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

ABSTRACT

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 hampers accurate prediction of drug candidates’ pharmacokinetic properties.

Areas Covered

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

Expert opinion

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

GRAPHICAL ABSTRACT

KEYWORDS:

• In-silico modeling
• Personalized medicine
• pharmacokinetics
• pharmacogenomics
• validation

Article highlights

• Predictive modeling in pharmacokinetics has evolved significantly, transitioning from traditional in-silico simulations to a more personalized approach tailored to individual patient characteristics.

• The advancement of computer technology has facilitated the development of sophisticated models like population pharmacokinetics (PopPK) models, analyze drug behavior in diverse populations, considering factors like age, weight, and sex, to tailor treatment plans accordingly and physiologically based pharmacokinetic (PBPK) models, utilize rich physiological data to simulate drug absorption, distribution, metabolism, and elimination (ADME), offering clinically relevant insights.

• In-silico simulations, such as molecular docking and molecular dynamics, play a pivotal role in scrutinizing ligand binding affinity and drug behavior within biological systems.

• Predictive modeling aids in identifying optimal drug candidates, offering insights into their therapeutic uses, and optimizing drug dosages for maximum efficacy.

• The trajectory of predictive modeling in pharmacokinetics has led to the emergence of personalized medicine, aiming to customize treatment plans based on individual patient characteristics and biomolecular processes. Personalized medicine endeavors to enhance patient outcomes by mitigating the risks of adverse drug reactions (ADRs) and optimizing therapy through tailored treatment plans.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17425255.2024.2330666

Additional information

Funding

This paper was not funded.