Dose-finding Trial Design Research Articles

Patient and public involvement and engagement in the development of innovative patient-centric early phase dose-finding trial designs

BackgroundIn light of the FDA’s Project Optimus initiative, there is fresh interest in leveraging Patient-reported Outcome (PRO) data to enhance the assessment of tolerability for investigational therapies within early phase dose-finding oncology trials. Typically, dose escalation in most trial designs is solely reliant on clinician assessed adverse events. Research has shown a disparity between patients and clinicians when assessing whether an investigational therapy is tolerable, leading to the recommendation of potentially intolerable doses for further investigation in subsequent trials.It is also increasingly recognized that patient and public involvement and engagement (PPIE) plays a pivotal role in enriching trial design and conduct. However, to our knowledge, no PPIE has explored the optimal integration of PROs in the development of advanced statistical trial designs within early phase dose-finding oncology trials.MethodsA virtual PPIE session was held with nine participants on 18th October 2023 to discuss the incorporation of PROs within a dose-finding trial design. This cross disciplinary session was developed and led by a team of statisticians, clinical specialists, qualitative experts, and trial methodologists. Following the session, in-depth perspectives were provided by two patient advocates who actively engaged in the PPIE session. We discuss the importance of PPIE in shaping advanced dose-finding trial designs, share insights from patients on integrating PROs to inform treatment tolerability, and present a template for meaningful patient involvement in trial design development.ResultsParticipants generally supported the introduction of PROs within dose-finding trials but showed some apprehensiveness as to how PROs may reduce the size of the recommended dose (and potentially efficacious effect). Some participants shared that they may be reluctant to record the real severity of their symptoms via PROs if it would mean that they would have to discontinue treatment. They discussed that PROs could be used to assess tolerability rather than toxicity of a dose.ConclusionsAmplifying patient voice in the development of patient-centric dose-finding trial designs is now essential. This paper offers an exemplary illustration of how trialists and methodologists can effectively incorporate patient voice in the future development of advanced dose-finding trial designs.

A generalized Bayesian optimal interval design for dose optimization in immunotherapy.

For novel immuno-oncology therapies, the primary purpose of a dose-finding trial is to identify an optimal dose (OD), defined as the tolerable dose having adequate efficacy and immune response under the unpredictable dose-outcome (toxicity, efficacy, and immune response) relationships. In addition, the multiple low or moderate-grade toxicities rather than dose-limiting toxicities (DLTs) and multiple levels of efficacy should be evaluated differently in dose-finding to determine true OD for developing novel immuno-oncology therapies. We proposed a generalized Bayesian optimal interval design for immunotherapy, simultaneously considering efficacy and toxicity grades and immune response outcomes. The proposed design, named gBOIN-ETI design, is model-assisted and easy to implement to develop immunotherapy efficiently. The operating characteristics of the gBOIN-ETI are compared with other dose-finding trial designs in oncology by simulation across various realistic settings. Our simulations show that the gBOIN-ETI design could outperform the other available approaches in terms of both the percentage of correct OD selection and the average number of patients allocated to the OD across various realistic trial settings.

Statistical methods and data visualisation of patient-reported outcomes in early phase dose-finding oncology trials: a methodological review

