Comparative review of novel model-assisted designs for phase I/II clinical trials.

In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting dose locations.

Confidence intervals for odds ratio from multistage randomized phase II trials.

A multi-stage randomized trial design can significantly improve efficiency by allowing early termination of the trial when the experimental arm exhibits either low or high efficacy compared to the control arm during the study. However, proper inference methods are necessary because the underlying distribution of the target statistic changes due to the multi-stage structure. This article focuses on multi-stage randomized phase II trials with a dichotomous outcome, such as treatment response, and proposes exact conditional confidence intervals for the odds ratio. The usual single-stage confidence intervals are invalid when used in multi-stage trials. To address this issue, we propose a linear ordering of all possible outcomes. This ordering is conditioned on the total number of responders in each stage and utilizes the exact conditional distribution function of the outcomes. This approach enables the estimation of an exact confidence interval accounting for the multi-stage designs.

Statistical considerations in model-based dose finding for binary responses under model uncertainty.

The statistical methodology for model-based dose finding under model uncertainty has attracted increasing attention in recent years. While the underlying principles are simple and easy to understand, developing and implementing an efficient approach for binary responses can be a formidable task in practice. Motivated by the statistical challenges encountered in a phase II dose finding study, we explore several key design and analysis issues related to the hybrid testing-modeling approaches for binary responses. The issues include candidate model selection and specifications, optimal design and efficient sample size allocations, and, notably, the methods for dose-response testing and estimation. Specifically, we consider a class of generalized linear models suited for the candidate set and establish D-optimal designs for these models. Additionally, we propose using permutation-based tests for dose-response testing to avoid asymptotic normality assumptions typically required for contrast-based tests. We perform trial simulations to enhance our understanding of these issues.

rBMA: A robust Bayesian Model Averaging Method for phase II basket trials based on informative mixture priors.

Oncology drug research in the last few decades has been driven by the development of targeted agents. In the era of targeted therapies, basket trials are often used to test the antitumor activity of a novel treatment in multiple indications sharing the same genomic alteration. As patient population are further fragmented into biomarker-defined subgroups in basket trials, novel statistical methods are needed to facilitate cross-indication learning to improve the statistical power in basket trial design. Here we propose a robust Bayesian model averaging (rBMA) technique for the design and analysis of phase II basket trials. We consider the posterior distribution of each indication (basket) as the weighted average of three different models which only differ in their priors (enthusiastic, pessimistic and non-informative). The posterior weights of these models are determined based on the effect of the experimental treatment in all the indications tested. In early phase oncology trials, different binary endpoints might be chosen for different indications (objective response, disease control or PFS at landmark times), which makes it even more challenging to borrow information across indications. Compared to previous approaches, the proposed method has the flexibility to support cross-indication learning in the presence of mixed endpoints. We evaluate and compare the performance of the proposed rBMA approach to competing approaches in simulation studies. R scripts to implement the proposed method are available at https://github.com/xwang317/rBMA.

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.

Statistical and practical considerations in planning and conduct of dose-optimization trials.

The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.

Risk-benefit trade-offs and precision utilities in phase I-II clinical trials.

BACKGROUND: Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life. PURPOSE: While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups. METHODS AND CONCLUSIONS: Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.

Nonalcoholic steatohepatitis (NASH) cirrhosis: a snapshot of therapeutic agents in clinical development and the optimal design for clinical trials.

INTRODUCTION: Patients with nonalcoholic steatohepatitis (NASH)-associated cirrhosis have the highest rates of major adverse liver outcomes (MALO) within the fatty liver disease spectrum and therefore have the highest unmet need for effective therapeutic agents. Several drugs are being tested for patients with NASH cirrhosis with different mechanisms of action and endpoints. AREAS COVERED: This article summarizes the available data on the natural history of NASH cirrhosis and the rates of developing MALO. We provide examples of ongoing clinical trials for NASH cirrhosis including the study design and endpoints. We then discuss the FDA-guidance on acceptable endpoints for NASH cirrhosis trials that will lead to approval. EXPERT OPINION: Metabolic and antifibrotic drugs are currently in phase 2b trials for NASH cirrhosis with outcomes ranging from histologic improvement on liver biopsy to the development of varices or MALO. We provide the readers with pragmatic advice on trial design for phase 2B and 3 NASH cirrhosis trials. The data presented in the article justify further development and investigation of therapeutic agents for the treatment of NASH cirrhosis.

