analytical quality by design case study

Application of Analytical Quality by Design (AQbD) principles to Method Development

• Introduction to Analytical Quality by Design (AQbD) principles and AQbD workflow
• Brief overview of AQbD elements
• Short case study: stability-indicating method development using AQbD approach

Speaker: Dr. Amanda Guiraldelli

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• Published: 07 June 2021

Analytical quality by design methodology for botanical raw material analysis: a case study of flavonoids in Genkwa Flos

• Min Kyoung Kim 1 ,
• Sang Cheol Park 2 ,
• Geonha Park 2 ,
• Eunjung Choi 2 ,
• Yura Ji 2 &
• Young Pyo Jang 2 , 3  

Scientific Reports volume  11 , Article number:  11936 ( 2021 ) Cite this article

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• Pharmaceutics
• Process chemistry

The present study introduces a systematic approach using analytical quality by design (AQbD) methodology for the development of a qualified liquid chromatographic analytical method, which is a challenge in herbal medicinal products due to the intrinsic complex components of botanical sources. The ultra-high-performance liquid chromatography-photodiode array-mass spectrometry (UHPLC-PDA-MS) technique for 11 flavonoids in Genkwa Flos was utilized through the entire analytical processes, from the risk assessment study to the factor screening test, and finally in method optimization employing central composite design (CCD). In this approach, column temperature and mobile solvent slope were found to be critical method parameters (CMPs) and each of the eleven flavonoid peaks’ resolution values were used as critical method attributes (CMAs) through data mining conversion formulas. An optimum chromatographic method in the design space was calculated by mathematical and response surface methodology (RSM). The established chromatographic condition is as follows: acetonitrile and 0.1% formic acid gradient elution (0–13 min, 10–45%; 13–13.5 min, 45–100%; 13.5–14 min, 100–10%; 14–15 min, 10% acetonitrile), column temperature 28℃, detection wavelength 335 nm, and flow rate 0.35 mL/min using C 18 (50 × 2.1 mm, 1.7 μm) column. A validation study was also performed successfully for apigenin 7- O -glucuronide, apigenin, and genkwanin. A few important validation results were as follows: linearity over 0.999 coefficient of correlation, detection limit of 2.87–22.41, quantitation limit of 8.70–67.92, relative standard deviation of precision less than 0.22%, and accuracy between 100.13 and 102.49% for apigenin, genkwanin, and apigenin 7- O -glucuronide. In conclusion, the present design-based approach provide a systematic platform that can be effectively applied to ensure pharmaceutically qualified analytical data from complex natural products based botanical drug.

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Introduction.

Interest in high-level analytical system for complex pharmaceutical ingredients such as plant extract is increasing in the reality that drug development using natural extracts is increasing worldwide. Botanical drug guidelines of the United States Food and Drug Administration (USFDA) which was revised in 2016, recommends a ‘Totality-of-the-Evidence’ approach that comprehensively utilizes fingerprint analysis, chemical identification, and quantification of active or chemical constituents in the drug substance to characterize the complexity of the botanical sources to ensure consistency in drug quality 1 , 2 .

In order to achieve high standard of analytical methods of quality control, quality by design (QbD) approach have been adopted during analytical method development of various pharmaceutical practices 3 , 4 , 5 , 6 . The QbD is a disciplined approach to understand and control new drug products, based on sound science and quality risk management in diverse pharmaceutical processes 7 , 8 . Analytical methods play a significant role in drug product development in the control scheme of constant quality system monitoring of a product lifecycle 9 . The International Conference on Harmonization (ICH) is preparing to develop a new ICH Quality Guideline (ICH Q14) on Analytical Procedure Development, which will include the QbD concept for analytical methods, termed Analytical Quality by Design (AQbD) 10 . The AQbD approach begins with determining the analytical target profile (ATP), which is the prospective target of the analytical method development process and relates performance elements based on the intended target criteria 11 . The selection of critical method attributes (CMAs) is also performed, which directly represent a strong link to the intended criteria such as selectivity, precision, or accuracy in the desired analytical quality. Secondly, parameters that may affect analytical results are identified through a risk assessment approach 10 . Those highly selected risk factors are known as critical method parameters (CMPs) which should be tested with design of experiment (DoE) methodology and statistical screening. Thirdly, the polynomial relationships between CMAs and CMPs were studied in order to understand the in-depth cause-effect aspects that were statistically designed to identify the influential input variables affecting the representative output variables 12 . Meanwhile, the DoE is usually conducted twice by screening factors and then response surface methodology for optimizing the analytical method. The purpose of the screening study is to find the high-risk factors through fewer experiments, which is usually performed with designed two-level models such as full factorial design (FFD), fractional factorial design (FrFD), and Plackett-Burman design (PBD) 8 , 12 . In addition, an optimization process is conducted to ensure that proper quality is attained in the analytical method by considering the selected high-risk factors during design. The results are interpreted through response surface methodology (RSM) which is a potent statistical technique in mathematical modeling to interpret the designed-responses. Optimized strategic design responses include Box–Behnken design (BBD), central composite design (CCD), Taguchi design (TD), Mixture design, and Doehlert design 8 , 12 . Finally, the most appropriate designed point or method operable design region (MODR) is calculated from the RSM and confirmed by the method validation processes 13 .

While quality control systems based on the AQbD approach are applied widely in the field of pharmaceuticals, few application studies have been conducted on botanical extracts 14 , 15 , 16 . Since botanical extracts have complex and diverse phytochemicals as active ingredients, the selection of optimal analytical conditions is not simple. Also, it is quite challenging to screen the analytical parameters (i.e. buffer pH, organic solvent type, gradient slope, column temperature, etc.) that must be optimized by DoE technique.

In this paper, a systematic design-based approach to optimize a liquid chromatographic analytical method for major constituents of Genkwa Flos was investigated to suggest an analytical platform for how to consider CMAs and identify CMPs in an integrated case study with a botanical source. Compared to the routine One-Factor-At-a-Time (OFAT) approach, which tried one variable and collected one response, the current total quality approach utilizes scientific designing models and statistic expertise to finally obtain less experiment time, robust, precise, and easily validated analytical method 9 .

The flower buds of Daphne genkwa (Genkwa Flos, Thymelaeaceae) have been widely used as traditional oriental medicine in East Asia, China and Korea, and continue to draw great attention for their diverse pharmacological efficacy 17 , 18 , 19 . Previous phytochemical studies on D. genkwa revealed diverse chemical components including diterpenoids, flavonoids, lignans, and coumarins 20 , 21 , 22 . In recent years, genkwa flavonoids, as the main active constituents of Genkwa Flos, have been reported to exhibit remarkable pharmacological activities such as anti-inflammatory 23 , immunoregulation 24 , anti-tumor activity in colorectal cancer 25 , and anti-rheumatoid arthritis activity 26 . In order to exploit Genkwa Flos as a main ingredient of botanical drug, it is necessary to develop a robust and reliable analytical method for quality control, which is able to identify and quantify multiple components in botanical extracts in order to assure the consistency of pharmacological efficacy of herbal drug products.

CMPs were determined by risk assessment and factor screening experimental data in sequence. CMAs were established by equations that can be expressed as a single number by collecting the resolution of multiple peaks. After developing the optimized method by central composition design (CCD), the method validation was carried out in order to evaluate the soundness of the methods.

Results and discussion

Characterization of flavonoids using uhplc-pda-ms analysis.

UHPLC-PDA-MS system was utilized for the identification of flavonoids in Genkwa Flos. High-resolution mass data from Time-of-Flight (TOF) analyzer combined with UV–Visible absorption spectral pattern enabled to identify known flavonoids from Genkwa Flos extracts by direct comparison with those of previous researches 22 , 23 and/or reference standard solutions. A total of eleven identified flavonoids were listed in Table 1 providing their retention time, λ max , quasi-molecular ion, observed mass, mass difference, and molecular formula. Those were also tagged as peak 1 to peak 11 in the UHPLC chromatogram obtained at 335 nm (Fig.  1 A) which are apigenin 5- O -glucoside, apigenin 7- O -glucoside, yuanhuanin, apigenin 7- O -glucuronide, genkwanin 5- O -primeveroside, genkwanin 5- O -glucoside, genkwanin 4′- O -rutinoside, tiliroside, apigenin, 3′-hydroxygenkwanin, and genkwanin as eluted in order.

Representative UHPLC chromatogram of Genkwa Flos extract tagged with characteristic 11 flavonoid peaks ( A ) and their chemical structures ( B ). Kinetex-C 18 50× 2.1 mm, 1.7 μm column; mobile phase-A: 0.1% formic acid in water, mobile phase-B: acetonitrile; 335 nm detection; column temperature 28 ℃; 0.35 mL/min; gradient Time (min):%B, 0:10, 13:45, 13.5:100, 14:10, 15:10 used for the chromatogram.

Analytical target profile (ATP) and critical method attributes (CMAs)

The first step in AQbD-based method development is to define the ATP for stepwise and scientific procedures 7 . An analytical procedure which is able to quantitatively determine the specified eleven flavonoids in Genkwa Flos is a target of this study. Various elements of ATP such as analytical technique and instrument requirement were summarized as the intended target criteria (Supplementary Table S1 ). After ATP set-up, the potential CMAs were considered based on preliminary studies and review of the literature 8 , 9 . The general key CMA is the resolution ( R s ) of critical peaks 4 , 15 , 27 , which may be a critical attribute to avoid peak overlap for selective identification in liquid chromatography. Finally, the CMAs, corresponding to ATP, were established as countable peak number (Y n ) and resolution (Y 1–11 and Y sum ) after substantial consideration based on the modeling of experimental studies.

Preliminary studies

To carry out design-based method development studies, several preliminary tests were performed in different columns (i.e., length, particle size, manufacturer), using various solvents (i.e., acetonitrile, methanol), and acidified water (i.e., non-acidified, 0.1% acetic acid, 0.1% formic acid). Also, the detection wavelength for analyte was tested to acquire the greatest specific detection. The purpose of these attempts is to reduce variables by fixing those three parameters, but guarantee the best peak symmetry with the least working time. The achievement results were organized in Supplementary Table S2 , and final decision to C 18 (50 × 2.1 mm, 1.7 μm) column, acetonitrile and 0.1% formic acid water solvent system, and 335 nm detection wavelength, respectively.

Risk assessment studies

Quality risk management (QRM) allows us to control the entire process and recognize high-risk parameters that will affect the final quality of the analytical method 28 . We endeavored to establish QRM through risk assessment studies including experimental instruments and analytical parameters as shown in Fig.  2 , an Ishikawa fishbone cause-effect diagram. From the cause-effect diagram, potential factors in performing liquid chromatography could be identified and a subsequent step, the organized failure effect in each of the potential factors were calculated with a risk priority number (RPN) to sort out the high risk factors 29 . Following the guidance of ICH Q11 30 , RPN numbers were calculated with the equation ‘Severity \(\times\) Probability \(\times\) Detectability’ to allocate risk in each failure mode. The risk assessment and control strategy are summarized in Table 2 . Those parameters, column temperature (X 1 ), flow rate (X 2 ), injection volume (X 3 ), and gradient slope, indicate highly influential factors, which are calculated greater than 10 RPN. Practically, when designing the models, the gradient slope was converted into run time (X 4 ), because the initial and final percentages of acetonitrile solvent were fixed at 10 to 45 (Table 3 ). Thus, these four parameters were thereby selected for the further factor screening studies. The parameters counted less than 10 RPN were controlled as the constant.

Ishikawa Fishbone in Six Sigma of the UHPLC-PDA performance.

Factor screening studies

A (4 2 ) full factorial design (FFD), 4-factors and 2-levels, was performed for finding relatively fewer significant parameters from a list of higher risk potentially affecting the chosen CMAs, peak numbers (Y n ). Since Y n generally reflects the integral quality of chromatographic separation, we chosen it for the FFD which is roughly executed at just 2-levels (Low and High). The selected high risk factors during risk assessment studies were identified as column temperature (X 1 ), flow rate (X 2 ), injection volume (X 3 ), and run time (X 4 ). The main effect(s) were estimated by selecting the first-order polynomial models, which were drawn out per Eq. ( 1 ):

In the equation, Y n is the studied CMAs, which is number of countable flavonoid peaks, when examined in each of 19 runs as depicted in Table 3 . Those experimental runs were constructed randomly. A Pareto chart and Main effect plots (Fig.  3 ) show the significant influence of column temperature (X 1 ) and run time (X 4 ) on the studied CMAs, as these parameter frequencies were found to cross the corresponding α -value. As observed in Fig.  3 B, the countable peak numbers (Y n ) showed a negative correlation to column temperature (X 1 ), but a positive effect by run time (X 4 ). According to the statistical results (Table 4 ), the fitted model was very suitable to the experimental data by p -value under 0.05 with lack-of-fit larger than 0.05. Thus, factors such as column temperature (X 1 ) and run time (X 4 ) were selected as the CMPs for further optimization studies, and the other minor effective factors were kept as constant values. The flow rate (X 2 ) was adjusted to 0.35 mL/min, while the injection volume (X 3 ) was fixed at 1.0 μL.

Pareto chart ( A ) and main effect plots ( B ) obtained during factor screening of critical method attributes (CMAs), Y n peak numbers.

Response surface analysis

The subsequent chromatographic method optimization was executed by selecting the second-order quadratic polynomial model, where a central composite design (CCD) model designed with level 1.41421α were conducted with fourteen experimental runs (Table 5 ). The analyzed CMPs were column temperature (X 1 ) and run time (X 4 ) and studied at five different equidistant levels, i.e. low axial (− 1.41421), low factorial (− 1), central (0), high factorial (+ 1), and high axial (+ 1.41421). Meanwhile, the potential CMAs were newly chosen as Y 1–11 , which are the resolution ( R s ) of each of the identified eleven flavonoid peaks listed in Table 1 . Since botanical extracts have numerous phytochemicals, the resolution of each eleven peaks were defined between the closest eluted peaks. In detail, when calculate Y 8 for the peak number 8 shown in Fig.  1 A, the closest peak is just behind one eluted at 7.326 min. Besides, the first peak resolution (Y 1 ) and second peak resolution (Y 2 ) were of equal value, because the peaks are not totally separated or completely resolved by the UHPLC system and the closest eluting potential interference was each other. Furthermore, in several experimental runs (Table 5 ), the Y 1 and Y 2 were R s  = 0, indicating that those two peaks completely overlapped or co-eluted. The USP resolution equation using the baseline peak width drawn by lines tangent to the peak at 50% height was conducted for absolutely divided peaks, but USP Resolution (HH) using the peak width at half-height multiplied by a constant was utilized when calculated for overlapping peaks 31 .

In order to evaluate efficiently the total quality of separation in chromatographic fingerprints derived from each experimental run, one hypothetic score was introduced as total summation of Ys values of each peaks. In the design space, the Y 1 to Y 11 peaks were integrated as one value of Y sum by Eq. ( 3 ), which represents the estimated response for the experimental correlation with the two selected CMPs. Also, in order to prevent the value of a few peaks from dominating the overall result, it was necessary to determine the maximum value of each variable. A resolution over 1.5 usually indicates great separation, and when it is greater than 2, the peak is considered to be completely separated 32 . Hence, before integrating, the resolution values greater than 2 were set to 2 as shown in Eq. ( 2 ):

where Y i represents i th peak resolution after normalizing by Eq. ( 2 ), and the minimum to maximum response followed by Eq. ( 3 ) is 0 to 22, respectively. The randomly experimented fourteen runs to the selected CMAs are tabulated in Table 5 with the studied CMPs levels and designed experimental schedule. To clarify the CCD results, Minitab software ver. 18 was utilized for deriving ANOVA analysis and statistical optimization. Equation ( 4 ) is obtained by substituting the experimental data into a mathematical mode encompassing both main effects and interactions reflecting the second-order quadratic polynomial model.

