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Quantitative clinical pharmacology and patient-centered healthcare technologies: perspectives 2030

https://doi.org/10.30895/1991-2919-2022-12-2-205-213

Abstract

One of the most promising trends in clinical pharmacology is pharmacometrics, a combination of pharmacology and statistics that implements quantitative approaches for characterising dose–response relationships and predicting the variability of these relationships attributable to patient-specific characteristics (covariates). The aim of the study was to evaluate the significance of quantitative clinical pharmacology and discuss opportunities for its development in the context of health systems moving towards the value-based care model. The study showed that two key prerequisites for pharmacometrics development were the advancements in mathematical and statistical methodology based upon non-linear mixed effects regression modelling and the emergence of a personalised medicine paradigm aimed at creation of strategies for individualised prescribing of medicinal products. The study demonstrated the necessity for using the dose–response relationship information obtained by exploratory analysis of data stored in existing and newly created bases. Further integration of pharmacostatistical modelling and real-world data processing technologies, as well as their incorporation into clinical and economic evaluation of health technologies, will streamline decision making and, thus, facilitate the transition of health systems to the value-based model.

About the Authors

V. I. Petrov
Volgograd State Medical University; Volgograd Medical Scientific Centre
Russian Federation

Vladimir I. Petrov, Academician of RAS, Dr. Sci. (Med.), Professor

1, Pavshikh Bortsov Sq., Volgograd 400131



B. E. Tolkachev
Volgograd State Medical University; Volgograd Medical Scientific Centre
Russian Federation

Boris E. Tolkachev, Cand. Sci. (Med.)

1, Pavshikh Bortsov Sq., Volgograd 400131



References

1. Clinical pharmacology in health care, teaching and research. Kachestvennaya klinicheskaya praktika = Good Clinical Practice. 2020;(2S):7–66 (In Russ.) https://doi.org/10.37489/2588-0519-2020-S2

2. Mentré F, Friberg LE, Duffull S, French J, Lauffenburger DA, Lang Li, et al. Pharmacometrics and systems pharmacology 2030. Clin Pharmacol Ther. 2020;107(1):76–8. https://doi.org/10.1002/cpt.1683

3. Pacanowski M, Liu Q. Precision Medicine 2030. Clin Pharmacol Ther. 2020;107(1):62–4. https://doi.org/10.1002/cpt.1675

4. Denny JC, Collins FS. Precision medicine in 2030—seven ways to transform healthcare. Cell. 2021;184(6):1415–9. https://doi.org/10.1016/j.cell.2021.01.015

5. Allegaert K, Flint R, Smits A. Pharmacokinetic modelling and Bayesian estimation-assisted decision tools to optimize vancomycin dosage in neonates: only one piece of the puzzle. Expert Opin Drug Metab Toxicol. 2019;15(9):735–49. https://doi.org/10.1080/17425255.2019.1655540

6. Mehrotra N, Bhattaram A, Earp JC, Florian J, Krudys K, Lee JE, et al. Role of quantitative clinical pharmacology in pediatric approval and labeling. Drug Metab Dispos. 2016;44(7):924–33. https://doi.org/10.1124/dmd.116.069559

7. Holford N, Karlsson MO. Time for quantitative clinical pharmacology: a proposal for a pharmacometrics curriculum. Clin Pharmacol Ther. 2007;82(1): 103–5. https://doi.org/10.1038/sj.clpt.6100231

8. Jean D, Naik K, Milligan L, Hall S, Huang SM, Isoherranen N, et al. Development of best practices in physiologically based pharmacokinetic modeling to support clinical pharmacology regulatory decision-making—A workshop summary. CPT: Pharmacometrics Syst Pharmacol. 2021;10(11):1271–5. https://doi.org/10.1002/psp4.12706

9. Brouwer KLR, Schmidt S, Floren LC, Johnson JA. Clinical pharmacology education — the decade ahead. Clin Pharmacol Ther. 2020;107(1):37–9. https://doi.org/10.1002/cpt.1652

10. Van Driest SL, Choi L. Real-world data for pediatric pharmacometrics: can we upcycle clinical data for research use? Clin Pharmacol Ther. 2019;106(1):84–6. https://doi.org/10.1002/cpt.1416

11. Venkatakrishnan K, Benincosa LJ. Diversity and inclusion in drug development: rethinking intrinsic and extrinsic factors with patient centricity. Clin Pharmacol Ther. Published online September 22, 2021. https://doi.org/10.1002/cpt.2416

12. Swift B, Jain L, White C, Chandrasekaran V, Bhandari A, Hughes DA, Jadhav PR. Innovation at the intersection of clinical trials and real-world data science to advance patient care. Clin Transl Sci. 2018;11(5):450–60. https://doi.org/10.1111/cts.12559

13. Shahin MH, Abdel-Rahman S, Hartman D, Johnson JA, Mitchell DY, Reynolds KS, et al. The patient-centered future of clinical pharmacology. Clin Pharmacol Ther. 2020;107(1):72–5. https://doi.org/10.1002/cpt.1681

14. Hill-McManus D, Marshall S, Liu J, Willke RJ, Hughes DA. Linked pharmacometric-pharmacoeconomic modeling and simulation in clinical drug development. Clin Pharmacol Ther. 2021;110(1):49–63. https://doi.org/10.1002/cpt.2051


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For citations:


Petrov V.I., Tolkachev B.E. Quantitative clinical pharmacology and patient-centered healthcare technologies: perspectives 2030. Bulletin of the Scientific Centre for Expert Evaluation of Medicinal Products. Regulatory Research and Medicine Evaluation. 2022;12(2):205-213. (In Russ.) https://doi.org/10.30895/1991-2919-2022-12-2-205-213

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ISSN 3034-3062 (Print)
ISSN 3034-3453 (Online)