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Quantitative Decision Criteria for Enhanced Decision Making in Drug Development

This paper discusses the importance of using quantitative decision criteria in drug development to reduce the likelihood of incorrect decisions. It explores six components of model-based drug development and emphasizes the need for collaboration among kineticists/modelers, statisticians, and clinicians. The authors provide examples and highlight the importance of calibrating models against data-derived statistics.

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Quantitative Decision Criteria for Enhanced Decision Making in Drug Development

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  1. Enhanced Quantitative Decision Making - Reducing the likelihood of incorrect decisions Mike K. Smith, Jonathan French, (Pfizer) Ken Kowalski, (A2PG) Wayne Ewy (formerly Pfizer, retired).

  2. Six Components ofModel-Based Drug Development* PK/PD & Disease Models Trial Performance Metrics Model-Based Drug Development Competitor Info. & Meta-Analysis Decision Criteria Design & Trial Execution Models Data Analysis Model * Lalonde et al, Clin Pharm & Ther, 2007; 82: pp21-32

  3. Quantitative Decision Criteria • “I’ll know it when I see it…” • “Evidence of an effect…” • “Reasonable efficacy and safety tradeoffs” • WRONG!!!

  4. Quantitative Decision Criteria • 2 points improvement over placebo. • Better. • At least it’s quantitative • How sure do you want to be? • Mean 2 points? • Lower CI 2 points? • Mean 2 points and lower CI > 0?

  5. P(Criteria|Data) • Not just P(… | Data) • Data • Prior data, model assumptions, parameter uncertainties • Trial design • Dropouts, imputation methods etc. • Data analytic method

  6. Truth vs Trial • For a given set of model parameters / assumptions there will be a “true” outcome against the decision criteria. • What is the chance of achieving 2 points improvement given current information? • For a given set of parameters we will know whether we achieve 2 points improvement or not. • Then for this same set of parameters, apply design, dropout / imputation models, analytic technique and assess decision criteria.

  7. Truth vs. Trial - Formally •  is the true (unknown) treatment effect • =f(, , ) is specified for a given set of model assumptions •   vector of fixed effects parameters •   covariance matrix for between-unit (subject or study) random effects •   covariance matrix for within-unit (subject or study) random effects

  8. Truth vs. Trial - Formally • Define quantitative decision rule under truth () and data-analytic results (T), e.g., • Truth: Go if TV, No Go if <TV • Data: Go if TTV, No Go if T<TV • Note TV denotes the Target Value • Note T could be a point estimate or confidence limit on estimate/prediction of 

  9. Operating Characteristics Trial Go Trial No Go Total “True” No Go “True” Go Total P(correct) PTS P(Go)

  10. Example • Comparing SC-75416 with ibuprofen in dental pain. • Published in Kowalski, K.G, et al. “Modeling and Simulation to Support Dose Selection and Clinical Development of SC-75416, a Selective COX-2 Inhibitor for the Treatment of Acute and Chronic Pain”. • Decision criteria based on 3 point difference from ibuprofen in TOTPAR6 endpoint.

  11. PTS = P(  3) = 67.2 Mean = 3.27, SD=0.60 Obs Mean = 3.3 Example From Kowalski et al: A model-based framework for quantitative decision-making in drug development Presentation at ACOP, Tuscon, AZ 2008.

  12. Example P(correct) = 70.72% P(Go) = 61.90% PTS = 67.20% 17 From Kowalski et al: A model-based framework for quantitative decision-making in drug development Presentation at ACOP, Tuscon, AZ 2008.

  13. “Nominal” values for OCs • P(Correct) can be fixed at >=80% • PTS for initiating a new trial depends on quadrant, portfolio, stage of development. • Perhaps minimal “dignity level” for starting a trial. • Fixing these two implies P(False GO) and P(False NO GO) must float, depend on P(Correct) and PTS. • Driven by decision criteria. • E.g. For P(Correct) = 80%, P(Incorrect) = 20%, spent across P(False GO), P(False NO GO).

  14. Iterate / Optimise If the operating characteristics “don’t look good”… Change the data analytic model Change the design constraints (↑ n /group) Change the data-analytic decision criteria for the trial. If we fix one or more of the above (e.g. n /group) then there is limited other things that can improve OCs. Change the data analytic model, change data-analytic decision criteria for the trial.

  15. The components may change over time “Truth” model / prior will be refined over time. P(“True” Go given current knowledge / model) changes. Decision criteria may change. Commercial viability changes. [This may change both our compound target criteria – truth decision rule, as well as the data-analytic decision rule] Acceptable level of confidence for Trial Go decision changes. [This applies only to data-analytic decision rule]

  16. Final Remarks (1) • Greater collaboration required among kineticists/modelers, statisticians and clinicians • Kineticists/modelers: • Explicit and transparent about the assumptions and limitations of their PK/PD and disease models • Think strategically about how model will be used to influence internal decision-making • Avoid excessive use of NONMEM-jargon and write reports to broader audience • Calibrate models against data-derived (non-model-based) statistics of interest

  17. Final Remarks (2) • Statisticians: • Embrace assumption-rich nonlinear models for decision-making especially in early clinical development • Avoid “Phase 3” mentality when designing Phase 2 studies…relying on empirical (assumption-poor) models to make decisions in early clinical development can be costly • Clinicians: • Quantitatively define clinically relevant effects and commercial targets • Explicitly and quantitatively defined decision rules

  18. Bibliography • Kowalski, K.G., Ewy, W., Hutmacher, M.M., Miller, R., and Krishnaswami, S. “Model-Based Drug Development – A New Paradigm for Efficient Drug Development”. Biopharmaceutical Report 2007;15:2-22. • Lalonde, R.L., et al. “Model-Based Drug Development”. Clin Pharm Ther 2007;82:21-32. • Kowalski, K.G., Olson, S., Remmers, A.E., and Hutmacher, M.M. “Modeling and Simulation to Support Dose Selection and Clinical Development of SC-75416, a Selective COX-2 Inhibitor for the Treatment of Acute and Chronic Pain”. Clin Pharm Ther, 2008; 83: 857-866. • Kowalski, K.G., French, J.L., Smith, M.K., Hutmacher, M.M. “A model-based framework for quantitative decision making in drug development”. Presentation at ACOP, Tuscon, AZ. 2008. http://tucson2008.go-acop.org/pdfs/8-Kowalski_FINAL.pdf

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