AI Medical Compendium Journal:
Expert opinion on drug metabolism & toxicology

Showing 1 to 10 of 12 articles

Mass balance and metabolite profiles in humans of tegoprazan, a novel potassium-competitive acid blocker, using C-radiolabelled techniques.

Expert opinion on drug metabolism & toxicology
BACKGROUND: Tegoprazan (LXI-15028), a novel potassium-competitive acid blocker, has shown great efficacy in treating acid-related disorders. However, its metabolic and excretion characteristics are not fully understood.

Pharmacokinetic and pharmacodynamic alterations in older people: what we know so far.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Healthcare professionals face increasing challenges when managing older patients, a group characterized by significant interindividual variability in comorbidity patterns, homeostatic capacity, frailty status, cognitive function, and li...

Evaluating the synergistic use of advanced liver models and AI for the prediction of drug-induced liver injury.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Drug-induced liver injury (DILI) is a leading cause of acute liver failure. Hepatotoxicity typically occurs only in a subset of individuals after prolonged exposure and constitutes a major risk factor for the termination of drug develop...

Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study.

Expert opinion on drug metabolism & toxicology
BACKGROUND: Cardiotoxicity is a major cause of drug withdrawal. The hERG channel, regulating ion flow, is pivotal for heart and nervous system function. Its blockade is a concern in drug development. Predicting hERG blockade is essential for identify...

Recent progress in machine learning approaches for predicting carcinogenicity in drug development.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies...

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hamp...

Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Dev...

Improving on in-silico prediction of oral drug bioavailability.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Although significant development has been made in high-throughput screening of oral drug absorption and oral bioavailability, prediction continues to play an important role in prediction of oral bioavailability and assisting in the pro...

Deep learning neural network derivation and testing to distinguish acute poisonings.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was e...