A Bayesian Dose-finding Design for Oncology Clinical Trials of Combinational Biological Agents

A Bayesian phase I-II clinical trial design to find the biological optimal dose on drug combination

In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I-II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose-response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well.

Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies

Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.

BOIN-ETC: A Bayesian optimal interval design considering efficacy and toxicity to identify the optimal dose combinations

One of the primary objectives of a dose-finding trial for novel anti-cancer agent combination therapies, such as molecular targeted agents and immune-oncology therapies, is to identify optimal dose combinations that are tolerable and therapeutically beneficial for subjects in subsequent clinical trials. The goal differs from that of a dose-finding trial for traditional cytotoxic agents, in which the goal is to determine the maximum tolerated dose combinations. This paper proposes the new design, named 'BOIN-ETC' design, to identify optimal dose combinations based on both efficacy and toxicity outcomes using the waterfall approach. The BOIN-ETC design is model-assisted, so it is expected to be robust, and straightforward to implement in actual oncology dose-finding trials. These characteristics are quite valuable from a practical perspective. Simulation studies show that the BOIN-ETC design has advantages compared with the other approaches in the percentage of correct optimal dose combination selection and the average number of patients allocated to the optimal dose combinations across various realistic settings.

Local continual reassessment methods for dose finding and optimization in drug-combination trials

Due to the limited sample size and large dose exploration space, obtaining a desirable dose combination is a challenging task in the early development of combination treatments for cancer patients. Most existing designs for optimizing the dose combination are model-based, requiring significant efforts to elicit parameters or prior distributions. Model-based designs also rely on intensive model calibration and may yield unstable performance in the case of model misspecification or sparse data. We propose to employ local, underparameterized models for dose exploration to reduce the hurdle of model calibration and enhance the design robustness. Building upon the framework of the partial ordering continual reassessment method, we develop local data-based continual reassessment method designs for identifying the maximum tolerated dose combination, using toxicity only, and the optimal biological dose combination, using both toxicity and efficacy, respectively. The local data-based continual reassessment method designs only model the local data from neighboring dose combinations. Therefore, they are flexible in estimating the local space and circumventing unstable characterization of the entire dose-exploration surface. Our simulation studies show that our approach has competitive performance compared to widely used methods for finding maximum tolerated dose combination, and it has advantages over existing model-based methods for optimizing optimal biological dose combination.

A Bayesian adaptive design for dual-agent phase I-II oncology trials integrating efficacy data across stages

Combination of several anticancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers phase I-II dose finding designs for dual-agent combinations, where one main objective is to characterize both the toxicity and efficacy profiles. We propose a two-stage Bayesian adaptive design that accommodates a change of patient population in-between. In stage I, we estimate a maximum tolerated dose combination using the escalation with overdose control (EWOC) principle. This is followed by a stage II, conducted in a new yet relevant patient population, to find the most efficacious dose combination. We implement a robust Bayesian hierarchical random-effects model to allow sharing of information on the efficacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random-effects distribution is specified for the main effects parameters to capture uncertainty about the between-stage differences. The inclusion of nonexchangeability assumption further enables that the stage-specific efficacy parameters have their own priors. The proposed methodology is assessed with an extensive simulation study. Our results suggest a general improvement of the operating characteristics for the efficacy assessment, under a conservative assumption about the exchangeability of the parameters a priori.

Antibody-drug conjugates: in search of partners of choice

Antibody-drug conjugates (ADCs) have become a credentialled class of anticancer drugs for both solid and hematological malignancies, with regulatory approvals mainly as single agents. Despite extensive preclinical and clinical efforts to develop rational ADC-based combinations, to date only a limited number have demonstrated survival improvements over standard of care. The most appealing partners for ADCs are those that offer additive or synergistic effects on tumor cells or their microenvironment without unacceptable overlapping toxicities. Coadministration with antiangiogenic compounds, HER2-targeting drugs, DNA-damage response agents and immune checkpoint inhibitors (ICIs) represent active forerunners. Through the identification of targets with tumor-specific expression, improved conjugation technologies, and novel linkers and payloads offering superior therapeutic indices, the next generation of ADCs brings optimism to combinatorial approaches.

Bayesian Adaptive Estimation with Theoretical Bound: An Exploration-Exploitation Approach

This paper investigates the theoretical bound to reduce the parameter uncertainty in Bayesian adaptive estimation for psychometric functions and proposes an exploration-exploitation (E-E) approach to improve the computation efficiency for parameter estimations. When the experimental trial goes on, the uncertainty of the parameters decreases dramatically and the space between the maximal mutual information and the theoretical bound gets narrower, so the advantage of classical Bayesian adaptive estimation algorithm diminishes. This approach tries to trade off the exploration (parameter posterior uncertainty) and the exploitation (parameter mean estimation). The experimental results show that the proposed E-E approach estimates parameters for psychometric functions with same convergence and reduces the computation time by more than 34.27%, compared with the classical Bayesian adaptive estimation.

