Abstract
Declining pharmaceutical industry productivity is well recognized by drug developers, regulatory authorities and patient groups. A key part of the problem is that clinical studies are increasingly expensive, driven by the rising costs of conducting Phase II and III trials. It is therefore crucial to ensure that these phases of drug development are conducted more efficiently and cost-effectively, and that attrition rates are reduced. In this article, we argue that moving from the traditional clinical development approach based on sequential, distinct phases towards a more integrated view that uses adaptive design tools to increase flexibility and maximize the use of accumulated knowledge could have an important role in achieving these goals. Applications and examples of the use of these tools — such as Bayesian methodologies — in early- and late-stage drug development are discussed, as well as the advantages, challenges and barriers to their more widespread implementation.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Concurrent isolation of hepatic stem cells and hepatocytes from the human liver
In Vitro Cellular & Developmental Biology - Animal Open Access 27 March 2020
-
Analysis of integrated clinical trial protocols in early phases of medicinal product development
European Journal of Clinical Pharmacology Open Access 18 September 2017
-
Classification of advanced stages of Parkinson’s disease: translation into stratified treatments
Journal of Neural Transmission Open Access 24 March 2017
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout



References
Booth, B & Zemmel, R. Prospects for productivity. Nature Rev. Drug Discov. 3, 451–456 (2004).
Gilbert, J., Henske, P. & Singh, A. Rebuilding big pharma's business model. In Vivo 21, 1–4 (2003).
Kola, I. & Landis, J. Can the pharmaceutical industry reduce attrition rates? Nature Rev. Drug Discov. 3, 711–716 (2004).
Budget US Govt., App., FY 1993–2003.
Parexel's Pharmaceutical R&D Statistical Sourcebook 2002/2003 (Parexel International, Waltham, USA, 2003).
Adams, C. P. & Brantner, V. V. Spending on new drug development. Health Econ. 26 Feb 2009 (doi: 10.1002/hec.1454).
DiMasi, J. A., Hansen, R. W. & Grabowski, H. G. The price of innovation: new estimates of drug development costs. J. Health Econ. 22, 151−185 (2003).
Adams, C. & Brantner, V. V. Estimating the cost of new drug development: is it really $802 million? Health Aff. 2, 420–428 (2006).
Pharmaceutical R&D Factbook 2007 (CMR International, London, UK, 2007).
KMR General Metrics Study (KMR Group, Chicago, USA, 2007).
Sheiner, L. B. & Steimer, J.-L. Pharmacokinetic/pharmacodynamic modeling in drug development. Ann. Rev. Pharmacol. Toxicol. 40, 67–95 (2000).
Lalonde, R. L. et al. Model-based drug development. Clin. Pharmacol. Ther. 82, 21–32 (2007).
Breimer, D. D. & Danhof, M. Relevance of the application of pharmacokinetic–pharmacodynamic modeling concepts in drug development. The “Wooden Shoe” paradigm. Clin. Pharmacokinet. 32, 259–267 (1997).
Danhof, M., Alvan, G., Dahl, S. G., Kuhlmann, J. & Paintaud, G. Mechanism-based pharmacokinetic/pharmacodynamic modeling — a new classification of biomarkers. Pharm. Res. 22, 1432–1437 (2005).
Holford, N. H. G., Kimko, H. C., Monteleone, J. P. R. & Peck, C. C. Simulation of clinical trials. Annu. Rev. Pharmacol. Toxicol. 40, 209–234 (2000).
Miller, R. et al. How modeling and simulation have enhanced decision making in new drug development. J. Pharmacokinet. Pharmacodyn. 32, 185–197 (2005).
Berry, D. A. Bayesian statistics. Med. Decis. Making 26, 429–430 (2006).
Müller, P., Berry, D. A., Grieve, A. P., Smith, M. & Krams, M. Simulation-based sequential Bayesian design. J. Stat. Plan. Inference 137, 3140–3150 (2007).
Goggin, T. et al. Modeling and simulation of clinical trials: an industrial perspective. In Simulation for Designing of Clinical Trials (eds Kimko, H. C. & Duffull, S. B.) 227–224 (Marcel Dekker, New York, USA, 2002).