Traditionally, within dose-finding clinical trials, treatment toxicity and tolerability are assessed by clinicians. Research has shown that clinician reporting may have inadequate inter-rater reliability, poor correlation with patient reported outcomes, and under capture the true toxicity burden. The introduction of patient-reported outcomes (PROs), where the patient can assess their own symptomatic adverse events or quality of life, has potential to complement current practice to aid dose optimisation. There are no international recommendations offering guidance for the inclusion of PROs in dose-finding trial design and analysis. Our review aimed to identify and describe current statistical methods and data visualisation techniques employed to analyse and visualise PRO data in published early phase dose-finding oncology trials (DFOTs). DFOTs published from June 2016-December 2022, which presented PRO analysis methods, were included in this methodological review. We extracted 35 eligible papers indexed in PubMed. Study characteristics extracted included: PRO objectives, PRO measures, statistical analysis and visualisation techniques, and whether the PRO was involved in interim and final dose selection decisions. Most papers (30, 85.7%) did not include clear PRO objectives. 20 (57.1%) papers used inferential statistical techniques to analyse PROs, including survival analysis and mixed-effect models. One trial used PROs to classify a clinicians' assessed dose-limiting toxicities (DLTs). Three (8.6%) trials used PROs to confirm the tolerability of the recommended dose. 25 trial reports visually presented PRO data within a figure or table within their publication, of which 12 papers presented PRO score longitudinally. This review highlighted that the statistical methods and reporting of PRO analysis in DFOTs are often poorly described and inconsistent. Many trials had PRO objectives which were not clearly described, making it challenging to evaluate the appropriateness of the statistical techniques used. Drawing conclusions based on DFOTs which are not powered for PROs may be misleading. With no guidance and standardisation of analysis methods for PROs in early phase DFOTs, it is challenging to compare study findings across trials. Therefore, there is a crucial need to establish international guidance to enhance statistical methods and graphical presentation for PRO analysis in the dose-finding setting. EA has been supported to undertake this work as part of a PhD studentship from the Institute of Cancer Research within the MRC/NIHR Trials Methodology Research Partnership. AM is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust, the Institute of Cancer Research and Imperial College.

Interval design to identify the optimal biological dose for immunotherapy

Immunotherapeutics have revolutionized the treatment of metastatic cancers and are expected to play an increasingly prominent role in the treatment of cancer patients. Recent advances in checkpoint inhibition show promising early results in a number of malignancies, and several treatments have been approved for use. However, the immunotherapeutic agents have been shown to have different mechanisms of antitumor activity from cytotoxic agents, and many limitations and challenges encountered in the traditional paradigm were recently pointed out for immunotherapy. I propose a desirability-based method to determine the optimal biological dose of immunotherapeutics by effectively using toxicity, immune response, and tumor response. Moreover, a new dose allocation algorithm of interval designs is proposed to incorporate immune response in addition to toxicity and tumor response. Simulation studies show that the proposed design has desirable operating characteristics compared to existing dose-finding designs. It also inherits the strengths of interval designs for dose-finding trials, yielding good performance with ease of implementation.

Early Completion of Model-Assisted Designs for Dose-Finding Trials

We propose a novel early completion method for phase I dose-finding trials using model-assisted designs. The trials can halt when a maximum tolerated dose (MTD) is estimated with sufficient accuracy. Early completion can reduce the average number of patients treated relative to the planned number, thereby allowing the trial to proceed to enrolling an expansion cohort for efficacy and enabling the trial to reach the next phase faster. Early completion is conducted on the basis of a dose-retainment probability using dose-assignment decisions. We evaluated early the completion for two actual trials. In addition, we performed a computer simulation to confirm the percentage of correctly selected MTDs, the early completion percentage, and the average number of patients treated. In the evaluation of the two actual trials, we confirmed that the trials completed early. In the simulation results, we confirmed that the percentages of correct MTD selection were maintained relative to the original model-assisted designs. The early completion percentages ranged from 50% to 90%, and the number of patients treated reduced from 20%-60% relative to the planned number of patients. We conclude that the early completion method can be applied unproblematically to the model-assisted design of phase I dose-finding trials.

Probability intervals of toxicity and efficacy design for dose-finding clinical trials in oncology.

