Adaptive randomized study of idarubicin and cytarabine versus troxacitabine and cytarabine versus troxacitabine and idarubicin in untreated patients 50 years or older with adverse karyotype acute myeloid leukemia

An 8-year pragmatic observation evaluation of the benefits of allogeneic HCT in older and medically infirm patients with AML

We designed a prospective, observational study enrolling patients presenting for treatment of acute myeloid leukemia (AML) at 13 institutions to analyze associations between hematopoietic cell transplantation (HCT) and survival, quality of life (QOL), and function in: the entire cohort, those aged ≥65 years, those with high comorbidity burden, intermediate cytogenetic risk, adverse cytogenetic risk, and first complete remission with or without measurable residual disease. Patient were assessed 8 times over 2 years. Time-dependent regression models were used. Among 692 patients that were evaluable, 46% received HCT with a 2-year survival of 58%. In unadjusted models, HCT was associated with reduced risks of mortality most of the subgroups. However, after accounting for covariates associated with increased mortality (age, comorbidity burden, disease risks, frailty, impaired QOL, depression, and impaired function), the associations between HCT and longer survival disappeared in most subgroups. Although function, social life, performance status, and depressive symptoms were better for those selected for HCT, these health advantages were lost after receiving HCT. Recipients and nonrecipients of HCT similarly ranked and expected cure as main goal of therapy, whereas physicians had greater expectations for cure than the former. Accounting for health impairments negates survival benefits from HCT for AML, suggesting that the unadjusted observed benefit is mostly owing to selection of the healthier candidates. Considering patients' overall expectations of cure but also the QOL burdens of HCT motivate the need for randomized trials to identify the best candidates for HCT. This trial was registered at www.clinicaltrials.gov as #NCT01929408.

A Bayesian adaptive randomized phase II multicenter trial of bevacizumab with or without vorinostat in adults with recurrent glioblastoma

BACKGROUND

Bevacizumab has promising activity against recurrent glioblastoma (GBM). However, acquired resistance to this agent results in tumor recurrence. We hypothesized that vorinostat, a histone deacetylase (HDAC) inhibitor with anti-angiogenic effects, would prevent acquired resistance to bevacizumab.

METHODS

This multicenter phase II trial used a Bayesian adaptive design to randomize patients with recurrent GBM to bevacizumab alone or bevacizumab plus vorinostat with the primary endpoint of progression-free survival (PFS) and secondary endpoints of overall survival (OS) and clinical outcomes assessment (MD Anderson Symptom Inventory Brain Tumor module [MDASI-BT]). Eligible patients were adults (≥18 y) with histologically confirmed GBM recurrent after prior radiation therapy, with adequate organ function, KPS ≥60, and no prior bevacizumab or HDAC inhibitors.

RESULTS

Ninety patients (bevacizumab + vorinostat: 49, bevacizumab: 41) were enrolled, of whom 74 were evaluable for PFS (bevacizumab + vorinostat: 44, bevacizumab: 30). Median PFS (3.7 vs 3.9 mo, P = 0.94, hazard ratio [HR] 0.63 [95% CI: 0.38, 1.06, P = 0.08]), median OS (7.8 vs 9.3 mo, P = 0.64, HR 0.93 [95% CI: 0.5, 1.6, P = 0.79]) and clinical benefit were similar between the 2 arms. Toxicity (grade ≥3) in 85 evaluable patients included hypertension (n = 37), neurological changes (n = 2), anorexia (n = 2), infections (n = 9), wound dehiscence (n = 2), deep vein thrombosis/pulmonary embolism (n = 2), and colonic perforation (n = 1).

CONCLUSIONS

Bevacizumab combined with vorinostat did not yield improvement in PFS or OS or clinical benefit compared with bevacizumab alone or a clinical benefit in adults with recurrent GBM. This trial is the first to test a Bayesian adaptive design with adaptive randomization and Bayesian continuous monitoring in patients with primary brain tumor and demonstrates the feasibility of using complex Bayesian adaptive design in a multicenter setting.

Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies.

Pragmatic, adaptive clinical trials: Is 2020 the dawning of a new age?

Given the high case fatality rate of SARS-CoV-2, for which there is no cure and no vaccine, clinicians are forced to make decisions about how best to manage patients with limited high-quality evidence to guide treatment. Traditional randomized controlled trials provide strong experimental evidence, however, tend to be slow, inflexible, and have limited generalizability. Adaptive and pragmatic designs are an attractive alternative, which meet our ethical obligation during the SARS-CoV-2 pandemic to balance speed, agility, and generalizability with both prospective study and scientific rigor.

The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal websites."To maximise the benefit to society, you need to not just do research but do it well" Douglas G Altman.

The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits.

Adaptive designs in clinical trials: why use them, and how to run and report them

Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.

A simulation study of outcome adaptive randomization in multi-arm clinical trials

Randomizing patients among treatments with equal probabilities in clinical trials is the established method to obtain unbiased comparisons. In recent years, motivated by ethical considerations, many authors have proposed outcome adaptive randomization, wherein the randomization probabilities are unbalanced, based on interim data, to favor treatment arms having more favorable outcomes. While there has been substantial controversy regarding the merits and flaws of adaptive versus equal randomization, there has not yet been a systematic simulation study in the multi-arm setting. A simulation study was conducted to evaluate four different Bayesian adaptive randomization methods and compare them to equal randomization in five-arm clinical trials. All adaptive randomization methods included an initial burn-in with equal randomization and some combination of other modifications to avoid extreme randomization probabilities. Trials either with or without a control arm were evaluated, using designs that may terminate arms early for futility and select one or more experimental treatments at the end. The designs were evaluated under a range of scenarios and sample sizes. For trials with a control arm and maximum same size 250 or 500, several commonly used adaptive randomization methods have very low probabilities of correctly selecting a truly superior treatment. Of those studied, the only adaptive randomization method with desirable properties has a burn-in with equal randomization and thereafter randomization probabilities restricted to the interval 0.10-0.90. Compared to equal randomization, this method has a favorable sample size imbalance but lower probability of correctly selecting a superior treatment. In multi-arm trials, compared to equal randomization, several commonly used adaptive randomization methods give much lower probabilities of selecting superior treatments. Aside from randomization method, conducting a multi-arm trial without a control arm may lead to very low probabilities of selecting any superior treatments if differences between the treatment success probabilities are small.

