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Paliwal A, Jain S, Kumar S, Wal P, Khandai M, Khandige PS, Sadananda V, Anwer MK, Gulati M, Behl T, Srivastava S. Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine. Expert Opin Drug Metab Toxicol 2024; 20:181-195. [PMID: 38480460 DOI: 10.1080/17425255.2024.2330666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/11/2024] [Indexed: 03/22/2024]
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.
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Affiliation(s)
- Ajita Paliwal
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, India
| | - Smita Jain
- Department of Pharmacy, Banasthali Vidyapith, Banasthali, India
| | - Sachin Kumar
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India
| | - Pranay Wal
- Department of Pharmacy, Pranveer Singh Institute of Technology, Pharmacy, Kanpur, India
| | - Madhusmruti Khandai
- Department of Pharmacy, Royal College of Pharmacy and Health Sciences, Berahmpur, India
| | - Prasanna Shama Khandige
- NGSM Institute of Pharmaceutical Sciences, Department of Pharmacology, Manglauru, NITTE (Deemed to be University), Manglauru, India
| | - Vandana Sadananda
- AB Shetty Memorial Institute of Dental Sciences, Department of Conservative Dentistry and Endodontics, NITTE (Deemed to be University), Mangaluru, India
| | - Md Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Monica Gulati
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, India
- ARCCIM, Health, University of Technology, Sydney, Ultimo, Australia
| | - Tapan Behl
- Amity School of Pharmaceutical Sciences, Amity University, Mohali, Punjab, India
| | - Shriyansh Srivastava
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, India
- Department of Pharmacology, Delhi Pharmaceutical Sciences and Research University (DPSRU), New Delhi, India
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