Immunotherapy, gene therapy or adoptive cell therapies, such as the chimeric antigen receptor+ T-cell therapies, have demonstrated promising therapeutic effects in oncology patients. We consider statistical designs for dose-finding adoptive cell therapy trials, in which the monotonic dose-response relationship assumed in traditional oncology trials may not hold. Building upon a previous design called "TEPI", we propose a new dose finding method - Probability Intervals of Toxicity and Efficacy (PRINTE), which utilizes toxicity and efficacy jointly in making dosing decisions, does not require a pre-elicited decision table and at the same time can handle Ockham's razor properly in the statistical inference. We show that optimizing the joint posterior expected utility of toxicity and efficacy under a 0-1 loss is equivalent to maximizing the marginal model posterior probability in the two-dimensional probability space. An extensive simulation study under various scenarios are conducted and results show that PRINTE outperforms existing designs in the literature since it assigns more patients to optimal doses and less to toxic ones, and selects optimal doses with higher percentages. The simple and transparent features together with good operating characteristics make PRINTE an improved design for dose-finding trials in oncology trials.

TEPI-2 and UBI: designs for optimal immuno-oncology and cell therapy dose finding with toxicity and efficacy

ABSTRACT Conventional dose finding designs in oncology drug development target on the identification of the maximum tolerated dose (MTD), with the assumption that the MTD has the most potential of clinical activity among those identified tolerable dose levels. However, immuno-oncology (I-O) and cell therapy area, may lack dose-efficacy monotonicity, posing significant challenges in the statistical designs for dose finding trials. A desirable design should empower the trial to identify the right dose level with tolerable toxicity and acceptable efficacy. Such dose is called as optimal biological dose (OBD), which is more appropriate to be considered as the primary objective of the first-in-human trial in I-O and cell therapy than MTD. We propose two model-assisted designs in this setting: the toxicity and efficacy probability interval-2 (TEPI-2) design and the utility-based interval (UBI) design that incorporate the toxicity and efficacy outcomes simultaneously and identify a dose that has high probability of acceptable efficacy with manageable toxicity. The proposed designs can generate decision tables before trial starts to facilitate practical and easy-to-implement applications. Through simulation studies, our proposed novel designs demonstrate superior performance in accuracy, efficiency, and safety. Additionally, they can reduce the number of patients and shorten clinical development timeline. We also illustrate the advantages of proposed methods by redesigning a CAR T-cell therapy phase I clinical trial for multiple myeloma and summarize our recommendations in the discussion section.

Design considerations for phase I/II dose finding clinical trials in Immuno-oncology and cell therapy

Immuno-oncology (IO) and cell therapy, the frontier of cancer treatment, is a rapidly developing area that brings new opportunities to patients. In IO and cell therapy clinical trial development, it is critical to identify the right dose level in early phase of trials thus improving the probability of success in confirmatory trials to test the superiority over other therapies. Given the complex mechanism interacting with immune system for IO drugs especially cell therapy, the traditional oncology dose finding trial designs may not serve the purpose. Specifically, it is questionable to believe the monotone relationship between dose level and safety/efficacy, which will likely result in inappropriate dose selection using designs with monotone assumption. Additionally, considering the immune system pathway, designs ignoring the heterogeneity of the patient populations may provide misleading dose decisions, which could be either unsafe or lead to selection mistakes for targeted population. Therefore, in our paper, we review and present the limitations of the traditional dose finding designs. Then we discuss improved dose finding designs that consider both safety and efficacy outcomes simultaneously. Furthermore, we propose novel dose finding designs for multiple populations: BNP-mTPI and fBNP-mTPI, which extend the modified toxicity probability interval designs by utilizing Bayesian non-parametric priors. The proposed designs consider patient population differences meanwhile flexibly borrowing information across populations with similar profiles to improve the efficiency of dose search and accuracy of estimation of optimal dose level. Simulations are provided to demonstrate the model performance. Finally, we conclude the recommendations for IO and cell therapy dose finding designs in the discussion and offer insights for future research direction.

Time-to-event model-assisted designs for dose-finding trials with delayed toxicity.

