A Bayesian design for phase I cancer therapeutic vaccine trials

Wang, Chenguang; Rosner, Gary L.; Roden, Richard B.S.

doi:10.1002/sim.8021

Cited by 7 publications

(6 citation statements)

References 43 publications

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“…Lastly, we found the doses for which the proportion of individuals that would experience grade 3+ adverse events above the thresholds of 30% and 17%. 30% of grade 3+ adverse events has been defined as a threshold for unacceptable toxicity in dose-escalation studies [ 22 , 23 , 24 ]. However, of the commonly CDC recommended vaccines, the largest grade 3+ adverse reaction rate is 17% for the Shingrix herpes zoster vaccine [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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Developing a vaccine against the global pandemic SARS-CoV-2 is a critical area of active research. Modelling can be used to identify optimal vaccine dosing; maximising vaccine efficacy and safety and minimising cost. We calibrated statistical models to published dose-dependent seroconversion and adverse event data of a recombinant adenovirus type-5 (Ad5) SARS-CoV-2 vaccine given at doses 5.0 × 1010, 1.0 × 1011 and 1.5 × 1011 viral particles. We estimated the optimal dose for three objectives, finding: (A) the minimum dose that may induce herd immunity, (B) the dose that maximises immunogenicity and safety and (C) the dose that maximises immunogenicity and safety whilst minimising cost. Results suggest optimal dose [95% confidence interval] in viral particles per person was (A) 1.3 × 1011 [0.8–7.9 × 1011], (B) 1.5 × 1011 [0.3–5.0 × 1011] and (C) 1.1 × 1011 [0.2–1.5 × 1011]. Optimal dose exceeded 5.0 × 1010 viral particles only if the cost of delivery exceeded £0.65 or cost per 1011 viral particles was less than £6.23. Optimal dose may differ depending on the objectives of developers and policy-makers, but further research is required to improve the accuracy of optimal-dose estimates.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…Previous studies (eg, Cai et al 33 ) show that ignoring the correlation between toxicity and efficacy has little impact on the performance of phase I-II designs. In cases where these assumptions may be violated, we can simply add d i and/or X i as covariates in Equations ( 6) and (7).…”

Section: Probability Modelsmentioning

confidence: 99%

“…The prior for 𝜂 c1 is taken to be 𝜂 c1 ∼ N(1.25, 0.75 2 ) so that there is a 90% prior probability that 𝜂 c1 is between 0 and 2.5 and a 5% prior probability that 𝜂 c1 is negative. With standardized Y , the elicitation of the prior for 𝜂 2 in the hazard function (7) is facilitated by the fact that it represents the log of the hazard ratio when the immune response Y increases by two SDs. We elicit from clinicians a range of hazard ratio that is practically feasible for 𝜂 2 , say (r 2,1 , r 2,2 ), and then assign 𝜂 2 a uniform prior distribution with range ( log(r 2,1 ), log(r 2,2 ) )…”

Section: Prior Distributionsmentioning

confidence: 99%

“…[1][2][3] A few dose-finding designs have been developed for immunotherapy that can incorporate an immune response. [4][5][6][7][8][9] However, all these existing designs assume the patient population is homogeneous and use the "one-dose-fits-all" rule for patient allocation and optimal dose identification, which is disconnected from medical practice as population heterogeneity is often expected for clinical trial studies. For example, many studies in various cancer types have demonstrated that the programmed cell death-ligand 1 (PD-L1) expression is a predictive biomarker for checkpoint inhibitor-based immunotherapy, with PD-L1 positive patients showing higher response rates, better progression-free survival (PFS), and overall survival, compared with PD-L1 negative patients.…”

Section: Introductionmentioning

confidence: 99%

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BIPSE: A biomarker‐based phase I/II design for immunotherapy trials with progression‐free survival endpoint

Guo

Zang

2021

Statistics in Medicine

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A Bayesian biomarker-based phase I/II design (BIPSE) is presented for immunotherapy trials with a progression-free survival (PFS) endpoint. The objective is to identify the subgroup-specific optimal dose, defined as the dose with the best risk-benefit tradeoff in each biomarker subgroup. We jointly model the immune response, toxicity outcome, and PFS with information borrowing across subgroups. A plateau model is used to describe the marginal distribution of the immune response. Conditional on the immune response, we model toxicity using probit regression and model PFS using the mixture cure rate model. During the trial, based on the accumulating data, we continuously update model estimates and adaptively randomize patients to doses with high desirability within each subgroup. Simulation studies show that the BIPSE design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.

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“…After several decades of development and optimization [5][6][7][8][9], there is now evidence that some cancer vaccines may improve clinical outcomes, in particular in combination with other active therapy [10][11][12][13]. The frequency and severity of adverse events observed in cancer vaccine approaches necessitate a change in the way early-phase clinical trials of these treatment strategies are designed and conducted [14][15][16][17]. There are no existing dose-finding methods that could directly address the multitude of challenges presented by the wide range of study research objectives, so our team needed to adapt relevant components of existing methods in developing a flexible design strategy.…”

Section: Introductionmentioning

confidence: 99%

Tailoring early-phase clinical trial design to address multiple research objectives

Wages

Slingluff

Bullock

et al. 2019

Cancer Immunol Immunother

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Introduction-In contemporary oncology drug development, implementation of novel earlyphase designs with the ability to address multiple research objectives are needed to better refine regimens. This paper describes an adaptive design strategy for identifying a range of optimal regimens based on two endpoints within multiple cohorts. The proposed design was developed to address objectives in an early-phase trial of cancer vaccines in combination with agonistic antibodies to CD40 and CD27.Materials and methods-We describe a model-based design strategy that was developed for a trial evaluating the safety and immunogenicity of vaccination with i) peptides plus CD40 antibody and TLR3 ligand, ii) systemic administration of an agonistic CD27 antibody, and c) to assess immune response from i) and ii) compared to optimal controls in participants with stage IIB-IV melanoma.Terms of use and reuse: academic research for non-commercial purposes, see here for full terms. https://www.springer.com/aamterms-v1

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A Bayesian design for phase I cancer therapeutic vaccine trials

Cited by 7 publications

References 43 publications

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

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

BIPSE: A biomarker‐based phase I/II design for immunotherapy trials with progression‐free survival endpoint

Tailoring early-phase clinical trial design to address multiple research objectives

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