Dose finding with drug combinations in cancer phase I clinical trials using conditional escalation with overdose control

Toxicity Adaptive Lists Design: A Practical Design for Phase I Drug Combination Trials in Oncology

PURPOSE

We introduce a novel algorithmic approach to design phase I trials for oncology drug combinations.

METHODS

Our proposed Toxicity Adaptive Lists Design (TALE) is straightforward to implement, requiring the prespecification of a small number of parameters that define rules governing dose escalation, de-escalation, or reassessment of previously explored dose levels. These rules effectively regulate dose exploration and control the number of toxicities. A key feature of TALE is the possibility of simultaneous assignment of multiple-dose combinations that are deemed safe by previously accrued data.

RESULTS

A numerical study shows that TALE shares comparable operative characteristics, in terms of identification of the maximum tolerated dose (MTD), to alternative approaches such as the Bayesian optimal interval design, the COPULA, the product of independent beta probabilities escalation, and the continual reassessment method for partial ordering designs while reducing the risk of overdosing patients.

CONCLUSION

The proposed TALE design provides a favorable balance between maintaining patient safety and accurately identifying the MTD. To facilitate the use of TALE, we provide a user-friendly R Shiny application and an R package for computing relevant operating characteristics, such as the risk of assigning highly toxic dose combinations.

Development of consensus-driven SPIRIT and CONSORT extensions for early phase dose-finding trials: the DEFINE study

BACKGROUND

Early phase dose-finding (EPDF) trials are crucial for the development of a new intervention and influence whether it should be investigated in further trials. Guidance exists for clinical trial protocols and completed trial reports in the SPIRIT and CONSORT guidelines, respectively. However, both guidelines and their extensions do not adequately address the characteristics of EPDF trials. Building on the SPIRIT and CONSORT checklists, the DEFINE study aims to develop international consensus-driven guidelines for EPDF trial protocols (SPIRIT-DEFINE) and reports (CONSORT-DEFINE).

METHODS

The initial generation of candidate items was informed by reviewing published EPDF trial reports. The early draft items were refined further through a review of the published and grey literature, analysis of real-world examples, citation and reference searches, and expert recommendations, followed by a two-round modified Delphi process. Patient and public involvement and engagement (PPIE) was pursued concurrently with the quantitative and thematic analysis of Delphi participants' feedback.

RESULTS

The Delphi survey included 79 new or modified SPIRIT-DEFINE (n = 36) and CONSORT-DEFINE (n = 43) extension candidate items. In Round One, 206 interdisciplinary stakeholders from 24 countries voted and 151 stakeholders voted in Round Two. Following Round One feedback, one item for CONSORT-DEFINE was added in Round Two. Of the 80 items, 60 met the threshold for inclusion (≥ 70% of respondents voted critical: 26 SPIRIT-DEFINE, 34 CONSORT-DEFINE), with the remaining 20 items to be further discussed at the consensus meeting. The parallel PPIE work resulted in the development of an EPDF lay summary toolkit consisting of a template with guidance notes and an exemplar.

CONCLUSIONS

By detailing the development journey of the DEFINE study and the decisions undertaken, we envision that this will enhance understanding and help researchers in the development of future guidelines. The SPIRIT-DEFINE and CONSORT-DEFINE guidelines will allow investigators to effectively address essential items that should be present in EPDF trial protocols and reports, thereby promoting transparency, comprehensiveness, and reproducibility.

TRIAL REGISTRATION

SPIRIT-DEFINE and CONSORT-DEFINE are registered with the EQUATOR Network ( https://www.equator-network.org/ ).

Modeling synergism in early phase cancer trials with drug combination with continuous dose levels: is there an added value?

In parametric Bayesian designs of early phase cancer clinical trials with drug combinations exploring a discrete set of partially ordered doses, several authors claimed that there is no added value in including an interaction term to model synergism between the two drugs. In this paper, we investigate these claims in the setting of continuous dose levels of the two agents. Parametric models will be used to describe the relationship between the doses of the two agents and the probability of dose limiting toxicity and efficacy. Trial design proceeds by treating cohorts of two patients simultaneously receiving different dose combinations and response adaptive randomization. We compare trial safety and efficiency of the estimated maximum tolerated dose (MTD) curve between models that include an interaction term with models without the synergism parameter with extensive simulations. Under a selected class of dose-toxicity models and dose escalation algorithm, we found that not including an interaction term in the model can compromise the safety of the trial and reduce the pointwise reliability of the estimated MTD curve.

