标题： 通过不确定性下的鲁棒优化进行个性化药物的计算设计 显示英文标题

标题： Computational design of personalized drugs via robust optimization under uncertainty

摘要： 有效的疾病治疗通常需要精确控制活性药物成分（API）的释放。 在本工作中，我们提出了一种计算逆向设计方法，以确定产生目标释放曲线的最佳药物组成。 我们假设药物释放由Noyes-Whitney模型控制，这意味着溶解发生在药物表面。 我们的逆向设计方法基于拓扑优化。 该方法根据目标释放曲线优化药物组成，考虑药物材料参数和最终药物的形状。 我们的方法是非参数的，适用于任意药物形状。 逆向设计方法通过鲁棒的拓扑优化得到补充，该优化考虑了随机的药物材料参数。 我们使用随机降阶方法（SROM）来传播溶解模型中的不确定性。 与蒙特卡洛方法不同，SROM所需的样本更少，并提高了计算性能。 我们将该方法应用于设计具有多个目标释放曲线的药物。 数值结果表明，设计药物的释放曲线非常接近目标曲线。 基于SROM的药物设计在释放曲线中表现出更小的不确定性，这表明我们的方法是一种具有不确定性的药物设计的有效方法。 显示英文摘要

摘要： Effective disease treatment often requires precise control of the release of the active pharmaceutical ingredient (API). In this work, we present a computational inverse design approach to determine the optimal drug composition that yields a target release profile. We assume that the drug release is governed by the Noyes-Whitney model, meaning that dissolution occurs at the surface of the drug. Our inverse design method is based on topology optimization. The method optimizes the drug composition based on the target release profile, considering the drug material parameters and the shape of the final drug. Our method is non-parametric and applicable to arbitrary drug shapes. The inverse design method is complemented by robust topology optimization, which accounts for the random drug material parameters. We use the stochastic reduced-order method (SROM) to propagate the uncertainty in the dissolution model. Unlike Monte Carlo methods, SROM requires fewer samples and improves computational performance. We apply our method to designing drugs with several target release profiles. The numerical results indicate that the release profiles of the designed drugs closely resemble the target profiles. The SROM-based drug designs exhibit less uncertainty in their release profiles, suggesting that our method is a convincing approach for uncertainty-aware drug design.