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Life Sciences & Pharmaceuticals AI Strategy: opportunities, use cases & the operating model
In pharma, AI's value concentrates in R&D and safety: compressing discovery timelines, sharpening trial design and recruitment, and industrializing pharmacovigilance, where each avoided delay is worth exclusivity-period revenue.
Where AI creates value
Target identificationMulti-omics and literature-driven target discovery
Molecule designGenerative chemistry and property prediction
Trial designProtocol optimization and synthetic control arms
Patient recruitmentSite selection and eligibility matching at scale
PharmacovigilanceAdverse event intake, coding, and signal detection
Regulatory and medical writingSubmission and CSR document generation
Top AI use cases
- Generative and structure-based design proposing candidate molecules with predicted ADMET
- Trial protocol optimization and feasibility using historical and real-world data
- Patient recruitment matching eligibility against EHR and registry populations
- Automated adverse event case intake, MedDRA coding, and safety signal detection
- Medical writing copilots drafting clinical study reports and regulatory submissions
What has to change (operating model)
- GxP validation and computer system validation extended to AI and machine learning models
- FDA and EMA expectations for transparency and evidence in AI-supported submissions
- Pharmacovigilance QPPV accountability for AI-assisted signal detection and reporting
- Data governance for real-world data provenance and patient privacy across R&D
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