Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules.

Background/objectives: Drug-Induced Kidney Injury (DIKI) presents a significant challenge in drug development, often leading to clinical-stage failures. The early prediction of DIKI risk can improve drug safety and development efficiency. Existing models tend to focus on physicochemical properties alone, often overlooking drug-target interactions crucial for DIKI.

G signaling in asthma.

Shortening of airway smooth muscle and bronchoconstriction are pathognomonic for asthma. Airway shortening occurs through calcium-dependent activation of myosin light chain kinase, and RhoA-dependent calcium sensitization, which inhibits myosin light chain phosphatase. The mechanism through which pro-contractile stimuli activate calcium sensitization is poorly understood.

Safety Implications of Modulating Nuclear Receptors: A Comprehensive Analysis from Non-Clinical and Clinical Perspectives.

The unintended modulation of nuclear receptor (NR) activity by drugs can lead to toxicities amongst the endocrine, gastrointestinal, hepatic cardiovascular, and central nervous systems. While secondary pharmacology screening assays include NRs, safety risks due to unintended interactions of small molecule drugs with NRs remain poorly understood. To identify potential nonclinical and clinical safety effects resulting from functional interactions with 44 of the 48 human-expressed NRs, we conducted a systematic narrative review of the scientific literature, tissue expression data, and used curated databases (OFF-X™) (Off-X, Clarivate) to organize reported toxicities linked to the functional modulation of NRs in a tabular and machine-readable format.