Reducing Subjectivity in Quality Risk Management: Aligning with ICH Q9(R1)

In a previous post, I discussed how overcoming subjectivity in risk management and decision-making requires fostering a culture of quality and excellence. This is an issue that it is important to continue to evaluate and push for additional improvement.

Overcoming Subjectivity in Risk Management and Decision Making Requires a Culture of Quality and Excellence

The revised ICH Q9(R1) guideline, finalized in January 2023, introduces critical updates to Quality Risk Management (QRM) practices, emphasizing the need to address subjectivity, enhance formality, improve risk-based decision-making, and manage product availability risks. These revisions aim to ensure that QRM processes are more science-driven, knowledge-based, and effective in safeguarding product quality and patient safety. Two years later it is important to continue to build on key strategies for reducing subjectivity in QRM and aligning with the updated requirements.

Understanding Subjectivity in QRM

Subjectivity in QRM arises from personal opinions, biases, heuristics, or inconsistent interpretations of risks by stakeholders. This can impact every stage of the QRM process—from hazard identification to risk evaluation and mitigation. The revised ICH Q9(R1) explicitly addresses this issue by introducing a new subsection, “Managing and Minimizing Subjectivity,” which emphasizes that while subjectivity cannot be entirely eliminated, it can be controlled through structured approaches.

The guideline highlights that subjectivity often stems from poorly designed scoring systems, differing perceptions of hazards and risks among stakeholders, and cognitive biases. To mitigate these challenges, organizations must adopt robust strategies that prioritize scientific knowledge and data-driven decision-making.

Strategies to Reduce Subjectivity

Leveraging Knowledge Management

ICH Q9(R1) underscores the importance of knowledge management as a tool to reduce uncertainty and subjectivity in risk assessments. Effective knowledge management involves systematically capturing, organizing, and applying internal and external knowledge to inform QRM activities. This includes maintaining centralized repositories for technical data, fostering real-time information sharing across teams, and learning from past experiences through structured lessons-learned processes.

By integrating knowledge management into QRM, organizations can ensure that decisions are based on comprehensive data rather than subjective estimations. For example, using historical data on process performance or supplier reliability can provide objective insights into potential risks.

To integrate knowledge management (KM) more effectively into quality risk management (QRM), organizations can implement several strategies to ensure decisions are based on comprehensive data rather than subjective estimations:

Establish Robust Knowledge Repositories

Create centralized, easily accessible repositories for storing and organizing historical data, lessons learned, and best practices. These repositories should include:

Process performance data
Supplier reliability metrics
Deviation and CAPA records
Audit findings and inspection observations
Technology transfer documentation

By maintaining these repositories, organizations can quickly access relevant historical information when conducting risk assessments.

Implement Knowledge Mapping

Conduct knowledge mapping exercises to identify key sources of knowledge within the organization. This process helps to:

Locate subject matter experts
Identify critical knowledge assets
Uncover knowledge gaps that may impact risk assessments

Use the resulting knowledge maps to guide risk assessment teams to relevant information and expertise.

Develop Data Analytics Capabilities

Invest in data analytics tools and capabilities to extract meaningful insights from historical data. For example:

Use statistical process control to identify trends in manufacturing performance
Apply machine learning algorithms to predict potential quality issues based on historical patterns
Utilize data visualization tools to present complex risk data in an easily understandable format

These analytics can provide objective, data-driven insights into potential risks and their likelihood of occurrence.

Integrate KM into QRM Processes

Embed KM activities directly into QRM processes to ensure consistent use of available knowledge:

Include a knowledge gathering step at the beginning of risk assessments
Require risk assessment teams to document the sources of knowledge used in their analysis
Implement a formal process for capturing new knowledge generated during risk assessments

This integration helps ensure that all relevant knowledge is considered and that new insights are captured for future use.

Foster a Knowledge-Sharing Culture

Encourage a culture of knowledge sharing and collaboration within the organization:

Implement mentoring programs to facilitate the transfer of tacit knowledge
Establish communities of practice around key risk areas
Recognize and reward employees who contribute valuable knowledge to risk management efforts

By promoting knowledge sharing, organizations can tap into the collective expertise of their workforce to improve risk assessments.

