标题： 基于物理信息神经网络的药代动力学建模：整合时变吸收率和分数阶微积分以提高预测准确性 显示英文标题

标题： Pharmacometrics Modeling via Physics-Informed Neural Networks: Integrating Time-Variant Absorption Rates and Fractional Calculus for Enhancing Prediction Accuracy

摘要： 我们提出了一种新方法来改进药代动力学建模，这是药物开发中的关键步骤。 传统模型经常无法完全表示药物吸收和分布的复杂性，这限制了其在个性化治疗策略中所需的预测能力。 我们的方法引入了两项创新以提高建模准确性：1. 时变参数：这种方法旨在适应药物吸收速率的动态特性。 2. 在表示延迟药物反应中使用分数阶微积分。 这种方法有效地捕捉了异常扩散现象，在描述药物延迟反应方面超越了传统模型，而无需进行广泛的隔室化。 显示英文摘要

摘要： We present a novel method to improve pharmacokinetics modeling, an essential step of drug development. Conventional models frequently fail to fully represent the intricacies of drug absorption and distribution, which limits their predictive abilities required for personalized treatment strategies. Our methodology introduces two innovations to enhance modeling accuracy: 1. Time-varying parameters: this approach is designed to accommodate the dynamic nature of drug absorption rates. 2. Fractional calculus in representing delayed drug response. This approach effectively captures anomalous diffusion phenomena, surpassing traditional models in describing drug delayed response without the need for extensive compartmentalization.