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. The current design workflows are ill-suited for the systematic exploration of the high-dimensional space of shapes, compositions, and release behaviors. We present PILL-CoDe, a polypill co-design framework that simultaneously optimizes tablet geometry and excipient distribution to match prescribed drug-release kinetics. The framework couples a supershape parametrization of the pill geometry with a coordinate-based neural network representation of the excipient distribution, and governs dissolution through a coupled system of modified Allen-Cahn and Fickian diffusion equations. Implemented in JAX, the entire pipeline is end-to-end differentiable, with automatic differentiation providing exact sensitivities for gradient-based co-optimization of shape and composition under manufacturability constraints. We demonstrate the method through single-phase and multi-excipient case studies, showing accurate matching of both monotonic and non-monotonic target release profiles.

翻译：多药片是一种将多种活性药物成分和辅料组合而成的单一口服剂型，可实现固定剂量联合治疗、协调多相释放以及患者特定治疗方案的精确定制。增材制造的最新进展促进了多材料辅料的物理实现，为目标释放曲线提供了优异的定制能力。然而，多药片配方仍依赖于特设的参数扫描进行调整。当前的设计流程不适用于系统探索形状、组成和释放行为的高维空间。我们提出PILL-CoDe，这是一个多药片协同设计框架，可同时优化药片几何形状和辅料分布，以匹配指定的药物释放动力学。该框架将药片几何形状的超形状参数化与基于坐标的神经网络表示的辅料分布相结合，并通过修正的Allen-Cahn和Fickian扩散方程的耦合系统控制溶出过程。基于JAX实现，整个流程是端到端可微的，自动微分在可制造性约束下为形状和组成的梯度基协同优化提供精确灵敏度。我们通过单相和多辅料案例研究证明了该方法，展示了单调和非单调目标释放曲线的精确匹配。