New approach methodologies for risk assessment using deep learning.

The advancement of technologies and the development of more efficient artificial intelligence (AI) enable the processing of large amounts of data in a very short time. Concurrently, the increase in information within biological databases, such as 3D molecular structures or networks of functional macromolecule associations, will facilitate the creation of new methods for risk assessment that can serve as alternatives to animal testing. Specifically, the predictive capabilities of AI as new approach methodologies (NAMs) are poised to revolutionise risk assessment approaches.

Determining Targets for Antiretroviral Drug Concentrations: A Causal Framework Illustrated With Pediatric Efavirenz Data From the CHAPAS-3 Trial.

Background: Determining a therapeutic window for maintaining antiretroviral drug concentrations within an appropriate range is required for identifying effective dosing regimens. The limits of this window are typically calculated using predictive models. We propose that target concentrations should instead be calculated based on counterfactual probabilities of relevant outcomes and describe a counterfactual framework for this.

Comparative genomics reveals carbohydrate enzymatic fluctuations and herbivorous adaptations in arthropods.

Background: Arthropods represent the largest and most diverse phylum on Earth, playing a pivotal role in the biosphere. One key to their evolutionary success is their ability to feed on plant material. However, their endogenous enzymatic repertoire, which contributes to plant digestion, remains largely unexplored and poorly understood.