Implementing Structured Risk-Based Decision-Making

The revised guideline introduces a dedicated section on risk-based decision-making, emphasizing the need for structured approaches that consider the complexity, uncertainty, and importance of decisions. Organizations should establish clear criteria for decision-making processes, define acceptable risk tolerance levels, and use evidence-based methods to evaluate options.

Structured decision-making tools can help standardize how risks are assessed and prioritized. Additionally, calibrating expert opinions through formal elicitation techniques can further reduce variability in judgments.

Addressing Cognitive Biases

Cognitive biases—such as overconfidence or anchoring—can distort risk assessments and lead to inconsistent outcomes. To address this, organizations should provide training on recognizing common biases and their impact on decision-making. Encouraging diverse perspectives within risk assessment teams can also help counteract individual biases.

For example, using cross-functional teams ensures that different viewpoints are considered when evaluating risks, leading to more balanced assessments. Regularly reviewing risk assessment outputs for signs of bias or inconsistencies can further enhance objectivity.

Enhancing Formality in QRM

ICH Q9(R1) introduces the concept of a “formality continuum,” which aligns the level of effort and documentation with the complexity and significance of the risk being managed. This approach allows organizations to allocate resources effectively by applying less formal methods to lower-risk issues while reserving rigorous processes for high-risk scenarios.

For instance, routine quality checks may require minimal documentation compared to a comprehensive risk assessment for introducing new manufacturing technologies. By tailoring formality levels appropriately, organizations can ensure consistency while avoiding unnecessary complexity.

Calibrating Expert Opinions

We need to recognize the importance of expert knowledge in QRM activities, but also acknowledges the potential for subjectivity and bias in expert judgments. We need to ensure we:

Implement formal processes for expert opinion elicitation
Use techniques to calibrate expert judgments, especially when estimating probabilities
Provide training on common cognitive biases and their impact on risk assessment
Employ diverse teams to counteract individual biases
Regularly review risk assessment outputs for signs of bias or inconsistencies

Calibration techniques may include:

Structured elicitation protocols that break down complex judgments into more manageable components
Feedback and training to help experts align their subjective probability estimates with actual frequencies of events
Using multiple experts and aggregating their judgments through methods like Cooke’s classical model
Employing facilitation techniques to mitigate groupthink and encourage independent thinking

By calibrating expert opinions, organizations can leverage valuable expertise while minimizing subjectivity in risk assessments.

Utilizing Cooke’s Classical Model

Cooke’s Classical Model is a rigorous method for evaluating and combining expert judgments to quantify uncertainty. Here are the key steps for using the Classical Model to evaluate expert judgment:

Select and calibrate experts:

Choose 5-10 experts in the relevant field
Have experts assess uncertain quantities (“calibration questions”) for which true values are known or will be known soon
These calibration questions should be from the experts’ domain of expertise

Elicit expert assessments:

Have experts provide probabilistic assessments (usually 5%, 50%, and 95% quantiles) for both calibration questions and questions of interest
Document experts’ reasoning and rationales

Score expert performance:

Evaluate experts on two measures:
a) Statistical accuracy: How well their probabilistic assessments match the true values of calibration questions
b) Informativeness: How precise and focused their uncertainty ranges are

Calculate performance-based weights:

Derive weights for each expert based on their statistical accuracy and informativeness scores
Experts performing poorly on calibration questions receive little or no weight

Combine expert assessments:

Use the performance-based weights to aggregate experts’ judgments on the questions of interest
This creates a “Decision Maker” combining the experts’ assessments

Validate the combined assessment:

Evaluate the performance of the weighted combination (“Decision Maker”) using the same scoring as for individual experts
Compare to equal-weight combination and best-performing individual experts

Conduct robustness checks:

Perform cross-validation by using subsets of calibration questions to form weights
Assess how well performance on calibration questions predicts performance on questions of interest

The Classical Model aims to create an optimal aggregate assessment that outperforms both equal-weight combinations and individual experts. By using objective performance measures from calibration questions, it provides a scientifically defensible method for evaluating and synthesizing expert judgment under uncertainty.

