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Population pharmacokinetics of erlotinib in patients with non-small cell lung cancer (NSCLC): A model-based meta-analysis.
Comput Biol Med
January 2025
Department of Pharmacy and Yonsei Institute of Pharmaceutical Sciences, Yonsei University, Incheon, Republic of Korea; Department of Pharmaceutical Medicine and Regulatory Science, Yonsei University, Incheon, Republic of Korea; Graduate Program of Industrial Pharmaceutical Science, Yonsei University, Incheon, Republic of Korea; Department of Integrative Biotechnology, Yonsei University, Incheon, Republic of Korea. Electronic address:
Background: Erlotinib is a potent first-generation epidermal growth factor receptor tyrosine kinase inhibitor. Due to its proximity to the upper limit of tolerability, dose adjustments are often necessary to manage potential adverse reactions resulting from its pharmacokinetic (PK) variability.
Methods: Population PK studies of erlotinib were identified using PubMed databases.
Short and mid-term research priorities for Veterans with multiple sclerosis: A modified Delphi process engaging Veterans, researchers, and operational partners.
Mult Scler Relat Disord
January 2025
Multiple Sclerosis Center of Excellence West, Veterans Affairs, USA; Rehabilitation Care Service, VA Puget Sound Health Care System, 1660 S Columbian Way, Seattle, Washington, 98108, USA; Department of Rehabilitation Medicine, University of Washington, 325 9th Avenue, Seattle, Washington, 98104, USA. Electronic address:
Background/objective: Identifying research priorities of Veterans, MS researchers, and key stakeholders is critical to advance high-quality, evidence-based, and Veteran-specific MS care.
Methods: We used a modified Delphi approach to identify research priorities for Veterans with MS. Electronic surveys were distributed to Veterans with MS (n = 50,975), MS researchers (n = 191), VA healthcare providers (1,337), and funding agency representatives (n = 6) asking about their 2-3 most important research questions that would benefit Veterans with MS for researchers to answer in the next 5-10 years.
Quantitative Evaluation of Multiple Treatment Regimens for Treatment-Resistant Depression.
Int J Neuropsychopharmacol
January 2025
Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai 201203, China.
Objective: This study aims to quantitatively evaluate the efficacy and safety of various treatment regimens for treatment-resistant depression (TRD) across oral, intravenous, and intranasal routes to inform clinical guidelines.
Methods: A systematic review identified randomized controlled trials on TRD, with efficacy measured by changes in the Montgomery-Åsberg Depression Rating Scale (MADRS). We developed pharmacodynamic and covariate models for different administration routes, using Monte Carlo simulations to estimate efficacy distribution.
Discovery of an Enzyme-Activated Fluorogenic Probe for Profiling of Acylaminoacyl-Peptide Hydrolase.
Anal Chem
January 2025
Department of Laboratory Medicine, School of Medicine, Yangtze University, Jingzhou 434023, P.R. China.
Acylaminoacyl-peptide hydrolase (APEH), a serine peptidase that belongs to the prolyl oligopeptidase (POP) family, catalyzes removal of N-terminal acetylated amino acid residues from peptides. As a key regulator of protein N-terminal acetylation, APEH was involved in many important physiological processes while its aberrant expression was correlated with progression of various diseases such as inflammation, diabetics, Alzheimer's disease (AD), and cancers. However, while emerging attention has been attracted in APEH-related disease diagnosis and drug discovery, the mechanisms behind APEH and related disease progression are still unclear; thus, further investigating the physiological role and function of APEH is of great importance.
View Article and Find Full Text PDFEnhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.
J Chem Inf Model
January 2025
Department of Chemical Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan.
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data.
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