Dose optimization for cancer treatments with considerations for late-onset toxicities

Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.

Should the choice of BOIN design parameter p.tox only depend on the target DLT rate?

IMPORTANCE

On December 10, 2021, the FDA published a Determination Letter, along with a Statistical Review and Evaluation Report, and concluded that under the non-informative prior, the local Bayesian optimal interval design (BOIN) design, in its revised form, can be designated fit-for-purpose for identifying the maximum tolerated dose (MTD) of a new drug, assuming that dose-toxicity relationship is monotonically increasing. Although setting the BOIN design parameter p.tox = 1.4 * target.DLT.rate is recommended in almost all BOIN methodology articles and is the default value in the R package BOIN, it's unclear if the choice of p.tox should only depend on the target DLT rate and whether certain range of p.tox could produce the same BOIN boundary table.

DESIGN

In this simulation study, following parameters were varied one at a time, using R package BOIN, to explore each parameter's effect on the equivalence intervals of p.saf and p.tox: 1) target DLT rate, 2) n.earlystop, 3) cutoff.eli, 4) cohortsize, and 5) ncohort. And a simple 3+3 design was used as an example to explore equivalent sets of BOIN design parameters that can generate the same boundary table.

RESULTS

When the early stopping parameter n.earlystop is relatively small or the cohortsize value is not optimized via simulation, it might be better to use p.tox < 1.4 * target.DLT.rate, or try out different cohort sizes, or increase n.earlystop, whichever is both feasible and provides better operating characteristics. This is because if the cohortsize was not optimized via simulation, even when n.earlystop = 12 and cohortsize > 3, the BOIN escalation/de-escalation rules generated using p.tox = 1.4 * target.DLT.rate could be exactly the same as those calculated using p.tox > 3 * target.DLT.rate, which might not be acceptable for some pediatric trials targeting 10% DLT rate.The traditional 3+3 design stops the dose finding process when 3 patients have been treated at the current dose level, 0 DLT has been observed, and the next higher dose has already been eliminated. If additional 3 patients were required to be treated at the current dose in the situation described above, the decision rules of this commonly used 3+3 design could be generated using BOIN design with target DLT rates ranging from 18% to 29%, p.saf ranging from 8% to 26%, and different p.tox values ranging from 39% to 99%. To generate this commonly used 3+3 design table, BOIN parameters also need to satisfy a set of conditions.

Combination MCP-Mod for two-drug combination dose-ranging studies

Combination therapies with multiple mechanisms of action can offer improved efficacy and/or safety profiles when compared to a single therapy with one mechanism of action. Consequently, the number of combination therapy studies have increased multi-fold, both in oncology and non-oncology indications. However, identifying the optimal doses of each drug in a combination therapy can require a large sample size and prolong study timelines, especially when full factorial designs are used. In this paper, we extend the MCP-Mod design of Bretz, Pinheiro, and Branson to a three-dimensional space to model the dose-response surface of a two-drug combination under the framework of Combination (Comb) MCP-Mod. The resulting model yields a set of dosages for each drug in the combination that elicits the target response so that an optimal dose for the combination can be selected for pivotal studies. We construct three-dimensional dose-response models for the combination and formulate the contrast test statistic to select the best model, which can then be used to select the optimal dose. Guidance to calculate power and sample size calculations are provided to assist study design. Simulation studies show that Comb MCP-Mod performs as well as the conventional multiple comparisons approach in controlling the family-wise error rate at the desired alpha level. However, Comb MCP-Mod is more powerful than the classical multiple comparisons approach in detecting dose-response relationships when treatment is non-null. The probability of correctly identifying the underlying dose-response relationship is generally higher when using Comb MCP-Mod than when using the multiple comparisons approach.

Interim decision making in seamless trial designs: An application in an adaptive dose-finding study in a rare kidney disease

Adaptive seamless trial designs, combining the learning and confirming cycles of drug development in a single trial, have gained popularity in recent years. Adaptations may include dose selection, sample size re-estimation and enrichment of the study population. Despite methodological advances and recognition of the potential efficiency gains such designs offer, their implementation, including how to enable efficient decision making on the adaptations in interim analyzes, remains a key challenge in their adoption. This manuscript uses a case study of an adaptive seamless proof-of-concept (Phase 2a)/dose-finding (Phase 2b) to showcase potential adaptive features that can be implemented in trial designs at earlier development stages and the role of simulations in assessing the design operating characteristics and specifying the decision rules for the adaptations. It further outlines the elements needed to support successful interim analysis decision making on the adaptations while safeguarding study integrity, including the role of different stakeholders, interactive simulation-based tools to facilitate decision making and operational aspects requiring preplanning. The benefits of the adaptive Phase 2a/2b design chosen compared to following the traditional two separate studies (2a and 2b) paradigm are discussed. With careful planning and appreciation of their complexity and components needed for their implementation, seamless adaptive designs have the potential to yield significant savings both in terms of time and resources.

Sample size calculation for multi-arm parallel design with restricted mean survival time

With the recent advances in oncology treatment, restricted mean survival time (RMST) is increasingly being used to replace the routine approach based on hazard ratios in randomized controlled trials for time-to-event outcomes. While RMST has been widely applied in single-arm and two-arm designs, challenges still exist in comparing RMST in multi-arm trials with three or more groups. In particular, it is unclear in the literature how to compare more than one intervention simultaneously or perform multiple testing based on RMST, and sample size determination is a major obstacle to its penetration to practice. In this paper, we propose a novel method of designing multi-arm clinical trials with right-censored survival endpoint based on RMST that can be applied in both phase II/III settings using a global χ 2 test as well as a modeling-based multiple comparison procedure. The framework provides a closed-form sample size formula built upon a multi-arm global test and a sample size determination procedure based on multiple-comparison in the phase II dose-finding study. The proposed method enjoys strong robustness and flexibility as it requires less a priori set-up than conventional work, and obtains a smaller sample size while achieving the target power. In the assessment of sample size, we also incorporate practical considerations, including the presence of non-proportional hazards and staggered patient entry. We evaluate the validity of our method through simulation studies under various scenarios. Finally, we demonstrate the accuracy and stability of our method by implementing it in the design of two real clinical trial examples.

A Monte Carlo simulation study comparing the up and down, biased-coin up and down and continual reassessment methods used to estimate an effective dose (ED95 or ED90) in anaesthesiology research

Background

Dose-finding studies in anaesthesiology aim to target the effective dose (ED) of an anaesthetic agent in a specific population. The common dose-finding designs used are the up and down method (UDM), the biased-coin up and down (BCD), and the continual reassessment method (CRM). Although the advantages of CRM over the UDM and BCD methods have been described in the statistical literature in terms of precision and direct estimation of ED, CRM may also offer attractive properties from an ethical point of view.

Methods

Based on Monte Carlo simulations, this article aims to compare the three methods with regard to 1) their ability to find as close an estimate as possible for the ED95 or ED90 and 2) the total number of patients needed to treat and the number of failures.

Results

In contrast to BCD and UDM, CRM does find an estimate for ED95 and ED90. UDM underestimates both ED95 and ED90. BCD is close to the targeted EDs when the starting dose does not exceed the ED of interest, otherwise it overestimates it. CRM with cohorts of two patients is closest to the ED of interest independently of the starting doses. CRM requires between 20 and 50 observations, UDM should include 90 patients, and BCD 100 or 60 observations. Lastly, CRM is associated with fewer failures, compared with BCD and UDM.

Conclusions

Based on Monte Carlo simulations, our work suggests that the UDM is not an adequate dose-finding method because it underestimates the ED of interest. Compared with BCD, CRM offers the advantages of being more efficient, requires fewer patients to be included, and is associated with fewer failures.

Concurrent dose-finding of a novel cancer drug with and without a second agent

Introduction

More complex research questions are being posed in early-phase oncology clinical trials, necessitating design strategies tailored to contemporary study objectives. This paper describes the proposed design of a Phase I trial concurrently evaluating the safety of a hematopoietic progenitor kinase-1 inhibitor (Agent A) as a single agent and in combination with an anti-PD-1 agent in patients with advanced malignancies. The study's primary objective was to concurrently determine the maximum tolerated dose (MTD) of Agent A with and without anti-PD-1 therapy among seven possible study dose levels.

Methods

Our solution to this challenge was to apply a continual reassessment method shift model to meet the research objectives of the study.