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.

[Progress and Application of Bayesian Approach in the Early Research and Development of New Anticancer Drugs]

Bayesian statistics is an approach for learning from evidences as it accumulates, combining prior distribution with current information on a quantity of interest, in which posterior distribution and inferences are being updated each time new data become available using Bayes' Theorem. Though frequentist approach has dominated medical studies, Bayesian approach has been more and more widely recognized by its flexibility and efficiency. Research and development (R&D) on anti-cancer new drugs have been so hot globally in recent years in spite of relatively high failure rate. It is the common demand of pharmaceutical enterprises and researchers to identify the optimal dose, regime and right population in the early-phase R&D stage more accurately and efficiently, especially when the following three major changes have been observed. The R&D on anticancer drugs have transformed from chemical drugs to biological products, from monotherapy to combination therapy, and the study design has also gradually changed from traditional way to innovative and adaptive mode. This also raises a number of subsequent challenges on decision-making of early R&D, such as inability to determine MTD, flexibility to deal with delayed toxicity, delayed response and dose-response changing relationships. It is because of the above emerging changes and challenges that the Bayesian approach is getting more and more attention from the industry. At least, Bayesian approach has more information for decision-making, which could potentially help enterprises achieve higher efficiency, shorter period and lower investment. This study also expounds the application of Bayesian statistics in the early R&D on anticancer new drugs, and compares and analyzes its idea and application scenarios with frequentist statistics, aiming to provide macroscopic and systematic reference for all related stakeholders.
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A Bayesian phase I/II biomarker-based design for identifying subgroup-specific optimal dose for immunotherapy

Immunotherapy is an innovative treatment that enlists the patient's immune system to battle tumors. The optimal dose for treating patients with an immunotherapeutic agent may differ according to their biomarker status. In this article, we propose a biomarker-based phase I/II dose-finding design for identifying subgroup-specific optimal dose for immunotherapy (BSOI) that jointly models the immune response, toxicity, and efficacy outcomes. We propose parsimonious yet flexible models to borrow information across different types of outcomes and subgroups. We quantify the desirability of the dose using a utility function and adopt a two-stage dose-finding algorithm to find the optimal dose for each subgroup. Simulation studies show that the BSOI design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.

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.

gBOIN-ET: The generalized Bayesian optimal interval design for optimal dose-finding accounting for ordinal graded efficacy and toxicity in early clinical trials

One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low- or moderate-grade toxicities than dose-limiting toxicities. Besides, efficacy should be evaluated as an overall response and stable disease in solid tumors and the difference between complete remission and partial remission in lymphoma. This paper proposes the generalized Bayesian optimal interval design for dose-finding accounting for efficacy and toxicity grades. The new design, named "gBOIN-ET" design, is model-assisted, simple, and straightforward to implement in actual oncology dose-finding trials than model-based approaches. These characteristics are quite valuable in practice. A simulation study shows that the gBOIN-ET design has advantages compared with the other model-assisted designs in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.

BIPSE: A biomarker-based phase I/II design for immunotherapy trials with progression-free survival endpoint

A Bayesian biomarker-based phase I/II design (BIPSE) is presented for immunotherapy trials with a progression-free survival (PFS) endpoint. The objective is to identify the subgroup-specific optimal dose, defined as the dose with the best risk-benefit tradeoff in each biomarker subgroup. We jointly model the immune response, toxicity outcome, and PFS with information borrowing across subgroups. A plateau model is used to describe the marginal distribution of the immune response. Conditional on the immune response, we model toxicity using probit regression and model PFS using the mixture cure rate model. During the trial, based on the accumulating data, we continuously update model estimates and adaptively randomize patients to doses with high desirability within each subgroup. Simulation studies show that the BIPSE design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.

BOIN: a novel Bayesian design platform to accelerate early phase brain tumor clinical trials

Despite decades of extensive research, the progress in developing effective treatments for primary brain tumors lags behind that of other cancers, largely due to the unique challenges of brain tumors (eg, the blood-brain barrier and high heterogeneity) that limit the delivery and efficacy of many therapeutic agents. One way to address this issue is to employ novel trial designs to better optimize the treatment regimen (eg, dose and schedule) in early phase trials to improve the success rate of subsequent phase III trials. The objective of this article is to introduce Bayesian optimal interval (BOIN) designs as a novel platform to design various types of early phase brain tumor trials, including single-agent and combination regimen trials, trials with late-onset toxicities, and trials aiming to find the optimal biological dose (OBD) based on both toxicity and efficacy. Unlike many novel Bayesian adaptive designs, which are difficult to understand and complicated to implement by clinical investigators, the BOIN designs are self-explanatory and user friendly, yet yield more robust and powerful operating characteristics than conventional designs. We illustrate the BOIN designs using a phase I clinical trial of brain tumor and provide software (freely available at www.trialdesign.org) to facilitate the application of the BOIN design.