Two useful strategies to speed up drug development are to increase the patient accrual rate and use novel adaptive designs. Unfortunately, these two strategies often conflict when the evaluation of the outcome cannot keep pace with the patient accrual rate and thus the interim data cannot be observed in time to make adaptive decisions. A similar logistic difficulty arises when the outcome is late-onset. Based on a novel formulation and approximation of the likelihood of the observed data, we propose a general methodology for model-assisted designs to handle toxicity data that are pending due to fast accrual or late-onset toxicity and facilitate seamless decision making in phase I dose-finding trials. The proposed time-to-event model-assisted designs consider each dose separately and the dose-escalation/de-escalation rules can be tabulated before the trial begins, which greatly simplifies trial conduct in practice compared to that under existing methods. We show that the proposed designs have desirable finite and large-sample properties and yield performance that is comparable to that of more complicated model-based designs. We provide user-friendly software for implementing the designs.

Implementing the EffTox dose-finding design in the Matchpoint trial

BackgroundThe Matchpoint trial aims to identify the optimal dose of ponatinib to give with conventional chemotherapy consisting of fludarabine, cytarabine and idarubicin to chronic myeloid leukaemia patients in blastic transformation phase. The dose should be both tolerable and efficacious. This paper describes our experience implementing EffTox in the Matchpoint trial.MethodsEffTox is a Bayesian adaptive dose-finding trial design that jointly scrutinises binary efficacy and toxicity outcomes. We describe a nomenclature for succinctly describing outcomes in phase I/II dose-finding trials. We use dose-transition pathways, where doses are calculated for each feasible set of outcomes in future cohorts. We introduce the phenomenon of dose ambivalence, where EffTox can recommend different doses after observing the same outcomes. We also describe our experiences with outcome ambiguity, where the categorical evaluation of some primary outcomes is temporarily delayed.ResultsWe arrived at an EffTox parameterisation that is simulated to perform well over a range of scenarios. In scenarios where dose ambivalence manifested, we were guided by the dose-transition pathways. This technique facilitates planning, and also helped us overcome short-term outcome ambiguity.ConclusionsEffTox is an efficient and powerful design, but not without its challenges. Joint phase I/II clinical trial designs will likely become increasingly important in coming years as we further investigate non-cytotoxic treatments and streamline the drug approval process. We hope this account of the problems we faced and the solutions we used will help others implement this dose-finding clinical trial design.Trial registrationMatchpoint was added to the European Clinical Trials Database (https://www.clinicaltrialsregister.eu/ctr-search/trial/2012-005629-65/GB) on 2013-12-30.

Embracing model-based designs for dose-finding trials

Background:Dose-finding trials are essential to drug development as they establish recommended doses for later-phase testing. We aim to motivate wider use of model-based designs for dose finding, such as the continual reassessment method (CRM).Methods:We carried out a literature review of dose-finding designs and conducted a survey to identify perceived barriers to their implementation.Results:We describe the benefits of model-based designs (flexibility, superior operating characteristics, extended scope), their current uptake, and existing resources. The most prominent barriers to implementation of a model-based design were lack of suitable training, chief investigators’ preference for algorithm-based designs (e.g., 3+3), and limited resources for study design before funding. We use a real-world example to illustrate how these barriers can be overcome.Conclusions:There is overwhelming evidence for the benefits of CRM. Many leading pharmaceutical companies routinely implement model-based designs. Our analysis identified barriers for academic statisticians and clinical academics in mirroring the progress industry has made in trial design. Unified support from funders, regulators, and journal editors could result in more accurate doses for later-phase testing, and increase the efficiency and success of clinical drug development. We give recommendations for increasing the uptake of model-based designs for dose-finding trials in academia.

