Straightforward Phase I Dose-Finding Design for Healthy Volunteers Accounting for Surrogate Activity Biomarkers

TL;DR

This paper introduces a two-stage phase I dose-finding design for healthy volunteers that balances safety with the goal of identifying the dose with optimal activity, especially for plateauing dose-activity curves.

Contribution

It proposes a novel two-stage design combining algorithmic escalation and model-based selection to improve dose-finding accuracy under safety constraints.

Findings

Outperforms Bayesian logistic regression in dose selection accuracy.

Effectively identifies the lowest dose with activity probability near the target.

Handles plateauing dose-activity relationships more reliably.

Abstract

Conventionally, a first-in-human phase I trial in healthy volunteers aims to confirm the safety of a drug in humans. In such situations, volunteers should not suffer from any safety issues and simple algorithm-based dose-escalation schemes are often used. However, to avoid too many clinical trials in the future, it might be appealing to design these trials to accumulate information on the link between dose and efficacy/activity under strict safety constraints. Furthermore, an increasing number of molecules for which the increasing dose-activity curve reaches a plateau are emerging.In a phase I dose-finding trial context, our objective is to determine, under safety constraints, among a set of doses, the lowest dose whose probability of activity is closest to a given target. For this purpose, we propose a two-stage dose-finding design. The first stage is a typical algorithm dose escalation phase that can both check the safety of the doses and accumulate activity information. The second stage is a model-based dose-finding phase that involves selecting the best dose-activity model according to the plateau location.Our simulation study shows that our proposed method performs better than the common Bayesian logistic regression model in selecting the optimal dose.