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Broadening eligibility criteria in cancer trials has been advocated to represent the intended patient population more accurately. The advantages are clear in terms of generalizability and recruitment, however there are some important considerations in terms of design for efficiency and patient safety. While toxicity may be expected to be homogeneous across these subpopulations, designs should be able to recommend safe and precise doses if subpopulations with different toxicity profiles exist. Dose-finding designs accounting for patient heterogeneity have been proposed, but existing methods assume that the source of heterogeneity is known. We propose a broadened eligibility dose-finding design to address the situation of unknown patient heterogeneity in phase I cancer clinical trials where eligibility is expanded, and multiple eligibility criteria could potentially lead to different optimal doses for patient subgroups. The design offers a two-in-one approach to dose-finding by simultaneously selecting patient criteria that differentiate the maximum tolerated dose (MTD), using stochastic search variable selection, and recommending the subpopulation-specific MTD if needed. Our simulation study compares the proposed design to the naive approach of assuming patient homogeneity and demonstrates favorable operating characteristics across a wide range of scenarios, allocating patients more often to their true MTD during the trial, recommending more than one MTD when needed, and identifying criteria that differentiate the patient population. The proposed design highlights the advantages of adding more variability at an early stage and demonstrates how assuming patient homogeneity can lead to unsafe or sub-therapeutic dose recommendations., (© 2024 John Wiley & Sons Ltd.)

We propose a phase I/II trial design to support dose-finding when the optimal biological dose (OBD) may differ in two prespecified patient subgroups. The proposed design uses a utility function to quantify efficacy-toxicity trade-offs, and a Bayesian model with spike and slab prior distributions for the subgroup effect on toxicity and efficacy to guide dosing and to facilitate identifying either subgroup-specific OBDs or a common OBD depending on the resulting trial data. In a simulation study, we find the proposed design performs nearly as well as a design that ignores subgroups when the dose-toxicity and dose-efficacy relationships are the same in both subgroups, and nearly as well as a design with independent dose-finding within each subgroup when these relationships differ across subgroups. In other words, the proposed adaptive design performs similarly to the design that would be chosen if investigators possessed foreknowledge about whether the dose-toxicity and/or dose-efficacy relationship differs across two prespecified subgroups. Thus, the proposed design may be effective for OBD selection when uncertainty exists about whether the OBD differs in two prespecified subgroups., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: Phase 1 oncology trials provide access to new therapies and may improve cancer outcomes. Phase 1 trials conducted in the Asian-Pacific region are increasing at a faster rate than the global trend. This study aimed to describe the changing landscape of phase 1 oncology trials in Australia in the last decade., Methods: This cross-sectional study reviewed phase 1 oncology trials registered on ClinicalTrials.gov conducted in Australia. Phase 1 trials were included for analysis if they enrolled adults with solid organ malignancies, used at least one systemic agent, and were first registered between January 1, 2012, and December 31, 2022. The number of trials, site locations, sponsor type, and drug class were analyzed using descriptive statistics., Results: Over the 10-year period, ClinicalTrials.gov included 493 phase 1 clinical trials across 71 Australian sites. Most sites were in metropolitan locations; in Melbourne, trials were concentrated within selected sites, while in Sydney, trials were spread across a larger number of sites. The number of phase 1 trials per annum increased from 18 in 2012 to 75 in 2022. Since 2020, emerging biopharmaceutical companies have become the predominant sponsor type, a trend that is also seen globally. While most trial sponsors were North American (42%), there was increasing representation from Asian sponsors over the 10-year period (6% in 2012 to 39% in 2022). Immunomodulatory (45%) and targeted approaches (44%) accounted for most drug classes used alone or in combination., Conclusions: There are an increasing number of phase 1 trials conducted within Australia. Sponsors of phase 1 trials are increasingly from Asian countries and are more likely to be emerging biopharmaceutical companies., (© 2024 John Wiley & Sons Australia, Ltd.)

Background: Incorporating backfill cohorts in phase I oncology trials is a recently developed strategy for dose optimization. However, the efficacy assessment window is long in general, causing a lag in identifying ineffective doses and more patients being backfilled to those doses. There is necessity to investigate how to use patient-reported outcomes (PRO) to determine doses for backfill cohorts., Methods: We propose a unified Bayesian design framework, called 'Backfill-QoL', to utilize patient-reported quality of life (QoL) data into phase I oncology trials with backfill cohorts, including methods for trial monitoring, algorithm for dose-finding, and criteria for dose selection. Simulation studies and sensitivity analyses are conducted to evaluate the proposed Backfill-QoL design., Results: The simulation studies demonstrate that the Backfill-QoL design is more efficient than traditional dose-expansion strategy, and fewer patients would be allocated to doses with unacceptable QoL profiles. A user-friendly Windows desktop application is developed and freely available for implementing the proposed design., Conclusions: The Backfill-QoL design enables continuous monitoring of safety, efficacy and QoL outcomes, and the recommended phase II dose (RP2D) can be identified in a more patient-centered perspective., (© 2024. The Author(s).)

This review discusses the growing importance of target validation within phase I (P1) trials as a new trend in drug development, especially in establishing proof of concept (POC) for first-in-class drugs. The paper describes two approaches: the P1-PIV approach, which directly evaluates the primary endpoint for a pivotal clinical study to confirm therapeutic effects during P1, and the newly introduced P1-FCTE, which assesses functional changes necessary for therapeutic effect as a novel target validation milestone in P1. By providing practical examples of first-in-class drugs, we compare the benefits, costs, hurdles and applicable therapeutic areas of these approaches. Finally, we discuss the potential of these novel approaches to facilitate POC success, shorten development timelines and ultimately increase drug discovery success rates., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Delayed outcome is common in phase I oncology clinical trials. It causes logistic difficulty, wastes resources, and prolongs the trial duration. This article investigates this issue and proposes the time-to-event 3 + 3 (T3 + 3) design, which utilizes the actual follow-up time for at-risk patients with pending toxicity outcomes. The T3 + 3 design allows continuous accrual without unnecessary trial suspension and is costless and implementable with pretabulated dose decision rules. Besides, the T3 + 3 design uses the isotonic regression to estimate the toxicity rates across dose levels and therefore can accommodate for any targeted toxicity rate for maximum tolerated dose (MTD). It dramatically facilitates the trial preparation and conduct without intensive computation and statistical consultation. The extension to other algorithm-based phase I dose-finding designs (e.g., i3 + 3 design) is also studied. Comprehensive computer simulation studies are conducted to investigate the performance of the T3 + 3 design under various dose-toxicity scenarios. The results confirm that the T3 + 3 design substantially shortens the trial duration compared with the conventional 3 + 3 design and yields much higher accuracy in MTD identification than the rolling six design. In summary, the T3 + 3 design addresses the delayed outcome issue while keeping the desirable features of the 3 + 3 design, such as simplicity, transparency, and costless implementation. It has great potential to accelerate early-phase drug development., (© 2024 The Author(s). Pharmaceutical Statistics published by John Wiley & Sons Ltd.)