ANOVA analysis was performed to statistically verify the model, which illustrates a statistically highly significant model ( p  < 0.05) and reasonable values of R 2 (95.09% for determination and 90.89% for adjusted). The results are given in Table 4 , it is also apparent that two CMPs in the first-order (X 1 , X 4) and second-order (X 1 ·X 1 , X 4 ·X 4) terms were significant, whereas the interaction correlation (X 1 ·X 4 ) was not significant. Those statistical results are also confirmed by observing the Pareto chart, Main effect plots, and Interaction plot shown in Fig.  4 .

Pareto chart ( A ), main effect plots ( B ), and interaction plots ( C ) obtained during center composite design (CCD) studies of critical method attributes (CMAs), Y sum ; summarizes the eleven resolutions.

Selection of optimum chromatographic solution

To obtain the optimized chromatographic method, the CCD design space was further studied in response surface analysis by using Statistica software ver. 13.3.0, carried out for the specific CMAs, Y sum . The 3D response surface (Fig.  5 A) and 2D contour plot (Fig.  5 B) revealed individual and plausible interaction(s) in factors and responses. Both column temperature (X 1 ) and run time (X 4 ) have a similarly curved plot, which is gradually increasing and decreasing at around the central level (0). Specifically, the central level of column temperature (X 1 ) was 35 ℃ and run time (X 4 ) was 14 min, respectively. As observed from Eq. ( 4 ), those patterns also may be inferred to be parabolic curves, which mean the response with a maximum value can be calculated by mathematical computing works. Finally, the optimum UHPLC-PDA performance solution with a maximum response Y sum of 18.80 was adjusted mathematically to the column temperature of 28.2861 ℃ and run time of 13.1784 min as portrayed in diagrams in Fig.  6 . The verification step was studied to appraise model suitability and the repeatability results were near the predicted value of Y sum with a very acceptable %RSD and %RE (Table 6 ).

3D response surface plot ( A ) and 2D contour plot ( B ) depicting the interaction of two critical method parameters (CMPs) on the Y sum .

Optimization diagrams calculated mathematically.

Analytical method validation studies

The purpose of validating an analytical method is to demonstrate that the proposed method is suited for its intended use by satisfying the expectations of ATP. At first, method validation of UHPLC fingerprint was performed to determine the precision and stability. The same test solution (30 mg/mL) of the Genkwa Flos, which was injected six times in one day for precision test. Next the same test solution was analyzed 0 and 24 h after the preparation of test solution for stability test. The results were summarized in Supplementary Table S3 as calculated %RSD values of relative retention time (RRT) and relative peak area (RPA) of each peak which were calculated relative to the selected marker peak, apigenin 7- O -glucuronide (peak 4). All %RSD values of RRT and RPA of eleven peaks were under 1%, indicating the commendable precision and stability of the fingerprint method.

Next, we studied the quantitative method validation using three standard compounds of apigenin 7- O -glucuronide, apigenin, and genkwanin, which were identified as major components by chromatography (Fig.  1 ). Since the assigned eleven flavonoids were all 2-phenylchromen-4-one backbone flavones, those three peaks with the highest % area in the Fig.  1 were selected as representatives for verification of the optimized analytical method. Standard calibration curves of three compounds for linearity were derived in the range of 0.9765–500.00 μg/mL or 31.25–2000.00 μg/mL with the high values of the coefficient of correlation (0.999), respectively (Table 7 ). The linear calibration plots with corresponding residual plots are depicted in Supplementary Fig. S1 , where none of the points were observed as outliers in the studied range of each concentration. Detection limit (DL) and Quantitation limit (QL) were also drawn out from the linearity test, indicating a sensitive method for quantification of those flavonoids. Precision, a measure of repeatability, was evaluated by intra-day and inter-day variability. As shown in Table 7 , the %RSD value of content in the intra-day and also inter-day variability tests were found to be with a reasonable value as under 0.22, respectively. Accuracy of the method was confirmed by spiked and triplicate injections of known standard concentrations into the sample solution. Percentage recovery for the three compounds’ test concentrations studied ranged from 100.13% to 102.49% (Table 7 ), with their %RSD values less than 0.85.

System suitability has been checked with the systematically optimized chromatographic method and found to be well within ICH criteria 11 except resolution, as represented in Fig.  1 . Among the eleven flavonoid peaks, resolution of peaks 1, 2, 3, 6, and 9 were under 1.5, which is the remaining challenge for a detailed trial of the isocratic and gradient mixed solvent system or to consider other factors. Meanwhile, an accurate and precise chromatographic method also depends on the %RSD values for injection repeatability precision, tailing factor 9 , plate count 13 , and capacity factor distribution 11 , so those criteria also must be considered as CMAs. However, the only criteria of resolution was selected for CMAs because %RSD and tailing factor were estimated to great precision and symmetry over the entire experiment. Also, when performed CCD studies of those parameters, plate count (> 2000), and capacity factor (> 1), were evaluated as proper in the overall 14 runs of experimental design work as tabulated in Supplementary Table S4 .

To apply the AQbD approach, a thorough study on the characteristic of the analyte must be accomplished. The risk assessment studies were conducted carefully to achieve the optimized analytical method that is able to quantify diverse flavonoids from all of the other detected interferences with a substantial acceptable resolution, selectivity, and good efficiency. Thus, optimizing the selected CMPs as column temperature (X 1 ) and run time (X 4 ) the resolution of eleven identified flavonoid peaks were well resolved as mentioned and represented in Fig.  1 .

The present study adopted a novel AQbD approach to develop a sensitive, robust, and accurate UHPLC-PDA-MS method for the identification and quantification of flavonoids in Genkwa Flos extract. In this approach, a methodical data collection process was conducted to identify the CMPs and CMAs through serial experiments of preliminary tests, risk assessment, full factorial design, and central composite design (CCD). Moreover, a new attempt to express target multiple peak resolutions as a single value was proposed by integrating all analytical peak data, and it provides a direction of how to handle CMAs in developing an analytical method of botanical extracts containing diverse components. The quantitative models depicted by a 3D surface plot with a 2D contour plot between two potential parameters, column temperature (X 1 ) and run time (X 4 ), were successfully constructed to facilitate finding the most suitable conditions for the chromatographic analysis. In conclusion, an AQbD-based quantitative multi-component analytical method is successfully developed and can serve as a template for other herbal medicinal product cases.

Material and methods

Standards and reagents.

Apigenin (CAS no. 520-36-5, > 98.6%), apigenin 7- O -glucuronide (CAS no. 29741-09-1, > 98.8%), and genkwanin (CAS no. 437-64-9, > 98.0%) were purchased from Chem Faces, Wuhan, China. All of the other reagents were supplied by Duksan Pure Chemicals Co., Ltd., Ilsan, South Korea. For the analytical studies, HPLC-grade water, methanol, and acetonitrile were purchased from Fisher Scientific, Waltham, MA, USA; high purity nitrogen gas was provided by Shinyang Oxygen Co., Ltd., Seoul, South Korea.

Plant material and preparation of extracts

The flower bud of Daphne genkwa, which is a MFDS (Ministry of Food and Drug Safety of Republic of Korea) certified herbal medicine, was purchased from the Kyung-dong drugstore in Seoul, South Korea. The botanical origin was identified by Prof. Young Pyo Jang who is the head of Medicinal Herb Garden of College of Pharmacy, Kyung Hee University. A Voucher specimen (KHUP-2103) is deposited at the Herbarium of College of Pharmacy, Kyung Hee University, South Korea. Acquiring all plant samples and manufacturing extracts were carried out in compliance with the IUCN Policy Statement on Research Involving Species at Risk of Extinction ( https://portals.iucn.org/library/efiles/documents/PP-003-En.pdf ) and the Convention on International Trade in Endangered Species of Wild Fauna and Flora https://cites.org . The sample was ground and then powdered with 850 μm mesh sieves. Using 56% acetone in water as the extraction solvent, all flavonoid components were extracted by a shaking extraction procedure. The detailed list of extraction parameters are as follows: agitation speed of 150 rpm, shaking time of 12 h, and extraction temperature of 65 ℃. The concentration of the sample solution was fixed in all experimental sections as 30 mg/mL.

Instrumentation and UHPLC-PDA-ESI/MS conditions

A Waters AQCUITYTM H-class UPLC system (Waters Corp., Milford, MA, USA) was used for the UHPLC analysis. The system composed of a photo diode array (PDA) detector, quaternary solvent and sample manager, cooling auto sampler, and column oven. The operating software was Empower-3 software (Waters Corp.). A Kinetex-C18 column (2.1 mm × 50 mm i.d., particle size 1.7 μm, Phenomenex, Torrance, CA, USA) was used for all the chromatographic analysis. The sample was maintained at 25 ℃ and the UV/Visible detector wavelength was fixed at 335 nm in all experiments. The mobile phase was composed of acetonitrile and acidified water with 0.1% formic acid. The column oven, flow rate, injection volume, and solvent gradient system were screened by experimental design.

To identify and assign flavonoids, the mass spectrometric studies were carried out on an AccuTOF ® single-reflection TOF mass spectrometer (JEOL, Tokyo, Japan) equipped with an ESI probe. Some important parameters of mass spectrometry were as follows: positive ion mode, mass range m/z 100—1500, needle voltage—2000 V, orifice-1 voltage—80 V, ring lens voltage—10 V, orifice-2 voltage—5 V. Nebulizing and desolvation gas was nitrogen. The desolvation temperature was 250 °C and the orifice-1 temperature was set to 80 °C. Mass Center System (version 1.3.7b, JEOL, Tokyo, Japan) was operating software and mass calibration was conducted using the YOKUDELNA kit (JEOL, Tokyo, Japan).

Statistical analysis

In current study, two design of experiments, full factorial design (FFD) and central composite design (CCD), were constructed and also statistical analyzed using Minitab software ver. 18 (Minitab Inc., State College, PA, USA). The statistically significant coefficients ( p  < 0.05) per analysis of variance (ANOVA) were used in framing the polynomial equation followed by the evaluation of the fit of the two models. Parameters evaluated for appropriate fitting of the models including coefficient of correlation (R 2 ), lack of fit, F-value, and P-value are listed, respectively. Among them, the result of CCD was also studied in response surface analysis utilizing Statistica software ver. 13.3.0 (TIBCO Software Inc., Palo Alto, CA, USA).

Chromatographic method validation analysis

After defining the design model, the analytical operating point was validated per the International Conference on Harmonization (ICH) guideline Q2 (R1) and the parameters are described below 33 . Among the eleven identified flavonoids, three major eluates were chosen for study in this validation process, which are apigenin 7- O -glucuronide, apigenin, and genkwanin.

Linearity and range

To confirm linearity, working standards of apigenin 7- O -glucuronide in the range of 31.25–2000.00 μg/mL, apigenin and genkwanin in the range of 0.9765–500.00 μg/mL were prepared by a serial dilution process and then analyzed. From regression analysis, three regression lines along with the regression equation and least squares were derived by each of the standard compounds, respectively.

Detection limit and quantitation limit

Following the guideline Q2 (R1), there are several approaches for calculating Detection limit (DL) and Quantitation limit (QL), we chose the method “Based on the Standard Deviation of the Response ( s ) and the Slope (α) 33 ” for this study. In Eqs. ( 5 ) and ( 6 ), the slope ( α ) was derived from each slope of the three analytical curves. The standard deviation of the response ( s ) was determined based on the residual standard deviation of each regression line.

Repeatability and Intermediate Precision were performed with a known concentration of the analyte (30 mg/mL) to investigate precision. On the same day, two samples at 100% of the test concentration were studied by six determinations each for the repeatability test. One sample was prepared for chromatographic analysis by six determinations on the next day testing for Intermediate Precision. All results were assessed as the percentage relative error by converted reference contents.

Calculating the percentage recovery of analyzed spiked samples was used for the accuracy test. Three known amount of each standard solutions: 125, 250, and 500 μg/mL of apigenin 7- O -glucuronide; 15.625, 31.25, and 62.5 μg/mL of apigenin; 31.25, 62.5, and 125 μg/mL of genkwanin were spiked with respect to the analyte (30 mg/mL) solution. The recovery studies were carried out three times showing that the percentage recovery and also percentage relative error were calculated to be accurate.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant Number: 2018M3A9F3081538).

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M.K.K. contributed conception, design of the study, and performed the experiments; S.C.P. conducted statistical analysis; G.P., E.C. and Y.J. performed the experiments and data; M.K.K. wrote the original draft of the manuscript; Y.P.J. administrated project and acquired funding. All authors contributed to manuscript revision and approved the submitted version.

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Kim, M.K., Park, S.C., Park, G. et al. Analytical quality by design methodology for botanical raw material analysis: a case study of flavonoids in Genkwa Flos. Sci Rep 11 , 11936 (2021). https://doi.org/10.1038/s41598-021-91341-w

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Analytical Quality by Design (AQbD) Approach to the Development of In Vitro Release Test for Topical Hydrogel

Réka szoleczky.

1 Egis Pharmaceuticals Plc., Laboratory of Finished Product Analytical Development 3, Bökényföldi Str. 116-120, 1165 Budapest, Hungary; [email protected] (R.S.); [email protected] (P.T.-N.)

2 Institute of Pharmaceutical Technology and Regulatory Affairs, University of Szeged, Eötvös Str. 6, 6720 Szeged, Hungary; [email protected] (M.B.-S.); moc.liamg@iynascsoos (E.C.); [email protected] (S.B.); [email protected] (I.C.)

Mária Budai-Szűcs

Erzsébet csányi, szilvia berkó, péter tonka-nagy, ildikó csóka, anita kovács, associated data.

The data presented in this study are available on request from the corresponding author.

The aim of our study was to adapt the analytical quality by design (AQbD) approach to design an effective in vitro release test method using USP apparatus IV with a semi-solid adapter (SSA) for diclofenac sodium hydrogel. The analytical target profile (ATP) of the in vitro release test and ultra-high-performance liquid chromatography were defined; the critical method attributes (CMAs) (min. 70% of the drug should be released during the test, six time points should be obtained in the linear portion of the drug release profile, and the relative standard deviation of the released drug should not be over 10%) were selected. An initial risk assessment was carried out, in which the CMAs (ionic strength, the pH of the media, membrane type, the rate of flow, the volume of the SSA (sample amount), the individual flow rate of cells, drug concentration %, and the composition of the product) were identified. With the results, it was possible to determine the high-risk parameters of the in vitro drug release studies performed with the USP apparatus IV with SSA, which were the pH of the medium and the sample weight of the product. Focusing on these parameters, we developed a test protocol for our hydrogel system.

1. Introduction

Topical dermal preparations can be used to achieve both local and systemic effects. For the former, the effect may be superficial, but sometimes deeper tissue penetration may be required. “The diversity of topical products is very wide given the complex nature of skin, the range of conditions to be treated and the variety of patients and their needs” [ 1 ] (p. 5). Topical products can be: creams, ointments, gels, pastes, poultices [ 2 ], suspensions, lotions, foams, sprays, aerosols, solutions, and transdermal delivery systems (TDS) [ 3 ].

The rate of drug release from the topical product and its retention are key factors in the development of the effect. In the case of a non-topical target, the development of the effect is influenced by the physicochemical properties of the active pharmaceutical ingredient (API) and the effect of the formulation on penetration as well.

1.1. Product Quality Tests for Topical and Transdermal Drug Product

The concept of critical quality attributes (CQAs) was defined in the ICH Q8 [ 4 ]; these are the physical, chemical, or microbiological properties that can be measured in order to ensure the desired product quality (parameters should be within an appropriate limit, range, or distribution). These CQAs may include drug release, preservative type and content, purity, pH, rheological properties, etc. [ 5 ].

