Adaptive Dose Selection Using Efficacy-Toxicity Trade-Offs: Illustrations and Practical Considerations

The purpose of this paper is to describe and illustrate an outcome-adaptive Bayesian procedure, proposed by Thall and Cook (2004), for assigning doses of an experimental treatment to successive cohorts of patients. The method uses elicited (efficacy, toxicity) probability pairs to construct a family of trade-off contours that are used to quantify the desirability of each dose. This provides a basis for determining a best dose for each cohort. The method combines the goals of conventional Phase I and Phase II trials, and thus may be called a “Phase I-II” design. We first give a general review of the probability model and dose-finding algorithm. We next describe an application to a trial of a biologic agent for treatment of acute myelogenous leukemia, including a computer simulation study to assess the design's average behavior. To illustrate how the method may work in practice, we present a cohort-by-cohort example of a particular trial. We close with a discussion of some practical issues that may arise during implementation.     

Impact of Dose Selection Strategies Used in Phase II on the Probability of Success in Phase III

The purpose of this study was to assess the impact of phase II dose-selection strategies on the likelihood of success of phase III clinical programs, comparing both traditional and adaptive approaches.

We evaluated the impact of the phase II approach to dose selection (including traditional, design-adaptive, and analysis-adaptive approaches), the sample size used in phase II, the number of doses studied in phase II, and the number of doses selected to advance into phase III on the probability of demonstrating efficacy, of demonstrating a lack of toxicity, of phase III trial success, and on the probability of overall success of the combined phase II/phase III programs. The expected net present value was used to quantify the financial implications of different strategies.

We found that adaptive dose allocation approaches (in particular, the Bayesian general adaptive dose allocation method) usually outperformed other fixed dose allocation approaches with respect to both probability of success and dose selection. Design-adaptive approaches were more efficient than analysis-adaptive approaches. The allocation of additional resources into phase II improved the probability of success in phase III and the expected net present value. Bringing two doses forward into phase III testing also increased the probability of success and improved the expected net present value. The overall probability of success in phase III ranged from 35% to 65%, consistent with recent industry experience. This success rate could likely be improved with additional investment in phase II, the use of design-adaptive dose-finding designs when possible, increasing the power of phase III trials, more explicit consideration of toxicity concerns, and better dose selection.     

Adaptive dose selection using efficacy-toxicity trade-offs: illustrations and practical considerations

The purpose of this paper is to describe and illustrate an outcome-adaptive Bayesian procedure, proposed by Thall and Cook (2004), for assigning doses of an experimental treatment to successive cohorts of patients. The method uses elicited (efficacy, toxicity) probability pairs to construct a family of trade-off contours that are used to quantify the desirability of each dose. This provides a basis for determining a best dose for each cohort. The method combines the goals of conventional Phase I and Phase II trials, and thus may be called a "Phase I-II" design. We first give a general review of the probability model and dose-finding algorithm. We next describe an application to a trial of a biologic agent for treatment of acute myelogenous leukemia, including a computer simulation study to assess the design's average behavior. To illustrate how the method may work in practice, we present a cohort-by-cohort example of a particular trial. We close with a discussion of some practical issues that may arise during implementation.     

Adaptive Penalized D-Optimal Designs for Dose Finding Based on Continuous Efficacy and Toxicity

When a new drug is under development, a conventional dose-finding study involves learning about the dose–response curve in order to bring forward right doses of the drug to late-stage development. We propose an adaptive procedure for dose-finding in clinical trials in the presence of both efficacy and toxicity endpoints. We use the principles of optimal experimental designs for bivariate continuous endpoints.

However, instead of using the traditional D-optimal design, which favors collective ethics but neglects the individual ethics, we consider the penalized D-optimal design that achieves an appropriate balance between the efficient treatment of patients in the trial (by penalizing allocation of patients to ineffective or toxic doses) and the precise estimation of the model parameters to be used in the identification of the target dose. This is compared with the traditional fixed allocation design in terms of allocation of subjects and precision of the identified dose–response curve and selection of the target dose.     