PIONEER-Panc: a platform trial for phase II randomized investigations of new and emerging therapies for localized pancreatic cancer.

BACKGROUND: Personalized and effective treatments for pancreatic ductal adenocarcinoma (PDAC) continue to remain elusive. Novel clinical trial designs that enable continual and rapid evaluation of novel therapeutics are needed. Here, we describe a platform clinical trial to address this unmet need. METHODS: This is a phase II study using a Bayesian platform design to evaluate multiple experimental arms against a control arm in patients with PDAC. We first separate patients into three clinical stage groups of localized PDAC (resectable, borderline resectable, and locally advanced disease), and further divide each stage group based on treatment history (treatment naïve or previously treated). The clinical stage and treatment history therefore define 6 different cohorts, and each cohort has one control arm but may have one or more experimental arms running simultaneously. Within each cohort, adaptive randomization rules are applied and patients will be randomized to either an experimental arm or the control arm accordingly. The experimental arm(s) of each cohort are only compared to the applicable cohort specific control arm. Experimental arms may be added independently to one or more cohorts during the study. Multiple correlative studies for tissue, blood, and imaging are also incorporated. DISCUSSION: To date, PDAC has been treated as a single disease, despite knowledge that there is substantial heterogeneity in disease presentation and biology. It is recognized that the current approach of single arm phase II trials and traditional phase III randomized studies are not well-suited for more personalized treatment strategies in PDAC. The PIONEER Panc platform clinical trial is designed to overcome these challenges and help advance our treatment strategies for this deadly disease. TRIAL REGISTRATION: This study is approved by the Institutional Review Board (IRB) of MD Anderson Cancer Center, IRB-approved protocol 2020-0075. The PIONEER trial is registered at the US National Institutes of Health (ClinicalTrials.gov) NCT04481204 .

An Overview of Phase 2 Clinical Trial Designs.

Clinical trials are studies to test new treatments in humans. Typically, these treatments are evaluated over several phases to assess their safety and efficacy. Phase 1 trials are designed to evaluate the safety and tolerability of a new treatment, typically with a small number of patients (eg, 20-80), generally spread across several dose levels. Phase 2 trials are designed to determine whether the new treatment has sufficiently promising efficacy to warrant further investigation in a large-scale randomized phase 3 trial, as well as to further assess safety. These studies usually involve a few hundred patients. This article provides an overview of some of the most commonly used phase 2 designs for clinical trials and emphasizes their critical elements and considerations. Key references to some of the most commonly used phase 2 designs are given to allow the reader to explore in more detail the critical aspects when planning a phase 2 trial. A comparison of 3 potential designs in the context of the NRG-HN002 trial is presented to complement the discussion about phase 2 trials.

Advantages of multi-arm non-randomised sequentially allocated cohort designs for Phase II oncology trials.

BACKGROUND: Efficient trial designs are required to prioritise promising drugs within Phase II trials. Adaptive designs are examples of such designs, but their efficiency is reduced if there is a delay in assessing patient responses to treatment. METHODS: Motivated by the WIRE trial in renal cell carcinoma (NCT03741426), we compare three trial approaches to testing multiple treatment arms: (1) single-arm trials in sequence with interim analyses; (2) a parallel multi-arm multi-stage trial and (3) the design used in WIRE, which we call the Multi-Arm Sequential Trial with Efficient Recruitment (MASTER) design. The MASTER design recruits patients to one arm at a time, pausing recruitment to an arm when it has recruited the required number for an interim analysis. We conduct a simulation study to compare how long the three different trial designs take to evaluate a number of new treatment arms. RESULTS: The parallel multi-arm multi-stage and the MASTER design are much more efficient than separate trials. The MASTER design provides extra efficiency when there is endpoint delay, or recruitment is very quick. CONCLUSIONS: We recommend the MASTER design as an efficient way of testing multiple promising cancer treatments in non-comparative Phase II trials.