Combining cytotoxic agents with continuous dose levels in seamless phase I-II clinical trials

Phase I-II cancer clinical trial designs are intended to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, it is common to divide the study in two stages: i) a first stage in which dose is escalated based only on toxicity data and we look for the maximum tolerated dose (MTD) set and ii) a second stage in which we search for the most efficacious dose within the MTD set. Current available approaches in the area of continuous dose levels involve fixing the MTD after stage I and discarding all collected stage I efficacy data. However, this methodology is clearly inefficient when there is a unique patient population present across stages. In this article, we propose a two-stage design for the combination of two cytotoxic agents assuming a single patient population across the entire study. In stage I, conditional escalation with overdose control (EWOC) is used to allocate successive cohorts of patients. In stage II, we employ an adaptive randomization approach to allocate patients to drug combinations along the estimated MTD curve, which is constantly updated. The proposed methodology is assessed with extensive simulations in the context of a real case study.

Accuracy and Safety of Novel Designs for Phase I Drug-Combination Oncology Trials*

Despite numerous innovative designs having been published for phase I drug-combination dose finding trials, their use in real applications is rather limited. As a working group under the American Statistical Association Biopharmaceutical Section, our goal is to identify the unique challenges associated with drug combination, share industry's experiences with combination trials, and investigate the pros and cons of the existing designs. Toward this goal, we review seven existing designs and distinguish them based on the criterion of whether their primary objectives are to find a single maximum tolerated dose (MTD) or the MTD contour (i.e., multiple MTDs). Numerical studies, based on either industry-specified fixed scenarios or randomly generated scenarios, are performed to assess their relative accuracy, safety, and ease of implementation. We show that the algorithm-based 3+3 design has poor performance and often fails to find the MTD. The performance of model-based combination trial designs is mixed: some demonstrate high accuracy of finding the MTD but poor safety, while others are safe but with compromised identification accuracy. In comparison, the model-assisted designs, such as BOIN and waterfall designs, have competitive and balanced performance in the accuracy of MTD identification and patient safety, and are also simple to implement, thus offering an attractive approach to designing phase I drug-combination trials. By taking into consideration the design's operating characteristics, ease of implementation and regulation, the need for advanced infrastructures, as well as the risk of regulatory acceptance, our paper offers practical guidance on the selection of a suitable dose-finding approach for designing future combination trials.

A nonparametric Bayesian method for dose finding in drug combinations cancer trials

We propose an adaptive design for early-phase drug-combination cancer trials with the goal of estimating the maximum tolerated dose (MTD). A nonparametric Bayesian model, using beta priors truncated to the set of partially ordered dose combinations, is used to describe the probability of dose limiting toxicity (DLT). Dose allocation between successive cohorts of patients is estimated using a modified continual reassessment scheme. The updated probabilities of DLT are calculated with a Gibbs sampler that employs a weighting mechanism to calibrate the influence of data vs the prior. At the end of the trial, we recommend one or more dose combinations as the MTD based on our proposed algorithm. We apply our method to a Phase I clinical trial of CB-839 and Gemcitabine that motivated this nonparametric design. The design operating characteristics indicate that our method is comparable with existing methods.

The Ci3+3 design for dual-agent combination dose-finding clinical trials

We propose a rule-based statistical design for combination dose-finding trials with two agents. The Ci3 + 3 design is an extension of the i3 + 3 design with simple up-and-down decision rules comparing the observed toxicity rates and equivalence intervals that define the maximum tolerated dose combination. Ci3 + 3 consists of two stages to allow fast and efficient exploration of the dose-combination space. Statistical inference is restricted to a beta-binomial model for dose evaluation, and the entire design is built upon a set of fixed rules. We show via simulation studies that the Ci3 + 3 design exhibits similar and comparable operating characteristics to more complex designs utilizing model-based inferences. Implementation of Ci3 + 3 for practical trials is simple for the first stage, where the up-and-down decisions may be carried out using a decision table based on the preselected escalation path and i3 + 3. The second stage is not simpler than model-based designs, however, since it also requires computation of posterior probabilities based on a Bayesian model. We believe that the Ci3 + 3 design may provide an alternative choice to help simplify the design and conduct of combination dose-finding trials in practice.

