Abstract
Model-based drug development (MBDD) has been recognized as a concept to improve the efficiency of drug development. The acceptance of MBDD from regulatory agencies, industry, and academia has been growing, yet today’s drug development practice is still distinctly distant from MBDD. This manuscript is aimed at clarifying the concept of MBDD and proposing practical approaches for implementing MBDD in the pharmaceutical industry. The following concepts are defined and distinguished: PK–PD modeling, exposure–response modeling, pharmacometrics, quantitative pharmacology, and MBDD. MBDD is viewed as a paradigm and a mindset in which models constitute the instruments and aims of drug development efforts. MBDD covers the whole spectrum of the drug development process instead of being limited to a certain type of modeling technique or application area. The implementation of MBDD requires pharmaceutical companies to foster innovation and make changes at three levels: (1) to establish mindsets that are willing to get acquainted with MBDD, (2) to align processes that are adaptive to the requirements of MBDD, and (3) to create a closely collaborating organization in which all members play a role in MBDD. Pharmaceutical companies that are able to embrace the changes MBDD poses will likely be able to improve their success rate in drug development, and the beneficiaries will ultimately be the patients in need.
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References
Congressional Budget Office. A CBO Study: Research and Development in the Pharmceutical Industry, The Congress of the United States, Washington, DC, 2006.
I. Kola, and J. Landis. Can the pharmaceutical industry reduce attrition rates. Nat. Rev. Drug Discov. 3:711–715 (2004).
S. Arlington, S. Barnett, S. Hughes, and J. Palo. Pharma 2010: The Threshold to Innovation, IBM Business Consulting Services, Somers, 2002.
S. Frantz. Pipeline problems are increasing the urge to merge. Nat. Rev. Drug Discov. 5:977–979 (2006).
Tufts Center for the Study of Drug Development. Impact Report: Fastest drug developers consistently best peers on key performance metrics, Tufts University, Boston, 2006.
Center for Drug Evaluation and Research. Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products, US Food and Drug Administration, Rockville, 2004.
C. Csajka, and D. Verotta. Pharmacokinetic-pharmacodynamic modelling: history and perspectives. J. Pharmacokinet. Pharmacodyn. 33:227–279 (2006).
V. A. Bhattaram, B. P. Booth R. P. Ramchandani et al. Impact of pharmacometrics on drug approval and labeling decisions: a survey of 42 new drug applications. Aaps J. 7:E503–E512 (2005).
D. D. Breimer, and M. Danhof. Relevance of the application of pharmacokinetic-pharmacodynamic modelling concepts in drug development. The “wooden shoe’ paradigm. Clin. Pharmacokinet. 32:259–267 (1997).
J. Y. Chien, S. Friedrich, M. A. Heathman, D. P. de Alwis, and V. Sinha. Pharmacokinetics/Pharmacodynamics and the stages of drug development: role of modeling and simulation. Aaps J. 7:E544–E559 (2005).
B. Meibohm, and H. Derendorf. Pharmacokinetic/pharmacodynamic studies in drug product development. J. Pharm. Sci. 91:18–31 (2002).
S. C. Olson, H. Bockbrader, R. A. Boyd, J. Cook, J. R. Koup, R. L. Lalonde, P. H. Siedlik, and J. R. Powell. Impact of population pharmacokinetic-pharmacodynamic analyses on the drug development process: experience at Parke-Davis. Clin. Pharmacokinet. 38:449–459 (2000).
B. G. Reigner, P. E. Williams, I. H. Patel, J. L. Steimer, C. Peck, and P. van Brummelen. An evaluation of the integration of pharmacokinetic and pharmacodynamic principles in clinical drug development. Experience within Hoffmann La Roche. Clin. Pharmacokinet. 33:142–152 (1997).
L. B. Sheiner. Learning versus confirming in clinical drug development. Clin. Pharmacol. Ther. 61:275–291 (1997).
J. S. Barrett, M. J. Fossler, K. D. Cadieu, and M. R. Gastonguay. Pharmacometrics: a multidisciplinary field to facilitate critical thinking in drug development and translational research settings. J. Clin. Pharmacol. 48:632–649 (2008).
K. Gough, M. Hutchison, O. Keene, B. Byrom, S. Ellis, L. Lacey, and J. McKellar. Assessment of dose proportionality: Report from the statisticians in the Pharmaceutical Industry/Pharmacokinetics UK Joint Working Party. Drug Inf. J. 29:1039–1048 (1995).
H. Derendorf, and B. Meibohm. Modeling of pharmacokinetic/pharmacodynamic (PK–PD) relationships: concepts and perspectives. Pharm Res. 16:176–185 (1999).
L. B. Sheiner, and J. L. Steimer. Pharmacokinetic/pharmacodynamic modeling in drug development. Annu. Rev. Pharmacol. Toxicol. 40:67–95 (2000).
L. Zhang, V. Sinha, S. T. Forgue, S. Callies, L. Ni, R. Peck, and S. R. Allerheiligen. Model-based drug development: the road to quantitative pharmacology. J. Pharmacokinet. Pharmacodyn. 33:369–393 (2006).
R. Krishna. Quantitative clinical pharmacology: Making paradigm shifts a reality. J. Clin. Pharmacol. 46:966–967 (2006).
T. H. Grasela, J. Fiedler-Kelly, C. A. Walawander, J. S. Owen, B. B. Cirincione, K. E. Reitz, E. A. Ludwig, J. A. Passarell, and C. W. Dement. Challenges in the transition to model-based development. Aaps J. 7:E488–E495 (2005).