The evolution of adjuvant/neoadjuvant trials for resectable localized sarcoma.

Soft-tissue sarcomas are rare tumors arising from mesenchymal tissues. As a heterogeneous group comprising more than 50 types, the development of clinical trials remains challenging. Decision-making for neoadjuvant or adjuvant chemotherapy and radiation therapy is based on the available evidence of contemporary trials and multidisciplinary clinical judgment.

Trace Amine-Associated Receptor 1 as a Target for the Development of New Antipsychotics: Current Status of Research and Future Directions.

Schizophrenia is a mental illness associated with an array of symptoms that often result in disability. The primary treatments for schizophrenia are termed antipsychotics. Although antipsychotics modulate a number of different receptor types and subtypes, all currently regulatory agency-approved antipsychotics share in common direct or functional antagonism at the dopamine type 2 receptor (D2R). The majority of people with schizophrenia do not achieve full resolution of their symptoms with antipsychotics, suggesting the need for alternative or complementary approaches. The primary focus of this review is to assess the evidence for the role of the trace amine-associated receptor 1 (TAAR-1) in schizophrenia and the role of TAAR-1 modulators as novel-mechanism antipsychotics. Topics include an overview of TAAR-1 physiology and pathophysiology in schizophrenia, interaction with other neurotransmitter systems, including the dopaminergic, glutamatergic and serotonergic system, and finally, a review of investigational TAAR-1 compounds that have reached Phase II clinical studies in schizophrenia: SEP-363856 (ulotaront) and RO6889450 (ralmitaront). Thus far, results are publicly available only for ulotaront in a relatively young (18-40 years) and acutely exacerbated cohort. These results showed positive effects for overall schizophrenia symptoms without significant tolerability concerns. An ongoing study of ralmitaront will assess specific efficacy in patients with persistent negative symptoms. If trials of TAAR-1 modulators, and other novel-mechanism targets for schizophrenia that are under active study, continue to show positive results, the definition of an antipsychotic may need to be expanded beyond the D2R target in the near future.

RaPiDS (GOG-3028): randomized Phase II study of balstilimab alone or in combination with zalifrelimab in cervical cancer.

Balstilimab (anti-programmed death 1) and zalifrelimab (anti-CTLA-4) are two new checkpoint inhibitors that have emerged as promising investigational agents for the treatment of cervical cancer, particularly in the setting of previously-treated, recurrent/metastatic disease. Here we describe the rationale and design of RaPiDS (NCT03894215), a two-arm Phase II study evaluating the safety, tolerability and efficacy of balstilimab administered alone or in combination with zalifrelimab in patients with advanced cervical cancer who progressed after first-line, platinum-based chemotherapy. Patients will be randomized in a 1:1 ratio. The primary end point is objective response rate, and key secondary objectives include safety, duration of response, progression-free survival, overall survival and quality of life outcomes.

Lay abstract Current treatment options for women with recurrent/metastatic cervical cancer are limited. Immunotherapy is altering the therapeutic landscape in this setting yet opportunities remain to improve on current outcomes. Dual blockade of different immune checkpoints is an approach shown to be highly effective in other cancers. Balstilimab (anti-programmed death 1) and zalifrelimab (anti-CTLA-4) are two new checkpoint inhibitors showing promise in patients with advanced cervical cancer. The RaPiDS trial is designed to characterize the safety and activity of balstilimab, alone and in combination with zalifrelimab, in patients with recurrent/metastatic cervical cancer who progressed after prior platinum-based chemotherapy. Clinical trial registration: NCT03894215 (ClinicalTrials.gov).

Multimodal Treatment with GEMOX Plus Helical Tomotherapy in Unresectable Locally Advanced Pancreatic Cancer: A Pooled Analysis of Two Phase 2 Studies.