Using Data to Support Decisions

ICH Q9(R1) emphasizes the importance of basing risk management decisions on scientific knowledge and data. The guideline encourages organizations to:

Develop robust knowledge management systems to capture and maintain product and process knowledge
Create standardized repositories for technical data and information
Implement systems to collect and convert data into usable knowledge
Gather and analyze relevant data to support risk-based decisions
Use quantitative methods where feasible, such as statistical models or predictive analytics

Specific approaches for using data in QRM may include:

Analyzing historical data on process performance, deviations, and quality issues to inform risk assessments
Employing statistical process control and process capability analysis to evaluate and monitor risks
Utilizing data mining and machine learning techniques to identify patterns and potential risks in large datasets
Implementing real-time data monitoring systems to enable proactive risk management
Conducting formal data quality assessments to ensure decisions are based on reliable information

Digitalization and emerging technologies can support data-driven decision making, but remember that validation requirements for these technologies should not be overlooked.

Improving Risk Assessment Tools

The design of risk assessment tools plays a critical role in minimizing subjectivity. Tools with well-defined scoring criteria and clear guidance on interpreting results can reduce variability in how risks are evaluated. For example, using quantitative methods where feasible—such as statistical models or predictive analytics—can provide more objective insights compared to qualitative scoring systems.

Organizations should also validate their tools periodically to ensure they remain fit-for-purpose and aligned with current regulatory expectations.

Leverage Good Risk Questions

A well-formulated risk question can significantly help reduce subjectivity in quality risk management (QRM) activities. Here’s how a good risk question contributes to reducing subjectivity:

Clarity and Focus

A good risk question provides clarity and focus for the risk assessment process. By clearly defining the scope and context of the risk being evaluated, it helps align all participants on what specifically needs to be assessed. This alignment reduces the potential for individual interpretations and subjective assumptions about the risk scenario.

Specific and Measurable Terms

Effective risk questions use specific and measurable terms rather than vague or ambiguous language. For example, instead of asking “What are the risks to product quality?”, a better question might be “What are the potential causes of out-of-specification dissolution results for Product X in the next 6 months?”. The specificity in the latter question helps anchor the assessment in objective, measurable criteria.

Factual Basis

A well-crafted risk question encourages the use of factual information and data rather than opinions or guesses. It should prompt the risk assessment team to seek out relevant data, historical information, and scientific knowledge to inform their evaluation. This focus on facts and evidence helps minimize the influence of personal biases and subjective judgments.

Standardized Approach

Using a consistent format for risk questions across different assessments promotes a standardized approach to risk identification and analysis. This consistency reduces variability in how risks are framed and evaluated, thereby decreasing the potential for subjective interpretations.

Objective Criteria

Good risk questions often incorporate or imply objective criteria for risk evaluation. For instance, a question like “What factors could lead to a deviation from the acceptable range of 5-10% for impurity Y?” sets clear, objective parameters for the assessment, reducing the room for subjective interpretation of what constitutes a significant risk.

Promotes Structured Thinking

Well-formulated risk questions encourage structured thinking about potential hazards, their causes, and consequences. This structured approach helps assessors focus on objective factors and causal relationships rather than relying on gut feelings or personal opinions.

Facilitates Knowledge Utilization

A good risk question should prompt the assessment team to utilize available knowledge effectively. It encourages the team to draw upon relevant data, past experiences, and scientific understanding, thereby grounding the assessment in objective information rather than subjective impressions.

By crafting risk questions that embody these characteristics, QRM practitioners can significantly reduce the subjectivity in risk assessments, leading to more reliable, consistent, and scientifically sound risk management decisions.

Fostering a Culture of Continuous Improvement

Reducing subjectivity in QRM is an ongoing process that requires a commitment to continuous improvement. Organizations should regularly review their QRM practices to identify areas for enhancement and incorporate feedback from stakeholders. Investing in training programs that build competencies in risk assessment methodologies and decision-making frameworks is essential for sustaining progress.

