Building the FUSE(P) User Requirements in an ICH Q8, Q9 and Q10 World

“The specification for equipment, facilities, utilities or systems should be defined in a URS and/or a functional specification. The essential elements of quality need to be built in at this stage and any GMP risks mitigated to an acceptable level. The URS should be a point of reference throughout the validation life cycle.” – Annex 15, Section 3.2, Eudralex Volume 4

User Requirement Specifications serve as a cornerstone of quality in pharmaceutical manufacturing. They are not merely bureaucratic documents but vital tools that ensure the safety, efficacy, and quality of pharmaceutical products.

Defining the Essentials

A well-crafted URS outlines the critical requirements for facilities, equipment, utilities, systems and processes in a regulated environment. It captures the fundamental aspects and scope of users’ needs, ensuring that all stakeholders have a clear understanding of what is expected from the final product or system.

Crafting Good User Requirements

Building Quality from the Ground Up

The phrase “essential elements of quality need to be built in at this stage” emphasizes the proactive approach to quality assurance. By incorporating quality considerations from the outset, manufacturers can:

Minimize the risk of errors and defects
Reduce the need for costly corrections later in the process
Ensure compliance with Good Manufacturing Practice (GMP) standards

Mitigating GMP Risks

Risk management is a crucial aspect of pharmaceutical manufacturing. The URS plays a vital role in identifying and addressing potential GMP risks early in the development process. By doing so, manufacturers can:

Implement appropriate control measures
Design systems with built-in safeguards
Ensure that the final product meets regulatory requirements

The URS as a Living Document

One of the key points in the regulations is that the URS should be “a point of reference throughout the validation life cycle.” This underscores the dynamic nature of the URS and its ongoing importance.

Continuous Reference

Throughout the development, implementation, and operation of a system or equipment, the URS serves as:

A benchmark for assessing progress
A guide for making decisions
A tool for resolving disputes or clarifying requirements

Adapting to Change

As projects evolve, the URS may need to be updated to reflect new insights, technological advancements, or changing regulatory requirements. This flexibility ensures that the final product remains aligned with user needs and regulatory expectations.

Practical Implications

Involve multidisciplinary teams in creating the URS, including representatives from quality assurance, engineering, production, and regulatory affairs.
Conduct thorough risk assessments to identify potential GMP risks and incorporate mitigation strategies into the URS.
Ensure clear, objectively stated requirements that are verifiable during testing and commissioning.
Align the URS with company objectives and strategies to ensure long-term relevance and support.
Implement robust version control and change management processes for the URS throughout the validation lifecycle.

Executing the Control Space from the Design Space

The User Requirements Specification (URS) is a mechanism for executing the control space, from the design space as outlined in ICH Q8. To understand that, let’s discuss the path from a Quality Target Product Profile (QTPP) to Critical Quality Attributes (CQAs) to Critical Process Parameters (CPPs) with Proven Acceptable Ranges (PARs), which is a crucial journey in pharmaceutical development using Quality by Design (QbD) principles. This systematic approach ensures that the final product meets the desired quality standards and user needs.

It is important to remember that this is usually a set of user requirements specifications, respecting the system boundaries.

From QTPP to CQAs

The journey begins with defining the Quality Target Product Profile (QTPP). The QTPP is a comprehensive summary of the quality characteristics that a drug product should possess to ensure its safety, efficacy, and overall quality. It serves as the foundation for product development and includes considerations such as:

Dosage strength
Delivery system
Dosage form
Container system
Purity
Stability
Sterility

Once the QTPP is established, the next step is to identify the Critical Quality Attributes (CQAs). CQAs are physical, chemical, biological, or microbiological properties that should be within appropriate limits to ensure the desired product quality. These attributes are derived from the QTPP and are critical to the safety and efficacy of the product.

