Adaptive designs for dual-agent phase I dose-escalation studies

DOD-Combo: Bayesian dose finding design in combination trials with meta-analytic-predictive prior

Combination therapy, a treatment modality that involves multiple treatment agents, has become imperative for improving treatment effectiveness and addressing resistance in the field of oncology. However, determining the most effective dose for these combinations, particularly when dealing with intricate drug interactions and diverse toxicity patterns, presents a substantial challenge. This paper introduces a novel Bayesian dose-finding design for combination therapies with information borrowing, named the DOD-Combo design. Leveraging historical single-agent trials and the meta-analytic-predictive (MAP) power prior, our approach utilizes a copula-type model to connect individual drug priors with joint toxicity probabilities in combination treatments. The MAP power prior allows the integration of information from multiple historical trials, constructing informative priors for each agent. Extensive simulations confirm our method's superior performance compared to combination designs with no information borrowing. By adaptively incorporating historical data, our approach reduces sample sizes and enhances efficiency in selecting the maximum tolerated dose (MTD), effectively addressing the intricate challenges presented by combination trials.

A new function for drug combination dose finding trials

Combination drugs play an essential role in treating cancers. The challenging part of the combination drugs are to specify the dose-toxicity ordering, which means the sequences of dose escalation and de-escalation in process of dose findings should be pre-determined. In the paper, we extend a novel function of the continual reassessment method based on the combination of the normal distribution for drug-combination dose-finding trials and systematically evaluate its performance using a template of four performance measures EARS (Efficiency, Accuracy, Reliability, Selection). Dose escalation and deescalation rules are based on the nearest neighborhood continual reassessment method for a combination drug, and we specify all possible dose-toxicity orderings in the trial. Simulation demonstrates that the new design is efficient, accurate and reasonably reliable.

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/ ).

Data-dependent early completion of dose-finding trials for drug-combination

PURPOSE

Model-assisted designs for drug combination trials have been proposed as novel designs with simple and superior performance. However, model-assisted designs have the disadvantage that the sample size must be set in advance, and trials cannot be completed until the number of patients treated reaches the pre-set sample size. Model-assisted designs have a stopping rule that can be used to terminate the trial if the number of patients treated exceeds the predetermined number, there is no statistical basis for the predetermined number. Here, I propose two methods for data-dependent early completion of dose-finding trials for drug combination: (1) an early completion method based on dose retainment probability, and (2) an early completion method in which the dose retainment probability is adjusted by a bivariate isotonic regression.

METHODS

Early completion is determined when the dose retainment probability using both trial data and the number of remaining patients is high. Early completion of a virtual trial was demonstrated. The performances of the early completion methods were evaluated by simulation studies with 12 scenarios.

RESULTS

The simulation studies showed that the percentage of early completion was an average of approximately 70%, and the number of patients treated was 25% less than the planned sample size. The percentage of correct maximum tolerated dose combination selection for the early completion methods was similar to that of non-early completion methods with an average difference of approximately 3%.

CONCLUSION

The performance of the proposed early completion methods was similar to that of the non-early completion methods. Furthermore, the number of patients for determining early completion before the trial starts was determined and a program code for calculating the dose retainment probability was proposed.

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.

On the feasibility of pediatric dose-finding trials in small samples with information from a preceding trial in adults

We consider the case of pediatric dose-finding trials with extremely limited sample size. The operating characteristics of the standard design, the Continual Reassessment Method (CRM), are only well described for sample sizes of about 20 patients or more. In this simulation study, we assume the situation of a pediatric trial with only 10 patients and a preceding dose-finding trial in adults. Based on the adult data, we reduce the set of pediatric doses and formulate (partially) informative prior distributions for the pediatric trial. Our simulations show that such small pediatric dose-finding trials with robustified priors may provide sufficient operating characteristics.

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.

Pragmatic patient engagement in designing pragmatic oncology clinical trials

Oncology trials are the cornerstone of effective and safe therapeutic discoveries. However, there is increasing demand for pragmatism and patient engagement in the design, implementation and dissemination of oncology trials. Many researchers are uncertain about making trials more practical and even less knowledgeable about how to meaningfully engage patients without compromising scientific rigor to meet regulatory requirements. The present work provides practical guidance for addressing both pragmaticism and meaningful patient engagement. Applying evidence-based approaches like PRECIS-2-tool and the 10-Step Engagement Framework offer practical guidance to make future trials in oncology truly pragmatic and patient-centered. Consequently, such patient-centered trials have improved participation, faster recruitment and greater retention, and uptake of innovative technologies in community-based care.