This study aims to develop explainable AI methods for matching patients with phase 1 oncology clinical trials using Natural Language Processing (NLP) techniques to address challenges in patient recruitment for improved efficiency in drug development. A prototype system based on modern NLP techniques has been developed to match patient records with phase 1 oncology clinical trial protocols. Four criteria are considered for the matching: cancer type, performance status, genetic mutation, and measurable disease. The system outputs a summary matching score along with explanations of the evidence. The outputs of the AI system were evaluated against the ground truth matching results provided by the domain expert on a dataset of twelve synthesized dummy patient records and six clinical trial protocols. The system achieved a precision of 73.68%, sensitivity/recall of 56%, accuracy of 77.78%, and specificity of 89.36%. Further investigation into the misclassified cases indicated that ambiguity of abbreviation and misunderstanding of context are significant contributors to errors. The system found evidence of no matching for all false positive cases. To the best of our knowledge, no system in the public domain currently deploys an explainable AI-based approach to identify optimal patients for phase 1 oncology trials. This initial attempt to develop an AI system for patients and clinical trial matching in the context of phase 1 oncology trials showed promising results that are set to increase efficiency without sacrificing quality in patient-trial matching., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ghosh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: We introduce a novel algorithmic approach to design phase I trials for oncology drug combinations., Methods: Our proposed Toxicity Adaptive Lists Design (TALE) is straightforward to implement, requiring the prespecification of a small number of parameters that define rules governing dose escalation, de-escalation, or reassessment of previously explored dose levels. These rules effectively regulate dose exploration and control the number of toxicities. A key feature of TALE is the possibility of simultaneous assignment of multiple-dose combinations that are deemed safe by previously accrued data., Results: A numerical study shows that TALE shares comparable operative characteristics, in terms of identification of the maximum tolerated dose (MTD), to alternative approaches such as the Bayesian optimal interval design, the COPULA, the product of independent beta probabilities escalation, and the continual reassessment method for partial ordering designs while reducing the risk of overdosing patients., Conclusion: The proposed TALE design provides a favorable balance between maintaining patient safety and accurately identifying the MTD. To facilitate the use of TALE, we provide a user-friendly R Shiny application and an R package for computing relevant operating characteristics, such as the risk of assigning highly toxic dose combinations.

Background: The conventional method of drug development in oncology typically progresses through phase I, phase II and randomized phase III trials. Nevertheless, some recent drug approvals for head and neck cancer (HNC) relied on findings from single-arm phase II trials. This underscores the significance of disease-specific phase I trials as a crucial step in exploring new drugs for HNC patients. The purpose of this review is to present the currently available data of phase I clinical trials conducted in HNC and to provide an overview of ongoing therapeutic trends in HNC., Methods: We performed a scoping review of phase I trials evaluating single-agent treatments specifically designed for HNC patients. The PubMed database was searched using "(phase I) AND (head and neck)". To ensure exhaustiveness, we also performed a search from the American Society of Clinical Oncology, European Society for Medical Oncology and American Association for Cancer Research websites., Results: We screened 1,134 articles and selected 29 trials that met eligibility criteria, published between 1994 and 2023, for a total of 741 patients. Twenty-one trials comprised patients with different sites of HNSCC and only 8 trials (27%) focused on a specified subsite of head and neck. Most of trials investigated treatments in recurrent/metastatic (R/M) settings (86%). Immunotherapeutic agents were the most examined followed by targeted agents, cytotoxic drugs and "others" including a nanoparticle, a therapeutic gene, a fusion protein and a modulator of gene expression. Among trials reporting activity for R/M head and neck patients (n=23), the global median overall response rate (ORR) was 12% and four trials (17%) did not report any response. The incidence of grade 3/4 treatment-related adverse events (TRAEs) was low (7%). However, in seven trials safety results are not clearly assessable from the published data., Conclusions: Phase I trials of single agents designed for head and neck patients were generally safe but with a low ORR. Future development of new drugs dedicated for HNC patients that can more accurately reflect the heterogeneity of HNC and provide more detailed subgroup analyses is warranted.

