Quality by Design (QbD)

It is a graded seminar presentation of Mohammad Abuzar Shaikh Umer on the topic of quality by design (QbD) with case study on naproxen enteric coated pallets model for QbD study.by using plackette burman boxe behnken design and statistical analysis by using ANOVA.

1. Presented by: Mohammad Abuzar Roll No:543 Department : Pharmaceutical Quality Assurance Guided by: Dr. Asha T homas Quality by Design ( QbD ) 1

2. Content: Introduction Concepts and background of QbD Key characterstics of QbD Terminology Elements of QbD ICH Guidelines of QbD Advantages of QbD Case study References 2

3. Introduction FDA announced a new initiative in 2002 for risk management i.e. ( cGMP for the 21st Century: A Risk based Approach) to modernize the FDAs regulation for maintaining better pharmaceutical quality setting up new regulatory framework focusing on QbD , risk management and quality maintaining system. QbD needs good knowledge of final product and in-process or process variables that affect end-product quality . T wo main documents were generated by International Conference on Harmonization (ICH), to guide the quality i.e. 1)ICH-Q8 : (Pharmaceutical Development) 2) ICH-Q9: (Quality Risk Management). 3

4. CONCEPT AND BACKGROUND OF QUALITY BY DESIGN ICH-Q8: (pharmaceutical development) is the guideline to understand the concept of QbD . It defines QbD as " QbD is a systematic approach to design a product of predefined quality and its production process to continually and consistently delivering intended performance of the final product ." The data collected from pharmaceutical development studies and manufacturing experiences, utilized for logical understanding for design space its specifications and process controls, based on sound science and quality risk management”. 4

5. Conventional verses QbD manufacturing Process Variable Starting Material Fixed Manufacturing Process Variable End Product Variable Starting Material Controlled Manufacturing Consistent Finished Product Conventional Process QbD Manufacturing Process 5

6. KEY CHARACTERISTICS OF QbD This is a useful tool for speeding up the development of new drugs. Is based on the notion that quality can be continuously incorporated into the design It has the potential to be used in the development of pharmaceutical products and substances (chemicals and biologics). It can be beneficial to analytical methods . It is possible to implement in full or in part. Is appropriate for use at every stage of the drug's life cycle. It is always encouraged by regulators . 6

7. Terminology Q uality target product profile ( QTT P) C ritical material attributes ( CMA ) Critical Quality Attributes (CQA s) Critical p rocess parameters (CPPs ) 7

8. Quality Target Product Profile (QTTP) According to ICH Q8(R2), QTTP is “Prospective summary of the quality characteristics of a drug product that ideally will be achieved to ensure the desired quality, taking into account safety and efficacy of the drug product”. Basically it is a tool for setting the strategy for drug development. Recently QTTP is widely used in development planning, clinical and commercial decision making, regulatory agency interactions, and risk management. 8

9. C ritical material attributes ( CMA ) It is critical to fail when a true change in a parameter causes it impossible for a product to meet a QTPP. It's important to consider how much adjustment one is willing to make as well as the uniqueness of each input material when deciding which parameters are important. CMAs that fall within an acceptable range or ranges must meet drug substance, excipient , and inprocess material quality. 9

10. Critical quality attributes (CQAs) Once QTPP has been identified, the next step is to identify the relevant CQAs. A CQA is defined as “A physical, chemical, biological or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality”. CQAs are generally associated with raw materials (drug substance, excipients ), intermediates (in-process materials), and drug product. 10

11. Critical p rocess parameters (CPPs ) This means that any measurable input or output of a method step must be managed in order to achieve the required product quality and method consistency. Each item in this read would be a method parameter. Here's how it'd work: Parameters are examined before or during procedures that can have a significant impact on the appearance, purity, and yield of the finished product 11

12. Elements of QbD Target Product Profile Identification of Quality Attributes Design Space Development Risk Assessment to Identification Process /Product Risk Control Strategy Life Cycle Management 12

13. Typical layout of QbD Labeled Use Safety & Efficacy Define Target Product Quality Profile Design Formulation Variables Design Process Variables Identification of Critical Materials Attributes Identification of Critical Process Parameter Establish Control Strategy Modelization & Validation 13

14. ICH Guidelines of QbD Quality systems (Q10) DESIRED STATE Pharmaceuti ca l Development (Q8) Quality risk management ( Q9) 14

15. 1)Pharmaceutical Development (Q8) ICH-Q8 is intended to provide guidelines of Pharmaceutical Development of drug products as explained in the scope of Module 3 of the CTD. While clinical trial stage of the product the guidelines of ICH-Q8 are not applicable for the data at the same time the principles in these guidelines are important to consider clinical trials. 2)Quality Risk Management (Q9) ICH-Q9 Guideline is mainly concerned with the principles and examples of quality risk management (QRM ) . It reviews the materials, processes,equipment used, storage conditions and intended use of the product. 15

