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The infrastructure decisions companies make today could determine how quickly they capitalize on new computational capabilities

Brox.AI’s Hamish Brocklebank on three levels of digital clone use cases — from HCP content testing and GLP-1 price sensitivity modeling to simulating physician adoption after FDA approval.

The top barrier to AI adoption among pharma commercial leaders is compliance uncertainty, and the fix may be governance that evaluates one use case at a time.

Teva acquires BioXcel’s BXCL501, under FDA review for at-home agitation use; Rumi Therapeutics gains an option on Enthorin’s Fragile X PDE2 inhibitor.

Brox.AI’s Hamish Brocklebank on the three approaches to digital twins in pharma — and why only one-to-one clones of real individuals deliver true accuracy.

Shionogi’s Dr. Draica on how XOCOVA blocks viral replication between exposure and symptoms — and why it works even with prior immunity.

Phagenyx’s Chad Hoskins on why AHA/ASA guideline inclusion gives stroke teams a basis to use PES earlier — and what the evidence shows.

Bristol Myers Squibb discloses a patient death in its Phase I CAR-T multiple sclerosis trial, Pathos AI launches multi-year collaborations with Bristol Myers Squibb and Tempus AI to advance Alzheimer’s drug development, and health economist Dr. Nadine Zawadzki speaks with The Ron Lanton Report on how value is defined across healthcare.

Pfizer’s talazoparib-enzalutamide combination moves to earlier line in prostate cancer, with eligibility tied to genetic testing and immature survival data.

Pathos AI launches multi-year collaborations to build an AI-driven dataset for Alzheimer’s and other neurological diseases.

Shionogi’s Dr. Draica on why COVID-19 still matters — and why the window between exposure and symptoms is where XOCOVA can intervene.

Pharmaceutical and health economist Dr. Nadine Zawadzki, Senior Director, FTI Consulting joins the Ron Lanton Report to discuss how value is defined and measured across healthcare.

Brox.AI’s Hamish Brocklebank on why it retired “digital twin” for “digital clone” — and why the distinction matters for pharma research accuracy.