Proportion of Patients in Phase I Oncology Trials Receiving Treatments That Are Ultimately Approved

BACKGROUND

Phase I oncology trials are often regarded as a therapeutic option for patients. However, such claims have relied on surrogate measures of benefit, such as objective response.

METHODS

Using a systematic search of publications, we assessed the therapeutic value of phase I cancer trial participation by determining the probability that patients will receive active doses of treatments that eventually receive FDA approval or a National Comprehensive Cancer Network (NCCN) guideline recommendation for their indication. ClinicalTrials.gov, PubMed, American Society of Clinical Oncology reports, NCCN guidelines, and Drugs@FDA were searched between May 1, 2018, and July 31, 2018. All statistical tests were 2-sided.

RESULTS

A total of 1000 phase I oncology trials initiated between 2005 and 2010 and enrolling 32 582 patients were randomly sampled from 3229 eligible trials on ClinicalTrials.gov. A total of 386 (1.2%) patients received a treatment that was approved by the US Food and Drug Administration for their malignancy at a dose delivered in the trial; including NCCN guideline recommendations, the number and proportion are 1168 (3.6%). Meta-regression showed a statistically significantly greater proportion of patients receiving a drug that was ultimately FDA approved in biomarker trials (rate ratio = 4.49, 95% confidence interval [CI] = 1.53 to 13.23; P = .006) and single-indication trials (rate ratio = 3.32, 95% CI = 1.21 to 9.15; P  = .02); proportions were statistically significantly lower for combination vs monotherapy trials (rate ratio = 0.09, 95% CI = 0.01 to 0.68; P  = .02).

CONCLUSIONS

One in 83 patients in phase I cancer trials received a treatment that was approved for their indication at the doses received. Given published estimates of serious adverse event rates of 10%-19%, this represents low therapeutic value for phase I trial participation.

Dynamic ordering design for dose finding in drug-combination trials

Drug-combination studies have become increasingly popular in oncology. One of the critical concerns in phase I drug-combination trials is the uncertainty in toxicity evaluation. Most of the existing phase I designs aim to identify the maximum tolerated dose (MTD) by reducing the two-dimensional searching space to one dimension via a prespecified model or splitting the two-dimensional space into multiple one-dimensional subspaces based on the partially known toxicity order. Nevertheless, both strategies often lead to complicated trials which may either be sensitive to model assumptions or induce longer trial durations due to subtrial split. We develop two versions of dynamic ordering design (DOD) for dose finding in drug-combination trials, where the dose-finding problem is cast in the Bayesian model selection framework. The toxicity order of dose combinations is continuously updated via a two-dimensional pool-adjacent-violators algorithm, and then the dose assignment for each incoming cohort is selected based on the optimal model under the dynamic toxicity order. We conduct extensive simulation studies to evaluate the performance of DOD in comparison with four other commonly used designs under various scenarios. Simulation results show that the two versions of DOD possess competitive performances in terms of correct MTD selection as well as safety, and we apply both versions of DOD to two real oncology trials for illustration.

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.

Phase I Study of Sorafenib and Vorinostat in Advanced Hepatocellular Carcinoma

OBJECTIVES

Preclinical data suggest histone deacetylase inhibitors improve the therapeutic index of sorafenib. A phase I study was initiated to establish the recommended phase 2 dose of sorafenib combined with vorinostat in patients with unresectable hepatocellular carcinoma.

MATERIALS AND METHODS

Patients received vorinostat (200 to 400 mg by mouth once daily, 5 of 7 d) and sorafenib at standard or reduced doses (400 mg [cohort A] or 200 mg [cohort B] by mouth twice daily). Patients who received 14 days of vorinostat in cycle 1 were evaluable for dose-limiting toxicity (DLT).

RESULTS

Sixteen patients were treated. Thirteen patients were evaluable for response. Three patients experienced DLTs, 2 in cohort A (grade [gr] 3 hypokalemia; gr 3 maculopapular rash) and 1 in cohort B (gr 3 hepatic failure; gr 3 hypophosphatemia; gr 4 thrombocytopenia). Eleven patients required dose reductions or omissions for non-DLTtoxicity. Ten patients (77%) had stable disease (SD). The median treatment duration was 4.7 months for response-evaluable patients. One patient with SD was on treatment for 29.9 months, and another patient, also with SD, was on treatment for 18.7 months. Another patient electively stopped therapy after 15 months and remains without evidence of progression 3 years later.

