The Confidence Interval

MIDD uses modeling and simulation to turn nonclinical, clinical, and real world data into evidence that supports smarter development decisions, from dose selection to trial design to regulatory submissions. As MIDD becomes a standard expectation rather than a nice to have, sponsors need practical guidance on how to plan, execute, and communicate model based evidence effectively. The articles below explore MIDD strategies across therapeutic areas, real world applications, and how leading teams are building MIDD into their development plans from the earliest stages.

NAMs encompass a growing set of in vitro, in silico, and other non-animal approaches that are changing how safety, toxicity, and efficacy are evaluated. Regulators and sponsors alike are exploring how these methods can improve predictivity, reduce cost and timelines, and decrease reliance on animal testing without compromising scientific rigor. The posts below look at the technologies behind NAMs, how they are being validated and adopted, and what it takes to build confidence in these methods for regulatory use.

Adopted in January 2026, the ICH M15 guideline gives the industry its first globally harmonized framework for assessing and communicating MIDD evidence. For sponsors, this means new expectations around model credibility, documentation, and early engagement with regulators. The posts below break down what the final guidance covers, how it differs from the draft, and what practical steps teams can take now to align their MIDD programs with these new global expectations.

AI and machine learning are moving from experimental tools to a core part of how drugs are discovered, developed, and brought to market. From identifying novel targets to optimizing trial design and streamlining regulatory writing, these technologies are reshaping workflows across the biopharma lifecycle. The articles below examine specific use cases, share lessons learned from early adopters, and discuss how to apply AI and machine learning responsibly while meeting the scientific and regulatory standards this industry demands.

Modern drug discovery blends traditional pharmacology with computational modeling, AI, and new experimental methods to identify and validate promising candidates faster than ever. As the tools available to discovery teams multiply, so do the decisions about which approaches to invest in and how to integrate them into existing workflows. The articles below cover emerging technologies, case studies, and strategic considerations for teams looking to modernize their discovery programs.

Consistent, well structured data is the foundation for everything from modeling and simulation to regulatory submissions, yet inconsistent standards remain a common source of delay and rework. Strong data standards make it easier to reuse data across programs, collaborate across teams, and meet regulatory expectations for transparency and traceability. The posts below cover standards like CDISC, common data challenges, and practical approaches for building data practices that scale across an organization.