Contemporary dose-escalation methods for early phase studies in the immunotherapeutics era

Phase 1 dose-escalation trials are crucial to drug development by providing a framework to assess the toxicity of novel agents in a stepwise and monitored fashion. Despite widely adopted, rule-based dose-escalation methods (such as 3 + 3) are limited in finding the maximum tolerated dose (MTD) and tend to treat a significant number of patients at subtherapeutic doses. Newer methods of dose escalation, such as model-based and model-assisted designs, have emerged and are more accurate in finding MTD. However, these designs have not yet been broadly embraced by investigators. In this review, we summarise the advantages and disadvantages of contemporary dose-escalation methods, with emphasis on model-assisted designs, including time-to-event designs and hybrid methods involving optimal biological dose (OBD). The methods reviewed include mTPI, keyboard, BOIN, and their variations. In addition, the challenges of drug development (and dose-escalation) in the era of immunotherapeutics are discussed, where many of these agents typically have a wide therapeutic window. Fictional examples of how the dose-escalation method chosen can alter the outcomes of a phase 1 study are described, including the number of patients enrolled, the trial's timeframe, and the dose level chosen as MTD. Finally, the recent trends in dose-escalation methods applied in phase 1 trials in the immunotherapeutics era are reviewed. Among 856 phase I trials from 2014 to 2019, a trend towards the increased use of model-based and model-assisted designs over time (OR = 1.24) was detected. However, only 8% of the studies used non-rule-based dose-escalation methods. Increasing familiarity with such dose-escalation methods will likely facilitate their uptake in clinical trials.

The intranasal dexmedetomidine plus ketamine for procedural sedation in children, adaptive randomized controlled non-inferiority multicenter trial (Ketodex): a statistical analysis plan

Background

Procedural sedation and analgesia (PSA) is frequently required to perform closed reductions for fractures and dislocations in children. Intravenous (IV) ketamine is the most commonly used sedative agent for closed reductions. However, as children find IV insertion a distressing and painful procedure, there is need to identify a feasible alternative route of administration. There is evidence that a combination of dexmedetomidine and ketamine (ketodex), administered intranasally (IN), could provide adequate sedation for closed reductions while avoiding the need for IV insertion. However, there is uncertainty about the optimal combination dose for the two agents and whether it can provide adequate sedation for closed reductions. The Intranasal Dexmedetomidine Plus Ketamine for Procedural Sedation (Ketodex) study is a Bayesian phase II/III, non-inferiority trial in children undergoing PSA for closed reductions that aims to address both these research questions. This article presents in detail the statistical analysis plan for the Ketodex trial and was submitted before the outcomes of the trial were available for analysis.

Methods/design

The Ketodex trial is a multicenter, four-armed, randomized, double-dummy controlled, Bayesian response adaptive dose finding, non-inferiority, phase II/III trial designed to determine (i) whether IN ketodex is non-inferior to IV ketamine for adequate sedation in children undergoing a closed reduction of a fracture or dislocation in a pediatric emergency department and (ii) the combination dose for IN ketodex that provides optimal sedation. Adequate sedation will be primarily measured using the Pediatric Sedation State Scale. As secondary outcomes, the Ketodex trial will compare the length of stay in the emergency department, time to wakening, and adverse events between study arms.

Discussion

The Ketodex trial will provide evidence on the optimal dose for, and effectiveness of, IN ketodex as an alternative to IV ketamine providing sedation for patients undergoing a closed reduction. The data from the Ketodex trial will be analyzed from a Bayesian perspective according to this statistical analysis plan. This will reduce the risk of producing data-driven results introducing bias in our reported outcomes.

Trial registration

ClinicalTrials.gov NCT04195256. Registered on December 11, 2019.

Probability intervals of toxicity and efficacy design for dose-finding clinical trials in oncology

Immunotherapy, gene therapy or adoptive cell therapies, such as the chimeric antigen receptor+ T-cell therapies, have demonstrated promising therapeutic effects in oncology patients. We consider statistical designs for dose-finding adoptive cell therapy trials, in which the monotonic dose-response relationship assumed in traditional oncology trials may not hold. Building upon a previous design called "TEPI", we propose a new dose finding method - Probability Intervals of Toxicity and Efficacy (PRINTE), which utilizes toxicity and efficacy jointly in making dosing decisions, does not require a pre-elicited decision table and at the same time can handle Ockham's razor properly in the statistical inference. We show that optimizing the joint posterior expected utility of toxicity and efficacy under a 0-1 loss is equivalent to maximizing the marginal model posterior probability in the two-dimensional probability space. An extensive simulation study under various scenarios are conducted and results show that PRINTE outperforms existing designs in the literature since it assigns more patients to optimal doses and less to toxic ones, and selects optimal doses with higher percentages. The simple and transparent features together with good operating characteristics make PRINTE an improved design for dose-finding trials in oncology trials.

