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Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

Mutagenicity and Carcinogenicity

Mutagenicity and Carcinogenicity

Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

Pharmacokinetic Models: Overview

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...

Mechanistic Models: Overview of Compartment Models

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

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Related Experiment Video

Updated: Jan 14, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

AI/ML-based computational models for toxicity prediction.

Sushmita Barua¹, Badhrinarayanan Balaji², Seetharaman Balaji³

¹Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Environmental Science and Pollution Research International

|January 12, 2026

Summary

This summary is machine-generated.

Computational toxicology and AI/ML models are advancing chemical safety evaluation. These tools predict toxicity, aiding regulatory efforts and reducing animal testing for better chemical safety assessment.

Keywords:

AI Animal toxicity Computational toxicity Ecotoxicity Human toxicity ML SDG 3, 6, 9, 12, 14

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Area of Science:

Computational toxicology
Artificial Intelligence (AI)
Machine Learning (ML)

Background:

Increasing demand for accurate toxicity assessment and reduced animal testing drives computational model development.
AI/ML models and online resources are crucial for modern computational toxicology research.

Purpose of the Study:

To review computational models and data coverage for toxicity prediction and chemical safety evaluation.
To highlight AI/ML tools for predicting various toxicity endpoints and discuss regulatory relevance.

Main Methods:

Focus on computational models, molecular descriptors, Quantitative Structure-Activity Relationship (QSAR) models.
Inclusion of AI/ML-based approaches, Explainable AI (XAI), and predictive methodologies.
Analysis of data coverage, accessibility, and regulatory considerations.

Main Results:

Computational models and AI/ML tools enable identification, prediction, and analysis of chemical toxicity across biological endpoints.
AI/ML tools are effective for predicting neurotoxicity, hepatotoxicity, cardiotoxicity, genotoxicity, and environmental toxicity.
Significant regulatory limitations and a lack of global conformity in chemical safety assessment were observed.

Conclusions:

Regulatory adaptability is essential due to the rapid evolution of AI.
Integrating AI/ML tools and interoperable frameworks can significantly advance predictive toxicology.
Global conformity in regulatory norms is a key focus for future chemical safety evaluations.