An FDA oncology analysis of immune activating products and first-in-human dose selection

As sub-therapeutic doses are not medically justifiable in patients with cancer, we retrospectively analyzed data on immune activating products, to assess approaches used in first-in-human (FIH) dose selection, the utility of animal toxicology studies in dose selection, and the length of time to complete FIH trials. The information collected included pharmacology and toxicology data, FIH dose and rationale, and dose-finding trial design. We used the principles of the Hill equation to estimate the FIH doses for antibodies and compared them to the doses administered to patients with acceptable toxicities. For approximately half the antibodies (44%) examined, the FIH doses were at least a hundred-fold lower than the doses safely administered to patients, indicating optimization of FIH dose selection and/or optimization of dose-finding trial design is needed to minimize patient exposure to sub-therapeutic doses. However, selection of the FIH dose for antibodies based on animal toxicology studies using 1/6th the HNSTD or 1/10th the NOAEL resulted in human doses that were unsafe for several antibodies examined. We also concluded that antibodies with Fc-modifications for increased effector function may be less tolerated, resulting in toxicities at lower doses than those without such modifications. There was insufficient information to evaluate CD3 bispecific products.

A robust Bayesian dose-finding design for phase I/II clinical trials.

We propose a Bayesian phase I/II dose-finding trial design that simultaneously accounts for toxicity and efficacy. We model the toxicity and efficacy of investigational doses using a flexible Bayesian dynamic model, which borrows information across doses without imposing stringent parametric assumptions on the shape of the dose-toxicity and dose-efficacy curves. An intuitive utility function that reflects the desirability trade-offs between efficacy and toxicity is used to guide the dose assignment and selection. We also discuss the extension of this design to handle delayed toxicity and efficacy. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. The results show that the proposed design possesses good operating characteristics and is robust to the shape of the dose-toxicity and dose-efficacy curves.

Development of a Bayesian response-adaptive trial design for the Dexamethasone for Excessive Menstruation study.

It is often unclear what specific adaptive trial design features lead to an efficient design which is also feasible to implement. This article describes the preparatory simulation study for a Bayesian response-adaptive dose-finding trial design. Dexamethasone for Excessive Menstruation aims to assess the efficacy of Dexamethasone in reducing excessive menstrual bleeding and to determine the best dose for further study. To maximise learning about the dose response, patients receive placebo or an active dose with randomisation probabilities adapting based on evidence from patients already recruited. The dose-response relationship is estimated using a flexible Bayesian Normal Dynamic Linear Model. Several competing design options were considered including: number of doses, proportion assigned to placebo, adaptation criterion, and number and timing of adaptations. We performed a fractional factorial study using SAS software to simulate virtual trial data for candidate adaptive designs under a variety of scenarios and to invoke WinBUGS for Bayesian model estimation. We analysed the simulated trial results using Normal linear models to estimate the effects of each design feature on empirical type I error and statistical power. Our readily-implemented approach using widely available statistical software identified a final design which performed robustly across a range of potential trial scenarios.

Dose-Finding Trial Designs for Combination Therapies in Oncology

Dose-Finding Trial Designs for Combination Therapies in Oncology

Bayesian Continual Reassessment Method for Dose-Finding Trials Infusing T Cells with Limited Sample Size

We consider the design of dose-finding trials for patients with malignancies when only a limited sample size is available. The small sample size may be necessary because (1) the modality of treatment is very expensive, and/or (2) the disease under investigation is rare, requiring a lengthy period to enroll a target patient population. Both of these are common in the field of adoptive immunotherapy, in which T cells are infused to prevent and treat infections and malignancies. The clinical trial described in this paper investigates a novel therapy to adoptively transfer genetically modified T cells in small pilot protocols enrolling patients with B-lineage malignancies. Due to the constraints of cost and infrastructure, the maximum sample size for this trial is fixed at 12 patients distributed among four doses of T cells. Given these limitations, an innovative statistical design has been developed to efficiently evaluate the safety, feasibility, persistence, and toxicity profiles of the trial doses. The proposed statistical design is specifically tailored for trials with small sample sizes in that it uses the toxicity outcomes from patients treated at different doses to make dose-finding decisions. Supplementary materials including an R function and a movie demo can be downloaded in the websites mentioned in the paper.