A Bayesian adaptive design for cancer phase I trials using a flexible range of doses

We present a Bayesian adaptive design for dose finding in cancer phase I clinical trials. The goal is to estimate the maximum tolerated dose (MTD) after possible modification of the dose range during the trial. Parametric models are used to describe the relationship between the dose and the probability of dose-limiting toxicity (DLT). We investigate model reparameterization in terms of the probabilities of DLT at the minimum and maximum available doses at the start of the trial. Trial design proceeds using escalation with overdose control (EWOC), where at each stage of the trial we seek the dose of the agent such that the posterior probability of exceeding the MTD of this agent is bounded by a feasibility bound. At any time during the trial, we test whether the MTD is below or above the minimum and maximum doses, respectively. If during the trial there is evidence that the MTD is outside the range of doses, we extend the range of doses and complete the trial with the planned sample size. At the end of the trial, a Bayes estimate of the MTD is proposed. We evaluate design operating characteristics in terms of safety of the trial design and efficiency of the MTD estimate under various scenarios and model misspecification. The methodology is further compared to the original EWOC design. We showed by comprehensive simulation studies that the proposed method is safe and can estimate the MTD more efficiently than the original EWOC design.     

Dose-finding designs for trials of molecularly targeted agents and immunotherapies

ABSTRACT

Recently, there has been a surge of early phase trials of molecularly targeted agents (MTAs) and immunotherapies. These new therapies have different toxicity profiles compared to cytotoxic therapies. MTAs can benefit from new trial designs that allow inclusion of low-grade toxicities, late-onset toxicities, addition of an efficacy endpoint, and flexibility in the specification of a target toxicity probability. To study the degree of adoption of these methods, we conducted a Web of Science search of articles published between 2008 and 2014 that describe phase 1 oncology trials. Trials were categorized based on the dose-finding design used and the type of drug studied. Out of 1,712 dose-finding trials that met our criteria, 1,591 (92.9%) utilized a rule-based design, and 92 (5.4%; range 2.3% in 2009 to 9.7% in 2014) utilized a model-based or novel design. Over half of the trials tested an MTA or immunotherapy. Among the MTA and immunotherapy trials, 5.8% used model-based methods, compared to 3.9% and 8.3% of the chemotherapy or radiotherapy trials, respectively. While the percentage of trials using novel dose-finding designs has tripled since 2007, the adoption of these designs continues to remain low.     

复方药物临床试验设计的统计学考虑

复方药物的创新研发在制药行业逐渐普及,设计科学合理的复方新药临床试验是复方药物成功上市的关键。本文围绕新药注册流程,介绍了复方药物在剂量探索阶段和确证性阶段所涉及的临床试验设计类型,并详细介绍了在剂量探索阶段析因设计的应用和安慰剂在该阶段设计中的必要性。     

Response rates,duration of response,and dose response effects in phase I studies of antineoplastics

Summary Over a period of 14 years, 7,960 patients were treated in 228 phase I trials. In these patients, there were 75 complete and 432 partial responses for an overall objective response rate of 6%. Complete responses lasted a median of six months (range 1–18), while partial responses lasted a median of three months (range 1–17). Of note is that no drug has made it to the market which has not had a response in phase I trials. Responses were noted in very diverse histologic types of tumors. Although there were responses at doses which were as low as 3–5% of the recommended dose for phase II trials, the majority of responses did occur at 80–120% of the dose recommended for phase II trials. Although the response rate in phase I trials is indeed low, responses do occur. This response rate information should help the clinician provide facts for the patient considering a phase I trial with new anticancer agents. These findings also emphasize that although phase I trials are characteristically dose-finding studies, if no responses are noted in phase I studies, it is unlikely the drug will be used routinely in the clinic.     

Sequential Designs for Logistic Phase I Clinical Trials

Both parametric and nonparametric sequential designs and estimation methods are implemented in phase I clinical trials. In this article, we take a systematic approach, consisting of a start-up design, a follow-on design, a sequential dose-finding design, and an estimation method, to find an efficient estimate of the maximum tolerated dose under the assumption that the dose-response curve has a true underlying logistic distribution. In particular, for the problem of the nonexistence of the maximum likelihood estimates of the logistic parameters, a constraint on the probability of an undetermined maximum likelihood estimator (MLE) is incorporated into the parametric sequential designs. In addition, this approach can also be extended to incorporate ethical considerations, which prohibit an administered dose from exceeding the maximum acceptable dose. Comparison based on simulation studies between the systematic designs and nonparametric designs are described both for continuous dose spaces and discrete dose spaces, respectively.     