Results

The application of this method is described herein, and a simulation study of the design's operating characteristics is conducted. This work was developed through collaboration and mentoring between the authors at the American Association for Cancer Research (AACR) and the American Society of Clinical Oncology (ASCO) annual AACR/ASCO Methods in Clinical Cancer Research Workshop.

Conclusions

The aim of this manuscript is to highlight examples of novel design applications as a means of augmenting the implementation of innovative designs in the future and to demonstrate the flexibility of adaptive designs in satisfying modern design conditions. Although the design is presented using an investigation of Agent A with and without anti-PD-1 therapy as an illustrative example, the approach described is not specific to these agents and could be applied to other concurrent monotherapy and combination therapy studies with well-defined binary safety endpoints.

Application of the patient-reported outcomes continual reassessment method to a phase I study of radiotherapy in endometrial cancer

This article considers the concept of designing Phase I clinical trials using both clinician- and patient-reported outcomes to adaptively allocate study participants to tolerable doses and determine the maximum tolerated dose (MTD) at the study conclusion. We describe an application of a Bayesian form of the patient-reported outcomes continual reassessment method (PRO-CRMB) in an ongoing Phase I study of adjuvant hypofractionated whole pelvis radiation therapy (WPRT) in endometrial cancer (NCT04458402). The study's primary objective is to determine the MTD per fraction of WPRT, defined by acceptable clinician- and patient-reported DLT rates. We conduct simulation studies of the operating characteristics of the design and compared them to a rule-based approach. We illustrate that the PRO-CRMB makes appropriate dose assignments during the study to give investigators and reviewers an idea of how the method behaves. In simulation studies, the PRO-CRMB demonstrates superior performance to a 5 + 2 stepwise design in terms of recommending target treatment courses and allocating patients to these courses. The design is accompanied by an easy-to-use R shiny web application to simulate operating characteristics at the design stage and sequentially update dose assignments throughout the trial's conduct.

Comparison of design methods for a safety run-in phase of a phase II clinical trial

BACKGROUND/AIMS

In pediatric oncology, a Phase II trial often utilizes a safety run-in phase followed by an efficacy phase that enrolls at the dose level selected based on the safety run-in. Different from a Phase I trial, a Phase II safety run-in often assesses a very small number of dose levels. In the context of a safety run-in that assesses two or three dose levels, this article aims to compare three design methods, including the algorithm-based designs 3 + 3 and Rolling 6, and the model-assisted designs such as the Bayesian optimal interval design.

METHODS

Extensive simulations were conducted to evaluate and compare operating characteristics of the three design methods for a safety run-in with two or three dose levels, varying the starting dose level.

RESULTS

The performance of algorithm-based and model-assisted designs can be influenced by selection of the starting dose level, with trials starting at a lower dose level having a higher probability of selecting a low dose or considering all doses as toxic. The impact is larger for 3 + 3 and Rolling 6 but to a lesser extent for Bayesian optimal interval design. For a safety run-in with two dose levels, using 3 + 3 or Rolling 6 and starting at the higher dose often lead to similar performance to Bayesian optimal interval design. For safety run-in with three dose levels, starting at the middle dose with 3 + 3, Rolling 6 or Bayesian optimal interval design is a good compromise between improving correct dose selection and imposing a toxic dose to less patients.

CONCLUSIONS

Despite being sensitive to the starting dose level, the 3 + 3, Rolling 6 and Bayesian optimal interval designs overall demonstrate reasonable performance, which can be further improved with wise selection of the starting dose level. The Rolling 6 design remains the recommended design method especially if pharmacokinetics is important or required with this design having the feature of treating six patients per dose level. When designing a safety run-in, selection of a design method or selection of a starting dose should consider both the performance of the design approaches with different choices of a starting dose level and the magnitude of safety concerns with the dose levels under investigation.

Performance of phase-I dose finding designs with and without a run-in intra-patient dose escalation stage

Dose-finding designs for phase-I trials aim to determine the recommended phase-II dose (RP2D) for further phase-II drug development. If the trial includes patients for whom several lines of standard therapy failed or if the toxicity of the investigated agent does not necessarily increase with dose, optimal dose-finding designs should limit the frequency of treatment with suboptimal doses. We propose a two-stage design strategy with a run-in intra-patient dose escalation part followed by a more traditional dose-finding design. We conduct simulation studies to compare the 3 + 3 design, the Bayesian Optimal Interval Design (BOIN) and the Continual Reassessment Method (CRM) with and without intra-patient dose escalation. The endpoints are accuracy, sample size, safety, and therapeutic efficiency. For scenarios where the correct RP2D is the highest dose, inclusion of an intra-patient dose escalation stage generally increases accuracy and therapeutic efficiency. However, for scenarios where the correct RP2D is below the highest dose, intra-patient dose escalation designs lead to increased risk of overdosing and an overestimation of RP2D. The magnitude of the change in operating characteristics after including an intra-patient stage is largest for the 3 + 3 design, decreases for the BOIN and is smallest for the CRM.

Isotonic design for phase I cancer clinical trials with late-onset toxicities

This article addresses the problem of identifying the maximum tolerated dose (MTD) in Phase I dose-finding clinical trials with late-onset toxicities. The main design challenge is how best to adaptively allocate study participants to tolerable doses when the evaluation window for the toxicity endpoint is long relative to the accrual rate of new participants. We propose a new design framework based on order-restricted statistical inference that addresses this challenge in sequential dose assignments. We illustrate the proposed method on real data from a Phase I trial of bortezomib in lymphoma patients and apply it to a Phase I trial of radiotherapy in prostate cancer patients. We conduct extensive simulation studies to compare our design's operating characteristics to existing published methods. Overall, our proposed design demonstrates good performance relative to existing methods in allocating participants at and around the MTD during the study and accurately recommending the MTD at the study conclusion.

A Bayesian phase I/II design to determine subgroup-specific optimal dose for immunotherapy sequentially combined with radiotherapy

Sequential administration of immunotherapy following radiotherapy (immunoRT) has attracted much attention in cancer research. Due to its unique feature that radiotherapy upregulates the expression of a predictive biomarker for immunotherapy, novel clinical trial designs are needed for immunoRT to identify patient subgroups and the optimal dose for each subgroup. In this article, we propose a Bayesian phase I/II design for immunotherapy administered after standard-dose radiotherapy for this purpose. We construct a latent subgroup membership variable and model it as a function of the baseline and pre-post radiotherapy change in the predictive biomarker measurements. Conditional on the latent subgroup membership of each patient, we jointly model the continuous immune response and the binary efficacy outcome using plateau models, and model toxicity using the equivalent toxicity score approach to account for toxicity grades. During the trial, based on accumulating data, we continuously update model estimates and adaptively randomize patients to admissible doses. Simulation studies and an illustrative trial application show that our design has good operating characteristics in terms of identifying both patient subgroups and the optimal dose for each subgroup.

Dose finding studies for therapies with late-onset toxicities: A comparison study of designs

An objective of phase I dose-finding trials is to find the maximum tolerated dose; the dose with a particular risk of toxicity. Frequently, this risk is assessed across the first cycle of therapy. However, in oncology, a course of treatment frequently consists of multiple cycles of therapy. In many cases, the overall risk of toxicity for a given treatment is not fully encapsulated by observations from the first cycle, and hence it is advantageous to include toxicity outcomes from later cycles in phase I trials. Extending the follow up period in a trial naturally extends the total length of the trial which is undesirable. We present a comparison of eight methods that incorporate late onset toxicities while not extensively extending the trial length. We conduct simulation studies over a number of scenarios and in two settings; the first setting with minimal stopping rules and the second setting with a full set of standard stopping rules expected in such a dose finding study. We find that the model-based approaches in general outperform the model-assisted approaches, with an interval censored approach and a modified version of the time-to-event continual reassessment method giving the most promising overall performance in terms of correct selections and trial length. Further recommendations are made for the implementation of such methods.

