Human pharmacokinetic (PK) predictions play a critical role in assessing the quality of potential clinical candidates where the accurate estimation of clearance, volume of distribution, bioavailability, and the plasma-concentration-time profiles are the desired end points. While many methods for conducting predictions utilize in vivo data, predictions can be conducted successfully from in vitro or in silico data, applying modeling and simulation techniques. This approach can be facilitated using commercially available prediction software such as GastroPlus which has been reported to accurately predict the oral PK profile of small drug-like molecules. Herein, case studies are described where GastroPlus modeling and simulation was employed using in silico or in vitro data to predict PK profiles in early discovery. The results obtained demonstrate the feasibility of adequately predicting plasma-concentration-time profiles with in silico derived as well as in vitro measured parameters and hence predicting PK profiles with minimal data. The applicability of this approach can provide key information enabling decisions on either dose selection, chemistry strategy to improve compounds, or clinical protocol design, thus demonstrating the value of modeling and simulation in both early discovery and exploratory development for predicting absorption and disposition profiles.
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Front Syst Neurosci
December 2024
Universidade Federal de Goias, School of Electrical, Mechanical and Computer Engineering, Goiânia, Brazil.
Dysfunction in fear and stress responses is intrinsically linked to various neurological diseases, including anxiety disorders, depression, and Post-Traumatic Stress Disorder. Previous studies using in vivo models with Immediate-Extinction Deficit (IED) and Stress Enhanced Fear Learning (SEFL) protocols have provided valuable insights into these mechanisms and aided the development of new therapeutic approaches. However, assessing these dysfunctions in animal subjects using IED and SEFL protocols can cause significant pain and suffering.
View Article and Find Full Text PDFFront Chem
December 2024
African Society for Bioinformatics and Computational Biology, Cape Town, South Africa.
Introduction: Dengue Fever continues to pose a global threat due to the widespread distribution of its vector mosquitoes, and . While the WHO-approved vaccine, Dengvaxia, and antiviral treatments like Balapiravir and Celgosivir are available, challenges such as drug resistance, reduced efficacy, and high treatment costs persist. This study aims to identify novel potential inhibitors of the Dengue virus (DENV) using an integrative drug discovery approach encompassing machine learning and molecular docking techniques.
View Article and Find Full Text PDFCureus
December 2024
Biostatistics, The Oxford Center, Brighton, USA.
Using simulated data with duplicate observational data points, this research aims to highlight the notable efficiency of repeated measures analysis of variance (ANOVA) compared to one-way ANOVA as a more powerful statistical model. One of the principal advantages of repeated measures ANOVA is its design, in which each subject acts as their own control. This methodology allows for the statistical mitigation of individual differences among subjects, thereby reducing extraneous variability (noise) that can obscure the effects of the experimental conditions under investigation.
View Article and Find Full Text PDFChem Sci
December 2024
VASP Software GmbH Berggasse 21 A-1090 Vienna Austria.
Constructing a self-consistent first-principles framework that accurately predicts the properties of electron transfer reactions through finite-temperature molecular dynamics simulations is a dream of theoretical electrochemists and physical chemists. Yet, predicting even the absolute standard hydrogen electrode potential, the most fundamental reference for electrode potentials, proves to be extremely challenging. Here, we show that a hybrid functional incorporating 25% exact exchange enables quantitative predictions when statistically accurate phase-space sampling is achieved thermodynamic integrations and thermodynamic perturbation theory calculations, utilizing machine-learned force fields and Δ-machine learning models.
View Article and Find Full Text PDFTher Adv Med Oncol
January 2025
Department of Oncology, Zhuzhou Second Hospital, Zhuzhou, Hunan 412000, China.
Background: Both the antibody-drug conjugate (ADC) enfortumab vedotin (EV) and programmed death-1 inhibitor pembrolizumab have been shown to provide survival benefits in patients previously treated with locally advanced or metastatic urothelial carcinoma (la/mUC). Cost-effectiveness is necessary to consider whether the increased efficacy of the two therapies will lead to higher prices for first-line treatment of previously untreated la/mUC.
Objectives: To guide the choice of EV plus pembrolizumab or chemotherapy for patients with previously untreated la/mUC.
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