CONCLUSIONS

Although some patients had durable disease control, the addition of vorinostat to sorafenib led to toxicities in most patients, requiring dose modifications that prevented determination of the recommended phase 2 dose. The combination is not recommended for further exploration with this vorinostat schedule in this patient population.

Immune checkpoint inhibitor-based combinations: is dose escalation mandatory for phase I trials?

BACKGROUND

Numerous phase I trials testing immune checkpoint inhibitors (CPI)-based combinations are currently being conducted to improve response rates observed with single agents. However, methodology varies across studies, especially regarding the use of dose escalation.

MATERIALS AND METHODS

A literature search was conducted in Pubmed and major oncology meetings libraries for phase I trials reported between 2011 and 2018, containing at least one CPI [CLTA-4 blocking antibody or a PD(L)1 blocking antibody] plus at least one second agent (e.g. tyrosine kinase inhibitor, chemotherapy). Dose escalation schemes, target doses and recommended phase II doses (RP2D) were captured in our database for each study. Combination RP2D (combo-RP2D) was compared with target dose.

RESULTS

We identified 113 different studies comprising a total of 120 individual cohorts. The backbone was an anti- cytotoxic T-lymphocyte antigen 4 (CTLA-4) in 40 cohorts and an anti-PD(L)1 in 80 cohorts. Dose escalation was used for the CPI in 29 (24%) cohorts [11% for anti-PD(L)1 and 50% for anti-CTLA-4] and for the second agent in 55 cohorts (46%). For 31 s agents (26%), the combo-RP2D was significantly lower than the expected target dose. Failure to reach the target dose was explained by the type of second agent form (e.g. small molecules versus monoclonal antibodies) (P < 0.001) and the choice of trial design for the second agent by investigators.

CONCLUSION

Design of studies investigating new CPI-based combinations must consider the type of second agent. Dose escalation is required for combinations with small molecules but is unnecessary with vaccine/virus/dendritic therapies and monoclonal antibodies.

A Bayesian model-free approach to combination therapy phase I trials using censored time-to-toxicity data

The product of independent beta probabilities escalation (PIPE) design for dual-agent phase I dose-escalation trials is a Bayesian model-free approach for identifying multiple maximum tolerated dose combinations of novel combination therapies. Despite only being published in 2015, the PIPE design has been implemented in at least two oncology trials. However, these trials require patients to have completed follow-up before clinicians can make dose-escalation decisions. For trials of radiotherapy or advanced therapeutics, this may lead to impractically long trial durations due to late-onset treatment-related toxicities. In this paper, we extend the PIPE design to use censored time-to-event (TITE) toxicity outcomes for making dose escalation decisions. We show via comprehensive simulation studies and sensitivity analyses that trial duration can be reduced by up to 35%, particularly when recruitment is faster than expected, without compromising on other operating characteristics.

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.

Design considerations for early-phase clinical trials of immune-oncology agents

BACKGROUND

With numerous and fast approvals of different agents including immune checkpoint inhibitors, monoclonal antibodies, or chimeric antigen receptor (CAR) T-cell therapy, immunotherapy is now an established form of cancer treatment. These agents have demonstrated impressive clinical activity across many tumor types, but also revealed different toxicity profiles and mechanisms of action. The classic assumptions imposed by cytotoxic agents may no longer be applicable, requiring new strategies for dose selection and trial design.

DESCRIPTION

This main goal of this article is to summarize and highlight main challenges of early-phase study design of immunotherapies from a statistical perspective. We compared the underlying toxicity and efficacy assumptions of cytotoxic versus immune-oncology agents, proposed novel endpoints to be included in the dose-selection process, and reviewed design considerations to be considered for early-phase trials. When available, references to software and/or web-based applications were also provided to ease the implementation. Throughout the paper, concrete examples from completed (pembrolizumab, nivolumab) or ongoing trials were used to motivate the main ideas including recommendation of alternative designs.

CONCLUSION

Further advances in the effectiveness of cancer immunotherapies will require new approaches that include redefining the optimal dose to be carried forward in later phases, incorporating additional endpoints in the dose selection process (PK, PD, immune-based biomarkers), developing personalized biomarker profiles, or testing drug combination therapies to improve efficacy and reduce toxicity.

Phase I study of pemetrexed with sorafenib in advanced solid tumors

Purpose

To determine if combination treatment with pemetrexed and sorafenib is safe and tolerable in patients with advanced solid tumors.

