Analysis of Clinical Dose–Response in Small-Molecule Drug Development: 2009–2014

Thomas, Neal; Roy, Dooti

doi:10.1080/19466315.2016.1256229

Cited by 13 publications

(14 citation statements)

References 28 publications

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“…The model selection component of MCP‐MOD can be placed within a long history of variable selection methods for regression models, eg, Hocking . The example used here to illustrate the MCP‐MOD method had high signal‐to‐noise, excellent conjectured curves, and a dosing design better than most currently used in practice . The biologically implausible quadratic model was nonetheless selected in 20% of samples, which illustrates the difficulty of selecting models based on the limited data available for a single drug.…”

Section: Discussionmentioning

confidence: 72%

Understanding MCP‐MOD dose finding as a method based on linear regression

Thomas

2017

Statistics in Medicine

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MCP-MOD is a testing and model selection approach for clinical dose finding studies. During testing, contrasts of dose group means are derived from candidate dose response models. A multiple-comparison procedure is applied that controls the alpha level for the family of null hypotheses associated with the contrasts. Provided at least one contrast is significant, a corresponding set of "good" candidate models is identified. The model generating the most significant contrast is typically selected. There have been numerous publications on the method. It was endorsed by the European Medicines Agency. The MCP-MOD procedure can be alternatively represented as a method based on simple linear regression, where "simple" refers to the inclusion of an intercept and a single predictor variable, which is a transformation of dose. It is shown that the contrasts are equal to least squares linear regression slope estimates after a rescaling of the predictor variables. The test for each contrast is the usual t statistic for a null slope parameter, except that a variance estimate with fewer degrees of freedom is used in the standard error. Selecting the model corresponding to the most significant contrast P value is equivalent to selecting the predictor variable yielding the smallest residual sum of squares. This criteria orders the models like a common goodness-of-fit test, but it does not assure a good fit. Common inferential methods applied to the selected model are subject to distortions that are often present following data-based model selection.

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Section: Discussionmentioning

confidence: 72%

Understanding MCP‐MOD dose finding as a method based on linear regression

Thomas

2017

Statistics in Medicine

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“…This finding is consistent with research spanning an earlier time period (2009–2014) focusing on 66 FDA‐approved small‐molecule drug indications. Here, 66% of the dose–response trials included three or less active doses, and moreover, a ≤4‐fold dose range was studied in “most” trials . In addition, 45% of the dose–response trials reported on clinicaltrials.gov (1999–2013) included three or fewer dosage groups .…”

Section: Discussionmentioning

confidence: 99%

“…Motivated by the recent focus on dose finding in clinical drug development, 6 this review aimed to evaluate its current practice in the pharmaceutical industry and to assess the totality of clinical evidence supporting the optimality of the label dose. To our knowledge, previous work has focused on dose-ranging trial design, 17,18 whereas clinical development paths and dose-exposure-response characterization for label-dose identification have not been reviewed. The current work has shown that, in practice, dose finding is not confined to a single trial, phase, or development stage.…”

Section: Discussion Summarymentioning

confidence: 99%

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Dose Finding in the Clinical Development of 60 US Food and Drug Administration–Approved Drugs Compared With Learning vs. Confirming Recommendations

Lyauk

Jonker

Lund

2019

Clinical Translational Sci

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This review characterizes clinical development that supported the label dose in 60 drug indications recently approved by the US Food and Drug Administration. With Lewis B. Sheiner's Learning vs. Confirming clinical drug development paradigm as a reference point, the clinical development paths, the design of dose‐ranging trials, and the dose–exposure–response characterization were examined using US Food and Drug Administration approval packages. It was found that 89% of clinical development programs included several doses in the first‐in‐patient trial, 43% proceeded directly to confirmatory trials after the first‐in‐patient trial, and 52% included multiple doses in confirmatory development. A low number of doses and narrow dose ranges were generally included in dose‐ranging trials, with only 20% including at least four doses over an at least 10‐fold dose range. In a third of approval packages, no dose–response or exposure–response evaluation was identified, and model‐based dose–exposure–response characterization was rarely alluded to, as only 2 of 60 approval packages mentioned the use of a model‐based approach. The findings suggest that confirmatory development may often be guided more toward learning than confirming, and furthermore that dose exposure response is robustly assessed in only a minority of clinical drug development programs, indicating that there may be room left for optimizing the benefit/risk profile of confirmatory/marketed dose(s). Significant deviation from Learning vs. Confirming may exist in clinical development practice on several levels, and the reasons for why this may be the case are discussed in light of contemporary literature.

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“…Bayesian approaches to the modeling of the dose response and longitudinal data can improve the precision in the statistical analysis, effectively providing more information for the same resources, however they are not commonly used to date. As indicated in a review of dose response studies for small‐molecule drugs approved by the US Food and Drug Administration (FDA) between 2009 and 2014, most study designs and their analyses focused on a small number of pairwise comparisons of dose groups to placebo . Consideration and evaluation of a range of plausible dose response models (eg, some assuming monotonically increasing function and others more flexible in the shape of dose response) may be desirable both for the interim and final analysis.…”

Section: Discussionmentioning

confidence: 99%

Applications of Bayesian statistical methodology to clinical trial design: A case study of a phase 2 trial with an interim futility assessment in patients with knee osteoarthritis

Smith

Jin

Raddad

et al. 2018

Pharmaceutical Statistics

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Development of new pharmacological treatments for osteoarthritis that address unmet medical needs in a competitive market place is challenging. Bayesian approaches to trial design offer advantages in defining treatment benefits by addressing clinically relevant magnitude of effects relative to comparators and in optimizing efficiency in analysis. Such advantages are illustrated by a motivating case study, a proof of concept, and dose finding study in patients with osteoarthritis. Patients with osteoarthritis were randomized to receive placebo, celecoxib, or 1 of 4 doses of galcanezumab. Primary outcome measure was change from baseline WOMAC pain after 8 weeks of treatment. Literature review of clinical trials with targeted comparator therapies quantified treatment effects versus placebo. Two success criteria were defined: one to address superiority to placebo with adequate precision and another to ensure a clinically relevant treatment effect. Trial simulations used a Bayesian dose response and longitudinal model. An interim analysis for futility was incorporated. Simulations indicated the study had ≥85% power to detect a 14‐mm improvement and ≤1% risk for a placebo‐like drug to pass. The addition of the second success criterion substantially reduced the risk of an inadequate, weakly efficacious drug proceeding to future development. The study was terminated at the interim analysis due to inadequate analgesic efficacy. A Bayesian approach using probabilistic statements enables clear understanding of success criteria, leading to informed decisions for study conduct. Incorporating an interim analysis can effectively reduce sample size, save resources, and minimize exposure of patients to an inadequate treatment.

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Analysis of Clinical Dose–Response in Small-Molecule Drug Development: 2009–2014

Cited by 13 publications

References 28 publications

Understanding MCP‐MOD dose finding as a method based on linear regression

Understanding MCP‐MOD dose finding as a method based on linear regression

Dose Finding in the Clinical Development of 60 US Food and Drug Administration–Approved Drugs Compared With Learning vs. Confirming Recommendations

Applications of Bayesian statistical methodology to clinical trial design: A case study of a phase 2 trial with an interim futility assessment in patients with knee osteoarthritis

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