Practical implementation of the partial ordering continual reassessment method in a Phase I combination-schedule dose-finding trial

There is a growing medical interest in combining several agents and optimizing their dosing schedules in a single trial in order to optimize the treatment for patients. Evaluating at doses of several drugs and their scheduling in a single Phase I trial simultaneously possess a number of statistical challenges, and specialized methods to tackle these have been proposed in the literature. However, the uptake of these methods is slow and implementation examples of such advanced methods are still sparse to date. In this work, we share our experience of proposing a model-based partial ordering continual reassessment method (POCRM) design for three-dimensional dose-finding in an oncology trial. In the trial, doses of two agents and the dosing schedule of one of them can be escalated/de-escalated. We provide a step-by-step summary on how the POCRM design was implemented and communicated to the trial team. We proposed an approach to specify toxicity orderings and their a-priori probabilities, and developed a number of visualization tools to communicate the statistical properties of the design. The design evaluation included both a comprehensive simulation study and considerations of the individual trial behavior. The study is now enrolling patients. We hope that sharing our experience of the successful implementation of an advanced design in practice that went through evaluations of several health authorities will facilitate a better uptake of more efficient methods in practice.

Surv-CRM-12: A Bayesian phase I/II survival CRM for right-censored toxicity endpoints with competing disease progression

The growing interest in new classes of anti-cancer agents, such as molecularly-targeted therapies and immunotherapies with modes of action different from those of cytotoxic chemotherapies, has changed the dose-finding paradigm. In this setting, the observation of late-onset toxicity endpoints may be precluded by treatment and trial discontinuation due to disease progression, defining a competing event to toxicity. Trial designs where dose-finding is modeled in the framework of a survival competing risks model appear particularly well-suited. We aim to provide a phase I/II dose-finding design that allows dose-limiting toxicity (DLT) outcomes to be delayed or unobserved due to competing progression within the possibly long observation window. The proposed design named the Survival-continual reassessment method-12, uses survival models for right-censored DLT and progression endpoints. In this competing risks framework, cause-specific hazards for DLT and progression-free of DLT were considered, with model parameters estimated using Bayesian inference. It aims to identify the optimal dose (OD), by minimizing the cumulative incidence of disease progression, given an acceptable toxicity threshold. In a simulation study, design operating characteristics were evaluated and compared to the TITE-BOIN-ET design and a nonparametric benchmark approach. The performance of the proposed method was consistent with the complexity of scenarios as assessed by the nonparametric benchmark. We found that the proposed design presents satisfying operating characteristics in selecting the OD and safety.

A hybrid design for dose-finding oncology clinical trials

Identifying the maximum tolerated dose (MTD) and recommending a phase II dose for an investigational treatment is crucial in cancer drug development. A suboptimal dose often leads to a failed late-stage trial, while an overly toxic dose causes harm to patients. There is a very rich literature on trial designs for dose-finding oncology clinical trials. We propose a novel hybrid design that maximizes the merits and minimizes the limitations of the existing designs. Building on 2 existing dose-finding designs: a model-assisted design (the modified toxicity probability interval) and a dose-toxicity model-based design, a hybrid design of the modified toxicity probability interval design and a dose-toxicity model such as the logistic regression model is proposed, incorporating optimal properties from these existing approaches. The performance of the hybrid design was tested in a real trial example and through simulation scenarios. The hybrid design controlled the overdosing toxicity well and led to a recommended dose closer to the true MTD due to its ability to calibrate for an intermediate dose. The robust performance of the proposed hybrid design is illustrated through the real trial dataset and simulations. The simulation results demonstrated that the proposed hybrid design can achieve excellent and robust operating characteristics compared with other existing designs and can be an effective model for determining the MTD and recommended phase II dose in oncology dose-finding trials. For practical feasibility, an R-shiny tool was developed and is freely available to guide clinicians in every step of the dose finding process.

Improving the performance of Bayesian logistic regression model with overdose control in oncology dose-finding studies

An accurately identified maximum tolerated dose (MTD) serves as the cornerstone of successful subsequent phases in oncology drug development. Bayesian logistic regression model (BLRM) is a popular and versatile model-based dose-finding design. However, BLRM with original overdose control strategy has been reported to be safe but "excessively conservative." In this article, we investigate the reason for conservativeness and point out that a major reason could be the lack of appropriate underdose control. We propose designs that balance overdose and underdose control to improve the performance over the original BLRM. Simulation results reveal that the new designs have better accuracy and treat more patients at MTD.

Stereotactic Body Radiotherapy of adrenal metastases - A dose-finding study

Optimal doses for the treatment of adrenal metastases with stereotactic radiotherapy (SBRT) are unknown. We aimed to identify dose-volume cut-points associated with decreased local recurrence rates (LRR). A multicenter database of patients with adrenal metastases of any histology treated with SBRT (biologically effective dose, BED10 ≥ 50Gy, ≤ 12 fractions) was analyzed. Details on dose-volume parameters were required (planning target volume: PTV-D98%, PTV-D50%, PTV-D2%; gross tumor volume: GTV-D50%, GTV-mean). Cut-points for LRR were optimized using the R maxstat package. 196 patients with 218 lesions were included, the largest histopathological subgroup was adenocarcinoma (n = 101). Cut-point optimization resulted in significant cut-points for PTV-D50% (BED10: 73.2Gy; p = 0.003), GTV-D50% (BED10: 74.2Gy; p = 0.006), GTV-mean (BED10: 73.0Gy; p = 0.007), and PTV-D2% (BED10: 78.0Gy; p = 0.02) but not for the PTV-D98% (p = 0.06). Differences in LRR were clinically relevant (LRR ≥ doubled for cut-points that were not achieved). Further dose-escalation was not associated with further improved LRR. PTV-D50%, GTV-D50%, and GTV-mean cut-points were also associated with significantly improved LRR in the adenocarcinoma subgroup. Separate dose optimizations indicated a lower cut-point for the PTV-D50% (BED10: 69.1Gy) in adenocarcinoma lesions, other values were similar (< 2% difference). Associations of cut-points with overall survival (OS) and progression-free survival were not significant but durable freedom from local recurrence was associated with OS in a landmark model (p < 0.001). To achieve a significant improvement of LRR for adrenal SBRT, a moderate escalation of PTV-D50% BED10 > 73.2Gy (adenocarcinoma: 69.1Gy) should be considered. This article is protected by copyright. All rights reserved.

A dose-finding design for dual-agent trials with patient-specific doses for one agent with application to an opiate detoxification trial

There is a growing interest in early phase dose-finding clinical trials studying combinations of several treatments. While the majority of dose finding designs for such setting were proposed for oncology trials, the corresponding designs are also essential in other therapeutic areas. Furthermore, there is increased recognition of recommending the patient-specific doses/combinations, rather than a single target one that would be recommended to all patients in later phases regardless of their characteristics. In this paper, we propose a dose-finding design for a dual-agent combination trial motivated by an opiate detoxification trial. The distinguishing feature of the trial is that the (continuous) dose of one compound is defined externally by the clinicians and is individual for every patient. The objective of the trial is to define the dosing function that for each patient would recommend the optimal dosage of the second compound. Via a simulation study, we have found that the proposed design results in high accuracy of individual dose recommendation and is robust to the model misspecification and assumptions on the distribution of externally defined doses.

Optimal adaptive allocation using deep reinforcement learning in a dose-response study

Estimation of the dose-response curve for efficacy and subsequent selection of an appropriate dose in phase II trials are important processes in drug development. Various methods have been investigated to estimate dose-response curves. Generally, these methods are used with equal allocation of subjects for simplicity; nevertheless, they may not fully optimize performance metrics because of nonoptimal allocation. Optimal allocation methods, which include adaptive allocation methods, have been proposed to overcome the limitations of equal allocation. However, they rely on asymptotics, and thus sometimes cannot efficiently optimize the performance metric with the sample size in an actual clinical trial. The purpose of this study is to construct an adaptive allocation rule that directly optimizes a performance metric, such as power, accuracy of model selection, accuracy of the estimated target dose, or mean absolute error over the estimated dose-response curve. We demonstrate that deep reinforcement learning with an appropriately defined state and reward can be used to construct such an adaptive allocation rule. The simulation study shows that the proposed method can successfully improve the performance metric to be optimized when compared with the equal allocation, D-optimal, and TD-optimal methods. In particular, when the mean absolute error was set to the metric to be optimized, it is possible to construct a rule that is superior for many metrics.