16. 3)Pharmaceutical Quality System (Q10) ICH-Q10 Guideline describes quality of drug substances and its products , which includes biotechnology and biological products, throughout the lifecycle of drug products. Each and every stage of product lifecycle should be validated by using elements of Q10 in an appropriate and proportionate manner. 16

17. Advantages of QbD Continuous Improvement Change Control Failure Prevention Reduced Control Right First Time Consistency Final Thought 17

18. Case study On QbD 18

19. Enteric coating is the most common method for manufacturing oral solid preparation especially when the drug acid stability/dissolution or irritation to gastric mucosa is an issue. The coating pellets process consists of two phases: 1) firstly, the pellets core containing drug should be obtained, 2) the pellets are coated with enteric-coating materials. 19

20. Case study flow Preparation of NAP-ECPs Determination of NAP In vitro dissolution of NAP-ECP Risk assessment PlacketteBurman design screening study BoxeBehnken design optimization study Confirmation tests of model Statistical analyses Test results Confirmation test C onclusin 20

21. Naproxen (NAP) , a non-steroidal anti-inflammatory drug (NSAID) T he solubility of which has a positive association of pH , meanwhile along with local gastric irritation, is used as a model compound to develop Naproxen enteric-coated pellets (NAP-ECPs). 21

22. The study aims to prepare naproxen enteric-coated pellets (NAP-ECPs) by fluid-bed coating using QbD principle. Risk assessment was firstly performed by using failure mode and effect analysis (FMEA) methodology. A Plackette Burman design was then used for as sessment of the most important variables affecting enteric-coated pellets characteristics. A Boxe Behnken design was subsequently used for investigating the main, inter active, and quadratic effects of these variables on the response . By FMEA we discovered that eight f actors should be considered to be high/important risk variables as compared with others . 22

23. The responses of acid resistance and cumulative drug release were taken as critical quality attributes (CQAs). Pareto ranking analyses indicated that the coating weight gain (X7), triethyl citrate percentage (X1) and glycerol mono stearate percentage (X2) were the most significant factors affecting the selected responses out of the eight high-risk variables. Optimization with response surface method (RSM) further fully clarified the relationship between X7, X1, X2 and CQAs, and design space was established based on the constraints se t on the responses. Due to the extreme coincidence of the predicted value generated by m odel with the observed value, the accuracy and robustness of the model were confirmed. It could be concluded that a promising NAP-ECPs was successfully designed using QbD approach in a laboratory scale . 23

24. QbD , risk management and quality management in formulation development 24

25. Risk assessment Fish-bone diagram was constructed to identify the potential risks and corresponding causes. Specifically, acid resistance and cumulative drug release were identified as the two CQAs. Based on previous knowledge and initial experimental data,failure mode and effect analysis (FMEA) method were further applied in the risk analysis of the parameters of the pellets coating. Each variable (potential failure mode) was scored in t erms of severity (S), detectability (D) and probability (P). 25

26. An Fish-bone diagram illustrating factors that may have impact on acid resistance and cumulative drug release 26

27. RPN = S×D× P which represents the overall magnitude of the risk . Score generally 1 to 5 5=Worst case 1=best case 3=moderate case Maximum RPN value=125 Minimum RPN value=1 Possible The RPN threshold was set at 60, and any formulation variable or process parameter with an RPN 60 or above was regarded as a potential critical factor 27

28. Pareto chart showing RPN scores for the operating parameters for ECPs coating process. Parameters that had RPN
scores higher than the threshold (RPN = 60) were considered for further experimentation. 28

29. PlacketteBurman design screening study : Based on the risk assessment results, PlacketteBurman study was used to screen significant factors influencing selected CQAs . The PlacketteBurman design screening study with each factor evaluated at low ( - 1) and high ( + 1) levels were summarized in Table 1. The determination of the low and high values was derived from the preliminary study results. The responses evaluated were Y1 and Y2. 29

30. Table 1.P lacketteBurman design screening study 30

31. BoxeBehnken design optimization study 31 Relied on the results of the PlacketteBurman screening study , RSM were applied in order to rapidly achieve the optimal NAP-ECPs. The relationship between the material attributes/process parameters and CQAs was delineated in the DS . DS was determined from the common region of successful operating ranges for multiple CQAs.(Table2) The successful operating ranges for the Y1, Y2, were determined Y1 10% and Y2 80 %,respectively . Based on the prior knowledge space, the CS was also determined. It is expected that operation within the CS will result in a product possessing the desired CQAs

