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Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases
Abstract
The advancement in the processing speeds of computing machines has facilitated the development of complex physiologically based pharmacokinetic (PBPK) models. These PBPK models can incorporate disease-specific data and could be used to predict pharmacokinetics (PK) of administered drugs in different chronic conditions. The present study aimed to develop and evaluate PBPK drug-disease models for captopril after incorporating relevant pathophysiological changes occurring in adult chronic kidney disease (CKD) and chronic heart failure (CHF) populations. The population-based PBPK simulator Simcyp was used as a modeling and simulation platform. The visual predictive checks and mean observed/predicted ratios (ratio (Obs/pred) ) of the PK parameters were used for model evaluation. The developed disease models were successful in predicting captopril PK in all three stages of CKD (mild, moderate, and severe) and CHF, as the observed and predicted PK profiles and the ratio (obs/pred) for the PK parameters were in close agreement. The developed captopril PBPK models can assist in tailoring captopril dosages in patients with different disease severity (CKD and CHF).
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“A two-fold error range (0.5–2.0) was used as a criterion to evaluate the predicted performance for PK parameters [50,51,52].”
From Predicting Food–Drug Interactions between Piperine and CYP3A4 Substrate Drugs Using PBPK Modeling (2024) · cites this paper as [51]
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The Simcyp ® Population-based ADME Simulator
Jamei, M., Marciniak, S., Feng, K., Barnett, A., Tucker, G., & Rostami-Hodjegan, A. (2009) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1517/17425250802691074 · Cited by 533
Parrott, N., Paquereau, N., Coassolo, P., & Lavé, T. (2005) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.20419 · Cited by 107
Research works printed but not linked: 50
- Rowland, M., Peck, C. & Tucker, G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu. Rev. Pharmacol. Toxicol. 51, 45–73 (2011). · DOI · Paper page
- Rowland, M., Lesko, L. & Rostami-Hodjegan, A. Physiologically based pharmacokinetics is impacting drug development and regulatory decision making. CPT Pharmacomet. Syst. Pharmacol. 4, 313–315 (2015). · DOI
- Rostami-Hodjegan, A. & Tucker, G. T. Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat. Rev. Drug Discov. 6, 140–148 (2007). · DOI
- Lin, L. & Wong, H. Predicting oral drug absorption: Mini review on physiologically-based pharmacokinetic models. Pharmaceutics 9, 41 (2017). · DOI
- Takubo, H., Taniguchi, T., Iwanaga, K. & Nomura, Y. Evaluation of the changes in exposure to thiol compounds in chronic kidney disease patients using the PBPK model. Xenobiotica 51, 31–39 (2020). · DOI
- Zhang, M., Pan, X., Jones, H., Wesche, D. & Almond, L. Impact of disease on plasma and lung exposure of chloroquine, hydroxy-chloroquine and azithromycin: application of PBPK modelling. Clin. Pharmacol. Ther. 108, 976–984 (2020). · DOI
- Rasool, M. F. et al. Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting rifampicin exposure in tuberculosis and cirrhosis populations. Pharmaceutics 11, 578 (2019). · DOI
- Marsousi, N., Desmeules, J. A., Rudaz, S. & Daali, Y. Usefulness of PBPK modeling in incorporation of clinical conditions in personalized medicine. J. Pharm. Sci. 106, 2380–2391 (2017). · DOI
- Rasool, M. F., Khalil, F. & Läer, S. Optimizing the clinical use of carvedilol in liver cirrhosis using a physiologically based pharmacokinetic modeling approach. Eur. J. Drug Metab. Pharmacokinet. 42, 383–396 (2017). · DOI
- Rowland Yeo, K., Aarabi, M., Jamei, M. & Rostami-Hodjegan, A. Modeling and predicting drug pharmacokinetics in patients with renal impairment. Expert Rev. Clin. Pharmacol. 4, 261–274 (2011). · DOI
- Rasool, M. F., Khalil, F. & Läer, S. Predicting stereoselective disposition of carvedilol in adult and pediatric chronic heart failure patients by incorporating pathophysiological changes in organ blood flows—A physiologically based pharmacokinetic approach. Drug Metab. Dispos. 44, 1103–1115 (2016). · DOI · Paper page
- Hanke, N. et al. A comprehensive whole-body physiologically based pharmacokinetic drug–drug–gene interaction model of metformin and cimetidine in healthy adults and renally impaired individuals. Clin. Pharmacokinet. 59, 1419–1431. https://doi.org/10.1007/s40262-020-00896-w (2020). · DOI
- Malik, P. R. V. & Yeung, C. H. T. A physiological approach to pharmacokinetics in chronic kidney disease. J. Clin. Pharmacol. 60(Suppl 1), S52-s62. https://doi.org/10.1002/jcph.1713 (2020). · DOI
