An overview of the BOIN design and its current extensions for novel early-phase oncology trials.
Ontology highlight
ABSTRACT:
SUBMITTER: Ananthakrishnan R
PROVIDER: S-EPMC9260438 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeUsing novel Bayesian adaptive designs has great potential to improve the efficiency of early-phase clinical trials. A major barrier for clinical researchers to adopt novel designs is the lack of easy-to-use software. Our purpose is to develop a user-friendly software platform to implement novel clinical trial designs that address various challenges in early-phase dose-finding trials.MethodsWe used R Shiny to develop a web-based software platform to facilitate the use of recent novel adaptive designs.ResultsWe developed a web-based software suite, called Bayesian optimal interval (BOIN) suite, which includes R Shiny applications to handle various clinical settings, including single-agent phase I trials with and without prior information, trials with late-onset toxicity, trials to find the optimal biological dose based on risk-benefit trade-off, and drug combination trials to find a single maximum tolerated dose (MTD) or the MTD contour. The applications are built using the same software architecture to ensure the best and a uniform user experience, and they are developed using a proven software development standard operating procedure to ensure accuracy, robustness, and reproducibility. The suite is freely available with internet access and a web browser without the need of installing any other software.ConclusionThe BOIN suite allows clinical researchers to design various types of early-phase clinical trials under a unified framework. This work is extremely important because it not only advances the clinical research and drug development by facilitating the use of novel trial designs with optimal performance but also enhances collaborations between biostatisticians and clinicians by disseminating novel statistical methodology to broader scientific communities through user-friendly software. The BOIN suite establishes a KISS principle: keep it simple, but smart.
| S-EPMC8462603 | biostudies-literature
Project description:Despite decades of extensive research, the progress in developing effective treatments for primary brain tumors lags behind that of other cancers, largely due to the unique challenges of brain tumors (eg, the blood-brain barrier and high heterogeneity) that limit the delivery and efficacy of many therapeutic agents. One way to address this issue is to employ novel trial designs to better optimize the treatment regimen (eg, dose and schedule) in early phase trials to improve the success rate of subsequent phase III trials. The objective of this article is to introduce Bayesian optimal interval (BOIN) designs as a novel platform to design various types of early phase brain tumor trials, including single-agent and combination regimen trials, trials with late-onset toxicities, and trials aiming to find the optimal biological dose (OBD) based on both toxicity and efficacy. Unlike many novel Bayesian adaptive designs, which are difficult to understand and complicated to implement by clinical investigators, the BOIN designs are self-explanatory and user friendly, yet yield more robust and powerful operating characteristics than conventional designs. We illustrate the BOIN designs using a phase I clinical trial of brain tumor and provide software (freely available at www.trialdesign.org) to facilitate the application of the BOIN design.
| S-EPMC8579094 | biostudies-literature
Project description:Bayesian optimal interval (BOIN) design is a model-assisted phase I dose-finding design to find the maximum tolerated dose (MTD). The hallmark of the BOIN design is its concise decision rule - making the decision of dose escalation and de-escalation by simply comparing the observed dose-limiting toxicity (DLT) rate at the current dose with a pair of optimal dose escalation and de-escalation boundaries. The interval 3+3 (i3+3) design is a recently proposed algorithm-based dose-finding design based on a similar decision rule with some modifications. The similarity in the appearance of the two designs has caused confusions among practitioners. In this article, we demystify the i3+3 design by elucidating its links with the BOIN design and compare their similarities and differences, as well as pros and cons. We perform comprehensive simulation studies to compare the operating characteristics of the two designs. Our results show that, compared to the algorithm-based i3+3 design, which are characterized by ad hoc and often scientifically and logically incoherent decision rules, the mode-assisted BOIN design is not only simpler, but also statistically more rigorous with better operating characteristics, thus providing a better design choice for phase I oncology trials.