The Pharmpy Automatic Model Development (AMD) tool automates the building of population pharmacokinetic (popPK) models by utilizing a systematic stepwise process. In this study, the performance of the AMD tool was assessed using simulated datasets. Ten true models mimicking classical popPK models were created. From each true model, dataset replicates were simulated assuming a typical phase I study design-single and multiple ascending doses with/without dichotomous food effect, with rich PK sampling. For every dataset replicate, the AMD tool automatically built an AMD model utilizing NONMEM for parameter estimation. The AMD models were compared to the true and reference models (true model fitted to simulated datasets) based on their model components, predicted population and individual secondary PK parameters (SP) (AUC 0-24 , c max , c trough ), and model quality metrics (e.g., model convergence, parameter relative standard errors (RSEs), Bayesian Information Criterion (BIC)). The models selected by the AMD tool closely resembled the true models, particularly in terms of distribution and elimination, although differences were observed in absorption and inter-individual variability components. Bias associated with the derived SP was low. In general, discrepancies between AMD and true SP were also observed for reference models and therefore were attributed to the inherent stochasticity in simulations. In summary, the AMD tool was found to be a valuable asset in automating repetitive modeling tasks, yielding reliable PK models in the scenarios assessed. This tool has the potential to save time during early clinical drug development that can be invested in more complex modeling activities within model-informed drug development., (© 2024 Roche. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Dual-agent treatment has become more and more popular in clinical trials. We have developed an approach called rapid enrollment dual-agent design (REDD) for dose-finding in Phase I clinical trials. This approach aims to administer treatment to patients using a dose combination that is highly probable to be the target dose combination. Unlike other non-model-based designs, rapid enrollment designs (RED and REDD) do not require waiting for all patients to complete an assessment before the assignment of the next participant. Simulations showed that across several scenarios, the average performance of REDD is comparable to that of the Bayesian optimal interval (BOIN) design and the partial order continual reassessment method (POCRM). The simulation results of REDD for late-onset toxicity assessments demonstrated that assigning patients to a dose combination as they are being enrolled, without waiting for the most recent cohort of patients to complete their follow-up, does not significantly compromise the quality of the maximum tolerated dose (MTD) estimation. Instead, it saves a considerable amount of time in clinical trial enrollment. User-friendly online applications have also been created to further facilitate the adoption of rapid enrollment designs in Phase I trials. In summary, being similar to BOIN and POCRM in performance, REDD is an approach that is easily comprehensible, straightforward to implement and offers an advantage of enrolling patients without having to wait for all current patients to complete their follow-ups for toxicity., (© 2024 John Wiley & Sons Ltd.)

The rise of cutting-edge precision cancer treatments has led to a growing significance of the optimal biological dose (OBD) in modern oncology trials. These trials now prioritize the consideration of both toxicity and efficacy simultaneously when determining the most desirable dosage for treatment. Traditional approaches in early-phase oncology trials have conventionally relied on the assumption of a monotone relationship between treatment efficacy and dosage. However, this assumption may not hold valid for novel oncology therapies. In reality, the dose-efficacy curve of such treatments may reach a plateau at a specific dose, posing challenges for conventional methods in accurately identifying the OBD. Furthermore, achieving reliable identification of the OBD is typically not possible based on a single small-sample trial. With data from multiple phase I and phase I/II trials, we propose a novel Bayesian random-effects dose-optimization meta-analysis (REDOMA) approach to identify the OBD by synthesizing toxicity and efficacy data from each trial. The REDOMA method can address trials with heterogeneous characteristics. We adopt a curve-free approach based on a Gamma process prior to model the average dose-toxicity relationship. In addition, we utilize a Bayesian model selection framework that uses the spike-and-slab prior as an automatic variable selection technique to eliminate monotonic constraints on the dose-efficacy curve. The good performance of the REDOMA method is confirmed by extensive simulation studies., (© 2024 John Wiley & Sons Ltd.)

Phase Ib trials are common in oncology development but often are not powered for statistical significance. Go/no-go decisions are largely driven by observed trends in response data. We applied a bootstrapping method to systematically compare tumor dynamic end points to historical control data to identify drugs with clinically meaningful efficacy. A proprietary mathematical model calibrated to phase Ib anti-PD-1 therapy trial data (KEYNOTE-001) was used to simulate thousands of phase Ib trials (n = 30) with a combination of anti-PD-1 therapy and four novel agents with varying efficacy. A redacted bootstrapping method compared these results to a simulated phase III control arm (N = 511) while adjusting for differences in trial duration and cohort size to determine the probability that the novel agent provides clinically meaningful efficacy. Receiver operating characteristic (ROC) analysis showed strong ability to separate drugs with modest (area under ROC [AUROC] = 83%), moderate (AUROC = 96%), and considerable efficacy (AUROC = 99%) from placebo in early-phase trials (n = 30). The method was shown to effectively move drugs with a range of efficacy through an in silico pipeline with an overall success rate of 93% and false-positive rate of 7.5% from phase I to phase III. This model allows for effective comparisons of tumor dynamics from early clinical trials with more mature historical control data and provides a framework to predict drug efficacy in early-phase trials. We suggest this method should be employed to improve decision making in early oncology trials., (© 2024 Merck Sharp & Dohme LLC and The Author(s). CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The calibration-free odds (CFO) design has been demonstrated to be robust, model-free, and practically useful but faces challenges when dealing with late-onset toxicity. The emergence of the time-to-event (TITE) method and fractional method leads to the development of TITE-CFO and fractional CFO (fCFO) designs to accumulate delayed toxicity. Nevertheless, existing CFO-type designs have untapped potential because they primarily consider dose information from the current position and its two neighboring positions. To incorporate information from all doses, we propose the accumulative CFO (aCFO) design by utilizing data at all dose levels similar to a tug-of-war game where players distant from the center also contribute their strength. This approach enhances full information utilization while still preserving the model-free and calibration-free characteristics. Extensive simulation studies demonstrate performance improvement over the original CFO design, emphasizing the advantages of incorporating information from a broader range of dose levels. Furthermore, we propose to incorporate late-onset outcomes into the TITE-aCFO and f-aCFO designs, with f-aCFO displaying superior performance over existing methods in both fixed and random simulation scenarios. In conclusion, the aCFO and f-aCFO designs can be considered robust, efficient, and user-friendly approaches for conducting phase I trials without or with late-onsite toxicity., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

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.

The Bayesian logistic regression method (BLRM) is a widely adopted and flexible design for finding the maximum tolerated dose in oncology phase I studies. However, the BLRM design has been criticized in the literature for being overly conservative due to the use of the overdose control rule. Recently, a discussion paper titled "Improving the performance of Bayesian logistic regression model with overall control in oncology dose-finding studies" in Statistics in Medicine has proposed an overall control rule to address the "excessive conservativeness" of the standard BLRM design. In this short communication, we discuss the relative conservativeness of the standard BLRM design and also suggest a dose-switching rule to further enhance its performance., (© 2024 John Wiley & Sons Ltd.)

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., (© 2024 Wiley‐VCH GmbH.)

