scholarly journals Optimal designs for phase II/III drug development programs including methods for discounting of phase II results

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Stella Erdmann ◽  
Marietta Kirchner ◽  
Heiko Götte ◽  
Meinhard Kieser
Keyword(s):  
Drug Development ◽  
Sample Size ◽  
Phase Ii ◽  
Treatment Effect ◽  
Development Program ◽  
Phase Iii ◽  
Effect Estimate ◽  
Development Programs ◽  
Treatment Effect Estimate ◽  
Phase Ii And Iii

Abstract Background Go/no-go decisions after phase II and sample size chosen for phase III are usually based on phase II results (e.g., the treatment effect estimate of phase II). Due to the decision rule (only promising phase II results lead to phase III), treatment effect estimates from phase II that initiate a phase III trial commonly overestimate the true treatment effect. Underpowered phase III trials are the consequence. Optimistic findings may then not be reproduced, leading to the failure of potentially expensive drug development programs. For some disease areas these failure rates are described to be quite high: 62.5%. Methods We integrate the ideas of multiplicative and additive adjustment of treatment effect estimates after go decisions in a utility-based framework for optimizing drug development programs. The design of a phase II/III program, i.e., the “right amount of adjustment”, the allocation of the resources to phase II and III in terms of sample size, and the rule applied to decide whether to stop or to proceed with phase III influences its success considerably. Given specific drug development program characteristics (e.g., fixed and variable per patient costs for phase II and III, probable gain in case of market launch), optimal designs with respect to the maximal expected utility can be identified by the proposed Bayesian-frequentist approach. The method will be illustrated by application to practical examples characteristic for oncological studies. Results In general, our results show that the program set-ups with adjusted treatment effect estimate used for phase III planning are superior to the “naïve” program set-ups with respect to the maximal expected utility. Therefore, we recommend considering an adjusted phase II treatment effect estimate for the phase III sample size calculation. However, there is no one-fits-all design. Conclusion Individual drug development planning for a specific program is necessary to find the optimal design. The optimal choice of the design parameters for a specific drug development program at hand can be found by our user friendly R Shiny application and package (both assessable open-source via [1]).

Tildrakizumab: A Review of Phase II and III Clinical Trials

2018 ◽  
Vol 53 (4) ◽  
pp. 413-418 ◽  
Author(s):  
Sree S. Kolli ◽  
Sarah D. Gabros ◽  
Adrian Pona ◽  
Abigail Cline ◽  
Steven R. Feldman
Keyword(s):  
Clinical Trial ◽  
Phase Ii ◽  
Biological Therapy ◽  
Common Adverse Effect ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Severe Psoriasis ◽  
Phase Iii ◽  
Physician Global Assessment ◽  
Phase Ii And Iii

Objective: Tildrakizumab, an inhibitor of the p19 subunit of interleukin (IL)-23, was recently Food and Drug Administration (FDA) approved for patients with moderate to severe psoriasis. This article will review the phase II and III clinical trial data of tildrakizumab. Data Sources: A PubMed search from January 2000 to September 2018 was done with the search terms tildrakizumab, guselkumab, risankizumab, p19, interleukin-23, and psoriasis. Study Selection and Data Extraction: Articles discussing phase II and III clinical trial data for tildrakizumab were selected. Data Synthesis: In phase II and phase III trials, tildrakizumab was safe and efficacious compared with placebo and etanercept. More patients achieved Psoriasis Area and Severity Index 75 receiving tildrakizumab (200 mg, 62%-74%; 100 mg, 61%-66%; 25 mg, 64%; 5 mg, 33%) compared with placebo (4%-6%, P < 0.0001) and etanercept (48%, P = 0.01). More patients achieved Physician Global Assessment (PGA) response of “clear” or “minimal” receiving tildrakizumab (200 mg, 59%; 100 mg, 55%-58%) than the placebo group (4%-7%, P < 0.0001). 59% of patients who received tildrakizumab 200 mg achieved a PGA response of “clear” or “minimal” compared with etanercept (48%, P = 0.0031). The most common adverse effect was infection. Relevance to Patient Care and Clinical Practice: Tildrakizumab is a new, FDA-approved, physician-administered biological therapy for patients with moderate to severe psoriasis. It appears to be efficacious and safe so far. Conclusion: Tildrakizumab is efficacious and safe for the treatment of patients with moderate to severe psoriasis. IL-23/p19 inhibitors are a promising class of biological therapy.