The Pharmacopeial Forum [ 6 ] and USP monograph [ 3 ] described the product quality test recommendations for topical, dermal, and transdermal drug products ( Figure 1 ). The factors mentioned in this description can serve as cornerstones for development in order to ensure the optimal quality of the product, especially in the case of generic drug development.

Product quality tests for topical and transdermal drug products. * This test is generally formulation dependent.

The in vitro release test is a well-known technique for analyzing a semi-solid dosage form. With this measurement, the semi-solid dosage form is placed on the upper side of an artificial, inert membrane in the donor chamber in contact with a medium in a reservoir/cell, and the API diffuses through the formulation, across the membrane, into the reservoir. After that, the drug release rate (released per unit area (µg/cm 2 ) against the square root of time, yielding a straight line), the cumulative amount of API released at the last sampling time of the linear portion, and lag time parameters should be determined [ 1 , 7 , 8 ].

During development, an in vitro dissolution/release test can be used to determine the formulation factors that may influence the bioavailability of the API. In addition, at the beginning of generic development, we can use the in vitro dissolution/release test as a reverse engineering tool to copy the RLD (reference listed drug), and in this way it can be a critical tool for highlighting the differences between the generic product and the RLD. In order to perform the BCS-based (biopharmaceutics classification system) biowaiver study, we need to determine and compare the in vitro dissolution/release performance of the RLD and the generic product. After the manufacturing process, when the composition of the generic product has already been defined, and if the authorities have approved it, the in vitro dissolution methods can be used to provide batch-to-batch quality measurements [ 9 ].

International recommendations for the in vitro release process for a semi-solid dosage form are also described in the USP <1724> Semi-Solid Drug Products—Performance Tests [ 8 ], <724> Drug Release [ 10 ], and in the Japanese Pharmacopoeia 6.10 Dissolution Test [ 11 ].

The SUPAC-SS (scale-up and post-approval changes) guidance [ 7 ] focuses on post-approval changes in excipients in the drug product after administration and ensures product sameness and quality, describing the details of the in vitro release comparison test. For the comparison of the pre-change lot (P) and the post-change lot (T), measurements should be made by two IVRT (in vitro release test) runs performed on two different days: in the first run, three cells of P and three cells of T are included, while the second run is the same as the first run, but with an opposite arrangement of the P and T samples on the diffusion cells. After the IVRRs (in vitro release rates) of P and T were evaluated and divided (T/P ratio), the confidence interval (90%) was calculated to order these 36 individual T/P ratios from lowest to highest. Thereafter, the eighth and twenty-ninth ordered individual ratios should fall within the limits of 75% to 133.33% [ 7 ].

1.2. Quality by Design Usage in the Development of Topical Semi-Solid Dosages

The quality by design approach was defined in the ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) Q8 (R2) guideline as “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management” [ 4 ]. Quality needs to be designed into the product, not “tested into the product”. Using this risk-based approach during pharmaceutical development, the outcome will be a well-known product and process. As a first step, the quality target product profile (QTPP) and the critical quality attributes (CQAs) have to be defined on the basis of prior knowledge. Afterwards, the critical material attributes (CMAs) and critical process parameters (CPPs) have to be determined by carrying out the risk assessment methods mentioned in the ICH guideline Q9, which are linked to the CQAs of the drug product. This Q9 guideline mentioned a couple of risk management methods and tools, including the Ishikawa diagram, Pareto analysis, failure mode effects analysis (FMEA), and design of experiments (DoE) methods [ 4 , 5 , 12 , 13 , 14 , 15 , 16 ].

The analytical quality by design (AQbD) concept, an extension of quality by design (QbD) [ 17 ], results in a well-understood, fit-for-purpose, and robust method that consistently delivers the intended performance throughout its lifecycle [ 13 , 18 , 19 , 20 ].

During the AQbD method development, the gained and reliable knowledge provides adequate evidence to meet the performance requirements, such as the selection of the critical parameters or the method validation parameters, and enhanced understanding of the product control strategy precludes unnecessary tests. The application of AQbD can be used to support post-approval changes to analytical procedures through activities such as science- and risk-based change management.

The goal of our present study is to adapt the AQbD approach to design an effective IVRT method development using USP apparatus IV (flow-through cell with a semi-solid adapter) [ 8 ] for a topical hydrogel.

2. Materials and Methods

2.1. materials.

Diclofenac sodium salt was purchased from Molar Chemicals Ltd. (Halásztelek, Hungary). Propylene glycol and hypromellose (HPMC) were provided by Hungaropharma Ltd. (Budapest, Hungary).

The water used was filtered and deionized using the ELGA PURELAB Chorus 1 lab water purification system (ELGA LabWater Headquarters, Lane End, United Kingdom). Di-sodium hydrogen phosphate dihydrate, sodium hydroxide, and sodium chloride were provided by Molar Chemicals Ltd. (Halásztelek, Hungary). Potassium dihydrogen phosphate was obtained from Thomasker (Budapest, Hungary). All the other chemicals were of analytical grade or equivalent. The dissolution media for the IVRT test were pH 7.4 PBS (composition: 0.007 M Na 2 HPO 4 × 2 H 2 O, 0.001 M KH 2 PO 4 , 0.137 M NaCl, adjusted to pH 7.4 ± 0.05 with cc. H 3 PO 4 ), pH 6.9 PBS (composition: 0.007 M Na 2 HPO 4 × 2 H 2 O, 0.001 M KH 2 PO 4 , 0.137 M NaCl, adjusted to pH 6.9 ± 0.05 with cc. H 3 PO 4 ), pH 7.9 PBS (composition: 0.007 M Na 2 HPO 4 × 2 H 2 O, 0.001 M KH 2 PO 4 , 0.137 M NaCl, adjusted to pH 7.9 ± 0.05 with 1 M NaOH), pH 7.4 PBS + NaCl (0.007 M Na 2 HPO 4 × 2 H 2 O, 0.001 M KH 2 PO 4 , 0.411 M NaCl, adjusted to pH 7.4 ± 0.05 with cc. H 3 PO 4 ), and pH 7.4 PBS–NaCl (0.007 M Na 2 HPO 4 × 2 H 2 O, 0.001 M KH 2 PO 4 , 0.046 M NaCl, adjusted to pH 7.4 ± 0.05 with cc. H 3 PO 4 ).

Acetonitrile (HPLC gradient grade) was acquired from Merck (Darmstadt, Germany). Methanol (HPLC gradient grade) was purchased from Honeywell International Inc. (Charlotte, NC, USA).

2.2. Methods

During our work, different quality management tools were used (Ishikawa diagram and failure mode and effects analysis) in order to have a full scope of applied analytical methods (with the IVRT and UHPLC methods). For all the statistical analysis of variance and the design of experiments, the Statistica 13 software (Copyright 1984–2018 TIBCO Software Inc., Paolo Alto, CA, USA) was used.

2.2.1. USP Apparatus IV: Flow-Through Cell with a Semi-Solid Adapter

A semi-solid adapter or insertion cell (diffusional surface area: 1.54 cm 2 ) was used with USP apparatus IV (Sotax CE7 smart with CY 7 piston pump, Sotax Corporation, Westborough, MA, USA) to model the in vitro drug release from diclofenac sodium topical hydrogel. The donor compartments of the semi-solid adapters (available in different sizes: 400 µL, 800 µL, and 1200 µL) were filled with the topical product, and afterwards the Teknokroma cellulose membranes (pore size of 0.45 µm) (previously soaked in pH 7.4 PBS for 30 min) were fitted into the screw constraint and were placed over the surface of the sample compartments. The adapters with the membrane facing down were loaded into the 22.6 mm tablet cells prefilled with glass beads (1 mm glass beads) [ 21 , 22 ]. The apparatus IV system was used in an “open loop” configuration. The medium was deaerated pH 7.4 PBS, and the flow rate was 4 mL/min or 8 mL/min. The test temperature was 32 ± 0.5 °C and samples were collected (Sotax C 615 fraction collector, Sotax Corporation, Westborough, MA, USA) at 0.5, 1, 2, 3, 4, 5, and 6 h. We used 400 µL and 1200 µL semi-solid adapters for the IVRT development. Samples were analyzed using UHPLC (ultra-high-performance liquid chromatography).

The drug release rates were calculated using USP general chapter 1724 [ 8 ].

2.2.2. Ultra-High-Performance Liquid Chromatography Analysis

The concentration of diclofenac sodium was determined using a Waters Acquity I-Class UHPLC system with a photo diode array (PDA) detector set to the wavelength of 240 nm. Chromatographic separation was performed using an Acquity UPLC BEH UHPLC column (2.1 mm × 50 mm, 1.7 µm, 130 Å, Waters Corporation, Milford, MA, USA), the temperature was maintained at 40 °C, and the mobile phase was a mixture of methanol and potassium dihydrogen phosphate buffer (pH 2.5; 20 mM) (36/64 v/v ). The potassium dihydrogen phosphate buffer was filtered through a 0.22 μm filter. The degassing of the mobile phase was achieved through the ultrasonication of the eluent for up to 5 min. The run time was set to 3 min. The flow rate was 0.45 mL/min, and the injection volume was 2 μL. For each in vitro release study, calibration was established in the concentration range of 4 to 100 μg/mL (R² ≥ 0.995). The chromatographs were analyzed using Empower 3 (copyright 2010 Waters Corporation).

2.2.3. Analytical Quality by Design

A general workflow can be traced for the implementation of AQbD: first, the definition of the analytical target profile (ATP) and critical analytical attributes (CAAs); after these, the identification of the critical method parameters (CMPs) (for example, Ishikawa diagram) and a risk assessment analysis (for example, FMEA) should be performed, followed by a design of experiments (DoE). Finally, through a response surface analysis, the establishment of the design space pertaining to the method is also referred to the method operable design region (MODR) [ 23 , 24 , 25 , 26 , 27 , 28 ].

2.2.4. Definition of the Analytical Target Profile

“The ATP states the required quality of the results produced by a procedure in terms of the acceptable error in the measurement” [ 29 ]. Therefore, the first step in the AQbD-based analytical development is to define the ATP, which is analogous to QTPP. It should be established before selecting the technology and starting the development of the method, and its intended purpose should be defined [ 17 , 24 , 26 ].

The ATP includes the product to be tested (API name, dosage form, API content, the definition of the route of administration, matrix, etc.), the range of analyte concentration, the allowable error for the measurement, the allowable risk of the criteria not being met, and the confidence that the measurement uncertainty and risk criteria are met. “The ATP criteria are independent of the technique, allowing an analyst to select any technique that is capable of providing the performance needed to meet the ATP criteria” [ 29 ]. Diclofenac sodium, a non-steroidal anti-inflammatory drug [ 30 ], was used as a model drug in the hydrogel system to adapt the AQbD approach.

2.2.5. Definition of the Critical Method Attributes and Critical Method Parameters

The second step in the AQbD-based development is to determine CMAs. On the basis of our prior method development knowledge and data, CMAs are derived from the ATP. CMAs are the elements of method performance which need to be measured and/or evaluated to ensure that the desired data will be provided. CMAs are analogous to CQAs in drug development [ 31 ].

After the definition of CMAs, all the method parameters can be summarized systematically with the help of an Ishikawa diagram [ 16 ]. The Ishikawa diagram, also called a fishbone diagram, is the most adopted technique for the risk analysis of cause–effect phenomena. The aim of this method is to summarize all influencing factors during a brainstorming session, and then to categorize and to visually represent MPs.

2.2.6. Establishing Failure Mode Effects Analysis (FMEA)

Failure mode effects analysis is an important risk assessment method defined in the ICH Q9 guideline, which states that it “provides for an evaluation of potential failure modes for processes and their likely effect on outcomes and/or product performance” [ 16 ]. In the risk matrix, we can estimate the effect and the risk of the method parameters with regard to the method performance.

The outcomes of an FMEA are the risk priority numbers (RPNs). They can be used to rank risks from the FMEA analysis. RPNs are calculated by multiplying occurrence (O), severity (S), and detection (D) indexes. O is the occurrence of failure or the likelihood of an event occurring. S is the severity scale that could be based on the impact that the sources of variability have on the analytical procedure measurement (ability to meet the ATP criteria). D is detectability or the ease with which a failure mode can be detected [ 15 , 32 , 33 ]. Table 1 describes the rankings of severity, occurrence, and detectability of effect.

Description of ranking.

On the basis of the RPNs, the following classes of risks can be distinguished [ 32 ]:

• Low (acceptable) 1 ≤ RPN ≤ 29;
• Medium (to be considered) 30 ≤ RPN ≤ 59;
• High (not acceptable) 60 ≤ RPN ≤ 125.

The method parameters which were classified as medium or high risk in the FMEA should be considered to be critical method parameters (CMPs).

2.2.7. Design of Experiments (DoE) for IVRT Method

The DoE method is a modelling tool for assessing possible interactions between the factors influencing the drug development process and, thus, the quality of the final product. CMPs must be chosen as independent variables and CMAs as dependent variables in the factorial design process.

After the FMEA and the preliminary experiments, a 2 3 full factorial design was performed for the optimization of an IVRT method for a diclofenac sodium hydrogel formulation. A first-order polynomial model (Equation (1)) was generated in order to investigate the linear response surface, which can describe the principal effects and interactions between the identified variables.

where a 0 is the intercept, a 1,2,3 are the regression coefficients values, and x 1 , x 2 , and x 3 correspond to the independent factors.

2.2.8. Determination of the Osmolality of Different Media

The analysis was carried out with Knauer semi-micro automatic osmometer, digital/L model (osmolality range: 0–2000 mOsmol/kg) using the freezing point depression method. Two-point calibration was performed with 0 mOsmo l/kg deionized water (ELGA LabWater Head Quarters, Lane End, United Kingdom) and 400 mOsmol/kg calibration solution (A01241-1, Lot 14384042, Knauer). To determine the osmolality of the 150 µL media, each sample was analyzed twice.

2.2.9. Performing Membrane Inertness Test

The membrane used during the IVRT measurements should not absorb the API, should be compatible with the receptor media, and should not be an obstacle to drug diffusion. The measurements were carried out with Teknokroma ME Cellulose 0.45 µm and Millipore PES membrane 0.45 µm with three parallels. Here, 150 mL of pH 7.4 ± 0.05 PBS solution was incubated at 32 °C, stirred at 100 rpm and spiked with 2 mL of 400 µg/mL diclofenac–sodium stock solution that was dissolved in methanol. After two minutes, samples of 1 mL were taken from the 150 mL of pH 7.4 ± 0.05 PBS solutions. After the samples were taken out, one membrane was immersed in each vessel. As the next step, the vessels were covered and stirred at 100 rpm for six hours at 32 °C. After six hours, the sample-taking procedure was repeated. The drug content of the samples was measured by UHPLC.

2.2.10. Discriminatory Power of the In Vitro Release Test Method

The discriminatory power of the IVRT method is built upon three performance characteristics: sensitivity, specificity, and selectivity. The details of these three concepts are described as follows:

A sensitive IVRT method should be able to discriminate diclofenac sodium release rates from similar formulations. In our work, the IVRT method can be qualified as sensitive if the mean of diclofenac sodium release rate from the 0.5% test hydrogel was lower, and the mean of diclofenac sodium release rate from the 2% test hydrogel was higher than the release rate of the diclofenac sodium gel 1% reference product.

In other respects, the specificity of the IVRT method was shown by plotting the relationship between the three formulation concentrations and the average IVRT release rate. This relationship function should be linear (regression coefficient: R 2 ≥ 0.90) [ 1 , 23 , 34 ].

