Computational design of personalized drugs via robust optimization under uncertainty

2025-07-22

AI Summary

This study addresses the challenge of achieving precise active pharmaceutical ingredient (API) release control in personalized drug delivery under material parameter uncertainty. We propose a computation-driven robust inverse design framework that integrates topology optimization, Noyes–Whitney–based dissolution kinetics modeling, stochastic reduced-order modeling (SROM) for efficient uncertainty propagation, and robust optimization to co-design composition and microstructure. Our key contributions are: (i) the first incorporation of SROM into pharmaceutical topology optimization, enabling non-parametric, geometry-agnostic design; (ii) a >40% reduction in release profile uncertainty compared to deterministic designs; and (iii) high-fidelity matching—average error <3.5%—to diverse target release profiles (e.g., zero-order, pulsatile, delayed release). The framework balances reliability and computational efficiency, establishing a scalable, digital design paradigm for personalized drug delivery systems.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Optimizing drug composition for target release profiles
• Handling uncertainty in drug material parameters
• Designing personalized drugs using computational methods

Innovation

• Computational inverse design for drug composition
• Robust topology optimization under uncertainty
• Stochastic reduced-order method for uncertainty propagation