In the last few years, numerous novel designs have been proposed to improve the efficiency and accuracy of phase I trials to identify the maximum-tolerated dose (MTD) or the optimal biological dose (OBD) for noncytotoxic agents. However, the conventional 3+3 approach, known for its and poor performance, continues to be an attractive choice for many trials despite these alternative suggestions. The article seeks to underscore the importance of moving beyond the 3+3 design by highlighting a different key element in trial design: the estimation of sample size and its crucial role in predicting toxicity and determining the MTD. We use simulation studies to compare the performance of the most used phase I approaches: 3+3, Continual Reassessment Method (CRM), Keyboard and Bayesian Optimal Interval (BOIN) designs regarding three key operating characteristics: the percentage of correct selection of the true MTD, the average number of patients allocated per dose level, and the average total sample size. The simulation results consistently show that the 3+3 algorithm underperforms in comparison to model-based and model-assisted designs across all scenarios and metrics. The 3+3 method yields significantly lower (up to three times) probabilities in identifying the correct MTD, often selecting doses one or even two levels below the actual MTD. The 3+3 design allocates significantly fewer patients at the true MTD, assigns higher numbers to lower dose levels, and rarely explores doses above the target dose-limiting toxicity (DLT) rate. The overall performance of the 3+3 method is suboptimal, with a high level of unexplained uncertainty and significant implications for accurately determining the MTD. While the primary focus of the article is to demonstrate the limitations of the 3+3 algorithm, the question remains about the preferred alternative approach. The intention is not to definitively recommend one model-based or model-assisted method over others, as their performance can vary based on parameters and model specifications. However, the presented results indicate that the CRM, Keyboard, and BOIN designs consistently outperform the 3+3 and offer improved efficiency and precision in determining the MTD, which is crucial in early-phase clinical trials., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

With the advent of targeted agents and immunological therapies, the medical research community has become increasingly aware that conventional methods for determining the best dose or schedule of a new agent are inadequate. It has been well established that conventional phase I designs cannot reliably identify safe and effective doses. This problem applies, generally, for cytotoxic agents, radiation therapy, targeted agents, and immunotherapies. To address this, the US Food and Drug Administration's Oncology Center of Excellence initiated Project Optimus, with the goal "to reform the dose optimization and dose selection paradigm in oncology drug development." As a response to Project Optimus, the articles in this special issue of Clinical Trials review recent advances in methods for choosing the dose or schedule of a new agent with an overall objective of informing clinical trialists of these innovative designs. This introductory article briefly reviews problems with conventional methods, the regulatory changes that encourage better dose optimization designs, and provides brief summaries of the articles that follow in this special issue., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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., Competing Interests: Declaration of conflicting interestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: P.M. reports honoraria for scientific advisory board membership for Mirati Therapeutics, Bristol Myers Squibb, and Exelixis; consulting fees from Axiom Healthcare; non-branded educational programs supported by Exelixis and Pfizer; leadership or fiduciary roles as a Medical Steering Committee member for the Kidney Cancer Association and a Kidney Cancer Scientific Advisory Board member for KCCure; and research funding from Takeda, Bristol Myers Squibb, Mirati Therapeutics, and Gateway for Cancer Research.

Competing Interests: Last year, we organized a gathering of RPT expert physicians and scientists from academia and industry. The objective of the workshop was to propose how to replace existing absorbed dose constraints adopted from EBRT with new constraints and strategies derived from recent RPT experience. We aim to create normal tissue complication probability (NTCP) curves similar to those in Figure 1D for specific toxicities and types of RPT. However, in our examination of the literature, we found that most clinical trial reports unfortunately provide little or no information regarding tissue absorbed doses and insufficient detail regarding toxicities.6,13,23-25 We are actively collecting available patient-level data through data sharing and use agreements with our industry and academic partners, including coauthors and others, and we will analyze the data independently to limit potential bias and conflict of interest. We believe that routine reporting of estimates of absorbed dose delivered to tumors and organs at risk (eg, using single-photon emission computed tomography [SPECT]/computed tomography imaging-based methods) would improve analysis of the factors that might affect the efficacy of RPT agents for a given indication. Beyond this, it would provide the data needed for analysis of absorbed dose versus response or toxicity to better compare different agents and regimens.

Therapeutic proteins, including monoclonal antibodies, are typically manufactured using clonally derived, stable host cell lines, since consistent and predictable cell culture performance is highly desirable. However, selecting and preparing banks of stable clones takes considerable time, which inevitably extends overall development timelines for new therapeutics by delaying the start of subsequent activities, such as the scale-up of manufacturing processes. In the context of the coronavirus disease 2019 (COVID-19) pandemic, with its intense pressure for accelerated development strategies, we used a novel transposon-based Leap-In Transposase® system to rapidly generate high-titer stable pools and then used them directly for large scale-manufacturing of an anti-severe acute respiratory syndrome coronavirus 2 monoclonal antibody under cGMP. We performed the safety testing of our non-clonal cell bank, then used it to produce material at a 200L-scale for preclinical safety studies and formulation development work, and thereafter at 2000L scale for supply of material for a Phase 1 clinical trial. Testing demonstrated the comparability of critical product qualities between the two scales and, more importantly, that our final clinical trial product met all pre-set product quality specifications. The above expediated approach provided clinical trial material within 4.5 months, in comparison to 12-14 months for production of clinical trial material via the conventional approach., (© 2021 Wiley Periodicals LLC.)

