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The AAPS Journal

, 11:653 | Cite as

Evaluation of Agile Designs in First-in-Human (FIH) Trials—A Simulation Study

  • Itay Perlstein
  • James A. Bolognese
  • Rajesh Krishna
  • John A. Wagner
Research Article Theme: Quantitative Pharmacology, a Roadmap for Rational, Model-Based Drug Development

Abstract

The aim of the investigation was to evaluate alternatives to standard first-in-human (FIH) designs in order to optimize the information gained from such studies by employing novel agile trial designs. Agile designs combine adaptive and flexible elements to enable optimized use of prior information either before and/or during conduct of the study to seamlessly update the study design. A comparison of the traditional 6 + 2 (active + placebo) subjects per cohort design with alternative, reduced sample size, agile designs was performed by using discrete event simulation. Agile designs were evaluated for specific adverse event models and rates as well as dose-proportional, saturated, and steep-accumulation pharmacokinetic profiles. Alternative, reduced sample size (hereafter referred to as agile) designs are proposed for cases where prior knowledge about pharmacokinetics and/or adverse event relationships are available or appropriately assumed. Additionally, preferred alternatives are proposed for a general case when prior knowledge is limited or unavailable. Within the tested conditions and stated assumptions, some agile designs were found to be as efficient as traditional designs. Thus, simulations demonstrated that the agile design is a robust and feasible approach to FIH clinical trials, with no meaningful loss of relevant information, as it relates to PK and AE assumptions. In some circumstances, applying agile designs may decrease the duration and resources required for Phase I studies, increasing the efficiency of early clinical development. We highlight the value and importance of useful prior information when specifying key assumptions related to safety, tolerability, and PK.

Key words

agile design simulation 

Supplementary material

12248_2009_9141_MOESM1_ESM.doc (52 kb)
Supplementary Table S1 (DOC 51.5 kb)

