Implementing the EffTox dose-finding design in the Matchpoint trial

Accelerated-phase CML: de novo and transformed

Despite the dramatic improvements in outcomes for the majority of chronic myeloid leukemia (CML) patients over the past 2 decades, a similar improvement has not been observed in the more advanced stages of the disease. Blast phase CML (BP-CML), although infrequent, remains poorly understood and inadequately treated. Consequently, the key initial goal of therapy in a newly diagnosed patient with chronic phase CML continues to be prevention of disease progression. Advances in genomic investigation in CML, specifically related to BP-CML, clearly demonstrate we have only scratched the surface in our understanding of the disease biology, a prerequisite to devising more targeted and effective therapeutic approaches to prevention and treatment. Importantly, the introduction of the concept of "CML-like" acute lymphoblastic leukemia (ALL) has the potential to simplify the differentiation between BCR::ABL1-positive ALL from de novo lymphoid BP-CML, optimizing monitoring and therapeutics. The development of novel treatment strategies such as the MATCHPOINT approach for BP-CML, utilizing combination chemotherapy with fludarabine, cytarabine, and idarubicin in addition to dose-modified ponatinib, may also be an important step in improving treatment outcomes. However, identifying patients who are high risk of transformation remains a challenge, and the recent 2022 updates to the international guidelines may add further confusion to this area. Further work is required to clarify the identification and treatment strategy for the patients who require a more aggressive approach than standard chronic phase CML management.

Early phase clinical trials in oncology: Realising the potential of seamless designs

BACKGROUND

The pharmaceutical industry's productivity has been declining over the last two decades and high attrition rates and reduced regulatory approvals are being seen. The development of oncology drugs is particularly challenging with low rates of approval for novel treatments when compared with other therapeutic areas. Reliably establishing the potential of novel treatment and the corresponding optimal dosage is a key component to ensure efficient overall development. A growing interest lies in terminating developments of poor treatments quickly while enabling accelerated development for highly promising interventions.

METHODS

One approach to reliably establish the optimal dosage and the potential of a novel treatment and thereby improve efficiency in the drug development pathway is the use of novel statistical designs that make efficient use of the data collected.

RESULTS

In this paper, we discuss different (seamless) strategies for early oncology development and illustrate their strengths and weaknesses through real trial examples. We provide some directions for good practices in early oncology development, discuss frequently seen missed opportunities for improved efficiency and some future opportunities that have yet to fully develop their potential in early oncology treatment development.

DISCUSSION

Modern methods for dose-finding have the potential to shorten and improve dose-finding and only small changes to current approaches are required to realise this potential.

Adaptive Cohort Size Determination Method for Bayesian Optimal Interval Phase I/II Design to Shorten Clinical Trial Duration

PURPOSE

Recently, the strategy for dose optimization in oncology has shifted toward conducting phase II randomized controlled trials with multiple doses. Optimal biologic dose (OBD) selection from phase I trial data to determine candidate doses for phase II trials has been gaining attention. Trials to identify the OBD have a fixed cohort size, which increases the trial duration. We propose a method to increase the cohort size using trial data and shorten the trial duration while maintaining accuracy.

METHODS

We propose a novel adaptive cohort size determination method in which the increase of cohort size is determined using desirability probability on the basis of toxicity and efficacy data. The desirability probability is a measure of how desirable a dose is and thus how close it is to the OBD. However, during the trial, the desirability probability does not need to be calculated. Instead, the cohort size expansion can be determined by a simple table generated in advance from toxicity and efficacy data. An illustrated example is provided and the performance was evaluated in a simulation study with 16 scenarios.

RESULTS

In the simulation study, the trial duration was reduced by an average of 20% compared with the conventional design. The percentages of correct OBD selection are almost the same as those with the conventional design.

CONCLUSION

The proposed adaptive cohort size determination method described in this study reduces trial duration while maintaining accuracy.

