IMPALA Consortium's Role in Advancing Biopharmaceutical Quality through Data Analytics

Inter-Company quality Analytics Industry (IMPALA) Consortium Patrick O’Sullivan, Global Quality Medicine Learning Manager, Boehringer Ingelheim Michael Pelosi, Director Quality Analytics, Astellas RQA Quality Conversation 27Aug2024

Overview of the Inter-Company quality Analytics Industry group (IMPALA) and its impact on modernizing GCP/PV Quality

Version 1.18 - July 2024 Approved for external use

The Inter coMPany quALity Analytics (IMPALA)

The Inter coMPany quALity Analytics (IMPALA) Vision

IMPALA aims to engage with Health Authorities inspectors on defining guiding principles for the use of advanced analytics to complement, enhance and accelerate current QA practices

The IMPALA ecosystem (industry, regulators, patients) will contribute to a change in paradigm for QA, i.e., where co-developed advanced analytics and best practices can help detect and mitigate issues faster, reduce the burden of retrospective, time-consuming traditional QA activities and ultimately accelerate approval and patient access to innovative drugs

The IMPALA Alliance will provide the strategic focus for working across the Biopharmaceutical ecosystem to develop and gain industry-wide consensus for the adoption of improved QA3using advanced analytics and best practices to be used across the industry; ultimately improving patient outcomes and quality care at greater efficiency and greater speed

“IMPALA’s mission is to transform the Biopharmaceutical Clinical Quality Assurance process in the Good Clinical Practice and Good Pharmacovigilance Practice areas, by using advanced analytics and promoting the adoption of this new approach and associated methodologies by key industry stakeholders (e.g. pharma quality professionals, Health Authorities) to assure safe use therefore building patient trust, accelerating approvals and ultimately benefiting patients globally.

The IMPALA Consortium will provide the strategic focus for working across the Biopharmaceutical ecosystem to develop and gain industry-wide consensus for the adoption of improved QA using advanced analytics and best practices to be used across the industry.

To achieve these objectives, key projects have been established and are ongoing in focused Work Products.”

The Inter coMPany quALity Analytics (IMPALA) Members In scope Knowledge sharing and best practices for GCP/PV quality and analytics

Joint Health Authorities engagement for GCP/PV quality 2.0 Group started on Jul-2019 Established as a non-for-profit consortium on Oct2022

Co-development of open source tools: analytics packages, templates, methodologies, etc.

Analytics for GCP/PV quality; quality for AI/ML (e.g. validation); quality briefs; etc.

Actionable work products - ongoing https://impala-consortium.org/work-products/

Ongoing work products Audit Site Risk Selection (methodology; open-source package) Anomaly detection clinical data (methodology; open-source package, templates) Quality Briefs (methodology; standard templates) Education (framework, maturity assessment) Service Provider Analytics Interest Group (methodology) AI Powered Regulatory Intelligence (methodology)

interest groups Service Provider Oversight and Analytics RAPID Audit Methodology RAPID Product Information Audit

Achievements to date https://impala-consortium.org/work-products/ 19 Members, 9 Events, 5 publications, 4 Completed work products Completed work products and publications Data Analytics for Quality Assurance in Pharmaceutical Development Framework Clinical Trial Anomalies open-source package Clinical Safety Reporting open-source package IMPALA Podcast Series (available on Spotify) Publications: Therapeutic Innovation and Regulatory Science publication – “An Open-Source R Package for Detection of Adverse Events Under-Reporting in Clinical Trials: Implementation and Validation” Publication in IEEE Spectrum “Big Data Could Accelerate Drug Approval” IMPALA white paper in CPT Pharmacometrics & System Pharmacology Publication in QUASAR “Launch of the IMPALA consortium” CPT: Pharmacometrics and Systems Pharmacology article – “Cross-company collaboration to leverage analytics for clinical quality and accelerate drug development: The IMPALA industry group”

Achievements to date https://impala-consortium.org/work-products/

Presentations/Webinars 2024 DIA China Sessions - Applying Data Analytics in Audit Planning and Assessment, Quality Briefs – Supporting Transparency, Innovation & Products to patients faster DIA Global 2024 Sessions IMPALA Webinar (2024) - Identifying Anomalies in Clinical Trial Data with CTAS Clinical Data Analytics Virtual Event (2024) Engagement sessions at DIA Global 2023 IMPALA Webinar (2023) - Embedding an Open-Source Analytics Package into Analytics-Based Audits RQA Session (2023) on “Embedding data analytics capabilities within R&D Quality” RQA Workshop on Data Analytics Write-Up (2022) RQA Coffee Sessions APEC Roundtable Dialogue (2022) - “Leveraging analytics for remote clinical quality oversight”

Data Analytics for Quality Assurance In Pharmaceutical Development First steps to fine tuning

IMPALA Structure & Education Work Product Team

ISTO-IEEE Board of Directors

Work Product Team

Work Product Team

Strategic Steering Committee Work Product Team

Education WPT • Supports IMPALA’s mission by through development of a framework the framework, including skills, capabilities and requirements needed to develop an analytics domain within biopharma quality organizations • Sharing of lessons learned and best practices through papers, conferences, and the IMPALA Podcast • IMPALA Podcast Series | Podcast on Spotify

