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Problem definition: We study early-stage dose-finding clinical trials with simultaneous consideration of efficacy and toxicity without parametric assumptions on the forms of the unknown dose-efficacy and dose-toxicity curves. We propose algorithms that adaptively allocate doses based on patient responses, in order to maximize the efficacy for the patients during the trial while minimizing the toxicity. Methodology/results: We leverage online learning to design the clinical trial and propose two algorithms. The first one follows dose-escalation principles and analyzes the efficacy and toxicity simultaneously. The second one uses bisection search to identify a safe dose range and then applies upper confidence bound algorithms within the safe range to identify efficacious doses. We show the matching upper and lower bounds for the regret of both algorithms. We find that observing the dose-escalation principle is costly, as the optimal regret of the first algorithm is in the order of N3/4 , worse than the optimal regret of the second algorithm, which is in the order of N2/3. We test our proposed algorithms with three benchmarks commonly used in practice on synthetic and real data sets, and the results show that they are competitive with or significantly outperform the benchmarks. Managerial implications: We provide a novel insight that following the dose-escalation principle inevitably leads to higher regret. The first proposed algorithm is suitable to use when little information about the dose-toxicity profile is available, whereas the second one is appealing when more information is available about the toxicity profile. Funding: This work was supported by the National Science Foundation [Grant 1651912]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0246. [ABSTRACT FROM AUTHOR]

Dose optimization is a critical challenge in drug development. Historically, dose determination in oncology has followed a divergent path from other non-oncology therapeutic areas due to the unique characteristics and requirements in Oncology. However, with the emergence of new drug modalities and mechanisms of drugs in oncology, such as immune therapies, radiopharmaceuticals, targeted therapies, cytostatic agents, and others, the dose-response relationship for efficacy and toxicity could be vastly varied compared to the cytotoxic chemotherapies. The doses below the MTD may demonstrate similar efficacy to the MTD with an improved tolerability profile, resembling what is commonly observed in non-oncology treatments. Hence, alternate strategies for dose optimization are required for new modalities in oncology drug development. This paper delves into the historical evolution of dose finding methods from non-oncology to oncology, highlighting examples and summarizing the underlying drivers of change. Subsequently, a practical framework and guidance are provided to illustrate how dose optimization can be incorporated into various stages of the development program. We provide the following general recommendations: 1) The objective for phase I is to identify a dose range rather than a single MTD dose for subsequent development to better characterize the safety and tolerability profile within the dose range. 2) At least two doses separable by PK are recommended for dose optimization in phase II. 3) Ideally, dose optimization should be performed before launching the confirmatory study. Nevertheless, innovative designs such as seamless II/III design can be implemented for dose selection and may accelerate the drug development program. [ABSTRACT FROM AUTHOR]

The Multiple Comparison Procedure and Modelling (MCPMod) approach has been shown to be a powerful statistical technique that can significantly improve the design and analysis of dose-finding studies under model uncertainty. Due to its frequentist nature, however, it is difficult to incorporate information into MCPMod from historical trials on the same drug. BMCPMod, a recently introduced Bayesian version of MCPMod, is designed to take into account historical information on the placebo dose group. We introduce a Bayesian hierarchical framework capable of incorporating historical information on an arbitrary number of dose groups, including both placebo and active ones, taking into account the relationship between responses of these dose groups. Our approach can also model both prognostic and predictive between-trial heterogeneity and is particularly useful in situations where the effect sizes of two trials are different. Our goal is to reduce the necessary sample size in the dose-finding trial while maintaining its target power. [ABSTRACT FROM AUTHOR]

Introduction: In targeted therapies and immunotherapies, the occurrence of low-grade (e.g., grade 1-2) toxicities (LGT) is common, while dose-limiting toxicities (DLT) are relatively rare. As a result, conventional phase I trial designs, solely based on DLTs and disregarding milder toxicities, are problematic when evaluating these novel therapies. Methods: To address this issue, we propose a novel phase I design called a multiple-constraint keyboard (MC-Keyboard) that integrates multiple toxicity constraints, accounting for both DLT and lGt, for precise dose escalation and de-escalation, and identification of the maximum tolerated dose (MTD). As a model-assisted design, an important feature of MC-Keyboard is that its dose-escalation or de-escalation rule can be pretabulated and incorporated into the trial protocol before the initiation of the trial, greatly simplifying its implementation. Results: The simulation study showed that the MC-Keyboard had high accuracy in identifying the MTD and is safer than some existing designs. Conclusion: The MC-Keyboard provides a novel, simple, and safe approach to assessing safety and identifying the MTD for targeted therapies and immunotherapies. [ABSTRACT FROM AUTHOR]

The U.S. Food and Drug Administration (FDA) has launched Project Optimus to shift dose selection from the maximum tolerated dose (MTD) to the dose that produces the optimal risk‐benefit tradeoff. One approach highlighted in the FDA's guidance involves conducting a randomized phase II trial following the completion of a phase I trial, where multiple doses (typically including the MTD and one or two doses lower than the MTD) are compared to identify the optimal dose that maximizes the benefit‐risk tradeoff. This article focuses on the design of such a multiple‐dose randomized trial, specifically the determination of the sample size. We generalized the standard definitions of type I error and power to accommodate the unique characteristics of dose optimization and derived a decision rule along with an algorithm to determine the optimal sample size. The resulting design is referred to as MERIT (Multiple‐dosE RandomIzed Trial design for dose optimization based on toxicity and efficacy). Simulation studies demonstrate that MERIT has desirable operating characteristics, and a sample size between 20 and 40 per dosage arm often offers reasonable power and type I errors to ensure patient safety and benefit. To facilitate the implementation of the MERIT design, we provide software, available at https://www.trialdesign.org. [ABSTRACT FROM AUTHOR]