Practical implementation of the partial ordering continual reassessment method in a Phase I combination-schedule dose-finding trial

There is a growing medical interest in combining several agents and optimizing their dosing schedules in a single trial in order to optimize the treatment for patients. Evaluating at doses of several drugs and their scheduling in a single Phase I trial simultaneously possess a number of statistical challenges, and specialized methods to tackle these have been proposed in the literature. However, the uptake of these methods is slow and implementation examples of such advanced methods are still sparse to date. In this work, we share our experience of proposing a model-based partial ordering continual reassessment method (POCRM) design for three-dimensional dose-finding in an oncology trial. In the trial, doses of two agents and the dosing schedule of one of them can be escalated/de-escalated. We provide a step-by-step summary on how the POCRM design was implemented and communicated to the trial team. We proposed an approach to specify toxicity orderings and their a-priori probabilities, and developed a number of visualization tools to communicate the statistical properties of the design. The design evaluation included both a comprehensive simulation study and considerations of the individual trial behavior. The study is now enrolling patients. We hope that sharing our experience of the successful implementation of an advanced design in practice that went through evaluations of several health authorities will facilitate a better uptake of more efficient methods in practice.

Mathematical Modelling for Optimal Vaccine Dose Finding: Maximising Efficacy and Minimising Toxicity

Vaccination is a key tool to reduce global disease burden. Vaccine dose can affect vaccine efficacy and toxicity. Given the expense of developing vaccines, optimising vaccine dose is essential. Mathematical modelling has been suggested as an approach for optimising vaccine dose by quantitatively establishing the relationships between dose and efficacy/toxicity. In this work, we performed simulation studies to assess the performance of modelling approaches in determining optimal dose. We found that the ability of modelling approaches to determine optimal dose improved with trial size, particularly for studies with at least 30 trial participants, and that, generally, using a peaking or a weighted model-averaging-based dose–efficacy relationship was most effective in finding optimal dose. Most methods of trial dose selection were similarly effective for the purpose of determining optimal dose; however, including modelling to adapt doses during a trial may lead to more trial participants receiving a more optimal dose. Clinical trial dosing around the predicted optimal dose, rather than only at the predicted optimal dose, may improve final dose selection. This work suggests modelling can be used effectively for vaccine dose finding, prompting potential practical applications of these methods in accelerating effective vaccine development and saving lives.

Ponatinib with fludarabine, cytarabine, idarubicin, and granulocyte colony-stimulating factor chemotherapy for patients with blast-phase chronic myeloid leukaemia (MATCHPOINT): a single-arm, multicentre, phase 1/2 trial

BACKGROUND

Outcomes for patients with blast-phase chronic myeloid leukaemia are poor. Long-term survival depends on reaching a second chronic phase, followed by allogeneic haematopoietic stem-cell transplantation (HSCT). We investigated whether the novel combination of the tyrosine-kinase inhibitor ponatinib with fludarabine, cytarabine, granulocyte colony-stimulating factor, and idarubicin (FLAG-IDA) could improve response and optimise allogeneic HSCT outcomes in patients with blast-phase chronic myeloid leukaemia. The aim was to identify a dose of ponatinib, which combined with FLAG-IDA, showed clinically meaningful activity and tolerability.

METHODS

MATCHPOINT was a seamless, phase 1/2, multicentre trial done in eight UK Trials Acceleration Programme-funded centres. Eligible participants were adults (aged ≥16 years) with Philadelphia chromosome-positive or BCR-ABL1-positive blast-phase chronic myeloid leukaemia, suitable for intensive chemotherapy. Participants received up to two cycles of ponatinib with FLAG-IDA. Experimental doses of oral ponatinib (given from day 1 to day 28 of FLAG-IDA) were between 15 mg alternate days and 45 mg once daily and the starting dose was 30 mg once daily. Intravenous fludarabine (30 mg/m2 for 5 days), cytarabine (2 g/m2 for 5 days), and idarubicin (8 mg/m2 for 3 days), and subcutaneous granulocyte colony-stimulating factor (if used), were delivered according to local protocols. We used an innovative EffTox design to investigate the activity and tolerability of ponatinib-FLAG-IDA; the primary endpoints were the optimal ponatinib dose meeting prespecified thresholds of activity (inducement of second chronic phase defined as either haematological or minor cytogenetic response) and tolerability (dose-limiting toxicties). Analyses were planned on an intention-to-treat basis. MATCHPOINT was registered as an International Standard Randomised Controlled Trial, ISRCTN98986889, and has completed recruitment; the final results are presented.

