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.

翻译：传统上，首次人体I期试验旨在确认药物在人体中的安全性。在此类情况下，志愿者不应遭受任何安全问题，因此常采用基于算法的简单剂量递增方案。然而，为避免未来开展过多临床试验，在严格的安全性约束下设计此类试验以积累剂量与疗效/活性关联的信息可能更具吸引力。此外，越来越多的分子其剂量-活性曲线在递增后呈现平台期。在I期剂量探索试验背景下，我们的目标是在安全性约束下，从一组剂量中确定其活性概率最接近给定目标值的最低剂量。为此，我们提出一种两阶段剂量探索设计。第一阶段为典型的算法剂量递增阶段，既可检验剂量安全性，又能积累活性信息。第二阶段为基于模型的剂量探索阶段，需根据平台期位置选择最佳的剂量-活性模型。我们的模拟研究表明，所提方法在选择最优剂量方面优于常见的贝叶斯逻辑回归模型。