In recently conducted phase III trials in a rare disease area, patients received monthly treatment at a high dose of the drug, which targets to lower a specific biomarker level, closely associated with the efficacy endpoint, to around 10% across patients. Although this high dose demonstrated strong efficacy, treatments were withheld due to the reports of serious adverse events. Dosing in these studies were later resumed at a reduced dosage which targets to lower the biomarker level to 15%–35% across patients. Two questions arose after this disruption. The first is whether the efficacy of this revised regimen as measured by the reduction in annualized event rate is adequate to support the continuation of the development and the second is whether the potential bias due to the loss of patients during this dosing gap process can be gauged. To address these questions, we built a prediction model that quantitatively characterizes biomarker vs. endpoint relationship and predicts efficacy at the 15%–35% range of the biomarker level using the available data from the original high dose. This model predicts favorable event rate in the target biomarker level and shows that the bias due to the loss of patients is limited. These results support the continued development of the revised regimen, however, given the limitation of the data available, this prediction is planned to be validated further when data under the revised regimen become available. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We consider a dose-optimization design for a first-in-human oncology trial that aims to identify a suitable dose for late-phase drug development. The proposed approach, called the Pharmacometrics-Enabled DOse OPtimization (PEDOOP) design, incorporates observed patient-level pharmacokinetics (PK) measurements and latent pharmacodynamics (PD) information for trial decision-making and dose optimization. PEDOOP consists of two seamless phases. In phase I, patient-level time-course drug concentrations, derived PD effects, and the toxicity outcomes from patients are integrated into a statistical model to estimate the dose-toxicity response. A simple dose-finding design guides dose escalation in phase I. At the end of the phase I dose finding, a graduation rule is used to assess the safety and efficacy of all the doses and select those with promising efficacy and acceptable safety for a randomized comparison against a control arm in phase II. In phase II, patients are randomized to the selected doses based on a fixed or adaptive randomization ratio. At the end of phase II, an optimal biological dose (OBD) is selected for late-phase development. We conduct simulation studies to assess the PEDOOP design in comparison to an existing seamless design that also combines phases I and II in a single trial. [ABSTRACT FROM AUTHOR]

Most existing dose-ranging study designs focus on assessing the dose–efficacy relationship and identifying the minimum effective dose. There is an increasing interest in optimizing the dose based on the benefit–risk tradeoff. We propose a Bayesian quasi-likelihood dose-ranging design that jointly considers safety and efficacy to simultaneously identify the minimum effective dose and the maximum utility dose to optimize the benefit–risk tradeoff. The binary toxicity endpoint is modeled using a beta-binomial model. The efficacy endpoint is modeled using the quasi-likelihood approach to accommodate various types of data (e.g. binary, ordinal or continuous) without imposing any parametric assumptions on the dose–response curve. Our design utilizes a utility function as a measure of benefit–risk tradeoff and adaptively assign patients to doses based on the doses' likelihood of being the minimum effective dose and maximum utility dose. The design takes a group-sequential approach. At each interim, the doses that are deemed overly toxic or futile are dropped. At the end of the trial, we use posterior probability criteria to assess the strength of the dose–response relationship for establishing the proof-of-concept. If the proof-of-concept is established, we identify the minimum effective dose and maximum utility dose. Our simulation study shows that compared with some existing designs, the Bayesian quasi-likelihood dose-ranging design is robust and yields competitive performance in establishing proof-of-concept and selecting the minimum effective dose. Moreover, it includes an additional feature for further maximum utility dose selection. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Combination therapy is increasingly being explored as a promising approach for improving cancer treatment outcomes. However, identifying effective dose combinations in early oncology drug development is challenging due to limited sample sizes in early-phase clinical trials. This task becomes even more complex when multiple agents are being escalated simultaneously, potentially leading to a loss of monotonic toxicity order with respect to the dose. Traditional single-agent trial designs are insufficient for this multi-dimensional problem, necessitating the development and implementation of dose-finding methods specifically designed for drug combinations. While, in practice, approaches to this problem have focused on preselecting combinations with a known toxicity order and applying single-agent designs, this limits the number of combinations considered and may miss promising dose combinations. In recent years, several novel designs have been proposed for exploring partially ordered drug combination spaces with the goal of identifying a maximum tolerated dose combination, based on safety, or an optimal dose combination, based on toxicity and efficacy. However, their implementation in clinical practice remains limited. In this article, we describe the application of the partial order continual reassessment method and its extensions for combination therapies in early-phase clinical trials. We present completed trials that use safety endpoints to identify maximum tolerated dose combinations and adaptively use both safety and efficacy endpoints to determine optimal treatment strategies. We discuss the effectiveness of the partial-order continual reassessment method and its extensions in identifying optimal treatment strategies and provide our experience with executing these novel adaptive designs in practice. By utilizing innovative dose-finding methods, researchers and clinicians can more effectively navigate the challenges of combination therapy development, ultimately improving patient outcomes in the treatment of cancer. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Traditional dose selection for oncology registration trials typically employs a one- or two-step single maximum tolerated dose (MTD) approach. However, this approach may not be appropriate for molecularly targeted therapy, which tends to have toxicity profiles that are markedly different than cytotoxic agents. The US Food and Drug Administration launched Project Optimus to reform dose optimization in oncology drug development and has recently released a related guidance for industry. Methods: We propose a “three steps toward dose optimization” procedure, in response to these initiatives, and discuss the details in dose-optimization designs and analyses. The first step is dose escalation to identify the MTD or maximum administered dose with an efficient hybrid design, which can offer good overdose control and increases the likelihood of the recommended MTD being close to the true MTD. The second step is the selection of appropriate recommended doses for expansion (RDEs), based on all available data, including emerging safety, pharmacokinetics, pharmacodynamics, and other biomarker information. The third step is dose optimization, which uses data from a randomized fractional factorial design with multiple RDEs explored in multiple tumor cohorts during the expansion phase to ensure a feasible dose is selected for registration trials, and that the tumor type most sensitive to the investigative treatment is identified. Conclusion: We believe using this three-step approach can increase the likelihood of selecting an optimal dose for a registration trial that demonstrates a balanced safety profile while retaining much of the efficacy observed at the MTD.

In the development of new cancer treatment, an essential step is to determine the maximum tolerated dose in a phase I clinical trial. In general, phase I trial designs can be classified as either model-based or algorithm-based approaches. Model-based phase I designs are typically more efficient by using all observed data, while there is a potential risk of model misspecification that may lead to unreliable dose assignment and incorrect maximum tolerated dose identification. In contrast, most of the algorithm-based designs are less efficient in using cumulative information, because they tend to focus on the observed data in the neighborhood of the current dose level for dose movement. To use the data more efficiently yet without any model assumption, we propose a novel approximate Bayesian computation approach to phase I trial design. Not only is the approximate Bayesian computation design free of any dose-toxicity curve assumption, but it can also aggregate all the available information accrued in the trial for dose assignment. Extensive simulation studies demonstrate its robustness and efficiency compared with other phase I trial designs. We apply the approximate Bayesian computation design to the MEK inhibitor selumetinib trial to demonstrate its satisfactory performance. The proposed design can be a useful addition to the family of phase I clinical trial designs due to its simplicity, efficiency and robustness.