Response-adaptive clinical trials: case studies in the medical literature

The past 15 years has seen many pharmaceutical sponsors consider and implement adaptive designs (AD) across all phases of drug development. Given their arrival at the turn of the millennium, we might think that they are a recent invention. That is not the case. The earliest idea of an AD predates Bradford Hill's MRC tuberculosis study, appearing in Biometrika in 1933. In this paper, we trace the development of response-ADs, designs in which the allocation to intervention arms depends on the responses of subjects already treated. We describe some statistical details underlying the designs, but our main focus is to describe and comment on ADs from the medical research literature. Copyright © 2016 John Wiley & Sons, Ltd.

Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis

The Bayesian approach and several of its advantages in drug and medical device development are described. One advantage from the perspective of analysis is that it provides a methodology for synthesizing information. However, taking a Bayesian approach to designing clinical trials is potentially more valuable than using this approach in analyzing trial results. Bayesian methodology provides a mechanism for updating what is known as results accumulate during a trial. Such updating can be incorporated completely explicitly and prospectively. An important way in which the Bayesian approach can be used is in calculating the predictive probability distribution of future results on the basis of current results. I show how to exploit predictive distributions in adapting to results that accumulate during the course of a trial. Possible adaptations including decreasing or increasing sample size, dropping treatment arms, and modifying the randomization proportions to the various arms depending on the interim results. Consequences of taking a Bayesian approach to clinical trial design are efficiency, better treatment of patients in the trial, and greater precision regarding the primary endpoints. An example of the last of these is Bayesian modeling of the relationship between early and longer term endpoints. Such modeling also enables earlier decision making. Case studies 2 and 3 deal with trials that were shorter and smaller, respectively, because of such modeling.

Idle thoughts of a 'well-calibrated' Bayesian in clinical drug development

The use of Bayesian approaches in the regulated world of pharmaceutical drug development has not been without its difficulties or its critics. The recent Food and Drug Administration regulatory guidance on the use of Bayesian approaches in device submissions has mandated an investigation into the operating characteristics of Bayesian approaches and has suggested how to make adjustments in order that the proposed approaches are in a sense calibrated. In this paper, I present examples of frequentist calibration of Bayesian procedures and argue that we need not necessarily aim for perfect calibration but should be allowed to use procedures, which are well-calibrated, a position supported by the guidance.

A Unified Family of Covariate-Adjusted Response-Adaptive Designs Based on Efficiency and Ethics

Response-adaptive designs have recently attracted more and more attention in the literature because of its advantages in efficiency and medical ethics. To develop personalized medicine, covariate information plays an important role in both design and analysis of clinical trials. A challenge is how to incorporate covariate information in response-adaptive designs while considering issues of both efficiency and medical ethics. To address this problem, we propose a new and unified family of covariate-adjusted response-adaptive (CARA) designs based on two general measurements of efficiency and ethics. Important properties (including asymptotic properties) of the proposed procedures are studied under categorical covariates. This new family of designs not only introduces new desirable CARA designs, but also unifies several important designs in the literature. We demonstrate the proposed procedures through examples, simulations, and a discussion of related earlier work.

Are outcome-adaptive allocation trials ethical?

Randomization is firmly established as a cornerstone of clinical trial methodology. Yet, the ethics of randomization continues to generate controversy. The default, and most efficient, allocation scheme randomizes patients equally (1:1) across all arms of study. However, many randomized trials are using outcome-adaptive allocation schemes, which dynamically adjust the allocation ratio in favor of the better performing treatment arm. Advocates of outcome-adaptive allocation contend that it better accommodates clinical equipoise and promotes informed consent, since such trials limit patient-subject exposure to sub-optimal care. In this essay, we argue that this purported ethical advantage of outcome-adaptive allocation does not stand up to careful scrutiny in the setting of two-armed studies and/or early-phase research.

A comparison of Bayesian adaptive randomization and multi-stage designs for multi-arm clinical trials

When several experimental treatments are available for testing, multi-arm trials provide gains in efficiency over separate trials. Including interim analyses allows the investigator to effectively use the data gathered during the trial. Bayesian adaptive randomization (AR) and multi-arm multi-stage (MAMS) designs are two distinct methods that use patient outcomes to improve the efficiency and ethics of the trial. AR allocates a greater proportion of future patients to treatments that have performed well; MAMS designs use pre-specified stopping boundaries to determine whether experimental treatments should be dropped. There is little consensus on which method is more suitable for clinical trials, and so in this paper, we compare the two under several simulation scenarios and in the context of a real multi-arm phase II breast cancer trial. We compare the methods in terms of their efficiency and ethical properties. We also consider the practical problem of a delay between recruitment of patients and assessment of their treatment response. Both methods are more efficient and ethical than a multi-arm trial without interim analyses. Delay between recruitment and response assessment attenuates this efficiency gain. We also consider futility stopping rules for response adaptive trials that add efficiency when all treatments are ineffective. Our comparisons show that AR is more efficient than MAMS designs when there is an effective experimental treatment, whereas if none of the experimental treatments is effective, then MAMS designs slightly outperform AR.