Moreover, fostering a culture that values transparency, collaboration, and accountability can empower teams to address subjectivity proactively. Encouraging open discussions about uncertainties or disagreements during risk assessments can lead to more robust outcomes.

Conclusion

The revisions introduced in ICH Q9(R1) represent a significant step forward in addressing long-standing challenges associated with subjectivity in QRM. By leveraging knowledge management, implementing structured decision-making processes, addressing cognitive biases, enhancing formality levels appropriately, and improving risk assessment tools, organizations can align their practices with the updated guidelines while ensuring more reliable and science-based outcomes.

It has been two years, it is long past time be be addressing these in your risk management process and quality system.

Ultimately, reducing subjectivity not only strengthens compliance with regulatory expectations but also enhances the quality of pharmaceutical products and safeguards patient safety—a goal that lies at the heart of effective Quality Risk Management.

Timely Equipment/Facility Upgrades

One of the many fascinating items in the recent Warning Letter to Sanofi is the FDA’s direction to provide a plan to perform “timely technological upgrades to the equipment/facility infrastructure.” This point drives home the point that staying current with technological advancements is crucial for maintaining compliance, improving efficiency, and ensuring product quality. Yet, I think it is fair to say we rarely see it this bluntly put as a requirement.

One of the many reasons this Warning Letter stands out is that this is (as far as I can tell) the same facility that won the ISPE’s Facility of the Year award in 2020. This means it is still a pretty new facility, and since it is one of the templates that many single-use biotech manufacturing facilities are based on, we had best pay attention. If a failure to maintain a state-of-the-art facility can contribute to this sort of Warning Letter, then many companies had best be paying close attention. There is a lot to unpack and learn here.

Establishing an Ongoing Technology Platform Process

To meet regulatory requirements and industry standards, facilities should implement a systematic approach to technological upgrades.

1. Conduct Regular Assessments

At least annually, perform comprehensive evaluations of your facility’s equipment, systems, and processes. This assessment should include:

Review of equipment performance and maintenance, including equipment effectiveness
Analysis of deviation reports and quality issues
Evaluation of current technologies against emerging industry standards
Assessment of facility design and layout for potential improvements

This should be captured as part of the FUSE metrics plan and appropriately evaluated as part of quality governance.

2. Stay Informed on Industry Trends

Keep abreast of technological advancements in biotech manufacturing at minimum by:

Attending industry conferences and workshops
Participating in working groups for key consensus standard writers, such as ISPE and ASTM
Subscribing to relevant publications and regulatory updates
Engaging with equipment vendors and technology providers

3. Develop a Risk-Based Approach

Prioritize upgrades based on their potential impact on product quality, patient safety, and regulatory compliance. Utilize living risk assessments to get a sense of where issues are developing. These should be the evolution of the risk management that built the facility.

4. Create a Technology Roadmap

Develop a long-term plan for implementing upgrades, considering:

Budget constraints and return on investment
Regulatory timelines for submissions and approvals
Production schedules and potential downtime
Integration with existing systems and processes

5. Implement Change Management Procedures

Ensure there is a robust change management process in place to ensure that upgrades are implemented safely and effectively. This should include:

Detailed documentation of proposed changes
Impact assessments on product quality and regulatory compliance
Appropriate verification for new and changed equipment and systems
Training programs for personnel

6. Appropriate Verification – Commissioning, Qualification and Validation

Conduct thorough verification activities to demonstrate that the upgraded equipment or systems meet predetermined specifications and regulatory requirements.