| S-EPMC8261789 | biostudies-literature
Project description:BACKGROUND:With numerous and fast approvals of different agents including immune checkpoint inhibitors, monoclonal antibodies, or chimeric antigen receptor (CAR) T-cell therapy, immunotherapy is now an established form of cancer treatment. These agents have demonstrated impressive clinical activity across many tumor types, but also revealed different toxicity profiles and mechanisms of action. The classic assumptions imposed by cytotoxic agents may no longer be applicable, requiring new strategies for dose selection and trial design. DESCRIPTION:This main goal of this article is to summarize and highlight main challenges of early-phase study design of immunotherapies from a statistical perspective. We compared the underlying toxicity and efficacy assumptions of cytotoxic versus immune-oncology agents, proposed novel endpoints to be included in the dose-selection process, and reviewed design considerations to be considered for early-phase trials. When available, references to software and/or web-based applications were also provided to ease the implementation. Throughout the paper, concrete examples from completed (pembrolizumab, nivolumab) or ongoing trials were used to motivate the main ideas including recommendation of alternative designs. CONCLUSION:Further advances in the effectiveness of cancer immunotherapies will require new approaches that include redefining the optimal dose to be carried forward in later phases, incorporating additional endpoints in the dose selection process (PK, PD, immune-based biomarkers), developing personalized biomarker profiles, or testing drug combination therapies to improve efficacy and reduce toxicity.
| S-EPMC6103998 | biostudies-literature
Project description:IntroductionEarly phase dose-finding (EPDF) studies are critical for the development of new treatments, directly influencing whether compounds or interventions can be investigated in further trials to confirm their safety and efficacy. There exists guidance for clinical trial protocols and reporting of completed trials in the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 and CONsolidated Standards Of Reporting Randomised Trials (CONSORT) 2010 statements. However, neither the original statements nor their extensions adequately cover the specific features of EPDF trials. The DEFINE (DosE-FIndiNg Extensions) study aims to enhance transparency, completeness, reproducibility and interpretation of EPDF trial protocols (SPIRIT-DEFINE) and their reports once completed (CONSORT-DEFINE), across all disease areas, building on the original SPIRIT 2013 and CONSORT 2010 statements.Methods and analysisA methodological review of published EPDF trials will be conducted to identify features and deficiencies in reporting and inform the initial generation of the candidate items. The early draft checklists will be enriched through a review of published and grey literature, real-world examples analysis, citation and reference searches and consultation with international experts, including regulators and journal editors. Development of CONSORT-DEFINE commenced in March 2021, followed by SPIRIT-DEFINE from January 2022. A modified Delphi process, involving worldwide, multidisciplinary and cross-sector key stakeholders, will be run to refine the checklists. An international consensus meeting in autumn 2022 will finalise the list of items to be included in both guidance extensions.Ethics and disseminationThis project was approved by ICR's Committee for Clinical Research. The Health Research Authority confirmed Research Ethics Approval is not required. The dissemination strategy aims to maximise guideline awareness and uptake, including but not limited to dissemination in stakeholder meetings, conferences, peer-reviewed publications and on the EQUATOR Network and DEFINE study websites.Registration detailsSPIRIT-DEFINE and CONSORT-DEFINE are registered with the EQUATOR Network.
| S-EPMC10069529 | biostudies-literature
Project description:BACKGROUND:Although phase I clinical trials are the gateway to progress in cancer therapies, this setting poses ethical challenges to ensure that patients provide consent free from misunderstandings of therapeutic intent or unrealistic expectations of benefit. The design of phase I oncology trials has evolved rapidly over time and today includes more targeted agents and combinations of experimental drugs with standard drugs, which may further complicate how patients understand phase I research participation. METHODS:We conducted semistructured interviews regarding motivations, decision making, and understanding of trial purpose nested within a phase I clinical trial of a novel PI3kinase inhibitor combined with a standard oral chemotherapy in 18 participants. RESULTS:Fewer than half of patients correctly identified the safety and dosing objectives. The inclusion of a targeted agent was attractive to participants and was perceived as an indicator of less toxic or more efficacious therapy, with less appreciation for added risks. The significance of a cellular drug target, without a known predictive biomarker of response, was unclear to patients. The inclusion of a standard drug in the regimen attracted patients with more treatment options than traditional first-in-human participants. Patients frequently expressed a realistic understanding of prognosis and uncertainty of benefit, but simultaneous hopes for extraordinary outcomes. CONCLUSION:Novel phase I oncology trial designs may attract patients with less constrained treatment options, but the inclusion of targeted drugs and combinations including standard chemotherapies is likely to complicate understanding of safety and dosing objectives and likelihood of personal benefit for purposes of informed consent.
