AIMC Topic: Administration, Oral
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A broader view on deriving a reference dose for THC traces in foods.
Critical reviews in toxicology
Feb 17, 2022
An Acute Reference Dose (ARfD) of 1 µg of -9-tetrahydrocannabinol (THC) per kilogram (kg) of body weight (bw) per day was recommended by the European Food Safety Authority (EFSA) for its assessment of possible acute health risks from the intake of in...
Pharmacokinetics of panduratin A following oral administration of a Boesenbergia pandurata extract to rats.
Journal of food and drug analysis
Dec 15, 2021
Boesenbergia pandurata and its major active ingredient, panduratin A (PAN), exhibit antibacterial, anti-oxidant, anti-inflammatory, and anti-obesity effects. We explored the time course of the plasma and tissue (in the major organs, gums and skin) co...
Active Learning for Drug Design: A Case Study on the Plasma Exposure of Orally Administered Drugs.
Journal of medicinal chemistry
Nov 15, 2021
The success of artificial intelligence (AI) models has been limited by the requirement of large amounts of high-quality training data, which is just the opposite of the situation in most drug discovery pipelines. Active learning (AL) is a subfield of...
Machine Learning Prediction of the Three Main Input Parameters of a Simplified Physiologically Based Pharmacokinetic Model Subsequently Used to Generate Time-Dependent Plasma Concentration Data in Humans after Oral Doses of 212 Disparate Chemicals.
Biological & pharmaceutical bulletin
Nov 2, 2021
Physiologically based pharmacokinetic (PBPK) modeling has the potential to play significant roles in estimating internal chemical exposures. The three major PBPK model input parameters (i.e., absorption rate constants, volumes of the systemic circula...
Magneto-Responsive Microneedle Robots for Intestinal Macromolecule Delivery.
Advanced materials (Deerfield Beach, Fla.)
Sep 17, 2021
Oral administration is the most convenient and commonly used approach for drug delivery, while it is still a challenge to overcome the complicated gastrointestinal barriers and realize efficient macromolecular drug absorption. Here, novel magneto-res...
CBDPS 1.0: A Python GUI Application for Machine Learning Models to Predict Bitter-Tasting Children's Oral Medicines.
Chemical & pharmaceutical bulletin
Aug 21, 2021
Bitter tastes are innately aversive and are thought to help protect animals from consuming poisons. Children are extremely sensitive to drug tastes, and their compliance is especially poor with bitter medicine. Therefore, judging whether a drug is bi...
Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology.
Molecules (Basel, Switzerland)
Apr 28, 2021
Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We descr...
An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design.
European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
Dec 7, 2020
Drugs in solid dispersion (SD) take advantage of fast and extended dissolution, thus attains a higher bioavailability than the crystal form. However, current development of SD relies on a random large-scale formulation screening method with low effic...
Robotically handled whole-tissue culture system for the screening of oral drug formulations.
Nature biomedical engineering
Apr 27, 2020
Monolayers of cancer-derived cell lines are widely used in the modelling of the gastrointestinal (GI) absorption of drugs and in oral drug development. However, they do not generally predict drug absorption in vivo. Here, we report a robotically hand...
Enhancing Acute Oral Toxicity Predictions by using Consensus Modeling and Algebraic Form-Based 0D-to-2D Molecular Encodes.
Chemical research in toxicology
May 17, 2019
Quantitative structure-activity relationships (QSAR) are introduced to predict acute oral toxicity (AOT), by using the QuBiLS-MAS (acronym for quadratic, bilinear and N-Linear maps based on graph-theoretic electronic-density matrices and atomic weigh...
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