On the coherence of model-based dose-finding designs for drug combination trials

The concept of coherence was proposed for single-agent phase I clinical trials to describe the property that a design never escalates the dose when the most recently treated patient has toxicity and never de-escalates the dose when the most recently treated patient has no toxicity. It provides a useful theoretical tool for investigating the properties of phase I trial designs. In this paper, we generalize the concept of coherence to drug combination trials, which are substantially different and more challenging than single-agent trials. For example, in the dose-combination matrix, each dose has up to 8 neighboring doses as candidates for dose escalation and de-escalation, and the toxicity orders of these doses are only partially known. We derive sufficient conditions for a model-based drug combination trial design to be coherent. Our results are more general and relaxed than the existing results and are applicable to both single-agent and drug combination trials. We illustrate the application of our theoretical results with a number of drug combination dose-finding designs in the literature.

A dose-finding approach for genomic patterns in phase I trials

Precision medicine is an emerging approach for disease treatment and prevention that accounts for individual variability in genes, environment, and lifestyle. Cancer is a genomic disease; therefore, the dose-efficacy and dose-toxicity relationships for molecularly targeted agents in cancer most likely differ, based on the genomic mutation pattern. The individualized optimal dose - the maximal efficacious dose with a clinically acceptable safety profile - may vary depending on the genomic mutation patterns and should be determined prior to the use of these agents in precision medicine. In addition, genes that influence the individualized optimal doses should be identified in early-phase development. In this study, we propose a novel dose-finding approach to identify the individualized optimal dose for molecularly targeted agents in phase I cancer trials. Individualized optimal dose determination and gene selection were conducted simultaneously based on L ₁ and L ₂ penalized regression. Similar to most reported dose-finding approaches, this study considers non-monotonic patterns for dose-efficacy and dose-toxicity relationships, as well as correlations between efficacy and toxicity outcomes based on multinomial distribution. Our dose-finding algorithm is based on the predictive probability calculated with an estimated penalized regression model. We compare the operating characteristics between the proposed and existing methods by simulation studies under various scenarios.

Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling

Identifying children who are at-risk for developmental delay, so that these children can have access to interventions as early as possible, is an important and challenging problem in developmental research. This research aimed to identify latent subgroups of children with developmental delay, by modelling and clustering developmental milestones. The main objectives were to (a) create a developmental profile for each child by modelling milestone achievements, from birth to three years of age, across multiple domains of development, and (b) cluster the profiles to identify groups of children who show similar deviations from typical development. The ensemble methodology used in this research consisted of three components: (1) Bayesian sequential updating was used to model the achievement of milestones, which allows for updated predictions of development to be made in real time; (2) a measure was created that indicated how far away each child deviated from typical development for each functional domain, by calculating the area between each child’s obtained sequence of posterior means and a sequence of posterior means representing typical development; and (3) Dirichlet process mixture modelling was used to cluster the obtained areas. The data used were 348 binary developmental milestone measurements, collected from birth to three years of age, from a small community sample of young children (N = 79). The model identified nine latent groups of children with similar features, ranging from no delays in all functional domains, to large delays in all domains. The performance of the Dirichlet process mixture model was validated with two simulation studies.

Bayesian adaptive linearization method for phase I drug combination trials with dimension reduction

Many phase I drug combination designs have been proposed to find the maximum tolerated combination (MTC). Due to the two-dimension nature of drug combination trials, these designs typically require complicated statistical modeling and estimation, which limit their use in practice. In this article, we propose an easy-to-implement Bayesian phase I combination design, called Bayesian adaptive linearization method (BALM), to simplify the dose finding for drug combination trials. BALM takes the dimension reduction approach. It selects a subset of combinations, through a procedure called linearization, to convert the two-dimensional dose matrix into a string of combinations that are fully ordered in toxicity. As a result, existing single-agent dose-finding methods can be directly used to find the MTC. In case that the selected linear path does not contain the MTC, a dose-insertion procedure is performed to add new doses whose expected toxicity rate is equal to the target toxicity rate. Our simulation studies show that the proposed BALM design performs better than competing, more complicated combination designs.

TITE-BOIN-ET: Time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes

One of the primary purposes of an oncology dose-finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de-escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late-onset outcomes. To solve these issues, we propose the time-to-event Bayesian optimal interval design to accelerate dose-finding based on cumulative and pending data of both efficacy and toxicity. The new design, named "TITE-BOIN-ET" design, is nonparametric and a model-assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose-finding trials compared with the model-based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE-BOIN-ET design has advantages compared with the model-based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE-BOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose-finding trials.