Sequential designs for logistic phase I clinical trials

Both parametric and nonparametric sequential designs and estimation methods are implemented in phase I clinical trials. In this article, we take a systematic approach, consisting of a start-up design, a follow-on design, a sequential dose-finding design, and an estimation method, to find an efficient estimate of the maximum tolerated dose under the assumption that the dose-response curve has a true underlying logistic distribution. In particular, for the problem of the nonexistence of the maximum likelihood estimates of the logistic parameters, a constraint on the probability of an undetermined maximum likelihood estimator (MLE) is incorporated into the parametric sequential designs. In addition, this approach can also be extended to incorporate ethical considerations, which prohibit an administered dose from exceeding the maximum acceptable dose. Comparison based on simulation studies between the systematic designs and nonparametric designs are described both for continuous dose spaces and discrete dose spaces, respectively.     

Phase II study of amonafide in gastric adenocarcinoma

Summary Twelve patients with recurrent, metastatic, or inoperable gastric adenocarcinoma were enrolled in an Illinois Cancer Center phase II trial of amonafide (nafidimide), a novel compound that acts as a DNA intercalator. Treatment consisted of a 60-minute infusion of amonafide which was administered daily for 5 consecutive days every 3 weeks at a starting dose of 300 mg/m²/d. Doses were modified according to the grade of toxicity experienced and eight patients underwent dose escalations. All 12 patients were evaluable for response and toxicities were predominantly hematologic. Stabilization of disease for at least 28 days was observed in seven patients and disease progression was noted in five. The median survival was 7.4 months. Doses were sufficient to produce severe bone marrow toxicity in one-third of the patients treated. None of the patients responded to therapy, implying a true response rate less than.221. Based on the results of this study, amonafide showed no activity against gastric adenocarcinoma; however toxicity appeared acceptable at the 300 mg/m₂/d x 5 consecutive days every 3 weeks dose and schedule.     

Dose Expansion Cohorts in Phase I Trials

A rapidly increasing number of Phase I dose-finding studies, those based on the standard 3+3 design in particular, are being prolonged with the inclusion of dose expansion cohorts (DEC) to better characterize the toxicity profiles of experimental agents and to study disease-specific cohorts. These trials consist of two phases: the usual dose escalation phase that aims to establish the maximum tolerated dose (MTD), and the dose expansion phase that accrues additional patients, often with different eligibility criteria, and where additional information is collected. Currently, not all protocols specify whether and how the MTD will be updated in the light of new data accumulated from the DEC. Here, we propose methods that allow monitoring of safety in the DEC by reevaluating the MTD in light of additional information. Our working assumption is that, regardless of the design being used for dose escalation, during the DEC we are experimenting in the neighborhood of a target dose with an acceptable rate of toxicity. We refine our initial estimate of the MTD by continuing experimentation in the immediate vicinity of the initial estimate of the MTD. The auxiliary information provided by such an evaluation will include toxicity, pharmacokinetic, efficacy, and other endpoints. We consider approaches specifically focused on the aims of DEC that examine efficacy alone or simultaneously with safety. Simulations provide further insight into the behavior of the proposed tests. Supplementary materials for this article are available online.     

Regulatory perspectives on multiplicity in adaptive design clinical trials throughout a drug development program

A clinical research program for drug development often consists of a sequence of clinical trials that may begin with uncontrolled and nonrandomized trials, followed by randomized trials or randomized controlled trials. Adaptive designs are not infrequently proposed for use. In the regulatory setting, the success of a drug development program can be defined to be that the experimental treatment at a specific dose level including regimen and frequency is approved based on replicated evidence from at least two confirmatory trials. In the early stage of clinical research, multiplicity issues are very broad. What is the maximum tolerable dose in an adaptive dose escalation trial? What should the dose range be to consider in an adaptive dose-ranging trial? What is the minimum effective dose in an adaptive dose-response study given the tolerability and the toxicity observable in short term or premarketing trials? Is establishing the dose-response relationship important or the ability to select a superior treatment with high probability more important? In the later stage of clinical research, multiplicity problems can be formulated with better focus, depending on whether the study is for exploration to estimate or select design elements or for labeling consideration. What is the study objective for an early-phase versus a later phase adaptive clinical trial? How many doses are to be studied in the early exploratory adaptive trial versus in the confirmatory adaptive trial? Is the intended patient population well defined or is the applicable patient population yet to be adaptively selected in the trial due to the potential patient and/or disease heterogeneity? Is the primary efficacy endpoint well defined or still under discussion providing room for adaptation? What are the potential treatment indications that may adaptively lead to an intended-to-treat patient population and the primary efficacy endpoint? In this work we stipulate the multiplicity issues with adaptive designs encountered in regulatory applications. For confirmatory adaptive design clinical trials, controlling studywise type I error and type II error is of paramount importance. For exploratory adaptive trials, we define the probability of correct selection of design features, e.g., dose, effect size, and the probability of correct decision for drug development. We assert that maximizing these probabilities would be critical to determine whether the drug development program continues or how to plan the confirmatory trials if the development continues.     

