Covariate-Adjusted Adaptive Dose-Finding in Early Phase Clinical Trials

2012 ◽  
pp. 369-379
Author(s):  
Peter Thall ◽  
Hoang Nguyen
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Dose Finding ◽  
Early Phase Clinical Trials

scholarly journals Adaptive dose-finding based on safety and feasibility in early-phase clinical trials of adoptive cell immunotherapy

2019 ◽  
Vol 17 (2) ◽  
pp. 157-165
Author(s):  
Nolan A Wages ◽  
Camilo E Fadul
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Safety Data ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Cell Immunotherapy ◽  
Adoptive Cell Immunotherapy ◽  
Early Phase Clinical Trials ◽  
Finding Method ◽  
Number Of Cells

Background/aims: Dose feasibility is a challenge that may arise in the development of adoptive T cell therapies for cancer. In early-phase clinical trials, dose is quantified either by a fixed or per unit body weight number of cells infused. It may not be feasible, however, to administer a patient’s assigned dose due to an insufficient number of cells harvested or functional heterogeneity of the product. The study objective becomes to identify the maximum tolerated dose with high feasibility of being administered. This article describes a new dose-finding method that adaptively accounts for safety and feasibility endpoints in guiding dose allocation. Methods: We propose an adaptive dose-finding method that integrates accumulating feasibility and safety data to select doses for participant cohorts in early-phase trials examining adoptive cell immunotherapy. We sequentially model the probability of dose-limiting toxicity and the probability of feasibility using independent beta-binomial models. The probability model for toxicity borrows information across all dose levels using isotonic regression, allowing participants infused at a lower dose than his or her planned dose to contribute safety data to the dose-finding algorithm. We applied the proposed methodology in a single simulated trial and evaluated its operating characteristics through extensive simulation studies. Results: In simulations conducted for a phase I study of adoptive immunotherapy for newly diagnosed glioblastoma, the proposed method demonstrates the ability to identify accurately the feasible maximum tolerated doses and to treat participants at and around these doses. Over 10 hypothesized scenarios studied, the percentage of correctly selecting the true feasible and maximum tolerated dose ranged from 50% to 90% with sample sizes averaging between 21 and 24 participants. A comparison to the only known existing method accounting for safety and feasibility yields competitive performance. Conclusion: We have developed a new practical adaptive dose-finding method to assess feasibility in early-phase adoptive cell therapy trials. A design that incorporates feasibility, as a function of the quantity and quality of the product manufactured, in addition to safety will have an impact on the recommended phase II doses in studies that evaluate patient outcomes.

scholarly journals Practical Model-Based Dose Finding in Early-Phase Clinical Trials

Stroke ◽  
2008 ◽  
Vol 39 (9) ◽  
pp. 2627-2636 ◽  
Author(s):  
Harry T. Whelan ◽  
John D. Cook ◽  
Catherine M. Amlie-Lefond ◽  
Collin A. Hovinga ◽  
Anthony K. Chan ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Dose Finding ◽  
Model Based ◽  
Practical Model ◽  
Early Phase Clinical Trials

Monitoring event times in early phase clinical trials: some practical issues

2005 ◽  
Vol 2 (6) ◽  
pp. 467-478 ◽  
Author(s):  
Peter F Thall ◽  
Leiko H Wooten ◽  
Nizar M Tannir
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Event Times ◽  
Early Phase Clinical Trials

scholarly journals Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shinjo Yada
Keyword(s):  
Neural Network ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Early Phase ◽  
Patient Specific ◽  
Specific Gene ◽  
Cancer Type ◽  
Background Characteristics ◽  
Number Of Patients ◽  
Early Phase Clinical Trials

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

scholarly journals Simian adenovirus vector production for early-phase clinical trials: A simple method applicable to multiple serotypes and using entirely disposable product-contact components

Vaccine ◽  
2019 ◽  
Vol 37 (47) ◽  
pp. 6951-6961 ◽  
Author(s):  
Sofiya Fedosyuk ◽  
Thomas Merritt ◽  
Marco Polo Peralta-Alvarez ◽  
Susan J Morris ◽  
Ada Lam ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Adenovirus Vector ◽  
Simple Method ◽  
Simian Adenovirus ◽  
Early Phase Clinical Trials

scholarly journals Targeting for Success: Demonstrating Proof-of-Concept with Mechanistic Early Phase Clinical Pharmacology Studies for Disease-Modification in Neurodegenerative Disorders

2021 ◽  
Vol 22 (4) ◽  
pp. 1615
Author(s):  
Maurits F. J. M. Vissers ◽  
Jules A. A. C. Heuberger ◽  
Geert Jan Groeneveld
Keyword(s):  
Clinical Trials ◽  
Failure Rate ◽  
Neurodegenerative Disorders ◽  
Early Phase ◽  
Proof Of Concept ◽  
Mesh Terms ◽  
Disease Modifying ◽  
Early Phase Clinical Trials ◽  
Pharmacodynamic Biomarkers ◽  
Response Biomarkers

The clinical failure rate for disease-modifying treatments (DMTs) that slow or stop disease progression has been nearly 100% for the major neurodegenerative disorders (NDDs), with many compounds failing in expensive and time-consuming phase 2 and 3 trials for lack of efficacy. Here, we critically review the use of pharmacological and mechanistic biomarkers in early phase clinical trials of DMTs in NDDs, and propose a roadmap for providing early proof-of-concept to increase R&D productivity in this field of high unmet medical need. A literature search was performed on published early phase clinical trials aimed at the evaluation of NDD DMT compounds using MESH terms in PubMed. Publications were selected that reported an early phase clinical trial with NDD DMT compounds between 2010 and November 2020. Attention was given to the reported use of pharmacodynamic (mechanistic and physiological response) biomarkers. A total of 121 early phase clinical trials were identified, of which 89 trials (74%) incorporated one or multiple pharmacodynamic biomarkers. However, only 65 trials (54%) used mechanistic (target occupancy or activation) biomarkers to demonstrate target engagement in humans. The most important categories of early phase mechanistic and response biomarkers are discussed and a roadmap for incorporation of a robust biomarker strategy for early phase NDD DMT clinical trials is proposed. As our understanding of NDDs is improving, there is a rise in potentially disease-modifying treatments being brought to the clinic. Further increasing the rational use of mechanistic biomarkers in early phase trials for these (targeted) therapies can increase R&D productivity with a quick win/fast fail approach in an area that has seen a nearly 100% failure rate to date.

Unified exact design with early stopping rules for single arm clinical trials with multiple endpoints

2021 ◽  
pp. 096228022110130
Author(s):  
Wei Wei ◽  
Denise Esserman ◽  
Michael Kane ◽  
Daniel Zelterman
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Decision Rules ◽  
Stopping Rules ◽  
Multiple Endpoints ◽  
Shiny App ◽  
R Shiny ◽  
Early Phase Clinical Trials ◽  
User Friendly ◽  
Exact Design

Adaptive designs are gaining popularity in early phase clinical trials because they enable investigators to change the course of a study in response to accumulating data. We propose a novel design to simultaneously monitor several endpoints. These include efficacy, futility, toxicity and other outcomes in early phase, single-arm studies. We construct a recursive relationship to compute the exact probabilities of stopping for any combination of endpoints without the need for simulation, given pre-specified decision rules. The proposed design is flexible in the number and timing of interim analyses. A R Shiny app with user-friendly web interface has been created to facilitate the implementation of the proposed design.

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