AIMC Topic: Biological Availability
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An oral robotic pill reliably and safely delivers teriparatide with high bioavailability in healthy volunteers: A phase 1 study.
British journal of clinical pharmacology
Apr 7, 2025
AIMS: The incidence of osteoporosis is projected to exceed 70 million people over the age of 65 years by 2030. Osteoanabolic agents, such as teriparatide and abaloparatide, are not only effective in reducing fracture incidence but also improve skelet...
RSM and AI based machine learning for quality by design development of rivaroxaban push-pull osmotic tablets and its PBPK modeling.
Scientific reports
Mar 7, 2025
The study is based on applying Artificial Neural Network (ANN) based machine learning and Response Surface Methodology (RSM) as simultaneous bivariate approaches in developing controlled-release rivaroxaban (RVX) osmotic tablets. The influence of dif...
Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation.
Scientific reports
Feb 8, 2025
In this study, the solubility of rivaroxaban, a poorly water-soluble drug, was investigated in mixed solvent systems to address challenges in pharmaceutical formulation and bioavailability enhancement. Solubility optimization is essential for the eff...
Preparation and optimisation of solid lipid nanoparticles of rivaroxaban using artificial neural networks and response surface method.
Journal of microencapsulation
Jan 5, 2025
AIMS: This study aimed to improve rivaroxaban delivery by optimising solid lipid nanoparticles (SLN) for minimal mean diameter and maximal entrapment efficiency (EE), enhancing solubility, bioavailability, and the ability to cross the blood-brain bar...
Quantifying the contributions of factors to bioaccessible Cd and Pb in soil using machine learning.
Journal of hazardous materials
Jan 2, 2025
The bioaccessibility of cadmium (Cd) and lead (Pb) in the gastrointestinal tract is crucial for health risk assessments of contaminated soils. However, variability in In vitro analytical conditions and soil properties introduces bias and uncertainty ...
Using machine learning to predict soil lead relative bioavailability.
Journal of hazardous materials
Nov 22, 2024
Although the relative bioavailability (RBA) can be applied to assess the effects of Pb on human health, there is no definition and no specific data of Pb-RBA to different soil sources and endpoints in vivo. In this study, we estimated the Pb-RBA from...
Machine learning driven bioequivalence risk assessment at an early stage of generic drug development.
European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
Oct 26, 2024
BACKGROUND: Bioequivalence risk assessment as an extension of quality risk management lacks examples of quantitative approaches to risk assessment at an early stage of generic drug development. The aim of our study was to develop a model-based approa...
Predicting bioavailability of potentially toxic elements (PTEs) in sediment using various machine learning (ML) models: A case study in Mahabad Dam and River-Iran.
Journal of environmental management
Jul 15, 2024
Considering the significant impact of potentially toxic elements (PTEs) on the ecosystem and human health, this paper, investigated the contamination level of four PTEs (Zn, Cu, Mo and Pb) and their mobility in sediments of Mahabad dam and river. Cho...
Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.
Journal of molecular modeling
Jul 12, 2024
CONTEXT: Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achie...
Assessing the anticholinergic cognitive burden classification of putative anticholinergic drugs using drug properties.
British journal of clinical pharmacology
Jun 11, 2024
AIMS: This study evaluated the use of machine learning to leverage drug absorption, distribution, metabolism and excretion (ADME) data together with physicochemical and pharmacological data to develop a novel anticholinergic burden scale and compare ...
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