Generation of Drug-Induced Cardiac Reactions towards Virtual Clinical Trials

• URL: http://arxiv.org/abs/2502.07297v2
• Date: Sun, 18 May 2025 08:05:51 GMT
• Title: Generation of Drug-Induced Cardiac Reactions towards Virtual Clinical Trials
• Authors: Qian Shao, Bang Du, Zepeng Li, Qiyuan Chen, Hongxia Xu, Jimeng Sun, Jian Wu, Jintai Chen,
• Abstract summary: Drug-Aware Diffusion Model (DADM) is a novel model for simulating drug-induced electrocardiogram (ECG) alterations.<n>EPK is used to adaptively constrain the morphology of the generated ECGs.<n>ControlNet is proposed to incorporate demographic and drug data, simulating individual drug reactions.
• Score: 27.928421986311005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical trials remain critical in cardiac drug development but face high failure rates due to efficacy limitations and safety risks, incurring substantial costs. In-silico trial methodologies, particularly generative models simulating drug-induced electrocardiogram (ECG) alterations, offer a potential solution to mitigate these challenges. While existing models show progress in ECG synthesis, their constrained fidelity and inability to characterize individual-specific pharmacological response patterns fundamentally limit clinical translatability. To address these issues, we propose a novel Drug-Aware Diffusion Model (DADM). Specifically, we construct a set of ordinary differential equations to provide external physical knowledge (EPK) of the realistic ECG morphology. The EPK is used to adaptively constrain the morphology of the generated ECGs through a dynamic cross-attention (DCA) mechanism. Furthermore, we propose an extension of ControlNet to incorporate demographic and drug data, simulating individual drug reactions. Compared to the other eight state-of-the-art (SOTA) ECG generative models: 1) Quantitative and expert evaluation demonstrate that DADM generates ECGs with superior fidelity; 2) Comparative results on two real-world databases covering 8 types of drug regimens verify that DADM can more accurately simulate drug-induced changes in ECGs, improving the accuracy by at least 5.79% and recall by 8%. In addition, the ECGs generated by DADM can also enhance model performance in downstream drug-effect classification tasks.

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