Cloretazine for the treatment of acute myeloid leukemia

Cloretazine is a new sulfonylhydrazine alkylating agent with antileukemic activity. Phase I studies have shown myelosuppression to be the dose limiting toxicity in both solid tumors and leukemias. A large Phase II study of single agent cloretazine (600 mg/m2) confirmed its activity in patients with relapsed acute myeloid leukemia, and in elderly patients with previously untreated acute myeloid leukemia or myelodysplastic syndrome. It also confirmed the limited nonhematological toxicity, even in elderly patients. Cloretazine can be safely combined with cytarabine, and this combination regimen is currently being tested in a large Phase III study in patients with relapsed acute myeloid leukemia. Cloretazine is a promising new antileukemic agent that may be incorporated into an intensive combination regimen.

Troxacitabine in acute leukemia

Troxacitabine (Troxatyl; BCH-4556; (-)-2'-deoxy-3'-oxacytadine) is the first synthetic l-nucleoside enantiomer to demonstrate broad spectrum cytotoxic activity. It was obtained by exchanging the sulphur endocyclic atom with oxygen in the structure of lamivudine, following the discovery that this agent had cytotoxic, as well as anti-viral activity. The unique "unnatural" stereochemistry of troxacitabine has produced impressive cytotoxic potency against a wide range of malignancies in the laboratory which led to its selection for clinical development. The initial trials with troxacitabine have established its efficacy in both solid and haematological malignancies, including those resistant to ara-C (cytarabine). This review will consider troxacitabine in terms of its pharmacology, mode of action, pharmacokinetics, tolerability and clinical efficacy.

Modeling of experts' divergent prior beliefs for a sequential phase III clinical trial

BACKGROUND

There have been few Bayesian analyses of phase III sequential clinical trials that model divergent expert opinions in a single distribution.

PURPOSE

We used modeling of experts' opinions to perform additional Bayesian analyses of a randomized clinical trial (designed as a sequential trial), particularly when a bimodal shape is observed. We provide an illustrative example based on a randomized trial conducted in patients aged between 65 and 75 years with multiple myeloma as the case study.

METHODS

The main endpoint of the trial was overall survival (OS). Prior distribution of the log hazard ratio of death in the experimental versus the control arm ( $$\theta $$ ) was constructed based on elicitation of experts using a mixture of normal distributions estimated by the Expectation-Maximisation (EM) algorithm. At each interim and terminal analysis, the posterior probability of $$\theta $$ and the resulting increases in median OS in the experimental arm compared to the control were computed. The results were compared to results obtained using either skeptical, enthusiastic, or a mixture of those priors. Finally, we discuss our results in light of the frequentist approach originally designed for the trial.

RESULTS

A total of 39 experts reported their opinion on the median OS in the experimental arm compared to the median control survival of 30 months. The resulting pooled distribution of the log hazard ratios exhibited a bimodal profile. When the prior mixture of the normal distribution was fitted to the data sets from the experts, 44% of the experts' opinions were optimistic and 56% were doubtful. At the final analysis, the percentage of doubting experts dropped to 18%. This corresponded to a posterior probability of an improved OS in the experimental arm compared to the control arm of at least 0.98, regardless of the prior. These findings are in agreement with the original conclusion of the trial regarding the beneficial effect of the experimental treatment in this population.

LIMITATIONS

Only 39 experts among the 120 questioned physicians responded to the inquiry. Our approach was hybrid because the prior mixture was estimated using the EM algorithm, and a full Bayesian approach may have been used.

CONCLUSIONS

Bayesian inference allows the quantification of increased survival in terms of probability distributions and provides investigators with an additional tool in the analysis of a randomized phase III clinical trial. Using a mixture of densities appears to be a promising strategy for incorporating the bimodal profile of prior opinion, with actualization of the two components along the trial as an illustration of the evolution of opinions as data are accumulated.

Optimal Adaptive Design in Clinical Drug Development: A Simulation Example

The objective of this article is to demonstrate optimal adaptive design as a methodology for improving the performance of phase II dose-response studies. Optimal adaptive design uses both information prior to the study and data accrued during the study to continuously update and refine the study design. Dose-response models include linear, log-linear, 4-parameter sigmoidal E(max), and exponential models. Where the response has both a placebo effect and plateau at higher doses, only the 4-parameter sigmoidal E(max) model behaves acceptably and hence is used to illustrate the methodology. Across 13 hypothetical dose-response scenarios considered, it was shown that the capability of the adaptive designs to "learn" the true dose response resulted in performances up to 180% more efficient than the best fixed optimal designs. This work exposes the common misconception that adaptive designs are somehow "risky." As shown in this simple simulation example, the converse is true. Adaptive designs perform extremely well both when prior information is accurate and inaccurate. This leads to improved dose-response models and dose selection in phase III. This benefits sponsors, regulators, and subjects alike by reducing sample size, increasing information, and providing better dose guidance.

Paradigms for adaptive statistical information designs: practical experiences and strategies

In the last decade or so, interest in adaptive design clinical trials has gradually been directed towards their use in regulatory submissions by pharmaceutical drug sponsors to evaluate investigational new drugs. Methodological advances of adaptive designs are abundant in the statistical literature since the 1970s. The adaptive design paradigm has been enthusiastically perceived to increase the efficiency and to be more cost-effective than the fixed design paradigm for drug development. Much interest in adaptive designs is in those studies with two-stages, where stage 1 is exploratory and stage 2 depends upon stage 1 results, but where the data of both stages will be combined to yield statistical evidence for use as that of a pivotal registration trial. It was not until the recent release of the US Food and Drug Administration Draft Guidance for Industry on Adaptive Design Clinical Trials for Drugs and Biologics (2010) that the boundaries of flexibility for adaptive designs were specifically considered for regulatory purposes, including what are exploratory goals, and what are the goals of adequate and well-controlled (A&WC) trials (2002). The guidance carefully described these distinctions in an attempt to minimize the confusion between the goals of preliminary learning phases of drug development, which are inherently substantially uncertain, and the definitive inference-based phases of drug development. In this paper, in addition to discussing some aspects of adaptive designs in a confirmatory study setting, we underscore the value of adaptive designs when used in exploratory trials to improve planning of subsequent A&WC trials. One type of adaptation that is receiving attention is the re-estimation of the sample size during the course of the trial. We refer to this type of adaptation as an adaptive statistical information design. Specifically, a case example is used to illustrate how challenging it is to plan a confirmatory adaptive statistical information design. We highlight the substantial risk of planning the sample size for confirmatory trials when information is very uninformative and stipulate the advantages of adaptive statistical information designs for planning exploratory trials. Practical experiences and strategies as lessons learned from more recent adaptive design proposals will be discussed to pinpoint the improved utilities of adaptive design clinical trials and their potential to increase the chance of a successful drug development.