Results

Thirty-seven patients were enrolled and 36 patients were treated (24 in cohort A; 12 in cohort B). The cohort A dose schedule resulted in problematic cumulative toxicity, while the cohort B dose schedule was found to be more tolerable. The maximum tolerated dose (MTD) was pemetrexed 750 mg/m² every 14 days with oral sorafenib 400 mg given twice daily on days 1–5. Because dosing delays and modifications were associated with the MTD, the recommended phase II dose was declared to be pemetrexed 500 mg/m² every 14 days with oral sorafenib 400 mg given twice daily on days 1–5. Thirty-three patients were evaluated for antitumor activity. One complete response and 4 partial responses were observed (15% overall response rate). Stable disease was seen in 15 patients (45%). Four patients had a continued response at 6 months, including 2 of 5 patients with triple-negative breast cancer.

Experimental Design

A phase I trial employing a standard 3 + 3 design was conducted in patients with advanced solid tumors. Cohort A involved a novel dose escalation schema exploring doses of pemetrexed every 14 days with continuous sorafenib. Cohort B involved a modified schedule of sorafenib dosing on days 1–5 of each 14-day pemetrexed cycle. Radiographic assessments were conducted every 8 weeks.

Conclusions

Pemetrexed and intermittent sorafenib therapy is a safe and tolerable combination for patients, with promising activity seen in patients with breast cancer.

Drug development and clinical trial design in pancreatico-biliary malignancies

Pancreatico-biliary (P-B) tumors arise from the pancreas, bile duct, and ampulla of Vater. Despite their close anatomical location, they have different etiology and biology. However, they uniformly share a poor prognosis, with no major improvements observed in overall survival over decades, even in the face of progress in diagnostic imaging and surgical techniques, and advances in systemic and loco-regional radiation therapies. To date, cytotoxic treatment has been associated with modest benefits in the advanced disease setting, and survival for patients with stage IV disease has not exceeded a year. Therefore, there is a pressing need to identify better treatments which may impact more significantly. Frequently, encouraging signals of potential efficacy for novel agents in early phase clinical trials have been followed by disappointing failures in larger phase III trials, raising the valid question of how drug development can be optimized for patients with pancreatic adenocarcinoma and biliary tract malignancies. In this article we summarize the current therapeutic options for these patients and their limitations. The biological context of these cancers is reviewed, highlighting features that may make them resistant to standard chemotherapeutics and could be potential therapeutic targets. We discuss the role of early phase clinical trials, defined as phase I and non-randomised phase II trials, within the clinical context and current therapeutic landscape of P-B tumors and postulate how translational studies and trial design may enable better realization of emerging targets together with a proposed model for future patient management. A detailed summary of current phase I clinical trials in P-B tumors is provided.

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.

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.

Some statistical considerations in the clinical development of cancer immunotherapies

Immuno-oncology has emerged as an exciting new approach to cancer treatment. Common immunotherapy approaches include cancer vaccine, effector cell therapy, and T-cell-stimulating antibody. Checkpoint inhibitors such as cytotoxic T lymphocyte-associated antigen 4 and programmed death-1/L1 antagonists have shown promising results in multiple indications in solid tumors and hematology. However, the mechanisms of action of these novel drugs pose unique statistical challenges in the accurate evaluation of clinical safety and efficacy, including late-onset toxicity, dose optimization, evaluation of combination agents, pseudoprogression, and delayed and lasting clinical activity. Traditional statistical methods may not be the most accurate or efficient. It is highly desirable to develop the most suitable statistical methodologies and tools to efficiently investigate cancer immunotherapies. In this paper, we summarize these issues and discuss alternative methods to meet the challenges in the clinical development of these novel agents. For safety evaluation and dose-finding trials, we recommend the use of a time-to-event model-based design to handle late toxicities, a simple 3-step procedure for dose optimization, and flexible rule-based or model-based designs for combination agents. For efficacy evaluation, we discuss alternative endpoints/designs/tests including the time-specific probability endpoint, the restricted mean survival time, the generalized pairwise comparison method, the immune-related response criteria, and the weighted log-rank or weighted Kaplan-Meier test. The benefits and limitations of these methods are discussed, and some recommendations are provided for applied researchers to implement these methods in clinical practice.

