Efficacy inference in early-phase non-controlled clinical trials via Bayesian biomarker deconvolution
Abstract
Early-phase clinical trials of therapies for acute organ injury are typically small, uncontrolled, and must infer treatment activity using only tissue-damage biomarker changes over time. Interpretation is confounded by the temporal overlap of ongoing tissue damage and biomarker clearance. We address this with a Bayesian deconvolution framework that fits individual patient biomarker trajectories with an exponentially-modified Gaussian model. This model separates injury kinetics (peak release rate, injury duration, time to peak) from biomarker clearance, using a historic cohort as a null distribution. In a simulated phase 1 dose-escalation regenerative therapy trial, the framework reduces the minimum detectable treatment effect from 67.5% to 24.5% (a 2.76-fold improvement) and supports dose selection. Applied to published case-series data for another therapeutic, the framework recovers per-patient pharmacodynamic signatures consistent with pre-clinical mechanistic studies and supports smaller prospective clinical trial design. With indication-specific recalibration, the framework architecture conceptually transfers to other acute organ injuries where serum biomarkers reflect tissue damage. This offers a route to significantly reducing clinical trial sizes, supporting therapy dose-finding in non-controlled clinical trials, and identifying pharmacodynamic signatures.
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Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence.
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