| S-EPMC5640413 | biostudies-literature
Project description:Existing statistical methodology on dose finding for combination chemotherapies has focused on toxicity considerations alone in finding a maximum tolerated dose combination to recommend for further testing of efficacy in a phase II setting. Recently, there has been increasing interest in integrating phase I and phase II trials in order to facilitate drug development. In this article, we propose a new adaptive phase I/II method for dual-agent combinations that takes into account both toxicity and efficacy after each cohort inclusion. The primary objective, both within and at the conclusion of the trial, becomes finding a single dose combination with an acceptable level of toxicity that maximizes efficacious response. We assume that there exist monotone dose-toxicity and dose-efficacy relationships among doses of one agent when the dose of other agent is fixed. We perform extensive simulation studies that demonstrate the operating characteristics of our proposed approach, and we compare simulated results to existing methodology in phase I/II design for combinations of agents.
| S-EPMC4258389 | biostudies-literature
Project description:BackgroundThe statistical plan of a phase II trial should balance minimizing the premature termination of potentially beneficial therapies (i.e. false negatives) and the further, costly testing of ineffective drugs (i.e. false positives). We sought to examine the methodology, reporting, and bias in the interpretation of outcomes of phase II oncology trials in recent years.Materials and methodsIn a retrospective cross-sectional analysis, we reviewed all full-length articles published on PubMed from 1 January 2021 to 20 June 2022. We searched for data regarding the sample size calculation (number, α value, power, and expected effect size), the primary and secondary outcomes and results, and the authors' conclusion of the study.ResultsAbout 5.4% of studies (n = 10) used a statistical power that was inferior to 80%, and 16.7% (n = 34) did not indicate the level of power for the sample size calculation. Approximately 16.7% (n = 31) of studies used a one-sided α level of ≤0.025; 17.7% (n = 33) of studies used a predefined threshold (no comparator effect size or difference between groups) to determine the sample size for efficacy. The percentage of studies with a positive authors' conclusion but not meeting the primary endpoint, or the endpoint was equivocal, was 27.4% (n = 51).ConclusionMany randomized phase II studies in oncology failed to report essential data for determining sample size calculations, many did not actually use a comparator to determine efficacy even though the studies were randomized, and many had positive conclusions even though the results were indeterminate or the primary endpoint was not met.
| S-EPMC10024120 | biostudies-literature
Project description:The clinical development of cancer drugs is rapidly moving from empirical "one drug fits all" or development-by-tumor-type approaches towards more personalized treatment models. A deeper understanding of cancer and the immune system, novel technologies, and powerful analytics have fueled an increase in precision oncology approaches integrating the molecular profiles of the tumor with the clinical profile of the patient. While this approach has been successful for targeted therapies, the complex mode of action of immunotherapies will likely require integration of clinical profiling with more comprehensive profiling of the tumor, of the tumor microenvironment, and of the immune system of the patient. Integration of precision oncology into clinical research for immunotherapies is viewed as a means to better select patients in the early clinical phase of drug development to (1) maximize the benefit-to-risk ratio for the patient, (2) generate early proof of concept and proof of relevance for the investigational drug, and (3) inform on how to best combine or sequence the therapeutic with other drugs. Here we discuss the upsides and challenges of incorporating precision immuno-oncology into early-phase clinical trials.
| S-EPMC6875515 | biostudies-literature
Project description:With the emergence of immunotherapy and other novel therapies, the traditional assumption that the efficacy of the study drug increases monotonically with dose levels is not always true. Therefore, dose-finding methods evaluating only toxicity data may not be adequate. In this paper, we have first compared the Modified Toxicity Probability Interval (mTPI) and Toxicity Equivalence Range (TEQR) dose-finding oncology designs for safety with identical stopping rules; we have then extended both designs to include efficacy in addition to safety - we determine the optimal dose for safety and efficacy using these designs by applying isotonic regression to the observed toxicity and efficacy rates, once the early phase trial is completed. We consider multiple types of underlying dose response curves, i.e., monotonically increasing, plateau, or umbrella-shaped. We conduct simulation studies to investigate the operating characteristics of the two proposed designs and compare them to existing designs. We found that the extended mTPI design selects the optimal dose for safety and efficacy more accurately than the other designs for most of the scenarios considered.
| S-EPMC5898482 | biostudies-literature