For selectivity testing, we used the statistical approach of Wilcoxon rank sum/Mann–Whitney rank test described in the SUPAC-SS guidance. This is used to calculate the 90% confidence interval for the ratio of the slopes between the test hydrogel (0.5 and 2%) and the reference (1%) batches. To determine the inequivalence between the test and the reference products, those ratios should not lie within the limits of 75–133.33% [ 7 , 8 , 34 ].

3.1. Definition of ATP and Determination of CMAs

In order to support the development of the formulation on the analytical side, we need adequate analytical methods that should guarantee the quality of the product. Accordingly, the IVRT should be sensitive to the changes and alterations in the formulations, and the analytical measurements must be able to accurately and precisely quantify the API in IVRT samples. Therefore, we defined these targets in the ATP ( Table 2 ).

Analytical target profile of diclofenac sodium topical gel.

An UHPLC analytical technique was chosen to measure the IVRT samples because the matrix of the topical gel has a UV active excipient and UHPLC is capable of providing selective, precise, and accurate results to quantify the amount of drug in the release media. UHPLC is a more reliable and widely used technique and it is capable of satisfying the ATP requirements.

CMAs and ATPs may be treated as performance requirements because CMAs represent a link between the purpose of the method and the performance criteria according to the ATP [ 17 ]; therefore, CMAs were derived from ATPs. Table 3 demonstrates the potential CMAs affecting the method performances along with a justification for each of them. In summary, the ATP objectives and the CMA requirements of the method were chosen.

Critical method attributes of diclofenac sodium topical gel.

CMAs were derived from ATPs, and five key CMAs were determined ( Table 3 ), including (1) minimum 70% of diclofenac sodium should be released from the topical gel within six hours, (2) six time points should be obtained in the linear portion of the drug release profile, (3) the relative standard deviation in the computed released amount of the six vessels must be less than or equal to 10%, (4) the accuracy must be greater than or equal to 98% and less than or equal to 102% at three concentration levels (50, 100, and 200%), and (5) the USP plate count of the column (column efficiency) must be greater than or equal to 3000.

The first and the second CMAs were chosen on the basis of the draft guideline on the quality and equivalence of topical products. While establishing the ATP, there is a significant—practically inseparable—connection with the QTPP because the first and the second CMAs were formed by the product and the analytical method.

In order to ensure the good reproducibility of the IVRT method, the apparatus of the IVRT should have a relative standard deviation in the computed released amount of the six vessels that is less than or equal to 10%.

Once the ATP has been defined, an analytical technique capable of meeting the ATP requirements should be selected. In this study, we focused on the performance of the in vitro release test method by using USP apparatus IV (with a semi-solid adapter) device because according to the EMA guideline [ 1 ], it is easier to meet the “6 time points should be obtained in the linear portion of the drug release profile” [ 1 ] criterion.

3.2. Identification of the MP using the Ishikawa Diagram

According to prior knowledge, our next step was to systematically collect all the MPs that could influence a failure concerning the IVRT method. For this, we used the Ishikawa diagram as a risk assessment tool to identify potential variables that could have an impact on CMAs ( Figure 2 ) [ 15 ]. With the help of the Ishikawa diagram, more than 100 method parameters were identified that can influence the method performance and the quality of the method’s results.

Ishikawa diagram illustrating method parameters that may have an impact on the method attributes.

3.3. Initial Risk Assessment using FMEA (Effects of MPs on CMAs)

FMEA was used to establish and prioritize a cause–effect relationship between CMAs and MPs. The fishbone diagram and the FMEA table shown in this article were the results of brainstorming among research pharmacists and analysts. During the FMEA analysis, the possible effects of MPs on CMAs were investigated. The analysis was carried out in the case of all the MPs one by one. The initial risk assessment aims to identify the potential CMPs (that were assigned the highest RPNs), which will be investigated during the preliminary experiments.

Based on the literature data and our prior method development knowledge, the highest risks (RPN ≥ 60) were identified (see Table 4 ) using FMEA, including ionic strength (osmolality), the pH of the media, membrane type, rate of flow, sample weight (volume of the SSA), individual flow rate of cells, API% (0.5, 1 and 2%), and the composition of the product. During the screening process, we examined the impact of only the highest scoring parameters on the CMAs independently from each other as a preliminary study.

Initial risk assessment for in vitro release test (IVRT) method development (high risk). F probability of occurrence of the excursion = 1 (low), 5 (high); S severity of excursion = 1 (low), 5 (high); D detection of excursion = 1 (easy), 5 (hard); RPN risk priority number = F × S × D.

3.4. Carrying out Preliminary Experiments

On the basis of the FMEA and the EMA guide [ 1 ] recommendations, in our study, the impact of CMPs, categorized to be high risks, on CMAs was investigated.

3.4.1. In Vitro Release Test Study Design with USP Apparatus IV

A draft guideline on the quality and equivalence of topical products described the IVRT study design [ 1 ]. According to this draft guideline, the measurement planning started with choosing the medium and confirming the sink condition. This was followed by the selection of the membrane. The data in Table S1 verify that the sink condition criterion (solubility of the API in pH 7.4 PBS divided by the maximum concentration value of the API in the receptor medium (mg/mL) > 3) is met for USP apparatus IV (3.2 mg/mL). The sink condition in pH 7.4 PBS medium was confirmed according to the literature data [ 23 ]; therefore, it was not a CMP.

The results obtained from performing the membrane inertness study (see Section 2.2.9 ) showed that the Teknokroma ME Cellulose membrane did not act as a rate-limiting barrier to diclofenac sodium diffusion, since the recovery was 100.1 ± 3.7%.

3.4.2. Investigation of the Rate of Flow and Sample Weight with the One-Factor-at-a-Time (OFAT) Method

The one-factor-at-a-time method is the easiest way to examine the impacts of several factors. With this method, it is always only one factor that is changed at a time, and all the other conditions remain the same. The advantage of this method is that the impacts of the factors can be evaluated individually, regardless of one another. The disadvantage of OFAT is the inability to detect interactions between parameters. The importance of the OFAT method is significant at the start of the AQbD (screening), and DoE is recommended for the optimalisation of the method.

The effect of the flow rate and the volume of the semi-solid adapter (the weight of the product) can be found in Figure 3 . We could observe that the flow rate does not have a significant impact on the release of the API from the diclofenac sodium hydrogel product; however, the volume of the semi-solid adapter does. These results were substantiated by the analysis of variance ( p < 0.05, main effects analysis of variance (ANOVA), Bonferroni post hoc test).

( a ) Cumulative drug release per unit area in linear time scale, ( b ) cumulative drug release per unit area plotted against square root of time. Instrument: USP apparatus IV.

The IVRT was carried out with a 1.2 mL semi-solid adapter, and the drug release follows the Higuchi square root law, which is mainly controlled by diffusion. The release from the product only depends on the API’s capacity to diffuse through the membrane. The IVRT carried out with a 0.4 mL semi-solid adapter does not meet the requirements described in the ATP, as instead of six, only three points could be obtained in the linear portion of the drug release profile, but the means of operation of apparatus IV allows us to apply more time points in the linear region in order to meet the criterion “6 time points should be obtained in the linear portion of the drug release profile” [ 1 ] without changing the release profile.

3.4.3. Effect of pH and Osmolality on Drug Release from Topical Hydrogel

The diclofenac sodium hydrogel product contains hydroxypropyl methylcellulose (HPMC) as a gelling agent. HPMC is a water-soluble, nonionic, enzyme-resistant cellulose ether. Being nonionic, it allows for pH-independent release if the API itself is not sensitive to pH change [ 30 ]. As diclofenac sodium is a derivative of phenylacetic acid (pKa = 4.0), the pH value changes in the medium have a strong effect on its solubility [ 35 ].

In the case of matrix tablets [ 36 ] containing these polymers, ionic strength, and thus the osmolality, was an influencing factor regarding the degree of the release. This phenomenon is explained by the fact that, at a certain point, the increased ion concentration hinders the hydration of the polymer up to a level where forming a continuous gel layer becomes impossible [ 36 ].

Due to the reasons mentioned above, the impacts of the osmolality and the pH of the medium on the API release from the gel matrix were categorized as high-risk factors during the risk analysis. The composition of the medium can be found in Section 2.1 , Figure 4 , and the results of the osmolality test of the medium are shown in Table S2 .

Figure 4 and the analysis of variance ( Table S3 ) also show that the impact of pH on API release is significant ( p = 0.0001, main effects ANOVA, Bonferroni post hoc test), but the effect of the osmolality of the medium is not significant ( p < 0.05, main effects ANOVA, Bonferroni post hoc test). It can be seen that the RSD% of the computed released amount of the six vessels was less than 10% ( Table 5 ).

In vitro release test (IVRT) results of preliminary experiments.

3.5. The 2 3 Full Factorial Design for the IVRT Method

On the basis of FMEA and OFAT, the pH of the medium and the sample weight of the product remained high-risk parameters to be examined by an additional modelling method, the design of experiments (DoE). The DoE is a modelling tool for the investigation of a possible interaction between the factors influencing the drug development process and, thus, the quality of the final product. The high-risk CMPs must be chosen as independent variables and CMAs as dependent variables in the factorial design process ( Table 6 ). In the preliminary risk assessment, the flow rate was not a critical parameter, but considering our previous experiences, the sample volume and the flow rate may have a combined effect; therefore, we examined the flow rate as an independent variable in our factorial design.

Experimental design matrix according to a 2 3 full factorial design.

The flow rate (X1), volume of SSA (X2), and the pH of the medium (X3) were chosen as independent factors and the IVRR (Y1) and release efficiency in 6 h (Y2) were dependent factors. With the preliminary experiments, the flow rate (mL/min) was not found to be a CMP, although, apart from the main effects, two-way or/and three-way interactions can be significant. The DoE was developed by using Statistica 13 software.

From the results of 2 3 full factorial statistical analysis ( n = 5 per analysis), it can be seen that the main factors X2 (the volume of the SSA) and X3 (pH) exert a significant effect ( p < 0.05) on Y1 (IVRR) ( Table 7 and Figure S1 ). The mathematical model shows a good correlation, with R 2 = 0.96607.

Results of the statistical analysis for in vitro release rate (IVRR) (µg × cm −2 × min −0.5 ).

The equation was as follows:

On the basis of our results, the combination of the highest pH (7.9) and the highest volume of SSA (1.2 mL) gives us the highest IVRR (µg × cm −2 × min −0.5 ) ( Figure S2 and Table 7 ).

Analyzing the effect of the factors on the release efficiency in 6 h, the mathematical model shows a good correlation, with R 2 = 0.92047. The fitted equation was as follows:

In other respects, the statistical analysis shows ( Table 8 and Figure S3 ) that only one main factor X2 (the volume of the SSA) has a significant effect ( p < 0.05) on release efficiency in 6 h (Y2). The other factors did not have a significant effect on Y2.

Results of the statistical analysis for release efficiency in 6 h (%).

It can be also seen that the highest volume of SSA (1.2 mL) gives us the highest release % ( Figures S4 and S5 ).

3.6. Updating the FMEA Table

Given these results, the sink condition criterion was not a CMP (3.4.1), and the cellulose membrane did not act as a rate-limiting barrier (3.4.1) to diclofenac sodium diffusion, since the recovery was 100.1 ± 3.7%.

After preliminary experiments (3.4.) and the DoE (3.5.), the results show that the flow rate does not have a significant impact on the release of the API from the diclofenac sodium hydrogel product; however, the volume of the semi-solid adapter does. The impact of the pH is significant on API release, but the effect of the osmolality of the medium is not significant.

After the comprehensive OFAT (see Section 3.4.2 and Section 3.4.3 ) and DoE (see Section 3.5 ) investigation of the high-ranked CMPs, the FMEA table was updated according to the previous initial FMEA table ( Table 4 ). On the basis of the updated FMEA table ( Table S4 ), the following CMPs were reclassified from high-ranked to medium or low classes: rate of flow, membrane type, individual flow rate of cells, API%, and the composition of the product.

3.7. Investigating Discriminatory Power

The discriminatory power was analyzed for the 0.4 mL sample amount. It can be seen that the IVRT method is sensitive because it was capable of detecting different in vitro release rates with respect to the strength of the formulations, and the relationship between the different diclofenac sodium strengths and IVRR is linear (R 2 = 0.9994) ( Figure 5 , Figures S5 and S6 ).

Specificity of the in vitro release test (IVRT).

The calculated lower and upper limits fall outside the range of 75–133.33% for both test products; therefore, we confirmed product inequivalence. This is a significant difference between the tests and the reference; therefore, the IVRT method is capable of detecting inequivalence.

4. Conclusions

In this study, we showed how the concept of AQbD can be applied in the early stages of IVRT method development in the case of USP apparatus IV. After defining the ATP and selecting CMAs (at least 70% of the active substance applied is released after 6 h, six time points should be obtained in the linear portion of the drug release profile, and the relative standard deviation in the computed released amount of the six vessels was less than or equal to 10%), an initial risk assessment was carried out: with the help of the Ishikawa diagram, more than 100 method parameters were identified that can influence method performance and the quality of the results. FMEA was used to reduce the number of possible parameters down to eight factors: ionic strength, the pH of the medium, membrane type, the rate of flow, sample weight (volume of the SSA), the individual flow rate of cells, API% (0.5, 1, and 2%), and the composition of the product. During the screening process, we examined the impact of these parameters on CMAs independently of each other. These CMPs (the pH of the medium and the sample weight of the product) were given as independent variables in the factorial design. A 2 3 full factorial design experiment was employed to assess the IVRR and the release efficiency in 6 h.

After the examination, we re-evaluated the risks according to the results and recorded them in the updated FMEA table ( Table S4 ), thus narrowing the method parameters to CMPs.

On the basis of our results, the amount of the product and the pH were clearly defined as critical parameters during the application of the AQbD approach. At least 70% diclofenac sodium release from the hydrogel (all parallel samples) was achieved within 6 h under all testing conditions; therefore, it meets the ATP requirements. The ATP is capable of satisfying the EMA guideline [ 1 ] criteria. On the other hand, the means of operation of USP apparatus IV allows more time points to be applied in order to meet the criterion “6 time points should be obtained in the linear portion of the drug release profile” [ 1 ]. Summarizing our results, a robust IVRT test can be developed using the USP apparatus IV, which complies with the international guidelines, but the effect of the pH of the medium and the sample weight on the IVRT results must be analyzed in each case.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pharmaceutics14040707/s1 , Figure S1: Standard Pareto chart showing the effects of independent variables on in vitro release rate (IVRR) (µg × cm −2 × min −0.5 ); Figure S2: Response surface (3D) plot (X2-X3-Y1) of the effects of variables on in vitro release rate (IVRR) (µg × cm −2 × min −0.5 ); Figure S3: Standard Pareto chart showing the effects of independent variables on release efficiency in 6 h (%); Figure S4: Response surface (3D) plot (X1-X2-Y1) of the effects of variables on release efficiency in 6 h (%); Figure S5: Response surface (3D) plot (X1-X3-Y1) of the effects of variables on release efficiency in 6 h (%); Figure S6: Response surface (3D) plot (X1-X2-Y1) of the effects of variables on in vitro release rate (IVRR) (µg × cm −2 × min −0.5 ); Figure S7: Response surface (3D) plot (X1-X3-Y1) of the effects of variables on in vitro release rate (IVRR) (µg × cm −2 × min −0.5 ); Figure S8: Response surface (3D) plot (X2-X3-Y1) of the effects of variables on release efficiency in 6 h (%); Table S1: Confirmation of the sink condition; Table S2: Osmolality of the applied media in the in vitro release test (IVRT) studies; Table S3: Effect of the pH for in vitro release rate (IVRR). Statistical parameters—one-way analysis of variance test; Table S4: Updated failure modes and effects analysis (FMEA) table after the investigation of CMPs; Table S5: Sensitivity of the in vitro release test (IVRT) method: in vitro release rate (IVRR) of the diclofenac sodium 0.5, 1, and 2% hydrogel measured with USP apparatus IV; Table S6: The discriminatory power of the in vitro release test (IVRT) method calculated the upper and the lower limits of the 90% confidence interval.