scholarly journals FDA relations during drug development

2000 ◽  
Vol 2 (3) ◽  
pp. 213-217
Keyword(s):  
Drug Development ◽  
Drug Application ◽  
Development Program ◽  
The United States ◽  
Legal Aspects ◽  
Development Programs ◽  
New Drug ◽  
Regulatory Affairs ◽  
New Drug Application ◽  
Drug Development Program

Working closely and cooperatively with regulatory authorities during drug development is vital to successful drug development programs. In the United States, the drug development team includes not only members of the key disciplines of drug discovery, clinical research, regulatory affairs, marketing, chemistry, toxicology, and legal aspects, but also the Food and Drug Administration (FDA). New regulations encourage meetings at the pre-investigational new drug (pre-IND), end-of-phase-2, and pre-new drug application (pre-NDA) submission phases. Appropriate informal discussions via fax and telephone are also encouraged. By proactively interacting with the FDA, the pharmaceutical industry increases the probability of a successful drug development program.

scholarly journals Estimation of the Overall Treatment Effect in the Presence of Interference in Cluster-Randomized Trials of Infectious Disease Prevention

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Nicole Bohme Carnegie ◽  
Rui Wang ◽  
Victor De Gruttola
Keyword(s):  
Infectious Disease ◽  
Disease Prevention ◽  
Social Interactions ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Effect Estimate ◽  
Treatment Effect Estimate ◽  
Partial Interference ◽  
The Difference ◽  
Cluster Randomized

AbstractAn issue that remains challenging in the field of causal inference is how to relax the assumption of no interference between units. Interference occurs when the treatment of one unit can affect the outcome of another, a situation which is likely to arise with outcomes that may depend on social interactions, such as occurrence of infectious disease. Existing methods to accommodate interference largely depend upon an assumption of “partial interference” – interference only within identifiable groups but not among them. There remains a considerable need for development of methods that allow further relaxation of the no-interference assumption. This paper focuses on an estimand that is the difference in the outcome that one would observe if the treatment were provided to all clusters compared to that outcome if treatment were provided to none – referred as the overall treatment effect. In trials of infectious disease prevention, the randomized treatment effect estimate will be attenuated relative to this overall treatment effect if a fraction of the exposures in the treatment clusters come from individuals who are outside these clusters. This source of interference – contacts sufficient for transmission that are with treated clusters – is potentially measurable. In this manuscript, we leverage epidemic models to infer the way in which a given level of interference affects the incidence of infection in clusters. This leads naturally to an estimator of the overall treatment effect that is easily implemented using existing software.

Optimal Designs for Multi-Arm Phase II/III Drug Development Programs

2020 ◽  
pp. 1-11 ◽  
Author(s):  
Stella Preussler ◽  
Marietta Kirchner ◽  
Heiko Götte ◽  
Meinhard Kieser
Keyword(s):  
Drug Development ◽  
Phase Ii ◽  
Optimal Designs ◽  
Development Programs

PrabotulinumtoxinA-xvfs for the Treatment of Moderate-to-Severe Glabellar Lines

2020 ◽  
pp. 106002802094352
Author(s):  
Mary B. Gadarowski ◽  
Rima I. Ghamrawi ◽  
Sarah L. Taylor ◽  
Steven R. Feldman
Keyword(s):  
Clinical Trials ◽  
Phase Ii ◽  
Data Extraction ◽  
Botulinum Toxin Type A ◽  
Botulinum Toxin Type ◽  
Phase Iii ◽  
Phase Iii Clinical Trials ◽  
Study Selection ◽  
Pubmed Database ◽  
Phase Ii And Iii

