Asian Journal of Biomedical and Pharmaceutical Sciences

Reach Us +1 (202) 780-3397

Opinion Article - Asian Journal of Biomedical and Pharmaceutical Sciences (2025) Volume 15, Issue 109

Pharmacokinetic modeling: Advancing drug development and personalized medicine.

Andrew Gawron *

Department of Biomedical Sciences, Karolinska Institute, Sweden

*Corresponding Author:
Andrew Gawron
Department of Biomedical Sciences, Karolinska Institute, Sweden
E-mail: andrew@gawron.com

Received: 01-Jan -2025, Manuscript No. AABPS-25-161144; Editor assigned: 02-Jan -2025, PreQC No. AABPS-25-161144(PQ); Reviewed: 18-Jan -2025, QC No. AABPS-25-161144; Revised: 22-Jan -2025, Manuscript No. AABPS-25-161144(R); Published: 29-Jan -2025, DOI: 10.35841/aabps-15.106.273

Citation: : Gawron A. Pharmacokinetic modeling: Advancing drug development and personalized medicine. Asian J Biomed Pharm Sci. 2025;15(109):273

Introduction  

Pharmacokinetic modeling has emerged as a crucial tool in drug development, clinical practice, and personalized medicine. It provides a mathematical framework to describe the absorption, distribution, metabolism, and excretion (ADME) of drugs in the human body. By integrating physiological, biochemical, and molecular data, pharmacokinetic models help optimize drug dosing, minimize toxicity, and improve therapeutic outcomes. The increasing availability of computational resources and biological data has propelled pharmacokinetic modeling to new heights, making it an indispensable aspect of modern pharmaceutical sciences [1].

Pharmacokinetics (PK) is the study of how drugs move through the body, while pharmacokinetic modeling involves creating mathematical models to predict these movements. The models are constructed based on experimental data and are used to simulate drug behavior under various physiological conditions. This approach helps in predicting drug levels in different compartments of the body and determining optimal dosing strategies [2].

Pharmacokinetic models are broadly classified into compartmental, physiologically based pharmacokinetic (PBPK), and non-compartmental models. Compartmental models assume the body is composed of compartments with drug distribution following predefined pathways. PBPK models incorporate detailed physiological parameters to simulate real biological systems. Non-compartmental models use statistical techniques to estimate drug exposure without assuming specific compartments [3].

Pharmacokinetic modeling plays a vital role in the drug development process by predicting drug behavior before clinical trials. It helps in optimizing drug formulations, identifying potential safety concerns, and determining appropriate dosing regimens. By reducing reliance on extensive in vivo studies, pharmacokinetic modeling accelerates the drug approval process and minimizes development costs [4].

The integration of pharmacokinetic modeling with pharmacogenomics has paved the way for personalized medicine. By considering individual variability in drug metabolism and genetic factors, personalized dosing strategies can be developed. This approach enhances drug efficacy while minimizing adverse effects, particularly for drugs with narrow therapeutic windows, such as chemotherapy agents and anticoagulants [5].

Pharmacokinetic models are often combined with pharmacodynamic (PD) models to create PK-PD models, which link drug concentrations with therapeutic or toxic effects. This integration provides a more comprehensive understanding of drug action and helps in optimizing therapeutic regimens. PK-PD modeling is widely used in antibiotic therapy, oncology, and immunosuppressive treatments [6].

Recent advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized pharmacokinetic modeling. AI-driven models can analyze vast datasets to identify patterns and predict drug behavior with higher accuracy. Machine learning algorithms enhance model efficiency, reduce experimental workload, and enable real-time decision-making in clinical settings [7].

Despite its advantages, pharmacokinetic modeling faces several challenges. Variability in biological systems, incomplete datasets, and the complexity of drug interactions pose difficulties in model accuracy. Additionally, integrating multi-omics data and ensuring regulatory compliance remain critical hurdles that need to be addressed for broader clinical applications [8].

The future of pharmacokinetic modeling lies in the integration of big data, computational biology, and AI-driven analytics. The development of more refined PBPK models and multi-scale simulations will enhance predictive capabilities. Collaborative efforts between pharmaceutical companies, regulatory agencies, and research institutions will be essential in advancing this field [9, 10].

Conclusion  

Pharmacokinetic modeling is a transformative tool in pharmaceutical sciences, offering insights into drug behavior and optimizing therapeutic strategies. Its role in drug development, personalized medicine, and AI-driven analytics underscores its growing significance. As technology advances, pharmacokinetic modeling will continue to shape the future of pharmacotherapy, ensuring safer and more effective drug treatments for diverse patient populations.

References

  1. Ingber DE. Human organs-on-chips for disease modelling, drug development and personalized medicine. Nat Rev Genet. 2022;23(8):467-91.

    2. Indexed at, Google Scholar, Cross Ref

  2. Serrano DR, Luciano FC, Anaya BJ, et al. ArtificialArtificial intelligence (AI) applications in drug discovery and drug delivery: Revolutionizing personalized medicine. Pharma. 2024;16(10):1328.

    3. Indexed at, Google Scholar, Cross Ref

  3. Taherdoost H, Ghofrani A. AI and the Evolution of Personalized Medicine in Pharmacogenomics. Inte Phar. 2024.

    4. Google Scholar

  4. Haymarket V. Advancing Research in Personalized Medicine. US Pharm. 2023;48(2):31-6.

    5. Google Scholar

  5. Madabushi R, Seo P, Zhao L, et al. Role of model-informed drug development approaches in the lifecycle of drug development and regulatory decision-making. Pharm Res. 2022;39(8):1669-80.

    6. Indexed at, Google Scholar, Cross Ref

  6. Darwich AS, Polasek TM, Aronson JK, et al. Model-informed precision dosing: background, requirements, validation, implementation, and forward trajectory of individualizing drug therapy. Annual review of pharmacology and toxicology. 2021;61(1):225-45.

    7. Indexed at, Google Scholar, Cross Ref

  7. Moingeon P, Kuenemann M, Guedj M. Artificial intelligence-enhanced drug design and development: Toward a computational precision medicine. Drug Discov Today. 2022;27(1):215-22.

    8. Indexed at, Google Scholar, Cross Ref

  8. Mizuno T, Dong M, Taylor ZL, et al. Clinical implementation of pharmacogenetics and model?informed precision dosing to improve patient care. Br J Clin Pharmacol. 2022;88(4):1418-26.

    9. Indexed at, Google Scholar, Cross Ref

  9. Gough A, Soto-Gutierrez A, Vernetti L, et al. Human biomimetic liver microphysiology systems in drug development and precision medicine. Nat Rev Gastroenterol Hepato. 2021;18(4):252-68.

    10. Indexed at, Google Scholar, Cross Ref

  10. Collin CB, Gebhardt T, Golebiewski M, et al. Computational models for clinical applications in personalized medicine guidelines and recommendations for data integration and model validation. Journal of personalized medicine. 2022 Jan 26;12(2):166.

    11. Indexed at, Google Scholar, Cross Ref

Get the App