FINDINGS

Between March 19, 2015, and April 26, 2018, 17 patients (12 men, five women) were recruited, 16 of whom were evaluable for the coprimary outcomes. Median follow-up was 41 months (IQR 36-48). The EffTox model simultaneously considered clinical responses and dose-limiting toxicities, and determined the optimal ponatinib dose as 30 mg daily, combined with FLAG-IDA. 11 (69%) of 16 patients were in the second chronic phase after one cycle of treatment. Four (25%) patients had a dose-limiting toxicity (comprising cardiomyopathy and grade 4 increased alanine aminotransferase, cerebral venous sinus thrombosis, grade 3 increased amylase, and grade 4 increased alanine aminotransferase), fulfilling the criteria for clinically relevant activity and toxicity. 12 (71%) of 17 patients proceeded to allogeneic HSCT. The most common grade 3-4 non-haematological adverse events were lung infection (n=4 [24%]), fever (n=3 [18%]), and hypocalcaemia (n=3 [18%]). There were 12 serious adverse events in 11 (65%) patients. Three (18%) patients died due to treatment-related events (due to cardiomyopathy, pulmonary haemorrhage, and bone marrow aplasia).

INTERPRETATION

Ponatinib-FLAG-IDA can induce second chronic phase in patients with blast-phase chronic myeloid leukaemia, representing an active salvage therapy to bridge to allogeneic HSCT. The number of treatment-related deaths is not in excess of what would be expected in this very high-risk group of patients receiving intensive chemotherapy. The efficient EffTox method is a model for investigating novel therapies in ultra-orphan cancers.

FUNDING

Blood Cancer UK and Incyte.

Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic

BACKGROUND

Modern designs for dose-finding studies (e.g., model-based designs such as continual reassessment method) have been shown to substantially improve the ability to determine a suitable dose for efficacy testing when compared to traditional designs such as the 3 + 3 design. However, implementing such designs requires time and specialist knowledge.

METHODS

We present a practical approach to developing a model-based design to help support uptake of these methods; in particular, we lay out how to derive the necessary parameters and who should input, and when, to these decisions. Designing a model-based, dose-finding trial is demonstrated using a treatment within the AGILE platform trial, a phase I/II adaptive design for novel COVID-19 treatments.

RESULTS

We present discussion of the practical delivery of AGILE, covering what information was found to support principled decision making by the Safety Review Committee, and what could be contained within a statistical analysis plan. We also discuss additional challenges we encountered in the study and discuss more generally what (unplanned) adaptations may be acceptable (or not) in studies using model-based designs.

CONCLUSIONS

This example demonstrates both how to design and deliver an adaptive dose-finding trial in order to support uptake of these methods.

Operating characteristics are needed to properly evaluate the scientific validity of phase I protocols

PURPOSE

Operating characteristics for proposed clinical trial designs provide insight into performance regarding safety and accuracy, allowing the study team and review entities to determine the design's suitability to achieve the study's proposed objectives. Advances in cancer therapeutics have augmented the needs of early phase clinical trial design. Additionally, advances in research on early-phase trial design have led to the availability of a wide range of methods that show vast improvement over outdated approaches.

METHODS

Three trials utilizing variations of the 3 + 3 decision rule are discussed. The protocols lacked detail, including operating characteristics and guidance for decision-making that deviated from the 3 + 3 decision rule and MTD determination. We provide a discussion of the statistical issues associated with each design and operating characteristics for the proposed design compared to alternatives better suited to achieve the aims of each trial.

RESULTS

Our results illustrate how operating characteristics inform a design's safety and accuracy. Operating characteristics can unmask poor behavior, such as a high percentage of particiapnts exposed to overly toxic doses, a low probability of correctly identifying the MTD, and inappropriate early study termination.

CONCLUSION

Selection of early-phase trial design has significant implications on a trial's ability to meet its objectives. Operating characteristics are a necessary component in the design and review of a protocol, determining if the study's objectives can be achieved and documenting the study's scientific validity. Continued use of outdated approaches due to historical acceptance hinders scientific rigor and the effort to move effective agents through the drug development process.