Pragmatic dose-escalation methods incorporating relative dose intensity assessment for molecularly targeted agents in phase I trials

The recommended phase 2 doses of molecularly targeted agents, determined by using an ordinal dose-finding method that only uses toxicity data at first cycle, may not be optimal. Some researchers have proposed the use of relative dose intensity that can account for late-onset, cumulative, and low-grade toxicities to determine the recommended phase 2 dose (RP2D). In this study, we proposed two dose escalation methods based on the observed relative dose intensities (RDIs) between the pre-specified intervals (cycles) for toxicity evaluation used in combination with DLT evaluation in the first cycle. First, we propose the modified 3 + 3 design that incorporates longitudinal RDI assessment. Second, we propose the sequential assessment method for longitudinal RDI (SARDI) to achieve faster dose escalation compared to that of the modified 3 + 3 design. Simulation studies demonstrated that the SARDI was, in many cases, superior to the ordinal and modified 3 + 3 designs in respect to the selection rate of true RP2D and study period. The two proposed methods could also in some cases decrease the average number of patients enrolled in the trial compared to that of the ordinary 3 + 3 design. Incorporation of the RDI assessment into the 3 + 3 design is not difficult and does not require the use of complex statistical techniques. Therefore, we believe that investigators who routinely use the 3 + 3 design in practice can easily use our proposed methods.

A flexible design for advanced Phase I/II clinical trials with continuous efficacy endpoints

There is growing interest in integrated Phase I/II oncology clinical trials involving molecularly targeted agents (MTA). One of the main challenges of these trials are nontrivial dose-efficacy relationships and administration of MTAs in combination with other agents. While some designs were recently proposed for such Phase I/II trials, the majority of them consider the case of binary toxicity and efficacy endpoints only. At the same time, a continuous efficacy endpoint can carry more information about the agent's mechanism of action, but corresponding designs have received very limited attention in the literature. In this work, an extension of a recently developed information-theoretic design for the case of a continuous efficacy endpoint is proposed. The design transforms the continuous outcome using the logistic transformation and uses an information-theoretic argument to govern selection during the trial. The performance of the design is investigated in settings of single-agent and dual-agent trials. It is found that the novel design leads to substantial improvements in operating characteristics compared to a model-based alternative under scenarios with nonmonotonic dose/combination-efficacy relationships. The robustness of the design to missing/delayed efficacy responses and to the correlation in toxicity and efficacy endpoints is also investigated.

Model-Assisted Designs for Early-Phase Clinical Trials: Simplicity Meets Superiority

Drug development enterprise is struggling because of prohibitively high costs and slow progress. There is urgent need for adoption of novel adaptive designs to improve the efficiency and success of clinical trials. A major barrier is that many conventional designs are inadequate for modern drug development, yet most novel adaptive designs are difficult to understand, require complicated statistical modeling, demand complex computation, and need expensive infrastructure for implementation. The objective of this article is to introduce and review a class of novel adaptive designs, known as model-assisted designs, to remove this barrier and increase the use of novel adaptive designs. Model-assisted designs enjoy superior performance comparable to more complicated, model-based adaptive designs, but their decision rule can be pretabulated and included in the protocol-thus implemented as simply as the conventional designs. We review state-of-the-art model-assisted designs for phase I clinical trials for single-agent, drug-combination and late-onset toxicity scenarios. We also briefly introduce model-assisted designs for phase II trials to handle binary, coprimary endpoints and delayed response. Freely available user-friendly software and trial examples (trialdesign.org) facilitate the adoption of model-assisted designs.

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.

Flexible Phase I-II design for partially ordered regimens with application to therapeutic cancer vaccines

Existing methodology for the design of Phase I-II studies has been intended to search for the optimal regimen, based on a trade-off between toxicity and efficacy, from a set of regimens comprised of doses of a new agent. The underlying assumptions guiding allocation are that the dose-toxicity curve is monotonically increasing, and that the dose-efficacy curve either plateaus or decreases beyond an intermediate dose. This article considers the problem of designing Phase I-II studies that violate these assumptions for both outcomes. The motivating application studies regimens that are not defined by doses of a new agent, but rather a peptide vaccine plus novel adjuvants for the treatment of melanoma. All doses of each adjuvant are fixed, and the regimens vary by the number and selection of adjuvants. This structure produces regimen-toxicity curves that are partially ordered, and regimen-efficacy curves that may deviate from a plateau or unimodal shape. Application of a Bayesian model-based design is described in determining the optimal biologic regimen, based on bivariate binary measures of toxicity and biologic activity. A simulation study of the design's operating characteristics is conducted, and its versatility in handling other Phase I-II problems is discussed.

An adaptive design for the identification of the optimal dose using joint modeling of continuous repeated biomarker measurements and time-to-toxicity in phase I/II clinical trials in oncology

We present a new adaptive dose-finding method, based on a joint modeling of longitudinal continuous biomarker activity measurements and time to first dose limiting toxicity, with a shared random effect. Estimation relies on likelihood that does not require approximation, an important property in the context of small sample sizes, typical of phase I/II trials. We address the important case of missing at random data that stem from unacceptable toxicity, lack of activity and rapid deterioration of phase I patients. The objective is to determine the lowest dose within a range of highly active doses, under the constraint of not exceeding the maximum tolerated dose. The maximum tolerated dose is associated to some cumulative risk of dose limiting toxicity over a predefined number of treatment cycles. Operating characteristics are explored via simulations in various scenarios.

