Integrating Pharmacokinetics and Pharmacodynamics Modeling with Quantum Regression for Predicting Herbal Compound Toxicity

TL;DR

This paper introduces a quantum regression model that combines pharmacokinetics and pharmacodynamics data to predict the toxicity of herbal compounds, aiming to improve safety assessments.

Contribution

The study presents a novel quantum regression approach integrating PK/PD data with toxicity information for herbal compounds.

Findings

Quantum regression effectively predicts toxicity severity.

Integration of PK/PD data enhances prediction accuracy.

Model demonstrates potential for safer herbal medicine development.

Abstract

Herbal compounds present complex toxicity profiles that are often influenced by both intrinsic chemical properties and pharmacokinetics (PK) governing absorption and clearance. In this study, we develop a quantum regression model to predict acute toxicity severity for herbal-derived compounds by integrating toxicity data from NICEATM with pharmacological features from TCMSP.