7. Monitor and Review Performance

Continuously monitor the performance of upgraded systems and equipment to ensure they meet expectations and comply with cGMP requirements. Conduct periodic reviews to identify any necessary adjustments or further improvements. This is all part of Stage 3 of the FDA’s process validation model focusing on ongoing assurance that the process remains in a state of control during routine commercial manufacture. This stage is designed to:

Anticipate and prevent issues before they occur
Detect unplanned deviations from the process
Identify and correct problems

Leveraging Advanced Technologies

To stay ahead of regulatory expectations and industry trends, consider incorporating advanced technologies into your upgrade plans:

Single-Use Systems (SUS): Implement disposable components to reduce cleaning and validation requirements while improving flexibility.
Modern Microbial Methods (MMM): Implement advanced techniques used in microbiology that offer significant advantages over traditional culture-based methods
Process Analytical Technology (PAT): Integrate real-time monitoring and control systems to enhance product quality and process understanding.
Data Analytics and Artificial Intelligence: Implement advanced data analysis tools to identify trends, predict maintenance needs, and optimize processes.

Conclusion

Maintaining a state-of-the-art biotech facility requires a proactive and systematic approach to technological upgrades. By establishing an ongoing process for identifying and implementing improvements, facilities can ensure compliance with FDA requirements, align with industry standards, and stay competitive in the rapidly evolving biotech landscape.

Remember that the goal is not just to meet current regulatory expectations but to anticipate future requirements and position your facility at the forefront of biotech manufacturing excellence. By following this comprehensive approach and staying informed on industry developments, you can create a robust, flexible, and compliant manufacturing environment that supports the production of high-quality biopharmaceutical products.

The Importance of a Quality Plan

In the ever-evolving landscape of pharmaceutical manufacturing, quality management has become a cornerstone of success. Two key frameworks guiding this pursuit of excellence are the ICH Q10 Pharmaceutical Quality System and the FDA’s Quality Management Maturity (QMM) program. At the heart of these initiatives lies the quality plan – a crucial document that outlines an organization’s approach to ensuring consistent product quality and continuous improvement.

What is a Quality Plan?

A quality plan serves as a roadmap for achieving quality objectives and ensuring that all stakeholders are aligned in their pursuit of excellence.

Key components of a quality plan typically include:

Organizational objectives to drive quality
Steps involved in the processes
Allocation of resources, responsibilities, and authority
Specific documented standards, procedures, and instructions
Testing, inspection, and audit programs
Methods for measuring achievement of quality objectives

Aligning with ICH Q10 Management Responsibilities

ICH Q10 provides a model for an effective pharmaceutical quality system that goes beyond the basic requirements of Good Manufacturing Practice (GMP). To meet ICH Q10 management responsibilities, a quality plan should address the following areas:

1. Management Commitment

The quality plan should clearly articulate top management’s commitment to quality. This includes allocating necessary resources, participating in quality system oversight, and fostering a culture of quality throughout the organization.

2. Quality Policy and Objectives

Align your quality plan with your organization’s overall quality policy. Define specific, measurable quality objectives that support the broader goals of quality realization, establishing and maintaining a state of control, and facilitating continual improvement.

3. Planning

Outline the strategic approach to quality management, including how quality considerations are integrated into product lifecycle stages from development through to discontinuation.

4. Resource Management

Detail how resources (human, financial, and infrastructural) will be allocated to support quality initiatives. This includes provisions for training and competency development of personnel.

5. Management Review

Establish a process for regular management review of the quality system’s performance. This should include assessing the need for changes to the quality policy, objectives, and other elements of the quality system.

Aligning with FDA’s Quality Management Maturity Model

The FDA’s QMM program aims to encourage pharmaceutical manufacturers to go beyond basic compliance and foster a culture of quality and continuous improvement. To align your quality plan with QMM principles, consider incorporating the following elements:

1. Quality Culture

Describe how your organization will foster a strong quality culture mindset. This includes promoting open communication, encouraging employee engagement in quality initiatives, and recognizing quality-focused behaviors.

2. Continuous Improvement

Detail processes for identifying areas where quality management practices can be enhanced. This might include regular assessments, benchmarking against industry best practices, and implementing improvement projects.

3. Risk Management

Outline a proactive approach to risk management that goes beyond basic compliance. This should include processes for identifying, assessing, and mitigating risks to product quality and supply chain reliability.

4. Performance Metrics

Define key performance indicators (KPIs) that will be used to measure and monitor quality performance. These metrics should align with the FDA’s focus on product quality, patient safety, and supply chain reliability.

