**Abstract:** This research proposes a novel multi-scale simulation framework for predicting pharmacokinetic (PK) parameters within virtual human microcirculation networks. Traditional PK modeling often simplifies vascular geometry and blood flow dynamics, leading to inaccurate predictions. Our framework integrates computational fluid dynamics (CFD) at the microvascular level with physiologically-based pharmacokinetic (PBPK) models to simulate drug distribution, metabolism, and excretion with unprecedented detail. The system employs a dynamically updating network of virtual capillaries constructed from patient-specific MRI data, coupled with a physics-informed neural network (PINN) to infer drug concentration profiles over time.

Drug acetylation polymorphism is of clinical importance in slow and rapid acetylators. There are therapeutic and toxic consequences of interindividual drug acetylation of, e.g. isoniazid, procainamide and various sulfonamides. For comparing...

Drug acetylation polymorphism is of clinical importance in slow and rapid acetylators. There are therapeutic and toxic consequences of interindividual drug acetylation of, e.g. isoniazid, procainamide and various sulfonamides. For comparing...

We have developed a robust and sensitive analytical method for evaluating the pharmacokinetic properties of TPD354, using ultra-performance liquid chromatography coupled with tandem mass spectrometry...

**Abstract:** Current pharmacokinetic (PK) modeling relies on compartmental models and empirical observations, often failing to accurately predict drug behavior *in vivo*. This research introduces a novel approach leveraging hyperdimensional encoding (HDE) coupled with graph neural networks (GNNs) to represent molecular transport pathways within cellular membranes and extracellular spaces. By transforming pathway complexity into high-dimensional vector spaces and utilizing GNNs to analyze pathway interactions, we achieve a 10x improvement in PK prediction accuracy compared to traditional methods, enabling personalized drug dosing strategies and accelerated drug development.

Euonymi herba (EH) was one of the traditional characteristic national medicines of Guangxi, which had various pharmacological activities such as hemos…

AbstractObjectives. To optimize vedolizumab therapy for Crohn’s disease, this study aimed to construct a pharmacokinetic/pharmacodynamic (PK/PD) model by a

This study centered on creating and validating a UPLC–MS/MS assay that is both reliable and simple, making it suitable for measuring peiminine levels in the plasma of beagle dogs. The established met...

Based on the results, the commercially available fixed-dose premixed formulations of daptomycin can be integrated into day-to-day clinical practice with the expected benefit of improved efficacy target...

The present study aims to evaluate the in vivo drug interactions between sotorasib and ondansetron to provide critical data for accidental or self-medication cases to ensure safe and effective concur...

**Abstract:** Current Population Pharmacokinetic/Pharmacodynamic (PK/PD) modeling relies heavily on expert-driven population parameter estimation, limiting scalability and introducing subjective bias. This study introduces a novel framework, Bayesian Hierarchical Modeling with Deep Generative Adversarial Network-Enhanced Prior Distributions (BHDN-PKPD), that automates PK/PD model building and parameter estimation, leveraging deep learning to generate realistic prior distributions. This results in a 10x improvement in modeling efficiency and a 20% reduction in model uncertainty for diverse PK/PD datasets.

In clinical pharmacology trials, pharmacokinetic samples are typically collected via venipuncture by trained staff. However, recent advances in blood collection devices have enabled participant self-...

JOURNAL OF MEDICAL INTERNET RESEARCH. Liu et al https://www.jmir.org/2026/1/e77917. J Med Internet Res 2026 | vol. 28 | e77917 | p. 1. (page number ...

Background: Adherence to anti-seizure medications (ASMs) constitutes a cornerstone of effective epilepsy management. However, current consensus guidelines for assessing medication adherence via therapeutic drug monitoring (TDM) may neglect individual patient characteristics, thereby compromising the accuracy of adherence assessment. Objective: This study proposes an innovative Bayesian-based pharmacokinetic (PK) framework integrated with TDM to address above limitations, with a focus on 14 widely prescribed ASMs, including brivaracetam, carbamazepine, clobazam, eslicarbazepine acetate, lacosamide, lamotrigine, levetiracetam, oxcarbazepine, perampanel, phenobarbital, topiramate, valproic acid, vigabatrin and zonisamide. Methods: Comprehensive clinical trial simulations were conducted to investigate the PK of ASMs in patients with epilepsy under conditions of full adherence versus various non-adherent dosing behaviors, including omission of the last dose and consecutive missed doses. Bayesian posterior probabilities of these dosing behaviors were derived by integrating validated population PK models, individual patient demographics (e.g., age, weight, creatinine clearance), dosing history, prior adherence probability and TDM measurements. Additionally, the influence of covariates on assessment outcomes was systematically evaluated. Results: The Bayesian-based PK approach demonstrated robust discriminative ability. Under idealized simulation conditions with minimized variabilities, the approach achieved accurate retrodiction of the last 1 or 2 doses across all 14 ASMs and partial retrodiction of extended non-adherence trajectories for six ASMs. Concentration thresholds for adherence classification vary significantly across drugs and are influenced by patient-specific factors, comedications, formulation, sampling time, and prior probability. To operationalize these insights, an adaptable web-based dashboard (https://mipd.shinyapps.io/adherence_ASM/) was engineered with shiny package in R to enable precise and real-time assessments of medication adherence. Conclusions: This study establishes a Bayesian-based PK approach to enhance medication adherence assessment of ASMs. This approach facilitates a paradigm shift from population-based management to patient-specific adherence profiling, offering a practical methodology for precise evaluation of medication-taking behavior.

**Abstract:** This research proposes a novel framework for predicting and optimizing anti-cancer drug pharmacokinetics (PK) through integration of patient-specific microbiome data and a Generative Adversarial Network (GAN)-based pharmacokinetic model. Traditionally, PK modeling relies on population-average parameters, often failing to account for inter-individual variability significantly influenced by the gut microbiome. Our approach, *Microbiome-Pharmacokinetic GAN (MPG-GAN)*, utilizes microbiome composition as input to a GAN, generating personalized PK profiles and allowing for in-silico drug dosage optimization.

The pharmacokinetics of naftopidil ((R,S)-1-[4-(2-methoxyphenyl)-1-piperazinyl]-3-(I-naphthyloxy)-2-propanol, CAS 57149-07-2) was studied in rats and dogs using 14C-labeled drug in pharmacodynamically effective doses (oral doses: 5/10 mg/kg...