From Famine to Feast: Developing Early-Phase Combination Immunotherapy Trials Wisely

Not until the turn of this century has immunotherapy become a fundamental component of cancer treatment. While monotherapy with immune modulators, such as immune checkpoint inhibitors, provides a subset of patients with durable clinical benefit and possible cure, combination therapy offers the potential for antitumor activity in a greater number of patients. The field of immunology has provided us with a plethora of potential molecules and pathways to target. This abundance makes it impractical to empirically test all possible combinations efficiently. We recommend that potential immunotherapy combinations be chosen based on sound rationale and available data to address the mechanisms of primary and acquired immune resistance. Novel trial designs may increase the proportion of patients receiving potentially efficacious treatments and, at the same time, better define the balance of clinical activity and safety. We believe that implementing a strategic approach in the early development of immunotherapy combinations will expedite the delivery of more effective therapies with improved safety and durable outcomes.

Toxicity-dependent feasibility bounds for the escalation with overdose control approach in phase I cancer trials

Phase I trials of anti-cancer therapies aim to identify a maximum tolerated dose (MTD), defined as the dose that causes unacceptable toxicity in a target proportion of patients. Both rule-based and model-based methods have been proposed for MTD recommendation. The escalation with overdose control (EWOC) approach is a model-based design where the dose assigned to the next patient is one that, given all available data, has a posterior probability of exceeding the MTD equal to a pre-specified value known as the feasibility bound. The aim is to conservatively dose-escalate and approach the MTD, avoiding severe overdosing early on in a trial. The EWOC approach has been applied in practice with the feasibility bound either fixed or varying throughout a trial, yet some of the methods may recommend incoherent dose-escalation, that is, an increase in dose after observing severe toxicity at the current dose. We present examples where varying feasibility bounds have been used in practice, and propose a toxicity-dependent feasibility bound approach that guarantees coherent dose-escalation and incorporates the desirable features of other EWOC approaches. We show via detailed simulation studies that the toxicity-dependent feasibility bound approach provides improved MTD recommendation properties to the original EWOC approach for both discrete and continuous doses across most dose-toxicity scenarios, with comparable performance to other approaches without recommending incoherent dose escalation. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Spotlight on landmark oncology trials: the latest evidence and novel trial designs

The era of precision oncology is marked with prominent successes in the therapy of advanced soft tissue sarcomas, breast cancer, ovarian cancer and haematological neoplasms, among others. Moreover, recent trials of immune checkpoint inhibitors in melanoma, non-small cell lung carcinoma, and head and neck cancers have significantly influenced the therapeutic landscape by providing promising evidence for immunotherapy efficacy in the adjuvant setting in high-risk locoregional disease. To speed up the introduction of targeted therapy for cancer patients, novel phase II trials are being designed, and may likely form the basis for the 'landmark trials' of the future. A special article collection in BMC Medicine, "Spotlight on landmark oncology trials", features articles from invited experts on recent clinical practice-changing trials.

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.

Approaches to modernize the combination drug development paradigm

Recent advances in genomic sequencing and omics-based capabilities are uncovering tremendous therapeutic opportunities and rapidly transforming the field of cancer medicine. Molecularly targeted agents aim to exploit key tumor-specific vulnerabilities such as oncogenic or non-oncogenic addiction and synthetic lethality. Additionally, immunotherapies targeting the host immune system are proving to be another promising and complementary approach. Owing to substantial tumor genomic and immunologic complexities, combination strategies are likely to be required to adequately disrupt intricate molecular interactions and provide meaningful long-term benefit to patients. To optimize the therapeutic success and application of combination therapies, systematic scientific discovery will need to be coupled with novel and efficient clinical trial approaches. Indeed, a paradigm shift is required to drive precision medicine forward, from the traditional "drug-centric" model of clinical development in pursuit of small incremental benefits in large heterogeneous groups of patients, to a "strategy-centric" model to provide customized transformative treatments in molecularly stratified subsets of patients or even in individual patients. Crucially, to combat the numerous challenges facing combination drug development-including our growing but incomplete understanding of tumor biology, technical and informatics limitations, and escalating financial costs-aligned goals and multidisciplinary collaboration are imperative to collectively harness knowledge and fuel continual innovation.

Monoclonal antibodies for treating gastric cancer: promises and pitfalls

INTRODUCTION

Gastric cancer (GC) presents dismal prognosis when diagnosed at advanced stages, standard chemotherapy having shown little efficacy. Introduction of biotherapies interfering with novel targets and signaling pathways is currently an emerging strategy.

