A Phase I Bayesian Adaptive Design to Simultaneously Optimize Dose and Schedule Assignments Both Between and Within Patients

Dose optimization for cancer treatments with considerations for late-onset toxicities

Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.

DICE: A Bayesian model for early dose finding in phase I trials with multiple treatment courses

Dose-finding clinical trials in oncology aim to determine the maximum tolerated dose (MTD) of a new drug, generally defined by the proportion of patients with short-term dose-limiting toxicities (DLTs). Model-based approaches for such phase I oncology trials have been widely designed and are mostly restricted to the DLTs occurring during the first cycle of treatment, although patients continue to receive treatment for multiple cycles. We aim to estimate the probability of DLTs over sequences of treatment cycles via a Bayesian cumulative modeling approach, where the probability of DLT is modeled taking into account the cumulative effect of the administered drug and the DLT cycle of occurrence. We propose a design, called DICE (Dose-fInding CumulativE), for dose escalation and de-escalation according to previously observed toxicities, which aims at finding the MTD sequence (MTS). We performed an extensive simulation study comparing this approach to the time-to-event continual reassessment method (TITE-CRM) and a benchmark. In general, our approach achieved a better or comparable percentage of correct MTS selection. Moreover, we investigated the DICE prediction ability.

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens

Most phase I trials in oncology aim to find the maximum tolerated dose (MTD) based on the occurrence of dose limiting toxicities (DLT). Evaluating the schedule of administration in addition to the dose may improve drug tolerance. Moreover, for some molecules, a bivariate toxicity endpoint may be more appropriate than a single endpoint. However, standard dose‐finding designs do not account for multiple dose regimens and bivariate toxicity endpoint within the same design. In this context, following a phase I motivating trial, we proposed modeling the first type of DLT, cytokine release syndrome, with the entire dose regimen using pharmacokinetics and pharmacodynamics (PK/PD), whereas the other DLT (DLT_o) was modeled with the cumulative dose. We developed three approaches to model the joint distribution of DLT, defining it as a bivariate binary outcome from the two toxicity types, under various assumptions about the correlation between toxicities: an independent model, a copula model and a conditional model. Our Bayesian approaches were developed to be applied at the end of the dose‐allocation stage of the trial, once all data, including PK/PD measurements, were available. The approaches were evaluated through an extensive simulation study that showed that they can improve the performance of selecting the true MTD‐regimen compared to the recommendation of the dose‐allocation method implemented. Our joint approaches can also predict the DLT probabilities of new dose regimens that were not tested in the study and could be investigated in further stages of the trial.

A Bayesian dose-finding design for outcomes evaluated with uncertainty

INTRODUCTION

In some phase I trial settings, there is uncertainty in assessing whether a given patient meets the criteria for dose-limiting toxicity.

METHODS

We present a design which accommodates dose-limiting toxicity outcomes that are assessed with uncertainty for some patients. Our approach could be utilized in many available phase I trial designs, but we focus on the continual reassessment method due to its popularity. We assume that for some patients, instead of the usual binary dose-limiting toxicity outcome, we observe a physician-assessed probability of dose-limiting toxicity specific to a given patient. Data augmentation is used to estimate the posterior probabilities of dose-limiting toxicity at each dose level based on both the fully observed and partially observed patient outcomes. A simulation study is used to assess the performance of the design relative to using the continual reassessment method on the true dose-limiting toxicity outcomes (available in simulation setting only) and relative to simple thresholding approaches.

RESULTS

Among the designs utilizing the partially observed outcomes, our proposed design has the best overall performance in terms of probability of selecting correct maximum tolerated dose and number of patients treated at the maximum tolerated dose.

CONCLUSION

Incorporating uncertainty in dose-limiting toxicity assessment can improve the performance of the continual reassessment method design.

