Decision‐making in early clinical drug development

Frewer, Paul; Mitchell, Pat; Watkins, Claire; Matcham, James

doi:10.1002/pst.1746

Cited by 34 publications

(47 citation statements)

References 8 publications

(9 reference statements)

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“…Our approach combines a multiple‐endpoint extension of the decision criteria in Lalonde et al and Frewer et al with a dose‐response methodology under model uncertainty . We describe an approach for jointly modeling efficacy while estimating dose, in the presence of uncertainty about the nonlinear dose response.…”

Section: Discussionmentioning

confidence: 99%

“…Other measures of dose uncertainty are illustrated below. The process of picking most appropriate target values and corresponding percentages is a development team activity, with commercial and scientific trade‐offs unique to the drug, indication, and marketplace . The operating characteristics of the decision criteria are then studied through simulation .…”

Section: Decision Framework For Multidimensional Performance Metricsmentioning

confidence: 99%

“…High probabilities of a yellow (uncertainty in efficacy) or light green (uncertainty in dose) outcome suggest issues with the current study design. In our programs, probabilities in the consider zone P (yellow or light green outcome) > 30% would generally be considered issues for exploration of both the decision criteria and the underlying study design . Robustness of the study design and decision criteria is then confirmed through simulation of a range of potential underlying efficacy responses.…”

Section: Decision Framework For Multidimensional Performance Metricsmentioning

confidence: 99%

“…Statistical methods for decision making have been increasingly focused on Bayesian methods, clinical assurance, and optimizing probability of technical success . Promising products may be accelerated and inferior products stopped as early as possible, improving overall pipeline production …”

Section: Introductionmentioning

confidence: 99%

“…Lalonde proposes a priori definitions of success (reasonably high probability of modest commercial feasibility) and futility (low probability of a strong commercial success). Frewer expanded on this approach and described real‐world experience in the drug development process . Both Lalonde and Frewer used efficacy as the basic measure of performance, although potential extensions were discussed.…”

Section: Introductionmentioning

confidence: 99%

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Integrating dose estimation into a decision‐making framework for model‐based drug development

Dunyak¹,

Mitchell²,

Hamrén

et al. 2018

Pharmaceutical Statistics

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Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response. Doses which are too high or low pose risk of clinical or commercial failure. This paper addresses this issue and continues the evolution of formal decision frameworks in drug development. Here, we consider the integration of both efficacy and dose-response estimation accuracy into the go/no-go decision process, using a model-based approach. Using prespecified target and lower reference values associated with both efficacy and dose accuracy, we build a decision framework to more completely characterize development risk. Given the limited knowledge of dose response in early development, our approach incorporates a set of dose-response models and uses model averaging. The approach and its operating characteristics are illustrated through simulation. Finally, we demonstrate the decision approach on a post hoc analysis of the phase 2 data for naloxegol (a drug approved for opioid-induced constipation).

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Section: Discussionmentioning

confidence: 99%

Section: Decision Framework For Multidimensional Performance Metricsmentioning

confidence: 99%

Section: Decision Framework For Multidimensional Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Integrating dose estimation into a decision‐making framework for model‐based drug development

Dunyak¹,

Mitchell²,

Hamrén

et al. 2018

Pharmaceutical Statistics

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Compound Progression and Go/No Go Criteria

Barrett¹

2022

Fundamentals of Drug Development

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Incorporation of healthy volunteers data on receptor occupancy into a phase II proof‐of‐concept trial using a Bayesian dynamic borrowing design

Di Stefano,

Rodrigues,

Galtier

et al. 2023

Biometrical J

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Receptor occupancy in targeted tissues measures the proportion of receptors occupied by a drug at equilibrium and is sometimes used as a surrogate of drug efficacy to inform dose selection in clinical trials. We propose to incorporate data on receptor occupancy from a phase I study in healthy volunteers into a phase II proof‐of‐concept study in patients, with the objective of using all the available evidence to make informed decisions. A minimal physiologically based pharmacokinetic modeling is used to model receptor occupancy in healthy volunteers and to predict it in the patients of a phase II proof‐of‐concept study, taking into account the variability of the population parameters and the specific differences arising from the pathological condition compared to healthy volunteers. Then, given an estimated relationship between receptor occupancy and the clinical endpoint, an informative prior distribution is derived for the clinical endpoint in both the treatment and control arms of the phase II study. These distributions are incorporated into a Bayesian dynamic borrowing design to supplement concurrent phase II trial data. A simulation study in immuno‐inflammation demonstrates that the proposed design increases the power of the study while maintaining a type I error at acceptable levels for realistic values of the clinical endpoint.

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Decision‐making in early clinical drug development

Cited by 34 publications

References 8 publications

Integrating dose estimation into a decision‐making framework for model‐based drug development

Integrating dose estimation into a decision‐making framework for model‐based drug development

Compound Progression and Go/No Go Criteria

Incorporation of healthy volunteers data on receptor occupancy into a phase II proof‐of‐concept trial using a Bayesian dynamic borrowing design

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