Generation of Drug-Induced Cardiac Reactions towards Virtual Clinical Trials
Journal:
arXiv
Published Date:
Feb 11, 2025
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.