Seamless phase I/II design for novel anticancer agents with competing disease progression

Molecularly targeted agents and immunotherapies have prolonged administration and complicated toxicity and efficacy profiles requiring longer toxicity observation windows and the inclusion of efficacy information to identify the optimal dose. Methods have been proposed to either jointly model toxicity and efficacy, or for prolonged observation windows. However, it is inappropriate to address these issues individually in the setting of dose-finding because longer toxicity windows increase the risk of patients experiencing disease progression and discontinuing the trial, with progression defining a competing event to toxicity, and progression-free survival being a commonly used efficacy endpoint. No method has been proposed to address this issue in a competing risk framework. We propose a seamless phase I/II design, namely the competing risks continual reassessment method (CR-CRM). Given an observation window, the objective is to recommend doses that minimize the progression probability, among a set of tolerable doses in terms of toxicity risk. In toxicity-centered stage of the design, doses are assigned based on toxicity alone, and in optimization stage of the design, doses are assigned integrating both toxicity and progression information. Design operating characteristics were examined in a simulation study compared with benchmark performances, including sensitivity to time-varying hazards and correlated events. The method performs well in selecting doses with acceptable toxicity risk and minimum progression risk across a wide range of scenarios.

Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

Apixaban in patients on haemodialysis: a single-dose pharmacokinetics study

BACKGROUND

Apixaban, a direct oral anticoagulant inhibiting factor Xa, has been proven to reduce the risk of atrial fibrillation-related stroke and thromboembolism in patients with mild to moderate renal insufficiency. Patients on renal replacement therapy, however, were excluded from randomized controlled trials. Therefore, uncertainty remains concerning benefits, dosing and timing of intake in haemodialysis population.

METHODS

We conducted a Phase II pharmacokinetics study in which 24 patients on maintenance haemodialysis were given a single dose (2.5 mg or 5 mg) of apixaban, either 30 min before or immediately after dialysis on the mid-week dialysis day.

RESULTS

Apixaban 5 mg resulted in higher area under the curve (AUC0-48) in comparison with 2.5 mg, although significance could only be reached for dosing pre-dialysis (2.5 mg versus 5 mg, P = 0.008). In line, peak concentrations (Cmax) after dosing pre-dialysis were significantly higher in the 5 mg than in the 2.5 mg groups (P = 0.02). In addition, dialysis resulted in significant reduction of drug exposure. AUC0-48 pre-dialysis were on average 48% (2.5 mg) and 26% (5 mg) lower than the AUC0-48 post-dialysis, in line with Cmax. As a result, a dose of 2.5 mg post-dialysis and a dose of 5 mg pre-dialysis resulted in similar AUC0-48. In contrast, significant differences were found between the 5 mg group post-dialysis and the 2.5 mg group pre-dialysis (P = 0.02).

CONCLUSIONS

Our data suggest that exposure to apixaban in patients on maintenance haemodialysis is dependent not only on drug dose but also on timing of intake relative to the haemodialysis procedure.

Optimal dose-finding for efficacy-safety models

Dose-finding is an important part of the clinical development of a new drug. The purpose of dose-finding studies is to determine a suitable dose for future development based on both efficacy and safety. Optimal experimental designs have already been used to determine the design of this kind of studies, however, often that design is focused on efficacy only. We consider an efficacy-safety model, which is a simplified version of the bivariate Emax model. We use here the clinical utility index concept, which provides the desirable balance between efficacy and safety. By maximizing the utility of the patients, we get the estimated dose. This desire leads us to locally c -optimal designs. An algebraic solution for c -optimal designs is determined for arbitrary c vectors using a multivariate version of Elfving's method. The solution shows that the expected therapeutic index of the drug is a key quantity determining both the number of doses, the doses itself, and their weights in the optimal design. A sequential design is proposed to solve the complication of parameter dependency, and it is illustrated in a simulation study.

Estimating Similarity of Dose-Response Relationships in Phase I Clinical Trials-Case Study in Bridging Data Package

Bridging studies are designed to fill the gap between two populations in terms of clinical trial data, such as toxicity, efficacy, comorbidities and doses. According to ICH-E5 guidelines, clinical data can be extrapolated from one region to another if dose–reponse curves are similar between two populations. For instance, in Japan, Phase I clinical trials are often repeated due to this physiological/metabolic paradigm: the maximum tolerated dose (MTD) for Japanese patients is assumed to be lower than that for Caucasian patients, but not necessarily for all molecules. Therefore, proposing a statistical tool evaluating the similarity between two populations dose–response curves is of most interest. The aim of our work is to propose several indicators to evaluate the distance and the similarity of dose–toxicity curves and MTD distributions at the end of some of the Phase I trials, conducted on two populations or regions. For this purpose, we extended and adapted the commensurability criterion, initially proposed by Ollier et al. (2019), in the setting of completed phase I clinical trials. We evaluated their performance using three synthetic sets, built as examples, and six case studies found in the literature. Visualization plots and guidelines on the way to interpret the results are proposed.

Bayesian optimization design for dose-finding based on toxicity and efficacy outcomes in phase I/II clinical trials

In phase I trials, the main goal is to identify a maximum tolerated dose under an assumption of monotonicity in dose-response relationships. On the other hand, such monotonicity is no longer applied to biologic agents because a different mode of action from that of cytotoxic agents potentially draws unimodal or flat dose-efficacy curves. Therefore, biologic agents require an optimal dose that provides a sufficient efficacy rate under an acceptable toxicity rate instead of a maximum tolerated dose. Many trials incorporate both toxicity and efficacy data, and drugs with a variety of modes of actions are increasingly being developed; thus, optimal dose estimation designs have been receiving increased attention. Although numerous authors have introduced parametric model-based designs, it is not always appropriate to apply strong assumptions in dose-response relationships. We propose a new design based on a Bayesian optimization framework for identifying optimal doses for biologic agents in phase I/II trials. Our proposed design models dose-response relationships via nonparametric models utilizing a Gaussian process prior, and the uncertainty of estimates is considered in the dose selection process. We compared the operating characteristics of our proposed design against those of three other designs through simulation studies. These include an expansion of Bayesian optimal interval design, the parametric model-based EffTox design, and the isotonic design. In simulations, our proposed design performed well and provided results that were more stable than those from the other designs, in terms of the accuracy of optimal dose estimations and the percentage of correct recommendations.

Continual reassessment method with regularization in phase I clinical trials

Many Phase I trial designs have been developed to improve upon the standard 3+3 design. These designs can be classified as long-memory designs, for example, the continual reassessment method (CRM), and short-memory designs such as the modified toxicity probability interval (mTPI) design. Long-term memory designs use all data but their performance can be negatively affected by the model misspecification. Short-term memory designs only use data at the current dose and might lose efficiency as a result. To overcome these issues, we propose a regularized CRM (rCRM). The rCRM offers a trade-off between long-term memory and short-term memory methods. The rCRM gives more weight to data obtained at the doses with the estimated probability of toxicity closer to the target toxicity rate. The addition of a regularization term has an effect of shrinking the dimension of the model and leads to improved performance of the 2-parameter CRM. The rCRM is a good design choice to guide assignments in an expansion cohort phase of a dose-finding trial since dose assignments do not seem to change as often as in corresponding CRMs.

Early phase dose-finding trials in virology

Little has been published in terms of dose-finding methodology in virology. Aside from a few papers focusing on HIV, the considerable progress in dose-finding methodology of the last 25 years has focused almost entirely on oncology. While adverse reactions to cytotoxic drugs may be life threatening, for anti-viral agents we anticipate something different: side effects that provoke the cessation of treatment. This would correspond to treatment failure. On the other hand, success would not be yes/no but would correspond to a range of responses, from small, no more than say 20% reduction in viral load to the complete elimination of the virus. Less than total success matters since this may allow the patient to achieve immune-mediated clearance. The motivation for this article is an upcoming dose-finding trial in chronic norovirus infection. We propose a novel methodology whose goal is twofold: first, to identify the dose that provides the most favorable distribution of treatment outcomes, and, second, to do this in a way that maximizes the treatment benefit for the patients included in the study.