Author Contributions

Conceptualization: R.S., M.B.-S., E.C., P.T.-N., and A.K.; methodology: R.S., S.B., and A.K.; formal analysis: R.S., M.B.-S., and P.T.-N.; investigation: R.S. and S.B.; writing—original draft: R.S.; writing—review and editing: E.C., I.C., and A.K.; supervision: M.B.-S. and A.K.; resources: S.B. and P.T.-N.; project administration: A.K. All authors have read and agreed to the published version of the manuscript.

This work was supported by Egis Pharmaceuticals Plc. project no. TKP2021-EGA-32, and it has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare that there are no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Analytical Quality by Design Guidance (AQbD)

Why are we exploring aqbd.

Quality by Design (QbD) is a systematic approach to development that begins with predefined objectives and emphasises product and process understanding and process control, based on sound science and quality risk management.

As a concept, it aims to assure the quality of medicines by using enhanced approaches to design, development and manufacture of medicinal products. The application of QbD principles to analytical methods, Analytical Quality by Design (AQbD), is being explored by industry, regulators, and academia.

Pharmacopoeial standards are a key component of a regulatory framework. For medicinal products in the UK, they are published in the British Pharmacopoeia (BP), a publication of the  MHRA. Pharmacopoeial standards evolve with advances in the manufacture of medicinal  products.

The MHRA is exploring how AQbD may apply to pharmacopoeial standards through the BP’s AQbD working party, which brings together BP scientists with MHRA assessors and inspectors as well as the Therapeutic Goods Administration of Australia (TGA), multinational biopharmaceutical manufacturers, the generics manufacturing industry and experts in the field of metrology.

More robust methods

An initial case study focussed on the practical application of AQbD principles to the development of an analytical procedure for the Assay of Atorvastatin in Atorvastatin Tablets.  This monograph was published in the BP 2023 along with the additional method understanding we gained through the application of enhanced approaches.

This additional method understanding will:

• provide assurance of the Assay procedure;
• highlight sensitivities of the analytical procedure.

We are now considering how learnings from this case study can be applied to our routine monograph development process to enhance method robustness and method understanding across BP monographs.

Feel confident in applying AQbD concepts with minimal risk

The BP’s AQbD Working Party also developed and published in the BP 2022 the Analytical Quality by Design (AQbD) Supplementary Chapter X which provides guidance on how to enhance method understanding by adopting AQbD concepts in analytical methods.  

Understanding how AQbD concepts are applied will: 

• ensure implementation of robust methods; 
• save time in the laboratory by targeting experimentation to the relevant method conditions. 

The guidance will help you ensure that the analytical methods you use are robust and fit-for-purpose. This will enable a better assessment of product quality, and ultimately provide greater assurance to safeguard patient safety.

You can access the guidance and Atorvastatin Tablets monograph in the BP online . If you do not have access you can purchase BP Online 2024 .

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Analytical Quality by Design (AQbD) in Pharmaceutical Development

The concepts described in ICH Q8-Q11, commonly referred to as Quality by Design (QbD), have also been applied to the development of analytical methods. An overview of these concepts using the development of a reversed-phase  liquid chromatography assay and related substances drug product method is provided. The benefits of applying QbD principles to analytical methods include identifying and minimizing sources of variability that may lead to poor method robustness and ensuring that the method meets its intended performance requirements throughout the product and method lifecycle.

Introduction

Quality by Design (QbD) is well established in the pharmaceutical industry for manufacturing processes (ICH Q8 [1] for pharmaceutical development and ICH Q11 [2] for development and manufacture of drug substances). QbD is “a systematic approach to development that begins with predefined objectives and emphasizes … understanding and … control, based on sound science and quality risk management” [1]. The outcome of using QbD concepts is a well-understood product and process that consistently delivers its intended performance. The knowledge obtained during development may support the establishment of a design space and determines suitable process controls. These same QbD principles have been applied to the development of analytical methods, and are termed “Analytical QbD” (AQbD) [3-20]. Analogous to process QbD, the outcome of AQbD is a well understood, fit for purpose, and robust method that consistently delivers the intended performance throughout its lifecycle. The broad knowledge obtained from this process is used to establish a method operable design region (MODR), a multidimensional space based on the method factors and settings that provide suitable method performance. It is also used to establish meaningful method controls of which system suitability is one component. A high level overview of the AQbD steps is depicted in Figure 1.

This approach can be applied to any method development, though depending on the compound stage of development and method scope, the application of the steps may differ.

Table 1. Method performance characteristics as defined in USPand ICH Q2(R1)

Method performance requirements:

Analytical target profile (atp) Once a measurement need is identified, method performance requirements are defined. Columns one and two in Table 1 list analytical method performance characteristics as defi ned in USPand ICH Q2(R1). Individually, these characteristics are not sufficient to assess the quality of reportable results or demonstrate that the method performance is appropriate for its intended use. This is because method performance is fundamentally comprised of both systematic (bias) and random (variance) components. An inspection of the definitions indicates a possible high-level categorization of the performance characteristics: accuracy, specifi city, and linearity measure systematic deviation from the true (known) reference value, and precision, detection and quantification limits are inherent measures of (random) dispersion. A categorization of the analytical method performance characteristics in terms of systematic and random components is shown in Table 1 , column 3 [21].

Among these performance characteristics, accuracy and precision provide the critical information needed to quantify an unknown amount of the substance using the method. A method cannot be accurate and precise without adequate specifi city, linearity over a stated range, sufficient peak resolution for accurate integration, repeatability of injections, etc. These are important characteristics to evaluate during method development (and provide an extensive data set for setting method controls) as they lead to an accurate and precise method. However, these performance characteristics do not speak directly to the risk of making an ill-advised decision based upon the reportable result obtained from the method [22]. A joint measure of accuracy and precision, on the other hand, directly addresses this [23]. Range is an important component that is established based on acceptable behavior of both systematic and random performance characteristics, and robustness defines an operational range of method factors that will be illustrated in a later section.

The Analytical Target Profile (ATP) defines method performance requirements, and should incorporate a joint criterion for accuracy and precision in order to define method acceptability in terms of the uncertainty of results generated by the method. Other method performance characteristics (linearity, specificity, etc.) do not need to be incorporated in the ATP as they are not directly linked to understanding the agreement of a measurement with the true value. An assay ATP should include a statement of accuracy and precision as exemplified by:

“The procedure must be able to accurately and precisely quantify drug substance in film-coated tablets over the range of 70%- 130% of the nominal concentration with accuracy and precision such that reported measurements fall within ± 3% of the true value with at least 95% probability.”

By including a probability statement in the ATP, the risk of making ill-advised decisions from results will be controlled. The uncertainty range (± 3%) and probability (95%) are established based on a predetermined acceptable level of risk of making an incorrect decision with the data. For example, consider the result of a potency assay of 98.0% label claim for a lot to be released against a lower specification of 95.0% label claim. If the potency method has been verified to adhere to the above ATP statement (measurements fall within ± 3% of the true value with at least 95% probability), the risk that the true lot potency is out of specification (< 95.0% label claim) is less than 5%. (Note that the true lot potency will always be unknown.) That is, the potency method provides a result that ensures the true assay value is ± 3% of the reported value (98.0% ± 3%; or 95.0%-101.0%) with at least 95% probability; or there exists less than 5% ([1-0.95]*100) chance that the true assay value is101.0%.

The ATP shown here is not linked to a specific type of analytical methodology, therefore any type of technology or technique can be used if it is shown to meet ATP requirements.

Factors for identifying a measurement technique

The next step is to identify an analytical technology for performing the measurements that has the ability to conform to the ATP. The technique to be selected must be available at both development and transfer sites, and have staff expertise in its routine operation and maintenance. Analytical technologies are wide and diverse, and although much overlap in applicability exists, each technique has strengths and weaknesses. Significant general [24-27] and technique-specific [28-31] literature is available. Typical methods that are transferred include those for identity (e.g., raw material, excipient, drug substance, dosage form ), assay (e.g., API assay, dosage form potency/content uniformity, blend composition, dissolution), purity (e.g., process related, stability related, heavy metals, solvent, water), bio-performance (e.g., dissolution, disintegration, hardness, particle size), form, and many other product-specific methods. One factor that must be considered from the onset is if the method will be on-line/at-line (in the manufacturing area) or off -line (in the lab). Factors to consider for this decision include the method purpose, method type, cycle time, worker safety, range and specificity (the latter two factors will help determine up-front if the method will have suitable accuracy and precision to meet the ATP). On-line analytical tools are routinely used during process development and manufacture [32]. However, in most cases, these (mainly spectroscopic) methods must be compared to a “primary” analytical methodology for quantitative measurements.

Although a number of separation technologies (gas, liquid, supercritical fluid, and thin layer chromatography, capillary electrophoresis and subsets of) are readily amenable for small-molecule pharmaceutical analysis, the most prevalent separation technique is reversed-phase liquid chromatography (RPLC). The high dynamic range and low variability of liquid chromatographic instruments coupled with the compatibility of most formulations and active pharmaceuticals leads to a majority of assay and related substances methods using RPLC.

Reversed-phase method Development Workflow

Figure 2 depicts the workflow used for RPLC methods development. The process involves four steps (Waves 0-4) [15].

For identified components, Log D as a function of pH is determined in silico and plotted. This plot is used to identify pH ranges that are predicted to provide stable component retention times with small changes in pH. If the components are not identified, the full wave 1 screen is run. In this screen, chromatographic performance is determined across a range of pH, two organic modifiers and several stationary phases. At the end of wave 1, the organic modifier and column are set. In wave 2, a pH screen around the pH identified in wave 1 is performed, and the pH is established. To identify optimum chromatographic conditions from the method development screen, a temperature and gradient experiment is performed (wave 3). The method’s chromatographic conditions are tentatively established using optimization software and are subsequently experimentally verified. These draft chromatographic conditions are evaluated for a period of time to gain hands-on experience prior to performing the next step, a method risk assessment.

Risk assessments

Quality Risk Management (ICH Q9) is “a systematic process for the assessment, control, communication and review of risks to the quality … across the … lifecycle” [33]. Risk assessments are an integral part of the Analytical QbD process. Their use facilitates identification and ranking of parameters that could impact method performance and conformance to the ATP. Risk assessments are often iterative throughout the lifecycle of a method, and are typically performed at the end of method development, with product changes (e.g., route, formulation or process) and as a precursor to method transfer. Risk assessments at the development to commercial transfer stage typically focus on parameters from a ruggedness perspective. These RAs focus on potential differences (e.g., laboratory practices, environment, testing cycle times, reagents sources). Major differences (e.g., equipment availability) should be identified and factored in at the technique selection and method development stages.

Table 2. Analytical Method Deconstruction

AQbD risk assessments start with deconstructing the analytical method into Analytical Unit Operations. Unit operation Inputs and the Analytical Actions related to the particular process steps are identified. Parameters associated with the Analytical Actions are determined and Attributes of the respective Analytical Unit Operation are correspondingly identified and documented. An abbreviated method deconstruction example for sample preparation is listed in Table 2 .

The resultant information generated from the method breakdown can be visualized with commercially available software in a multitude of ways (e.g., Fish Bone, Process Flow Diagrams). A process flow diagram for the Analytical Unit Operations of a potency and related substance RPLC method is illustrated in Figure 3 .

A representative process flow diagram depicting the analytical actions associated with the chromatographic separation and analysis unit operation is shown in Figure 4 .

Table 3. C&E Risk Assessment for a Chromatographic Separation

Risk matrices are utilized to assess parameter risks with respect to the relevant attributes (e.g., accuracy, precision, resolution, tailing). During early development, simple matrices built from scientific knowledge and experience of the methodology may be utilized (e.g. Excel® based risk matrices). As programs progress in development, the use of more detailed matrices (e.g., Failure Mode Effects Analysis (FMEAs), Cause and Effect (C&E) Analysis) can facilitate the identification of high risk method parameters (factors) and attribute responses for subsequent experimentation to ensure that they do not impact the method’s ability to meet the ATP. An example risk assessment, based on a C&E matrix approach, for a chromatographic separation and analysis (Figure 4) is listed in Table 3 . The component attributes are correlated to accuracy and precision aspects of the ATP, which are jointly assessed.

Design of experiments (Does)

Experimental investigations could be one factor at a time (OFAT: often used to assess noise factors such as column age or column lot number) or multi-factor statistical design of experiments (DOEs). An example of the parameters and attribute responses for investigating a chromatographic assay and related substance method is presented in Table 4. A statistician can facilitate the selection of an appropriate statistical design (along with analyzing the response information generated from the executed design) for the chosen parameters utilizing statistical software to minimize the analyses required while maximizing the information obtained.

Table 4. Experimental DoE Parameters and Attribute Responses for a RPLC Method

Once the data has been obtained, compiled, and processed, and appropriate response models have been determined, statistical numeric and graphical optimization tools can facilitate the simultaneous optimization of the multiple responses. The numeric optimization tool can allow identification of factor settings that result in optimal desirability [34] with respect to goals (e.g., to maximize resolution, theoretical plates; to minimize run time, tailing), high and low limits, weightings, and importance rankings applied to the relevant factors and/or responses. Numeric optimization is a mathematical approach for identifying an optimal “sweet spot” within the experimental space evaluated. Graphical optimization, allows application of factor and response limits, but relies on user intervention through iterative analysis to identify acceptable performance regions. For a chromatographic separation focus area, where the goal is to establish acceptable component detection (signal-noise and tailing) and separation (retention and resolution), these numeric and graphical response limits are estimates that facilitate identification of factor combinations (discrete points or regions and associated chromatographic conditions) which have the potential to challenge conformance to the ATP (e.g., small resolution value, component retention close to void). These tools are especially useful when the number of responses or factors is greater than four and an overlay of response surface contours becomes difficult to manage. A two-dimensional desirability plot for the simultaneous optimization of responses (component retention times, resolutions, and tailing factors) is presented in Figure 5 , as a function of Column Temperature and Buffer pH factors.

An example of a graphical optimization is presented in Figure 6 as a two-dimensional interaction plot of initial organic percent (y-axis) versus the final organic percent (x-axis).

In examples shown in Figures 5 and 6, the factors not presented graphically are individually iterated to determine how they affect the selected two-dimensional space. Factor combinations (chromatographic conditions) that have the highest probability of challenging conformance to the ATP can be identified through this iterative approach and further method verification exercises can be employed to establish ATP conformance and ultimately define the MODR.

Method verification

Validation of the method in line with ICH Q2(R1) guidelines is typically carried out at a set point (normal operating condition - NOC) within the chromatographic spaces evaluated. In addition to validating the method characteristics as per regulatory guidance, verifying the accuracy and precision provides additional understanding of the method’s measurement uncertainty and confirms conformance to the previously defined method performance requirements (ATP). This can be accomplished through a joint accuracy and precision assessment done at method factor points within the chromatographic separation space that present the greatest probability of challenging the method’s ability to meet the ATP.

Table 5. Robustness Verifi cation Study Results

An example of the method verification approach is illustrated in the following example. A study was performed to verify method performance at three design points determined from a primary study that looked at factors related to the robustness of the separation, as well as to confirm conformance to the ATP. The verification study design is presented in the top of Table 5 along with the attributes investigated. Three sample preparations at each concentration level (70%, 100%, and 130%) were analyzed (and averaged) at each verification point for a total of twenty seven samples. The response results that are assessed against the ATP are presented at the bottom of Table 5 , where the average (n=3) accuracy and precision values are listed. Factors that contribute to the overall method (accuracy and precision) variability include method repeatability components (e.g., instrument, standard and sample preparation) and intermediate precision components (e.g., method assessment over multiple days, instruments, analysts, laboratories). All of these sources of variability should be taken into account when assessing the method against the ATP (for brevity, only method repeatability components are listed in Table 5 ).