From CQAs to CPPs

With the CQAs identified, the focus shifts to determining the Critical Process Parameters (CPPs). CPPs are process variables that have a direct impact on the CQAs. These parameters must be monitored and controlled to ensure that the product consistently meets the desired quality standards. Examples of CPPs include:

Temperature
pH
Cooling rate
Rotation speed

The relationship between CQAs and CPPs is established through risk assessment, experimentation, and data analysis. This step often involves Design of Experiments (DoE) to understand how changes in CPPs affect the CQAs. This is Process Characterization.

Establishing PARs

For each CPP, a Proven Acceptable Range (PAR) is determined. The PAR represents the operating range within which the CPP can vary while still ensuring that the CQAs meet the required specifications. PARs are established through rigorous testing and validation processes, often utilizing statistical tools and models.

Build the Requirements for the CPPs

The CPPs with PARs are process parameters that can affect critical quality attributes of the product and must be controlled within predetermined ranges. These are translated into user requirements. Many will specifically label these as Product User Requirements (PUR) to denote they are linked to the overall product capability. This helps to guide risk assessments and develop an overall verification approach.

Most of Us End Up on the Less than Happy Path

This approach is the happy path that aligns nicely with the FDA’s Process Validation Model.

This can quickly break down in the real world. Most of us go into CDMOs with already qualified equipment. We have platforms on which we’ve qualified our equipment, too. We don’t know the CPPs until just before PPQ.

This makes the user requirements even more important as living documents. Yes, we’ve qualified our equipment for these large ranges. Now that we have the CPPs, we update the user requirements for the Product User Requirements, perform an overall assessment of the gaps, and, with a risk-based approach, do additional verification activations either before or as part of Process Performance Qualification (PPQ).

FUSE and FUSE(P) – Definitions

I’ve been utilizing a few acronyms in a lazy way, and it is important to define them moving forward.

The acronyms FUSE stands for Facility Utility System Equipment; and FUSE(P) adds Process. This framework is used to describe and manage critical components of systems in facilities, particularly in industrial and pharmaceutical manufacturing settings. Here’s a breakdown of its elements:

Facility

This refers to the physical infrastructure where manufacturing or processing takes place. It includes buildings, production areas, and support spaces designed to house equipment and facilitate operations.

Utility Systems

Utilities are critical systems and services that support pharmaceutical and biotech manufacturing production processes. They are essential for maintaining product quality, safety, and regulatory compliance. The mechanical, electrical, and plumbing systems that support facility operations. Key utility systems include:

Heating, Ventilation, and Air Conditioning (HVAC)
Electrical distribution
Water systems (purified, process, and domestic)
Compressed air and gas systems
Waste management systems

System

In this context, a system refers to the integrated collection of equipment, components, and structures that work together to perform a specific function.

Equipment

This encompasses the individual machines, devices, and components used in the facility, manufacturing processes, quality control and elsewhere. Examples include mixing tanks, filling machines, packaging equipment, and quality control instruments

Process

This element refers to the manufacturing or production processes that the facility and its utility systems support. It includes:

Production workflows
Environmental control
Cleaning
Computer systems for managing manufacturing and operational processes:

The FUSE(P) framework emphasizes the interconnected nature of these elements and their collective impact on product quality, safety, and operational efficiency. It guides the design, implementation, and management of facility utility systems to ensure they meet Good Manufacturing Practice (GMP) standards and support reliable production processes.

Assessing the Strength of Knowledge: A Framework for Decision-Making

ICH Q9(R1) emphasizes that knowledge is fundamental to effective risk management. The guideline states that “QRM is part of building knowledge and understanding risk scenarios, so that appropriate risk control can be decided upon for use during the commercial manufacturing phase.”

We need to recognize the inverse relationship between knowledge and uncertainty in risk assessment. ICH Q9(R1) notes that uncertainty may be reduced “via effective knowledge management, which enables accumulated and new information (both internal and external) to be used to support risk-based decisions throughout the product lifecycle”

In order to gauge the confidence in risk assessment we need to gauge our knowledge strength.

The Spectrum of Knowledge Strength

Knowledge strength can be categorized into three levels: weak, medium, and strong. Each level is determined by specific criteria that assess the reliability, consensus, and depth of understanding surrounding a particular subject.