32. Table 2. BoxeBehnken design optimization study 32

33. Basing on the result of the screening study, it is concluded that the responses were impacted significantly by coating weight gain, TEC percentage and GMS percentage. These three parameters were further examined for their interactions and their effects on product quality attributes via the BoxeBehnken DOE. It inferred that spray rate has an inverse impact on the Y1 and Y2 . 33

34. 34

35. T he values of the regression coefficients (coded) of the variables are associated with the influence on the CQAs. The largest part of the absolute values for the coefficients (coded) meant the variables had the most potential effect on the r esponse. 35

36. Analysis of variance (ANOVA) was performed to evaluate the model significance . A model will be considered statistically significant if the P-value represented by “ Prob > F” is 0.05 or less. F-ratio is the “mean square between” divided by the ‘‘mean square within’’ . Low F-ration =more error Adequecy of developed model Estimated by 1) “lack of fit”, R2 2)adjust R2 [R2( adj )] 3)predicted R2 [R2 ( pred )] 36

37. The “lack of fit” estimates t he error variance independently of the model. A significant ‘‘ Lack of Fit’’ (P > 0.05) indicates that the variability measured by the replicates does not explain the gap between predicted a nd experimental data points. 37

38. Response surface (3D) plot of the effects of variables on the acid resistance and on the cumulative drug release of
prepared ECPs. Counter and response surface plots were also analyzed to visualize the effects of the parameters and their interactions on the responses. The quadratic response surface of CQAs as a function of selected variables was given in Fig . 38

39. ( A) coating weight gain/GMS and (B) coating weight gain/TEC were on the acid resistance; (C) coating weight
gain/GMS and (D) coating weight gain/TEC were on cumulative drug release. 39

40. In Design Expert, the desirability response values were set Y1 ≤10 % and Y2≥ 80%. When GMS was at low and high limits set in experiment, Fig. A and B showed the proposed DS, comprised of the yellow overlap region of ranges for the two CQAs. As depicted in Fig. C . the overlay part of the yellow region in Fig. A with B satisfied both Y1≤ 10 % and Y2≥ 80 %, in which GMS was from 3% to 10%. However , coating weight gain and TEC percentage were variables and it was difficult to determine the exact value in real operation during development. 40

41. Design space of prepared ECPs comprised of the overlap region of ranges for the three CQAs using GMS percentage of
(A) 3% and (B) 10%; (C) the theory region and (D) the operating region 41

42. Determination of control strategy of the prepared NAP-ECPs Fig. The control space of the prepared NAP-ECPs 42

43. Confirmation tests To evaluate the accuracy and robustness of the obtainedmodel , a confirmation test was carried out with low, medium and high value of all the eight factors . 43

44. Conclusion This current case study demonstrated how QbD approach can be applied toward the development of the ECPs preparation. Fish-bone paragraph and FMEA analysis favors to identify c ritical formulation and process parameters that affect ECPs product quality. The final aim of this approach is to achieve a process model of the ECPs preparation, thus a DS can be established based on it,and a CS could be further obtained. Confirmation tests were carried out at three levels of low, medium and high of the variables and the results manifested that the prediction and experimental observation were in a good agreement, which confirmed the accuracy and robustness of the model. 44

45. Software Used for Qbd Software available for QbD includes Design Expert®, MODDE®, Unscrambler ®, JMP®, Statstica ®, minitab ® etc, are at the software usually provide interface guide at every step during the entire product development cycle. Software provides support for chemo-metric analysis through multivariate technique like PCA(principle component analysis), PLS(partial least squares) etc. 45

46. References 1.Savitha S, Devi K, Quality by Design ( QbD ): A Review, Journal of Drug Delivery and Therapeutics. 2022; 12(2-s):234-239 2.Bhise JJ, Bhusnure OG, Mule ST, Mujewar IN, Gholve SB, Jadhav PU, A Review on Quality by Design Approach (QBD) for Pharmaceuticals, Journal of Drug Delivery and Therapeutics. 2019; 9(3-s):1137-1146 3.DR. Lalit Singh,Dr Vijay Sharma,Quality by Design ( QbD ) Approach in Pharmaceuticals: Status,Challenges and Next Steps, Article   in  Current Drug Delivery · December 2014;5,2-8 4.Shuling Kan, Jing Lu, Jianping Liu, Junlin Wang ,Yi Zhao,A quality by design ( QbD ) case study on enteric coated pellets: Screening of critical variables and establishment of design space at laboratory scale, Asian journal of pharmaceutical Sciences (2014) 1-11 5.Gandhi A, Roy C; Quality by Design (QbD) in Pharmaceutical Industry: Tools, Perspectives and Challenges; PharmaTutor; 2016; 4(11); 12-20 46

47. Thank you 47