Volume : 12, Issue : 10, October – 2025
Title:
BAYESIAN THEORY APPLICATION IN POPULATION PHARMACOKINETICS
Authors :
Maria Anam*, Sushma Desai
Abstract :
Bayesian theory is a very powerful tool in population pharmacokinetics and personalized medication therapy as it provides an instrument for statistical analysis for integrating the new data with the priority obtained information. Healthcare professionals can design treatment plan with this method, which is very helpful in-case of sparse or noisy data, less sampling and unpredictability. Bayesian theory is a useful application for making decisions because of uncertainty, even in-case of problems with prior assumption or biasness. This article addresses the concept of Bayesian theory, mathematical equations, its application in various field, advantages and challenges along with its use in future research areas.
Keywords: Bayesian theory, pharmacokinetic parameters, population pharmacokinetics.
Cite This Article:
Please cite this article in press Maria Anam et al., Bayesian Theory Application In Population Pharmacokinetics, Indo Am. J. P. Sci, 2025; 12(10).
REFERENCES:
(1) Aarons L. Population pharmacokinetics: theory and practice. Br J Clin Pharmacol. 1991 Dec;32(6):669-70. PMID: 1768557; PMCID: PMC1368544.
(2) Fox, C. (2018). Bayesian inference. In Springer textbooks in earth sciences, geography and environment (pp. 75–92). https://doi.org/10.1007/978-3-319-72953-4_6
(3) Introna, M., Van Den Berg, J. P., Eleveld, D. J., & Struys, M. M. R. F. (2022). Bayesian statistics in anesthesia practice: a tutorial for anesthesiologists. Journal of Anesthesia, 36(2), 294–302. https://doi.org/10.1007/s00540-022-03044-9
(4) Van De Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., & Van Aken, M. A. (2013). A gentle introduction to Bayesian Analysis: Applications to Developmental Research. Child Development, 85(3), 842–860. https://doi.org/10.1111/cdev.12169
(5) Schumacher GE, Barr JT. Bayesian approaches in pharmacokinetic decision making. Clin Pharm. 1984 Sep-Oct;3(5):525-30. PMID: 6488735.
(6) Carol K.H. Hon, Chenjunyan Sun, Bo Xia, Nerina L. Jimmieson, Kïrsten A. Way, Paul Pao-Yen Wu; Applications of Bayesian approaches in construction management research: a systematic review. Engineering, Construction and Architectural Management 31 May 2022; 29 (5): 2153–2182. https://doi.org/10.1108/ECAM-10-2020-0817
(7) Krauss M, Tappe K, Schuppert A, Kuepfer L, Goerlitz L. Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations. PLoS One. 2015 Oct 2;10(10):e0139423. doi: 10.1371/journal.pone.0139423. PMID: 26431198; PMCID: PMC4592188.
(8) Smith, A., & Wakefield, J. (1994). The hierarchical Bayesian approach to population pharmacokinetic modelling. International Journal of Bio-Medical Computing, 36(1–2), 35–42. https://doi.org/10.1016/0020-7101(94)90093-0
(9) Nanga, T. M., Woillard, J., Rousseau, A., Marquet, P., & Prémaud, A. (2022). Population pharmacokinetics and Bayesian estimation of mycophenolate mofetil in patients with autoimmune hepatitis. British Journal of Clinical Pharmacology, 88(11), 4732–4741. https://doi.org/10.1111/bcp.15389
(10) Monchaud, C., De Winter, B. C., Knoop, C., Estenne, M., Reynaud-Gaubert, M., Pison, C., Stern, M., Kessler, R., Guillemain, R., Marquet, P., & Rousseau, A. (2012). Population Pharmacokinetic modelling and design of a Bayesian estimator for therapeutic drug monitoring of tacrolimus in lung transplantation. Clinical Pharmacokinetics, 51(3), 175–186. https://doi.org/10.2165/11594760-000000000-00000
(11) Zhao, W., Cella, M., Della Pasqua, O., Burger, D., & Jacqz‐Aigrain, E. (2011). Population pharmacokinetics and maximum a posteriori probability Bayesian estimator of abacavir: application of individualized therapy in HIV‐infected infants and toddlers. British Journal of Clinical Pharmacology, 73(4), 641–650. https://doi.org/10.1111/j.1365-2125.2011.04121.x
(12) Krauss, M., Burghaus, R., Lippert, J., Niemi, M., Neuvonen, P., Schuppert, A., Willmann, S., Kuepfer, L., & Görlitz, L. (2013). Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification. In Silico Pharmacology, 1(1). https://doi.org/10.1186/2193-9616-1-6
(13) Cai, X., Li, R., Sheng, C., Tao, Y., Zhang, Q., Zhang, X., Li, J., Shen, C., Qiu, X., Wang, Z., & Jiao, Z. (2020). Systematic external evaluation of published population pharmacokinetic models for tacrolimus in adult liver transplant recipients. European Journal of Pharmaceutical Sciences, 145, 105237. https://doi.org/10.1016/j.ejps.2020.105237
(14) Savchuk, Vladimir, and Chris P. Tsokos. Bayesian theory and methods with applications. Vol. 1. Springer Science & Business Media, 2011.
(15) Chen, A., Gupta, A., Huy, D., DO, & Nazer, L. H. (2022). Bayesian method application: Integrating mathematical modeling into clinical pharmacy through vancomycin therapeutic monitoring. Pharmacology Research & Perspectives, 10(6). https://doi.org/10.1002/prp2.1026
(16) Lewis, M. G., & Nair, N. S. (2015). Review of applications of Bayesian meta-analysis in systematic reviews. https://nicpd.ac.in/ojs-/index.php/gjmedph/article/view/3978
(17) Gupta, S. K. (2012). Use of Bayesian statistics in drug development: Advantages and challenges. International Journal of Applied and Basic Medical Research, 2(1), 3. https://doi.org/10.4103/2229-516x.96789
(18) Webb, M., & Sidebotham, D. (2020). Bayes’ formula: a powerful but counterintuitive tool for medical decision-making. BJA Education, 20(6), 208–213. https://doi.org/10.1016/j.bjae.2020.03.002
(19) Khalili, H., Wimmer, M. A., & Lotzmann, U. (2024). Bayesian Deep Learning and Bayesian Statistics to analyze the European countries’ SARS-COV-2 policies. Mathematics, 12(16), 2574. https://doi.org/10.3390/math12162574
(20) Cao, L., Chen, H., Fan, X., Gama, J., Ong, Y., & Kumar, V. (2023). Bayesian Federated Learning: a survey. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2304.13267