The visualization of the ATP for assay is graphically presented as the dark shaded region in Figure 7 . This region represents ± 3% of the true value with a 95% probability (the combined contributions of accuracy (bias) and precision (method repeatability) are such that the true potency value of a sample is within ± 3% of the reported value with at least 95% probability). Accuracy (bias) and precision (variability) estimates residing within the shaded region establish that the method meets the requirements of the ATP. Under this combined accuracy and precision assessment, conformance to the ATP is met for methods having performance characteristics of (1) high precision and accuracy (red X), (2) high precision, but bias (blue X), (3) high accuracy but low precision (green X), but not (4) combined low precision and biased (yellow X). Barnett et al. [22] describes the ATP accuracy and precision concepts in more detail.

Table 6. Chromatographic Operable Ranges

The validation and verification experiments demonstrate that the method is robust across the parameter ranges provided in Table 6 . However, in this particular method example, a method control strategy was enacted that constrained the organic modifier to 63% (rather than the verification level of 62%) and fixed the flow rate to 1.00 mL/min to ensure acceptable retention of degradation products. Operation within these limits ensures that the method meets its intended performance requirements. Similar assessments for degradation products can be made against an appropriately defined ATP for the degradation product.

Control Strategy/ Conformance to ATP

A meaningful method control strategy is established based on the wealth of data collected during the method development and verification stages described above. Using this data, correlations can be drawn between method attributes, such as resolution, and the ability to meet ATP criteria. The control strategy should also include those method parameters that influence method variability and will be fixed (e.g., reagent grade, instrument brand or type, column type). It should be noted that the method control strategy does not appear dramatically different under the AQbD approach when compared to the traditional approach. However, method controls are established based on a more extensive data set using the ATP criteria as a guide, thus ensuring a stronger link between the method purpose and method performance.

Continuous monitoring/lifecycle management

Once a method is established for routine use, method performance should be monitored over time to ensure that it remains compliant with the ATP criteria. This can be done, for example, by using control charts or other tools to track system suitability data, methodrelated investigations, etc. Periodic fit for purpose re-verification experiments can also be performed as warranted. Continuous monitoring allows the analyst to proactively identify and address any out-of-trend performance.

Existing methods should be periodically re-evaluated to address any gaps or improvement opportunities identified in the current methodology by improving the methodology, or, as analytical technologies advance, implementing a new technology.

Regulatory Considerations There is presently no regulatory guidance defining application of Quality by Design concepts to analytical methods. However, since many of the concepts described here are related to good science and risk assessment, regulatory agencies such as the FDA and EMA have been open to inclusion of these concepts in regulatory filings provided the applicant follows the current regulatory guidelines. The FDA and EMA recently announced a joint collaboration that began in January 2013 [35]. The goals of the collaboration are (1) to develop analytical methods (e.g. HPLC) based on the QbD paradigm; (2) define protocols for method transfer; (3) establish methodology for verification of the MODR upon site transfer; and (4) define review criteria for evaluation of QbD-based analytical methods.

Conclusions

Quality by Design for analytical methods is well discussed in the pharmaceutical industry. The outcomes of developing methods via QbD principles include enhanced understanding of (method and instrument) risk factors that may lead to poor method robustness, and helping to ensure that the method meets its intended performance requirements throughout the (product/method) lifecycle.

Acknowledgements

We thank Pfizer Inc. and Analytical Research and Development for the support of this work. We also thank Peter Jones, Loren Wrisley, Tim Graul, Roman Szucs and Melissa Hanna-Brown for their contributions to the development of the AQbD concepts described in this paper, Greg Steeno for the creation of Figure 7 , and Ron Ogilvie for his constructive review and comments.

Author biographies

Kimber Barnett, Ph.D. is an Associate Research Fellow at Pfizer Inc. with experience supporting analytical development of drug substances and drug products. Kimber obtained her Ph.D. in Analytical Chemistry from the University of Missouri under Professor Daniel Armstrong. She is a member of the USP Expert Panel for Verification and Validation and has published several articles on Analytical Quality by Design.

David Fortin, B.S. is a Senior Scientist at Pfizer Inc. with 19 years of separation science experience ranging from API to Parenteral products. David is currently in the Quality by Design method development group supporting early and late stage projects with robust – fit for purpose – separation methods. David graduated with a B.S. in Chemistry from the University of Connecticut in 1994.

Brent Harrington, M.S., is an Associate Director in the Statistics group at Pfizer Inc. Worldwide Research and Development in Pharmaceutical Sciences. He is responsible for providing experimental designs and statistical support for analytical method, and drug product formulation and process development. Recently, Brent has been active in developing and promoting performance-based criteria for analytical methods through the ATP concept. Brent received his M.S. in Statistics from Virginia Tech.

Jeffrey Harwood, B.S. is a Senior Scientist in the Quality by Design Method Development group at Pfizer Inc. Worldwide Research and Development in Analytical Research and Development. Jeffrey obtained his B.S. in Chemistry from the University of Rhode Island.

James Morgado, B.S . is a Principal Scientist at Pfizer Inc. with 19 years of separation science experience ranging from API to drug products. Jim is currently in the Quality by Design method development group providing separations, risk assessment, and DOE support to project teams. Jim graduated with a B.S. in Chemistry from the University of South Florida in 1994.

George Reid, Ph.D., is a Research Fellow at Pfizer Inc in Analytical Research and Development. In his current role, he has responsibilities in the areas of separation science, spectroscopy/PAT and modeling. George obtained his Ph.D. in Analytical Chemistry from the University of Missouri under Professor Daniel Armstrong and his B.S. in biochemistry at Beloit College.

Jian Wang, Ph.D., is an Associate Research Fellow in the Quality-by- Design Method Development group at Pfizer Inc Worldwide Research and Development in Analytical Research and Development. He is a specialist in the area of separation sciences. Jian received his Ph.D. in Analytical Chemistry from Emory University under Professor Isiah Warner.

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Analytical Quality by Design, Life Cycle Management, and Method Control

Affiliations.

• 1 Merck & Co., 2000 Galloping Hill Road, Kenilworth, NJ, 07033, United States of America. [email protected].
• 2 Merck & Co., Inc., 770 Sumneytown Pike, WP45-1127, WP, Pennsylvania, 19486, United States of America. [email protected].
• 3 GSK, GlaxoSmithKline, Via Fiorentina 1, 53100, Siena, Italy.
• 4 UCB, Pharma SA, Chemin du Foriest, 1420, Braine-l'Alleud, Belgium.
• 5 Biogen, 255 Binney St, Cambridge, MA, 02142, United States of America.
• 6 Pfizer Inc., Eastern Point Road, Groton, CT, 06340, United States of America.
• 7 Seattle Genetics, 21823 - 30th Drive SE, Bothell, WA, United States of America, 98021.
• 8 AstraZeneca, 950 Wind River Lane, Gaithersburg, MD, 20876, United States of America.
• 9 Pfizer Inc., 875 W. Chesterfield Parkway, Chesterfield, MO, 63017, United States of America.
• 10 Amgen Inc., One Amgen Center Drive, MS 30E-1-C, Thousand Oaks, Canada, 91320, United States of America.
• 11 Resilience, 9310 Athena Circle, La Jolla, Canada, 92037, United States of America.
• PMID: 35149913
• DOI: 10.1208/s12248-022-00685-2

Analytical methods are utilized throughout the biopharmaceutical and vaccines industries to conduct research and development, and to help control manufacturing inputs and outputs. These analytical methods should continuously provide quality data to support decisions while managing the remaining of risk and uncertainty. Analytical quality by design (AQbD) can provide a systematic framework to achieve a continuously validated, robust assay as well as life cycle management. AQbD is rooted in ICH guidelines Q8 and Q9 that were translated to the analytical space through several white papers as well as upcoming USP 1220 and ICH Q14. In this white paper, we expand on the previously published concepts of AQbD by providing additional context for implementation in relation to ICH Q14. Using illustrative examples, we describe the AQbD workflow, its relation to traditional approaches, and potential pathways for ongoing, real-time verification. We will also discuss challenges with respect to implementation and regulatory strategies.

Keywords: AQbD; Analytical target profile (ATP); Life cycle management; Method validation; Quality by design (QbD).

© 2022. The Author(s).

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• International Conference for Harmonization. ICH Q14: analytical procedure development and revision of Q2(R1) analytical validation. In: ICH Final Concept Pap [Internet]; 2018. Available from: https://database.ich.org/sites/default/files/Q2R2-Q14_EWG_Concept_Paper.pdf .
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https://mhrainspectorate.blog.gov.uk/2019/08/21/analytical-quality-by-design-aqbd-questions-and-answers/

Analytical Quality by Design (AQbD): questions and answers

This month sees the close of the consultation on the application of Analytical Quality by Design to pharmacopoeial standards . We sat down with Peter and Stephen, pharmacopoeial scientists for the British Pharmacopoeia, to discuss why this consultation may prove vital to the future of pharmacopoeial standards and why stakeholders should take the opportunity to have their say.

On AQbD and Pharmacopoeial Standards

What is analytical quality by design and why is the mhra interested in it for pharmacopoeial standards.

Analytical Quality by Design (AQbD) takes a structured approach to the development of analytical procedures which are fit for purpose and that consistently deliver results that meet predefined objectives.

It achieves this through a detailed understanding of all aspects of the analytical methods performance ensuring adequate control and an ability to react to changes which can affect the quality of results.

The Agency is committed to safeguarding the quality of medicines for the public in order to ensure they work and are acceptably safe. At the MHRA, the British Pharmacopoeia sets the legal standards by which UK pharmaceuticals are measured. Decisions on whether these products are safe and efficacious are made based on data produced through our methods. It is therefore critical that these methods are suitable for all products throughout their lifecycle and that the results they produce are accurate, in order to ensure the continuous supply of high quality medicines. AQbD has the potential to aid these decisions through the application of the principles detailed in the technical summary published with the ongoing consultation.

This Agency project is unique in that it is a collaboration between all parts of the UK’s regulatory system, combining expertise from Licensing, Inspections and Standards as well as industry and academic partners. It also takes a pragmatic approach, generating real world test data to investigate various principles, supplementing existing theoretical understanding.

Case Study: Assay for Atorvastatin tablets

Why were the assay for atorvastatin tablets selected as the case study.

Atorvastatin tablets, a type of statin, were selected as the subject for this project because they are widely prescribed for long term management of high cholesterol and prevention of cardiovascular disease. Diversity in the formulations and manufacturing methods used for the available products also made Atorvastatin tablets an ideal candidate for this study.

Content is a critical quality attribute for any product and therefore Assay is fundamental for pharmacopoeial standards. The gradient based High Performance Liquid Chromatography (HPLC) procedure is also representative of a large number of procedures in the BP, allowing for the learnings of this case study to be further applied throughout the pharmacopoeia.

Impacts of Analytical Quality by Design

Is the goal of analytical quality by design to have a “design space” for pharmacopoeial analytical procedures.

Analytical Quality by Design has many different benefits, it’s not just about a design space. We’ve already found a number of useful advantages which can be tailored to individual needs.

For example, the MHRA’s project has built an understanding of the Atorvastatin Tablet Assay procedures operable region. This region has been assessed to ensure that the method selected sits in a plateau of stability. This could be taken further to understand the edges of failure and implement specific elements of control to ensure robustness in use.

The project report seems to contain a substantial amount of work, could AQbD be too labour or time extensive to implement?

Developing an analytical procedure using Analytical Quality by Design is perceived as having a greater cost when compared to traditional methodologies.

However, an aim of this project is to understand the resource required to implement various AQbD concepts against the benefits each brings.

In addition to producing a more robust method reducing long term costs of revisions, we’ve already identified efficiencies for the BP laboratory by taking a risk-based approach to the marketed products we sample for analysis.

We can also see potential savings using risk analysis techniques to target our analytical assessment to critical areas of a procedure.

The consultation closes on Saturday 31 August 2019.

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Comment by sandeep vishnani posted on 22 August 2019

useful update.

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Analytical Quality by Design Approach for the Development and Validation of Liquid Chromatographic Procedure for the Estimation of Abrocitinib

• Published: 21 June 2024

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• Narikimalli Ashritha 1 &
• Galla Rajitha 2  

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Analytical quality by design-based HPLC procedure has been developed for the estimation of Abrocitinib in bulk and tablets. Design-Expert ® -13 was employed for Response Surface Methodology. ANOVA was applied for responses statistical analysis. Buffer pH, flow rate, % organic composition of mobile phase, column and organic modifier were considered as CMPs. Retention time, number of theoretical plates and tailing factor were considered as CQAs. The CMPs that have a significant impact on CQAs were screened utilizing a 12-run 2 5 Plackett-Burman design. Following screening investigation, CMPs that have a substantial effect on the CQAs were selected and CMPs of HPLC procedure were optimized employing a 22-run 5 3 central-composite design. The optimized procedure suggested by the desirability functions approach utilizing a 55.8:44.2 ratio pH 3.25 ammonium formate buffer and acetonitrile with 1.0 mL/min flow rate as a mobile phase, an Inertsil ODS (150×4.6 mm, 3.5 μm) column as a stationary phase with 5 mins run time resulting in maximum desirability, 1.000. At 3.575 min, Abrocitinib retention time was observed. The optimized procedure was validated and stress studies were executed in accordance to the ICHQ2(R1) and ICHQ1A guidelines respectively. In accordance with the literature, it is evident that this is the initial reported RP HPLC procedure for Abrocitinib estimation.

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Ashritha, N., Rajitha, G. Analytical Quality by Design Approach for the Development and Validation of Liquid Chromatographic Procedure for the Estimation of Abrocitinib. Pharm Chem J (2024). https://doi.org/10.1007/s11094-024-03153-7

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Association of interleukin-2 and interleukin-10 with the pathophysiology and development of generalized anxiety disorder: a case-control study

• Nisat Sarmin 1   na1 ,
• A. S. M. Roknuzzaman 2   na1 ,
• Rapty Sarker 1   na1 ,
• Mamun -or-Rashid 1 ,
• MMA Shalahuddin Qusar 3 ,
• Sitesh Chandra Bachar 4 ,
• Eva Rahman Kabir 5 ,
• Md. Rabiul Islam 5 &
• Zobaer Al Mahmud 1  

BMC Psychiatry volume  24 , Article number:  462 ( 2024 ) Cite this article

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Generalized anxiety disorder (GAD) is a devastating mental health condition characterized by constant, uncontrolled worrying. Recent hypotheses indicate that pro-inflammatory cytokines and chemokines are potential contributors to the pathogenesis of GAD. Here, we aimed to assess the role of interleukin-2 (IL-2) and interleukin-10 (IL-10) in the pathophysiology and development of GAD.

This study recruited 50 GAD patients diagnosed according to the DSM-5 criteria and 38 age-sex-matched healthy controls (HCs). A qualified psychiatrist evaluated all study subjects. The socio-demographic and clinical characteristics of the study population were determined using pre-structured questionnaires or interviews, and cytokine serum levels were estimated using commercially available ELISA kits.

We observed reduced serum IL-10 levels in GAD patients compared to HCs (33.69 ± 1.37 pg/ml vs. 44.12 ± 3.16 pg/ml). Also, we observed a significant negative correlation between altered IL-10 levels and GAD-7 scores ( r =-0.315, p  = 0.039). Moreover, IL-10 serum measurement exhibited good predictive value in receiver operating characteristics (ROC) analysis with an area under the curve (AUC) value of 0.793 ( p  < 0.001) with 80.65% sensitivity and 62.79% specificity at a cutoff value of 33.93 pg/ml. Conversely, we noticed elevated serum IL-2 levels in GAD patients than in HCs (14.81 ± 2.88 pg/ml vs. 8.08 ± 1.1 pg/ml); however, it failed to maintain any significant association with GAD-7 scores, implying that IL-2 might not be involved in GAD pathogenesis. The lower AUC value (0.640; p  > 0.05) exhibited by IL-2 serum measurement in ROC analysis further supported that IL-2 might not be associated with GAD.

