How AbbVie accelerates drug discovery with Claude

Sarah Nam, VP of AI Strategy and Partnerships at AbbVie, views AI as a “once in a generation opportunity” to reimagine every function within the pharmaceutical industry and accelerate impact for patients. AbbVie has established a dedicated function for AI Strategy and Partnerships to define strategic priorities and lead business development related to AI.

A Value-Chain Approach to AI Integration

AbbVie is systematically identifying core priorities for AI across each business function, rather than applying it sporadically.

Drug Discovery

The primary goal is to better understand human biology to design, test, and validate new therapies more effectively and at scale. Key focus areas include:

• Multiparametric Optimization: Using AI for the complex optimization of efficacy, safety, and pharmacokinetics in both small molecule and biologic drug design.
• Indication Expansion: Integrating clinical, genomic, and other multimodal data to identify new applications for existing therapies and guide combination studies.
• Precision Medicine: Leveraging techniques like digital pathology to deliver more precise medicines tailored to specific patient populations.

Clinical Development

AI presents significant opportunities to overhaul how clinical trials are designed and conducted.

• Smarter Trial Design: AI is used to refine inclusion/exclusion criteria and develop adaptive clinical trial designs. This allows for the identification of patient subpopulations who are more likely to respond to a drug, which is particularly valuable for heterogeneous diseases.
• Operational Automation: AI is being deployed to automate numerous processes related to running clinical trials, including the authoring of thousands of regulatory and submission documents.
• Data Surveillance: Leveraging AI to continuously monitor incoming clinical data from trials, enabling teams to make adjustments and informed decisions in near real-time.

Concrete Use Cases and Partnerships

AbbVie’s collaboration with Anthropic has produced tangible results and tools that are already creating value.

• GenAIsys: A generative AI tool developed to enhance sales force effectiveness. It assists with call planning, and early results show “very significant improvements” in both the efficiency and effectiveness of sales representatives.
• GAIA: A document authoring tool for clinical development that leverages large language models. It automates the writing of complex study documents, starting with NDA (New Drug Application) and PSUR (Periodic Safety Update Report) documents. This has resulted in 40% to 60% efficiency gains in time spent on writing these materials.

Driving Enterprise-Wide AI Adoption

Successfully transforming a large enterprise requires more than just technology; it demands a focus on people and processes. AbbVie’s change management strategy is built on three pillars:

1. Upskilling: Implementing comprehensive AI training programs for employees at all levels, from beginners to proficient practitioners.
2. Demonstrating Early Wins: Focusing on a few high-impact use cases that generate near-term ROI and demonstrate value against key business metrics. These early successes help self-fund subsequent initiatives and build organizational momentum.
3. Empowering Champions: Establishing small, dedicated AI teams within each business function to act as champions who drive AI transformation within their specific domains.

Framework for Evaluating AI Partners

AbbVie has developed a systematic, four-pillar diligence framework for assessing potential AI-driven partnerships:

1. Strategic Fit: How well does the partnership align with the organization’s strategic objectives?
2. Technical Foundation: A deep dive into the partner’s technology, assessing the differentiation of their AI models, data generation capabilities, and comparative advantages.
3. Management Team: Seeking “bilingual” teams with deep expertise in both the pharmaceutical domain and AI/ML. This dual understanding is considered critical for success.
4. External Validation: Looking for concrete evidence of impact through benchmarks and real-world case studies, especially for drug discovery partners.

The Future of AI in Pharma (3-5 Year Outlook)

Sarah Nam is most excited about three key areas where AI will push the frontiers of drug discovery:

• De Novo Design: Moving beyond predicting molecular properties to using generative models for the de novo design of novel small molecules and biologics.
• Agentic Models: Developing AI agents that can actively reason across and integrate insights from complex, multimodal datasets (genomics, proteomics, clinical data, real-world data) to solve fundamental biological problems.
• Advanced Patient Stratification: Enhancing the ability to identify specific patient subgroups, which will improve both the discovery of new therapeutics and the design of future clinical trials.

• Tags:
• Generative AI
• Drug Discovery
• Anthropic
• Change Management
• AbbVie
• Pharmaceuticals
• Clinical Trials