In locally advanced pancreatic cancer (LAPC), the combination of chemotherapy and radiotherapy is a widely used treatment option. We performed a pooled analysis, including an exploratory analysis for prognostic and predictive factors, of two phase 2 trials including 73 patients with LAPC, treated with gemcitabine and oxaliplatin (GEMOX) and hypofractionated tomotherapy. With a median follow-up of 36 months (range 1-65), median progression-free (PFS) and overall survival (OS) were 10.2 (95% confidence interval [CI] 7.8-13.2) and 14.3 (95% CI 12.0-18.1) months, respectively. The overall resectability rate was 23.3% (95% CI 13.6-33.0), and the R0 resection rate was 13.7% (95% CI 5.8-21.6). In the multivariate analysis, ECOG performance status (PS) 0 and low levels of CA 19-9 were associated with improved OS and PFS. Concerning OS, log(CA19-9) resulted in a hazard ratio (HR) of 1.20 (95% CI 1.02-1.42), p = 0.027. For ECOG PS 0, HR was 1.00; for PS 1, HR was 2.69 (95% CI 1.46-4.96); for PS 2, HR was 4.18 (95% CI 0.90-19.46); p = 0.003. Low CA19-9 levels were also predictive for resection, with an odds ratio of 0.71 (95% CI 0.52-0.97), p = 0.034. In conclusion, GEMOX and hypofractionated radiotherapy is a treatment option in LAPC. Further studies are needed to identify differences in tumor biology, which may help to predict resectability and prognosis.

The quality of clinical trials in neuroendocrine tumours; have we learnt from our mistakes? An evaluation of phase II and phase III clinical trials.

The quality and reporting of neuroendocrine tumour (NET) clinical trials has previously been found to be heterogeneous impairing trial interpretability. We aimed to perform an updated review of the quality of phase II/III clinical trials in NET to assess if trial design and reporting have improved since 2011. We performed a PubMed search for phase II/III trials evaluating systemic anticancer therapies or liver-directed therapies published between 2011 and 2018. Data collected comprised administrative data, study population characteristics, endpoints, reporting and statistical design parameters, and results. Sixty studies were included (5218 patients): 50 phase II and 10 phase III trials. Study populations were heterogeneous: 52% of trials allowed tumours from various primary sites, 28% allowed both well- and poorly-differentiated tumour morphology or did not specify, and 57% did not report proliferative indices and/or tumour grade in ≥80% of the study population. Only 36% of trials mandated radiological disease progression on participant enrolment using a validated measure. Statistical design and primary endpoint were clearly defined in 67% and 88% of trials, respectively. Toxicity (88%), radiological response rate (85%) and progression-free survival/time to progression (82%) were well reported in a majority of trials, but health-related quality of life was included in the minority. Of the randomised trials (n = 11), study populations were more homogeneous and study design was more often clearly defined; however, only 45% mandated radiological progression at baseline as measured per Response Evaluation Criteria In Solid Tumours, and reporting of health-related quality of life (55%) remained suboptimal. The design and reporting of NET clinical trials, predominantly of single-arm phase II trials, remains suboptimal and has not considerably improved over time despite the growth in our knowledge of the biology and unique characteristics of NETs. Higher quality is seen in randomised trials, although certain design and reporting elements remain inadequate in some studies. We must prioritise the design and conduct of NET clinical trials to effectively inform future research and guide practice change.

Optimal Bayesian hierarchical model to accelerate the development of tissue-agnostic drugs and basket trials.

Tissue-agnostic trials and basket trials enroll patients based on their genetic biomarkers, not tumor type, in an attempt to determine if a new drug can successfully treat disease conditions based on biomarkers. The Bayesian hierarchical model (BHM) provides an attractive approach to design phase II tissue-agnostic trials by allowing information borrowing across multiple disease types. In this article, we elucidate two intrinsic and inevitable issues that may limit the use of BHM to tissue-agnostic trials: sensitivity to the prior specification of the shrinkage parameter and the competing "interest" among disease types in increasing power and controlling type I error. To address these issues, we propose the optimal BHM (OBHM) and clustered OBHM (COBHM) approaches. In these approach, we first specify a flexible utility function to quantify the tradeoff between type I error and power across disease types based on the study objectives, and then we select the prior of the shrinkage parameter to optimize the utility function of clinical and regulatory interest. COBHM further utilizes a simple Bayesian rule to cluster tumor types into sensitive and insensitive subgroups to achieve more accurate information borrowing. Simulation study shows that the OBHM and especially COBHM have desirable operating characteristics, outperforming some existing methods. COBHM effectively balances power and type I error, addresses the sensitivity of the prior selection, and reduces the "unwarranted" subjectivity in the prior selection. It provides a systematic, rigorous way to apply BHM and solve the common problem of blindingly using a non-informative inverse-gamma prior (with a large variance) or priors arbitrarily chosen that may lead to problematic statistical properties.