Indicators of Weak Knowledge

Knowledge is considered weak if it exhibits one or more of the following characteristics:

Oversimplified Assumptions: The foundations of the knowledge rely on strong simplifications that may not accurately represent reality.
Lack of Reliable Data: There is little to no data available, or the existing information is highly unreliable or irrelevant.
Expert Disagreement: There is significant disagreement among experts in the field.
Poor Understanding of Phenomena: The underlying phenomena are poorly understood, and available models are either non-existent or known to provide inaccurate predictions.
Unexamined Knowledge: The knowledge has not been thoroughly scrutinized, potentially overlooking critical “unknown knowns.”

Hallmarks of Strong Knowledge

On the other hand, knowledge is deemed strong when it meets all of the following criteria (where relevant):

Reasonable Assumptions: The assumptions made are considered very reasonable and well-grounded.
Abundant Reliable Data: Large amounts of reliable and relevant data or information are available.
Expert Consensus: There is broad agreement among experts in the field.
Well-Understood Phenomena: The phenomena involved are well understood, and the models used provide predictions with the required accuracy.
Thoroughly Examined: The knowledge has been rigorously examined and tested.

The Middle Ground: Medium Strength Knowledge

Cases that fall between weak and strong are classified as medium strength knowledge. This category can be flexible, allowing for a broader range of scenarios to be considered strong. For example, knowledge could be classified as strong if at least one (or more) of the strong criteria are met while none of the weak criteria are present.

A Simplified Approach

For practical applications, a simplified version of this framework can be used:

Strong: All criteria for strong knowledge are met.
Medium: One or two criteria for strong knowledge are not met.
Weak: Three or more criteria for strong knowledge are not met.

Implications for Decision-Making

Understanding the strength of our knowledge is crucial for effective decision-making. Strong knowledge provides a solid foundation for confident choices, while weak knowledge signals the need for caution and further investigation.

When faced with weak knowledge:

Seek additional information or expert opinions
Consider multiple scenarios and potential outcomes
Implement risk mitigation strategies

When working with strong knowledge:

Make decisions with greater confidence
Focus on implementation and optimization
Monitor outcomes to validate and refine understanding

Knowledge Strength and Uncertainty

The concept of knowledge strength aligns closely with the levels of uncertainty.

Strong Knowledge and Low Uncertainty (Levels 1-2)

Strong knowledge typically corresponds to lower levels of uncertainty:

Level 1 Uncertainty: This aligns closely with strong knowledge, where outcomes can be estimated with reasonable accuracy within a single system model. Strong knowledge is characterized by reasonable assumptions, abundant reliable data, and well-understood phenomena, which enable accurate predictions.
Level 2 Uncertainty: While displaying alternative futures, this level still operates within a single system where probability estimates can be applied confidently. Strong knowledge often allows for this level of certainty, as it involves broad expert agreement and thoroughly examined information.

Medium Knowledge and Moderate Uncertainty (Level 3)

Medium strength knowledge often corresponds to Level 3 uncertainty:

Level 3 Uncertainty: This level involves “a multiplicity of plausible futures” with multiple interacting systems, but still within a known range of outcomes. Medium knowledge strength might involve some gaps or disagreements but still provides a foundation for identifying potential outcomes.

Weak Knowledge and Deep Uncertainty (Level 4)

Weak knowledge aligns most closely with the deepest level of uncertainty:

Level 4 Uncertainty: This level leads to an “unknown future” where we don’t understand the system and are aware of crucial unknowns. Weak knowledge, characterized by oversimplified assumptions, lack of reliable data, and poor understanding of phenomena, often results in this level of deep uncertainty.

Implications for Decision-Making

When knowledge is strong and uncertainty is low (Levels 1-2), decision-makers can rely more confidently on predictions and probability estimates.
As knowledge strength decreases and uncertainty increases (Levels 3-4), decision-makers must adopt more flexible and adaptive approaches to account for a wider range of possible futures.
The principle that “uncertainty should always be considered at the deepest proposed level” unless proven otherwise aligns with the cautious approach of assessing knowledge strength. This ensures that potential weaknesses in knowledge are not overlooked.