Bayesian logistic regression model (BLRM) is widely used to guide dose escalation decisions in phase 1 oncology trials. An important feature of BLRM design is the appealing safety performance due to its escalation with overdose control (EWOC). However, some recent literature indicates that BLRM with EWOC may have a relatively low probability to find the maximum tolerated dose (MTD) compared to some other dose escalation designs. This work discusses this design problem and proposes a practical solution to improve the performance of BLRM design. Specifically, we suggest increasing the EWOC cutoff from routine value 0.25 to a value between 0.3 and 0.4, which will increase the chance of finding the correct MTD with minimal compromise to overdosing risk. Our comparative simulation studies indicate that BLRM with an increased EWOC cutoff has comparable operating characteristics on the correct MTD selection and over-toxicity control as other dose escalation designs (BOIN, mTPI, keyboard, etc.). Moreover, we compare the methodology and operating characteristics of BLRM designs with various decision rules that allow more flexible overdosing control. A case study of dose escalation in a recent phase 1 oncology trial is provided to show how BLRM with optimal EWOC cutoff operates well in practice. [ABSTRACT FROM AUTHOR]

Cell therapies comprise one of the most important advances in oncology. One of the biggest challenges in the early development of cell therapies is to recommend safe and feasible doses to carry forward to middle development. The treatment involves extracting cells from a patient, expanding the cells and infusing the cells back into the patient. Each dose level being studied is defined by the number of cells infused into the trial participant. The manufacturing process may not generate enough cells for a given patient to receive their assigned dose level, making it infeasible to administer their intended dose. The primary design challenge is to efficiently use accumulated data from participants treated away from their assigned dose to efficiently allocate future trial participants and recommend a feasible maximum tolerated dose (FMTD) at the study conclusion. Currently, there are few available options for designing and implementing Phase I trials of cell therapies that can incorporate a dose feasibility endpoint. Moreover, the application of these designs is limited to a traditional dose-finding framework, where the dose-limiting toxicity (DLT) endpoint is observed in early cycles of therapy. This paper presents a novel phase I trial design for adoptive cell therapy that simultaneously accounts for dose feasibility and late-onset toxicities. We apply our design to a phase I dose-escalation trial of Rituximab-based bispecific activated T-cells combined with a fixed dose of Nivolumab. Our simulation results demonstrate that our proposed method can reduce trial duration without significantly hindering trial accuracy. [ABSTRACT FROM AUTHOR]

A generalized phase 1-2-3 design, Gen 1-2-3, that includes all phases of clinical treatment evaluation is proposed. The design extends and modifies the design of Chapple and Thall (2019), denoted by CT. Both designs begin with a phase 1-2 trial including dose acceptability and optimality criteria, and both select an optimal dose for phase 3. The Gen 1-2-3 design has the following key differences. In stage 1, it uses phase 1-2 criteria to identify a set of candidate doses rather than 1 dose. In stage 2, which is intermediate between phase 1-2 and phase 3, it randomizes additional patients fairly among the candidate doses and an active control treatment arm and uses survival time data from both stage 1 and stage 2 patients to select an optimal dose. It then makes a Go/No Go decision of whether or not to conduct phase 3 based on the predictive probability that the selected optimal dose will provide a specified substantive improvement in survival time over the control. A simulation study shows that the Gen 1-2-3 design has desirable operating characteristics compared to the CT design and 2 conventional designs. [ABSTRACT FROM AUTHOR]

We propose a model‐based, semi‐mechanistic dose‐finding (SDF) design for phase I oncology trials that incorporates pharmacokinetic/pharmacodynamic (PK/PD) information when modeling the dose‐toxicity relationship. This design is motivated by a phase Ib/II clinical trial of anti‐CD20/CD3 T cell therapy in non‐Hodgkin lymphoma patients; it extends a recently proposed SDF model framework by incorporating measurements of a PD biomarker relevant to the primary dose‐limiting toxicity (DLT). We propose joint Bayesian modeling of the PK, PD, and DLT outcomes. Our extensive simulation studies show that on average the proposed design outperforms some common phase I trial designs, including modified toxicity probability interval (mTPI) and Bayesian optimal interval (BOIN) designs, the continual reassessment method (CRM), as well as an SDF design assuming a latent PD biomarker (SDF‐woPD), in terms of the percentage of correct selection of maximum tolerated dose (MTD) and average number of patients allocated to MTD, under a variety of dose‐toxicity scenarios. When the working PK model and the class of link function between the cumulative PD effect and DLT probability is correctly specified, the proposed design also yields better estimated dose‐toxicity curves than CRM and SDF‐woPD. Our sensitivity analyses suggest that the design's performance is reasonably robust to prior specification for the parameter in the link function, as well as misspecification of the PK model and class of the link function. [ABSTRACT FROM AUTHOR]

In oncology, it is commonplace to treat patients with a combination of drugs that deliver different effects from different disease-curing or cancer-elimination perspectives. Such drug combinations can often achieve higher efficacy in comparison with single-drug treatment due to synergy or non-overlapping toxicity. Due to the small sample size, there is a growing need for efficient designs for phase I clinical trials, especially for drug-combination trials. In the existing experimental design for phase I drug-combination trials, most of the proposed methods are parametric and model-based, either requiring tuning parameters or prior knowledge of the drug toxicity probabilities. We propose a two-dimensional calibration-free odds (2dCFO) design for drug-combination trials, which utilizes not only the current dose information but also that from all the neighborhood doses (i.e., along the left, right, up and down directions). In contrast to interval-based designs which only use the current dose information, the 2dCFO is more efficient and makes more accurate decisions because of its additional leverage over richer resources of neighborhood data. Because our design makes decisions completely based on odds ratios, it does not rely upon any dose--toxicity curve assumption. The simulations show that the 2dCFO delivers satisfactory performances in terms of accuracy and efficiency as well as demonstrating great robustness due to its non-parametric or model-free nature. More importantly, the 2dCFO only requires the minimal specification of the target toxicity probability, which greatly eases the design process from the clinicians' aspects. [ABSTRACT FROM AUTHOR]

