Quality by design (QbD) in Pharma manufacturing .pptx

Quality by design (QbD)

1. Quality by design ( QbD ) Dr. Mayank Sharma

2. Quality: The suitability of either a drug substance or a drug product for its intended use. This term includes such attributes as the identity, strength, and purity (ICH Q6A) Quality by Design ( QbD ) As per Q8(R2) guidance for industry, QbD is 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. It is the essential tool to ensure the quality in the finished products. It’s a scientific, risk-based, holistic, and proactive approach to product development.

3. QbD involves following elements: Target the product profile- Quality Target Product Profile (QTPP). Determine the critical quality attributes (CQAs). Link raw material attributes, formulation and process parameters to CQAs and perform the risk assessment. Develop the design space. Design and implement the control strategy. Manage product life cycle including continual improvement

4. Quality Target Product Profile (QTPP) A 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. The quality target product profile forms the basis of design for the development of the product. Considerations for the QTPP could include: Intended use in clinical setting, route of administration, dosage form, delivery systems;

5. Dosage strength(s); Container closure system; Therapeutic moiety release or delivery and attributes affecting pharmacokinetic characteristics (e.g., dissolution, aerodynamic performance) appropriate to the drug product dosage form being developed; Drug product quality criteria (e.g., sterility, purity, stability and drug release) appropriate for the intended marketed product.

6. Critical quality attributes (CQAs) 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. Knowledge of the QTPP for the product, in combination with information from the prior knowledge, risk assessments, and/or experimentation can be used to develop the list of product CQAs. The list of product CQAs can be modified as product development progresses and new knowledge is gained.

7. Risk assessment Risk assessment and process development experiments can leads to understanding of univariate and multivariate relationships between material attributes (MAs) and process parameters (PPs) and how they affect the product CQAs. Ishikawa-fish bone diagram is used for the establishment of cause-effect relationship among the input variables affecting the quality traits of the drug product. Prioritization exercise is carried out employing the initial risk assessment and quality risk management (QRM) techniques for identifying the “prominent few” input variables, termed as critical material attributes (CMAs) and critical process parameters (CPPs) from the “plausible so many”. This Process is popularly known as “factor screening”.

8. Examples of commonly used risk assessment techniques include: Comparison matrix (CM), Risk assessment matrix (RAM) Failure mode effect analysis (FMEA), Hazard operability analysis (HAZOP) Using these techniques various MAs and PPs are assigned with different risk levels viz. low, medium, and high-risk based on the severity and level of occurrence. Low resolution first order experimental designs e.g. fractional factorial, Plakett-Burman , and Taguchi design are highly helpful for screening and factor influence studies. Before venturing into product/process optimization prioritization of CMAs or CPPs using such QRM and/or screening studies is obligatory.

9. Design guided experimentation and analysis Response surface methodology is considered as pivotal part of the entire QbD exercise for optimization of product and/or process variables recognized from the risk assessment and screening studies. Response surface methodology includes Design of experiment (DOE) and response surface analysis (RSA).

10. Design of experiment (DOE) DOE represents a statistically organized experimental plan that provides the information with high precision on the variation of product/ process response as a function of change in the input variables. DOE is one of the essential element of QbD approach, which was discovered by British statistician sir Ronald Fisher in 1925. DOE primarily have 3 basic objectives: Screening (Identification of critical variables and their levels). Optimization (Identification of optimum input variable composition to achieve optimum response), and Robustness (Identification of sensitivity of response to the small changes in the factor).

11. Fundamentals of DOE: Variables: Pharmaceutical product development is associated with a number of processing and manufacturing parameters that may affect the quality of the finished product. These parameters are considered as variables. Variables are further classified as input and output variables. Input variables: The Variables that have direct influence on the output or response of the finished product and that can be controlled by formulation scientist are termed as input variables or independent variables. Input variables can be determined risk assessment analysis or with prior knowledge.

12. Qualitative Input variables: Affect qualitatively to the finished products. E.g. type of disintegrant , type of surfactant, grades of polymer, granulation method, etc. Quantitative Input variables: Affect quantitatively to the finished products. E.g. Concentration of disintegrants , ratio of surfactants, compression speed, etc. Output variables: These are the measured properties of the process, also called as dependent/response variables. The output variables can be decided based on the QTPP and CQAs. E.g. Disintegration time, hardness of tablet, drug release, particle size, assay, etc.

13. Levels: Values assigned to the variables, can be identified from factors influencing studies. Effect: Extent of change in output or response obtained by changing the input variables. Effect can be explained in terms of orthogonality , interaction, and confounding. Orthogonality : The effect that is linearly proportional to changes in the input variables; i.e. response is mainly attributed to the factor of interest.

14. Interaction: The effect that is not linearly proportional to changes in the input variables and thereby represents a lack of orthogonality . Interaction represents inherent quality of input data and can be assessed quantitatively. Confounding : Confounding is a lack of orthogonality ; i.e. effect obtained is not directly related to the changes in the input variables. Confounding effect represents the improper selection of factors, levels, expt. Design and data analysis. This effect needs to be controlled to avoid misinterpretation of the data. The degree of confounding can be assessed qualitatively by the resolution method.

15. Coding of variables: Transformation of natural variables into a non-dimensional coded variables to facilitate ease in math Calculation, orthogonality of effect, its interpretation, and interaction among the factors. Experimental domain: It is a dimensional space defined by coded variables that is investigated experimentally.

16. Design Space The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality. It is an imaginary area bound by extremes of the tested factors. Working within the design space is not considered as a change. Movement out of the design space is considered to be a change and would normally initiate a regulatory post approval change process .

17. Experimental designs employed during QbD -based product development are as follows

18. Modelization and validation of QbD methodology Modelization is carried out by selection of the optimum mathematical models like linear, quadratic, and cubic models to generate the 2D and 3D-response surface to relate the response variables or CQAs with the input variables or CMAs/CPPs for identifying the underlying interactions among them. Multivariate chemometric techniques Key multivariate chemometric techniques employed for modelization to distinguish the factor response relationship are: Multiple Linear Regression Analysis (MLRA), Partial Least Square Analysis (PLS), Principle Component Analysis (PCA).

19. Model diagnostic plots : Perturbation charts, outlier plot, leverage plot, Cook’s distance plot, and Box-Cox plot are used. Search for optimum solution is accomplished through numerical and graphical optimization techniques like: Desirability function, overlay plot, canonical function, artificial neural network, Brute-force methodology, etc. Subsequent to optimum search, the optimized formulation is located in the design and control space.

20. Control Strategy: As defined in ICH Q10, Control strategy is a planned set of controls, derived from current product and process understanding that assures process performance and product quality. The overall purpose of control strategy is to ensure that the CQAs are within the appropriate range, limit, or distribution to assure drug substance and product quality.

21. Softwares available for DOE optimization Softwares providing support for chemometric analysis through multivariate technique Softwares for QRM execution using Fish-bone diagram, REM, FMEA tech Design-Expert® MNLRA Minitab® Statistica® PCA Risk® Minitab® PLS Statgraphics MODDE® MODDE® FMEA-Pro, Unscrambler® Unscrambler® iGrafx JMP® SIMCA     CODDESA