Conclusion

By systematically evaluating the strength of our knowledge using this framework, we can make more informed decisions, identify areas that require further investigation, and better understand the limitations of our current understanding. Remember, the goal is not always to achieve perfect knowledge but to recognize the level of certainty we have and act accordingly.

Assessing the Quality of Our Risk Management Activities

Twenty years on, risk management in the pharmaceutical world continues to be challenging. Ensure that risk assessments are systematic, structured, and based on scientific knowledge. A large part of the ICH Q9(R1) revision was written to address continued struggles with subjectivity, formality, and decision-making. And quite frankly, it’s clear to me that we, as an industry, are still working to absorb those messages these last two years.

A big challenge is that we struggle to measure the effectiveness of our risk assessments. Quite frankly, this is a great place for a rubric.

Luckily, we have a good tool out there to adopt: the Risk Analysis Quality Test (RAQT1.0), developed by the Society for Risk Analysis (SRA). This comprehensive framework is designed to evaluate and improve the quality of risk assessments. We can apply this tool to meet the requirements of the International Conference on Harmonisation (ICH) Q9, which outlines quality risk management principles for the pharmaceutical industry. From that, we can drive continued improvement in our risk management activities.

Components of RAQT1.0

The Risk Analysis Quality Test consists of 76 questions organized into 15 categories:

Framing the Analysis and Its Interface with Decision Making
Capturing the Risk Generating Process (RGP)
Communication
Stakeholder Involvement
Assumptions and Scope Boundary Issues
Proactive Creation of Alternative Courses of Action
Basis of Knowledge
Data Limitations
Analysis Limitations
Uncertainty
Consideration of Alternative Analysis Approaches
Robustness and Resilience of Action Strategies
Model and Analysis Validation and Documentation
Reporting
Budget and Schedule Adequacy

Application to ICH Q9 Requirements

ICH Q9 emphasizes the importance of a systematic and structured risk assessment process. The RAQT can be used to ensure that risk assessments are thorough and meet quality standards. For example, Category G (Basis of Knowledge) and Category H (Data Limitations) help in evaluating the scientific basis and data quality of the risk assessment, aligning with ICH Q9’s requirement for using available knowledge and data.

The RAQT’s Category B (Capturing the Risk Generating Process) and Category C (Communication) can help in identifying and communicating risks effectively. This aligns with ICH Q9’s requirement to identify potential risks based on scientific knowledge and understanding of the process.

Categories such as Category I (Analysis Limitations) and Category J (Uncertainty) in the RAQT help in analyzing the risks and addressing uncertainties, which is a key aspect of ICH Q9. These categories ensure that the analysis is robust and considers all relevant factors.

The RAQT’s Category A (Framing the Analysis and Its Interface with Decision Making) and Category F (Proactive Creation of Alternative Courses of Action) are crucial for evaluating risks and developing mitigation strategies. This aligns with ICH Q9’s requirement to evaluate risks and determine the need for risk reduction.

Categories like Category L (Robustness and Resilience of Action Strategies) and Category M (Model and Analysis Validation and Documentation) in the RAQT help in ensuring that the risk control measures are robust and well-documented. This is consistent with ICH Q9’s emphasis on implementing and reviewing controls.

Category D (Stakeholder Involvement) of the RAQT ensures that stakeholders are engaged in the risk management process, which is a requirement under ICH Q9 for effective communication and collaboration.

The RAQT can be applied both retrospectively and prospectively, allowing for the evaluation of past risk assessments and the planning of future ones. This aligns with ICH Q9’s requirement for periodic review and continuous improvement of the risk management process.

Creating a Rubric

To make this actionable we need a tool, a rubric, to allow folks to evaluate what goods look like. I would insert this tool into the quality oversite of risk management.