Model-assisted designs, a new class of dose-finding designs for determining the maximum tolerated dose (MTD), model only the dose-limiting toxicity (DLT) data observed at the current dose based on a simple binomial model and offer the boundaries of DLT for the determination of dose escalation, retention, or de-escalation before beginning the trials. The boundaries for dose-escalation and de-escalation decisions are relevant to the operating characteristics of the design. The well-known model-assisted design, Bayesian Optimal Interval (BOIN), selects these boundaries to minimize the probability of incorrect decisions at each dose allocation but does not distinguish between overdose and underdose allocations caused by incorrect decisions when calculating the probability of incorrect decisions. Distinguishing between overdose and underdose based on the decision error in the BOIN design is expected to increase the accuracy of MTD determination. In this study, we extended the BOIN design to account for the decision probabilities of incorrect overdose and underdose allocations separately. To minimize the two probabilities simultaneously, we propose utilizing multiple objective optimizations and formulating an approach for determining the boundaries for dose escalation and de-escalation. Comprehensive simulation studies using fixed and randomly generated scenarios of DLT probability demonstrated that the proposed method is superior or comparable to existing interval designs, along with notably better operating characteristics of the proposed method. [ABSTRACT FROM AUTHOR]

Background: In the USA, the primary cause of death and morbidity continues to be cardiovascular disease (CVD). Numerous trials have shown that statin medication reduces the likelihood of CVD events; it is a cornerstone of CVD prevention. However, studies have also indicated that up to 60% of the estimated 26.8 million Americans prescribed primary prevention statin treatment are nonadherent during the first year. Multi-component behavioral change technique (BCT) therapies have shown moderate promise in improving medication adherence as well as other positive health behaviors (such as physical activity). However, no research has looked at the duration of multi-component BCT intervention needed to result in a clinically significant improvement in statin adherence behaviors. This study aims to determine the necessary dose of a multi-component BCT intervention (defined as duration in weeks) to promote adherence to statin medication among those on primary prevention statin treatment by utilizing the modified time-to-event continuous reassessment method (TiTE-CRM). Methods and design: The study will utilize the modified TiTE-CRM in 42 participants, recruited in 14 cohorts of 3 participants each. The goal of this analysis is to identify the minimum effective dose (MED) of a multi-behavior change technique (BCT) intervention required to increase adherence to statins by 20% between baseline and follow-up periods. Using the TiTE-CRM method, the dose of the behavior intervention in weeks will be assigned to each cohort based on the performance of the prior cohort. At the end of the study, the intervention dose that has been found to be associated with a 20% increase in statin adherence among 80% of participants assigned to that dose will be identified as the MED. Discussion: If successful, the current trial will provide additional guidance to researchers and clinicians seeking to increase statin medication adherence using a BCT intervention by identifying the dose (i.e., the duration) of an intervention required to meaningfully increase adherence. Trial registration: ClinicalTrials.gov NCT05273736. Registered on March 10, 2022. https://www.clinicaltrials.gov/ct2/show/NCT05273736 [ABSTRACT FROM AUTHOR]

Abstract This study aimed to determine the values of the half‐effective dose (ED50) and 95% effective dose (ED95) of remimazolam besylate used in the procedural sedation of endoscopic retrograde cholangiopancreatography (ERCP). Sixty patients who fulfilled the inclusion and exclusion criteria of this study were selected. Sufentanil was administered intravenously and remimazolam besylate was administered 2 min later. ERCP treatment was feasible when the modified alertness/sedation (MOAA/S) score was ≤2. If choking or movement occurred during duodenoscope placement, it was considered as a positive reaction. The dose was increased in the next patient; otherwise, it was considered as a negative reaction, and the dose was reduced in the next patient. The ED50 and ED95 values and 95% confidence interval (CI) of remimazolam besylate were calculated by Probit regression analysis. All 60 patients completed the trial. The ED50 and ED95 values of remimazolam besylate were 0.196 and 0.239 mg/kg, respectively, for the procedural sedation of ERCP. The time of MOAA/S score ≤ 2 was (82.58 ± 21.70) s, and the mean time of awakening was (9.03 ± 5.64) min. Transient hypotension was observed in two patients without medical intervention. The ED50 and ED95 values of remimazolam besylate used in the procedural sedation of ERCP were 0.196 and 0.239 mg/kg, and the dose of the medications has definite efficacy and good safety.

Abstract Background In the USA, the primary cause of death and morbidity continues to be cardiovascular disease (CVD). Numerous trials have shown that statin medication reduces the likelihood of CVD events; it is a cornerstone of CVD prevention. However, studies have also indicated that up to 60% of the estimated 26.8 million Americans prescribed primary prevention statin treatment are nonadherent during the first year. Multi-component behavioral change technique (BCT) therapies have shown moderate promise in improving medication adherence as well as other positive health behaviors (such as physical activity). However, no research has looked at the duration of multi-component BCT intervention needed to result in a clinically significant improvement in statin adherence behaviors. This study aims to determine the necessary dose of a multi-component BCT intervention (defined as duration in weeks) to promote adherence to statin medication among those on primary prevention statin treatment by utilizing the modified time-to-event continuous reassessment method (TiTE-CRM). Methods and design The study will utilize the modified TiTE-CRM in 42 participants, recruited in 14 cohorts of 3 participants each. The goal of this analysis is to identify the minimum effective dose (MED) of a multi-behavior change technique (BCT) intervention required to increase adherence to statins by 20% between baseline and follow-up periods. Using the TiTE-CRM method, the dose of the behavior intervention in weeks will be assigned to each cohort based on the performance of the prior cohort. At the end of the study, the intervention dose that has been found to be associated with a 20% increase in statin adherence among 80% of participants assigned to that dose will be identified as the MED. Discussion If successful, the current trial will provide additional guidance to researchers and clinicians seeking to increase statin medication adherence using a BCT intervention by identifying the dose (i.e., the duration) of an intervention required to meaningfully increase adherence. Trial registration ClinicalTrials.gov NCT05273736. Registered on March 10, 2022. https://www.clinicaltrials.gov/ct2/show/NCT05273736