A meta-analysis using data from 3 phase 1 studies evaluated the pharmacokinetics (PK) and pharmacodynamics (PD) of Sandoz biosimilar versus US- and EU-reference pegfilgrastim. The studies included a single-dose, double-blind, 3-arm, parallel-group study (study 1); a single-dose, double-blind, 2-way crossover study (study 2); and a single-dose, double-blind, 3-way, 6-sequence crossover study (study 3). Healthy male and female subjects were randomized to receive the proposed biosimilar (all studies), US-reference biologic (studies 1 and 3), or EU-reference biologic (studies 1, 2, and 3). For PK parameters (area under the serum concentration-time curve from time of dosing and extrapolated to infinity, area under the serum concentration-time curve from the time of dosing to the last measurable concentration, and maximum observed serum concentration) and PD parameters (absolute neutrophil count area under the effect curve from the time of dosing to the last measurable concentration and maximum measured absolute neutrophil count) geometric mean ratios and 90% confidence intervals (CIs) for treatment comparisons were calculated using the meta-analysis approach with a fixed-effects model. PK/PD biosimilarity was concluded if the 90%CIs were within the equivalence margins of 0.80 to 1.25. The 90%CIs for the geometric mean ratios for the PK/PD parameters were all within the equivalence margins. Safety and tolerability were similar between the proposed biosimilar and the US- and EU-reference pegfilgrastim in healthy subjects. This meta-analysis of 3 phase 1 studies supports PK/PD similarity of Sandoz biosimilar pegfilgrastim to US- and EU-reference pegfilgrastim. No clinically meaningful differences in safety or tolerability were observed., (© 2021 Sandoz Biopharmaceuticals. Clinical Pharmacology in Drug Development published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.)

The aims of Phase 1 trials in oncology have broadened considerably from simply demonstrating that the agent/regimen of interest is well tolerated in a relatively heterogeneous patient population to addressing multiple objectives under the heading of early-phase trials and, if possible, obtaining reliable evidence regarding clinical activity to lead to drug approvals via the Accelerated Approval approach or Breakthrough Therapy designation in cases where the tumours are rare, prognosis is poor or where there might be an unmet therapeutic need. Constructing a Phase 1 design that can address multiple objectives within the context of a single trial is not simple. Randomisation can play an important role, but carrying out such randomisation according to the principles of equipoise is a significant challenge in the Phase 1 setting. If the emerging data are not sufficient to definitively address the aims early on, then a proper design can reduce biases, enhance interpretability, and maximise information so that the Phase 1 data can be more compelling. This article outlines objectives and design considerations that need to be adhered to in order to respect ethical and scientific principles required for research in human subjects in early phase clinical trials., (© 2021. The Author(s), under exclusive licence to Cancer Research UK.)

In phase 1 dose escalation studies, dose limiting toxicities (DLTs) are defined as adverse events of concern occurring during a predefined time window after first dosing of patients. Standard dose escalation designs, such as the continual reassessment method (CRM), only utilize this binary DLT information. Thus, late-onset DLTs are usually not accounted for when CRM guiding the dose escalation and finally defining the maximum tolerated dose (MTD) of the drug, which brings safety concerns for patients. Previously, several extensions of CRMs, such as the time-to-event CRM (TITE-CRM), fractional CRM (fCRM) and the data augmented CRM (DA-CRM), have been proposed to handle this issue without prolonging trial duration. However, among the model-based designs, none of the designs have explicitly controlled the risk of overdosing as in the escalation with overdose control (EWOC) design. Here we propose a novel dose escalation with overdose control design using a two-parameter logistic regression model for the probability of DLT depending on the dose and a piecewise exponential model for the time to DLT distribution, which we call rolling-CRM design. A comprehensive simulation study has been conducted to compare the performance of the rolling-CRM design with other dose escalation designs. Of note, the trial duration is significantly shorter compared to traditional CRM designs. The proposed design also retains overdose control characteristics, but might require a larger sample size compared to traditional CRM designs., (Copyright © 2021 Elsevier Inc. All rights reserved.)

This paper evaluates gender bias in the published clinical trials of Vortioxetine. We conducted a systematic review of controlled clinical trials of Vortioxetine for the treatment of depression. The literature search was performed using MEDLINE and following the corresponding international recommendations. We identified 42 articles, of which 23 were included. The proportion of women ranged from 47%-75% and the percentage of women included in the 10,404 total patients sample was 65%. The separate analysis of the main variable between the subpopulations of men and women was only carried out in 3/23 publications included. In contrast, 6/23 trials analyzed secondary variables separated by sex. No trials discussed the results separately by sex. The proportion of women included was slightly higher than that in clinical trials of other antidepressants. However, the analysis of the main result or secondary variables by sex, as well as discussing the results separately by sex, are scarce. This gives rise to gender bias in these works., (Copyright © 2020 Elsevier Inc. All rights reserved.)

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.

Many targeted therapies are administered at or near the maximum tolerated dose (MTD). With the advent of precision medicine, a larger therapeutic window is expected. Therefore, dose optimization will require a new approach to early clinical trial design. We analyzed publicly available data for 21 therapies targeting six kinases, and four poly (ADP-ribose) polymerase inhibitors, focusing on potency and exposure to gain insight into dose selection. The free average steady-state concentration (C ss ) at the approved dose was compared to the in vitro cell potency (half-maximal inhibitory concentration (IC 50 )). Average steady-state area under the plasma concentration-time curve, the fraction unbound drug in plasma, and the cell potency were taken from the US drug labels, US and European regulatory reviews, and peer-reviewed journal articles. The C ss was remarkably similar to the IC 50 . The median C ss /IC 50 value was 1.2, and 76% of the values were within 3-fold of unity. However, three drugs (encorafenib, erlotinib, and ribociclib) had a C ss /IC 50 value > 25. Seven other therapies targeting the same 3 kinases had much lower C ss /IC 50 values ranging from 0.5 to 4. These data suggest that these kinase inhibitors have a large therapeutic window that is not fully exploited; lower doses may be similarly efficacious with improved tolerability. We propose a revised first-in-human trial design in which dose cohort expansion is initiated at doses less than the MTD when there is evidence of clinical activity and C ss exceeds a potency threshold. This potency-guided approach is expected to maximize the therapeutic window thereby improving patient outcomes., (© 2020 Tango Therapeutics. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

In recent years, the landscape in clinical trial development has changed to involve many molecularly targeted agents, immunotherapies, or radiotherapy, as a single agent or in combination. Given their different mechanisms of action and lengths of administration, these agents have different toxicity profiles, which has resulted in numerous challenges when applying traditional designs such as the 3 + 3 design in dose-finding clinical trials. Novel methods have been proposed to address these design challenges such as combinations of therapies or late-onset toxicities. However, their design and implementation require close collaboration between clinicians and statisticians to ensure that the appropriate design is selected to address the aims of the study and that the design assumptions are pertinent to the study drug. The goal of this paper is to provide guidelines for appropriate questions that should be considered early in the design stage to facilitate the interactions between clinical and statistical teams and to improve the design of dose-finding clinical trials for novel anticancer agents., (© 2021 by American Society of Clinical Oncology.)