An adaptive randomized trial of an intermittent dosing schedule of aerosolized ribavirin in patients with cancer and respiratory syncytial virus infection

A continuous dosing schedule of aerosolized ribavirin has been used for respiratory syncytial virus (RSV) upper respiratory tract infection and lower respiratory tract infection (LRTI) but is associated with high cost and inconvenient administration. We conducted an adaptive randomized trial to evaluate the effectiveness of an intermittent dosing schedule of ribavirin versus that of a continuous dosing schedule of ribavirin in preventing RSV LRTIs in 50 hematopoietic stem cell transplant recipients or patients with hematologic malignancies. LRTI occurred in 3 patients (9%) receiving the intermittent schedule and in 4 (22%) receiving the continuous schedule, with a 0.889 posterior probability. Because the intermittent schedule is easy to administer and has a higher efficacy than the continuous schedule, we recommend the intermittent schedule for patients who are at risk for RSV LRTI. Clinical Trials Registration. NCT00500578.

Potential of adaptive clinical trial designs in pharmacogenetic research

Adaptive trial designs can be beneficial in pharmacogenetic research when prior uncertainty exists regarding the exact role and clinical relevance of genetic variability in drug response. This type of design enables us to learn about the effect of the genetic variability on drug response and to immediately use this information for the remainder of the study. For different types of adaptive trial designs, we discuss when and how the designs are suitable for pharmacogenetic research: adaptation of randomization, adaptation of patient enrollment and adaptive enrichment. To illustrate the potential benefits of an adaptive design over a fixed design, we simulated an adaptive trial based on the results of the IPASS trial. With a simple model we show that for this example an adaptive enrichment design would have led to a smaller trial, with less EGF receptor mutation-negative patients unnecessarily exposed to the drug, without compromising the α level or reducing power.

The methods of comparative effectiveness research

This review describes methods used in comparative effectiveness research (CER). The aim of CER is to improve decisions that affect medical care at the levels of both policy and the individual. The key elements of CER are (a) head-to-head comparisons of active treatments, (b) study populations typical of day-to-day clinical practice, and (c) a focus on evidence to inform care tailored to the characteristics of individual patients. These requirements will stress the principal methods of CER: observational research, randomized trials, and decision analysis. Observational studies are especially vulnerable because they use data that directly reflect the decisions made in usual practice. CER will challenge researchers and policy makers to think deeply about how to extract more actionable information from the vast enterprise of the daily practice of medicine. Fortunately, the methods are largely applicable to research in the public health system, which should therefore benefit from the intense interest in CER.

A point-of-care clinical trial comparing insulin administered using a sliding scale versus a weight-based regimen

Background Clinical trials are widely considered the gold standard in comparative effectiveness research (CER) but the high cost and complexity of traditional trials and concerns about generalizability to broad patient populations and general clinical practice limit their appeal. Unsuccessful implementation of CER results limits the value of even the highest quality trials. Planning for a trial comparing two standard strategies of insulin administration for hospitalized patients led us to develop a new method for a clinical trial designed to be embedded directly into the clinical care setting thereby lowering the cost, increasing the pragmatic nature of the overall trial, strengthening implementation, and creating an integrated environment of research-based care.

Purpose We describe a novel randomized clinical trial that uses the informatics and statistics infrastructure of the Veterans Affairs Healthcare System (VA) to illustrate one key component (called the point-of-care clinical trial – POC-CT) of a ‘learning healthcare system,’ and settles a clinical question of interest to the VA.

Methods This study is an open-label, randomized trial comparing sliding scale regular insulin to a weight-based regimen for control of hyperglycemia, using the primary outcome length of stay, in non-ICU inpatients within the northeast region of the VA. All non-ICU patients who require in-hospital insulin therapy are eligible for the trial, and the VA’s automated systems will be used to assess eligibility and present the possibility of randomization to the clinician at the point of care. Clinicians will indicate their approval for informed consent to be obtained by study staff. Adaptive randomization will assign up to 3000 patients, preferentially to the currently ‘winning’ strategy, and all care will proceed according to usual practices. Based on a Bayesian stopping rule, the study has acceptable frequentist operating characteristics (Type I error 6%, power 86%) against a 12% reduction of median length of stay from 5 to 4.4 days. The adaptive stopping rule promotes implementation of a successful treatment strategy.

Limitations Despite clinical equipoise, individual healthcare providers may have strong treatment preferences that jeopardize the success and implementation of the trial design, leading to low rates of randomization. Unblinded treatment assignment may bias results. In addition, generalization of clinical results to other healthcare systems may be limited by differences in patient population. Generalizability of the POC-CT method depends on the level of informatics and statistics infrastructure available to a healthcare system.

Conclusions The methods proposed will demonstrate outcome-based evaluation of control of hyperglycemia in hospitalized veterans. By institutionalizing a process of statistically sound and efficient learning, and by integrating that learning with automatic implementation of best practice, the participating VA Healthcare Systems will accelerate improvements in the effectiveness of care.