A dose-finding approach for genomic patterns in phase I trials

Precision medicine is an emerging approach for disease treatment and prevention that accounts for individual variability in genes, environment, and lifestyle. Cancer is a genomic disease; therefore, the dose-efficacy and dose-toxicity relationships for molecularly targeted agents in cancer most likely differ, based on the genomic mutation pattern. The individualized optimal dose - the maximal efficacious dose with a clinically acceptable safety profile - may vary depending on the genomic mutation patterns and should be determined prior to the use of these agents in precision medicine. In addition, genes that influence the individualized optimal doses should be identified in early-phase development. In this study, we propose a novel dose-finding approach to identify the individualized optimal dose for molecularly targeted agents in phase I cancer trials. Individualized optimal dose determination and gene selection were conducted simultaneously based on L ₁ and L ₂ penalized regression. Similar to most reported dose-finding approaches, this study considers non-monotonic patterns for dose-efficacy and dose-toxicity relationships, as well as correlations between efficacy and toxicity outcomes based on multinomial distribution. Our dose-finding algorithm is based on the predictive probability calculated with an estimated penalized regression model. We compare the operating characteristics between the proposed and existing methods by simulation studies under various scenarios.

Targeting the rheumatoid arthritis synovial fibroblast via cyclin dependent kinase inhibition: An early phase trial

INTRODUCTION

Targeted biologic therapies demonstrate similar efficacies in rheumatoid arthritis despite distinct mechanisms of action. They also exhibit a ceiling effect, with 10% to 20% of patients achieving remission in clinical trials. None of these therapies target synovial fibroblasts, which drive and maintain synovitis. Seliciclib (R-roscovitine) is an orally available cyclin-dependent kinase inhibitor that suppresses fibroblast proliferation, and is efficacious in preclinical arthritis models. We aim to determine the toxicity and preliminary efficacy of seliciclib in combination with biologic therapies, to inform its potential as an adjunctive therapy in rheumatoid arthritis.

METHODS AND ANALYSIS

TRAFIC is a non-commercial, multi-center, rolling phase Ib/IIa trial investigating the safety, tolerability, and efficacy of seliciclib in patients with moderate to severe rheumatoid arthritis receiving biologic therapies. All participants receive seliciclib with no control arm. The primary objective of part 1 (phase Ib) is to determine the maximum tolerated dose and safety of seliciclib over 4 weeks of dosing. Part 1 uses a restricted 1-stage Bayesian continual reassessment method based on a target dose-limiting toxicity probability of 35%. Part 2 (phase IIa) assesses the potential efficacy of seliciclib, and is designed as a single arm, single stage early phase trial based on a Fleming-A'Hern design using the maximum tolerated dose recommended from part 1. The primary response outcome after 12 weeks of therapy is a composite of clinical, histological and magnetic resonance imaging scores. Secondary outcomes include adverse events, pharmacodynamic and pharmacokinetic parameters, autoantibodies, and fatigue.

ETHICS AND DISSEMINATION

The study has been reviewed and approved by the North East - Tyne & Wear South Research Ethics Committee (reference 14/NE/1075) and the Medicines and Healthcare Products Regulatory Agency (MHRA), United Kingdom. Results will be disseminated through publication in relevant peer-reviewed journals and presentation at national and international conferences.

TRIALS REGISTRATION

ISRCTN, ISRCTN36667085. Registered on September 26, 2014; http://www.isrctn.com/ISRCTN36667085Current protocol version: Protocol version 11.0 (March 21, 2019).

An adaptive power prior for sequential clinical trials - Application to bridging studies

During drug evaluation trials, information from clinical trials previously conducted on another population, indications or schedules may be available. In these cases, it might be desirable to share information by efficiently using the available resources. In this work, we developed an adaptive power prior with a commensurability parameter for using historical or external information. It allows, at each stage, full borrowing when the data are not in conflict, no borrowing when the data are in conflict or "tuned" borrowing when the data are in between. We propose to apply our adaptive power prior method to bridging studies between Caucasians and Asians, and we focus on the sequential adaptive allocation design, although other design settings can be used. We weight the prior information in two steps: the effective sample size approach is used to set the maximum desirable amount of information to be shared from historical data at each step of the trial; then, in a sort of Empirical Bayes approach, a commensurability parameter is chosen using a measure of distribution distance. This approach avoids elicitation and computational issues regarding the usual Empirical Bayes approach. We propose several versions of our method, and we conducted an extensive simulation study evaluating the robustness and sensitivity to prior choices.

Development of a Subcutaneous Fixed-Dose Combination of Pertuzumab and Trastuzumab: Results From the Phase Ib Dose-Finding Study

Adding pertuzumab to trastuzumab (both monoclonal antibodies targeting human epidermal growth factor receptor 2 [HER2]) has proven survival benefits when combined with chemotherapy for patients with HER2-positive breast cancer. The combination of pertuzumab and trastuzumab together in 1 vial for subcutaneous (SC) administration is being developed as a ready-to-use formulation to reduce the treatment burden on patients while improving healthcare efficiency. An open-label, 2-part, phase Ib dose-finding study (NCT02738970) was undertaken in healthy male volunteers (part 1) and female patients with HER2-postive early breast cancer who had completed standard (neo)adjuvant treatment (part 2). This study aimed to identify an SC pertuzumab dose given with recombinant human hyaluronidase that results in comparable exposure to that of the intravenous (IV) pertuzumab dose, based on pertuzumab serum trough concentration and area under the serum concentration-time curve. Pharmacokinetics (PK), safety, and tolerability of a single dose of SC pertuzumab given alone or in a fixed-dose combination (comixed or coformulated) with trastuzumab were also assessed. A maintenance dose of 600 mg for SC pertuzumab resulted in an equivalent exposure to that of IV pertuzumab, and no new safety signals were identified for SC pertuzumab or trastuzumab. A loading dose of 1200 mg for SC pertuzumab was selected based on approximate dose proportionality. The PK and safety results support further development of a fixed-dose coformulation combination of pertuzumab and trastuzumab for SC administration, which will be investigated in an upcoming phase III trial in patients with HER2-positive early breast cancer.

An adaptive multi-stage phase I dose-finding design incorporating continuous efficacy and toxicity data from multiple treatment cycles

Phase I designs traditionally use the dose-limiting toxicity (DLT), a binary endpoint from the first treatment cycle, to identify the maximum-tolerated dose (MTD) assuming a monotonically increasing relationship between dose and efficacy. In this article, we establish a general framework for a multi-stage adaptive design where we jointly model a continuous efficacy outcome and continuous/quasi-continuous toxicity endpoints from multiple treatment cycles. The normalized Total Toxicity Profile (nTTP) is used as an illustration for quasi-continuous toxicity endpoints, and we replace DLT with nTTP to take into account multiple grades and types of toxicities. In addition, the proposed design accommodates non-monotone dose-efficacy relationships, and longitudinal toxicity data in effort to capture the adverse events from multiple cycles. Stage 1 of our design uses toxicity data to perform dose-escalation and identify a set of initially allowable (safe) doses; stage 2 of our design incorporates an efficacy outcome to update the set of allowable doses for each new cohort and randomizes the new cohort of patients to the allowable doses with emphasis towards those with higher predicted efficacy. Stage 3 uses all data from all treated patients at the end of the trial to make final recommendations. Simulations showed that the design had a high probability of making the correct dose selection and good overdose control across various dose-efficacy and dose-toxicity scenarios. In addition, the proposed design allows for early termination when all doses are too toxic. To our best knowledge, the proposed dual-endpoint dose-finding design is the first such study to incorporate multiple cycles of toxicities and a continuous efficacy outcome.

Statistical justification of expansion cohorts in phase 1 cancer trials

BACKGROUND

Phase I cancer trials increasingly incorporate dose-expansion cohorts (DECs), reflecting a growing demand to acquire more information about investigational drugs. Protocols commonly fail to provide a sample-size justification or analysis plan for the DEC. In this study, we develop a statistical framework for the design of DECs.

METHODS

We assume the maximum tolerated dose (MTD) for the investigational drug has been identified in the dose-escalation stage of the trial. We use the 80% lower confidence bound and the 90% upper confidence bound for the response and toxicity rates, respectively, as decision thresholds for the dose-expansion stage. We calculate the operating characteristics with reference to prespecified minimum effective response rates and maximum safe DLT rates.

RESULTS

We apply our framework to specify a system of DEC plans. The design comprises three components: 1) the number of subjects enrolled at the MTD, 2) the minimum number of responses necessary to indicate provisional drug efficacy, and 3) the maximum number of dose-limiting toxicities (DLTs) permitted to indicate drug safety. We demonstrate our method in an application to a cancer immunotherapy trial.

CONCLUSIONS

Our simple and practical tool enables creation of DEC designs that appropriately address the safety and efficacy objectives of the trial.