An information theoretic phase I-II design for molecularly targeted agents that does not require an assumption of monotonicity

For many years phase I and phase II clinical trials have been conducted separately, but there has been a recent shift to combine these phases. Although a variety of phase I-II model-based designs for cytotoxic agents have been proposed in the literature, methods for molecularly targeted agents (TAs) are just starting to develop. The main challenge of the TA setting is the unknown dose-efficacy relationship that can have either an increasing, plateau or umbrella shape. To capture these, approaches with more parameters are needed or, alternatively, more orderings are required to account for the uncertainty in the dose-efficacy relationship. As a result, designs for more complex clinical trials, e.g. trials looking at schedules of a combination treatment involving TAs, have not been extensively studied yet. We propose a novel regimen finding design which is based on a derived efficacy-toxicity trade-off function. Because of its special properties, an accurate regimen selection can be achieved without any parametric or monotonicity assumptions. We illustrate how this design can be applied in the context of a complex combination-schedule clinical trial. We discuss practical and ethical issues such as coherence, delayed and missing efficacy responses, safety and futility constraints.

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 hierarchal modeling approach to shortening phase I/II trials of anticancer drug combinations

In phase I/II anticancer drug-combination trials, trial design to evaluate toxicity and efficacy has been studied by dividing the trial into 2 stages, followed by seamless execution of the 2 stages. In the first stage, admissible dose combinations in toxicity are identified, followed by patient assignment among the identified admissible dose combinations using adaptive randomization in the second stage. When patients are assigned using adaptive randomization, it is desirable to determine a more appropriate dose combination by taking into consideration both drug efficacy and toxicity; however, during the course of this determination and evaluation of toxicity and efficacy, there remains a concern that the trial duration might be prolonged. Therefore, we proposed a trial design to assign patients adaptively to more appropriate dose combinations in both toxicity and efficacy and to shorten trial duration without compromising trial performance. When selecting the dose combination for subsequent cohorts, unobserved data are treated as missing data, which are imputed using a data augmentation algorithm involving a gamma process. Probabilities associated with toxicity and efficacy are estimated applying a Bayesian hierarchical model to the imputed data, thereby allowing more patients to be assigned more appropriate dose combinations in both toxicity and efficacy through adaptive randomization. Results of simulation studies suggested that the proposed approach shortened trial duration without significantly compromising the performance of the trial as compared with existing approaches. We believe that the proposed approach will expedite drug development time and reduce costs associated with clinical development.

Analyses of drug combinations using missing data shortens trial periods in phase I/II oncology trials

In previous phase I/II oncology trials for drug combinations, a number of methods have been studied to determine the dose combination for the next cohort. However, there is a risk that trial durations will be unfeasibly long if methods for evaluating safety and efficacy are based on the best overall response and toxicity during trial design. In this study, we propose an approach to shorten the duration of drug trials in oncology. In this method, the dose combination to be allocated to the next cohort is decided before all data for patients in the current cohort is known and best overall response is determined. The efficacy of drug combinations in patients for whom the best overall response has not been determined is treated as missing data. The missing data mechanism is modeled by nonparametric prior processes. The probabilities of efficacy and toxicity are estimated after applying data augmentation to missing data, and the dose combination to be allocated to the next cohort is decided using these probabilities. Simulation studies from the present study show that this proposed approach would shorten trial durations without the low-performing of the trial design in comparison to existing approaches. Shortening trial durations would enable patients with the targeted disease to receive effective therapy at an earlier stage. This also enables clinical trial sponsors to use fewer patients in drug trials, which would lead to a reduction in the costs associated with clinical development.

BOIN-ET: Bayesian optimal interval design for dose finding based on both efficacy and toxicity outcomes

One of the main purposes of a phase I dose-finding trial in oncology is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent phase II and III trials. Many phase I dose-finding methods based solely on toxicity considerations have been proposed under the assumption that both toxicity and efficacy monotonically increase with the dose level. Such an assumption may not be necessarily the case, however, when evaluating the OD for molecular targeted, cytostatic, and biological agents, as well as immune-oncology therapy. To address this issue, we extend the Bayesian optimal interval (BOIN) design, which is nonparametric and thus does not require the assumption used in model-based designs, in order to identify an OD based on both efficacy and toxicity outcomes. The new design is named "BOIN-ET." A simulation study is presented that includes a comparison of this proposed method to the model-based approaches in terms of both efficacy and toxicity responses. The simulation shows that BOIN-ET has advantages in both the percentages of correct ODs selected and the average number of patients allocated to the ODs across a variety of realistic settings.