Adaptive Bayesian phase I clinical trial designs for estimating the maximum tolerated doses for two drugs while fully utilizing all toxicity information

The combined treatments with multiple drugs are very common in the contemporary medicine, especially for medical oncology. Therefore, we developed a Bayesian adaptive Phase I clinical trial design entitled escalation with overdoing control using normalized equivalent toxicity score for estimating maximum tolerated dose (MTD) contour of two drug combination (EWOC-NETS-COM) used for oncology trials. The normalized equivalent toxicity score (NETS) as the primary endpoint of clinical trial is assumed to follow quasi-Bernoulli distribution and treated as quasi-continuous random variable in the logistic linear regression model which is used to describe the relationship between the doses of the two agents and the toxicity response. Four parameters in the dose-toxicity model were re-parameterized to parameters with explicit clinical meanings to describe the association between NETS and doses of two agents. Noninformative priors were used and Markov chain Monte Carlo was employed to update the posteriors of the four parameters in dose-toxicity model. Extensive simulations were conducted to evaluate the safety, trial efficiency, and MTD estimation accuracy of EWOC-NETS-COM under different scenarios, using the EWOC as reference. The results demonstrated that EWOC-NETS-COM not only efficiently estimates MTD contour of multiple drugs but also provides better trial efficiency by fully utilizing all toxicity information.

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.

A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Ordinal Toxicity Grades

We propose a Bayesian adaptive design for early phase drug combination cancer trials incorporating ordinal grade of toxicities. Parametric models are used to describe the relationship between the dose combinations and the probabilities of the ordinal toxicities under the proportional odds assumption. Trial design proceeds by treating cohorts of two patients simultaneously receiving different dose combinations. Specifically, at each stage of the trial, we seek the dose of one agent by minimizing the Bayes risk with respect to a loss function given the current dose of the other agent. We consider two types of loss functions corresponding to the Continual Reassessment Method (CRM) and Escalation with Overdose Control (EWOC). At the end of the trial, we estimate the MTD curve as a function of Bayes estimates of the model parameters. We evaluate design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD by comparing this design to the one that uses a binary indicator of DLT. The methodology is further adapted to the case of a pre-specified discrete set of dose combinations.

A surface-free design for phase I dual-agent combination trials

In oncology, there is a growing number of therapies given in combination. Recently, several dose-finding designs for Phase I dose-escalation trials for combinations were proposed. The majority of novel designs use a pre-specified parametric model restricting the search of the target combination to a surface of a particular form. In this work, we propose a novel model-free design for combination studies, which is based on the assumption of monotonicity within each agent only. Specifically, we parametrise the ratios between each neighbouring combination by independent Beta distributions. As a result, the design does not require the specification of any particular parametric model or knowledge about increasing orderings of toxicity. We compare the performance of the proposed design to the model-based continual reassessment method for partial ordering and to another model-free alternative, the product of independent beta design. In an extensive simulation study, we show that the proposed design leads to comparable or better proportions of correct selections of the target combination while leading to the same or fewer average number of toxic responses in a trial.

Targeting the Versatile Wnt/β-Catenin Pathway in Cancer Biology and Therapeutics: From Concept to Actionable Strategy

This expert review offers a critical synthesis of the latest insights and approaches at targeting the Wnt/β-catenin pathway in various cancers such as colorectal cancer, melanoma, leukemia, and breast and lung cancers. Notably, from organogenesis to cancer, the Wnt/β-catenin signaling displays varied and highly versatile biological functions in animals, with virtually all tissues requiring the Wnt/β-catenin signaling in one way or the other. Aberrant expression of the members of the Wnt/β-catenin has been implicated in many pathological conditions, particularly in human cancers. Mutations in the Wnt/β-catenin pathway genes have been noted in diverse cancers. Biochemical and genetic data support the idea that inhibition of Wnt/β-catenin signaling is beneficial in cancer therapeutics. The interaction of this important pathway with other signaling systems is also noteworthy, but remains as an area for further research and discovery. In addition, formation of different complexes by components of the Wnt/β-catenin pathway and the precise roles of these complexes in the cytoplasmic milieu are yet to be fully elucidated. This article highlights the latest medical technologies in imaging, single-cell omics, use of artificial intelligence (e.g., machine learning techniques), genome sequencing, quantum computing, molecular docking, and computational softwares in modeling interactions between molecules and predicting protein-protein and compound-protein interactions pertinent to the biology and therapeutic value of the Wnt/β-catenin signaling pathway. We discuss these emerging technologies in relationship to what is currently needed to move from concept to actionable strategies in translating the Wnt/β-catenin laboratory discoveries to Wnt-targeted cancer therapies and diagnostics in the clinic.

AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual-agent dose finding trials

We propose a flexible design for the identification of optimal dose combinations in dual-agent dose finding clinical trials. The design is called AAA, standing for three adaptations: adaptive model selection, adaptive dose insertion and adaptive cohort division. The adaptations highlight the need and opportunity for innovation for dual-agent dose finding and are supported by the numerical results presented in the proposed simulation studies. To our knowledge, this is the first design that allows for all three adaptations at the same time. We find that AAA enhances the chance of finding the optimal dose combinations and shortens the trial duration. A clinical trial is being planned to apply the AAA design and a Web tool is being developed for both statisticians and non-statisticians.

Accelerating anticancer drug development - opportunities and trade-offs

The traditional approach to drug development in oncology, with discrete phases of clinical testing, is becoming untenable owing to expansion of the precision medicine paradigm, whereby patients are stratified into multiple subgroups according to the underlying cancer biology. Seamless approaches to drug development in oncology hold great promise of accelerating the accessibility of novel therapeutic agents to the public but are also accompanied by important trade-offs, including the limited availability of information on the clinical benefit and safety of novel agents at the time of market entry. In this Perspectives article, we describe several opportunities, in the form of novel trial designs or modelling strategies, to improve the efficiency of drug development in oncology, as well as new mechanisms to obtain information about anticancer therapies throughout their life cycle, such as innovative functional imaging techniques or the use of real-world clinical data.

Cancer phase I trial design using drug combinations when a fraction of dose limiting toxicities is attributable to one or more agents

Drug combination trials are increasingly common nowadays in clinical research. However, very few methods have been developed to consider toxicity attributions in the dose escalation process. We are motivated by a trial in which the clinician is able to identify certain toxicities that can be attributed to one of the agents. We present a Bayesian adaptive design in which toxicity attributions are modeled via copula regression and the maximum tolerated dose (MTD) curve is estimated as a function of model parameters. The dose escalation algorithm uses cohorts of two patients, following the continual reassessment method (CRM) scheme, where at each stage of the trial, we search for the dose of one agent given the current dose of the other agent. The performance of the design is studied by evaluating its operating characteristics when the underlying model is either correctly specified or misspecified. We show that this method can be extended to accommodate discrete dose combinations.

Two-stage design for phase I-II cancer clinical trials using continuous dose combinations of cytotoxic agents

We present a two-stage phase I/II design of a combination of two drugs in cancer clinical trials. The goal is to estimate safe dose combination regions with a desired level of efficacy. In stage I, conditional escalation with overdose control is used to allocate dose combinations to successive cohorts of patients and the maximum tolerated dose curve is estimated as a function of Bayes estimates of the model parameters. In stage II, we propose a Bayesian adaptive design for conducting the phase II trial to determine dose combination regions along the MTD curve with a desired level of efficacy. The methodology is evaluated by extensive simulations and application to a real trial.

A Bayesian Adaptive Design in Cancer Phase I Trials using Dose Combinations in the Presence of a Baseline Covariate

A Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility is described. The estimation of the maximum tolerated dose (MTD) curves is a function of a baseline covariate using two cytotoxic agents. A logistic model is used to describe the relationship between the doses, baseline covariate, and the probability of dose limiting toxicity (DLT). Trial design proceeds by treating cohorts of two patients simultaneously using escalation with overdose control (EWOC), where at each stage of the trial, the next dose combination corresponds to the α quantile of the current posterior distribution of the MTD of one of two agents at the current dose of the other agent and the next patient's baseline covariate value. The MTD curves are estimated as function of Bayes estimates of the model parameters at the end of trial. Average DLT, pointwise average bias, and percent of dose recommendation at dose combination neighborhoods around the true MTD are compared to the design that uses the covariate to the one that ignores the baseline characteristic. We also examine the performance of the approach under model misspecifications for the true dose-toxicity relationship. The methodology is further illustrated by the case of a pre-specified discrete set of dose combinations.

