How AI is reshaping drug development in Pharma: 3 high-value use cases

Solution: An AI conversational search with semantic capabilities, letting scientists retrieve relevant information based on the meaning of their query rather than exact keywords. It draws on critical external sources such as PubMed, FDA and EMA regulatory databases, and patent databases, as well as internal documents including clinical trial files and proprietary reports. Built internally to better enforce data governance rules, the platform produces comprehensive summaries with reliable sources so users can dig deeper into any finding as needed. To meet the pharmacovigilance team’s GxP compliance requirement, outputs were designed to be fully traceable and explainable, with a clear audit trail of queries and precise data sources. The platform also extracts relevant quantitative tables from the identified literature.

Value: $150M+ in annual value, driven primarily by the acceleration of drug time to market.

1. Shortened critical path activities, through faster and better literature analytics.
2. Reduction of manual workload from internal teams, by automating repetitive tasks, thus allowing them to focus on activities requiring their domain expertise.
3. Reduced reliance on external vendors, by automating manual literature search tasks that previously required external support.

With thousands of scientists each spending a meaningful share of their time on manual search, even small time savings compound quickly across the organization.

Solution: A knowledge platform acting as a central hub for reliable safety information across the organization. It draws on critical sources including toxicology databases, clinical trial documents, real-world data such as electronic health records and patient registries, and internal repositories. Non-technical scientists can access predictive models built by analytical teams and run scenario analyses and virtual simulations with customizable parameters on drug benefit-risk profiles without requiring technical expertise, enabling a more proactive approach to safety decision-making. It includes a dashboard view pulling together the key components: AI-generated safety insights, predictive analytics, and mechanistic models such as physiologically based pharmacokinetic (PBPK) modeling.

Value: $300M+ in annual value, driven primarily by an increased probability of regulatory success.

1. Stronger drug safety profile at the point of submission, with safety risks detected and remediated earlier in development.
2. Acceleration of drug time to market, through faster go/no-go decisions across the process with better data, thereby giving confidence to both internal teams and external regulators to move forward faster.

Avoiding a single late-stage failure can save more than the entire investment in the platform.

Solution: A centralized self-service hub built on an internal platform already in use elsewhere in the organization, avoiding any net-new build efforts. It uses data extraction, AI summarization, and quality assurance capabilities to generate a strong first draft for each report, based on critical sources including historical regulatory submissions, clinical safety data, Reference Safety Information (RSI) and Benefit-Risk Assessments (BRA). Functions always review and complete that draft to ensure accuracy and comprehensiveness of the output before submission.

Value: $3M+ in annual value, driven by expanded output coverage for internal teams and lower external vendor costs.

1. Expanded output coverage, by automating repetitive authoring tasks, freeing internal teams to focus on higher-value scientific work and produce additional regulatory tables that are not being generated today for lack of time.
2. Reduced reliance on external vendors, through internal platform capabilities that absorb a higher volume of tasks previously requiring external support.

Across large pipelines and high document volumes, these savings add up, even within a narrower scope than the first two use cases. Running on an internal platform already in place also keeps the build cost low.