References

  1. 1.
    Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov. 2004;3(8):711–5.CrossRefPubMedGoogle Scholar
  2. 2.
    Boyd R, Lalonde R. Nontraditional approaches to first-in-human studies to increase efficiency in drug development: will microdose studies make a significant impact? Clin Pharmacol Ther. 2007;81:24–6.CrossRefPubMedGoogle Scholar
  3. 3.
    Karara AH, Edeki T, McLeod J, Tonelli AP, Wagner JA. PARMA survey on the conduct of first-in-human clinical trials under exploratory INDs. J Clin Pharm. 2009 in press.Google Scholar
  4. 4.
    Robinson WT. Innovative early development regulatory approaches: expIND, expCTA. Microdosing Clin Pharmacol Ther. 2008;83:358–60.CrossRefGoogle Scholar
  5. 5.
    U.S. Department of Health and Human Services, Food and Drug Administration, Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products; 2004. Link: http://www.fda.gov/downloads/ScienceResearch/SpecialTopics/CriticalPathInitiative/CriticalPathOpportunitiesReports/ucm113411.pdf
  6. 6.
    Sheiner LB. Learning vs. confirming in clinical drug development. Clin Pharmacol Ther. 1997;61:275–91.CrossRefPubMedGoogle Scholar
  7. 7.
    Krishna R, Bolognese JA. Novel clinical trial designs in clinical pharmacology and experimental medicine. In: Krishna R, editor. Dose optimization in drug development. New York: Marcel Dekker; 2006.Google Scholar
  8. 8.
    Buoen Bjerrum OJ, Thomsen MS. How first-time-in-human studies are being performed: a survey of phase I dose-escalation trials in healthy volunteers published between 1995 and 2004. J Clin Pharmacol. 2005;45:1123–36.CrossRefGoogle Scholar
  9. 9.
    Sheiner LB, Beal SL, Sambol NC. Study designs for dose-ranging. Clin Pharmacol Ther. 1989;46(1):63–77.PubMedGoogle Scholar
  10. 10.
    Sheiner LB. Clinical pharmacology and the choice between theory and empiricism. Clin Pharmacol Ther. 1989;46:605–15.PubMedGoogle Scholar
  11. 11.
    Peck CC, Cross JT. "Getting the dose right": facts, a blueprint, and encouragements. Clin Pharmacol Ther. 2007;82:12–4.CrossRefPubMedGoogle Scholar
  12. 12.
    Dodds MG, Hooker AC, Vicini P. Robust population pharmacokinetic experiment design. J Pharmacokinet Pharmacodyn. 2005;32:33–64.CrossRefPubMedGoogle Scholar
  13. 13.
    Atkinson AC, Donev AN. Optimum experimental designs. Oxford: Oxford University Press; 1992.Google Scholar
  14. 14.
    Berry DA. Bayesian clinical trials. Nat Rev Drug Discov. 2006;5:27–36.CrossRefPubMedGoogle Scholar
  15. 15.
    Heyd JM, Carlin BP. Adaptive design improvements in the continual reassessment method for phase I studies. Stat Med. 1999;18:1307–21.CrossRefPubMedGoogle Scholar
  16. 16.
    Barrett J, Skolnik J, Jayaraman B, Patel D, Adamson P. Discrete event simulation applied to pediatric phase I oncology designs. Clin Pharmacol Ther. 2008;84(6):729–33.CrossRefPubMedGoogle Scholar
  17. 17.
    Barrett JS, Jayaraman B, Patel D, Skolnik JM. A SAS-based solution to evaluate study design efficiency of phase I pediatric oncology trials via discrete event simulation. Comput Methods Programs Biomed. 2008;90(3):240–50.CrossRefPubMedGoogle Scholar
  18. 18.
    Koyfman SA, et al. Risks and benefits associated with novel phase 1 oncology trial designs. Cancer. 2007;110:1115–24.CrossRefPubMedGoogle Scholar
  19. 19.
    Meille C, Gentet JC, Barbolosi D, Andre N, Doz F, Iliadis A. New adaptive method for phase I trials in oncology. Clin Pharmacol Ther. 2008;83:873–81.CrossRefPubMedGoogle Scholar
  20. 20.
    Skolnik JM, Barrett JS, Jayaraman B, Patel D, Adamson PC. Shortening the timeline of pediatric phase I trials: the rolling six design. J Clin Oncol. 2008;26(2):190–5.CrossRefPubMedGoogle Scholar
  21. 21.
    Maloney A, Karlsson MO, Simonsson US. Optimal adaptive design in clinical drug development: a simulation example. J Clin Pharmacol. 2007;47(10):1231–43.CrossRefPubMedGoogle Scholar
  22. 22.
    Golub HL. The need for more efficient trial designs. Stat Med. 2006;25:3231–5.CrossRefPubMedGoogle Scholar
  23. 23.
    Iwamoto M, Wenning LA, Petry AS, Laethem M, De Smet M, Kost JT, et al. Safety, tolerability, and pharmacokinetics of raltegravir after single and multiple doses in healthy subjects. Clin Pharmacol Ther. 2008;83(2):293–9.CrossRefPubMedGoogle Scholar
  24. 24.
    Cohen D, Lindvall M, Costa P. An introduction to agile methods. In: Zelkowitz MV, editor. In advances in software engineering (advances in computers 62). Amsterdam: Elsevier; 2004. p. 2–67.Google Scholar
  25. 25.
    Wegman AC, van der Windt DA, Stalman WA, de Vries TP. Conducting research in individual patients: lessons learnt from two series of N-of-1 trials. BMC Fam Pract. 2006;19(7):54.CrossRefGoogle Scholar
  26. 26.
    European Medicines Agency (EMEA) Committee for Medicinal Products for Human Use (CHMP): Guideline on clinical trials in small populations, 2006. Doc. Ref. CHMP/EWP/83561/2005. Link: http://www.emea.europa.eu/pdfs/human/ewp/8356105en.pdf
  27. 27.
    Yin YA, Chen C. Optimizing first-time-inhuman trial design for studying dose proportionality. Drug Inf J. 2001;35:1065–78.Google Scholar
  28. 28.
    Robertson T, Wright FT, Dykstra RL. Order restricted statistical inference. New York: Wiley; 1988. p. 1988.Google Scholar
  29. 29.
    Lee DP, Skolnik JM, Adamson PC. Pediatric phase I trials in oncology: an analysis of study conduct efficiency. J Clin Oncol. 2005;23:8431–41.CrossRefPubMedGoogle Scholar
  30. 30.
    Loke YC, Tan SB, Cai Y, et al. A Bayesian dose finding design for dual endpoint phase I trials. Stat Med. 2006;25:3–22.CrossRefPubMedGoogle Scholar
  31. 31.
    Chu H, Zha J, Roy A, Ette EI. Determination of the efficiency of first time-in-man designs in healthy volunteers. Clin Res Regul Aff. 2008;25:157–72.CrossRefGoogle Scholar
  32. 32.
    Chu H, Zha J, Roy A, Ette EI. Designs for first-time-in-man in nononcology indications. In: Ette EI, Williams PJ, editors. In pharmacometrics: the science of quantitative pharmacology. New York: Wiley; 2007. p. 761–80.Google Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2009

Authors and Affiliations

  • Itay Perlstein
    • 1
  • James A. Bolognese
    • 2
  • Rajesh Krishna
    • 1
  • John A. Wagner
    • 1
  1. 1.Department of Clinical PharmacologyMerck Research Laboratories, Merck & Co., Inc.RahwayUSA
  2. 2.Statistical ServicesCytel Inc.CambridgeUSA

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