Bayesian dose regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics

Phase I dose‐finding trials in oncology seek to find the maximum tolerated dose of a drug under a specific schedule. Evaluating drug schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose regimen assessment method (DRtox) using pharmacokinetics/pharmacodynamics (PK/PD) to estimate the maximum tolerated dose regimen (MTD‐regimen) at the end of the dose‐escalation stage of a trial. We modeled the binary toxicity via a PD endpoint and estimated the dose regimen toxicity relationship through the integration of a dose regimen PD model and a PD toxicity model. For the first model, we considered nonlinear mixed‐effects models, and for the second one, we proposed the following two Bayesian approaches: a logistic model and a hierarchical model. In an extensive simulation study, the DRtox outperformed traditional designs in terms of proportion of correctly selecting the MTD‐regimen. Moreover, the inclusion of PK/PD information helped provide more precise estimates for the entire dose regimen toxicity curve; therefore the DRtox may recommend alternative untested regimens for expansion cohorts. The DRtox was developed to be applied at the end of the dose‐escalation stage of an ongoing trial for patients with relapsed or refractory acute myeloid leukemia (NCT03594955) once all toxicity and PK/PD data are collected.

Population Pharmacodynamic Modeling of Epoetin Alfa in End-Stage Renal Disease Patients Receiving Maintenance Treatment Using Bayesian Approach

The ability to control dosage regimens of erythropoiesis-stimulating agents (ESAs) to maintain a desired hemoglobin (HGB) target is still elusive. We utilized a Bayesian approach and informative priors to characterize HGB profiles, using simulated drug concentrations, in patients with end-stage renal disease receiving maintenance doses of epoetin alfa. We also demonstrated an adaptive Bayesian method, applied to individual patients, to improve the accuracy of HGB predictions over time. The results showed that sparse HGB data from daily clinical practice were characterized successfully. The adaptive Bayesian method effectively improved the accuracy of HGB predictions by updating the individual model with new data accounting for within-subject changes over time. The Bayesian approach presented leverages existing knowledge of the model parameters and has a potential utility in clinical practice to individualize dosage regimens of epoetin alfa and ESAs to achieve target HGB. Further studies are warranted to develop an application for practical use.

A Bayesian time-to-event pharmacokinetic model for phase I dose-escalation trials with multiple schedules

Phase I dose-escalation trials must be guided by a safety model in order to avoid exposing patients to unacceptably high risk of toxicities. Traditionally, these trials are based on one type of schedule. In more recent practice, however, there is often a need to consider more than one schedule, which means that in addition to the dose itself, the schedule needs to be varied in the trial. Hence, the aim is finding an acceptable dose-schedule combination. However, most established methods for dose-escalation trials are designed to escalate the dose only and ad hoc choices must be made to adapt these to the more complicated setting of finding an acceptable dose-schedule combination. In this article, we introduce a Bayesian time-to-event model which takes explicitly the dose amount and schedule into account through the use of pharmacokinetic principles. The model uses a time-varying exposure measure to account for the risk of a dose-limiting toxicity over time. The dose-schedule decisions are informed by an escalation with overdose control criterion. The model is formulated using interpretable parameters which facilitates the specification of priors. In a simulation study, we compared the proposed method with an existing method. The simulation study demonstrates that the proposed method yields similar or better results compared with an existing method in terms of recommending acceptable dose-schedule combinations, yet reduces the number of patients enrolled in most of scenarios. The R and Stan code to implement the proposed method is publicly available from Github ( https://github.com/gunhanb/TITEPK_code).

An adaptive trial design to optimize dose-schedule regimes with delayed outcomes

This paper proposes a two-stage phase I-II clinical trial design to optimize dose-schedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decision-making is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design's performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.

Bayesian Adaptive Design for Finding the Maximum Tolerated Sequence of Doses in Multicycle Dose-Finding Clinical Trials

Purpose

Statistical designs for traditional phase I dose-finding trials consider dose-limiting toxicity in the first cycle of treatment. In reality, patients often go through multiple cycles of treatment and may experience toxicity events in more than one cycle. Therefore, it is desirable to identify the maximum tolerated sequence of three doses across three cycles of treatment.

Methods

Motivated by a three-cycle dose-finding clinical trial for a rare cancer with a JAK inhibitor, we proposed and implemented a simple Bayesian adaptive dose-cycle finding (BaSyc) design that allows intercycle and intrapatient dose modification. Because of the patient-specific dosing strategy over cycles, the BaSyc design is suited as a method in precision oncology.

Results

BaSyc is simple and transparent because its algorithm can be summarized as two tabulated decision rules before the trial starts, allowing physicians to visually examine these rules. In addition, BaSyc employs a time-saving enrollment scheme that speeds up the trial. Extensive simulation studies show that BaSyc has desirable operating characteristics in identifying the maximum tolerated sequence.