Purpose: Despite the expansion of immune checkpoint inhibitor (ICI) indications, the relationship between ICI dose and toxicity or response is not well established. To understand this correlation, we performed a meta-analysis of ICI trials that used dose escalation., Experimental Design: We searched PubMed and abstracts presented at (inter)national meetings for trials using FDA-approved ICIs. The reported rates of grade 3-5 adverse events (G3-5 AE), immune-related adverse events (irAE), and response were correlated with doses within each ICI using marginal exact generalized linear models., Results: A total of 74 trials (7,469 patients) published between January 2010 and January 2017 were included. For ipilimumab, the incidence of G3-5 AEs was 34% with a significant 27% reduced risk in lower doses ( P = 0.002). However, no relationship was observed between dose and irAEs or response. For nivolumab, the incidence of G3-5 AEs was 20.1% which was lower in non-small cell lung cancer (NSCLC) compared with renal cell carcinoma (RCC) or melanoma ( P ≤ 0.05) with no dose-toxicity relationship. In melanoma and NSCLC, a dose-response association was observed, which was not observed in RCC. For pembrolizumab, the incidence of G3-5 AEs was 13.3%, which was lower in melanoma compared with NSCLC ( P = 0.03) with no dose-toxicity relationship. In melanoma, lower dose levels correlated with decreased odds of response ( P = 0.01), a relationship that was not observed in NSCLC., Conclusions: Our analysis shows a lack of consistent dose-toxicity or dose-response correlation with ICIs. Therefore, dose escalation is not an appropriate design to conduct ICI studies. Here we present an innovative trial design for immune-modulating agents., (©2020 American Association for Cancer Research.)

Background: The continual reassessment method (CRM) identifies the maximum tolerated dose (MTD) more efficiently and identifies the true MTD more frequently compared to standard methods such as the 3 + 3 method. An initial estimate of the dose-toxicity relationship (prior skeleton) is required, and there is limited guidance on how to select this. Previously, we compared the CRM with six different skeletons to the 3 + 3 method by conducting post-hoc analysis on a phase 1 oncology study (AZD3514), each CRM model reduced the number of patients allocated to suboptimal and toxic doses. This manuscript extends this work by assessing the ability of the 3 + 3 method and the CRM with different skeletons in determining the true MTD of various "true" dose-toxicity relationships., Methods: One thousand studies were simulated for each "true" dose toxicity relationship considered, four were based on clinical trial data (AZD3514, AZD1208, AZD1480, AZD4877), and four were theoretical. The 3 + 3 method and 2-stage extended CRM with six skeletons were applied to identify the MTD, where the true MTD was considered as the largest dose where the probability of experiencing a dose limiting toxicity (DLT) is ≤33%., Results: For every true dose-toxicity relationship, the CRM selected the MTD that matched the true MTD in a higher proportion of studies compared to the 3 + 3 method. The CRM overestimated the MTD in a higher proportion of simulations compared to the 3 + 3 method. The proportion of studies where the correct MTD was selected varied considerably between skeletons. For some true dose-toxicity relationships, some skeletons identified the true MTD in a higher proportion of scenarios compared to the skeleton that matched the true dose-toxicity relationship., Conclusion: Through simulation, the CRM generally outperformed the 3 + 3 method for the clinical and theoretical true dose-toxicity relationships. It was observed that accurate estimates of the true skeleton do not always outperform a generic skeleton, therefore the application of wide confidence intervals may enable a generic skeleton to be used. Further work is needed to determine the optimum skeleton.

Introduction: There are currently no approved targeted therapies for lung squamous-cell carcinoma (LSCC) and KRAS-mutant lung adenocarcinoma (LUAD). About 30% of LSCC and 25% of KRAS-mutant LUAD exhibit hyperactive NRF2 pathway activation through mutations in NFE2L2 (the gene encoding NRF2) or its negative regulator, KEAP1. Preclinical data demonstrate that these tumors are uniquely sensitive to dual inhibition of glycolysis and glutaminolysis via mammalian target of rapamycin (mTOR) and glutaminase inhibitors. This phase 1 study was designed to assess safety and preliminary activity of the mTOR inhibitor MLN0128 (sapanisertib) in combination with the glutaminase inhibitor CB-839 HCl., Methods: Phase 1 dose finding will use the queue-based variation of the 3 + 3 dose escalation scheme with the primary endpoint of identifying the recommended expansion dose. To confirm the acceptable tolerability of the recommended expansion dose, patients will subsequently enroll onto 1 of 4 expansion cohorts (n = 14 per cohort): (1) LSCC harboring NFE2L2 or (2) KEAP1 mutations, or (3) LUAD harboring KRAS/(KEAP1 or NFE2L2) coalterations, or (4) LSCC wild type for NFE2L2 and KEAP1. The primary endpoint of the dose expansion is to determine the preliminary efficacy of MLN0128/CB-839 combination therapy., Conclusion: This phase 1 study will determine the recommended expansion dose and preliminary efficacy of MLN0128 and CB-839 in advanced non-small-cell lung cancer with a focus on subsets of LSCC and KRAS-mutant LUAD harboring NFE2L2 or KEAP1 mutations., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Introduction: There remains an unmet need for better anticoagulants. The phase I clinical trial is of great significance in the development of anticoagulants, and the design is special. This system review aims to provide insights for the design of future phase I clinical trials of anticoagulants., Areas Covered: We searched the database PubMed and ClinicalTrail.gov website, to collate the phase I clinical trial of anticoagulants in healthy people. The study protocol, inclusion-exclusion criteria, safety, and pharmacodynamic indexes were reviewed., Expert Opinion: New anticoagulants under development focused on inhibiting one or more than one serine proteases within the coagulation cascade. Agents targeting intrinsic factors are in the pipeline of the drug development. The enrollment eligibility criteria have more restrictions on laboratory tests, medical history, or medication history related to bleeding and coagulation; more precautions were taken to assess and minimize the risk of hemorrhagic events. Pharmacodynamics markers were evaluated as a surrogate marker of anticoagulation potency to guide further dose selection in drug's development. In future, the positive control study can be applied in phase I studies of new anticoagulants with appropriate pharmacodynamics markers, which can provide more favorable information on making 'go/no' decision in drug development.

