Literature DB >> 32550936

Flexible Phase I-II design for partially ordered regimens with application to therapeutic cancer vaccines.

Nolan A Wages1, Craig L Slingluff2.   

Abstract

Existing methodology for the design of Phase I-II studies has been intended to search for the optimal regimen, based on a trade-off between toxicity and efficacy, from a set of regimens comprised of doses of a new agent. The underlying assumptions guiding allocation are that the dose-toxicity curve is monotonically increasing, and that the dose-efficacy curve either plateaus or decreases beyond an intermediate dose. This article considers the problem of designing Phase I-II studies that violate these assumptions for both outcomes. The motivating application studies regimens that are not defined by doses of a new agent, but rather a peptide vaccine plus novel adjuvants for the treatment of melanoma. All doses of each adjuvant are fixed, and the regimens vary by the number and selection of adjuvants. This structure produces regimen-toxicity curves that are partially ordered, and regimen-efficacy curves that may deviate from a plateau or unimodal shape. Application of a Bayesian model-based design is described in determining the optimal biologic regimen, based on bivariate binary measures of toxicity and biologic activity. A simulation study of the design's operating characteristics is conducted, and its versatility in handling other Phase I-II problems is discussed.

Entities:  

Keywords:  Adaptive design; Bayesian method; Immuno-oncology; Optimal biologic regimen; Phase I-II

Year:  2019        PMID: 32550936      PMCID: PMC7302057          DOI: 10.1007/s12561-019-09245-3

Source DB:  PubMed          Journal:  Stat Biosci        ISSN: 1867-1764


  25 in total

1.  Dose-finding designs for HIV studies.

Authors:  J O'Quigley; M D Hughes; T Fenton
Journal:  Biometrics       Date:  2001-12       Impact factor: 2.571

2.  Calibration of prior variance in the Bayesian continual reassessment method.

Authors:  Shing M Lee; Ying Kuen Cheung
Journal:  Stat Med       Date:  2011-03-17       Impact factor: 2.373

3.  Implementation of a Model-Based Design in a Phase Ib Study of Combined Targeted Agents.

Authors:  Nolan A Wages; Craig A Portell; Michael E Williams; Mark R Conaway; Gina R Petroni
Journal:  Clin Cancer Res       Date:  2017-07-21       Impact factor: 12.531

4.  Scientific Review of Phase I Protocols With Novel Dose-Escalation Designs: How Much Information Is Needed?

Authors:  Alexia Iasonos; Mithat Gönen; George J Bosl
Journal:  J Clin Oncol       Date:  2015-05-04       Impact factor: 44.544

Review 5.  Implementation of adaptive methods in early-phase clinical trials.

Authors:  Gina R Petroni; Nolan A Wages; Gautier Paux; Frédéric Dubois
Journal:  Stat Med       Date:  2016-02-29       Impact factor: 2.373

6.  Seamless Phase I/II Adaptive Design for Oncology Trials of Molecularly Targeted Agents.

Authors:  Nolan A Wages; Christopher Tait
Journal:  J Biopharm Stat       Date:  2014-06-06       Impact factor: 1.051

7.  Adaptive randomization to improve utility-based dose-finding with bivariate ordinal outcomes.

Authors:  Peter F Thall; Hoang Q Nguyen
Journal:  J Biopharm Stat       Date:  2012       Impact factor: 1.051

8.  A Bayesian Dose-finding Design for Oncology Clinical Trials of Combinational Biological Agents.

Authors:  Chunyan Cai; Ying Yuan; Yuan Ji
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2014-01-01       Impact factor: 1.864

9.  A randomized pilot trial testing the safety and immunologic effects of a MAGE-A3 protein plus AS15 immunostimulant administered into muscle or into dermal/subcutaneous sites.

Authors:  Craig L Slingluff; Gina R Petroni; Walter C Olson; Mark E Smolkin; Kimberly A Chianese-Bullock; Ileana S Mauldin; Kelly T Smith; Donna H Deacon; Nikole E Varhegyi; Sean B Donnelly; Caroline M Reed; Kristy Scott; Nadejda V Galeassi; William W Grosh
Journal:  Cancer Immunol Immunother       Date:  2015-11-18       Impact factor: 6.968

10.  An information theoretic phase I-II design for molecularly targeted agents that does not require an assumption of monotonicity.

Authors:  Pavel Mozgunov; Thomas Jaki
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2018-06-15       Impact factor: 1.864

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  2 in total

1.  Mathematical Modelling for Optimal Vaccine Dose Finding: Maximising Efficacy and Minimising Toxicity.

Authors:  John Benest; Sophie Rhodes; Thomas G Evans; Richard G White
Journal:  Vaccines (Basel)       Date:  2022-05-11

2.  Optimising Vaccine Dose in Inoculation against SARS-CoV-2, a Multi-Factor Optimisation Modelling Study to Maximise Vaccine Safety and Efficacy.

Authors:  John Benest; Sophie Rhodes; Matthew Quaife; Thomas G Evans; Richard G White
Journal:  Vaccines (Basel)       Date:  2021-01-22
  2 in total

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