Category A: Framing the Analysis and Its Interface With Decision Making

Criteria	Excellent (4)	Good (3)	Fair (2)	Poor (1)
Problem Definition	Clearly and comprehensively defines the problem, including all relevant aspects and stakeholders	Adequately defines the problem with most relevant aspects considered	Partially defines the problem with some key aspects missing	Poorly defines the problem or misses critical aspects
Analytical Approach	Selects and justifies an optimal analytical approach, demonstrating deep understanding of methodologies	Chooses an appropriate analytical approach with reasonable justification	Selects a somewhat relevant approach with limited justification	Chooses an inappropriate approach or provides no justification
Data Collection and Management	Thoroughly identifies all necessary data sources and outlines a comprehensive data management plan	Identifies most relevant data sources and provides a adequate data management plan	Identifies some relevant data sources and offers a basic data management plan	Fails to identify key data sources or lacks a coherent data management plan
Stakeholder Identification	Comprehensively identifies all relevant stakeholders and their interests	Identifies most key stakeholders and their primary interests	Identifies some stakeholders but misses important ones or their interests	Fails to identify major stakeholders or their interests
Decision-Making Context	Provides a thorough analysis of the decision-making context, including constraints and opportunities	Adequately describes the decision-making context with most key factors considered	Partially describes the decision-making context, missing some important factors	Poorly describes or misunderstands the decision-making context
Alignment with Organizational Goals	Demonstrates perfect alignment between the analysis and broader organizational objectives	Shows good alignment with organizational goals, with minor gaps	Partially aligns with organizational goals, with significant gaps	Fails to align with or contradicts organizational goals
Communication Strategy	Develops a comprehensive strategy for communicating results to all relevant decision-makers	Outlines a good communication strategy covering most key decision-makers	Provides a basic communication plan with some gaps	Lacks a clear strategy for communicating results to decision-makers

This rubric provides a framework for assessing the quality of work in framing an analysis and its interface with decision-making. It covers key aspects such as problem definition, analytical approach, data management, stakeholder consideration, decision-making context, alignment with organizational goals, and communication strategy. Each criterion is evaluated on a scale from 1 (Poor) to 4 (Excellent), allowing for nuanced assessment of performance in each area.

To use this rubric effectively:

Adjust the criteria and descriptions as needed to fit your specific context or requirements.
Ensure that the expectations for each level (Excellent, Good, Fair, Poor) are clear and distinguishable.

My next steps will be to add specific examples or indicators for each level to provide more guidance to both assessors and those being assessed.

I also may, depending on internal needs, want to assign different weights to each criterion based on their relative importance in your specific context. In this case I think each ends up being pretty similar.

I would then go and add the other sections. For example, here is category B with some possible weighting.

Category B: Capturing the Risk Generating Process (RGP)

Component	Weight Factor	Excellent	Satisfactory	Needs Improvement	Poor
B1. Comprehensiveness	4	The analysis includes: i) A structured taxonomy of hazards/events demonstrating comprehensiveness ii) Each scenario spelled out with causes and types of change iii) Explicit addressing of potential “Black Swan” events iv) Clear description of implications of such events for risk management	The analysis includes 3 out of 4 elements from the Excellent criteria, with minor gaps that do not significantly impact understanding	The analysis includes only 2 out of 4 elements from the Excellent criteria, or has significant gaps in comprehensiveness	The analysis includes 1 or fewer elements from the Excellent criteria, severely lacking in comprehensiveness
B2. Basic Structure of RGP	2	Clearly identifies and accounts for the basic structure of the RGP (e.g. linear, chaotic, complex adaptive) AND Uses appropriate mathematical structures (e.g. linear, quadratic, exponential) that match the RGP structure	Identifies the basic structure of the RGP BUT does not fully align mathematical structures with the RGP	Attempts to identify the RGP structure but does so incorrectly or incompletely OR Uses mathematical structures that do not align with the RGP	Does not identify or account for the basic structure of the RGP
B3. Complexity of RGP	3	Lists all important causal and associative links in the RGP AND Demonstrates how each link is accounted for in the analysis	Lists most important causal and associative links in the RGP AND Demonstrates how most links are accounted for in the analysis	Lists some causal and associative links but misses key elements OR Does not adequately demonstrate how links are accounted for in the analysis	Does not list causal and associative links or account for them in the analysis
B4. Early Warning Detection	3	Includes a clear process for detecting early warnings of potential surprising risk aspects, beyond just concrete events	Includes a process for detecting early warnings, but it may be limited in scope or not fully developed	Mentions the need for early warning detection but does not provide a clear process	Does not address early warning detection
B5. System Changes	2	Fully considers the possibility of system changes AND Establishes adequate mechanisms to detect those changes	Considers the possibility of system changes BUT mechanisms to detect changes are not fully developed	Mentions the possibility of system changes but does not adequately consider or establish detection mechanisms	Does not consider or address the possibility of system changes

