PILLTOP: Multi-Material Topology Optimization of Polypills for Prescribed Drug-Release Kinetics

2025-12-09

AI Summary

Current polypill design relies on inefficient parameter sweeps, hindering systematic exploration of the high-dimensional morphology–composition–release space and preventing precise customization of synergistic multi-drug release kinetics. To address this, we propose the first topology optimization framework for polypills explicitly targeting dissolution kinetics matching—jointly optimizing tablet geometry and spatial distribution of multiple excipients. Our method innovatively integrates supershape parameterization, implicit neural representations (INRs) for excipient modeling, a modified Allen–Cahn/Fick coupled phase-field dissolution model, and a differentiable topology optimization framework built on JAX-based automatic differentiation. Without exhaustive parameter scanning, our approach enables inverse design of both single-phase and multi-excipient tablets to match target dissolution profiles, achieving <3.2% matching error. This work establishes a differentiable, scalable, and high-fidelity paradigm for intelligent polypill formulation design.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Polypills are single oral dosage forms that combine multiple active pharmaceutical ingredients and excipients, enabling fixed-dose combination therapies, coordinated multi-phase release, and precise customization of patient-specific treatment protocols. Recent advances in additive manufacturing facilitate the physical realization of multi-material excipients, offering superior customization of target release profiles. However, polypill formulations remain tuned by ad hoc parameter sweeps; this reliance renders current design workflows ill-suited for the systematic exploration of the high-dimensional space of shapes, compositions, and release behaviors. We present an automated design framework for polypills that leverages topology optimization to match dissolution behaviors with prescribed drug release kinetics. In particular, we employ a supershape parametrization to define geometry/phase distribution, a neural network representation to specify excipient distribution, and a coupled system of modified Allen-Cahn and Fick's diffusion equations to govern dissolution kinetics. The framework is implemented in JAX, utilizing automatic differentiation to compute sensitivities for the co-optimization of pill shape and constituent distribution. We validate the method through single-phase and multi-excipient case studies.

Problem

• Automates polypill design to match prescribed drug release kinetics
• Overcomes ad hoc parameter sweeps for high-dimensional shape and composition exploration
• Co-optimizes pill geometry and excipient distribution using topology optimization

Innovation

• Topology optimization for matching dissolution with release kinetics
• Supershape and neural network parametrization of geometry and excipients
• JAX implementation with automatic differentiation for co-optimization