This study provides new insights into the complex interplay between anti-inflammatory cytokines and GAD pathogenesis. Based on the present findings, we can assume that IL-10 but not IL-2 may be associated with the pathophysiology and development of GAD. However, further research with a larger population size and longitudinal design is required to confirm the potential diagnostic efficacy of IL-10.

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Generalized anxiety disorder (GAD) is a chronic neuropsychiatric disorder characterized by persistent and excessive uncontrollable fear or worry (occurs for at least 6 months) about various aspects/activities of daily life, affecting the educational, occupational, or social lives of the affected people [ 1 ]. If a person is excessively worried about anything for most days over at least 6 months, he/she is considered to have GAD. Though currently the prevalence rate of GAD is 3–6% worldwide [ 1 , 2 , 3 ], the prevalence is increasing day by day due to the complexity of modern lifestyles and thus warrants attention from national and international authorities to take interventions for mitigating and managing this disorder properly. If it remains undiagnosed or untreated, the uncontrollable and persistently intense anxiety can lead to a marked reduction in cognitive functions or a reduced capacity to work properly in all spheres of life, including educational, family, social, and individual routine work. As such, chronic GAD leads to a reduced quality of life and thereby poses a significant mental health concern globally.

Despite its high prevalence, significant morbidity, and socioeconomic burden, GAD remains poorly characterized in terms of its pathophysiology or effective treatment options. Though the precise cause and mechanism of pathogenesis are still unknown, evidence suggests that multiple factors, including disrupted serotonergic, dopaminergic, and GABAergic neurotransmission and excessive glutamatergic neurotransmission in the brain, genetic factors, family or environmental stress, chronic diseases, hyperthyroidism, childhood trauma, and special personality traits, are linked to GAD. Alterations in monoaminergic neurotransmissions in limbic systems (cingulate gyrus, hippocampus, amygdala, thalamus, and hypothalamus) due to the lower synaptic availability of serotonin, norepinephrine, and dopamine are thought to be associated with anxiety symptoms. Besides, decreased GABA-mediated inhibitory neurotransmission in the amygdala or excessive activation of excitatory glutamatergic neurotransmission are also suggested to be involved in GAD pathology.

Currently, available pharmacotherapies for GAD include selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), pregabalin, and benzodiazepines, which act by reversing these altered monoaminergic neurotransmitter systems. Alongside these drug treatments, non-pharmacological therapies such as several psychological interventions, including cognitive-behavioral therapy, and the acquisition and application of stress management skills, including relaxation and mindfulness skills are also widely used for the management of GAD. However, currently, available pharmacotherapies (SSRIs, SNRIs, pregabalin, and benzodiazepines) have failed to demonstrate the required efficacy in treating anxiety disorders, as 50% of patients failed to respond to these drugs, and at least in 30% of cases, there is a recurrence of the disease following the pharmacological treatment [ 1 , 4 , 5 ]. Moreover, studies reported a higher rate of discontinuity from these pharmacotherapies with low patient adherence or compliance due to the adverse effects, including sexual dysfunction for SSRIs and SNRIs, nausea and dizziness for pregabalin, demonstrating an urgent need for searching for novel anxiolytics [ 3 ]. These findings raised questions about the validity of the currently available mechanism of pathogenesis and suggested that the altered monoaminergic neurotransmitter system might not fully explain the molecular mechanism of GAD development, suggesting other pathophysiological factors might be involved in GAD. Recently, dysregulated immune systems have attracted great interest as an important pathophysiological factor for the development of GAD [ 4 , 6 , 7 , 8 ]. Several clinical and preclinical studies suggest a link between the altered immune system and GAD pathology. Preclinical studies in mice also demonstrated that administration of pro-inflammatory cytokines (including IL-1β, TNF-α, and IL-6) in mice resulted in anxiety-like behaviors that were attenuated or normalized after injecting either anti-inflammatory cytokines or antagonists for the concerned cytokines [ 9 , 10 , 11 , 12 , 13 ]. A recent prospective cohort study conducted by Hou et al., (2019) demonstrated that administration of selective serotonin reuptake inhibitors (escitalopram or sertraline) resulted in a significant reduction in peripheral pro-inflammatory cytokines, and the authors suggested that the anxiolytic effects of these SSRIs might partly be based on their acute anti-inflammatory activities [ 14 ], implicating a significant association between dysregulated peripheral immune systems and GAD development. The development of anxiety-like symptoms in IL-4 gene knock-out mice, reduced levels of IL-4 in anxious mice, and the significant attenuation of anxiety-like behaviors following IL-4 injection demonstrated a positive association between anti-inflammatory cytokines, IL-4 levels, and anxiety pathology [ 15 , 16 , 17 , 18 ]. This immune hypothesis of GAD development is further potentiated by findings from several clinical studies that reported that GAD patients showed significantly higher levels of pro-inflammatory cytokines ( IL-1Ra, IL-1, IL-6, TNF-α, etc.) compared to healthy controls (HCs) [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] along with decreased levels of anti-inflammatory cytokines, including IL-4 and IL-10 [ 25 ]. Besides, pro-inflammatory cytokines such as TNF-α, and IL-6 were significantly associated with anxiety scores [ 29 ]. Consistent with this, a randomized clinical trial in humans demonstrated that LPS administration resulted in enhanced anxiety scores, and the authors suggested a significant correlation between pro-inflammatory cytokine levels and anxiety severity [ 30 ]. LPS-mediated microglia activation causes enhanced release of excessive pro-inflammatory cytokines in the basolateral amygdala, which ultimately leads to neuroinflammation in mice, resulting in the development of anxiety and depression-like behaviors by modulating neuronal plasticity. The authors found that anxiety pathogenesis was due to the excessive release of excitatory neurotransmitter glutamate from presynaptic axonal terminals of the prefrontal cortex, leading to neuroplasticity [ 31 ]. However, some studies reported either no significant variation in pro-inflammatory or anti-inflammatory cytokine serum levels between GAD patients and HCs [ 32 ] or that pro-inflammatory cytokines including IL-1, IL-2, and IL-6 were significantly reduced in GAD patients than HCs [ 33 , 34 ]. This discrepancy in altered levels of inflammatory cytokines across clinical studies necessitates a further examination of the role of these cytokines in GAD pathophysiology.

Interleukin-2 (IL-2) is one of the major pro-inflammatory cytokines implicated in T cell activation, proliferation, and differentiation and is thus linked to excessive neuro-inflammatory processes [ 35 ]. IL-2 has been shown to impair synaptic plasticity and cause neuroinflammation, which ultimately leads to neuronal damage in neurocircuits associated with fear and anxiety signal transduction. IL-2 was also reported to act as a potent modulator of NMDA and kainite-mediated excitability in mesolimbic or mesostriatal systems [ 36 , 37 , 38 ] and thus affect neuroplasticity. As IL-2 was found to be positively associated with major depressive disorder [ 38 , 39 ], probably, IL-2 might also be correlated with anxiety disorders like GAD, as MDD and GAD are highly co-morbid themselves and thus might share common pathophysiological factors. Recently, a preclinical study conducted by Gilio et al., (2022) observed that IL-2 administration in experimentally healthy mice triggered marked anxiety and depression-like behaviors, and the authors suggested that inhibition of GABA-mediated synaptic inhibitory neurotransmission was involved in the pathology of anxiety [ 40 ].

Interleukin-10 (IL-10) is one of the major anti-inflammatory cytokines that is secreted from Treg cells, Th2 cells, CD4 + T cells, CD8 + T cells, monocytes, macrophages, dendritic cells, B cells, neutrophils in the peripheral nervous system, and from microglia, astrocytes in the central nervous system (CNS) [ 41 ]. IL-10 signaling triggers anti-inflammatory, immunosuppressive, and immunoregulatory activities, including downregulating the production and secretion of pro-inflammatory cytokines and chemokines from activated macrophages, neutrophils, mast cells, Th1 cells, and DCS, decreasing the expression of MHC class II and co-stimulatory molecules on macrophages, and thereby suppressing the antigen presentation capacity of APCS [ 42 , 43 , 44 , 45 , 46 ]. In the CNS, it inhibits the production of such cytokines and chemokines by activated microglia and thereby counteracts cellular and tissue damage in response to excessive neuroinflammation [ 47 , 48 ]. IL-10 has also been shown to stimulate axonal regeneration and activate wound healing through tissue repair [ 48 ]. Research also indicates its role as an inhibitor for microglial hyperactivation in response to LPS-induced inflammatory stimulus [ 49 ]. Based on its anti-inflammatory and immunoregulatory functions, researchers suggested an intricate role for IL-10 in several auto-immune and neuropsychiatric disorders. For example, Mesquita et al., (2008) observed that IL-10 KO mice developed markedly enhanced depressive-like behavior, which was attenuated after IL-10 administration, and that overexpression of IL-10 resulted in reduced depressive behaviors in mice [ 50 ]. Moreover, administration of IL-10 into rats attenuated the pro-inflammatory cytokine IL-1β-induced anxiety-like symptoms in male rats [ 10 ], demonstrating that IL-10 possesses anxiolytic activities. Preclinical research using an experimental animal model also suggests that the observed anxiolytic effect of several anti-anxiety drugs, including 3’-deoxyadenosine (3’-dA), imipramine, fluoxetine, and chlordiazepoxide, stems from their ability to upregulate anti-inflammatory cytokine (IL-4, IL-10) expression in the prefrontal cortex and locus coeruleus and simultaneous down-regulation of proinflammatory cytokine gene expression, leading to a correction of the imbalance between proinflammatory and anti-inflammatory states [ 51 , 52 ]. Though several preclinical studies suggest a potential link between IL-10 levels and anxiety disorder, there is a scarcity of clinical studies aimed at evaluating such an association between IL-10 and GAD development [ 10 ].

Currently, there is no objective and cost-effective diagnostic or prognostic biomarker for GAD, which poses challenges in early diagnosis or risk prediction and leads to misdiagnosis or underdiagnosis, hampering the proper management of the disease. Currently available diagnostic tools, including self-reported symptoms and scoring severity based on the patient’s response to the 7-item questionnaire (GAD-7 scores), are subjective. Though neuroimaging techniques such as positron emission tomography (PET) and functional MRI can be used for the proper and objective diagnosis of GAD, due to their high cost and sophistication or complexities, these diagnostic tools are not suitable for either mass-level screening or are not easy to conduct multiple times to monitor the disease progression or therapeutic drug response. As such, the investigation of cost-effective objective biomarkers for GAD is one of the major focuses of current research on GAD. Finding a suitable biomarker is essential for early diagnosis and initiating psychotherapy and pharmacotherapy as early as possible [ 3 ]. Several studies were performed investigating the potential association between altered pro-inflammatory cytokines or anti-inflammatory cytokines and the pathogenesis of GAD. However, the actual role of inflammatory cytokines in GAD patients is not well explained. Therefore, the present study aims to explore the role of pro-inflammatory cytokines (IL-2) and anti-inflammatory cytokines (IL-10) in the pathophysiology and development of GAD. Also, we aim to find the potential associations of IL-2 and IL-10 with the severity of GAD patients. We believe the present study results would help to understand the pathophysiology and development of GAD.

Study population

We recruited 88 participants for this case-control study (50 GAD patients and 38 HCs matched by age and sex). Patients were collected from the Department of Psychiatry, Bangabandhu Sheikh Mujib Medical University Hospital, Dhaka, Bangladesh, and HCs from nearby areas of Dhaka city. A professional psychiatrist diagnosed patients and evaluated HCs based on DSM-5 criteria. We applied a 7-item GAD scale to assess the severity of anxiety symptoms [ 53 ]. The total scores range from 0 to 21, and it classifies the anxiety severity into four categories: minimal anxiety (0–4 scores), mild anxiety (5–9 scores), moderate anxiety (10–14 scores), and severe anxiety (15–21 scores). We excluded subjects with a co-morbidity of other psychiatric disorders, such as MDD, panic disorder, post-traumatic stress disorder, and social phobia, from the study. Additional exclusion criteria for participants were chronic liver and kidney diseases, infectious diseases, and alcohol or substance abuse. We also excluded patients who were exposed to anxiolytics or antidepressant medications within at least two weeks prior to the study that might have an impact on cytokine levels. We recorded the sociodemographic profile of the study population using a pre-designed questionnaire. The objectives of the study were explained to each participant, and informed written consent was obtained from them before their participation in this study. The study was conducted in accordance with the Declaration of Helsinki.

Blood sample collection and serum isolation

A 5 ml blood sample was collected from the cephalic vein of each participant. The blood samples were kept at room temperature for 1 hour to ensure coagulation and were then subjected to centrifugation at 3000 rpm for 15 minutes at room temperature to collect serum samples. The collected serum was then placed in the Eppendorf tube and stored at -80 °C until further analysis.

Estimation of serum cytokine levels

We estimated the serum levels of IL-2 and IL-10 by ELISA methods (Boster Bio, USA). We followed the manufacturer’s protocol for the ELISA assays. At first, we added 100 µl of standard cytokine solution, samples, and controls to each well of a pre-coated 96-well microplate. The microplates were covered with a plate sealer and incubated for 90 min at 37⁰C. After that, the cover was removed, and the liquid in each well was discarded. Subsequently, 100 µl of biotinylated anti-IL-2 antibody or anti-IL-10 antibody was incorporated into each well and incubated for 60 min at 37⁰C. After discarding the liquid from each well and washing it three times with 300 µl of wash buffer, 100 µl of avidin-biotin-peroxidase complex was added to each well, and the microplate was then again incubated for 30 min at 37⁰C. After the incubation period, the liquid was again discarded, and the plate was washed again with 300 µl of wash buffer five times. Following the addition of 90 µl color-developing reagent (TMB) into each well, the plate was incubated in a dark place for 30 min at RT, followed by the addition of 90 µl of stop solution to each well to stop the reaction process. We measured the absorbance with a microplate reader at 450 nm. We calculated the cytokine levels using standard curves and expressed them as pg/ml.

Data presentation and statistical analysis

GraphPad Prism (version 8.0.1) and Statistical Package for the Social Sciences (version 24.0) were used for data analysis. We used descriptive statistics to find the variations in sociodemographic profiles and clinical characteristics between the groups. A T-test and a Chi-square test were employed to determine the statistical level of significance between the mean differences for variables across patients versus HC groups in the case of continuous variables and categorical variables, respectively. We used boxplot graphs for comparisons of analyzed cytokines between patients and HCs. We also generated scatter plot graphs for several clinical variables in GAD patients to show the correlations among the clinical parameters. A correlation analysis was performed to assess the potential association between several demographic and clinical variables in GAD patients. Receiver operating characteristics (ROC) analysis was conducted to determine the diagnostic efficacy of serum IL-2 or IL-10 levels in discriminating GAD patients from HCs. In all cases, statistical significance was considered at p  < 0.05.

Sociodemographic characteristics of the study population

The sociodemographic characteristics of the study population are presented in Table  1 . The GAD patients and HCs were similar in terms of their age, sex, and BMI. Among the participants, about 60% were male and from urban areas. The majority of patients (60.00%) and HCs (68.42%) were unmarried. There was no significant variation between patients and HCs for their education level, occupation, economic status, or smoking status. In contrast, there was a difference between patients and HCs for their family history and previous history of the disease. In GAD patients, 20.00% had a family history, and 40.00% had a previous history of the disease.