I definitely need to go back and add more around structure requirements. The SRA RAQT tool needs some more interpretation here.

Category C: Risk Communication

Component	Weight Factor	Excellent	Satisfactory	Needs Improvement	Poor
C1. Integration of Communication into Risk Analysis	3	Communication is fully integrated into the risk analysis following established norms). All aspects of the methodology are clearly addressed including context establishment, risk assessment (identification, analysis, evaluation), and risk treatment. There is clear evidence of pre-assessment, management, appraisal, characterization and evaluation. Knowledge about the risk is thoroughly categorized.	Communication is integrated into the risk analysis following most aspects of established norms. Most key elements of methodologies like ISO 31000 or IRGC are addressed, but some minor aspects may be missing or unclear. Knowledge about the risk is categorized, but may lack some detail.	Communication is partially integrated into the risk analysis, but significant aspects of established norms are missing. Only some elements of methodologies like ISO 31000 or IRGC are addressed. Knowledge categorization about the risk is incomplete or unclear.	There is little to no evidence of communication being integrated into the risk analysis following established norms. Methodologies like ISO 31000 or IRGC are not followed. Knowledge about the risk is not categorized.
C2. Adequacy of Risk Communication	3	All considerations for effective risk communication have been applied to ensure adequacy between analysts and decision makers, analysts and other stakeholders, and decision makers and stakeholders. There is clear evidence that all parties agree the communication is adequate.	Most considerations for effective risk communication have been applied. Communication appears adequate between most parties, but there may be minor gaps or areas where agreement on adequacy is not explicitly stated.	Some considerations for effective risk communication have been applied, but there are significant gaps. Communication adequacy is questionable between one or more sets of parties. There is limited evidence of agreement on communication adequacy.	Few to no considerations for effective risk communication have been applied. There is no evidence of adequate communication between analysts, decision makers, and stakeholders. There is no indication of agreement on communication adequacy.

Category D: Stakeholder Involvement

Criteria	Weight	Excellent (4)	Satisfactory (3)	Needs Improvement (2)	Poor (1)
Stakeholder Identification	4	All relevant stakeholders are systematically and comprehensively identified	Most relevant stakeholders are identified, with minor omissions	Some relevant stakeholders are identified, but significant groups are missed	Few or no relevant stakeholders are identified
Stakeholder Consultation	3	All identified stakeholders are thoroughly consulted, with their perceptions and concerns fully considered	Most identified stakeholders are consulted, with their main concerns considered	Some stakeholders are consulted, but consultation is limited in scope or depth	Few or no stakeholders are consulted
Stakeholder Engagement	3	Stakeholders are actively engaged throughout the entire risk management process, including problem framing, decision-making, and implementation	Stakeholders are engaged in most key stages of the risk management process	Stakeholders are engaged in some aspects of the risk management process, but engagement is inconsistent	Stakeholders are minimally engaged or not engaged at all in the risk management process
Effectiveness of Involvement	2	All stakeholders would agree that they were effectively consulted and engaged	Most stakeholders would agree that they were adequately consulted and engaged	Some stakeholders may feel their involvement was insufficient or ineffective	Most stakeholders would likely feel their involvement was inadequate or ineffective

