Computational design of personalized drugs via robust optimization under uncertainty

TL;DR

This paper introduces a robust, computational inverse design method using topology optimization and stochastic reduced-order modeling to create personalized drugs with precise release profiles, accounting for material uncertainties.

Contribution

It presents a novel, non-parametric inverse design approach for drug formulation that incorporates uncertainty quantification via SROM, improving design robustness.

Findings

Designed drugs closely match target release profiles

SROM reduces computational cost compared to Monte Carlo

Robust designs show less variability in release profiles

Abstract

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.