Early Phase Clinical Trial Designs - State of Play and Adapting for the Future

The process of anti-cancer drug development is complex, with high attrition rates. Factors that may optimise this process include well-constructed and relevant pre-clinical testing and use of biomarkers for patient selection. However, the design of early phase clinical trials will probably play a vital role in both the robust clinical investigation of new targeted therapies and in streamlining drug development. In this overview, we assess current concepts in phase I clinical trials, highlighting issues and opportunities to improve their meaningfulness. The particular challenge of how to design combination trials is addressed, with focus on the potential of new adaptive and model-based designs.

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

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

Identifying a maximum tolerated contour in two-dimensional dose finding

The majority of phase I methods for multi-agent trials have focused on identifying a single maximum tolerated dose combination (MTDC) among those being investigated. Some published methods in the area have been based on the notion that there is no unique MTDC and that the set of dose combinations with acceptable toxicity forms an equivalence contour in two dimensions. Therefore, it may be of interest to find multiple MTDCs for further testing for efficacy in a phase II setting. In this paper, we present a new dose-finding method that extends the continual reassessment method to account for the location of multiple MTDCs. Operating characteristics are demonstrated through simulation studies and are compared with existing methodology. Some brief discussion of implementation and available software is also provided. Copyright © 2016 John Wiley & Sons, Ltd.

Dose Finding for Drug Combination in Early Cancer Phase I Trials using Conditional Continual Reassessment Method

We describe a dose escalation algorithm for drug combinations in cancer phase I clinical trials. Parametric models for describing the association between the doses and the probability of dose limiting toxicity are used assuming univariate monotonicity of the dose-toxicity relationship. Trial design proceeds using the continual reassessment method, where at each stage of the trial, we seek the dose of one agent with estimated probability of toxicity closest to a target probability of toxicity given the current dose of the other agent. A Bayes estimate of the maximum tolerated dose (MTD) curve is proposed at the conclusion of the trial for continuous doses or a set of MTDs is determined in the case of discrete dose levels. We evaluate design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD under various model generated scenarios and misspecification. The method is further assessed for varying algorithms enrolling cohorts of two and three patients receiving different doses and compared to previous approaches such as escalation with overdose control and two-dimensional design.

A Bayesian adaptive design for estimating the maximum tolerated dose curve using drug combinations in cancer phase I clinical trials

We present a cancer phase I clinical trial design of a combination of two drugs with the goal of estimating the maximum tolerated dose curve in the two-dimensional Cartesian plane. A parametric model is used to describe the relationship between the doses of the two agents and the probability of dose limiting toxicity. The model is re-parameterized in terms of the probabilities of toxicities at dose combinations corresponding to the minimum and maximum doses available in the trial and the interaction parameter. Trial design proceeds using cohorts of two patients receiving doses according to univariate escalation with overdose control (EWOC), where at each stage of the trial, we seek a dose of one agent using the current posterior distribution of the MTD of this agent given the current dose of the other agent. The maximum tolerated dose curve is estimated as a function of Bayes estimates of the model parameters. Performance of the trial is studied by evaluating its design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD curve and under model misspecifications for the true dose-toxicity relationship. The method is further extended to accommodate discrete dose combinations and compared with previous approaches under several scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

A Bayesian Adaptive Design for Combination of Three Drugs in Cancer Phase I Clinical Trials

We describe a Bayesian adaptive design for early phase cancer trials of a combination of three agents. This is an extension of an earlier work by the authors by allowing all three agents to vary during the trial and by assigning different drug combinations to cohorts of three patients. The primary objective is to estimate the Maximum Tolerated Dose (MTD) surface in the three-dimensional Cartesian space. A class of linear models on the logit of the probability of Dose Limiting Toxicity (DLT) are used to describe the relationship between doses of the three drugs and the probability of DLT. Trial design proceeds using conditional escalation with overdose control, where at each stage of the trial, we seek a dose of one agent using the current posterior distribution of the MTD of this agent given the current doses of the other two agents. The MTD surface is estimated at the end of the trial as a function of Bayes estimates of the model parameters. Operating characteristics are evaluated with respect to trial safety and percent of dose recommendation at dose combination neighborhoods around the true MTD surface.