Optical control of sphingolipid biosynthesis using photoswitchable sphingosines.

Sphingolipid metabolism comprises a complex interconnected web of enzymes, metabolites and modes of regulation that influence a wide range of cellular and physiological processes. Deciphering the biological relevance of this network is challenging as numerous intermediates of sphingolipid metabolism are short-lived molecules with often opposing biological activities. Here, we introduce clickable, azobenzene-containing sphingosines, termed caSphs, as light-sensitive substrates for sphingolipid biosynthesis.

Semi-mechanistic population PK/PD model to aid clinical understanding of myelodysplastic syndromes following treatment with Venetoclax and Azacitidine.

Myelodysplastic syndromes (MDS) represent a group of bone marrow disorders involving cytopenias, hypercellular bone marrow, and dysplastic hematopoietic progenitors. MDS remains a challenge to treat due to the complex interplay between disease-induced and treatment-related cytopenias. Venetoclax, a selective BCL-2 inhibitor, in combination with azacitidine, a hypomethylating agent, is currently being investigated in patients with previously untreated higher-risk MDS.

Optical control of sphingolipid biosynthesis using photoswitchable sphingosines.

Sphingolipid metabolism comprises a complex interconnected web of enzymes, metabolites and modes of regulation that influence a wide range of cellular and physiological processes. Deciphering the biological relevance of this network is challenging as numerous intermediates of sphingolipid metabolism are short-lived molecules with often opposing biological activities. Here, we introduce clickable, azobenzene-containing sphingosines, termed s, as light-sensitive substrates for sphingolipid biosynthesis.

Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.

Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature-based interpretability method, which can be seamlessly integrated into supervised ML models to gain a deeper understanding of their predictions, thereby enhancing their transparency and trustworthiness. This tutorial focuses on the application of SHAP analysis to standard ML black-box models for regression and classification problems.

Population Pharmacokinetic and Exposure-Response Modeling to Inform Risankizumab Dose Selection in Patients With Ulcerative Colitis.

Data from phase IIb/III and phase III studies were used to characterize the population pharmacokinetics of risankizumab and its exposure-response relationships for efficacy and safety in ulcerative colitis (UC) patients. A two-compartment model with first-order absorption and elimination accurately described risankizumab pharmacokinetics. Although certain covariates, namely, body weight, serum albumin, fecal calprotectin, sex, corticosteroid use, advanced therapy inadequate response, and pancolitis, were statistically correlated with risankizumab clearance, their impact on exposure was not clinically meaningful for efficacy or safety.