Commentary|Videos|April 17, 2026

The Evidentiary Risks of Natural History Data and How to Approach External Control Comparability

In this video interview, Mwango Kashoki, MD, MPH, senior vice president and global head of regulatory strategy at Parexel, discusses how heterogeneity in disease course and patient characteristics creates confounding risk when using natural history data as an external control, and what sponsors need to do to achieve meaningful patient matching.

Full interview summary

In a recent video interview with Applied Clinical Trials, Mwango Kashoki, MD, MPH, senior vice president and global head of regulatory strategy at Parexel, discussed the FDA's plausible mechanism framework and what it means for sponsors developing individualized therapies for ultra-rare and life-threatening conditions. She opened by identifying the most significant shift the framework introduces: the possibility that a first-in-human study could serve as the pivotal trial supporting product approval, a consideration the agency historically would not have entertained so early in development. For sponsors of genome editing and other individualized therapies, this creates earlier clarity on the clinical, CMC, and non-clinical data needed to support licensure.

Kashoki went on to explain how the agency thinks about substantial evidence of effectiveness when patient populations are too small for traditional trial designs, emphasizing that the determination has always gone beyond trial numbers or statistical significance to encompass the totality of relevant data. Under the plausible mechanism framework, that totality includes well-characterized mechanistic and biomarker evidence that the product targets the underlying genetic or molecular abnormality and produces measurable clinical or surrogate benefit.

She addressed the vulnerabilities of master protocol designs under this framework, cautioning that basket designs require careful biological justification for each mutation and condition being studied, and that differences in patient age, dosing, disease severity, and progression can quickly undermine what looks straightforward in concept. On external controls and natural history data, she stressed that heterogeneity in disease course and patient characteristics is a persistent confounding risk, and that achieving meaningful patient matching typically requires a natural history database far larger than the interventional study itself.

Kashoki closed with practical guidance for sponsors navigating the framework in its current draft state, urging early agency engagement before first-in-human trials, robust non-clinical programs, adaptive and seamless trial designs, and a clearly articulated confirmatory evidence package that gives regulators what they need to make a substantial evidence determination.