Determining the optimal dose of atrasentan by evaluating the exposure-response relationships of albuminuria and bodyweight

This study aimed to identify the optimal dose of the endothelin-1 receptor antagonist atrasentan with maximal albuminuria reduction and minimal signs of sodium retention, as manifested by increase in bodyweight. Data from the RADAR-JAPAN studies were used, evaluating the effect of 0.75 or 1.25 mg/d of atrasentan in 161 patients with type 2 diabetes and kidney disease. Individual pharmacokinetic parameters were estimated using a population pharmacokinetic approach. Subsequently, changes in the urinary albumin-to-creatinine ratio (UACR) and bodyweight from baseline after 2 weeks' exposure were modelled as a function of the pharmacokinetic parameters. The 0.75 and 1.25 mg doses showed a mean UACR reduction of 34.0% and 40.1%, whereas mean bodyweight increased by 0.9 and 1.1 kg, respectively. A large variation between individuals was observed in the UACR and bodyweight responses. Individual pharmacokinetic parameters correlated significantly with both individual UACR and bodyweight responses (P < .01). The individual response curves for UACR and bodyweight crossed at approximately the mean trough concentration of 0.75 mg atrasentan, indicating that 0.75 mg/d of atrasentan is the optimal dose for kidney protection with maximal efficacy (albuminuria reduction) and safety (minimal sodium retention).

Dose-finding study of oxaliplatin associated to capecitabine-based preoperative chemoradiotherapy in locally advanced rectal cancer

Introduction

Proper administration timing, dose-intensity, efficacy/toxicity ratio of oxaliplatin added to fluoropyrimidin should be improved to safely perform two-drugs intensive preoperative chemoradiotherapy in locally advanced rectal cancer (LARC). This dose-finding study investigated recommended oxaliplatin dose, safety of oxaliplatin/capecitabine regimen and preliminary activity.

Methods

Schedule: oxaliplatin dose-levels, 35-40 mg/m²/week; capecitabine 825 mg/m²/ twice daily, radiotherapy on rectum/nodes, 50/45 Gy, 45 and 9 boost/45 Gy, in first 5 and subsequent patients, 5 days/week, respectively; for 5 weeks. Pathologic complete response (pCR) 10% was projected in order to positively affect clinical outcome.

Results

Seventeen fit <75 years patients enrolled: median age 60; young-elderly 4 (23%); T3/T4, 15/2, N0/N1/N2, 7/9/1. At first dose-level, no dose-limiting toxicity (DLT). At second, 2 DLT, G3 mucositis, G3 thrombocytopenia, in 2/6 patients (33%). Oxaliplatin recommended dose, 40 mg/m²/week. Cumulative G3-4 toxicities: mucositis 6%, thrombocytopenia 6%. Limiting toxicity syndromes 18%, 25% in young-elderly, all single site. Objective response rate intent-to-treat 94%. Sphinter preservation 87%, pCR 6%. After 17 months follow-up, progression-free survival and overall survival were not reached.

Conclusions

Oxaliplatin can be safely added to preoperative capecitabine-based chemoradiotherapy at the recommended dose 40 mg/m²/week, in LARC, with promising pCR and high activity.

Dose-response relationship of a new Timothy grass pollen allergoid in comparison with a 6-grass pollen allergoid

BACKGROUND

Subcutaneous allergen immunotherapy with grass pollen allergoids has been proven to be effective and safe in the treatment of patients with allergic rhinoconjunctivitis. Based on the extensive cross-reactivity among Pooideae species, it has been suggested that grass pollen extracts could be prepared from a single species, rather than from a multiple species mixture.

OBJECTIVE

To find the optimal dose of a Phleum pratense (P. pratense) allergoid preparation and compare its efficacy and safety to a 6-grass pollen allergoid preparation.

METHODS

In this double-blind, placebo-controlled study (EudraCT: 2011-000674-58), three doses of P. pratense allergoid (1800 therapeutic units (TU), standard-dose 6000 TU and 18 000 TU) were compared with placebo and the marketed 6-grass pollen allergoid (6000 TU). In a pre-seasonal dosing regimen, 102 patients were randomized to five treatment groups and received nine subcutaneous injections. The primary efficacy endpoint was the change in weal size (late-phase reaction [LPR]) in response to the intracutaneous testing (ICT) before and after treatment, comparing the active allergoids to placebo. Secondary outcomes were the change in Total Nasal Symptom Score (TNSS) assessed in the allergen exposure chamber (AEC), the changes in P. pratense-serum-specific IgG₄ and the incidence of adverse events (AEs).

RESULTS

All three doses of the P. pratense and the 6-grass pollen allergoid preparations were significantly superior to placebo for the primary outcome, whereas there were no significant differences in the change in TNSS. Compared to the standard-dose, the high-dose of P. pratense did not produce any additional significant benefit, but showed a slight increase in AEs. Yet this increase in AEs was lower than for the 6-grass pollen preparation.

CONCLUSIONS & CLINICAL RELEVANCE

The standard-dose of the new P. pratense allergoid was comparable to the marketed 6-grass pollen preparation at equal dose for the parameters measured.

Isotonic designs for phase I trials in partially ordered groups

BACKGROUND/AIMS

Dose-finding trials can be conducted such that patients are first stratified into multiple risk groups before doses are allocated. The risk groups are often completely ordered in that, for a fixed dose, the probability of toxicity is monotonically increasing across groups. In some trials, the groups are only partially ordered. For example, one of several groups in a trial may be known to have the least risk of toxicity for a given dose, but the ordering of the risk among the remaining groups may not be known. The aim of the article is to introduce a method for designing dose-finding trials of cytotoxic agents in completely or partially ordered groups of patients.

METHODS

This article presents a method for dose-finding that combines previously proposed mathematical models, augmented with results using order restricted inference. The resulting method is computationally convenient and allows for dose-finding in trials with completely or partially ordered groups. Extensive simulations are done to evaluate the performance of the method, using randomly generated dose-toxicity curves where, within each group, the risk of toxicity is an increasing function of dose.

RESULTS

Our simulations show that the hybrid method, in which order-restricted estimation is applied to parameters of a parsimonious mathematical model, gives results that are similar to previously proposed methods for completely ordered groups. Our method generalizes to a wide range of partial orders among the groups.

CONCLUSION

The problem of dose-finding in partially ordered groups has not been extensively studied in the statistical literature. The proposed method is computationally feasible, and provides a potential solution to the design of dose-finding studies in completely or partially ordered groups.

Bottlenecks in the development of topical analgesics: molecule, formulation, dose-finding, and phase III design

Topical analgesics can be defined as topical formulations containing analgesics or co-analgesics. Since 2000, interest in such formulations has been on the rise. There are, however, four critical issues in the research and development phases of topical analgesics: 1) The selection of the active pharmaceutical ingredient. Analgesics and co-analgesics differ greatly in their mechanism of action, and it is required to find the most optimal fit between such mechanisms of action and the pathogenesis of the targeted (neuropathic) pain. 2) Issues concerning the optimized formulation. For relevant clinical efficacy, specific characteristics for the selected vehicle (eg, cream base or gel base) are required, depending on the physicochemical characteristics of the active pharmaceutical ingredient(s) to be delivered. 3) Well-designed phase II dose-finding studies are required, and, unfortunately, such trials are missing. In fact, we will demonstrate that underdosing is one of the major hurdles to detect meaningful and statistically relevant clinical effects of topical analgesics. 4) Selection of clinical end points and innovatively designed phase III trials. End point selection can make or break a trial. For instance, to include numbness together with tingling as a composite end point for neuropathic pain seems stretching the therapeutic impact of an analgesic too far. Given the fast onset of action of topical analgesics (usually within 30 minutes), enrichment designs might enhance the chances for success, as the placebo response might decrease. Topical analgesics may become promising inroads for the treatment of neuropathic pain, once sufficient attention is given to these four key aspects.

Designs for phase I trials in ordered groups

We propose a new design for dose finding for cytotoxic agents in two ordered groups of patients. By ordered groups, we mean that prior to the study there is clinical information that would indicate that for a given dose one group would be more susceptible to toxicities than patients in the other group. The designs are evaluated relative to two previously proposed designs for ordered groups over a range of scenarios generated randomly from a family of dose-toxicity curves. Copyright © 2016 John Wiley & Sons, Ltd.