Conclusion

The BaSyc design provides a first-of-kind multicycle approach for dose finding and will likely lead to better and safer patient care and drug development.

A Bayesian adaptive phase I-II trial design for optimizing the schedule of therapeutic cancer vaccines

Phase I-II clinical trials refer to the class of designs that evaluate both the safety and efficacy of a novel therapeutic agent in a single trial. Typically, Phase I-II oncology trials take the form of dose-escalation studies, where initial subjects are treated at the lowest dose level and subsequent subjects are treated at progressively higher doses until the optimal dose is identified. While dose-escalation designs are well-motivated in the case of traditional chemotherapeutic agents, an alternate approach may be considered for therapeutic cancer vaccines, where an investigator's main objective is to evaluate the safety and efficacy of a set of dosing schedules or adjuvant combinations rather than to compare the safety and efficacy of progressively higher dose levels. We present a two-stage, Bayesian adaptive Phase I-II trial design to evaluate the safety and efficacy of therapeutic cancer vaccines. In the first stage, we determine whether a vaccination schedule achieves a minimum level of performance by comparing the toxicity and immune response rates to historical benchmarks. Vaccination schedules that achieve a minimum level of performance are compared using their magnitudes of immune response. If the superiority of a single schedule cannot be established after the first stage, Bayesian posterior predictive probabilities are used to determine the additional sample size required to identify the optimal vaccination schedule in a second stage. Copyright © 2016 John Wiley & Sons, Ltd.

Novel dose-finding designs and considerations on practical implementations in oncology clinical trials

One of the main objectives in phase I oncology trials is to evaluate safety and tolerability of an experimental treatment by estimating the maximum tolerated dose (MTD) based on the rate of dose-limiting toxicities (DLT). To meet emerging challenges in dose-finding studies, over the past two decades, extensive research has been conducted by statistical and medical researchers to create innovative dose finding designs that perform better than the standard 3 + 3 design, which often exhibits undesirable statistical and operational properties. However, clinical implementation and practical usage of these new designs have been limited. This article begins with a review of the most recent literature and then provides some perspectives on implementing novel adaptive dose finding designs in oncology phase I trials from a pharmaceutical industry perspective. Statistical planning and logistical considerations on how to effectively execute such designs in multi-center clinical trials are discussed using two recent case studies.

Adaptive Phase I clinical trial design using Markov models for conditional probability of toxicity

Many Phase I trials in oncology involve multiple-dose administrations on the same patient over multiple cycles, with a typical cycle lasting 3 weeks and having about six cycles per patient with a goal to find the maximum tolerated dose (MTD) and study the dose-toxicity relationship. A patient's dose is unchanged over the cycles and the data are reduced to a binary endpoint and the occurrence of a toxicity and analyzed by considering the toxicity either from the first dose or from any cycle on the study. In this article, an alternative approach allowing an assessment of toxicity from each cycle and dose variations for patient over cycles is presented. A Markov model for the conditional probability of toxicity on any cycle given no toxicity in previous cycles is formulated as a function of the current and previous doses. The extra information from each cycle provides more precise estimation of the dose-toxicity relationship. Simulation results demonstrating gains in using the Markov model as compared to analyses of a single binary outcome are presented. Methods for utilizing the Markov model to conduct a Phase I study, including choices for selecting doses for the next cycle for each patient, are developed and presented via simulation.

Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity

A phase I/II clinical trial design is proposed for adaptively and dynamically optimizing each patient's dose in each of two cycles of therapy based on the joint binary efficacy and toxicity outcomes in each cycle. A dose-outcome model is assumed that includes a Bayesian hierarchical latent variable structure to induce association among the outcomes and also facilitate posterior computation. Doses are chosen in each cycle based on posteriors of a model-based objective function, similar to a reinforcement learning or Q-learning function, defined in terms of numerical utilities of the joint outcomes in each cycle. For each patient, the procedure outputs a sequence of two actions, one for each cycle, with each action being the decision to either treat the patient at a chosen dose or not to treat. The cycle 2 action depends on the individual patient's cycle 1 dose and outcomes. In addition, decisions are based on posterior inference using other patients' data, and therefore the proposed method is adaptive both within and between patients. A simulation study of the method is presented, including comparison to two-cycle extensions of the conventional 3+3 algorithm, continual reassessment method, and a Bayesian model-based design, and evaluation of robustness.