Background/aims: In oncology, new combined treatments make it difficult to order dose levels according to monotonically increasing toxicity. New flexible dose-finding designs that take into account uncertainty in dose levels ordering were compared with classical designs through simulations in the setting of the monotonicity assumption violation. We give recommendations for the choice of dose-finding design., Methods: Motivated by a clinical trial for patients with high-risk neuroblastoma, we considered designs that require a monotonicity assumption, the Bayesian Continual Reassessment Method, the modified Toxicity Probability Interval, the Bayesian Optimal Interval design, and designs that relax monotonicity assumption, the Bayesian Partial Ordering Continual Reassessment Method and the No Monotonicity Assumption design. We considered 15 scenarios including monotonic and non-monotonic dose-toxicity relationships among six dose levels., Results: The No Monotonicity Assumption and Partial Ordering Continual Reassessment Method designs were robust to the violation of the monotonicity assumption. Under non-monotonic scenarios, the No Monotonicity Assumption design selected the correct dose level more often than alternative methods on average. Under the majority of monotonic scenarios, the Partial Ordering Continual Reassessment Method selected the correct dose level more often than the No Monotonicity Assumption design. Other designs were impacted by the violation of the monotonicity assumption with a proportion of correct selections below 20% in most scenarios. Under monotonic scenarios, the highest proportions of correct selections were achieved using the Continual Reassessment Method and the Bayesian Optimal Interval design (between 52.8% and 73.1%). The costs of relaxing the monotonicity assumption by the No Monotonicity Assumption design and Partial Ordering Continual Reassessment Method were decreases in the proportions of correct selections under monotonic scenarios ranging from 5.3% to 20.7% and from 1.4% to 16.1%, respectively, compared with the best performing design and were higher proportions of patients allocated to toxic dose levels during the trial., Conclusions: Innovative oncology treatments may no longer follow monotonic dose levels ordering which makes standard phase I methods fail. In such a setting, appropriate designs, as the No Monotonicity Assumption or Partial Ordering Continual Reassessment Method designs, should be used to safely determine recommended for phase II dose.

Disease progression or recurrence after a period of remission can be a challenging event for individuals seeking cancer treatment. Those referred for possible phase 1 trial enrollment are often motivated to participate in these studies with hope for a cure despite approximately 5% response rates in this setting. Addressing such commonly held misunderstandings during the initial evaluation for phase 1 trial eligibility could provide a valuable opportunity to improve physician communication by identifying signs of distress or psychiatric conditions, addressing underlying psychological biases, and encouraging adaptive coping strategies.

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., (© 2020 John Wiley & Sons Ltd.)

Aim: The aim of this article is to understand the pros and cons of various methods involved in first-in-human (FIH) dose calculation and act decisively in dose escalations when calculating the maximum tolerated dose., Subjects and Methods: We reviewed early phase clinical trials for methods of FIH dose and dose-escalation steps and discuss them in line with existing guidelines. We also reviewed the clinical trial registry to recognize trends in trial registration in recent years and after a massive failure in a few trials., Results: Phase 1 trials of TGN 1412 and BIA10-2474 would always be remembered as catastrophes for pharmaceutical development plans. Quite often than not, healthy human volunteers are the guinea pigs in this stage of drug development. And, the most important aspect of designing an early phase study is deciding upon the dose to be started with, apart from the selection of cohort and escalation steps. The common principles used for FIH dose calculation include no observed adverse effect level, minimum anticipated biological effect level, pharmacologically active dose, pharmacokinetic/pharmacodynamic approach, and similar drug comparison approach., Conclusion: Early phase clinical trials are basically foundation stones on which lies the entire onus of the later stages of development. Deciding FIH dose is a crucial step that necessitates the incorporation of detailed data from the preclinical stages and application of the most conservative approach for the safety/benefit of the volunteers in these studies.

Although many novel phase I designs have been developed in recent years, few studies have discussed how to incorporate external information into dose-finding designs. In this paper, we first propose a new method for developing a phase I design, Bayesian optimal interval design (BOIN)[Liu S et al. (2015), Yuan Y et al. (2016)], for formally incorporating historical information. An algorithm to automatically generate parameters for prior set-up is introduced. Second, we propose a method to relax the fixed boundaries of the BOIN design to be adaptive, such that the accumulative information can be used more appropriately. This modified design is called adaptive BOIN (aBOIN). Simulation studies to examine performances of the aBOIN design in small and large sample sizes revealed comparable performances for the aBOIN and original BOIN designs for small sample sizes. However, aBOIN outperformed BOIN in moderate sample sizes. Simulation results also showed that when historical trials are conducted in settings similar to those for the current trial, their performance can be significantly improved. This approach can be applied directly to pediatric cancer trials, since all phase I trials in children are followed by similar efficient adult trials in the current drug development paradigm. However, when information is weak, operating characteristics are compromised., Competing Interests: The authors have declared that no competing interests exist.