A phase I study of different doses and frequencies of pegylated recombinant human granulocyte-colony stimulating factor (PEG rhG-CSF) in patients with standard-dose chemotherapy-induced neutropenia

Objective

The recommended dose of prophylactic pegylated recombinant human granulocyte-colony stimulating factor (PEG rhG-CSF) is 100 μg/kg once per cycle for patients receiving intense-dose chemotherapy. However, few data are available on the proper dose for patients receiving less-intense chemotherapy. The aim of this phase I study is to explore the proper dose and administration schedule of PEG rhG-CSF for patients receiving standard-dose chemotherapy.

Methods

Eligible patients received 3-cycle chemotherapy every 3 weeks. No PEG rhG-CSF was given in the first cycle. Patients experienced grade 3 or 4 neutropenia would then enter the cycle 2 and 3. In cycle 2, patients received a single subcutaneous injection of prophylactic PEG rhG-CSF on d 3, and received half-dose subcutaneous injection in cycle 3 on d 3 and d 5, respectively. Escalating doses (30, 60, 100 and 200 μg/kg) of PEG rhG-CSF were investigated.

Results

A total of 26 patients were enrolled and received chemotherapy, in which 24 and 18 patients entered cycle 2 and cycle 3 treatment, respectively. In cycle 2, the incidence of grade 3 or 4 neutropenia for patients receiving single-dose PEG rhG-CSF of 30, 60, 100 and 200 μg/kg was 66.67%, 33.33%, 22.22% and 0, respectively, with a median duration less than 1 (0-2) d. No grade 3 or higher neutropenia was noted in cycle 3 in all dose cohorts.

Conclusions

The pharmacokinetic and pharmacodynamic profiles of PEG rhG-CSF used in cancer patients were similar to those reported, as well as the safety. Double half dose administration model showed better efficacy result than a single dose model in terms of grade 3 neutropenia and above. The single dose of 60 μg/kg, 100 μg/kg and double half dose of 30 μg/kg were recommended to the phase II study, hoping to find a preferable method for neutropenia treatment.

A practical Bayesian design to identify the maximum tolerated dose contour for drug combination trials

Drug combination therapy has become the mainstream approach to cancer treatment. One fundamental feature that makes combination trials different from single-agent trials is the existence of the maximum tolerated dose (MTD) contour, that is, multiple MTDs. As a result, unlike single-agent phase I trials, which aim to find a single MTD, it is often of interest to find the MTD contour for combination trials. We propose a new dose-finding design, the waterfall design, to find the MTD contour for drug combination trials. Taking the divide-and-conquer strategy, the waterfall design divides the task of finding the MTD contour into a sequence of one-dimensional dose-finding processes, known as subtrials. The subtrials are conducted sequentially in a certain order, such that the results of each subtrial will be used to inform the design of subsequent subtrials. Such information borrowing allows the waterfall design to explore the two-dimensional dose space efficiently using a limited sample size and decreases the chance of overdosing and underdosing patients. To accommodate the consideration that doses on the MTD contour may have very different efficacy or synergistic effects because of drug-drug interaction, we further extend our approach to a phase I/II design with the goal of finding the MTD with the highest efficacy. Simulation studies show that the waterfall design is safer and has higher probability of identifying the true MTD contour than some existing designs. The R package "BOIN" to implement the waterfall design is freely available from CRAN. Copyright © 2016 John Wiley & Sons, Ltd.

A robust two-stage design identifying the optimal biological dose for phase I/II clinical trials

We propose a robust two-stage design to identify the optimal biological dose for phase I/II clinical trials evaluating both toxicity and efficacy outcomes. In the first stage of dose finding, we use the Bayesian model averaging continual reassessment method to monitor the toxicity outcomes and adopt an isotonic regression method based on the efficacy outcomes to guide dose escalation. When the first stage ends, we use the Dirichlet-multinomial distribution to jointly model the toxicity and efficacy outcomes and pick the candidate doses based on a three-dimensional volume ratio. The selected candidate doses are then seamlessly advanced to the second stage for dose validation. Both toxicity and efficacy outcomes are continuously monitored so that any overly toxic and/or less efficacious dose can be dropped from the study as the trial continues. When the phase I/II trial ends, we select the optimal biological dose as the dose obtaining the minimal value of the volume ratio within the candidate set. An advantage of the proposed design is that it does not impose a monotonically increasing assumption on the shape of the dose-efficacy curve. We conduct extensive simulation studies to examine the operating characteristics of the proposed design. The simulation results show that the proposed design has desirable operating characteristics across different shapes of the underlying true dose-toxicity and dose-efficacy curves. The software to implement the proposed design is available upon request. Copyright © 2016 John Wiley & Sons, Ltd.

Implementation of adaptive methods in early-phase clinical trials

There has been constant development of novel statistical methods in the design of early-phase clinical trials since the introduction of model-based designs, yet the traditional or modified 3+3 algorithmic design remains the most widely used approach in dose-finding studies. Research has shown the limitations of this traditional design compared with more innovative approaches yet the use of these model-based designs remains infrequent. This can be attributed to several causes including a poor understanding from clinicians and reviewers into how the designs work, and how best to evaluate the appropriateness of a proposed design. These barriers are likely to be enhanced in the coming years as the recent paradigm of drug development involves a shift to more complex dose-finding problems. This article reviews relevant information that should be included in clinical trial protocols to aid in the acceptance and approval of novel methods. We provide practical guidance for implementing these efficient designs with the aim of augmenting a broader transition from algorithmic to adaptive model-guided designs. In addition we highlight issues to consider in the actual implementation of a trial once approval is obtained. Copyright © 2016 John Wiley & Sons, Ltd.

Gonadotropin-releasing hormone agonist trigger in oocyte donors co-treated with a gonadotropin-releasing hormone antagonist: a dose-finding study

OBJECTIVE

To determine the optimal GnRH agonist dose for triggering of oocyte maturation in oocyte donors.

DESIGN

Single-center, randomized, parallel, investigator-blinded trial.

SETTING

IVFMD, My Duc Hospital, Ho Chi Minh City, Vietnam.

PATIENT(S)

One hundred sixty-five oocyte donors (aged 18-35 years, body mass index [BMI] <28 kg/m(2), antimüllerian hormone level >1.25 ng/mL, and antral follicle count ≥6).

INTERVENTION(S)

Ovulation trigger with 0.2, 0.3, or 0.4 mg triptorelin in a GnRH antagonist cycle.

MAIN OUTCOME MEASURE(S)

The primary end point was number of metaphase II oocytes. Secondary end points were fertilization and cleavage rates, number of embryos and top-quality embryos, steroid levels, ovarian volume, and ongoing pregnancy rate (PR) in recipients.

RESULT(S)

There were no significant differences between the triptorelin 0.2, 0.3, and 0.4 mg trigger groups with respect to number of metaphase II oocytes (16.0 ± 8.5, 15.9 ± 7.8, and 14.7 ± 8.4, respectively), embryos (13.2 ± 7.8, 11.7 ± 6.9, 11.8 ± 7.0), and number of top-quality embryos (3.8 ± 2.9, 3.6 ± 3.0, 4.1 ± 3.0). Luteinizing hormone levels at 24 hours and 36 hours after trigger was significantly higher with triptorelin 0.4 mg versus 0.2 mg and 0.3 mg (9.8 ± 7.1 IU/L vs. 7.3 ± 4.1 IU/L and 7.2 ± 3.7 IU/L, respectively; 4.6 ± 3.2 IU/L vs. 3.2 ± 2.3 IU/L and 3.3 ± 2.1 IU/L, respectively. Progesterone level at oocyte pick-up +6 days was significantly higher in the 0.4-mg group (2.2 ± 3.7 ng/ml) versus 0.2 mg (1.1 ± 1.0 ng/ml) and 0.3 mg (1.2 ± 1.6 ng/ml). One patient developed early-onset severe ovarian hyperstimulation syndrome (OHSS).

CONCLUSION(S)

No significant differences between triptorelin doses of 0.2, 0.3, and 0.4 mg used for ovulation trigger in oocyte donors were seen with regard to the number of mature oocytes and top-quality embryos.

CLINICAL TRIAL REGISTRATION NUMBER

NCT02208986.