Safety and tolerability of fixed-dose combinations of ibuprofen and acetaminophen: pooled analysis of phase 1-3 clinical trials.

OBJECTIVES: An ibuprofen (IBU)/acetaminophen (APAP) fixed-dose combination (FDC) for over-the-counter (OTC) use was developed with the goal of providing the same effective analgesic activity as full doses of the individual monocomponents, while reducing individual monocomponent drug exposures. Here, the safety and tolerability of the FDC is characterized using pooled safety data from phase 1-3 clinical trials in the FDC development program. METHODS: We conducted a pooled safety analysis of data from 7 clinical trials: three phase 1 pharmacokinetic trials, a phase 2 proof-of-concept trial, and three phase 3 trials (a single- and a multiple-dose trial in a dental pain model and a single-dose trial in an induced-fever model). Safety and tolerability of the FDC were assessed by adverse events (AEs) for the total group and subgroups (age, sex, race). RESULTS: A total of 1,477 participants were enrolled in the 7 trials; 715 were treated with FDC IBU/APAP, 432 with IBU monotherapy, 330 with APAP monotherapy, and 156 with placebo. Most subjects were white (86.5%), and 44% were female. Two trials enrolling 195 adolescents accounted for 13.2% of the overall study population. All-causality treatment-emergent AEs (TEAEs) occurred in 19.7% of the 1477 participants. Nausea (13.5%), vomiting (7.4%), dizziness (4.5%), headache (1.2%), and feeling hot (1.0%) were the only TEAEs reported in ≥1% of subjects. Treatment-related AEs occurred in 1.8% of the subjects in the overall population. The incidence of AEs, including treatment-related AEs, was consistently lower in all active treatment groups than in the placebo group; this also applied to subgroups according to sex, race, and age, including adolescents aged 12-17 years. The higher rate of AEs with placebo was likely due to lack of pain/fever control. CONCLUSION: Single-dose or short-course FDC IBU/APAP OTC use was well tolerated, with an AE profile similar to its IBU and APAP monocomponents. CLINICALTRIALS.GOV REGISTRATION: NCT01559259; NCT02912650; NCT02837952; NCT02761980. The pharmacokinetic studies (n = 3) did not require registration.

Use of endpoints in multiple myeloma randomized controlled trials over the last 15 years: A systematic review.

Surrogate endpoints are being used more frequently in randomized controlled trials, even though they do not consistently corelate with patient outcomes. We systemically evaluated the use of surrogate endpoints in multiple myeloma randomized controlled trials over the past 15 years. We searched three databases (Pubmed, Embase, Cochrane) for multiple myeloma randomized controlled trials from January 1, 2005 to December 30, 2019. The primary outcome of our study was the proportion of randomized controlled trials that used overall survival as their primary endpoint. Secondary outcomes included the use of surrogate endpoints, and trends over time, and whether they differed based on study sponsorship. We included 151 randomized controlled trials in our analysis. The primary endpoint was overall survival (OS) in 17 (11.3%) of studies, progression free survival (PFS) or event-defined endpoints in 91 studies (60.3%) and response-based endpoints in 44 studies (29.1%). Quality of life was a primary endpoint in only three studies (2%). The use of OS as a primary endpoint decreased from 28.5% of trials from 2005 to 2009 to 5.5% from 2015 to 2019. There has been a decrease in the clinically meaningful endpoint of OS over the past 15 years in multiple myeloma randomized controlled trials. Use of quality of life as a primary endpoint remains exceedingly low. It remains paramount to recognize that the use of surrogate endpoints is imperfect, and care based upon them requires constant physician and patient re-analysis.