Abstract Background Early phase dose-finding (EPDF) trials are crucial for the development of a new intervention and influence whether it should be investigated in further trials. Guidance exists for clinical trial protocols and completed trial reports in the SPIRIT and CONSORT guidelines, respectively. However, both guidelines and their extensions do not adequately address the characteristics of EPDF trials. Building on the SPIRIT and CONSORT checklists, the DEFINE study aims to develop international consensus-driven guidelines for EPDF trial protocols (SPIRIT-DEFINE) and reports (CONSORT-DEFINE). Methods The initial generation of candidate items was informed by reviewing published EPDF trial reports. The early draft items were refined further through a review of the published and grey literature, analysis of real-world examples, citation and reference searches, and expert recommendations, followed by a two-round modified Delphi process. Patient and public involvement and engagement (PPIE) was pursued concurrently with the quantitative and thematic analysis of Delphi participants’ feedback. Results The Delphi survey included 79 new or modified SPIRIT-DEFINE (n = 36) and CONSORT-DEFINE (n = 43) extension candidate items. In Round One, 206 interdisciplinary stakeholders from 24 countries voted and 151 stakeholders voted in Round Two. Following Round One feedback, one item for CONSORT-DEFINE was added in Round Two. Of the 80 items, 60 met the threshold for inclusion (≥ 70% of respondents voted critical: 26 SPIRIT-DEFINE, 34 CONSORT-DEFINE), with the remaining 20 items to be further discussed at the consensus meeting. The parallel PPIE work resulted in the development of an EPDF lay summary toolkit consisting of a template with guidance notes and an exemplar. Conclusions By detailing the development journey of the DEFINE study and the decisions undertaken, we envision that this will enhance understanding and help researchers in the development of future guidelines. The SPIRIT-DEFINE and CONSORT-DEFINE guidelines will allow investigators to effectively address essential items that should be present in EPDF trial protocols and reports, thereby promoting transparency, comprehensiveness, and reproducibility. Trial registration SPIRIT-DEFINE and CONSORT-DEFINE are registered with the EQUATOR Network ( https://www.equator-network.org/ ).

Background: The elderly population is expanding globally. This gives numerous challenges especially regarding hip fracture patients. In the US alone over 300.000 hip fracture patients are treated each year, and a large amount of those develop opoid addiction. Hip fractures require surgical intervention within 24 h and is associated with significant pain even at rest. Postoperative analgesic treatment need to be optimized to ensure adequate pain relief and to prevent subsequent opioid addiction. Previous studies have shown that methadone effectively decreases post-operative opioid consumption but the studies focused on younger patients undergoing elective surgery. This study focus on the use of methadone on the elderly, fragile patients undergoing acute surgery, by first determining the maximal tolerable dose.The hypothesis is the maximal tolerable doses of these hip-fracture patients lies between 0.10 mg/kg and 0.20 mg/kg. This trial aims to estimate the maximum tolerable dose of methadone when administered to elderly patients undergoing surgery for a hip fracture. Method: This project is an adaptive dose-finding trial. The continuous reassessment method will estimate the maximum tolerable dose of methadone. The primary outcome will be respiratory depression. The statistical analysis plan will be published a priori to the closure of patient recruitment and statistical analysis of database results. Conclusion: The results of this study will give valuable information about the maximally tolerated dose of methadone for postoperative pain relief for elderly patients with hip fractures and potential adverse events.This trial is registered on clinicaltrials.gov with trial registration: NCT05581901. Registered 17 October 2022, https://www.clinicaltrials.gov/ct2/show/NCT05581901?term=methadone&cond = hip&draw = 2&rank = 1.

Abstract Dose optimization studies of new therapeutic agents aim to identify one or more promising doses for further evaluation in subsequent studies. Traditionally, dose optimization has focused on finding the maximum tolerated dose (MTD), assuming that drug activity and efficacy generally increase with increasing dose. For modern targeted agents, the dose–activity relationship is often non-monotone and such that activity starts to plateau or even decline before reaching the MTD. Finding the optimal biological dose (OBD) for a targeted agent requires considering both toxicity and activity in dose optimization. This article proposes a new design for finding the OBD that uses generalized likelihood ratios (GLRs) to measure statistical evidence regarding key scientific questions on toxicity and activity. This GLR-based design requires no parametric modeling assumptions and only assumes that the dose–toxicity relationship is monotone and that the dose–activity relationship follows a two-sided isotonic regression model. Compared with existing designs that operate under similar assumptions, the GLR-based design is more general and more flexible, and performs competitively in simulation experiments where drug activity starts to plateau or decline before reaching the MTD. [ABSTRACT FROM AUTHOR]

Recent success of sequential administration of immunotherapy following radiotherapy (RT), often referred to as immunoRT, has sparked the urgent need for novel clinical trial designs to accommodate the unique features of immunoRT. For this purpose, we propose a Bayesian phase I/II design for immunotherapy administered after standard‐dose RT to identify the optimal dose that is personalized for each patient according to his/her measurements of PD‐L1 expression at baseline and post‐RT. We model the immune response, toxicity, and efficacy as functions of dose and patient's baseline and post‐RT PD‐L1 expression profile. We quantify the desirability of the dose using a utility function and propose a two‐stage dose‐finding algorithm to find the personalized optimal dose. Simulation studies show that our proposed design has good operating characteristics, with a high probability of identifying the personalized optimal dose. [ABSTRACT FROM AUTHOR]

