1. The evolution of precision medicine
From population-level therapies to patient-specific interventions, precision medicine reframes drug development around measurable biological distinctions (tumour genotype, biomarker profile, immune signature) rather than a single average patient.
2. Challenges in oncology
Tumour heterogeneity, data sparsity in rare indications, and operational friction between wet-lab discovery and clinical delivery conspire to slow precision-medicine programs. Without a precise data ontology, per-patient decisions don't scale.
3. The role of automation and AI
Automation removes manual error and operator variability from the assay. AI compresses the diagnostic-to-decision cycle: ML-assisted antibody design, GenAI-accelerated literature review, and structured decision-support for Tumour Boards all feed a faster, more defensible therapeutic choice.
4. Real-world case studies
Three inite engagements illustrate what "working AI in regulated R&D" actually looks like: the Roche Digital Biorepository (10× cell-registration speed), ASPA's fully automated compound service, and a turnaround PM on eight parallel digitalisation projects. See Work → for summaries.
5. Future innovations
A data strategy that is lean, extendable, and precise (streamlined pipelines, scalable storage, structured ontologies) is the foundation on which the next decade of oncology R&D will be built. The firms that get this right will move candidates through pre-clinical pipelines faster and with higher confidence.
Talk to inite
Personalized Solutions is inite's 4-week engagement that turns one R&D bottleneck into a working PoC. Fixed scope. Read the details →