Do commonly used clinical trial designs reflect clinical reality?

This paper contends that commonly used clinical trial designs do not reflect clinical reality as viewed by patients or physicians. Specifically, randomized phase III designs focus on improvements that are more significant statistically than medically and put an emphasis on avoiding a false positive result that is more appropriate for diseases that are curable, in contrast to acute leukemias. The resultant large sample sizes needed for each treatment restrict the trial to one or two new treatments, although historical reality suggests the difficulty in knowing, without clinical data, whether these are the best of several new treatments. The p value-based statistics discourage use of data from previous patients in the trial to inform treatment of subsequent patients, contravening patients' assumptions. Standard phase II trials focus on a single outcome, ignoring the complexity of medical practice, and ignore prognostic heterogeneity. Finally, although patients are more interested in whether a new treatment is better than another, rather than whether it is active, randomization between different treatments does not begin until phase II trials have been completed. This paper proposes alternatives based on the Bayesian statistical approach. The thesis that I will develop here is that commonly used clinical trial designs are unrealistic in the sense that they do not correspond well to patients' views of medical practice and greatly over-simplify such practice. By emphasizing Bayesian rather than p value-based statistics and focusing on acute myeloid leukemia, I hope to familiarize physicians with some of the many new published designs that address these problems.

New nucleoside analogs for patients with hematological malignancies

INTRODUCTION

In the last few years, several new purine and pyrimidine nucleoside analogs have been synthesized and made available for both preclinical studies and clinical trials.

AREAS COVERED

This article summarizes recent achievements in the mechanism of action, pharmacological properties and clinical activity and toxicity as well as the emerging role of newer purine and pyrimidine nucleoside analogs potentially active in lymphoid and myeloid malignancies. A literature review was conducted from the MEDLINE database PubMed for articles in English. Publications from 2000 to October 2010 were scrutinized. The search terms used were clofarabine, nelarabine, forodesine, 8-chloroadenosine, LMP-420, azacitidine, decitabine, sapacitabine, troxacitabine, thiarabine and zebularine in conjunction with hematologic malignancies, leukemia and lymphoma. Conference proceedings from the previous 5 years of the American Society of Hematology, European Hematology Association, and American Society of Clinical Oncology were searched manually. Additional relevant publications were obtained by reviewing the references from the chosen articles.

EXPERT OPINION

Several new nucleoside analogs are currently under investigation in preclinical and clinical studies concerning hematological malignancies. Clofarabine, nelarabine, azacitidine and decitabine have been recently approved for the treatment of leukemias and/or myelodysplastic syndromes. Other agents including forodesine, 8-chloroadenosine, LMP-420, sapacitabine, troxacitabine, thiarabine and zebularine seem to be promising for the treatment of lymphoid and myeloid malignancies. However, definitive data from ongoing and future clinical trials will aid in better defining their status in the treatment of hematological disorders.

Outcome--adaptive randomization: is it useful?

Outcome-adaptive randomization is one of the possible elements of an adaptive trial design in which the ratio of patients randomly assigned to the experimental treatment arm versus the control treatment arm changes from 1:1 over time to randomly assigning a higher proportion of patients to the arm that is doing better. Outcome-adaptive randomization has intuitive appeal in that, on average, a higher proportion of patients will be treated on the better treatment arm (if there is one). In both the randomized phase II and phase III settings with a short-term binary outcome, we compare outcome-adaptive randomization with designs that use 1:1 and 2:1 fixed-ratio randomizations (in the latter, twice as many patients are randomly assigned to the experimental treatment arm). The comparisons are done in terms of required sample sizes, the numbers and proportions of patients having an inferior outcome, and we restrict attention to the situation in which one treatment arm is a control treatment (rather than the less common situation of two experimental treatments without a control treatment). With no differential patient accrual rates because of the trial design, we find no benefits to outcome-adaptive randomization over 1:1 randomization, and we recommend the latter. If it is thought that the patient accrual rates will be substantially higher because of the possibility of a higher proportion of patients being randomly assigned to the experimental treatment (because the trial will be more attractive to patients and clinicians), we recommend using a fixed 2:1 randomization instead of an outcome-adaptive randomization.

Phase II clinical trials in oncology: are we hitting the target?

The number of novel and molecularly targeted agents in the last decade that need screening for preliminary efficacy in Phase II trials has increased. Many of these agents have a cytostatic mode of action that is difficult to assess using traditional Phase II designs. These new agents require detailed evaluation to optimize their dosing, to evaluate their effects on their target and to define early markers that predict for a definitive benefit. This review focuses on the options for Phase II trial designs. The different end points, single versus multiarm and randomized designs, the use of biomarkers and Bayesian approaches are also reviewed. The final design chosen will depend on the characteristics and circumstances of each individual study.

Current and emerging therapies for acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is a clonal disease characterized by the proliferation and accumulation of myeloid progenitor cells in the bone marrow, which ultimately leads to hematopoietic failure. The incidence of AML increases with age, and older patients typically have worse treatment outcomes than do younger patients.

OBJECTIVE

This review is focused on current and emerging treatment strategies for nonpromyelocytic AML in patients aged <60 years.

METHODS

A literature review was conducted of the PubMed database for articles published in English. Publications from 1990 through March 2009 were scrutinized, and the search was updated on August 26, 2009. The search terms used were: acute myeloid leukemia in conjunction with treatment, chemotherapy, stem cell transplantation, and immunotherapy. Clinical trials including adults with AML aged > or =19 years were selected for analysis. Conference proceedings from the previous 5 years of The American Society of Hematology, The European Hematology Association, and The American Society for Blood and Marrow Transplantation were searched manually. Additional relevant publications were obtained by reviewing the references from the chosen articles.

