110 papers
Pharmacology and toxicology explore how substances interact with living systems, ranging from the development of life-saving medicines to understanding the dangers of chemical exposure. This field sits at the critical intersection of chemistry and biology, asking essential questions about how drugs work, how the body processes them, and what happens when things go wrong. It is a dynamic area where researchers constantly strive to improve patient safety while discovering new therapeutic possibilities.
At Gist.Science, we bridge the gap between complex research and public understanding by curating the latest preprints from bioRxiv in this vital category. Our team processes every new submission from bioRxiv as it appears, transforming dense scientific data into both plain-language overviews and detailed technical summaries. This ensures that whether you are a specialist or a curious reader, you can grasp the significance of these emerging findings without getting lost in jargon.
Below are the most recent pharmacology and toxicology papers from bioRxiv, each accompanied by our expert analysis to help you navigate the latest scientific breakthroughs.
This study redefines CVN636 not as an allosteric agonist but as a positive allosteric modulator that selectively enhances orthosteric agonist responses specifically at mGlu7/7 homodimers, distinguishing it from activity at heterodimers.
This study demonstrates that Cell Painting PLUS applied to human RPTEC/TERT1 cells effectively identifies distinct morphological signatures for over 300 compounds, enabling mechanistic nephrotoxicity profiling and highlighting the critical importance of using physiologically relevant cell models for Next Generation Risk Assessment.
This study demonstrates that combining Cell Painting with machine learning is a sensitive and effective approach for identifying specific hepatotoxic constituents in botanical extracts and generating mechanistic hypotheses about their modes of action in primary human hepatocytes.
This study demonstrates that integrating Large Language Models with the Adverse Outcome Pathway (AOP) framework significantly enhances developmental neurotoxicity assessment by enabling accurate prediction of adverse outcomes and plausible biological mechanisms, particularly when utilizing few-shot prompting to overcome challenges in reconstructing downstream pathway events.
This study demonstrates that coupling patient-derived human liver organoids with high-content imaging and machine learning enables high-throughput, reproducible morphological profiling that accurately identifies hepatotoxicants and prioritizes compounds for clinical risk assessment while preserving donor-specific variability.
The paper introduces TRIDENT-2, a multimodal AI model trained on extensive toxicity data that accurately predicts chemical toxicity across diverse eukaryotic species, effectively overcoming critical data gaps to improve ecological risk assessment and biodiversity protection.
This study demonstrates that integrating whole-body PET microdosing data with therapeutic-dose pharmacokinetics in non-human primates within a PBPK framework successfully predicts first-in-human exposure and tissue distribution for dolutegravir, validating a novel workflow to inform dose selection.
This study demonstrates that patient-derived hiPSC-cardiomyocytes and engineered heart tissues carrying specific HCM and DCM mutations exhibit distinct genotype-dependent molecular, structural, and functional phenotypes, including differential responses to hypertrophic stimulation and unique patterns of tissue failure, thereby validating these 3D models for investigating inherited cardiomyopathy mechanisms and drug responses.
This study demonstrates that short-term prepubertal exposure to sodium valproate in mice does not cause overt gonadotoxicity or alter steroidogenic gene expression in either sex, although a non-significant trend toward reduced testicular germ cell density in males suggests a need for further investigation into long-term fertility risks.
This study develops novel dissymmetric ionizable lipid-based nanoparticles that efficiently deliver ferroptosis-related siRNAs to treat diabetic metabolic disorders and restore islet function in mouse models, offering a safe and potent therapeutic strategy for type 2 diabetes.