Background: Early phase dose-finding (EPDF) trials are crucial for the development of a new intervention and influence whether it should be investigated in further trials. Guidance exists for clinical trial protocols and completed trial reports in the SPIRIT and CONSORT guidelines, respectively. However, both guidelines and their extensions do not adequately address the characteristics of EPDF trials. Building on the SPIRIT and CONSORT checklists, the DEFINE study aims to develop international consensus-driven guidelines for EPDF trial protocols (SPIRIT-DEFINE) and reports (CONSORT-DEFINE). Methods: The initial generation of candidate items was informed by reviewing published EPDF trial reports. The early draft items were refined further through a review of the published and grey literature, analysis of real-world examples, citation and reference searches, and expert recommendations, followed by a two-round modified Delphi process. Patient and public involvement and engagement (PPIE) was pursued concurrently with the quantitative and thematic analysis of Delphi participants' feedback. Results: The Delphi survey included 79 new or modified SPIRIT-DEFINE (n = 36) and CONSORT-DEFINE (n = 43) extension candidate items. In Round One, 206 interdisciplinary stakeholders from 24 countries voted and 151 stakeholders voted in Round Two. Following Round One feedback, one item for CONSORT-DEFINE was added in Round Two. Of the 80 items, 60 met the threshold for inclusion (≥ 70% of respondents voted critical: 26 SPIRIT-DEFINE, 34 CONSORT-DEFINE), with the remaining 20 items to be further discussed at the consensus meeting. The parallel PPIE work resulted in the development of an EPDF lay summary toolkit consisting of a template with guidance notes and an exemplar. Conclusions: By detailing the development journey of the DEFINE study and the decisions undertaken, we envision that this will enhance understanding and help researchers in the development of future guidelines. The SPIRIT-DEFINE and CONSORT-DEFINE guidelines will allow investigators to effectively address essential items that should be present in EPDF trial protocols and reports, thereby promoting transparency, comprehensiveness, and reproducibility. Trial registration: SPIRIT-DEFINE and CONSORT-DEFINE are registered with the EQUATOR Network (https://www.equator-network.org/). [ABSTRACT FROM AUTHOR]

Designs for early phase dose finding clinical trials typically are either phase I based on toxicity, or phase I‐II based on toxicity and efficacy. These designs rely on the implicit assumption that the dose of an experimental agent chosen using these short‐term outcomes will maximize the agent's long‐term therapeutic success rate. In many clinical settings, this assumption is not true. A dose selected in an early phase oncology trial may give suboptimal progression‐free survival or overall survival time, often due to a high rate of relapse following response. To address this problem, a new family of Bayesian generalized phase I‐II designs is proposed. First, a conventional phase I‐II design based on short‐term outcomes is used to identify a set of candidate doses, rather than selecting one dose. Additional patients then are randomized among the candidates, patients are followed for a predefined longer time period, and a final dose is selected to maximize the long‐term therapeutic success rate, defined in terms of duration of response. Dose‐specific sample sizes in the randomization are determined adaptively to obtain a desired level of selection reliability. The design was motivated by a phase I‐II trial to find an optimal dose of natural killer cells as targeted immunotherapy for recurrent or treatment‐resistant B‐cell hematologic malignancies. A simulation study shows that, under a range of scenarios in the context of this trial, the proposed design has much better performance than two conventional phase I‐II designs. [ABSTRACT FROM AUTHOR]

Purpose: Sedentary time is ubiquitous in developed nations and is associated with deleterious health outcomes. Physical activity guidelines recommend reductions in sedentary time; however, quantitative guidelines that inform how often and how long sedentary time should be interrupted have not been provided. The purpose of this study was to examine the acute effects of multiple doses of a sedentary break intervention on cardiometabolic risk factors, concurrently evaluating efficacy of varying frequencies and durations of sedentary breaks. Methods: In a randomized crossover study, middle- and older-age adults (n = 11) completed the following 8-h conditions on five separate days: 1 uninterrupted sedentary (control) condition and four acute (experimental) trials that entailed different sedentary break frequency/duration combinations: every 30 min for 1 min, every 30 min for 5 min, every 60 min for 1 min, and every 60 min for 5 min. Sedentary breaks entailed light-intensity walking. Glucose and blood pressure (BP) were measured every 15 and 60 min, respectively. Results: Compared with control, glucose incremental area under the curve was significantly attenuated only for the every 30 min for 5-min dose (−11.8[4.7]; P = 0.017). All sedentary break doses yielded significant net decreases in systolic BP from baseline compared with control (P < 0.05). The largest reductions in systolic BP were observed for the every 60 min for 1 min (−5.2 [1.4] mm Hg) and every 30 min for 5 min (−4.3[1.4] mm Hg) doses. Conclusions: The present study provides important information concerning efficacious sedentary break doses. Higher-frequency and longer-duration breaks (every 30 min for 5 min) should be considered when targeting glycemic responses, whereas lower doses may be sufficient for BP lowering. [ABSTRACT FROM AUTHOR]

In modern oncology drug development, adaptive designs have been proposed to identify the recommended phase 2 dose. The conventional dose finding designs focus on the identification of maximum tolerated dose (MTD). However, designs ignoring efficacy could put patients under risk by pushing to the MTD. Especially in immuno‐oncology and cell therapy, the complex dose‐toxicity and dose‐efficacy relationships make such MTD driven designs more questionable. Additionally, it is not uncommon to have data available from other studies that target on similar mechanism of action and patient population. Due to the high variability from phase I trial, it is beneficial to borrow historical study information into the design when available. This will help to increase the model efficiency and accuracy and provide dose specific recommendation rules to avoid toxic dose level and increase the chance of patient allocation at potential efficacious dose levels. In this paper, we propose iBOIN‐ET design that uses prior distribution extracted from historical studies to minimize the probability of decision error. The proposed design utilizes the concept of skeleton from both toxicity and efficacy data, coupled with prior effective sample size to control the amount of historical information to be incorporated. Extensive simulation studies across a variety of realistic settings are reported including a comparison of iBOIN‐ET design to other model based and assisted approaches. The proposed novel design demonstrates the superior performances in percentage of selecting the correct optimal dose (OD), average number of patients allocated to the correct OD, and overdosing control during dose escalation process. [ABSTRACT FROM AUTHOR]

We revisit our earlier extension of the CRM and introduce a new version of the dose escalation procedure. Its performance is assessed by simulation, with respect to a template of five operating measures BEARS (Benchmark, Efficiency, Accuracy, Reliability, Safety) that was used in Zhang, Wang and Yang. Our goal is to correctly identify the maximum tolerated dose with a high probability while protecting trial subjects from undue risk of toxicity. Simulation results show that this may be achieved for some considered scenarios, selective models, and considered competing designs. We discuss the pros and cons of the new version of the dose escalation procedure in the face of treatment versus experimentation dilemma. [ABSTRACT FROM AUTHOR]