Extensions of the mTPI and TEQR designs to include non-monotone efficacy in addition to toxicity for optimal dose determination for early phase immunotherapy oncology trials

With the emergence of immunotherapy and other novel therapies, the traditional assumption that the efficacy of the study drug increases monotonically with dose levels is not always true. Therefore, dose-finding methods evaluating only toxicity data may not be adequate. In this paper, we have first compared the Modified Toxicity Probability Interval (mTPI) and Toxicity Equivalence Range (TEQR) dose-finding oncology designs for safety with identical stopping rules; we have then extended both designs to include efficacy in addition to safety – we determine the optimal dose for safety and efficacy using these designs by applying isotonic regression to the observed toxicity and efficacy rates, once the early phase trial is completed. We consider multiple types of underlying dose response curves, i.e., monotonically increasing, plateau, or umbrella-shaped. We conduct simulation studies to investigate the operating characteristics of the two proposed designs and compare them to existing designs. We found that the extended mTPI design selects the optimal dose for safety and efficacy more accurately than the other designs for most of the scenarios considered.

Adaptive phase I/II clinical trials for drug combination assessment in oncology using the outcomes of each cycle

Many new anticancer agents can be combined with existing drugs, as combining a number of drugs may be expected to have a better therapeutic effect than monotherapy owing to synergistic effects. Furthermore, to drive drug development and to reduce the associated cost, there has been a growing tendency to combine these as phase I/II trials. With respect to phase I/II oncology trials for the assessment of dose combinations, in the existing methodologies in which efficacy based on tumor response and safety based on toxicity are modeled as binary outcomes, it is not possible to enroll and treat the next cohort of patients unless the best overall response has been determined in the current cohort. Thus, the trial duration might be potentially extended to an unacceptable degree. In this study, we proposed a method that randomizes the next cohort of patients in the phase II part to the dose combination based on the estimated response rate using all the available observed data upon determination of the overall response in the current cohort. We compared the proposed method to the existing method using simulation studies. These demonstrated that the percentage of optimal dose combinations selected in the proposed method is not less than that in the existing method and that the trial duration in the proposed method is shortened compared to that in the existing method. The proposed method meets both ethical and financial requirements, and we believe it has the potential to contribute to expedite drug development.

Implementation of a Model-Based Design in a Phase Ib Study of Combined Targeted Agents

In recent years, investigators have recognized the rigidity of single-agent, safety-only, traditional designs, rendering them ineffective for conducting contemporary early-phase clinical trials, such as those involving combinations and/or biological agents. Novel approaches are required to address these research questions, such as those posed in trials involving targeted therapies. We describe the implementation of a model-based design for identifying an optimal treatment combination, defined by low toxicity and high efficacy, in an early-phase trial evaluating a combination of two oral targeted inhibitors in relapsed/refractory mantle cell lymphoma. Operating characteristics demonstrate the ability of the method to effectively recommend optimal combinations in a high percentage of trials with reasonable sample sizes. The proposed design is a practical, early-phase, adaptive method for use with combined targeted therapies. This design can be applied more broadly to early-phase combination studies, as it was used in an ongoing study of a melanoma helper peptide vaccine plus novel adjuvant combinations. Clin Cancer Res; 23(23); 7158-64. ©2017 AACR.

Toxicity and Efficacy Probability Interval Design for Phase I Adoptive Cell Therapy Dose-Finding Clinical Trials

Recent trials of adoptive cell therapy (ACT), such as the chimeric antigen receptor (CAR) T-cell therapy, have demonstrated promising therapeutic effects for cancer patients. A main issue in the product development is to determine the appropriate dose of ACT. Traditional phase I trial designs for cytotoxic agents explicitly assume that toxicity increases monotonically with dose levels and implicitly assume the same for efficacy to justify dose escalation. ACT usually induces rapid responses, and the monotonic dose-response assumption is unlikely to hold due to its immunobiologic activities. We propose a toxicity and efficacy probability interval (TEPI) design for dose finding in ACT trials. This approach incorporates efficacy outcomes to inform dosing decisions to optimize efficacy and safety simultaneously. Rather than finding the maximum tolerated dose (MTD), the TEPI design is aimed at finding the dose with the most desirable outcome for safety and efficacy. The key features of TEPI are its simplicity, flexibility, and transparency, because all decision rules can be prespecified prior to trial initiation. We conduct simulation studies to investigate the operating characteristics of the TEPI design and compare it to existing methods. In summary, the TEPI design is a novel method for ACT dose finding, which possesses superior performance and is easy to use, simple, and transparent. Clin Cancer Res; 23(1); 13-20. ©2016 AACR.

A practical Bayesian design to identify the maximum tolerated dose contour for drug combination trials

Drug combination therapy has become the mainstream approach to cancer treatment. One fundamental feature that makes combination trials different from single-agent trials is the existence of the maximum tolerated dose (MTD) contour, that is, multiple MTDs. As a result, unlike single-agent phase I trials, which aim to find a single MTD, it is often of interest to find the MTD contour for combination trials. We propose a new dose-finding design, the waterfall design, to find the MTD contour for drug combination trials. Taking the divide-and-conquer strategy, the waterfall design divides the task of finding the MTD contour into a sequence of one-dimensional dose-finding processes, known as subtrials. The subtrials are conducted sequentially in a certain order, such that the results of each subtrial will be used to inform the design of subsequent subtrials. Such information borrowing allows the waterfall design to explore the two-dimensional dose space efficiently using a limited sample size and decreases the chance of overdosing and underdosing patients. To accommodate the consideration that doses on the MTD contour may have very different efficacy or synergistic effects because of drug-drug interaction, we further extend our approach to a phase I/II design with the goal of finding the MTD with the highest efficacy. Simulation studies show that the waterfall design is safer and has higher probability of identifying the true MTD contour than some existing designs. The R package "BOIN" to implement the waterfall design is freely available from CRAN. Copyright © 2016 John Wiley & Sons, Ltd.