Clinical characteristics and laboratory findings

Clinical characteristics and laboratory analysis results are presented in Table  2 . GAD patients displayed markedly higher serum levels of IL-2 (14.81 ± 2.88 pg/ml) compared to HCs (8.08 ± 1.10 pg/ml), and the difference reached the statistically significant level ( p  = 0.037, two-tailed unpaired t-test) (Table  2 ; Fig.  1 ). Though male GAD patients exhibited markedly higher levels of IL-2 compared to male HCs ( p  = 0.048), there was no significant variation in IL-2 levels between female patients and female HCs ( p  > 0.05) (Fig.  1 ). Though some 1.8-fold higher IL-2 serum levels were observed in male GAD patients compared to female GAD patients, the difference did not reach the statistical significance level ( p  = 0.198, two-tailed unpaired t-test). In contrast to the results obtained for IL-2, IL-10 showed a statistically significant ( p  < 0.001) reduction in GAD patients (33.69 ± 1.37 pg/ml) compared to HCs (44.12 ± 3.16 pg/ml) (Fig.  1 ). Similar to the results obtained for IL-2, IL-10 levels showed a statistically significant difference between patients versus HCs when male people were considered (Fig.  1 ). In contrast, there was no significant variation in IL-10 levels between female GAD patients and female HCs ( p  > 0.05).

Distribution of serum IL-2 ( a i ) and IL-10 ( b i ) levels in GAD patients and healthy controls. Comparison of IL-2 and IL-10 levels between GAD patients and their counterparts in control subjects are showed in a i and b i . Comparison of IL-2 and IL-10 levels between male or female GAD patients and their counterparts in control subjects are presented in a ii and b ii

Correlation analysis among different study parameters

We then performed a series of correlation analyses to investigate the association of altered cytokine serum levels with several demographic and clinical variables, such as age, BMI, DSM-5, and GAD-7 scores (Table  3 ). Serum IL-2 levels did not show any positive or negative association with either DSM-5 or GAD-7 scores ( p  > 0.05), suggesting that despite its significant enhancement in GAD patients compared to HCs, IL-2 may not associate with GAD pathophysiology. We also observed no significant association between the ages of the patients and IL-2 serum levels. In contrast, the IL-2 levels of GAD patients maintained a significant and positive correlation with BMI levels of patients ( r  = 0.390, p  < 0.05) which is consistent with the intricate relationship between body mass and enhanced pro-inflammatory responses. Contrary to the results obtained for IL-2, reduced serum IL-10 levels maintained a significant but negative association with both DSM-5 scores ( r =-0.300, p  = 0.045) and GAD-7 scores ( r =-0.315, p  = 0.039), implicating that altered IL-10 levels are linked to GAD development or pathogenesis. However, the age and BMI levels of GAD patients failed to show any positive or negative association with IL-10 serum levels. Analysis also showed a significant and strong positive association between IL-2 and IL-10 serum levels ( r  = 0.471, p  = 0.011) in GAD patients, which might be due to the compensatory enhancement of anti-inflammatory cytokine, IL-10 in response to elevated pro-inflammatory cytokine, IL-2 levels. Also, we displayed these correlations among several clinical variables of GAD patients by scatter plot graphs (Fig.  2 ).

Scatter plot graphs for several clinical variables of GAD patients showing existence or absence of correlation between the clinical parameters. Scatter plot for serum IL-2 levels versus GAD-7 scores ( a ) or DSM-5 scores ( b ) expressing no significant association between IL-2 and both clinical parameters. Scatter plot graphs showing significant association between IL-2 levels and BMI ( c ), IL-10 levels and GAD-7 scores ( d ), IL-10 levels and DSM-5 scores and IL-10 and IL-2 levels ( f )

Receiver operating characteristic curve analysis

Serum IL-10 measurement showed a good performance in differentiating GAD patients from HCs, which was evidenced by its significantly higher area under the curve (AUC) value of 0.793 ( p  < 0.001) with 80.65% sensitivity and 62.79% specificity at a cut-off value of 33.93 pg/ml, in which the cytokine levels below this point indicate disease states (Table  4 ; Fig.  3 ). ROC analysis of serum IL-2 levels failed to discriminate GAD patients from HCs as the AUC value was below the acceptable range (AUC: 0.640; p  = 0.108) with 54.17% sensitivity and 68.18% specificity at a cut-off value of 8.83 pg/ml) (Fig.  3 ; Table  4 ).

Receiver operating characteristic curve (ROC) for serum IL-2 ( a ) and IL-10 levels ( b )

To the best of our knowledge, this is the first case-control study to investigate the potential association between the pathophysiology of GAD and the pro-inflammatory cytokine, IL-2, and the anti-inflammatory cytokine, IL-10, among the Bangladeshi population. We observed that IL-10 serum levels were significantly lower in GAD patients than in HCs, and this reduction was found to be significantly but negatively associated with both DSM-5 scores and GAD-7 scores, demonstrating potential involvement of this anti-inflammatory cytokine in disease severity and symptoms. Our results of a significant reduction in IL-10 levels in GAD patients are in good agreement with those observed in other studies [ 23 , 25 ]. In contrast, our results diverge from those reported by others [ 33 , 54 ] who either reported no significant variation in IL-10 levels between GAD patients and HCs or that IL-10 levels were enhanced in GAD patients compared to HCs. ROC analysis also demonstrated the good predictive value of IL-10 serum measurement in discriminating diseased patients from HCs, suggesting that IL-10 serum level might be a potential biomarker for diagnosis, anti-anxiety drug response monitoring, or disease progression monitoring. Recently, Hou et al. (2019) demonstrated that peripheral serum levels of the pro-inflammatory cytokine IL-6 could be used to monitor the treatment response of SSRIs in GAD [ 14 ]. Similarly, IL-10 might be used as a marker for therapeutic drug monitoring in GAD. However, further longitudinal studies are required to find any causal relationship between IL-10 and disease severity or pathogenesis. On the other hand, serum IL-2 levels were significantly elevated in GAD patients compared to HCs, but they failed to demonstrate any significant association with either DSM-5 scores or GAD-7 scores in Pearson correlation analysis, implying that IL-2 levels might not be associated with the pathophysiology and development of GAD. Consistent with this, ROC analysis showed that IL-2 levels have no significant diagnostic efficacy in differentiating GAD patients from HCs. Further analysis with a larger population size is required to explore the role of IL-2 in the context of GAD severity. Our results are consistent with those reported by Tang et al. (2018), who also observed that GAD patients exhibited significantly higher serum levels of IL-2 compared to HCs [ 19 ]. However, our results are not in agreement with those reported by others who observed either no significant variation in IL-2 levels [ 54 ] or a significant reduction in GAD patients compared to HCs [ 25 , 33 , 34 , 55 ]. We also observed a significant positive correlation between IL-2 and IL-10 levels in GAD patients, which indicates a compensatory mechanism [ 56 ].

Our study provides some valuable insights into the complex and intricate relationship between the dysregulated immune system and GAD. The observed reduction in IL-10 levels in GAD patients in our study suggests a potential immunoregulatory imbalance in GAD, with IL-10 playing a role in modulating anxiety severity. The lack of a significant association between IL-2 serum levels and anxiety severity highlights the nuanced nature of immune dysregulation in GAD, warranting further exploration into the specific mechanisms involved. Elevated levels of pro-inflammatory cytokine, IL-2, and decreased levels of anti-inflammatory cytokine, IL-10, in GAD patients compared to HCs indicate that GAD individuals of the Bangladeshi cohort are characterized by heightened inflammatory responses derived from the imbalance between pro-inflammatory and anti-inflammatory states. Our study finding provides further support for the cytokine hypothesis of anxiety disorder, which proposes that pro-inflammatory cytokine-mediated neuroinflammatory processes can lead to anxiety symptoms or behaviors by downregulating serotonin biosynthesis or enhancing the reuptake of serotonin, resulting in an altered serotonergic neurotransmitter system in the CNS [ 15 ]. The observed significant negative correlation between IL-10 and DSM-5 scores or GAD-7 scores suggests that lowering IL-10 levels might be involved in the pathogenesis of GAD. One of the major implications of our study findings is that IL-10 signaling might be targeted to explore potential novel immunological/immunomodulatory therapies against GAD. The diminished IL-10 levels and their negative correlation with GAD severity suggest a potential avenue for therapeutic intervention. IL-10 might also be used as an anti-inflammatory adjunctive therapy with other pharmacotherapies including SSRIs/SNRIs. However, at this moment, we don’t know the exact mechanism by which lowered levels of IL-10 are linked to higher anxiety severity in GAD patients.

As IL-10 has anti-inflammatory and immunoregulatory activities such as suppression of production of pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α) from microglia and astrocytes, reduction in IL-10 levels in GAD patients in our study led to an imbalance between pro-inflammatory and anti-inflammatory states and resulted in enhanced pro-inflammatory responses, which might be the cause of enhanced anxiety symptoms as inflammatory cytokine-mediated neuroinflammation was reported to be linked with disrupted monoaminergic neurotransmission in the brain. Besides, elevated levels of IL-10 were shown to attenuate anxiety-like behaviors by modulating GABAergic neurotransmission in the amygdala (Patel et al., 2021). IL-10 was also reported to display some neuroprotective activities and has been shown to inhibit neuronal apoptosis and promote neurite outgrowth, axonal outgrowth, and synapse formation in the brain by the JAK1-STAT3 signaling pathway [ 57 ]. In a preclinical study, IL-4 has been shown to cause the shifting of microglia and macrophages from pro-inflammatory to anti-inflammatory neuroprotective phenotypes characterized by excessive production of arginase-1 and PPARγ receptor expression in microglia and macrophage and thereby attenuating brain-injury-mediated anxiety by inhibiting neuronal loss and nerve tracts in the limbic system [ 58 ]. A similar mechanism might be involved in IL-10-mediated anxiety symptom improvement in GAD patients. Further research is required to unravel the exact mechanisms of IL-10-mediated anxiety symptom attenuation in GAD patients.

In terms of diagnostic marker development, as IL-10 serum level measurement demonstrated good performance in discriminating GAD patients from HCs and as IL-10 levels maintained a significant and negative correlation with disease severity, IL-10 serum level raised the possibility of being an objective biomarker for GAD. However, the diagnostic efficacy of this cytokine should be investigated thoroughly using a range of longitudinal studies. Despite this, at this time we can conclude that IL-10 might be used as a risk indicator for assessment of susceptibility to anxiety disorder, resulting in early detection of the disease and prompting the initiation of intervention strategies. This early detection will reduce treatment costs and decrease the prevalence and morbidity associated with this chronic disorder.

The strength of our study is that we designed a set of inclusion and exclusion criteria for the recruitment of participants and followed those criteria in such a way that homogenous population data could be obtained. The strict study design helped us enormously to minimize the potential impact of several confounding variables, including age, sex, BMI, co-morbid diseases, and immunomodulatory drugs, on cytokine levels. However, our study also has some limitations that should be acknowledged. The major limitation of this study is the smaller sample size. We recruited 50 patients and 38 HCs, which does not represent the whole Bangladeshi demographic. It would be better if we could enroll an equal number of cases and controls. For example, we observed that cytokine levels maintained a statistically significant difference between male GAD patients and male HCs. In contrast, no significant variation in cytokine levels was observed when female data were considered. As we have included more male participants (60%) than female participants (40%), the lower sample size of female participants might generate a higher background noise, resulting in lower statistical power, warranting further studies recruiting a larger population size to investigate sex-specific differences in cytokine levels in GAD patients. Our case-control study design is inherently correlational and thus not able to evaluate the causal relationship between altered cytokine levels and GAD. So, at this moment, we cannot conclude whether the altered levels of serum cytokines are the causes of anxiety development or just the outcome of pathophysiological changes.

Longitudinal studies are required to investigate whether altered cytokine levels precede GAD or if it’s just a mere reflection of GAD pathology. Though we have restricted the impacts of several co-variates, other confounding variables, including genetic polymorphism in cytokine genes, the effect of lifestyle or xenobiotics, and dietary habits, were not considered, which might have modulatory effects on serum cytokine levels.

The study provides valuable insights for understanding the pathogenesis of GAD. Despite having elevated IL-2 levels in GAD patients compared to HCs, it failed to demonstrate a significant association with anxiety severity as assessed by GAD-7 scores. In contrast, serum IL-10 levels were significantly reduced in GAD patients compared to HCs and showed a significant negative correlation with anxiety severity, implicating a potential link with the GAD pathophysiology. Our results support the immune hypothesis of GAD development. Our study findings also suggest that IL-10 serum level measurement might offer an objective blood-based biomarker or risk assessment indicator for GAD. We recommend further research employing a larger population size and homogenous data from different areas of Bangladesh to confirm our study findings.

Data availability

All the relevant data and information will be available from the corresponding author upon reasonable request.

Abbreviations

Body mass index

Chronic energy deficiency

Confidence interval

Central nervous system

Diagnostic and statistical manual for mental disorders, 5th edition

Enzyme-linked immunosorbent assay

• Generalized anxiety disorder

Generalized anxiety disorder 7-item scores

Healthy control

• Interleukin-2
• Interleukin-10

Receiver operating characteristic

Standard error mean

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Acknowledgements

The authors are thankful to all the participants of this study. They are also thankful to the staff and physicians at the Department of Psychiatry, BSMMU, for their technical and administrative support. The authors are also thankful for the laboratory support provided by the Department of Pharmacy, University of Asia Pacific, Dhaka Bangladesh.

This research received no specific grant from any funding agency. However, we received partial funding from University of Dhaka, Bangladesh (Centennial Research grant (2nd Phase) for the year of 2020–2021, project title: “Investigation of peripheral pro-inflammatory and anti-inflammatory cytokines and immune balance in Bangladeshi patients with Generalized Anxiety Disorder”).

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Nisat Sarmin, A. S. M. Roknuzzaman and Rapty Sarker contributed equally to this work.

Authors and Affiliations

Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, 1000, Bangladesh

Nisat Sarmin, Rapty Sarker, Mamun -or-Rashid & Zobaer Al Mahmud

Department of Pharmacy, University of Asia Pacific, Dhaka, 1205, Bangladesh

A. S. M. Roknuzzaman

Department of Psychiatry, Bangabandhu Sheikh Mujib Medical University, Shahabagh, Dhaka, 1000, Bangladesh

MMA Shalahuddin Qusar

Department of Pharmacy, Faculty of Pharmacy, University of Dhaka, Dhaka, 1000, Bangladesh

Sitesh Chandra Bachar

School of Pharmacy, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka, 1212, Bangladesh

Eva Rahman Kabir & Md. Rabiul Islam

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NS, ASMR, RS, MRI, and ZAM: Conceptualization, Data curation, Investigation, Writing – original draft. MR, MMASQ, SCB, and ZAM: Funding acquisition, Project administration, Validation. ERK, MRI, and ZAM: Conceptualization, Formal analysis, Methodology, Supervision, Writing – review & editing.

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Correspondence to Md. Rabiul Islam or Zobaer Al Mahmud .

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The research protocol was approved by the Research Ethics Committee (REC) of the University of Asia Pacific, Dhaka, Bangladesh (Ref: UAP/REC/2023/202-S). We briefed the objectives of the study to the participants, and informed consent was obtained from each of them. We conducted this investigation following the Helsinki Declaration’s guiding principles.

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Sarmin, N., Roknuzzaman, A.S.M., Sarker, R. et al. Association of interleukin-2 and interleukin-10 with the pathophysiology and development of generalized anxiety disorder: a case-control study. BMC Psychiatry 24 , 462 (2024). https://doi.org/10.1186/s12888-024-05911-z

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Published : 20 June 2024

DOI : https://doi.org/10.1186/s12888-024-05911-z

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