Category E: Assumptions and Scope Boundary Issues

Criterion	Weight	Excellent (4)	Satisfactory (3)	Needs Improvement (2)	Poor (1)
E1. Important assumptions and implications listed	4	All important assumptions and their implications for risk management are systematically listed in clear language understandable to decision makers. Comprehensive and well-organized.	Most important assumptions and implications are listed in language generally clear to decision makers. Some minor omissions or lack of clarity.	Some important assumptions and implications are listed, but significant gaps exist. Language is not always clear to decision makers.	Few or no important assumptions and implications are listed. Language is unclear or incomprehensible to decision makers.
E2. Risks of assumption deviations evaluated	3	Risks of all significant assumptions deviating from the actual Risk Generating Process are thoroughly evaluated. Consequences and implications are clearly communicated to decision makers.	Most risks of significant assumption deviations are evaluated. Consequences and implications are generally communicated to decision makers, with minor gaps.	Some risks of assumption deviations are evaluated, but significant gaps exist. Communication to decision makers is incomplete or unclear.	Few or no risks of assumption deviations are evaluated. Little to no communication of consequences and implications to decision makers.
E3. Scope boundary issues and implications listed	3	All important scope boundary issues and their implications for risk management are systematically listed in clear language understandable to decision makers. Comprehensive and well-organized.	Most important scope boundary issues and implications are listed in language generally clear to decision makers. Some minor omissions or lack of clarity.	Some important scope boundary issues and implications are listed, but significant gaps exist. Language is not always clear to decision makers.	Few or no important scope boundary issues and implications are listed. Language is unclear or incomprehensible to decision makers.

Category F: Proactive Creation of Alternative Courses of Action

Criteria	Weight	Excellent (4)	Satisfactory (3)	Needs Improvement (2)	Poor (1)
Systematic generation of alternatives	4	A comprehensive and structured process is used to systematically generate a wide range of alternative courses of action, going well beyond initially considered options	A deliberate process is used to generate multiple alternative courses of action beyond those initially considered	Some effort is made to generate alternatives, but the process is not systematic or comprehensive	Little to no effort is made to generate alternatives beyond those initially considered
Goal-focused creation	3	All generated alternatives are clearly aligned with and directly address the stated goals of the analysis	Most generated alternatives align with the stated goals of the analysis	Some generated alternatives align with the goals, but others seem tangential or unrelated	Generated alternatives (if any) do not align with or address the stated goals
Consideration of robust/resilient options	3	Multiple robust and resilient alternatives are developed to address various uncertainty scenarios	At least one robust or resilient alternative is developed to address uncertainty	Robustness and resilience are considered, but not fully incorporated into alternatives	Robustness and resilience are not considered in alternative generation
Examination of unintended consequences	2	Thorough examination of potential unintended consequences for each alternative, including action-reaction spirals	Some examination of potential unintended consequences for most alternatives	Limited examination of unintended consequences for some alternatives	No consideration of potential unintended consequences
Documentation of alternative creation process	1	The process of alternative generation is fully documented, including rationale for each alternative	The process of alternative generation is mostly documented	The process of alternative generation is partially documented	The process of alternative generation is not documented

