Decision‐making in drug development using a composite definition of success

Saint‐Hilary, Gaëlle; Robert, Véronique; Gasparini, Mauro

doi:10.1002/pst.1870

Cited by 9 publications

(7 citation statements)

References 36 publications

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“…The power of 50% in our example may raise concerns for some readers, but it is inherent to the utilization of a dual‐criterion design based on statistical significance and clinical relevance (Roychoudhury et al., 2018; Saint‐Hilary et al., 2018). In dual‐criterion designs, the trial's success is determined not only by achieving statistical significance but also by exceeding a clinically meaningful threshold for the treatment effect estimate.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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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“…Due to its robust framework, PoS is commonly evaluated by sponsors in conjunction with a treatment's commercial potential when deciding whether to proceed to phase III for a given treatment and indication. 11,12 However, PoS is not a viable tool for drug repurposing since candidate treatments have established safety profiles allowing them to proceed directly to phase III. Without phase II data, it is difficult to construct a reliable prior for the unknown effect size which is required for PoS calculations.…”

Section: Introductionmentioning

confidence: 99%

Network meta analysis to predict the efficacy of an approved treatment in a new indication

Proper,

Chu,

Prajapati

et al. 2023

Research Synthesis Methods

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Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally‐intensive analytical methods that are not widely used in practice. Instead, repurposing decisions are often based on subjective judgments from limited empirical evidence. In this article, we develop a novel Bayesian network meta‐analysis (NMA) framework that can predict the efficacy of an approved treatment in a new indication and thereby identify candidate treatments for repurposing. We obtain predictions using two main steps: first, we use standard NMA modeling to estimate average relative effects from a network comprised of treatments studied in both indications in addition to one treatment studied in only one indication. Then, we model the correlation between relative effects using various strategies that differ in how they model treatments across indications and within the same drug class. We evaluate the predictive performance of each model using a simulation study and find that the model minimizing root mean squared error of the posterior median for the candidate treatment depends on the amount of available data, the level of correlation between indications, and whether treatment effects differ, on average, by drug class. We conclude by discussing an illustrative example in psoriasis and psoriatic arthritis and find that the candidate treatment has a high probability of success in a future trial.

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“…Several quantitative frameworks have been proposed to support decision making [8][9][10][11][12][13] at key development milestones. These frameworks can use likelihood-based approaches to set the decision criteria, 11 modeling and simulation, 14 or Bayesian metrics to quantify risk such as assurance [15][16][17][18][19][20][21][22] and/or the expected net present value (eNPV) described below. [22][23][24] In this paper, we focus on the probability of success (PoS), which is a single number expressed as a percentage reflecting the multitude of risks that may influence the final outcome of a program.…”

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confidence: 99%

“…These frameworks can use likelihood-based approaches to set the decision criteria, 11 modeling and simulation, 14 or Bayesian metrics to quantify risk such as assurance [15][16][17][18][19][20][21][22] and/or the expected net present value (eNPV) described below. [22][23][24] In this paper, we focus on the probability of success (PoS), which is a single number expressed as a percentage reflecting the multitude of risks that may influence the final outcome of a program. 22 PoS is one of a number of drivers of go/ no-go decision making comprising company strategy, public health needs, costs, expected financial rewards, and balance between lower-and higher risk programs deemed acceptable by the company.…”

mentioning

confidence: 99%

“…[22][23][24] In this paper, we focus on the probability of success (PoS), which is a single number expressed as a percentage reflecting the multitude of risks that may influence the final outcome of a program. 22 PoS is one of a number of drivers of go/ no-go decision making comprising company strategy, public health needs, costs, expected financial rewards, and balance between lower-and higher risk programs deemed acceptable by the company. 13 PoS is also used to calculate expected financial rewards.…”

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confidence: 99%

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A New Comprehensive Approach to Assess the Probability of Success of Development Programs Before Pivotal Trials

Holzhauer

Bornkamp

Kahn

et al. 2021

Clin Pharma and Therapeutics

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The point at which clinical development programs transition from early phase to pivotal trials is a critical milestone. Substantial uncertainty about the outcome of pivotal trials may remain even after seeing positive early phase data, and companies may need to make difficult prioritization decisions for their portfolio. The probability of success (PoS) of a program, a single number expressed as a percentage reflecting the multitude of risks that may influence the final program outcome, is a key decision‐making tool. Despite its importance, companies often rely on crude industry benchmarks that may be “adjusted” by experts based on undocumented criteria and which are typically misaligned with the definition of success used to drive commercial forecasts, leading to overly optimistic expected net present value calculations. We developed a new framework to assess the PoS of a program before pivotal trials begin. Our definition of success encompasses the successful outcome of pivotal trials, regulatory approval and meeting the requirements for market access as outlined in the target product profile. The proposed approach is organized in four steps and uses an innovative Bayesian approach to synthesize all relevant evidence. The new PoS framework is systematic and transparent. It will help organizations to make more informed decisions. In this paper, we outline the rationale and elaborate on the structure of the proposed framework, provide examples, and discuss the benefits and challenges associated with its adoption.

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Decision‐making in drug development using a composite definition of success

Cited by 9 publications

References 36 publications

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

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

Network meta analysis to predict the efficacy of an approved treatment in a new indication

A New Comprehensive Approach to Assess the Probability of Success of Development Programs Before Pivotal Trials

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