AREAS COVERED

Only two monoclonal antibodies (trastuzumab and ramucirumab) have been approved, mostly in association with cytotoxics. Conversely, testing other promising biotherapies (panitumumab, cetuximab, bevacizumab, rilotumumab) have yielded conflicting results, since encouraging early clinical trials have failed to be confirmed in larger phase-III studies. Empirical and underpowered strategies when designing combinational studies, lack of comprehensive knowledge of pharmacokinetics/pharmacodynamics (PK/PD) relationships, and underestimation of the large inter-patient variability in drug exposure levels with monoclonal antibodies, could explain the failures in developing biotherapies in gastric cancer. This review covers the achievements and limits of monoclonal antibodies in gastric cancer and proposes clues to overcome current failures.

EXPERT OPINION

Trastuzumab efficacy could be improved thanks to its combination with triplet chemotherapy or with another anti-HER2 agents or in continuation during second-line chemotherapy. Concerning ramucirumab, further studies are necessary to prove its interest in first line treatment of advanced GC, to use the optimal dose in each patient-given the large inter-patients variability, and to find predictive biomarkers of efficacy.

The performance of model-based versus rule-based phase I clinical trials in oncology : A quantitative comparison of the performance of model-based versus rule-based phase I trials with molecularly targeted anticancer drugs over the last 2 years

Phase I studies with anticancer drugs are used to evaluate safety and tolerability and to choose a recommended phase II dose (RP2D). Traditionally, phase I trial designs are rule-based, but for several years there is a trend towards model-based designs. Simulations have shown that model-based designs perform better, faster and are safer to establish the RP2D than rule-based designs. However, the superiority of model-based designs has never been confirmed based on true trial performance in practice. To aid evidence-based decisions for designing phase I trials, we compared publications of model-based and rule-based phase I trials in oncology. We reviewed 172 trials that have been published in the last 2 years and assessed the following operating characteristics: efficiency (trial duration, population size, dose-levels), patient safety (dose-limiting toxicities (DLTs)) and treatment optimality (percentage of patients treated below and at or above the recommended phase 2 dose). Our results showed a non-significant but clinically relevant difference in trial duration. Model-based trials needed 10 months less than rule-based trials (26 versus 36 months; p = 0.25). Additionally, fewer patients were treated at dose-levels below the RP2D (31 % versus 40 %; p = 0.73) while safety was preserved (13 % DLTs versus 14 % DLTs). In this review, we provide evidence to encourage the use of model-based designs for future phase I studies, based on a median of 10 months of time gain, acceptable toxicity rates and minimization of suboptimal treatment.

Modelling semi-attributable toxicity in dual-agent phase I trials with non-concurrent drug administration

In oncology, combinations of drugs are often used to improve treatment efficacy and/or reduce harmful side effects. Dual‐agent phase I clinical trials assess drug safety and aim to discover a maximum tolerated dose combination via dose‐escalation; cohorts of patients are given set doses of both drugs and monitored to see if toxic reactions occur. Dose‐escalation decisions for subsequent cohorts are based on the number and severity of observed toxic reactions, and an escalation rule. In a combination trial, drugs may be administered concurrently or non‐concurrently over a treatment cycle. For two drugs given non‐concurrently with overlapping toxicities, toxicities occurring after administration of the first drug yet before administration of the second may be attributed directly to the first drug, whereas toxicities occurring after both drugs have been given some present ambiguity; toxicities may be attributable to the first drug only, the second drug only or the synergistic combination of both. We call this mixture of attributable and non‐attributable toxicity semi‐attributable toxicity. Most published methods assume drugs are given concurrently, which may not be reflective of trials with non‐concurrent drug administration. We incorporate semi‐attributable toxicity into Bayesian modelling for dual‐agent phase I trials with non‐concurrent drug administration and compare the operating characteristics to an approach where this detail is not considered. Simulations based on a trial for non‐concurrent administration of intravesical Cabazitaxel and Cisplatin in early‐stage bladder cancer patients are presented for several scenarios and show that including semi‐attributable toxicity data reduces the number of patients given overly toxic combinations. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Novel dose-finding designs and considerations on practical implementations in oncology clinical trials

One of the main objectives in phase I oncology trials is to evaluate safety and tolerability of an experimental treatment by estimating the maximum tolerated dose (MTD) based on the rate of dose-limiting toxicities (DLT). To meet emerging challenges in dose-finding studies, over the past two decades, extensive research has been conducted by statistical and medical researchers to create innovative dose finding designs that perform better than the standard 3 + 3 design, which often exhibits undesirable statistical and operational properties. However, clinical implementation and practical usage of these new designs have been limited. This article begins with a review of the most recent literature and then provides some perspectives on implementing novel adaptive dose finding designs in oncology phase I trials from a pharmaceutical industry perspective. Statistical planning and logistical considerations on how to effectively execute such designs in multi-center clinical trials are discussed using two recent case studies.