Objective: PrabotulinumtoxinA-xvfs (Jeuveau), a botulinum toxin type A, was approved by the Food and Drug Administration for the temporary improvement in the appearance of moderate-to-severe glabellar lines in February 2019. This article will review phase II and III clinical trials to assess the efficacy, safety, and clinical application of this novel, aesthetic-only drug. Data sources: A systematic literature review was performed using the terms “glabellar lines AND prabotulinumtoxinA” in the PubMed database. ClinicalTrials.gov was searched to identify nonpublished studies. Study Selection and Data Extraction: Articles written in English between November 2019 and June 2020 discussing phase II and phase III clinical trials were evaluated. Data Synthesis: By the primary efficacy end point on day 30, more patients achieved a greater than 2-point improvement on the Glabellar Line Scale (GLS) at maximum frown compared with baseline on day 0. The proportions of participants who responded to treatment with prabotulinumtoxinA were 67.5% and 70.4% versus 1.2% and 1.3% in placebo groups across 2 identical clinical trials ( P < 0.001). Patients receiving prabotulinumtoxinA experienced greater improvement in GLS at maximum frown on day 30 (87.2%) compared with onabotulinumtoxinA (82.8%) and placebo (4.2%; P < 0.001). PrabotulinumtoxinA was well tolerated across all studies. Relevance to Patient Care and Clinical Practice: This review provides a detailed analysis of the safety and efficacy of prabotulinumtoxinA-xvfs and includes special considerations to help guide patients and clinicians. Conclusion: PrabotulinumtoxinA is a safe and effective new addition to the repository of available treatments for the appearance of glabellar lines.

scholarly journals Beyond p-values: A phase II dual-criterion design with statistical significance and clinical relevance

2018 ◽  
Vol 15 (5) ◽  
pp. 452-461 ◽  
Author(s):  
Satrajit Roychoudhury ◽  
Nicolas Scheuer ◽  
Beat Neuenschwander
Keyword(s):  
Sample Size ◽  
Phase Ii ◽  
Clinical Relevance ◽  
Statistical Significance ◽  
Effect Estimate ◽  
Type I ◽  
Operating Characteristics ◽  
Phase Ii Trials ◽  
Standard Design ◽  
Standard Designs

Background Well-designed phase II trials must have acceptable error rates relative to a pre-specified success criterion, usually a statistically significant p-value. Such standard designs may not always suffice from a clinical perspective because clinical relevance may call for more. For example, proof-of-concept in phase II often requires not only statistical significance but also a sufficiently large effect estimate. Purpose We propose dual-criterion designs to complement statistical significance with clinical relevance, discuss their methodology, and illustrate their implementation in phase II. Methods Clinical relevance requires the effect estimate to pass a clinically motivated threshold (the decision value (DV)). In contrast to standard designs, the required effect estimate is an explicit design input, whereas study power is implicit. The sample size for a dual-criterion design needs careful considerations of the study’s operating characteristics (type I error, power). Results Dual-criterion designs are discussed for a randomized controlled and a single-arm phase II trial, including decision criteria, sample size calculations, decisions under various data scenarios, and operating characteristics. The designs facilitate GO/NO-GO decisions due to their complementary statistical–clinical criterion. Limitations While conceptually simple, implementing a dual-criterion design needs care. The clinical DV must be elicited carefully in collaboration with clinicians, and understanding similarities and differences to a standard design is crucial. Conclusion To improve evidence-based decision-making, a formal yet transparent quantitative framework is important. Dual-criterion designs offer an appealing statistical–clinical compromise, which may be preferable to standard designs if evidence against the null hypothesis alone does not suffice for an efficacy claim.

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