A dose-schedule finding design for phase I-II clinical trials

Dose-finding methods aiming at identifying an optimal dose of a treatment with a given schedule may be at a risk of misidentifying the best treatment for patients. In this article we propose a phase I/II clinical trial design to find the optimal dose-schedule combination. We define schedule as the method and timing of administration of a given total dose in a treatment cycle. We propose a Bayesian dynamic model for the joint effects of dose and schedule. The proposed model allows us to borrow strength across dose-schedule combinations without making overly restrictive assumptions on the ordering pattern of the schedule effects. We develop a dose-schedule-finding algorithm to sequentially allocate patients to a desirable dose-schedule combination, and select an optimal combination at the end of the trial. We apply the proposed design to a phase I/II clinical trial of a γ-secretase inhibitor in patients with refractory metastatic or locally advanced solid tumours, and examine the operating characteristics of the design through simulations.

Phase I/II dose-finding design for molecularly targeted agent: Plateau determination using adaptive randomization

Conventionally, phase I dose-finding trials aim to determine the maximum tolerated dose of a new drug under the assumption that both toxicity and efficacy monotonically increase with the dose. This paradigm, however, is not suitable for some molecularly targeted agents, such as monoclonal antibodies, for which efficacy often increases initially with the dose and then plateaus. For molecularly targeted agents, the goal is to find the optimal dose, defined as the lowest safe dose that achieves the highest efficacy. We develop a Bayesian phase I/II dose-finding design to find the optimal dose. We employ a logistic model with a plateau parameter to capture the increasing-then-plateau feature of the dose–efficacy relationship. We take the weighted likelihood approach to accommodate for the case where efficacy is possibly late-onset. Based on observed data, we continuously update the posterior estimates of toxicity and efficacy probabilities and adaptively assign patients to the optimal dose. The simulation studies show that the proposed design has good operating characteristics. This method is going to be applied in more than two phase I clinical trials as no other method is available for this specific setting. We also provide an R package dfmta that can be downloaded from CRAN website.

A robust Bayesian dose-finding design for phase I/II clinical trials

We propose a Bayesian phase I/II dose-finding trial design that simultaneously accounts for toxicity and efficacy. We model the toxicity and efficacy of investigational doses using a flexible Bayesian dynamic model, which borrows information across doses without imposing stringent parametric assumptions on the shape of the dose-toxicity and dose-efficacy curves. An intuitive utility function that reflects the desirability trade-offs between efficacy and toxicity is used to guide the dose assignment and selection. We also discuss the extension of this design to handle delayed toxicity and efficacy. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. The results show that the proposed design possesses good operating characteristics and is robust to the shape of the dose-toxicity and dose-efficacy curves.

Quality improvement and practice-based research in neurology using the electronic medical record

We describe quality improvement and practice-based research using the electronic medical record (EMR) in a community health system-based department of neurology. Our care transformation initiative targets 10 neurologic disorders (brain tumors, epilepsy, migraine, memory disorders, mild traumatic brain injury, multiple sclerosis, neuropathy, Parkinson disease, restless legs syndrome, and stroke) and brain health (risk assessments and interventions to prevent Alzheimer disease and related disorders in targeted populations). Our informatics methods include building and implementing structured clinical documentation support tools in the EMR; electronic data capture; enrollment, data quality, and descriptive reports; quality improvement projects; clinical decision support tools; subgroup-based adaptive assignments and pragmatic trials; and DNA biobanking. We are sharing EMR tools and deidentified data with other departments toward the creation of a Neurology Practice-Based Research Network. We discuss practical points to assist other clinical practices to make quality improvements and practice-based research in neurology using the EMR a reality.

TEAMS: Toxicity- and Efficacy-based Dose Insertion Design with Adaptive Model Selection for Phase I/II Dose-Escalation Trials in Oncology

In many oncology clinical trials it is necessary to insert new candidate doses when the prespecified doses are poorly elicited. Formal statistical designs with dose insertion are lacking. We propose a dose insertion design for phase I/II clinical trials in oncology based on both efficacy and toxicity outcomes. We also implement Bayesian model selection during the course of the trial so that better models can be adaptively chosen to achieve more accurate inference. The new design, TEAMS, achieves great operating characteristics in extensive simulation studies due to its ability to adaptively insert new doses as well as perform model selection. As a result, appropriate doses are inserted when necessary and desirable doses are selected with higher probabilities at the end of the trial.