A curve-free Bayesian decision-theoretic design for two-agent Phase I trials

Although Bayesian statistical methods are gaining attention in the medical community, as they provide a natural framework for incorporating prior information, the complexity of these methods limited their adoptions in clinical trials. This article proposes a Bayesian design for two-agent Phase I trials that is relatively easy for clinicians to understand and implement, yet performs comparably to more complex designs, so that it is more likely to be adopted in actual trials. In order to reduce model complexity and computational burden, we choose a working model with conjugate priors so that the posterior distributions have analytical expressions. Furthermore, we provide a simple strategy to facilitate the specification of priors based on the toxicity information accrued from single-agent Phase I trials. The proposed method should be useful in terms of the ease of implementation and the savings in sample size without sacrificing performance. Moreover, the conservativeness of the dose-finding algorithm renders it a relatively safe method.

Treatment approaches for EGFR-inhibitor-resistant patients with non-small-cell lung cancer

Discovery of activating mutations in EGFR and their use as predictive biomarkers to tailor patient therapy with EGFR tyrosine kinase inhibitors (TKIs) has revolutionised treatment of patients with advanced EGFR-mutant non-small-cell lung cancer (NSCLC). At present, first-line treatment with EGFR TKIs (gefitinib, erlotinib, and afatinib) has been approved for patients harbouring exon 19 deletions or exon 21 (Leu858Arg) substitution EGFR mutations. These agents improve response rates, time to progression, and overall survival. Unfortunately, patients develop resistance, limiting patient benefit and posing a challenge to oncologists. Optimum treatment after progression is not clearly defined. A more detailed understanding of the biology of EGFR-mutant NSCLC and the mechanisms of resistance to targeted therapy mean that an era of treatment approaches based on rationally developed drugs or therapeutic strategies has begun. Combination approaches-eg, dual EGFR blockade-to overcome resistance have been trialled and seem to be promising but are potentially limited by toxicity. Third-generation EGFR-mutant-selective TKIs, such as AZD9291 or rociletininb, which target Thr790Met-mutant tumours, the most common mechanism of EGFR TKI resistance, have entered clinical trials, and exciting, albeit preliminary, efficacy data have been reported. In this Review, we summarise the scientific literature and evidence on therapy options after EGFR TKI treatment for patients with NSCLC, aiming to provide a guide to oncologists, and consider how to maximise therapeutic advances in outcomes in this rapidly advancing area.

A comparative study of adaptive dose-finding designs for phase I oncology trials of combination therapies

Little is known about the relative performance of competing model-based dose-finding methods for combination phase I trials. In this study, we focused on five model-based dose-finding methods that have been recently developed. We compared the recommendation rates for true maximum-tolerated dose combinations (MTDCs) and over-dose combinations among these methods under 16 scenarios for 3 × 3, 4 × 4, 2 × 4, and 3 × 5 dose combination matrices. We found that performance of the model-based dose-finding methods varied depending on (1) whether the dose combination matrix is square or not; (2) whether the true MTDCs exist within the same group along the diagonals of the dose combination matrix; and (3) the number of true MTDCs. We discuss the details of the operating characteristics and the advantages and disadvantages of the five methods compared.

Design of phase I combination trials: recommendations of the Clinical Trial Design Task Force of the NCI Investigational Drug Steering Committee

Anticancer drugs are combined in an effort to treat a heterogeneous tumor or to maximize the pharmacodynamic effect. The development of combination regimens, while desirable, poses unique challenges. These include the selection of agents for combination therapy that may lead to improved efficacy while maintaining acceptable toxicity, the design of clinical trials that provide informative results for individual agents and combinations, and logistic and regulatory challenges. The phase I trial is often the initial step in the clinical evaluation of a combination regimen. In view of the importance of combination regimens and the challenges associated with developing them, the Clinical Trial Design (CTD) Task Force of the National Cancer Institute Investigational Drug Steering Committee developed a set of recommendations for the phase I development of a combination regimen. The first two recommendations focus on the scientific rationale and development plans for the combination regimen; subsequent recommendations encompass clinical design aspects. The CTD Task Force recommends that selection of the proposed regimens be based on a biologic or pharmacologic rationale supported by clinical and/or robust and validated preclinical evidence, and accompanied by a plan for subsequent development of the combination. The design of the phase I clinical trial should take into consideration the potential pharmacokinetic and pharmacodynamic interactions as well as overlapping toxicity. Depending on the specific hypothesized interaction, the primary endpoint may be dose optimization, pharmacokinetics, and/or pharmacodynamics (i.e., biomarker).