- Kubo, S. H. & Cody, R. J. Clinical pharmacokinetics of the angiotensin converting enzyme inhibitors. Clin. Pharmacokinet. 10, 377–391 (1985). · DOI
- Hu, M. & Amidon, G. L. Passive and carrier-mediated intestinal absorption components of captopril. J. Pharm. Sci. 77, 1007–1011 (1988). · DOI
- Romankiewicz, J., Brogden, R., Heel, R., Speight, T. & Avery, G. Captopril: an update review of its pharmacological properties and therapeutic efficacy in congestive heart failure. Drugs 25, 6–40 (1983). · DOI
- Duchin, K. L., Singhvi, S. M., Willard, D. A., Migdalof, B. H. & McKinstry, D. N. Captopril kinetics. Clin. Pharmacol. Ther. 31, 452–458 (1982). · DOI
- Kripalani, K. J. et al. Disposition of captopril in normal subjects. Clin. Pharmacol. Ther. 27, 636–641 (1980). · DOI
- Ondetti, M. A., Rubin, B. & Cushman, D. W. Design of specific inhibitors of angiotensin-converting enzyme: new class of orally active antihypertensive agents. Science 196, 441–444 (1977). · DOI
- Brogden, R. N., Todd, P. A. & Sorkin, E. M. Captopril. Drugs 36, 540–600 (1988). · DOI · Paper page
- Öhman, K. P., Kågedal, B., Larsson, R. & Karlberg, B. E. Pharmacokinetics of captopril and its effects on blood pressure during acute and chronic administration and in relation to food intake. J. Cardiovasc. Pharmacol. 7, S20–S24 (1985). · DOI
- Singhvi, S. M., Mckinstry, D. N., Shaw, J. M., Willard, D. A. & Migdalof, B. H. Effect of food on the bioavailability of captopril in healthy subjects. J. Clin. Pharmacol. 22, 135–140 (1982). · DOI
- Williams, G. & Sugerman, A. in Journal of Clinical Pharmacology. A18-A18 (LIPPINCOTT-RAVEN PUBL 227 EAST WASHINGTON SQ, PHILADELPHIA, PA 19106).
- Creasey, W., Morrison, R., Singhvi, S. & Willard, D. Pharmacokinetics of intravenous captopril in healthy men. Eur. J. Clin. Pharmacol. 35, 367–370 (1988). · DOI
- Giudicelli, J., Chaignon, M., Richer, C., Giroux, B. & Guedon, J. Influence of chronic renal failure on captopril pharmacokinetics and clinical and biological effects in hypertensive patients. Br. J. Clin. Pharmacol. 18, 749–758 (1984). · DOI
- Duchin, K. L., Pierides, A. M., Heald, A., Singhvi, S. M. & Rommel, A. J. Elimination kinetics of captopril in patients with renal failure. Kidney Int. 25, 942–947 (1984). · DOI
- Plosker, G. L. & McTavish, D. Captopril. Drugs Aging 7, 226–253 (1995). · DOI
- Khalil, F. & Läer, S. Physiologically based pharmacokinetic models in the prediction of oral drug exposure over the entire pediatric age range—Sotalol as a model drug. AAPS J. 16, 226–239 (2014). · DOI · Paper page
- Sager, J. E., Yu, J., Ragueneau-Majlessi, I. & Isoherranen, N. Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metab. Dispos. 43, 1823–1837 (2015). · DOI
- Benet, L. Z., Broccatelli, F. & Oprea, T. I. BDDCS applied to over 900 drugs. AAPS J. 13, 519–547 (2011). · DOI
- Poulin, P. & Theil, F.-P. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution. J. Pharm. Sci. 91, 129–156. https://doi.org/10.1002/jps.10005 (2002). · DOI · Paper page
- Abdalrb, G. A., Mircioiu, I., Amzoiu, M., Belu, I. & Anuta, V. In vitro and in vivo evaluation of different solid dosage forms containing captopril. Curr. Health Sci. J. 43, 214–219. https://doi.org/10.12865/CHSJ.43.03.05 (2017). · DOI
- Rodgers, T. & Rowland, M. Mechanistic approaches to volume of distribution predictions: Understanding the processes. Pharm. Res. 24, 918–933. https://doi.org/10.1007/s11095-006-9210-3 (2007). · DOI · Paper page
- Lea-Henry, T. N., Carland, J. E., Stocker, S. L., Sevastos, J. & Roberts, D. M. Clinical pharmacokinetics in kidney disease. Fundam. Principles 13, 1085–1095. https://doi.org/10.2215/cjn.00340118 (2018). · DOI
- Yee, K. L. et al. Evaluation of model-based prediction of pharmacokinetics in the renal impairment population. J. Clin. Pharmacol. 58, 364–376 (2018). · DOI
- Sayama, H., Takubo, H., Komura, H., Kogayu, M. & Iwaki, M. Application of a physiologically based pharmacokinetic model informed by a top-down approach for the prediction of pharmacokinetics in chronic kidney disease patients. AAPS J. 16, 1018–1028 (2014). · DOI
- Leithe, M. E., Margorien, R. D., Hermiller, J. B., Unverferth, D. V. & Leier, C. V. Relationship between central hemodynamics and regional blood flow in normal subjects and in patients with congestive heart failure. Circulation 69, 57–64 (1984). · DOI
- Lee, R. E., Beard, J. D. & Aldoori, M. I. Cardiac Output and Regional Flow in Health and Disease 505–522 (Springer, 1993). · DOI
- Chik, Z. et al. A Bioequivalence comparison of two captopril formulations (25 mg tablets): An open-label, randomized, two-treatment, two-way crossover study in healthy volunteers. J. Bioequivalence Bioavailab. 6, 80 (2014).
