How AbbVie accelerates drug discovery with Claude

Key Concepts

• AI Transformation in Pharma: The overarching theme of leveraging Artificial Intelligence to fundamentally change and improve all aspects of the pharmaceutical industry.
• Value Chain Approach: A strategy of identifying and prioritizing AI applications across different stages of the pharmaceutical value chain, from drug discovery to clinical development and commercialization.
• Generative AI: AI models capable of creating new content, such as text, images, or molecular structures, used in tools like GenAIsys and GAIA.
• Multimodal Data: The integration and analysis of diverse data types, including clinical, genomic, proteomic, transcriptomic, and real-world data, to gain deeper biological insights.
• Agentic Models: AI models designed to actively reason and glean insights from complex datasets.
• Patient Stratification: Using AI to identify specific patient subgroups who are more likely to respond to a particular treatment, improving drug discovery and clinical trial design.
• Change Management: The process of managing the human and organizational aspects of AI adoption, including upskilling, demonstrating early wins, and empowering champions.
• Diligence Framework: A structured approach for evaluating potential AI technology partners, focusing on strategic fit, technical foundation, management team expertise, and external validation.

AbbVie's AI Strategy and Deployment

Sarah Nam, Vice President of AI Strategy and Partnerships at AbbVie, outlines her role in developing a new function focused on enterprise AI strategy and external innovation. Her team defines strategic priorities for AI across AbbVie's business, addressing cross-cutting enablers like tech architecture, data modernization, and change management. Additionally, her team handles business development and external innovation related to AI.

AbbVie views AI as a "once in a generation opportunity" to reimagine every function within the pharmaceutical industry and accelerate progress for patients. They employ a value chain-based approach to identify core AI priorities and deploy use cases.

Drug Discovery

• Understanding Human Biology: AI is used to better comprehend human biology, enabling more effective design, manufacturing, testing, and validation of new therapies at scale.
• Multiparametric Optimization: Focus on optimizing human efficacy, safety, and pharmacokinetics for both small molecule and biologic drug design.
• Indication Expansion and Combination Studies: Leveraging clinical, genomic, and multimodal data to drive indication expansion and combination studies more effectively at scale.
• Precision Medicine: Initial focus on digital pathology, with expansion into delivering medicines in a more precise manner for patients.

Clinical Development

• Clinical Trial Design:
  • Informing inclusion/exclusion criteria for trials.
  • Developing adaptive clinical trial designs.
  • Identifying patient subpopulations likely to respond to drugs in heterogeneous diseases.
• Clinical Trial Operations:
  • Automating processes related to clinical trials.
  • Authoring documents for regulatory submissions.
• Data Surveillance: Leveraging AI to monitor incoming clinical data for programs and make necessary adjustments.

Key AI Use Cases and Partnerships

AbbVie has partnered with Anthropic on several initiatives:

• GenAIsys: A tool utilizing generative AI to enhance sales force effectiveness through improved call planning. Early results indicate significant improvements in efficiency and effectiveness.
• GAIA: A clinical development document authoring tool that employs large language models (LLMs) to automate the writing of study documents, starting with NDA and PSUR documents, and extending to thousands of other document types. This has resulted in approximately 40-60% time savings in document authoring.

Change Management in AI Transformation

Sarah Nam emphasizes that AI transformation is not solely a technology challenge but also requires addressing people's mindsets and managing processes. Key change management strategies at AbbVie include:

• Upskilling: Implementing AI training programs for all levels of the organization, from beginners to advanced practitioners.
• Demonstrating Early Wins: Focusing on use cases that generate near-term ROI, financial returns, and patient impact, thereby moving key business metrics.
• Empowering Champions: Establishing small AI teams within each function to act as champions for AI transformation in their respective domains.

Evaluating AI Technology Partners

AbbVie employs a detailed diligence framework with four key pillars for evaluating AI-driven partnerships:

1. Strategic Fit: How the partnership aligns with the organization's strategic objectives.
2. Technical Foundation: The differentiation of the AI offering, including data generation capabilities and model comparative differentiation.
3. Management Team: The partner's domain expertise and deep AI/ML experience, requiring a "bilingualism" in understanding both pharma and AI.
4. External Validation: Benchmarking real-world impact through case studies, particularly for AI-driven drug discovery partnerships.

Advice for Pharma Executives on AI Transformation

Sarah Nam advises other pharma executives to:

• Start Simple: Identify a few areas that can serve as quick wins and early demonstrations of impact.
• Demonstrate ROI: Early successes and their return on investment can help self-fund further AI initiatives and drive organizational transformation.

She highlights that the "risk of inaction is just too great" given the rapid innovation in the AI space.

Future Excitement in AI and Pharma (Next 3-5 Years)

Sarah Nam expresses excitement about AI's potential to push the frontiers of drug discovery:

• Generative Models: Advancing beyond predicting molecular properties to aiding in the de novo design of small molecules and biologics.
• Agentic Models: Developing models that can actively reason against multimodal datasets (genomics, proteomics, transcriptomics, clinical, real-world data) to integrate insights and address biological problems.
• Patient Stratification: Enhancing not only therapeutic discovery but also the design of future clinical trials.