GUEST COLUMNISTS

• Bayesian Digital Twins Show Potential For Predicting Prognosis And Treatment Response

  Learn how Bayesian digital twins are trained, validated, and trusted in clinical settings and explore the potential for integrating these models into interventional trials.

• Bridging Language And Medicine: Best Practices In French Regulatory Medical Writing

  Poor translations are not just clunky. They can be risky. Medical-linguistic consultant Farah Ayadi, PharmD, explains its implications for safety, regulatory submissions, and downstream operational or financial outcomes.

• Research Center Layout Helps Lower Barriers To Cross-Industry Collaboration

  Discover how one research center, uniquely built for collaboration, enables the collection and analysis of high-quality specimens and data that will fuel both current and future research collaborations.

• Elsa's AI Model Migration: Technical, Compliance, And Regulatory Risks For Sponsors (Part 2)

  In the second part of their series on Elsa AI model migration, Kimberly Chew, Esq., and Michael Yang, Esq., analyze the risks to compliance and data residency, as well as the integrity of the regulatory record.

• Site Staffing On The Upswing, But Still A Challenge

  In this final article of the series, Jimmy Bechtel and Ken Getz discuss how workforce pressures have shaped site performance and what’s being done to strengthen site staff retention and career growth.

• From Real-World Data To Real-World Impact: Building The Evidence Capability Pharma Actually Needs

  Learn how companies that treat RWE as an operational capability will be better positioned to design more efficient trials, accelerate therapy adoption, strengthen payer negotiations, and demonstrate value across the healthcare ecosystem.

• Zymeworks Shares Its Design Rationale (And Wins) In Early-Phase ADC Trials

  Zymeworks CMO Sabeen Mekan, MD, explores how scientific gains in ADC therapies have influenced first-in‑human trial design.

• A Clinical Machine Learning Operations (MLOps) Maturity Framework For Biopharma

  Pharma has invested substantially in machine learning applications, but investment in the operational infrastructure — the MLOps layer — has lagged. The time to build that infrastructure is now — not after your next trial fails.