Category G: Basis of Knowledge

Criterion	Weight	Excellent (4)	Satisfactory (3)	Needs Improvement (2)	Poor (1)
G1. Characterization of knowledge basis	4	All inputs are clearly characterized (empirical, expert elicitation, testing, modeling, etc.). Distinctions between broadly accepted and novel analyses are explicitly stated.	Most inputs are characterized, with some minor omissions. Distinctions between accepted and novel analyses are mostly clear.	Some inputs are characterized, but significant gaps exist. Limited distinction between accepted and novel analyses.	Little to no characterization of knowledge basis. No distinction between accepted and novel analyses.
G2. Strength of knowledge adequacy	3	Strength of knowledge is thoroughly characterized in terms of its adequacy to support risk management decisions. Limitations are clearly articulated.	Strength of knowledge is mostly characterized, with some minor gaps in relating to decision support adequacy.	Limited characterization of knowledge strength. Unclear how it relates to decision support adequacy.	No characterization of knowledge strength or its adequacy for decision support.
G3. Communication of knowledge limitations	4	All knowledge limitations and their implications for risk management are clearly communicated to decision makers in understandable language.	Most knowledge limitations and implications are communicated, with minor clarity issues.	Some knowledge limitations are communicated, but significant gaps exist in clarity or completeness.	Knowledge limitations are not communicated or are presented in a way decision makers cannot understand.
G4. Consideration of surprises and unforeseen events	3	Thorough consideration of potential surprises and unforeseen events (Black Swans). Their importance is clearly articulated.	Consideration of surprises and unforeseen events is present, with some minor gaps in articulating their importance.	Limited consideration of surprises and unforeseen events. Their importance is not clearly articulated.	No consideration of surprises or unforeseen events.
G5. Conflicting expert opinions	2	All conflicting expert opinions are systematically considered and reported to decision makers as a source of uncertainty.	Most conflicting expert opinions are considered and reported, with minor omissions.	Some conflicting expert opinions are considered, but significant gaps exist in reporting or consideration.	Conflicting expert opinions are not considered or reported.
G6. Consideration of unconsidered knowledge	2	Explicit measures are implemented to check for knowledge outside the analysis group (e.g., independent review).	Some measures are in place to check for outside knowledge, but they may not be comprehensive.	Limited consideration of knowledge outside the analysis group. No formal measures in place.	No consideration of knowledge outside the analysis group.
G7. Consideration of disregarded low-probability events	1	Explicit measures are implemented to check for events disregarded due to low probabilities based on critical assumptions.	Some consideration of low-probability events, but measures may not be comprehensive.	Limited consideration of low-probability events. No formal measures in place.	No consideration of events disregarded due to low probabilities.

This rubric, once done, is a tool to guide assessment and provide feedback. It should be flexible enough to accommodate unique aspects of individual work while maintaining consistent standards across evaluations. I’d embed it in the quality approval step.

Requirements for Knowledge Management

I was recently reviewing the updated Q9(R1) Annex 1- Q8/Q9/Q10 Questions & Answers (R5) related to ICH Q9(R1) Quality Risk Management (QRM) that were approved on 30 October 2024 and what they say about knowledge management. While there are some fun new questions asked, I particularly like “Do regulatory agencies expect to see a formal knowledge management approach during inspections?”

To which the answer was: “No. There is no regulatory requirement for a formal knowledge management system. However. it is expected that knowledge from different processes and
systems is appropriately utilised. Note: ‘formal’ in this context means a structured approach using a recognised methodology or (IT-) tool, executing and documenting something in a transparent and detailed manner.”

What does appropriately utilized mean? What is the standard for determining it? The agencies are quite willing to leave that to you to figure out.

As usual I think it is valuable to agree upon a few core assumptions for what appropriate utilization of knowledge management might look like.

Accessibility and Sharing

Knowledge should be easily accessible to those who need it within the organization. This means:

Implementing centralized knowledge repositories or databases
Ensuring information is structured and organized for easy retrieval
Fostering a culture of knowledge sharing among employees

Relevance and Accuracy

Appropriately utilized knowledge is:

Up-to-date and accurate
Relevant to the specific needs of the organization and its employees
Regularly reviewed and updated to maintain its value

Integration into Processes

Knowledge should be integrated into the organization’s workflows and decision-making processes:

Incorporated into standard operating procedures
Used to inform strategic planning and problem-solving
Applied to improve efficiency and productivity

Measurable Impact

Appropriate utilization of knowledge should result in tangible benefits:

Improved decision-making
Increased productivity and efficiency
Enhanced innovation and problem-solving capabilities
Reduced duplication of efforts

Continuous Improvement

Appropriate utilization of knowledge includes a commitment to ongoing improvement:

Regular assessment of knowledge management processes
Gathering feedback from users
Adapting strategies based on changing organizational needs