Patient recruitment strategies for adaptive enrichment designs with time-to-event endpoints.

BACKGROUND: Adaptive enrichment designs for clinical trials have great potential for the development of targeted therapies. They enable researchers to stop the recruitment process for a certain population in mid-course based on an interim analysis. However, adaptive enrichment designs increase the total trial period owing to the stoppage in patient recruitment to make interim decisions. This is a major drawback; it results in delays in the submission of clinical trial reports and the appearance of drugs on the market. Here, we explore three types of patient recruitment strategy for the development of targeted therapies based on the adaptive enrichment design. METHODS: We consider recruitment methods which provide an option to continue recruiting patients from the overall population or only from the biomarker-positive population even during the interim decision period. A simulation study was performed to investigate the operating characteristics by comparing an adaptive enrichment design using the recruitment methods with a non-enriched design. RESULTS: The number of patients was similar for both recruitment methods. Nevertheless, the adaptive enrichment design was beneficial in settings in which the recruitment period is expected to be longer than the follow-up period. In these cases, the adaptive enrichment design with continued recruitment from the overall population or only from the biomarker-positive population even during the interim decision period conferred a major advantage, since the total trial period did not differ substantially from that of trials employing the non-enriched design. By contrast, the non-enriched design should be used in settings in which the follow-up period is expected to be longer than the recruitment period, since the total trial period was notably shorter than that of the adaptive enrichment design. Furthermore, the utmost care is needed when the distribution of patient recruitment is concave, i.e., when patient recruitment is slow during the early period, since the total trial period is extended. CONCLUSIONS: Adaptive enrichment designs that entail continued recruitment methods are beneficial owing to the shorter total trial period than expected in settings in which the recruitment period is expected to be longer than the follow-up period and the biomarker-positive population is promising.

A Bayesian hierarchal modeling approach to shortening phase I/II trials of anticancer drug combinations.

In phase I/II anticancer drug-combination trials, trial design to evaluate toxicity and efficacy has been studied by dividing the trial into 2 stages, followed by seamless execution of the 2 stages. In the first stage, admissible dose combinations in toxicity are identified, followed by patient assignment among the identified admissible dose combinations using adaptive randomization in the second stage. When patients are assigned using adaptive randomization, it is desirable to determine a more appropriate dose combination by taking into consideration both drug efficacy and toxicity; however, during the course of this determination and evaluation of toxicity and efficacy, there remains a concern that the trial duration might be prolonged. Therefore, we proposed a trial design to assign patients adaptively to more appropriate dose combinations in both toxicity and efficacy and to shorten trial duration without compromising trial performance. When selecting the dose combination for subsequent cohorts, unobserved data are treated as missing data, which are imputed using a data augmentation algorithm involving a gamma process. Probabilities associated with toxicity and efficacy are estimated applying a Bayesian hierarchical model to the imputed data, thereby allowing more patients to be assigned more appropriate dose combinations in both toxicity and efficacy through adaptive randomization. Results of simulation studies suggested that the proposed approach shortened trial duration without significantly compromising the performance of the trial as compared with existing approaches. We believe that the proposed approach will expedite drug development time and reduce costs associated with clinical development.

Adaptive phase I/II clinical trials for drug combination assessment in oncology using the outcomes of each cycle.

Many new anticancer agents can be combined with existing drugs, as combining a number of drugs may be expected to have a better therapeutic effect than monotherapy owing to synergistic effects. Furthermore, to drive drug development and to reduce the associated cost, there has been a growing tendency to combine these as phase I/II trials. With respect to phase I/II oncology trials for the assessment of dose combinations, in the existing methodologies in which efficacy based on tumor response and safety based on toxicity are modeled as binary outcomes, it is not possible to enroll and treat the next cohort of patients unless the best overall response has been determined in the current cohort. Thus, the trial duration might be potentially extended to an unacceptable degree. In this study, we proposed a method that randomizes the next cohort of patients in the phase II part to the dose combination based on the estimated response rate using all the available observed data upon determination of the overall response in the current cohort. We compared the proposed method to the existing method using simulation studies. These demonstrated that the percentage of optimal dose combinations selected in the proposed method is not less than that in the existing method and that the trial duration in the proposed method is shortened compared to that in the existing method. The proposed method meets both ethical and financial requirements, and we believe it has the potential to contribute to expedite drug development.

Application of Bayesian hierarchical models for phase I/II clinical trials in oncology.

Treatment during cancer clinical trials sometimes involves the combination of multiple drugs. In addition, in recent years there has been a trend toward phase I/II trials in which a phase I and a phase II trial are combined into a single trial to accelerate drug development. Methods for the seamless combination of phases I and II parts are currently under investigation. In the phase II part, adaptive randomization on the basis of patient efficacy outcomes allocates more patients to the dose combinations considered to have higher efficacy. Patient toxicity outcomes are used for determining admissibility to each dose combination and are not used for selection of the dose combination itself. In cases where the objective is not to find the optimum dose combination solely for efficacy but regarding both toxicity and efficacy, the need exists to allocate patients to dose combinations with consideration of the balance of existing trade-offs between toxicity and efficacy. We propose a Bayesian hierarchical model and an adaptive randomization with consideration for the relationship with toxicity and efficacy. Using the toxicity and efficacy outcomes of patients, the Bayesian hierarchical model is used to estimate the toxicity probability and efficacy probability in each of the dose combinations. Here, we use Bayesian moving-reference adaptive randomization on the basis of desirability computed from the obtained estimator. Computer simulations suggest that the proposed method will likely recommend a higher percentage of target dose combinations than a previously proposed method.

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

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.

Analyses of drug combinations using missing data shortens trial periods in phase I/II oncology trials.

In previous phase I/II oncology trials for drug combinations, a number of methods have been studied to determine the dose combination for the next cohort. However, there is a risk that trial durations will be unfeasibly long if methods for evaluating safety and efficacy are based on the best overall response and toxicity during trial design. In this study, we propose an approach to shorten the duration of drug trials in oncology. In this method, the dose combination to be allocated to the next cohort is decided before all data for patients in the current cohort is known and best overall response is determined. The efficacy of drug combinations in patients for whom the best overall response has not been determined is treated as missing data. The missing data mechanism is modeled by nonparametric prior processes. The probabilities of efficacy and toxicity are estimated after applying data augmentation to missing data, and the dose combination to be allocated to the next cohort is decided using these probabilities. Simulation studies from the present study show that this proposed approach would shorten trial durations without the low-performing of the trial design in comparison to existing approaches. Shortening trial durations would enable patients with the targeted disease to receive effective therapy at an earlier stage. This also enables clinical trial sponsors to use fewer patients in drug trials, which would lead to a reduction in the costs associated with clinical development.