5-Scientific Approach to Validation.pptx

Validation is a Science and even the most mundane tasks in healthcare environments, like hand washing, must be validated (to ensure correct method and other factors like correct hand wash agent) and also verified - to create an acceptable baseline for post handwash counts.

1. DEVELOPMENT AND MANUFACTURE OF DRUG SUBSTANCES

2. OVERALL PROCESS DEVELOPMENT SUMMARY ICH Q11: 3.2.1 - Overall Process Development Summary “It is recommended that the manufacturing process development section begin with a narrative summary that describes important milestones in the development of the process and explains how they are linked to assuring that the intended quality of the drug substance is achieved. The following should be included in the summary: List of drug substance CQAs; Brief description of the stages in the evolution of the manufacturing process and relevant changes to the control strategy; Brief description of the material attributes and process parameters identified as impacting drug substance CQAs; Brief description of the development of any design spaces”.

3. THE CONTROL STRATEGY THE KEY TO PRODUCT LIFECYCLE MANAGEMENT, PROCESS UNDERSTANDING AND CONTINUOUS PROCESS VERIFICATION AND IMPROVEMENT A planned set of controls, derived from current product and process understanding, that assures process performance and product quality. The controls can include parameters and attributes related to drug substance and drug product materials and components, facility and equipment operating conditions, in-process controls, finished product specifications, and the associated methods and frequency of monitoring and control. (ICH Q10)

4. Approaches to developing a control strategy ICH (Q8) Approaches to Product Development Aspect Minimal Approaches Enhanced, Quality by Design Approaches Overall Pharmaceutical Developme n t Mai n l y empir ic al Developmental research often conducted one variable at a time Systematic, relating mechanistic understanding of material attributes and process parameters to drug product CQAs Multivariate experiments to understand product and process Establishment of design space PAT tools utilized Manufacturing Process Fixed Validation primarily based on initial full‐ scale batches Focus on optimization and reproducibility Adjustable within design space Lifecycle approach to validation and, ideally, continuous process verification Focus on control strategy and robustness Use of statistical process control methods Process Controls In‐process tests primarily for go/no go decisions Off ‐ lin e analysis PAT tools utilized with appropriate feed forward and feedback controls Process operations tracked and Product Specifications Primary means of control Based on batch data available at time of registration Part of the overall quality control strategy Based on desired product performance with relevant supportive data Control Strategy Drug product quality controlled primarily by intermediates (in‐ process materials) and end product testing Drug product quality ensured by risk‐ based control strategy for well understood product and process Quality controls shifted upstream, with the possibility of real‐time release testing or reduced end‐ product testing

5. Approaches to developing a control strategy The traditional (MINIMAL) approach Disadvantages: With a reduced or observation-based product/process knowledge and understanding, the linkage between input materials control, in-process controls, and final product quality unclear. Empirical product and process knowledge is verifiable by observation or experience rather than theory or pure logic. It is often difficult to ensure that a control performed at a certain step of the process ensures conformance to the final CQA range or QTPP.

6. Approaches to developing a control strategy The Quality by Design approac h Advantages: With an improved product/process knowledge and understanding, the linkage between input materials control, in-process controls, and final product quality becomes clearer. Product and process knowledge is verifiable by demonstrated theory using DOE techniques and multi-variable curve fitting to optimize the product QTPP.

7. Lifecycle of the control strategy Control Strategy Lifecycle Initial product and process characterization and understanding Regulatory File Submission including proposal for future management to process parameters and controls. Monitoring and Continuous Process Verification over time Pharmaceutical Development Technology Transfer Commercial Manufacture Product Lifecycle