Phase II study of fludarabine phosphate for gastric adenocarcinoma

Summary In an Illinois Cancer Center phase II trial, fludarabine phosphate was administered to a total of 14 patients (9 men, 5 women) with advanced, measurable, gastric adenocarcinoma. Fludarabine phosphate was given as a rapid intravenous (IV) bolus at a starting dose of 20 mg/m²/d for the first 5 days of a 28-day cycle. For subsequent cycles, the dose was escalated in increments of 2 mg/m²/d, provided that no toxicities greater than grade 1 were noted. In cases of grade 3 toxicity, dose reductions of 2 mg/m²/d were required, and patients who experienced grade 4 toxicities were removed from study. Receiving one complete 5-day course of fludarabine phosphate and surviving for 4 weeks on study were required for a patient to be evaluable for response. None of the patients responded to treatment. Although fludarabine phosphate was ineffective against gastric adenocarcinoma in this study, toxicity was acceptable at the 20 mg/m²/d times 5 every 28 days dose and schedule.     

An adaptive multi-stage phase I dose-finding design incorporating continuous efficacy and toxicity data from multiple treatment cycles

Phase I designs traditionally use the dose-limiting toxicity (DLT), a binary endpoint from the first treatment cycle, to identify the maximum-tolerated dose (MTD) assuming a monotonically increasing relationship between dose and efficacy. In this article, we establish a general framework for a multi-stage adaptive design where we jointly model a continuous efficacy outcome and continuous/quasi-continuous toxicity endpoints from multiple treatment cycles. The normalized Total Toxicity Profile (nTTP) is used as an illustration for quasi-continuous toxicity endpoints, and we replace DLT with nTTP to take into account multiple grades and types of toxicities. In addition, the proposed design accommodates non-monotone dose-efficacy relationships, and longitudinal toxicity data in effort to capture the adverse events from multiple cycles. Stage 1 of our design uses toxicity data to perform dose-escalation and identify a set of initially allowable (safe) doses; stage 2 of our design incorporates an efficacy outcome to update the set of allowable doses for each new cohort and randomizes the new cohort of patients to the allowable doses with emphasis towards those with higher predicted efficacy. Stage 3 uses all data from all treated patients at the end of the trial to make final recommendations. Simulations showed that the design had a high probability of making the correct dose selection and good overdose control across various dose-efficacy and dose-toxicity scenarios. In addition, the proposed design allows for early termination when all doses are too toxic. To our best knowledge, the proposed dual-endpoint dose-finding design is the first such study to incorporate multiple cycles of toxicities and a continuous efficacy outcome.     

Single-Arm Phase II Group Sequential Trial Design With Survival Endpoint at a Fixed Time Point

In this article, three nonparametric test statistics are proposed to design single-arm phase II group sequential trials for monitoring survival probability. The small-sample properties of these test statistics are studied through simulations. Sample size formulas are derived for the fixed sample test. The Brownian motion property of the test statistics allowed us to develop a flexible group sequential design using a sequential conditional probability ratio test procedure. An example is given to illustrate the trial design by using the proposed method.     