Practical designs for Phase I combination studies in oncology

Phase I trials evaluating the safety of multidrug combinations are becoming more common in oncology. Despite the emergence of novel methodology in the area, it is rare that innovative approaches are used in practice. In this article, we review three methods for Phase I combination studies that are easy to understand and straightforward to implement. We demonstrate the operating characteristics of the designs through illustration in a single trial, as well as through extensive simulation studies, with the aim of increasing the use of novel approaches in Phase I combination studies. Design specifications and software capabilities are also discussed.

Bayesian optimal interval design for dose finding in drug-combination trials

Interval designs have recently attracted enormous attention due to their simplicity and desirable properties. We develop a Bayesian optimal interval design for dose finding in drug-combination trials. To determine the next dose combination based on the cumulative data, we propose an allocation rule by maximizing the posterior probability that the toxicity rate of the next dose falls inside a prespecified probability interval. The entire dose-finding procedure is nonparametric (model-free), which is thus robust and also does not require the typical "nonparametric" prephase used in model-based designs for drug-combination trials. The proposed two-dimensional interval design enjoys convergence properties for large samples. We conduct simulation studies to demonstrate the finite-sample performance of the proposed method under various scenarios and further make a modication to estimate toxicity contours by parallel dose-finding paths. Simulation results show that on average the performance of the proposed design is comparable with model-based designs, but it is much easier to implement.

A practical design for a dual-agent dose-escalation trial that incorporates pharmacokinetic data

Traditionally, model-based dose-escalation trial designs recommend a dose for escalation based on an assumed dose-toxicity relationship. Pharmacokinetic data are often available but are currently only utilised by clinical teams in a subjective manner to aid decision making if the dose-toxicity model recommendation is felt to be too high. Formal incorporation of pharmacokinetic data in dose-escalation could therefore make the decision process more efficient and lead to an increase in the precision of the resulting recommended dose, as well as decreasing the subjectivity of its use. Such an approach is investigated in the dual-agent setting using a Bayesian design, where historical single-agent data are available to advise the use of pharmacokinetic data in the dual-agent setting. The dose-toxicity and dose-exposure relationships are modelled independently and the outputs combined in the escalation rules. Implementation of stopping rules highlight the practicality of the design. This is demonstrated through an example which is evaluated using simulation.

A product of independent beta probabilities dose escalation design for dual-agent phase I trials

Dual-agent trials are now increasingly common in oncology research, and many proposed dose-escalation designs are available in the statistical literature. Despite this, the translation from statistical design to practical application is slow, as has been highlighted in single-agent phase I trials, where a 3 + 3 rule-based design is often still used. To expedite this process, new dose-escalation designs need to be not only scientifically beneficial but also easy to understand and implement by clinicians. In this paper, we propose a curve-free (nonparametric) design for a dual-agent trial in which the model parameters are the probabilities of toxicity at each of the dose combinations. We show that it is relatively trivial for a clinician's prior beliefs or historical information to be incorporated in the model and updating is fast and computationally simple through the use of conjugate Bayesian inference. Monotonicity is ensured by considering only a set of monotonic contours for the distribution of the maximum tolerated contour, which defines the dose-escalation decision process. Varied experimentation around the contour is achievable, and multiple dose combinations can be recommended to take forward to phase II. Code for R, Stata and Excel are available for implementation.

Evaluating DAPK as a therapeutic target

Death associated protein kinase 1 (DAPK) is an important serine/theoreine kinase involved in various cellular processes such as apoptosis, autophagy and inflammation. DAPK expression and activity are misregulated in multiple diseases including cancer, neuronal death, stoke, et al. Methylation of the DAPK gene is common in many types of cancer and can lead to loss of DAPK expression. In this review, we summarize the pathological status and functional roles of DAPK in disease and compare the published reagents that can manipulate the expression or activity of DAPK. The pleiotropic functions of DAPK make it an intriguing target and the barriers and opportunities for targeting DAPK for future clinical application are discussed.