E. A. Wintner, and C. C. Moallemi. Quantized surface complementarity diversity (QSCD): a model based on small molecule-target complementarity. J. Med. Chem. 43:1993–2006 (2000).
B. Schoeberl, U. B. Nielsen, and R. Paxson. Model-based design approaches in drug discovery, a parallel to traditional engineering approaches. IBS J. Res Dev. 50:645–653 (2006).
P. Arce, and M. Aznar. Modeling of phase equilibirum of binary mixtures composed by polystyrene and chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons and supercritical fluids using cubic and non-cubic equations of state. J. Supercrit. Fluids. 42:134–145 (2008).
A. Roncaglioni, and E. Benfenati. In silico-aided prediction of biological properties of chemicals: oestrogen receptor-mediated effects. Chem. Soc. Rev. 37:441–450 (2008).
M. Pfister, N. E. Martin, L. P. Haskell, and J. S. Barrett. Optimizing dose selection with modeling and simulation: application to the vasopeptidase inhibitor M100240. J. Clin. Pharmacol. 44:621–631 (2004).
B. Garcia-Mora, C. Santamaria, G. Rubio, and J. Luis Pontones. Modeling the recurrence-progression process in bladder carcinoma. Comput. Math Appl. 53:619–630 (2008).
K. Larsen, K. E. Hvass, T. B. Hansen, P. B. Thomsen, and K. Soballe. Effectiveness of accelerated perioperative care and rehabilitation intervention compared to current intervention after hip and knee arthroplasty. A before-after trial of 247 patients with a 3-month follow-up. BMC Musculoskelet. Disord. 9:59 (2008).
L. A. Kenna, L. Labbe, J. S. Barrett, and M. Pfister. Modeling and simulation of adherence: approaches and applications in therapeutics. Aaps J. 7:E390–E407 (2005).
M. A. Koopmanschap, J. N. van Exel, B. van den Berg, and W. B. Brouwer. An overview of methods and applications to value informal care in economic evaluations of healthcare. Pharmacoeconomics. 26:269–280 (2008).
R. L. Lalonde, K. G. Kowalski M. M. Hutmacher et al. Model-based drug development. Clin. Pharmacol. Ther. 82:21–32 (2007).
D. Stanski. Model-based drug development: a critical path opportunity. http://www.fda.gov/oc/initiatives/criticalpath/stanski/stanski.html (accessed 8/13/08).
P. Chaikin, G. R. Rhodes, R. Bruno, S. Rohatagi, and C. Natarajan. Pharmacokinetics/pharmacodynamics in drug development: an industrial perspective. J. Clin. Pharmacol. 40:1428–1438 (2000).
T. H. Grasela, C. W. Dement, O. G. Kolterman, M. S. Fineman, D. M. Grasela, P. Honig, E. J. Antal, T. D. Bjornsson, and E. Loh. Pharmacometrics and the transition to model-based development. Clin. Pharmacol. Ther. 82:137–142 (2007).
L. J. Lesko. Paving the critical path: how can clinical pharmacology help achieve the vision? Clin. Pharmacol. Ther. 81:170–177 (2007).
S. C. D. Johnson. The role of simulation in the managememnt of research: What can the pharmaceutical industry leanr from the aerospace industry? Drug Inf. J. 32:961–969 (1998).
C. A. O’Reilly, and M. L. Tushman. Winning through innovation: a practical guide to leading organizational change and renewal, Harvard Business School Press, Boston, 2002.
P. M. Senge, B. Smith, S. Schley, and N. Kruschwitz. The necessary revolution: how individuals and organisations are working together to create a sustainable world, Doubleday Business, New York, 2008.
V. A. Bhattaram, C. Bonapace D. M. Chilukuri et al. Impact of pharmacometric reviews on new drug approval and labeling decisions–a survey of 31 new drug applications submitted between 2005 and 2006. Clin. Pharmacol. Ther. 81:213–221 (2007).
C. Veyrat-Follet, R. Bruno, R. Olivares, G. R. Rhodes, and P. Chaikin. Clinical trial simulation of docetaxel in patients with cancer as a tool for dosage optimization. Clin. Pharmacol. Ther. 68:677–687 (2000).
Y. Wang, A. V. Bhattaram, P. R. Jadhav, L. J. Lesko, R. Madabushi, J. R. Powell, W. Qiu, H. Sun, D. S. Yim, J. J. Zheng, and J. V. Gobburu. Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: impact of FDA pharmacometrics during 2004–2006. J. Clin. Pharmacol. 48:146–156 (2008).
R. Miller, W. Ewy, B. W. Corrigan, D. Ouellet, D. Hermann, K. G. Kowalski, P. Lockwood, J. R. Koup, S. Donevan, A. El-Kattan, C. S. Li, J. L. Werth, D. E. Feltner, and R. L. Lalonde. How modeling and simulation have enhanced decision making in new drug development. J. Pharmacokinet. Pharmacodyn. 32:185–197 (2005).
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This article is based on a symposium held jointly by the American Association of Pharmaceutical Scientists (AAPS) and the American College of Clinical Pharmacology during the 2006 AAPS Annual Meeting in San Antonio, TX, USA.
Zhang, Pfister and Meibohm contributed equally to the development of this manuscript.
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Zhang, L., Pfister, M. & Meibohm, B. Concepts and Challenges in Quantitative Pharmacology and Model-Based Drug Development. AAPS J 10, 552–559 (2008). https://doi.org/10.1208/s12248-008-9062-3
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DOI: https://doi.org/10.1208/s12248-008-9062-3