Getting Started

1 Clarity on the “WHY”

2

3

4

5

6

Analytics strategy

Identify roles and responsibilities

Tangible use cases

Senior Leadership support & Advocates and champions

Rules and regulations & Potential barriers

Skill Sets Needed

Soft Skills

Intermediate

Advanced

• Regulatory Knowledge • Data Analytics Basics • Pharmaceutical Domain Expertise • Quality Assurance Domain Expertise

• Data Visualization: • Statistical Analysis • Data Storytelling

• Data Manipulation • Database Management • Machine Learning and Predictive Analytics • Coding and Programming

• Communication • Ethical Considerations • Influencing • Critical Thinking • Collaboration • Adaptability • Problem-Solving • Attention to Detail

• Interest in data and statistical analysis • Curiosity about the potential of data analysis and the application of new tools and methods

QA Data Analytics Professional

Hard Skills

Basic

• Display a genuine interest in exploring data • Display a genuine interest in exploring data and a willingness to dig into the “why” behind trends and patterns • “Storyteller” attitude • Growth Mindset / Continuous Learning • Persistence – Data Analysis • Problem-Solving Orientation • Adaptability and Agility

Assessing Analytics Maturity Operational

Strategic

Transformational

Basic

Opportunistic

Systematic

Differentiating

Transformational

Reactionary Analysis

Formalized Data Availability

Strategy and Capabilities

Cross-functional Data Analytics Collaboration

Data Informs QA Strategy

Immature Data Infrastructure

Underdeveloped QA Analytics Strategy

Access to Internal Data Only

Executive Leadership Support

Robust Data Infrastructure

Areas of Application

Assurance to Senior Leaders

Leading Indicators

Improving the Business

Detecting Errors

QA Operations

Quality of QA Activities

Lagging Indicators

Identifying and Controlling Risk Procedural Framework

Determine Required Analytics (Use Cases)

First, seek the following information:

Then, probe further

• What are your department goals or strategic objectives?

List the actions and decisions needed to achieve the strategic goals • What are the key business questions that will drive the department goals or strategic objectives?

• What are the problems that stakeholders are experiencing • and do these lend themselves to being solved with data?

• What business questions aren't you asking?

• What are your business processes (where do data capabilities fit in)?

strategic objectives?

• If we give you data /report/analytics, what decisions or actions will you take to achieve your goals?

• Who is the target audience and their depth of knowledge of the data and analytics?

• What do you and your target audience need to be able to understand and do with the information from the analytics?

• What do you think you or your target audience would do differently than what you /they do now?

• If you didn’t have analytics, what different actions would you /your target audience do?

• How will the data / report /analytics request achieve your

Don't drive data as a question. Drive the business questions and resulting actions

Prioritizing Use Cases High Use Case 1 Use Case 2 Business Value (Risk to Submission /Approvability, Patient Safety, Data Integrity, Impact to the Business)

Low Low

High Feasibility (data/complexity/time) How feasible is the use case considering data, complexity and cost?

Priority Use Cases

Identifying Data that Needs to be Analysed Data obtained should be based on a use case

MoSCoW Method

Data location, availability and accessibility

Data quality and data integrity

Types of data needed for use case

Must Have – essential data elements (non-negotiable) Should Have – high priority data elements; included whenever possible Could Have – desired data elements; may be left out due to resource constraints Won’t Have – least critical data elements; will be left out

Data Analysis Tools

Business Intelligence Tools Computing and Programming

Office Suite

Consider the organizati on’s IT Infrastruct ure Identify the goals of your QA analytics strategy

Data Storytelling Combining data analytics with memorable and cogent storytelling is a key component of conveying meaning and adding impact to presentations, reports, meetings and workshops. Stories are memorable to people and therefore teaching the art of storytelling with data is a vital skill to learn and implement when implementing a data analytics strategy.

Learning Aims and Concepts One can build a “story” with data education program by focusing on learning the following concepts: • Understanding data analytics basics • Understanding your existing data platforms and analysis tools (e.g., Power BI, Tableau, Python etc.)

• Identifying the key message/s you want to convey Training Methods • Books, other documents, videos and e-Learnings, e.g., for data analytics basic knowledge • Workshops / focus Sessions (specific data platform training) • Assessments (learner satisfaction, knowledge and skills attained) • Try-outs to present your “story” to your peers and receive constructive feedback • Accolades and certificates for successful completion (also fosters cultural changes) • Innovative and “wow effect” training methods, for example, employing Virtual Reality / Augmented Reality systems to illustrate how “digital” stories might be told in virtual environments

Lessons Learned Data Quality is Critical • Invest in Data Governance

Data Accessibility at Scale • Establish Enterprise Data Lake & Data Mesh

Resistance to Change

Regulatory Compliance

• Leadership Support

• Proactively Engage with Health Authorities

Data Security & Privacy

Interdisciplinary Collaboration

Resource Constraints

• Train in Regulations & Best Practices

• Maintain Regular Communication & Goal Alignment

• Prioritize Use Cases Based on Return on Investment (ROI)

Culture and Change Management Changing a QA organization towards data analytics is a transformation rather than an incremental change and so it also require cultural adjustments. Approaches would have similarities to any change management initiative.

Culture • Leadership • Communication and Training/Upskilling • Analytics maturity • Change Management • Change Agents / Champions and Analytics Translators • Stakeholder Management and Collaboration

https://impala-consortium.org/ Also, on LinkedIn - https://www.linkedin.com/company/86955237/ Get a copy of the framework Data Analytics for Quality Assurance In Pharmaceutical Development Framework