Designing Phase I clinical trials is challenging when accrual is slow or sample size is limited. The corresponding key question is: how to efficiently and reliably identify the maximum tolerated dose (MTD) using a sample size as small as possible? We propose model‐assisted and model‐based designs with adaptive intrapatient dose escalation (AIDE) to address this challenge. AIDE is adaptive in that the decision of conducting intrapatient dose escalation depends on both the patient's individual safety data, as well as other enrolled patient's safety data. When both data indicate reasonable safety, a patient may perform intrapatient dose escalation, generating toxicity data at more than one dose. This strategy not only provides patients the opportunity to receive higher potentially more effective doses, but also enables efficient statistical learning of the dose‐toxicity profile of the treatment, which dramatically reduces the required sample size. Simulation studies show that the proposed designs are safe, robust, and efficient to identify the MTD with a sample size that is substantially smaller than conventional interpatient dose escalation designs. Practical considerations are provided and R code for implementing AIDE is available upon request. [ABSTRACT FROM AUTHOR]

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

In targeted therapies and immunotherapies, the occurrence of low-grade (e.g., grade 1-2) toxicities (LGT) is common, while dose-limiting toxicities (DLT) are relatively rare. As a result, conventional phase I trial designs, solely based on DLTs and disregarding milder toxicities, are problematic when evaluating these novel therapies. Methods: To address this issue, we propose a novel phase I design called a multiple-constraint keyboard (MC-Keyboard) that integrates multiple toxicity constraints, accounting for both DLT and LGT, for precise dose escalation and de-escalation, and identification of the maximum tolerated dose (MTD). As a model-assisted design, an important feature of MC-Keyboard is that its dose-escalation or de-escalation rule can be pretabulated and incorporated into the trial protocol before the initiation of the trial, greatly simplifying its implementation. Results: The simulation study showed that the MC-Keyboard had high accuracy in identifying the MTD and is safer than some existing designs. Conclusion: The MC-Keyboard provides a novel, simple, and safe approach to assessing safety and identifying the MTD for targeted therapies and immunotherapies., Competing Interests: Conflicts of Interest: None.

An important tool to evaluate the performance of a dose-finding design is the nonparametric optimal benchmark that provides an upper bound on the performance of a design under a given scenario. A fundamental assumption of the benchmark is that the investigator can arrange doses in a monotonically increasing toxicity order. While the benchmark can be still applied to combination studies in which not all dose combinations can be ordered, it does not account for the uncertainty in the ordering. In this article, we propose a generalization of the benchmark that accounts for this uncertainty and, as a result, provides a sharper upper bound on the performance. The benchmark assesses how probable the occurrence of each ordering is, given the complete information about each patient. The proposed approach can be applied to trials with an arbitrary number of endpoints with discrete or continuous distributions. We illustrate the utility of the benchmark using recently proposed dose-finding designs for Phase I combination trials with a binary toxicity endpoint and Phase I/II combination trials with binary toxicity and continuous efficacy. [ABSTRACT FROM AUTHOR]

Introduction: Acute radiodermatitis is a common, though severe, side effect of radiotherapy against cancer that may lead to an interruption or even abortion of the radiotherapy. Mouse models provide an excellent tool to study pathomechanisms of a radiation-induced dermatitis as well as to test and develop novel innovative treatment strategies. Objective: The aim of this study was to provide an overview of different mouse models and irradiation devices that have been used so far and to describe the process of the induction of a radiation dermatitis in an immune proficient nude mouse model (SKH1-Hrhr) using a IBL 637 cesium-137γ-ray machine. Methods: This process includes the construction of a radiation shielding chamber, restricting the radiation to the right hind leg of the mouse, a dosimetry, and a dose finding study to identify the appropriate irradiation dose to induce a moderate radiation dermatitis. Results: A radiation shielding chamber was successfully constructed allowing selective irradiation of the right hind leg. A moderate radiodermatitis is induced with irradiation doses in the range of 60–70 Gy under the here described conditions. Symptoms peak about 8 days after irradiation and decrease relatively quickly thereafter. Histological analyses confirmed typical signs of inflammation. Conclusion: This study describes for the first time a protocol to induce a moderate radiodermatitis in the nude mouse model SKH1-Hrhr using a IBL 637 gamma irradiator. This protocol will allow researchers to study novel treatment strategies to alleviate the burden of a radiodermatitis as a side effect of cancer treatment. [ABSTRACT FROM AUTHOR]

Despite numerous innovative designs having been published for phase I drug-combination dose finding trials, their use in real applications is rather limited. As a working group under the American Statistical Association Biopharmaceutical Section, our goal is to identify the unique challenges associated with drug combination, share industry's experiences with combination trials, and investigate the pros and cons of the existing designs. Toward this goal, we review seven existing designs and distinguish them based on the criterion of whether their primary objectives are to find a single maximum tolerated dose (MTD) or the MTD contour (i.e., multiple MTDs). Numerical studies, based on either industry-specified fixed scenarios or randomly generated scenarios, are performed to assess their relative accuracy, safety, and ease of implementation. We show that the algorithm-based 3 + 3 design has poor performance and often fails to find the MTD. The performance of model-based combination trial designs is mixed: some demonstrate high accuracy of finding the MTD but poor safety, while others are safe but with compromised identification accuracy. In comparison, the model-assisted designs, such as BOIN and waterfall designs, have competitive and balanced performance in the accuracy of MTD identification and patient safety, and are also simple to implement, thus offering an attractive approach to designing phase I drug-combination trials. By taking into consideration the design's operating characteristics, ease of implementation and regulation, the need for advanced infrastructures, as well as the risk of regulatory acceptance, our article offers practical guidance on the selection of a suitable dose-finding approach for designing future combination trials. [ABSTRACT FROM AUTHOR]

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

Recent revolution in oncology treatment has witnessed emergence and fast development of the targeted therapy and immunotherapy. In contrast to traditional cytotoxic agents, these types of treatment tend to be more tolerable and thus efficacy is of more concern. As a result, seamless phase I/II trials have gained enormous popularity, which aim to identify the optimal biological dose (OBD) rather than the maximum tolerated dose (MTD). To enhance the accuracy and robustness for identification of OBD, we develop a calibration-free odds (CFO) design. For toxicity monitoring, the CFO design casts the current dose in competition with its two neighboring doses to obtain an admissible set. For efficacy monitoring, CFO selects the dose that has the largest posterior probability to achieve the highest efficacy under the Bayesian paradigm. In contrast to most of the existing designs, the prominent merit of CFO is that its main dose-finding component is model-free and calibration-free, which can greatly ease the burden on artificial input of design parameters and thus enhance the robustness and objectivity of the design. Extensive simulation studies demonstrate that the CFO design strikes a good balance between efficiency and safety for MTD identification under phase I trials, and yields comparable or sometimes slightly better performance for OBD identification than the competing methods under phase I/II trials. [ABSTRACT FROM AUTHOR]