5. Knowledge Management

Describe systems and processes for capturing, sharing, and utilizing knowledge gained throughout the product lifecycle. This supports informed decision-making and continuous improvement.

The SOAR Analysis

A SOAR Analysis is a strategic planning framework that focuses on an organization’s positive aspects and future potential. The acronym SOAR stands for Strengths, Opportunities, Aspirations, and Results.

Key Components

Strengths: This quadrant identifies what the organization excels at, its assets, capabilities, and greatest accomplishments.
Opportunities: This section explores external circumstances, potential for growth, and how challenges can be reframed as opportunities.
Aspirations: This part focuses on the organization’s vision for the future, dreams, and what it aspires to achieve.
Results: This quadrant outlines the measurable outcomes that will indicate success in achieving the organization’s aspirations.

Characteristics and Benefits

Positive Focus: Unlike SWOT analysis, SOAR emphasizes strengths and opportunities rather than weaknesses and threats.
Collaborative Approach: It engages stakeholders at all levels of the organization, promoting a shared vision.
Action-Oriented: SOAR is designed to guide constructive conversations and lead to actionable strategies.
Future-Focused: While addressing current strengths and opportunities, SOAR also projects a vision for the future.

Application

SOAR analysis is typically conducted through team brainstorming sessions and visualized using a 2×2 matrix. It can be applied to various contexts, including business strategy, personal development, and organizational change.

By leveraging existing strengths and opportunities to pursue shared aspirations and measurable results, SOAR analysis provides a framework for positive organizational growth and strategic planning.

The SOAR Analysis for Quality Plan Writing

Utilizing a SOAR (Strengths, Opportunities, Aspirations, Results) analysis can be an effective approach to drive the writing of a quality plan. This strategic planning tool focuses on positive aspects and future potential, making it particularly useful for developing a forward-looking quality plan. Here’s how you can leverage SOAR analysis in this process:

Conducting the SOAR Analysis

Strengths

Begin by identifying your organization’s current strengths related to quality. Consider:

Areas where your organization excels in quality management
Significant quality-related accomplishments
Unique quality offerings that set you apart from competitors

Ask questions like:

What are our greatest quality-related assets and capabilities?
Where do we consistently meet or exceed quality standards?

Opportunities

Next, explore external opportunities that could enhance your quality initiatives. Look for:

Emerging technologies that could improve quality processes
Market trends that emphasize quality
Potential partnerships or collaborations to boost quality efforts

Consider:

How can we leverage external circumstances to improve our quality?
What new skills or resources could elevate our quality standards?

Aspirations

Envision your preferred future state for quality in your organization. This step involves:

Defining what you want to be known for in terms of quality
Aligning quality goals with overall organizational vision

Ask:

What is our ideal quality scenario?
How can we integrate quality excellence into our long-term strategy?

Results

Finally, determine measurable outcomes that will indicate success in your quality initiatives. This includes:

Specific, quantifiable quality metrics
Key performance indicators (KPIs) for quality improvement
Key behavior indicators (KBIs) and Key risk indicators (KRIs)

Consider:

How will we measure progress towards our quality goals?
What tangible results will demonstrate our quality aspirations have been achieved?

Writing the Quality Plan

With the SOAR analysis complete, use the insights gained to craft your quality plan:

Executive Summary: Provide an overview of your quality vision, highlighting key strengths and opportunities identified in the SOAR analysis.
Quality Objectives: Translate your aspirations into concrete, measurable objectives. Ensure these align with the strengths and opportunities identified.
Strategic Initiatives: Develop action plans that leverage your strengths to capitalize on opportunities and achieve your quality aspirations. For each initiative, specify:
- Resources required
- Timeline for implementation
- Responsible parties
Performance Metrics: Establish a system for tracking the results identified in your SOAR analysis. Include both leading and lagging indicators of quality performance.
Continuous Improvement: Outline processes for regular review and refinement of the quality plan, incorporating feedback and new insights as they emerge.
Resource Allocation: Based on the strengths and opportunities identified, detail how resources will be allocated to support quality initiatives.
Training and Development: Address any skill gaps identified during the SOAR analysis, outlining plans for employee training and development in quality-related areas.
Risk Management: While SOAR focuses on positives, acknowledge potential challenges and outline strategies to mitigate risks to quality objectives.