- Jankowski, A. et al. Captopril: Determination in blood and pharmacokinetics after single oral dose. J. Pharm. Biomed. Anal. 13, 655–660 (1995). · DOI
- Massana, E. et al. No sex-related pharmacokinetic and pharmacodynamic differences of captopril. Pharmacol. Res. 36, 41–47 (1997). · DOI
- Creasey, W. A., Funke, P. T., McKinstry, D. N. & Sugerman, A. A. Pharmacokinetics of captopril in elderly healthy male volunteers. J. Clin. Pharmacol. 26, 264–268 (1986). · DOI
- Shaw, T. et al. Plasma free captopril concentrations during short and long term treatment with oral captopril for heart failure. Heart 54, 160–165 (1985). · DOI
- Zhang, Y., Huo, M., Zhou, J. & Xie, S. PKSolver: An add-in program for pharmacokinetic and pharmacodynamic data analysis in Microsoft Excel. Comput. Methods Programs Biomed. 99, 306–314 (2010). · DOI · Paper page
- Cui, C. et al. Development of a physiologically based pharmacokinetic (PBPK) population model for Chinese elderly subjects. Br. J. Clin. Pharmacol. https://doi.org/10.1111/bcp.14609 (2020). · DOI
- Park, M.-H. et al. Prediction of pharmacokinetics and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach: A case study of caffeine and ciprofloxacin. KJPP 21, 107–115. https://doi.org/10.4196/kjpp.2017.21.1.107 (2016). · DOI
- Kovar, L. et al. Physiologically-based pharmacokinetic (PBPK) modeling of buprenorphine in adults, children and preterm neonates. Pharmaceutics 12, 578 (2020). · DOI
- Duchin, K., McKinstry, D., Cohen, A. & Migdalof, B. Pharmacokinetics of captopril in healthy subjects and in patients with cardiovascular diseases. Clin. Pharmacokinet. 14, 241–259 (1988). · DOI
- Stader, F. et al. Physiologically based pharmacokinetic modelling to identify pharmacokinetic parameters driving drug exposure changes in the elderly. Clin. Pharmacokinet. 59, 383–401. https://doi.org/10.1007/s40262-019-00822-9 (2020). · DOI
- Rasool, M. F., Khalil, F. & Laer, S. A physiologically based pharmacokinetic drug-disease model to predict carvedilol exposure in adult and paediatric heart failure patients by incorporating pathophysiological changes in hepatic and renal blood flows. Clin. Pharmacokinet. 54, 943–962. https://doi.org/10.1007/s40262-015-0253-7 (2015). · DOI · Paper page
Cited by
Predicting Food–Drug Interactions between Piperine and CYP3A4 Substrate Drugs Using PBPK Modeling
Lin, F., Hu, Y., Zhang, Y., Zhao, L., Zhong, D., & Liu, J. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252010955 · Cited by 20
Metadata
- Authors
- Rasool, M. F., Ali, S., Khalid, S., Khalid, R., Majeed, A., Imran, I., Saeed, H., Usman, M., Ali, M., Alali, A. S., AlAsmari, A. F., Ali, N., Asiri, A. M., Alasmari, F., & Alqahtani, F.
- Venue
- Scientific Reports
- Published
- 2021-04-21
- Volume
- 11
- Issue
- 1
- ISSN
- 2045-2322
- Cited by
- 44
- References
- 2
- Language
- en
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
- Funders
- Deanship of Scientific Research, King Saud University (RG-1441-340.)
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