RESULTS

Cytarabine (AraC) is the cornerstone of induction therapy and consolidation therapy for AML. A standard form of induction therapy consists of AraC (100-200 mg/m(2)), administered by a continuous infusion for 7 days, combined with an anthracycline, administered intravenously for 3 days. Consolidation therapy comprises treatment with additional courses of intensive chemotherapy after the patient has achieved a complete remission (CR), usually with higher doses of the same drugs as were used during the induction period. High-dose AraC (2-3 g/m(2)) is now a standard consolidation therapy for patients aged <60 years. Despite substantial progress in the treatment of newly diagnosed AML, 20% to 40% of patients do not achieve remission with the standard induction chemotherapy, and 50% to 70% of first CR patients are expected to relapse within 3 years. The optimum strategy at the time of relapse, or for patients with the resistant disease, remains uncertain. Allogeneic stem cell transplantation has been established as the most effective form of antileukemic therapy in patients with AML in first or subsequent remission. New drugs are being evaluated in clinical studies, including immunotoxins, monoclonal antibodies, nucleoside analogues, hypomethylating agents, farnesyltransferase inhibitors, alkylating agents, FMS-like tyrosine kinase 3 inhibitors, and multidrug-resistant modulators. However, determining the success of these treatment strategies ultimately requires well-designed clinical trials, based on stratification of the patient risk, knowledge of the individual disease, and the drug's performance status.

CONCLUSIONS

Combinations of AraC and anthracyclines are still the mainstay of induction therapy, and use of high-dose AraC is now a standard consolidation therapy in AML patients aged <60 years. Although several new agents have shown promise in treating AML, it is unlikely that these agents will be curative when administered as monotherapy; it is more likely that they will be used in combination with other new agents or with conventional therapy.

[Phases 0, 1 and 2 oncology clinical trials: current questions]

We propose here a general review of current questions related to early trials, including the choice of the primary endpoint, role of bayesian designs, role of stratification and randomization for phase 2 trials, patient selection, and new designs for phase 1 and phase 0 trials. We also discuss the difficulties to apply such methodologies to molecular targeted therapies development.

Bayesian statistics in oncology: a guide for the clinical investigator

The rise of evidence-based medicine as well as important progress in statistical methods and computational power have led to a second birth of the >200-year-old Bayesian framework. The use of Bayesian techniques, in particular in the design and interpretation of clinical trials, offers several substantial advantages over the classical statistical approach. First, in contrast to classical statistics, Bayesian analysis allows a direct statement regarding the probability that a treatment was beneficial. Second, Bayesian statistics allow the researcher to incorporate any prior information in the analysis of the experimental results. Third, Bayesian methods can efficiently handle complex statistical models, which are suited for advanced clinical trial designs. Finally, Bayesian statistics encourage a thorough consideration and presentation of the assumptions underlying an analysis, which enables the reader to fully appraise the authors' conclusions. Both Bayesian and classical statistics have their respective strengths and limitations and should be viewed as being complementary to each other; we do not attempt to make a head-to-head comparison, as this is beyond the scope of the present review. Rather, the objective of the present article is to provide a nonmathematical, reader-friendly overview of the current practice of Bayesian statistics coupled with numerous intuitive examples from the field of oncology. It is hoped that this educational review will be a useful resource to the oncologist and result in a better understanding of the scope, strengths, and limitations of the Bayesian approach.

Clinical trials and rare diseases

Whenever possible, standard methodological approaches should be applied in the design and analysis of a clinical trial that warrant adequate informative value. However, there are circumstances when the number of experimental subjects is unavoidably small. In such circumstances it is justified to consider abandoning standard statistical methodology in place of alternative approaches. Performing a small clinical trial however it should be pointed out, that a such trial can never be as meaningful and provide as much evidence as a larger trial. In the present text, basic concepts are presented, that apply to small clinical trials in general. Moreover, several specific methodological approaches are presented, that either enhance the efficiency of standard statistical procedures or evolve from the idea of abandoning classical paradigms in the design and analysis of clinical trials. Within the scope of the former approach, (Bayesian) adaptive randomisation, group sequential (adaptive) designs, repeated measurement designs for longitudinal data, and meta-analyses are illustrated and discussed. The latter approach comprises alternative strategies such as (non-randomised) risk-based allocation designs, statistical prediction designs, ranking and selection designs, as well as the application of Bayesian statistics.

Bayesian clinical trials at the University of Texas M. D. Anderson Cancer Center

BACKGROUND

The Bayesian approach is being used increasingly in medical research. In particular, it has become a standard in designing clinical trials at the University of Texas M. D. Anderson Cancer Center.

PURPOSE

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METHODS

To address the extent and nature of Bayesian trials conducted at M. D. Anderson, we reviewed the protocols registered in the Protocol Document Online System between 2000 and early 2005. We summarize our findings and give details for three innovative trials that typify those in which a Bayesian approach has played a major role at the center.

RESULTS

Of 964 protocols reviewed, 59% were conducted solely at M. D. Anderson and the rest were multicenter trials. Bayesian designs and analyses were used in about 20% (195/964) of the protocols that we reviewed. Of the 520 protocols identified as phase I or II drug trials, about 34% were Bayesian. Most of the 195 Bayesian trials were designed by M. D. Anderson statisticians. The Bayesian design features most commonly used were the continuous reassessment method in phase I (toxicity) trials, adaptive randomization in phase II trials, and designs to monitor efficacy and toxicity simultaneously. We also provide an insider's view regarding some practical considerations that have made the design and implementation of so many Bayesian trials possible.

LIMITATIONS

We reviewed only a subset of all M. D. Anderson protocols, but did not exclude any available in electronic form.