Is more better? An analysis of toxicity and response outcomes from dose-finding clinical trials in cancer

BACKGROUND

The overwhelming majority of dose-escalation clinical trials use methods that seek a maximum tolerable dose, including rule-based methods like the 3+3, and model-based methods like CRM and EWOC. These methods assume that the incidences of efficacy and toxicity always increase as dose is increased. This assumption is widely accepted with cytotoxic therapies. In recent decades, however, the search for novel cancer treatments has broadened, increasingly focusing on inhibitors and antibodies. The rationale that higher doses are always associated with superior efficacy is less clear for these types of therapies.

METHODS

We extracted dose-level efficacy and toxicity outcomes from 115 manuscripts reporting dose-finding clinical trials in cancer between 2008 and 2014. We analysed the outcomes from each manuscript using flexible non-linear regression models to investigate the evidence supporting the monotonic efficacy and toxicity assumptions.

RESULTS

We found that the monotonic toxicity assumption was well-supported across most treatment classes and disease areas. In contrast, we found very little evidence supporting the monotonic efficacy assumption.

CONCLUSIONS

Our conclusion is that dose-escalation trials routinely use methods whose assumptions are violated by the outcomes observed. As a consequence, dose-finding trials risk recommending unjustifiably high doses that may be harmful to patients. We recommend that trialists consider experimental designs that allow toxicity and efficacy outcomes to jointly determine the doses given to patients and recommended for further study.

Moving Beyond 3+3: The Future of Clinical Trial Design

Misgivings have been raised about the operating characteristics of the canonical 3+3 dose-escalation phase I clinical trial design. Yet, the traditional 3+3 design is still the most commonly used. Although it has been implied that adhering to this design is due to a stubborn reluctance to adopt change despite other designs performing better in hypothetical computer-generated simulation models, the continued adherence to 3+3 dose-escalation phase I strategies is more likely because these designs perform the best in the real world, pinpointing the correct dose and important side effects with an acceptable degree of precision. Beyond statistical simulations, there are little data to refute the supposed shortcomings ascribed to the 3+3 method. Even so, to address the unique nuances of gene- and immune-targeted compounds, a variety of inventive phase 1 trial designs have been suggested. Strategies for developing these therapies have launched first-in-human studies devised to acquire a breadth of patient data that far exceed the size of a typical phase I design and blur the distinction between dose selection and efficacy evaluation. Recent phase I trials of promising cancer therapies assessed objective tumor response and durability at various doses and schedules as well as incorporated multiple expansion cohorts spanning a variety of histology or biomarker-defined tumor subtypes, sometimes resulting in U.S. Food and Drug Administration approval after phase I. This article reviews recent innovations in phase I design from the perspective of multiple stakeholders and provides recommendations for future trials.

Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.

Bayesian optimization design for dose-finding based on toxicity and efficacy outcomes in phase I/II clinical trials

In phase I trials, the main goal is to identify a maximum tolerated dose under an assumption of monotonicity in dose-response relationships. On the other hand, such monotonicity is no longer applied to biologic agents because a different mode of action from that of cytotoxic agents potentially draws unimodal or flat dose-efficacy curves. Therefore, biologic agents require an optimal dose that provides a sufficient efficacy rate under an acceptable toxicity rate instead of a maximum tolerated dose. Many trials incorporate both toxicity and efficacy data, and drugs with a variety of modes of actions are increasingly being developed; thus, optimal dose estimation designs have been receiving increased attention. Although numerous authors have introduced parametric model-based designs, it is not always appropriate to apply strong assumptions in dose-response relationships. We propose a new design based on a Bayesian optimization framework for identifying optimal doses for biologic agents in phase I/II trials. Our proposed design models dose-response relationships via nonparametric models utilizing a Gaussian process prior, and the uncertainty of estimates is considered in the dose selection process. We compared the operating characteristics of our proposed design against those of three other designs through simulation studies. These include an expansion of Bayesian optimal interval design, the parametric model-based EffTox design, and the isotonic design. In simulations, our proposed design performed well and provided results that were more stable than those from the other designs, in terms of the accuracy of optimal dose estimations and the percentage of correct recommendations.

