How AI is reshaping pharma and life sciences
Artificial intelligence is moving from pilot projects to core infrastructure in pharmaceutical and life sciences companies, with implications that extend into veterinary medicine as animal health companies adopt the same drug discovery, manufacturing, quality, and genomics tools used in human health. A recent PharmaShots publication by Rahul Mittal frames AI as a force reshaping how medicines are discovered, developed, manufactured, approved, supplied, and commercialized, and broader industry reporting supports that view: FDA has already reviewed more than 500 drug and biologic submissions with AI components since 2016, and its draft guidance now lays out a risk-based framework for establishing AI model credibility in regulatory decision-making. McKinsey and Deloitte, meanwhile, describe an industry that’s investing heavily in generative AI, but still struggling to move from experimentation to scaled value. In genomics, PharmaShots also highlighted Inocras’ push to turn whole-genome sequencing from a research tool into a standardized clinical workflow, using AI-driven bioinformatics refined on thousands of real-world cases to generate more actionable insights for oncology and rare disease care. (fda.gov)
Why it matters: For veterinary professionals, the near-term impact isn't that AI will replace scientific judgment. It's that AI-enabled workflows are likely to influence the products coming through the pipeline, from target identification and formulation support to pharmacovigilance, manufacturing quality, and genomics-based diagnostics. FDA's guidance is notable because it explicitly spans human and animal drug development, signaling that sponsors using AI in veterinary therapeutics will face the same pressure to document context of use, model credibility, oversight, and risk controls. The genomics angle matters too: companies like Inocras are pairing whole-genome sequencing with clinical reporting, genetic counseling, and trial matching to make complex genomic data usable in routine care, a model that could eventually inform companion animal oncology, inherited disease testing, and precision medicine. Industry groups are also warning that success will depend less on flashy tools than on governance, validation, cybersecurity, and integration into regulated quality systems. (fda.gov)
What to watch: Watch for more formal regulator expectations, more AI-enabled partnerships in drug discovery and genomics, and a shift from proof-of-concept projects to governed, inspection-ready systems across both human and animal health. Also watch how whole-genome platforms evolve from specialized research offerings into routine diagnostic services, because that transition may preview how AI-supported precision tools eventually enter veterinary practice. (fda.gov)