Results 41 to 50 of about 1,092 (147)

Development of in vitro - in vivo correlations for newly optimized Nimesulide formulations. [PDF]

PLoS ONE, 2018
Use of the human volunteers in bioequivalence studies is being discouraged by the Food and drug administration after the introduction of biowaiver approaches.
Muhammad Hanif, Muhammad Harris Shoaib, Rabia Ismail Yousuf, Farya Zafar   +3 more
doaj   +1 more source

Delivering Proteins and Enzymes to Solid Tumors Through the Development of Stimuli‐Responsive Nanocarriers

ChemNanoMat, Volume 12, Issue 6, June 2026.
Stimuli‐responsive strategies to achieve controlled release of protein‐based therapeutic agents into solid tumors. Proteins, enzymes, and peptides have long been recognized as valuable therapeutic agents, particularly in oncology. However, their complex chemical structures render them more sensitive than smaller chemotherapeutic drugs.
Guillermo Plaza‐Palomo, Diana Benita‐Donadios, Sandra Jiménez‐Falcao, Alejandro Baeza   +3 more
wiley   +1 more source

Direct, differential-equation-based in-vitro-in-vivo correlation (IVIVC) method

, 2003
A new, differential equation-based in-vitro-in-vivo correlation (IVIVC) method is proposed that directly relates the time-profiles of in-vitro dissolution rates and in-vivo plasma concentrations by using one- or multi-compartment pharmacokinetic models ...
Buchwald, Peter
core   +1 more source

Machine Learning Meets Pharmacokinetics: A Comparative Analysis of Predictive Models for Plasma Concentration‐Time Profiles

CPT: Pharmacometrics &Systems Pharmacology, Volume 15, Issue 5, May 2026.
ABSTRACT Predicting pharmacokinetic (PK) profiles from molecular structures represents a significant advancement in pharmaceutical research with substantial implications for expediting drug discovery processes. We evaluated five approaches to systematically compare five distinct methodological frameworks for predicting rat plasma concentration‐time ...
Felix Jost, Clemens Giegerich, Christoph Grebner, Hans Matter, Henrik Cordes   +4 more
wiley   +1 more source

Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development

CPT: Pharmacometrics &Systems Pharmacology, Volume 15, Issue 4, April 2026.
ABSTRACT This review examines the application of machine learning (ML) in physiologically based pharmacokinetic (PBPK) modeling through improved prediction of input parameters, particularly via quantitative structure–activity relationship (QSAR) models, for absorption, distribution, metabolism, and excretion (ADME) properties across drug development ...
Xinyue Chen, Zhoumeng Lin
wiley   +1 more source

Neurons enhance blood–brain barrier function via upregulating claudin-5 and VE-cadherin expression due to glial cell line-derived neurotrophic factor secretion

eLife
Blood–brain barrier (BBB) prevents neurotoxins from entering central nervous system. We aimed to establish and characterize an in vitro triple co-culture BBB model consisting of brain endothelial cells hCMEC/D3, astrocytoma U251 cells, and neuroblastoma ...
Lu Yang, Zijin Lin, Ruijing Mu, Wenhan Wu, Hao Zhi, Xiaodong Liu, Hanyu Yang, Li Liu   +7 more
doaj   +1 more source

Modeling Polymeric Drug Release: The Emerging Role of Machine Learning

WIREs Nanomedicine and Nanobiotechnology, Volume 18, Issue 2, March/April 2026.
A review of mechanistic, empirical, and machine learning (ML) approaches for modeling drug release from polymeric systems, highlighting how data‐driven methods uncover relationships between formulation parameters and release behavior to guide future drug delivery design.
Ryan N. Woodring, Kristy M. Ainslie
wiley   +1 more source

Microbial metabolites in tumor epigenetic regulation

iMeta, Volume 5, Issue 1, February 2026.
The gut microbiome modulates tumor epigenetic regulation through bioactive metabolites derived from dietary substrates. Microbiota‐produced SCFAs, secondary BAs, one‐carbon metabolites, and tryptophan‐derived ligands regulate histone acetylation, DNA methylation, and chromatin remodeling via HDAC, DNMT, AhR, and metabolic cofactor‐dependent pathways ...
Wangzheqi Zhang, Haoling Zhang, Yan Liao, Shuya Jiang, Haolong Zhang, Chenwei Huang, Zhijing Song, Rui Zhao, Ping Lu, Xiaohong Kang, Eryan Kong, Yalin Zhu, Wei Wang, Xuesong Liu, Yisheng Chen, Zhiwen Luo, Zhaoyu Li, Di Wang, Na Xing, Yadong Guo, Jingjing Zhang, Xiaojing Wang, Wenyi Wang, Mowaffaq Adam, Bakiah Shaharuddin, Muhamad Yusri Musa, Doblin Sandai, Chenglong Zhu, Aimin Jiang, Peng Luo, Zhijie Zhao, Zui Zou   +31 more
wiley   +1 more source

A Correlative Model to Predict In Vivo AUC for Nanosystem Drug Delivery with Release Rate-Limited Absorption

Journal of Pharmacy & Pharmaceutical Sciences, 2012
Purpose. Drug release from nanosystems at the sites of either absorption or effect biophase is a major determinant of its biological action. Thus, in vitro drug release is of paramount importance in gaining insight for the systems performance in vivo ...
Mohammad Barzegar-Jalali, Kaivan Mohammadi, Ghobad Mohammadi, Hadi Valizadeh, Azim Barzegar-Jalali, Khosro Adibkia, Ali Nokhodchi   +6 more
doaj   +1 more source

Development and validation of an in vitro–in vivo correlation (IVIVC) model for propranolol hydrochloride extended-release matrix formulations

, 2013
The objective of this study was to develop an in vitro–in vivo correlation (IVIVC) model for hydrophilic matrix extended-release (ER) propranolol dosage formulations.
Cheng, Chinhwa, Wu, Pao-Chu, Hsu, Kuang-Yang, Lee, Hsin-Ya   +3 more
core   +1 more source