8. The Scientific Approach to Validation

9. Identifying, reporting, and mitigating sources of uncertainty in a research study. Plant AL, Becker CA, Hanisch RJ, Boisvert RF, Possolo AM, et al. (2018) How measurement science can improve confidence in research results. PLOS Biology 16(4): e2004299. https://doi.org/10.1371/journal.pbio.2004299 , https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2004299 1. State the plan Clearly articulate the goals of the study and the basis for generalizability to other settings, species, conditions, etc., if claimed in the conclusions. State the experimental design, including variables to be tested, numbers of samples, statistical models to be used, how sampling is performed, etc. Provide preliminary data or evaluations that support the selection of protocols and statistical models. Identify and evaluate assumptions related to anticipated experiments, theories, and methods for analyzing results. 2. Look for systemic sources of bias and uncertainty Characterize reagents and control samples (e.g., composition, purity, activity, etc.). Ensure that experimental equipment is responding correctly (e.g., through use of calibration materials and verification of vendor specifications). Show that positive and negative control samples are appropriate in composition, sensitivity, and other characteristics to be meaningful indictors of the variables being tested. Evaluate the experimental environment (e.g., laboratory conditions such as temperature and temperature fluctuations, humidity, vibration, electronic noise, etc.). 3. Characterize the quality and robustness of data and protocols Acquire supplementary data that provide indicators of the quality of experimental data. These indicators include precision (i.e., repeatability, with statistics such as standard deviation and variance), accuracy (which can be assessed by applying alternative [orthogonal] methods or by comparison to a reference material), sensitivity to environmental or experimental perturbates (by testing for assay robustness to putatively insignificant experimental protocol changes), and the dynamic range and response function of the experimental protocol or assay (and assuring that data points are within that valid range). Reproduce the data using different technicians, laboratories, instruments, methods, etc. (i.e., meet the conditions for reproducibility as defined in the VIM). 4. Minimize bias in data reduction and interpretation of results Justify the basis for the selected statistical analyses. Quantify the combined uncertainties of the values measured using methods in the GUM [23] and other sources [27]. Evaluate the robustness and accuracy of algorithms, code, software, and analytical models to be used in analysis of data (e.g., by testing against reference datasets). Compare data and results with previous data and results (yours and others’). Identify other uncontrolled potential sources of bias or uncertainty in the data. Consider feasible alternative interpretations of the data. Evaluate the predictive power of models used. 5. Minimize confusion and uncertainty in reporting and dissemination Make available all supplementary material that fully describes the experiment/simulation and its analysis. Release well‐documented data and code used in the study. Collect and archive metadata that provide documentation related to process details, reagents, and other variables; include with numerical data as part of the dataset.

10. *“It is important that the manufacturer prepare a written validation protocol which specifies the procedures (and tests) to be conducted and the data to be collected. The purpose for which data are collected must be clear, and data must reflect facts and be collected carefully and accurately. The protocol should specify a sufficient number of replicate process runs to demonstrate reproducibility and provide an accurate measure of variability among successive runs. ” Validation does not equal three batches a common misconception about process validation *This seminar was presented to United States Food and Drug Administration policy advisors, management, and field staff in Silver Spring, Maryland, in May 2012. It summarizes the regulatory drivers that led to the publication of FDA’s 2011 Process Validation Guidance for industry. In particular, the article emphasizes that process validation is a meaningful scientific endeavor that strives to ensure process control and product quality rather than a discrete and isolated activity.

11. Validation Protocols MUST reflect a scientific approach Is empirical (i.e. based on, concerned with, or verifiable by observation or experience rather than theory or pure logic. Relies upon data (i.e. uses some type of measurement to analyze results and feeding these back to theories about what we know of the world. Is intellectual and visionary (i.e. the visionary part of science is relating findings back to the real world. This process is known as induction. Uses experiments to test predictions (i.e. this can also mean observation of the natural world and through this process of induction and generalization make predictions of how we think something should behave and design an experiment to test it. Is systematic and methodical (i.e. it takes more than one experiment to satisfy statistical models.

12. Process Validation – Definitions (and the solution in the EU definition to the “worst-case” scenario The documented evidence that the process, operated within established parameters , can perform effectively and reproducibly to produce a medicinal product meeting its predetermined specifications and quality attributes. EU GMP Guide, Annex 15 & EU Draft Guideline, 29 Mar 2012 “The collection and evaluation of data, from the process design stage throughout production, which establishes scientific evidence that a process is capable of consistently delivering quality products.” USA FDA Guideline – Jan 2011 What are the keywords in these two definitions? How are they different? What is “Scientific Evidence”?