In the era of immunotherapies and targeted therapies, the focus of early phase clinical trials has shifted from finding the maximum tolerated dose to identifying the optimal biological dose (OBD), which maximizes the toxicity‐efficacy trade‐off. One major impediment to using adaptive designs to find OBD is that efficacy or/and toxicity are often late‐onset, hampering the designs' real‐time decision rules for treating new patients. To address this issue, we propose the model‐assisted TITE‐BOIN12 design to find OBD with late‐onset toxicity and efficacy. As an extension of the BOIN12 design, the TITE‐BOIN12 design also uses utility to quantify the toxicity‐efficacy trade‐off. We consider two approaches, Bayesian data augmentation and an approximated likelihood method, to enable real‐time decision making when some patients' toxicity and efficacy outcomes are pending. Extensive simulations show that, compared to some existing designs, TITE‐BOIN12 significantly shortens the trial duration while having comparable or higher accuracy to identify OBD and a lower risk of overdosing patients. To facilitate the use of the TITE‐BOIN12 design, we develop a user‐friendly software freely available at http://www.trialdesign.org. [ABSTRACT FROM AUTHOR]

Abstract Background The overwhelming majority of dose-escalation clinical trials use methods that seek a maximum tolerable dose, including rule-based methods like the 3+3, and model-based methods like CRM and EWOC. These methods assume that the incidences of efficacy and toxicity always increase as dose is increased. This assumption is widely accepted with cytotoxic therapies. In recent decades, however, the search for novel cancer treatments has broadened, increasingly focusing on inhibitors and antibodies. The rationale that higher doses are always associated with superior efficacy is less clear for these types of therapies. Methods We extracted dose-level efficacy and toxicity outcomes from 115 manuscripts reporting dose-finding clinical trials in cancer between 2008 and 2014. We analysed the outcomes from each manuscript using flexible non-linear regression models to investigate the evidence supporting the monotonic efficacy and toxicity assumptions. Results We found that the monotonic toxicity assumption was well-supported across most treatment classes and disease areas. In contrast, we found very little evidence supporting the monotonic efficacy assumption. Conclusions Our conclusion is that dose-escalation trials routinely use methods whose assumptions are violated by the outcomes observed. As a consequence, dose-finding trials risk recommending unjustifiably high doses that may be harmful to patients. We recommend that trialists consider experimental designs that allow toxicity and efficacy outcomes to jointly determine the doses given to patients and recommended for further study.

Background: Relevant improvement in first-line treatment of metastatic pancreatic cancer (mPC) was provided by FOLFIRINOX and by gemcitabine (gem) plus nab-paclitaxel (Nab-p) regimens. Regardless of the first-line treatment survival benefit, most patients survive less than 1 year. Aim: The objectives of this multicenter phase I/II study were to evaluate as first-line chemotherapy (CT) two modified regimens of FOLFIRINOX, replacing either oxaliplatin (Oxa) or irinotecan with Nab-p, in patients with mPC. Methods: The primary objectives of phase 1 were the definition of the dose limit binations, while for phase II they were the characterization of safety and activity of Nab-FOLFIRI and Nab-FOLFOX in mPC. Results: Sixty-three patients received Nab-FOLFIRI or Nab-FOLFOX in phase I. We defined MTD at 120 mg/m2 for Nab-p with FOLFIRI and 160 mg/m2 with FOLFOX. In phase II, we randomized 42 patients for each arm with the following results: (1) overall response rate (ORR) was 31% for both schedules; (2) a clinical benefit rate (CBR) of 69% and 71%; (3) 1-year survival was 41% and 50%; (4) progression free survival (PFS) was 6 months and 5.6 months; (5) median overall survival (OS) was 10.2 and 10.4 months for Nab-FOLFIRI and Nab-FOLFOX, respectively. (6) Neutropenia was the most common grade ≥3 adverse event in our regimens, significantly lower than that reported for the FOLFIRINOX triplet. Conclusion: Nab-FOLFIRI and Nab-FOLFOX might be hopeful first-line CT options for mPC patients, with promising activity and a good safety profile.

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

Background: /Aims: In early-phase cell therapy trials, each dose level being studied is defined by the number of cells infused into the trial participant. The issue of dose feasibility presents itself when the desired number of cells is not reached in the expansion process. Consequently, dose assignments for some patients may deviate from the planned dose according to the chosen design. Widely used algorithmic designs aren't flexible enough to handle this complication and can lead to the exclusion of safety data from the dose assignment algorithm. This article studies the impact of dose feasibility challenges on the behavior of the 3 + 3 decision rule. Methods: We conducted a simulation study across six dose-feasibility and dose-toxicity scenarios. Trials are simulated using the 3 + 3 algorithm. We present a novel algorithm for random feasibility curve generation. We used this algorithm to conduct a large-scale simulation study across 100 random scenarios. Results: We found that the 3 + 3 has problematic characteristics due to the exclusion of safety data from the algorithm. Ignoring toxicity data can complicate the allocation of subsequent patients in the trial and can bias the final maximum tolerated dose recommendation for the next phase of drug development. Conclusion: Our study demonstrates that excluding safety data from the 3 + 3 algorithm can be detrimental to trial conduct. Furthermore, there are existing methods that are flexible enough to include data that is observed away from the planned dose. We recommend that these methods be used in conducting phase I cell therapy trials.

Multiple phase I clinical trials may be performed to determine specific maximum tolerated doses (MTD) for specific races or cancer types. In these situations, borrowing historical information has potential to improve the accuracy of estimating toxicity rate and increase the probability of correctly targeting MTD. To utilize historical information in phase I clinical trials, we proposed using the Meta-Analytic-Predictive (MAP) priors to automatically estimate the heterogeneity between historical trials and give a relatively reasonable amount of borrowed information. We then applied MAP priors in some famous phase I trial designs, such as the continual reassessment method (CRM), Keyboard design and Bayesian optimal interval design (BOIN), to accomplish the process of dose finding. A clinical trial example and extended simulation studies show that our proposed methods have robust and efficient statistical performance, compared with those designs which do not consider borrowing information. [ABSTRACT FROM AUTHOR]