Reigner, B. G. et al. An evaluation of the integration of pharmacokinetic and pharmacodynamic principles in clinical drug development. Experience within Hoffman La Roche. Clin. Pharmacokinet. 33, 142–152 (1997).
Lachmann, H. J. et al. Use of canakinumab in the cryopyrin-associated periodic syndrome. N. Engl. J. Med. 360, 2416–2425 (2009).
Berry, D. A. Bayesian clinical trials. Nature Rev. Drug Discov. 5, 27–36 (2006).
Berry, D. A. Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis. Clin. Trials 2, 295–300 (2005).
Berry, D. A. Adaptive trial design. Clin. Adv. Hematol. Oncol. 5, 522–524 (2007).
Krams, M. et al. Adaptive designs in clinical drug development: opportunities, challenges, and scope reflections following PhRMA's November 2006 workshop. J. Biopharm. Stat. 17, 957–964 (2007).
Gallo, P. et al. Adaptive designs in clinical drug development — an executive summary of the PhRMA Working Group. J. Biopharm. Stat. 16, 275–283 (2006).
Bornkamp, B. et al. Innovative approaches for designing and analyzing adaptive dose-ranging trials. J. Biopharm. Stat. 17, 965–995 (2007).
Weir, C. J., Spiegelhalter, D. J. & Grieve, A. P. Flexible design and efficient implementation of adaptive dose-finding studies. J. Biopharm. Stat. 17, 1033–1050 (2007).
Bretz, F., Schmidli, H., König, F., Racine, A. & Maurer, W. Confirmatory seamless Phase II/III clinical trials with hypotheses selection at interim: general concepts. Biom. J. 48, 623–634 (2006).
Mehta, C. R. & Patel, N. R. Adaptive, group sequential and decision theoretic approaches to sample size determination. Stat. Med. 25, 3250–3269 (2006).
Chuang-Stein, C., Anderson, K., Gallo, P. & Collins, S. Sample size reestimation: a review and recommendations. Drug Inf. J. 40, 475–484 (2006).
Golub, H. L. The need for more efficient clinical trials. Stat. Med. 25, 3231–3235 (2006).
Tsiatis, A. A. & Mehta, C. On the inefficiency of the adaptive design for monitoring clinical trials. Biometrika 90, 367–378 (2003).
Acknowledgements
A special acknowledgment to W. Dere (Amgen), S. Cummings (UCSF), A. Lee (Pfizer) and E. Berndt (MIT-CBI) for significant contributions to discussions leading to this manuscript. Also, many thanks to the McKinsey Trial Design Team for their support (M. E., E. F., N. S. and T. T.).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
Some authors are employed by, or have consulting relationships with, pharmaceutical companies, biotech companies, contract research organizations, academic institutions, consulting firms and/or research foundations, that might be perceived as a competing interest.
Supplementary information
Supplementary information S1 (box)
Case study: dose selection in type 2 diabetes mellitus (PDF 920 kb)
Supplementary information S2 (box)
Case study: dyslipidaemia proof-of-concept study (PDF 434 kb)
Supplementary information S3 (figure)
Advantages of a seamless/adaptive trial design compared with classical Phase IIb and III studies (PDF 259 kb)
Related links
Rights and permissions
About this article
Cite this article
Orloff, J., Douglas, F., Pinheiro, J. et al. The future of drug development: advancing clinical trial design. Nat Rev Drug Discov 8, 949–957 (2009). https://doi.org/10.1038/nrd3025
Published:
Issue Date:
DOI: https://doi.org/10.1038/nrd3025
This article is cited by
-
Concurrent isolation of hepatic stem cells and hepatocytes from the human liver
In Vitro Cellular & Developmental Biology - Animal (2020)
-
Classification of advanced stages of Parkinson’s disease: translation into stratified treatments
Journal of Neural Transmission (2017)
-
Analysis of integrated clinical trial protocols in early phases of medicinal product development
European Journal of Clinical Pharmacology (2017)
-
Future Challenges in the Generation of Hepatocyte-Like Cells From Human Pluripotent Stem Cells
Current Pathobiology Reports (2017)
-
Attitudes and opinions regarding confirmatory adaptive clinical trials: a mixed methods analysis from the Adaptive Designs Accelerating Promising Trials into Treatments (ADAPT-IT) project
Trials (2016)