A Bayesian phase 2 model based adaptive design to optimise antivenom dosing: Application to a dose-finding trial for a novel Russell's viper antivenom in Myanmar

For most antivenoms there is little information from clinical studies to infer the relationship between dose and efficacy or dose and toxicity. Antivenom dose-finding studies usually recruit too few patients (e.g. fewer than 20) relative to clinically significant event rates (e.g. 5%). Model based adaptive dose-finding studies make efficient use of accrued patient data by using information across dosing levels, and converge rapidly to the contextually defined 'optimal dose'. Adequate sample sizes for adaptive dose-finding trials can be determined by simulation. We propose a model based, Bayesian phase 2 type, adaptive clinical trial design for the characterisation of optimal initial antivenom doses in contexts where both efficacy and toxicity are measured as binary endpoints. This design is illustrated in the context of dose-finding for Daboia siamensis (Eastern Russell's viper) envenoming in Myanmar. The design formalises the optimal initial dose of antivenom as the dose closest to that giving a pre-specified desired efficacy, but resulting in less than a pre-specified maximum toxicity. For Daboia siamensis envenoming, efficacy is defined as the restoration of blood coagulability within six hours, and toxicity is defined as anaphylaxis. Comprehensive simulation studies compared the expected behaviour of the model based design to a simpler rule based design (a modified '3+3' design). The model based design can identify an optimal dose after fewer patients relative to the rule based design. Open source code for the simulations is made available in order to determine adequate sample sizes for future adaptive snakebite trials. Antivenom dose-finding trials would benefit from using standard model based adaptive designs. Dose-finding trials where rare events (e.g. 5% occurrence) are of clinical importance necessitate larger sample sizes than current practice. We will apply the model based design to determine a safe and efficacious dose for a novel lyophilised antivenom to treat Daboia siamensis envenoming in Myanmar.

Early phase dose-finding trials in virology

Little has been published in terms of dose-finding methodology in virology. Aside from a few papers focusing on HIV, the considerable progress in dose-finding methodology of the last 25 years has focused almost entirely on oncology. While adverse reactions to cytotoxic drugs may be life threatening, for anti-viral agents we anticipate something different: side effects that provoke the cessation of treatment. This would correspond to treatment failure. On the other hand, success would not be yes/no but would correspond to a range of responses, from small, no more than say 20% reduction in viral load to the complete elimination of the virus. Less than total success matters since this may allow the patient to achieve immune-mediated clearance. The motivation for this article is an upcoming dose-finding trial in chronic norovirus infection. We propose a novel methodology whose goal is twofold: first, to identify the dose that provides the most favorable distribution of treatment outcomes, and, second, to do this in a way that maximizes the treatment benefit for the patients included in the study.

A curve free Bayesian decision-theoretic design for phase Ia/Ib trials considering both safety and efficacy outcomes

A curve-free, Bayesian decision-theoretic two-stage design is proposed to select biological efficacious doses (BEDs) for phase Ia/Ib trials in which both toxicity and efficacy signals are observed. No parametric models are assumed to govern the dose-toxicity, dose-efficacy, and toxicity-efficacy relationships. We assume that the dose-toxicity curve is monotonic non-decreasing and the dose-efficacy curve is unimodal. In the phase Ia stage, a Bayesian model on the toxicity rates is used to locate the maximum tolerated dose. In the phase Ib stage, we model the dose-efficacy curve using a step function while continuing to monitor the toxicity rates. Furthermore, a measure of the goodness of fit of a candidate step function is proposed, and the interval of BEDs associated with the best fitting step function is recommended. At the end of phase Ib, if some doses are recommended as BEDs, a cohort of confirmation is recruited and assigned at these doses to improve the precision of estimates at these doses. Extensive simulation studies show that the proposed design has desirable operating characteristics across different shapes of the underlying true toxicity and efficacy curves.

