111 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 extensive atomistic molecular dynamics simulations to demonstrate that cholesterol stabilizes the human Sigma-1 receptor trimer and that distinct agonist and antagonist ligands differentially modulate its oligomeric dynamics through specific interactions involving the 6-strand and residue W136.
This study demonstrates that incorporating taste-masking agents, such as Bitter Drug Powder or a combination of artificial sweeteners and a thickening agent, into a palatable vehicle significantly improves the reliability of voluntary oral dosing for bitter compounds in rats and mice, offering a refined alternative to oral gavage.
This study successfully extended the integrated glucose-insulin (IGI) model to quantify disease progression in individuals with impaired glucose tolerance by characterizing the annual decline in insulin secretion and sensitivity, while demonstrating that lifestyle intervention significantly slows this deterioration and improves beta-cell function.
This paper presents an AI-assisted workflow that streamlines the implementation of spatial Partial Differential Equation (PDE) models in NONMEM by automatically translating reaction-diffusion equations into executable Method of Lines (MOL) ODE systems, thereby overcoming the operational complexity of manual coding to enable practical, transparent, and maintainable modeling of intra-tumoral drug distribution.
This study demonstrates that while common post-hoc outlier filtering and exponential-tail residual models can be unreliable due to variance masking or insufficient tail heaviness, adopting a Student's t-distribution for residual errors provides a more robust and stable approach for handling extreme outliers in population pharmacokinetic modeling.
This paper demonstrates that the Cauchy distribution serves as a computationally efficient and robust alternative to the Student's t-distribution for handling outliers and below-quantification data in CAR-T kinetics, while a semi-mechanistic model with smooth, asynchronous transition functions enhances the physiological plausibility of expansion, conversion, and decay timing.
This study advances CAR-T cellular kinetics modeling by integrating smooth S-shaped gating and delay differential equations to better capture expansion dynamics and biological lags, revealing that a delay in effector-to-memory conversion best fits pooled data from BCMA- and CD19-targeted therapies while highlighting distinct product-specific kinetic profiles.
This study optimized a green deep eutectic solvent extraction method for total flavonoids from *Hemerocallis citrina* and validated their multi-target antidepressant mechanism, which involves suppressing neuroinflammation and modulating the HPA axis via the PI3K-AKT pathway, through integrated network pharmacology and in vitro experiments.
This paper employs mathematical modeling to demonstrate that the concavity of an antibiotic's dose-response curve is the critical factor in determining whether constant low-dose or repeated high-dose regimens are optimal for specific antibiotic-bacteria pairings, thereby challenging the universal "hit hard and hit early" approach to combat antimicrobial resistance.
This study utilizes high-throughput transcriptomic profiling of normal breast cells from diverse donors to demonstrate that exposure to specific environmental chemicals, particularly those with documented racial disparities, induces biological changes linked to aggressive breast cancer hallmarks, such as cell cycle dysregulation and altered cellular plasticity, at concentrations relevant to the US population.