By utilizing the SOAR analysis framework, your quality plan will be grounded in your organization’s strengths, aligned with external opportunities, inspired by aspirational goals, and focused on measurable results. This approach ensures a positive, forward-looking quality strategy that engages stakeholders and drives continuous improvement.

A well-crafted quality plan serves as a bridge between regulatory requirements, industry best practices, and an organization’s specific quality goals. By aligning your quality plan with ICH Q10 management responsibilities and the FDA’s Quality Management Maturity model, you create a robust framework for ensuring product quality, fostering continuous improvement, and building a resilient, quality-focused organization.

When to Widen the Investigation

“there is no retrospective review of batch records for batches within expiry, to identify any other process deviations performed without the appropriate corresponding documentation including risk assessment(s).” – 2025 Warning Letter from the US FDA to Sanofi

This comment is about an instance where Sanofi deviated from the validated process by using an unvalidated single use component. Instead of self-identifying, creating a deviation and doing the right change control activities, the company just kept on deviating by using a non-controlled document.

This is a big problem for lots of reasons, from uncontrolled documents, to not using the change control system, to breaking the validated state. What the language quoted above really brings to bear is the question, when should we evaluate our records for other similar instances of this happening, so we can address it.

When a deviation investigation reveals recurring bad decision-making, it is crucial to expand the investigation and conduct a retrospective review of batch records. A good cutoff of this can be only for batches within expiry. This expanded investigation helps identify any other process deviations that may have occurred but were not discovered or documented at the time. Here’s when and how to approach this situation:

Triggers for Expanding the Investigation

Recurring Deviations: If the same or similar deviations are found to be recurring, it indicates a systemic issue that requires a broader investigation.
Pattern of Human Errors: When a pattern of human errors or poor decision-making is identified, it suggests potential underlying issues in training, procedures, or processes.
Critical Deviations: For deviations classified as critical, a more thorough investigation is typically warranted, including a retrospective review.
Potential Impact on Product Quality: If there’s a strong possibility that undiscovered deviations could affect product quality or patient safety, an expanded investigation becomes necessary.

Conducting the Retrospective Review

Timeframe: Review batch records for all batches within expiry, typically covering at least two years of production. Similarily for issues in the FUSE program you might look since the last requalification, or from a decide to go backwards in concentric circles based on what you find.
Scope: Examine not only the specific process where the deviation was found but also related processes or areas that could be affected. Reviewing related processes is critical.
Data Analysis: Utilize statistical tools and trending analysis techniques to identify patterns or anomalies in the historical data.
Cross-Functional Approach: Involve a team of subject matter experts from relevant departments to ensure a comprehensive review.
Documentation Review: Examine batch production records, laboratory control records, equipment logs, and any other relevant documentation.
Root Cause Analysis: Apply root cause analysis techniques to understand the underlying reasons for the recurring issues.

Key Considerations

Risk Assessment: Prioritize the review based on the potential risk to product quality and patient safety.
Data Integrity: Ensure that any retrospective data used is reliable and has maintained its integrity.
Corrective Actions: Develop and implement corrective and preventive actions (CAPAs) based on the findings of the expanded investigation.
Regulatory Reporting: Assess the need for notifying regulatory authorities based on the severity and impact of the findings.

By conducting a thorough retrospective review when recurring bad decision-making is identified, companies can uncover hidden issues, improve their quality systems, and prevent future deviations. This proactive approach not only enhances compliance but also contributes to continuous improvement in pharmaceutical manufacturing processes.

In the case of an issue that rises to a regulatory observation this becomes a firm must. The agency has raised a significant concern and they will want proof that this is a limited issue or that you are holistically dealing with it across the organization.