CONCLUSIONS

The large number of Bayesian trials conducted at M. D. Anderson testifies to the receptivity to the Bayesian approach within the center, including principal investigators, regulatory review committees, and patients. Statisticians who take a Bayesian perspective can successfully work to establish a culture of innovation in clinical trial design.

Bayesian adaptive non-inferiority with safety assessment: retrospective case study to highlight potential benefits and limitations of the approach

Adaptive trial design applied to randomized clinical trials of psychiatric medicines offers the potential to make clinical trials more efficient. In the current analysis, we retrospectively applied Bayesian adaptive allocation methods to a case study in agitated patients with schizophrenia and related diseases. The original study used a randomized, double-blind, parallel design. The objective of this analysis was to demonstrate the potential benefits of Bayesian adaptive designs by shortening the study duration and therefore limiting patient exposure to ineffective placebo or an active comparator with a known side effect. Bayesian methods allowed us to fully leverage historical data along with data observed as the study was ongoing to calculate predictive probabilities of patient response to treatment without experiencing a specified side effect. Using the Bayesian adaptive approach would have required less than half the number of patients as the original study to draw the same conclusion. Sample size was reduced from 311 to 156 patients, thereby decreasing the number of patients exposed to placebo from 54 to 30 and the number exposed to the active control with a known side effect from 126 to 60.

Emerging treatment strategies for acute myeloid leukemia (AML) in the elderly

Acute myeloid leukemia (AML) is more prevalent in older adults, with an incidence in the United States of 17.6 per 100,000 for those 65 years of age, compared with an incidence of 1.8 per 100,000 for those <65 years of age. While there have been improvements in survival during the last decade for younger patients, prognosis in elderly patients is still poor; approximately 50% achieve complete responses, but many of them relapse. With increasing age, more patients are suboptimal candidates for standard induction chemotherapy due to poor performance status, pre-existing myelodysplasia, unfavorable cytogenetics, treatment-related AML, multidrug resistance protein expression, and CD34 positivity, which are often characteristic of this patient population. In addition, the presence of comorbid conditions make many treatment options less tolerable for elderly patients. Several investigators have described subgroups showing no benefit after intensive treatment approaches in recent years. However, several novel agents have been developed to treat elderly AML patients. These include new chemotherapeutic agents, such as nucleoside analogs, as well as targeted therapies like farnesyltransferase inhibitors, tyrosine kinase inhibitors, epigenetic drugs, and antibodies. On the other hand new insights into the biology of the disease lead to a better understanding of its heterogeneity. Thus, with a variety of novel substances at hand it is increasingly important to introduce a risk-adapted approach for the optimal management of patients. This review will identify subgroups not likely to benefit from intensive chemotherapy and highlight the efficacy and tolerability of new agents in the treatment of AML.

Current and emerging strategies for the management of acute myeloid leukemia in the elderly

Acute myeloid leukemia (AML) accounts for approximately 80% of acute leukemias diagnosed in adults. The elderly are disproportionately affected by AML, as 35% of newly diagnosed patients are aged >or=75 and the median age at diagnosis is 67. Elderly individuals also respond less well to standard chemotherapy than do younger individuals, as reflected by lower complete remission and relapse-free survival rates in major clinical trials. A higher prevalence of comorbid conditions as well as the unique biological features of elderly AML patients account for the relatively poor response to therapy observed in this population. Compared with AML in younger individuals, for example, AML in the elderly more often emerges from a preceding myelodysplastic syndrome and is more frequently associated with poor-prognosis karyotypes such as 5q- or 7q-. The introduction of novel therapies over the past decade has already altered the treatment paradigm of elderly individuals with AML. The first of these to emerge was gemtuzumab ozogamicin. Other agents are currently under evaluation in clinical trials, including inhibitors of multidrug resistance, farnesyltransferase inhibitors, novel nucleoside analogues, and inhibitors of the FMS-like tyrosine kinase-3. This review describes the biological features of AML in the elderly and summarizes both the current and emerging strategies for the treatment of this disease in older individuals.

Potential role of novel nucleoside analogs in the treatment of acute myeloid leukemia

PURPOSE OF REVIEW

Nucleoside analogs remain a cornerstone in acute myeloid leukemia therapy. As many new nucleosides are being investigated in clinical trials, this review aims to update the current state of experience with these new compounds and where they may fit into treatment strategies for acute myeloid leukemia.

RECENT FINDINGS

Many new nucleoside analogs are emerging with novel metabolic properties and mechanisms of action. Some have entered clinical trials and are actively investigated in the context of acute myeloid leukemia therapy. Clofarabine is the most-developed compound, and single-agent experience and combinations with other active agents in acute myeloid leukemia are being explored. Troxacitabine and sapacitabine are still in single-agent phases of their development and clinical experience is accumulating quickly.

SUMMARY

Nucleosides remain the most important class of drugs in acute myeloid leukemia and the interest in new compounds is strong. The plethora of new analogs continues to provide ample opportunity to expand the effectiveness of these drugs in acute myeloid leukemia therapy. Furthermore, their unique mechanisms of action provide possibilities for mechanism-based combinations.

Design and conduct of phase II studies of targeted anticancer therapy: recommendations from the task force on methodology for the development of innovative cancer therapies (MDICT)

The Methodology for the Development of Innovative Cancer Therapies (MDICT) task force considered aspects of the design and conduct of phase II studies for molecular targeted agents during their 2007 meeting. The task force recommended that multinomial endpoints and designs should be considered for phase II studies of targeted agents, that both single arm as well as randomised designs remain appropriate in certain settings, and that further assessment of novel endpoints (tumour growth kinetic assessment, biomarker or functional imaging) and designs (randomised discontinuation or Bayesian adaptive design) be encouraged. The MDICT cautioned on the use of small randomised trials which have a number of statistical pitfalls and dangers and strongly encouraged the complete reporting, including negative trials, in the scientific literature.

