Integrating ensemble machine learning and multi-omics approaches to identify Dp44mT as a novel anti-Candida albicans agent targeting cellular iron homeostasis

Frontiers in Pharmacology, Год журнала: 2025, Номер 16

Опубликована: Апрель 24, 2025

Candidiasis, mainly caused by Candida albicans, poses a serious threat to human health. The escalating drug resistance in C. albicans and the limited antifungal options highlight critical need for novel therapeutic strategies. We evaluated 12 machine learning models on self-constructed dataset with known anti-C. activity. Based their performance, optimal model was selected screen our separate in-house compound library unknown activity potential agents. of compounds confirmed through vitro susceptibility assays, hyphal growth biofilm formation assays. Through transcriptomics, proteomics, iron rescue experiments, CTC staining, JC-1 DAPI molecular docking, dynamics simulations, we elucidated mechanism underlying compound. Among models, best predictive an ensemble constructed from Random Forests Categorical Boosting using soft voting. It predicts that Dp44mT exhibits potent tests further verified this finding can inhibit planktonic growth, formation, albicans. Mechanistically, exerts disrupting cellular homeostasis, leading collapse mitochondrial membrane ultimately causing apoptosis. This study presents practical approach predicting com-pounds provides new insights into development homeostasis

Язык: Английский

Amiodarone and Ravuconazole Combination Potentiates Chemotherapy against Drug-Resistant Trypanosoma cruzi Strains

ACS Omega, Год журнала: 2025, Номер unknown

Опубликована: Май 16, 2025

Язык: Английский