Concentric Circles of Investigation

Each layer of the investigation may require holistic looks. Utilizing the example above we have:

Layer of Problem	Further Investigation to Answer
Use of unassessed component outside of GMP controls	What other unassessed components were used in the manufacturing process(s)
Failure to document a temporary change	Where else were temporary changes not executed
Deviated from validated process	Where else were there significant deviations from validated processes there were not reported
Problems with components	What other components are having problems that are not being reported and addressed

Take a risk-based approach here is critical.

The Culture Wars Strike Clinical Trials

FDA purges material on clinical trial diversity from its site, showing stakes of Trump DEI ban

In recent years, the importance of diversity in clinical trials has gained significant attention in the medical research community. This focus is not just a matter of inclusivity; it’s a crucial scientific and ethical imperative that directly impacts the quality and applicability of medical research.

Why Diversity in Clinical Trials is Essential

Scientific Validity and Generalizability

Different populations may respond differently to the same treatment due to variations in genetics, lifestyle, and environmental factors. By including diverse participants, researchers can better understand how a treatment works across various groups, leading to more accurate and widely applicable results.

Addressing Health Disparities

Minority groups often experience poorer health outcomes in various diseases. Including these groups in clinical trials is a crucial step towards understanding and addressing these disparities, potentially leading to more targeted and effective treatments for underserved populations.

Innovation and Discovery

Diversity in clinical trials can lead to unexpected discoveries. For instance, the identification of PCSK9, which revolutionized our understanding of cholesterol homeostasis, was a result of studying variations in cardiovascular risk factors among different racial groups.

Alignment with ICH Guidelines

The International Council for Harmonisation (ICH) has recognized the importance of diversity in its updated guidelines, particularly in ICH E6(R3) and ICH E8(R1).

ICH E6(R3)

This guideline emphasizes the importance of including diverse patient populations in clinical trials. It encourages the use of innovative trial designs and technologies to enable wider participation and inclusion of diverse populations. The guideline also stresses the need for quality by design (QbD) and a focus on critical-to-quality factors, which inherently includes considerations of diversity to ensure the reliability of trial results.

ICH E8(R1)

ICH E8(R1) focuses on the general considerations for clinical studies and emphasizes the importance of engaging with a broader range of stakeholders, including patients and patient advocacy groups. This approach naturally leads to more diverse perspectives in trial design and conduct, potentially increasing participation from underrepresented groups.

The Impact of Recent Policy Changes

The recent purge of FDA pages on clinical trial diversity, as reported by STAT News, raises significant concerns about the future of inclusive clinical. This action, part of a wider executive order banning diversity, equity, and inclusion (DEI) initiatives, could have far-reaching consequences:

Reduced Guidance: The removal of these resources may leave researchers and pharmaceutical companies with less clear direction on how to ensure diverse representation in their trials.
Potential Setbacks: Years of progress in improving trial diversity could be undermined, potentially leading to less representative studies and, consequently, less generalizable results.
Health Equity Concerns: This move could exacerbate existing health disparities by reducing the focus on including underrepresented groups in clinical research.
Scientific Integrity: The quality and applicability of clinical trial data may be compromised if diversity is not actively pursued, potentially affecting the safety and efficacy of new treatments for certain populations.

Moving Forward

Despite this setback, the scientific and pharma community must continue to prioritize diversity in clinical trials. The principles outlined in ICH E6(R3) and E8(R1) provide a strong foundation for this effort. Researchers, pharmaceutical companies, and regulatory bodies should:

Continue to develop innovative recruitment strategies to reach diverse populations.
Engage with community leaders and organizations to build trust and awareness about clinical trials.
Design trials with flexibility to improve access for all populations, including the use of decentralized trial elements.
Maintain a focus on quality by design, ensuring that diversity considerations are built into trial planning from the outset.

It is important to remember that E6(r3) is the regulation in Europe, while it is a guidance in the US. So companies need to follow it for their EMA approval possibilities.

In conclusion, diversity in clinical trials is not just a matter of equity; it’s a scientific necessity that ensures the development of safe and effective treatments for all populations. While recent policy changes may present challenges, the medical research community must remain committed to this crucial aspect of clinical research, guided by international standards and ethical imperatives.