94 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 utilizes transcriptomic analysis in *Daphnia pulex* to demonstrate that nutritional status, particularly phosphorus limitation, significantly modulates arsenic toxicity through antagonistic interactions that disrupt critical cellular pathways like DNA repair and energy homeostasis.
The authors developed an open-source, AI-enabled screening platform using *C. elegans* to rapidly identify FDA-approved drugs that enhance heat stress resistance, leading to the discovery of 31 compounds that extend lifespan in worms and exhibit anti-senescence activity in human cells.
This study proposes a schema-grounded multitask instruction-tuning framework using parameter-efficient LoRA adaptation on LLMs, which significantly outperforms zero-shot baselines in jointly extracting biomedical entities and relations for pharmacovigilance while demonstrating that schema alignment is more critical than model scale for reliable structured extraction.
This paper proposes a semiclassical reinterpretation of drug release kinetics in polymeric nanocapsules by modeling the potential barrier as a time-decaying function rather than a static one, demonstrating through global optimization of ex-vivo data that an exact transmission formula combined with an exponential decay hypothesis provides a superior and more parsimonious fit than previous multifractal or rational decay models.
This study demonstrates that transcriptomic liberality serves as an unbiased, dimensionality-reducing metric to quantify TCDD-induced liver perturbations in mice, revealing that longer exposure durations cause more extensive alterations in transcriptomic architecture compared to shorter durations.
This study demonstrates that despite distinct physicochemical properties and kinetic profiles, ultrafine particulate matter, black carbon, and polystyrene nanoplastics all induce immunotoxicity through a conserved mitochondrial stress response network centered on the OMA1-DELE1 axis, while also exhibiting particle-specific dynamics that determine the severity and persistence of cellular damage.
This study evaluates the anthropogenic impact on surface water quality in Ghana's Ahafo Ano Southwest District, revealing significant seasonal and spatial variations driven by agriculture, mining, and deforestation that degrade water quality and pose ecological risks to aquatic life.
This comparative study demonstrates that while ivermectin exhibits similar pharmacokinetic profiles in both young and adult cattle, its therapeutic efficacy varies drastically between Argentina and Italy due to widespread anthelmintic resistance in Argentine farms versus high susceptibility in Italian herds, underscoring the critical need for resistance diagnostics before treatment.
This study demonstrates that the methanol extract of *Blumea balsamifera* leaves exhibits dose-dependent anxiolytic-like effects in ICR mice with a favorable safety profile, providing the first scientific evidence supporting its traditional use as a natural anxiety treatment.
This study developed and validated a computational model that accurately predicts long-term tuberculosis relapse in mice using only 4-week data on colony-forming units and ribosomal RNA synthesis ratios, thereby enabling the efficient screening and prioritization of novel short-course treatment regimens.