Randomized phase II study of gemcitabine and docetaxel compared with gemcitabine alone in patients with metastatic soft tissue sarcomas: results of sarcoma alliance for research through collaboration study 002 [corrected]

PURPOSE

Gemcitabine as a single agent and the combination of gemcitabine and docetaxel have activity in patients with metastatic soft tissue sarcoma. To determine if the addition of docetaxel to gemcitabine improved clinical outcome of patients with metastatic soft tissue sarcomas, we compared a fixed dose rate infusion of gemcitabine versus a lower dose of gemcitabine with docetaxel.

PATIENTS AND METHODS

In this open-label phase II clinical trial, the primary end point was tumor response, defined as complete or partial response or stable disease lasting at least 24 weeks. A Bayesian adaptive randomization procedure was used to produce an imbalance in the randomization in favor of the superior treatment, accounting for treatment-subgroup interactions.

RESULTS

One hundred nineteen of 122 randomly assigned patients had assessable outcomes. The adaptive randomization assigned 73 patients (60%) to gemcitabine-docetaxel and 49 patients (40%) to gemcitabine alone, indicating gemcitabine-docetaxel was superior. The objective Response Evaluation Criteria in Solid Tumors response rates were 16% (gemcitabine-docetaxel) and 8% (gemcitabine). Given the data, the posterior probabilities that gemcitabine-docetaxel was superior for progression-free and overall survival were 0.98 and 0.97, respectively. Median progression-free survival was 6.2 months for gemcitabine-docetaxel and 3.0 months for gemcitabine alone; median overall survival was 17.9 months for gemcitabine-docetaxel and 11.5 months for gemcitabine. The posterior probability that patients receiving gemcitabine-docetaxel had a shorter time to discontinuation for toxicity compared with gemcitabine alone was .999.

CONCLUSION

Gemcitabine-docetaxel yielded superior progression-free and overall survival to gemcitabine alone, but with increased toxicity. Adaptive randomization is an effective method to reduce the number of patients receiving inferior therapy.

Troxacitabine in leukemia

Troxacitabine (Troxatyl; BCH-4556; (-)-2'-deoxy-3'-oxacytadine) is the first synthetic l-nucleoside enantiomer to demonstrate broad spectrum cytotoxic activity. It was obtained by exchanging the sulphur endocyclic atom with oxygen in the structure of lamivudine, following the discovery that this agent had cytotoxic, as well as anti-viral activity. The unique "unnatural" stereochemistry of troxacitabine has produced impressive cytotoxic potency against a wide range of malignancies in the laboratory which led to its selection for clinical development. The initial trials with troxacitabine have established its efficacy in both solid and haematological malignancies, including those resistant to ara-C (cytarabine). This review will consider troxacitabine in terms of its pharmacology, mode of action, pharmacokinetics, toxicities, and clinical efficacy.

Evaluation of thel-stereoisomeric nucleoside analog troxacitabine for the treatment of acute myeloid leukemia

Troxacitabine (BCH-4556; [-]-2'-deoxy-3'-oxacytidine) is a synthetic dioxolane that represents the first nucleoside analog with an L-isomer configuration to have shown important cytotoxic activity. Troxacitabine was obtained by exchanging the sulfur endocyclic atom with oxygen in the structure of lamivudine (3TC). Its unnatural stereochemistry renders it insensitive to some mechanisms of resistance of tumor cells to D-nucleosides, such as deamination by deoxycytidine deaminase and decreased active uptake by nucleoside transporters. These characteristics make troxacitabine a suitable alternative for patients with acute myelogenous leukemia as a potential way for overcoming resistance to ara-C therapy, which is the mainstay of acute myelogenous leukemia therapy at present. Clinically significant activity has been reported in Phase I studies in patients with advanced hematologic malignancies and has prompted troxacitabine to enter a series of Phase II trials in patients with refractory and relapsed acute myelogenous leukemia.

Cloretazine (VNP40101M), a Novel Sulfonylhydrazine Alkylating Agent, in Patients Age 60 Years or Older With Previously Untreated Acute Myeloid Leukemia

Purpose

Cloretazine (VNP40101M) is a sulfonylhydrazine alkylating agent with significant antileukemia activity. A multicenter phase II study of cloretazine was conducted in patients 60 years of age or older with previously untreated acute myeloid leukemia (AML) or high-risk myelodysplastic syndrome (MDS).

Patients and Methods

Cloretazine 600 mg/m2was administered as a single intravenous infusion. Patients were stratified by age, performance score, cytogenetic risk category, type of AML, and comorbidity.

Results

One hundred four patients, median age 72 years (range, 60 to 84 years), were treated on study. Performance status was 2 in 31 patients (30%) and no patient had a favorable karyotype. Forty-seven patients (45%) had cardiac disease, 25 patients (24%) had hepatic disease, and 19 patients (18%) had pulmonary disease, defined as per the Hematopoietic Cell Transplantation–Specific Comorbidity Index, at study entry. The overall response rate was 32%, with 29 patients (28%) achieving complete response (CR) and four patients (4%) achieving CR with incomplete platelet recovery. Response rates in 44 de novo AML patients, 45 secondary AML patients, and 15 high-risk MDS patients were 50%, 11%, and 40%, respectively. Response by cytogenetic risk category was 39% in 56 patients with intermediate cytogenetic risk and 24% in 46 patients with unfavorable cytogenetic risk. Nineteen (18%) patients died within 30 days of receiving cloretazine therapy. Median overall survival was 94 days, with a 1-year survival of 14%; the median duration of survival was 147 days, with a 1-year survival of 28% for those who achieved CR.

Conclusion

Cloretazine has significant activity and modest extramedullary toxicity in elderly patients with AML or high-risk MDS. Response rates remain consistent despite increasing age and comorbidity.