A Two-Stage Adaptive Design in Phase 2 Clinical Trials for Acute Treatment of Migraine

In the development of acute migraine treatments, traditional phase 2 dose-finding trial designs are particularly challenging when a wide dose range needs to be investigated. In this article, an adaptive two-stage dose-finding design is introduced. The proposed design combines traditional phase 2a and 2b to serve the dual purpose of proof of concept and dose finding through the use of an unblinded interim analysis that provides an opportunity for adaptation. The design has a fixed total sample size but includes provisions for discontinuation of less effective doses at the interim analysis to allow for the allocation of patients into a more promising range of doses. The interim adaptation is designed to increase the amount of information collected on more effective doses and increase study power as compared to a traditional nonadaptive dose-finding design. On the other hand, despite the interim look and adaptation of the design, conventional statistical analyses of the final data can still be used for the final analysis and provide proper inferences with adequately controlled type I error and an ignorable amount of bias.

Combining Proof-of-Concept With Dose-Finding: Utilization of Adaptive Designs in Migraine Clinical Trials

There is an obvious need to improve clinical trial designs with respect to efficiency, duration and the number of patients recruited. Adaptive (flexible) designs may be valuable in this respect. We simulated the properties of a two-stage adaptive proof-of-concept and dose-finding trial design in adult migraine patients with moderate to severe headache, with or without aura. We also assessed the usefulness of a combined Bayesian and frequentist approach in the estimation of the probability of success of subsequent Phase III studies. Applying such an innovative approach would result in a reduction of the required sample size by 30 patients and no prolongation of the trial duration. The probability of success in Phase III is > 81%. An innovative adaptive design can facilitate testing of investigational migraine medications by reducing patient numbers and improving predictivity of success in Phase III.

Escalation, group andA +B designs for dose-finding trials

In this paper, rules are given on how to construct escalation, A + B, and group designs for dose-finding trials. Operational characteristics of these designs are discussed and compared via simulations. Dose-finding designs for trials with ordinal toxicity outcome are considered.

Modeling of transfer kinetics at the serum-cerebrospinal fluid barrier in rabbits with experimental meningitis: application to grepafloxacin.

The goals of the present study were to model the population kinetics of in vivo influx and efflux processes of grepafloxacin at the serum-cerebrospinal fluid (CSF) barrier and to propose a simulation-based approach to optimize the design of dose-finding trials in the meningitis rabbit model. Twenty-nine rabbits with pneumococcal meningitis receiving grepafloxacin at 15 mg/kg of body weight (intravenous administration at 0 h), 30 mg/kg (at 0 h), or 50 mg/kg twice (at 0 and 4 h) were studied. A three-compartment population pharmacokinetic model was fit to the data with the program NONMEM (Nonlinear Mixed Effects Modeling). Passive diffusion clearance (CL(diff)) and active efflux clearance (CL(active)) are transfer kinetic modeling parameters. Influx clearance is assumed to be equal to CL(diff), and efflux clearance is the sum of CL(diff), CL(active), and bulk flow clearance (CL(bulk)). The average influx clearance for the population was 0.0055 ml/min (interindividual variability, 17%). Passive diffusion clearance was greater in rabbits receiving grepafloxacin at 15 mg/kg than in those treated with higher doses (0.0088 versus 0.0034 ml/min). Assuming a CL(bulk) of 0.01 ml/min, CL(active) was estimated to be 0.017 ml/min (11%), and clearance by total efflux was estimated to be 0.032 ml/min. The population kinetic model allows not only to quantify in vivo efflux and influx mechanisms at the serum-CSF barrier but also to analyze the effects of different dose regimens on transfer kinetic parameters in the rabbit meningitis model. The modeling-based approach also provides a tool for the simulation and prediction of various outcomes in which researchers might be interested, which is of great potential in designing dose-finding trials.