How to design a dose-finding study using the continual reassessment method

INTRODUCTION

The continual reassessment method (CRM) is a model-based design for phase I trials, which aims to find the maximum tolerated dose (MTD) of a new therapy. The CRM has been shown to be more accurate in targeting the MTD than traditional rule-based approaches such as the 3 + 3 design, which is used in most phase I trials. Furthermore, the CRM has been shown to assign more trial participants at or close to the MTD than the 3 + 3 design. However, the CRM's uptake in clinical research has been incredibly slow, putting trial participants, drug development and patients at risk. Barriers to increasing the use of the CRM have been identified, most notably a lack of knowledge amongst clinicians and statisticians on how to apply new designs in practice. No recent tutorial, guidelines, or recommendations for clinicians on conducting dose-finding studies using the CRM are available. Furthermore, practical resources to support clinicians considering the CRM for their trials are scarce.

METHODS

To help overcome these barriers, we present a structured framework for designing a dose-finding study using the CRM. We give recommendations for key design parameters and advise on conducting pre-trial simulation work to tailor the design to a specific trial. We provide practical tools to support clinicians and statisticians, including software recommendations, and template text and tables that can be edited and inserted into a trial protocol. We also give guidance on how to conduct and report dose-finding studies using the CRM.

RESULTS

An initial set of design recommendations are provided to kick-start the design process. To complement these and the additional resources, we describe two published dose-finding trials that used the CRM. We discuss their designs, how they were conducted and analysed, and compare them to what would have happened under a 3 + 3 design.

CONCLUSIONS

The framework and resources we provide are aimed at clinicians and statisticians new to the CRM design. Provision of key resources in this contemporary guidance paper will hopefully improve the uptake of the CRM in phase I dose-finding trials.

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.

Challenges in implementing model-based phase I designs in a grant-funded clinical trials unit

BACKGROUND

For a clinical trials unit to run its first model-based, phase I trial, the statistician, chief investigator, and trial manager must all acquire a new set of skills. These trials also require a different approach to funding and data collection.

CHALLENGES AND DISCUSSION

From the statisticians' viewpoint, we highlight what is needed to move from running rule-based, early-phase trials to running a model-based phase I study as we experienced it in our trials unit located in the United Kingdom. Our example is CHARIOT, a dose-finding trial using the time-to-event continual reassessment method. It consists of three stages and aims to discover the maximum tolerated dose of the combination of radiotherapy, chemotherapy, and the ataxia telangiectasia mutated Rad3-related inhibitor M6620 (previously known as VX-970) in patients with oesophageal cancer. We present the challenges we faced in designing this trial and how we overcame them as a way of demystifying the conduct of a model-based trial in a grant-funded clinical trials unit.

CONCLUSIONS

Although we appreciate that undertaking model-based trials requires additional time and effort, they are feasible to implement and, once suitable tools such as guiding publications and document templates become available, the design and set-up process will be easier and more efficient.

Dose Transition Pathways: The Missing Link Between Complex Dose-Finding Designs and Simple Decision-Making

The ever-increasing pace of development of novel therapies mandates efficient methodologies for assessment of their tolerability and activity. Evidence increasingly support the merits of model-based dose-finding designs in identifying the recommended phase II dose compared with conventional rule-based designs such as the 3 + 3 but despite this, their use remains limited. Here, we propose a useful tool, dose transition pathways (DTP), which helps overcome several commonly faced practical and methodologic challenges in the implementation of model-based designs. DTP projects in advance the doses recommended by a model-based design for subsequent patients (stay, escalate, de-escalate, or stop early), using all the accumulated information. After specifying a model with favorable statistical properties, we utilize the DTP to fine-tune the model to tailor it to the trial's specific requirements that reflect important clinical judgments. In particular, it can help to determine how stringent the stopping rules should be if the investigated therapy is too toxic. Its use to design and implement a modified continual reassessment method is illustrated in an acute myeloid leukemia trial. DTP removes the fears of model-based designs as unknown, complex systems and can serve as a handbook, guiding decision-making for each dose update. In the illustrated trial, the seamless, clear transition for each dose recommendation aided the investigators' understanding of the design and facilitated decision-making to enable finer calibration of a tailored model. We advocate the use of the DTP as an integral procedure in the co-development and successful implementation of practical model-based designs by statisticians and investigators. Clin Cancer Res; 23(24); 7440-7. ©2017 AACR.