A mechanistic neural network model predicts both potency and toxicity of antimicrobial combination therapies
Arora, H. S.; Lev, K.; Robida, A.; Velmurugan, R.; Chandrasekaran, S.
Show abstract
Antimicrobial resistance poses a major global threat due to the diminishing efficacy of current treatments and limited new therapies. Combination therapy with existing drugs offers a promising solution, yet current empirical methods often lead to suboptimal efficacy and inadvertent toxicity. The high cost of experimentally testing numerous combinations underscores the need for data-driven methods to streamline treatment design. We introduce CALMA, an approach that predicts the potency and toxicity of multi-drug combinations in Escherichia coli and Mycobacterium tuberculosis. CALMA identified synergistic antimicrobial combinations involving vancomycin and isoniazid that were antagonistic for toxicity, which were validated using in vitro cell viability assays in human cell lines and through mining of patient health records that showed reduced side effects in patients taking combinations identified by CALMA. By combining mechanistic modelling with deep learning, CALMA improves the interpretability of neural networks, identifies key pathways influencing drug interactions, and prioritizes combinations with enhanced potency and reduced toxicity.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Model-informed Deep Q-Networks to Guide Infliximab Dosing in Pediatric Crohn's Disease 92%
- DrugWAS: Leveraging drug-wide association studies to facilitate drug repurposing for COVID-19 91%
- Algorithmic identification of treatment-emergent adverse events from clinical notes using large language models: a pilot study in inflammatory bowel disease 91%
Similar papers in this journal
Similar papers in this journal
- Defining subpopulations of differential drug response to reveal novel target populations 94%
- Stratification and prediction of drug synergy based on target functional similarity 94%
- Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA Damage Response inhibitors 94%
Similar papers in this journal
- SynToxProfiler: an approach for top drug combination selection based on integrated profiling of synergy, toxicity and efficacy 95%
- A machine learning and network framework to discover new indications for small molecules 94%
- Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with Deep Graph Networks 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.