13. Critical Process Parameters and Critical Quality Attributes – the key to any validation effort ICH Q8(R2) provides the following definitions using the term critical: Critical process parameter (CPP). A process parameter whose variability has an impact on a critical quality attribute and, therefore, should be monitored or controlled to ensure the process produces the desired quality. Critical quality attribute (CQA). 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. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, ICH Q8 Pharmaceutical Development

14. By using risk analysis as a means to determine criticality, an opportunity arises to resolve these potential conflicts in CQA. CQAs should be classified based on the potential risks to the patient. CPPs should be separated into those that have substantial impact on the CQAs and those with minor or no impact. The binary yes/no decision transforms into a continuum of criticality ranging from high impact to low impact critical to not critical. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, ICH Q8 Pharmaceutical Development Critical Process Parameters and Critical Quality Attributes – the key to any validation effort

15. Definition of a Validation Protocol A validation protocol describes prospectively, the experimental method to be followed, and acceptance criteria to be achieved, to successfully qualify a particular critical parameter of the process [or system] to be validated.

16. General Findings on Validation Protocols and Reports The Acceptance Criteria MUST be linked to the Critical Process Parameters and the Validation Protocol in turn must show a causal link between CPP and CQA. There is a huge excess of “Acceptance Criteria” in all VP that have nothing at all to do with the CPP, which must be identified. The pages and pages of false “Acceptance Criteria” are wasted and will attract adverse comment from experienced auditors. The documents are sometimes very detailed with several attachments so the opening page, the Table of Contents or Index should reference all documents and attachments in the protocol.

17. General Findings on Validation Protocols and Reports The first few pages should clearly identify the following: Identification of the process to be validated Identification of device(s) to be manufactured using this process Objective and measurable criteria for a successful validation – these criteria must be able to be evaluated from the data collected and MUST NOT refer to compliance to another SOP. Length and duration of the validation Shifts, operators, equipment to be used in the process Identification of utilities for the process equipment and quality of the utilities Identification of operators and required operator qualification Complete description of the process

18. General Findings on Validation Protocols and Reports The first few pages should clearly identify the following (continued): Relevant specifications that relate to the product, components, manufacturing materials, etc. Any special controls or conditions to be placed on preceding processes during the validation Process parameters to be monitored, and methods for controlling and monitoring Product characteristics to be monitored and method for monitoring Any subjective criteria used to evaluate the product Definition of what constitutes non-conformance for both measurable and subjective criteria Statistical methods for data collection and analysis Consideration of maintenance and repairs of manufacturing equipment Criteria for revalidation

19. Revalidation criteria must be described and may include: Change(s) in the actual process that may affect quality or its validation status Negative trend(s) in quality indicators (e.g. “Runs” charts) Change(s) in the product design which affects the process (e.g. new closure system) transfer of processes from one facility to another (even within the same site). change of the application of the process. General Findings on Validation Protocols and Reports Acceptance criteria must be related to Critical Process Parameters (CPP) and not just be steps as in a batch processing document, e.g. “hang dipstick to dry”. Re-qualification protocols must minimally reference the original founding protocol’s acceptance criteria and qualify the process against those. Photographs and illustrations are very good aids but MUST be annotated and explained.

20. When does cGMP and a Scientific approach start? Compliance with cGMPs is required from Phase 1 (IND) onward adequate documentation (traceability) and facilities sterility assurance QC/QA oversight Certain cGMPs develop with product defined in-process controls full process and assay validation (not mandated for Phase 1 IND) but data collection for QbD can begin at Phase 1.

21. Common IND researcher responses to cGMP “Our product is different …” “Its just a lot of unnecessary paperwork…” “Our product is too complex …” “We already know everything about our product …” “We already know this process is sound …” “We are confident these methods will work …” “It isn’t technically possible …” “I didn’t know we were going to submit this…”

22. Common IND researcher responses to cGMP “It will be too difficult …” “Trust us, we’re scientists …” “Why do we need to audit the data? It’s been published…” “Management won’t allow us …” “We just want to get through Phase II …” “The reviewer didn’t ask about this before …” “The FDA/EMA really wants this product …”