Towards using a full spectrum of early clinical trial data: a retrospective analysis to compare potential longitudinal categorical models for molecular targeted therapies in oncology

Following the pattern of phase I clinical trials for cytotoxic drugs, dose-finding clinical trials in oncology of molecularly targeted agents (MTA) aim at determining the maximum tolerated dose (MTD). In classical phase I clinical trials, MTD is generally defined by the number of patients with short-term major treatment toxicities (usually called dose-limiting toxicities, DLT), occurring during the first cycle of study treatment (e.g. within the first 3weeks of treatment). However, S. Postel-Vinay (2011) highlighted that half of grade 3 to 4 toxicities, usually considered as DLT, occur after the first cycle of MTA treatment. In addition, MTAs could induce other moderate (e.g. grade 2) toxicities which could be taken into account depending on their clinical importance, chronic nature and duration. Ignoring these late toxicities may lead to an underestimation of the drug toxicity and to wrong dose recommendations for phase II and III clinical trials. Some methods have been proposed, such as the time-to-event continuous reassessment method (Cheung 2000 and Mauguen 2011), to take into account the late toxicities. We suggest approaches based on longitudinal models (Doussau 2013). We compare several models for longitudinal data, such as transitional or marginal models, to take into account all relevant toxicities occurring during the entire length of the patient treatment (and not just the events within a predefined short-term time-window). These models allow the statistician to benefit from a larger amount of safety data which could potentially improve that accuracy in MTD assessment.

Efficacy and safety of the long-acting β2-agonist olodaterol over 4 weeks in Japanese patients with chronic obstructive pulmonary disease

Background

Olodaterol is a novel long-acting β₂-agonist with proven ≥24-hour duration of action in preclinical and clinical studies.

Objective

This randomized, double-blind, placebo-controlled, parallel-group study evaluated the dose response of once-daily (QD) olodaterol based on bronchodilator efficacy, safety, and pharmacokinetics over 4 weeks in Japanese patients with chronic obstructive pulmonary disease (COPD).

Methods

All eligible patients were randomized to receive 2 µg, 5 µg, or 10 µg of olodaterol or placebo for 4 weeks via the Respimat^® Soft Mist™ inhaler. The primary end point was the change from baseline in trough forced expiratory volume in 1 second (FEV₁) after 4 weeks of olodaterol treatment. Secondary end points included trough FEV₁ after 1 week and 2 weeks of treatment, FEV₁ area under the curve from 0 hour to 3 hours (AUC_0–3), peak FEV₁ from 0 hour to 3 hours (peak FEV₁), and corresponding forced vital capacity (FVC) responses. Rescue medication use, COPD symptoms, physician global evaluation, pharmacokinetics, and safety were also assessed.

Results

A total of 328 patients with COPD were randomized to receive treatment. All olodaterol doses assessed in the study showed statistically significant increases in trough FEV₁ compared to placebo at Day 29 (P<0.0001). Mean increases in peak FEV₁ and FEV₁ AUC_0–3 compared to placebo were also significant (P<0.0001). A clear dose–response relationship was observed across all treatment groups. FVC responses (trough and FVC AUC_0–3) supported FEV₁ outcomes. All doses of olodaterol were well tolerated, and no safety concerns were identified.

Conclusion

QD olodaterol demonstrated 24-hour bronchodilator efficacy and was well tolerated in Japanese patients with COPD.

Trial registration

ClinicalTrials.gov: NCT00824382.

Dose-finding evaluation of once-daily treatment with olodaterol, a novel long-acting β2-agonist, in patients with asthma: results of a parallel-group study and a crossover study

BACKGROUND

Olodaterol is a novel, inhaled long-acting β2-agonist (LABA) with >24-hour duration of action investigated in asthma and chronic obstructive pulmonary disease.

METHODS

Two multicentre studies examined the efficacy and safety of 4 weeks' once-daily (QD) olodaterol (2, 5, 10 and 20 μg, with background inhaled corticosteroids) in patients with asthma. One randomised, double-blind, parallel-group study (1222.6; 296 patients) administered treatment in the morning. Pulmonary function tests (PFTs) were performed pre-dose (trough) and ≤3 hours post-dose (weeks 1 and 2), and ≤6 hours post-dose after 4 weeks; primary end point was trough forced expiratory volume in 1 second (FEV1) response (change from baseline mean FEV1) after 4 weeks. A second randomised, double-blind, placebo- and active-controlled (formoterol 12 μg twice-daily) incomplete-block crossover study (1222.27; 198 patients) administered QD treatments in the evening. PFTs were performed over a 24-hour dosing interval after 4 weeks; primary end point was FEV1 area under the curve from 0-24 hours (AUC0-24) response (change from study baseline [mean FEV1] after 4 weeks).

RESULTS

Study 1222.6 showed a statistically significant increase in trough FEV1 response with olodaterol 20 μg (0.147 L; 95 % confidence interval [CI]: 0.059, 0.234; p = 0.001) versus placebo, with more limited efficacy and no evidence of dose response compared to placebo across the other olodaterol doses (2, 5 and 10 μg). Study 1222.27 demonstrated increases in FEV1 AUC0-24 responses at 4 weeks with all active treatments (p < 0.0001); adjusted mean (95 % CI) differences from placebo were 0.140 (0.097, 0.182), 0.182 (0.140, 0.224), 0.205 (0.163, 0.248) and 0.229 (0.186, 0.272) L for olodaterol 2, 5, 10 and 20 μg, respectively, and 0.169 (0.126, 0.211) for formoterol, providing evidence of increased efficacy with higher olodaterol dose. Olodaterol was generally well tolerated, with a few events associated with known sympathomimetic effects, mainly with 20 μg.

CONCLUSIONS

The LABA olodaterol has >24-hour duration of action. In patients with asthma, evidence of bronchodilator efficacy was demonstrated with statistically and clinically significant improvements in the primary end point of trough FEV1 response measured in clinics over placebo for the highest administered dose of 20 μg in Study 1222.6, and statistically and clinically significant improvements versus placebo in FEV1 AUC0-24 responses at 4 weeks for all doses tested in Study 1222.27, which also exhibited a dose response. Bronchodilator efficacy was seen over placebo for all olodaterol doses for morning and evening peak expiratory flow in both studies. All doses were well tolerated.

TRIAL REGISTRATIONS

NCT00467740 (1222.6) and NCT01013753 (1222.27).

Adaptive Phase I clinical trial design using Markov models for conditional probability of toxicity

Many Phase I trials in oncology involve multiple-dose administrations on the same patient over multiple cycles, with a typical cycle lasting 3 weeks and having about six cycles per patient with a goal to find the maximum tolerated dose (MTD) and study the dose-toxicity relationship. A patient's dose is unchanged over the cycles and the data are reduced to a binary endpoint and the occurrence of a toxicity and analyzed by considering the toxicity either from the first dose or from any cycle on the study. In this article, an alternative approach allowing an assessment of toxicity from each cycle and dose variations for patient over cycles is presented. A Markov model for the conditional probability of toxicity on any cycle given no toxicity in previous cycles is formulated as a function of the current and previous doses. The extra information from each cycle provides more precise estimation of the dose-toxicity relationship. Simulation results demonstrating gains in using the Markov model as compared to analyses of a single binary outcome are presented. Methods for utilizing the Markov model to conduct a Phase I study, including choices for selecting doses for the next cycle for each patient, are developed and presented via simulation.

Characterization of dose-response for count data using a generalized MCP-Mod approach in an adaptive dose-ranging trial

Understanding the dose-response relationship is a key objective in Phase II clinical development. Yet, designing a dose-ranging trial is a challenging task, as it requires identifying the therapeutic window and the shape of the dose-response curve for a new drug on the basis of a limited number of doses. Adaptive designs have been proposed as a solution to improve both quality and efficiency of Phase II trials as they give the possibility to select the dose to be tested as the trial goes. In this article, we present a 'shapebased' two-stage adaptive trial design where the doses to be tested in the second stage are determined based on the correlation observed between efficacy of the doses tested in the first stage and a set of pre-specified candidate dose-response profiles. At the end of the trial, the data are analyzed using the generalized MCP-Mod approach in order to account for model uncertainty. A simulation study shows that this approach gives more precise estimates of a desired target dose (e.g. ED70) than a single-stage (fixed-dose) design and performs as well as a two-stage D-optimal design. We present the results of an adaptive model-based dose-ranging trial in multiple sclerosis that motivated this research and was conducted using the presented methodology.