Use of double-blind placebo-controlled N-of-1 trials among stimulant-treated youths in The Netherlands: a descriptive study

OBJECTIVES: An N-of-1 trial is a double-blind placebo-controlled randomized trial to objectively and systematically evaluate the individual's response. This approach seems extraordinarily suitable for assessing the efficacy of stimulants in the treatment of attention deficit hyperactivity disorder (ADHD). The aim is to examine the use of N-of-1 trials among youths in the Netherlands, the protocols used, and the continuation of stimulant treatment thereafter. METHODS: Physicians requesting N-of-1 trials with stimulants were interviewed about their rationale and protocol. Prevalence and continuation were investigated by extracting N-of-1 trials among youths <20 years of age from a large pharmacy dispensing database for 2000-2004. RESULTS: The main purpose of N-of-1 trials mentioned by physicians was the assessing of individuals' response and dose-finding. Trial length, dosing schedule and efficacy assessment differed per physician. Trials consisted of a maximum of two treatment periods per dose. The annual percentage of youths starting stimulant treatment with an N-of-1 trial fluctuated between 0.6% (3/462) and 3.3% (10/301). No statistical significant difference could be detected between the continuation of stimulant treatment with or without an N-of-1 trial (p = 0.71). CONCLUSIONS: N-of-1 trials with stimulants are infrequently and not optimally used in the Netherlands. The results of N-of-1 protocols described by physicians are of questionable value, due to the small number of treatment periods per dose. More uniformity in the protocols would make it easier to encompass the N-of-1 methodology in physicians' daily practice.     

Randomized dose-escalation designs for drug combination cancer trials with immunotherapy

This work considers Phase I cancer dual-agent dose-escalation clinical trials in which one of the compounds is an immunotherapy. The distinguishing feature of trials considered is that the dose of one agent, referred to as a standard of care, is fixed and another agent is dose-escalated. Conventionally, the goal of a Phase I trial is to find the maximum tolerated combination (MTC). However, in trials involving an immunotherapy, it is also essential to test whether a difference in toxicities associated with the MTC and the standard of care alone is present. This information can give useful insights about the interaction of the compounds and can provide a quantification of the additional toxicity burden and therapeutic index. We show that both, testing for difference between toxicity risks and selecting MTC can be achieved using a Bayesian model-based dose-escalation design with two modifications. Firstly, the standard of care administrated alone is included in the trial as a control arm and each patient is randomized between the control arm and one of the combinations selected by a model-based design. Secondly, a flexible model is used to allow for toxicities at the MTC and the control arm to be modeled directly. We compare the performance of two-parameter and four-parameter logistic models with and without randomization to a current standard of such trials: a one-parameter model. It is found that at the cost of a small reduction in the proportion of correct selections in some scenarios, randomization provides a significant improvement in the ability to test for a difference in the toxicity risks. It also allows a better fitting of the combination-toxicity curve that leads to more reliable recommendations of the combination(s) to be studied in subsequent phases.     

A phase I and pharmacokinetic study of intravenous vinzolidine

The semi-synthetic vinca alkaloid vinzolidine was administered to advanced cancer patients as an intravenous bolus on a three day schedule every 21 days. Forty-two patients were treated in this phase I trial. Five partial remissions (breast-1, melanoma-2, renal cancer-2) were seen in 30 evaluable patients. The dose limiting toxicities were myelosuppression and neuropathy. Erratic myelosuppression from course to course within the same patient as seen in previous trials with oral vinzolidine, was not observed with the intravenous formulation. The measured pharmacokinetic parameters conformed best to a 2-compartment model with a mean terminal half-life of 23 hours. The anti-tumor activity observed during this phase I trial and acceptable toxicity provide the basis for initiating phase II studies in selected forms of cancer.Dr. Taylor is the recipient of a Clinical Oncology Career Development Award from the American Cancer Society.     

Bayesian Continual Reassessment Method for Dose-Finding Trials Infusing T Cells with Limited Sample Size

We consider the design of dose-finding trials for patients with malignancies when only a limited sample size is available. The small sample size may be necessary because (1) the modality of treatment is very expensive, and/or (2) the disease under investigation is rare, requiring a lengthy period to enroll a target patient population. Both of these are common in the field of adoptive immunotherapy, in which T cells are infused to prevent and treat infections and malignancies. The clinical trial described in this paper investigates a novel therapy to adoptively transfer genetically modified T cells in small pilot protocols enrolling patients with B-lineage malignancies. Due to the constraints of cost and infrastructure, the maximum sample size for this trial is fixed at 12 patients distributed among four doses of T cells. Given these limitations, an innovative statistical design has been developed to efficiently evaluate the safety, feasibility, persistence, and toxicity profiles of the trial doses. The proposed statistical design is specifically tailored for trials with small sample sizes in that it uses the toxicity outcomes from patients treated at different doses to make dose-finding decisions. Supplementary materials including an R function and a movie demo can be downloaded in the websites mentioned in the paper.