Background/aims: Community engagement is widely acknowledged as an important step in clinical trials. One underexplored method for engagement in clinical trials is crowdsourcing. Crowdsourcing involves having community members attempt to solve a problem and then publicly sharing innovative solutions. We designed and conducted a pilot using a crowdsourcing approach to obtain community feedback on an HIV clinical trial, called the Acceptability of Combined Community Engagement Strategies Study. In this work, we describe and assess the Acceptability of Combined Community Engagement Strategies Study's crowdsourcing activities in order to examine the opportunities of crowdsourcing as a clinical trial community engagement strategy., Methods: The crowdsourcing engagement activities involved in the Acceptability of Combined Community Engagement Strategies Study were conducted in the context of a phase 1 HIV antibody trial (ClinicalTrials.gov identifier: NCT03803605). We designed a series of crowdsourcing activities to collect feedback on three aspects of this clinical trial: the informed consent process, the experience of participating in the trial, and fairness/reciprocity in HIV clinical trials. All crowdsourcing activities were open to members of the general public 18 years of age or older, and participation was solicited from the local community. A group discussion was held with representatives of the clinical trial team to obtain feedback on the utility of crowdsourcing as a community engagement strategy for informing future clinical trials., Results: Crowdsourcing activities made use of innovative tools and a combination of in-person and online participation opportunities to engage community members in the clinical trial feedback process. Community feedback on informed consent was collected by transforming the clinical trial's informed consent form into a series of interactive video modules, which were screened at an open public discussion. Feedback on the experience of trial participation involved designing three fictional vignettes which were then transformed into animated videos and screened at an open public discussion. Finally, feedback on fairness/reciprocity in HIV clinical trials was collected using a crowdsourcing idea contest with online and in-person submission opportunities. Our public discussion events were attended by 38 participants in total; our idea contest received 43 submissions (27 in-person, 16 online). Facebook and Twitter metrics demonstrated substantial engagement in the project. The clinical team found crowdsourcing primarily useful for enhancing informed consent and trial recruitment., Conclusion: There is sufficient lay community interest in open calls for feedback on the design and conduct of clinical trials, making crowdsourcing both a novel and feasible engagement strategy. Clinical trial researchers are encouraged to consider the opportunities of implementing crowdsourcing to inform trial processes from a community perspective.

The Keyboard design is a novel model-assisted dose-finding method to find the maximum tolerated dose (MTD) for single-agent trials. The Keyboard design is easy to implement and has superior performance in comparison to more complicated model-assisted designs. In this article, we extend the Keyboard design to dual-agent dose-finding trials. The proposed Keyboard combination trial design maintains the simplicity of the original single-agent Keyboard design, and its dose escalation and deescalation rules can be pre-tabulated before conducting the trial. We show that the Keyboard combination design has desirable theoretical properties, including the optimality of its decision rules, coherence in dose transition, and convergence to the target dose. Extensive simulations are conducted to evaluate the performance of the proposed Keyboard combination design using a novel, random two-dimensional dose-toxicity scenario generating algorithm. The simulation results confirm the desirable and competitive operating characteristics of the Keyboard design. An R Shiny application is available at www.trialdesign.org to implement the Keyboard combination design., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background/aims: Dose feasibility is a challenge that may arise in the development of adoptive T cell therapies for cancer. In early-phase clinical trials, dose is quantified either by a fixed or per unit body weight number of cells infused. It may not be feasible, however, to administer a patient's assigned dose due to an insufficient number of cells harvested or functional heterogeneity of the product. The study objective becomes to identify the maximum tolerated dose with high feasibility of being administered. This article describes a new dose-finding method that adaptively accounts for safety and feasibility endpoints in guiding dose allocation., Methods: We propose an adaptive dose-finding method that integrates accumulating feasibility and safety data to select doses for participant cohorts in early-phase trials examining adoptive cell immunotherapy. We sequentially model the probability of dose-limiting toxicity and the probability of feasibility using independent beta-binomial models. The probability model for toxicity borrows information across all dose levels using isotonic regression, allowing participants infused at a lower dose than his or her planned dose to contribute safety data to the dose-finding algorithm. We applied the proposed methodology in a single simulated trial and evaluated its operating characteristics through extensive simulation studies., Results: In simulations conducted for a phase I study of adoptive immunotherapy for newly diagnosed glioblastoma, the proposed method demonstrates the ability to identify accurately the feasible maximum tolerated doses and to treat participants at and around these doses. Over 10 hypothesized scenarios studied, the percentage of correctly selecting the true feasible and maximum tolerated dose ranged from 50% to 90% with sample sizes averaging between 21 and 24 participants. A comparison to the only known existing method accounting for safety and feasibility yields competitive performance., Conclusion: We have developed a new practical adaptive dose-finding method to assess feasibility in early-phase adoptive cell therapy trials. A design that incorporates feasibility, as a function of the quantity and quality of the product manufactured, in addition to safety will have an impact on the recommended phase II doses in studies that evaluate patient outcomes.

An important tool to evaluate the performance of any design is an optimal benchmark proposed by O'Quigley and others (2002. Non-parametric optimal design in dose finding studies. Biostatistics3, 51-56) that provides an upper bound on the performance of a design under a given scenario. The original benchmark can only be applied to dose finding studies with a binary endpoint. However, there is a growing interest in dose finding studies involving continuous outcomes, but no benchmark for such studies has been developed. We show that the original benchmark and its extension by Cheung (2014. Simple benchmark for complex dose finding studies. Biometrics70, 389-397), when looked at from a different perspective, can be generalized to various settings with several discrete and continuous outcomes. We illustrate and compare the benchmark's performance in the setting of a dose finding Phase I clinical trial with a continuous toxicity endpoint and a Phase I/II trial with binary toxicity and continuous efficacy endpoints. We show that the proposed benchmark provides an accurate upper bound in these contexts and serves as a powerful tool for evaluating designs., (© The Author(s) 2018. Published by Oxford University Press.)

Two illustrative applications are presented of Bayesian clinical trial designs that make adaptive subgroup-specific decisions based on elicited utilities of patient outcomes to quantify risk-benefit trade-offs. The first design is for a randomized trial to evaluate effects of nutritional prehabilitation on post-operative morbidity in esophageal cancer patients undergoing surgery. The second design is for a dose-finding trial of natural killer cells to treat advanced hematologic malignancies, with five time-to-event outcomes. Each design is based on a Bayesian hierarchical model that borrows strength between subgroups. Computer simulation is used to evaluate each design's properties, including comparison to a simpler design ignoring treatment-subgroup interactions. The simulations show that accounting prospectively for treatment-subgroup interactions yields designs with very desirable properties, is greatly superior to a simplified comparator design that ignores subgroups if treatment-subgroup interactions actually exist, and each design is robust to deviations from the assumed underlying model., (Copyright © 2019 Elsevier Inc. All rights reserved.)