Time-to-Event Bayesian Optimal Interval Design to Accelerate Phase I Trials

Fractional accumulative calibration-free odds (f-aCFO) design for delayed toxicity in phase I clinical trials

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

STRILL: Phase I Trial Evaluating Stereotactic Body Radiotherapy (SBRT) Dose Escalation for Re-Irradiation of Inoperable Peripheral Lung Lesions

Few data are available on the role of SBRT re-irradiation for isolated recurrences. We designed a prospective phase I study to evaluate the maximum tolerated dose (MTD) of SBRT for thoracic re-irradiation, for peripheral lung lesions. RT was delivered with a dose escalation design from 30 Gy in five fractions up to 50 Gy in five fractions. The primary end point was the definition of the maximum tolerated dose (MTD) of SBRT for thoracic re-irradiation. The dose-limiting toxicity was pneumonia ≥G3. Fifteen patients were enrolled. No cases of pneumonia ≥G3 occurred in any of our cohorts. Only one patient developed pneumonia G1 during treatment. Three patients developed acute toxicities that included dyspnea G1, cardiac failure G3, and chest wall pain. One patient developed G3 late toxicity with acute coronary syndrome. After a median follow-up of 21 months (range 3.6-29.1 months), six patients (40%) had a local relapse. Distant relapse occurred in five patients (33.3%). At the last follow-up, six patients died, all but two due to progressive disease. SBRT dose escalation for thoracic re-irradiation is an effective and well-tolerated option for patients with inoperable lung lesions after a first thoracic RT with acceptable acute and late toxicities.

T3 + 3: 3 + 3 Design With Delayed Outcomes

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

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.

The 3 + 3 design in dose-finding studies with small sample sizes: Pitfalls and possible remedies

In the last few years, numerous novel designs have been proposed to improve the efficiency and accuracy of phase I trials to identify the maximum-tolerated dose (MTD) or the optimal biological dose (OBD) for noncytotoxic agents. However, the conventional 3+3 approach, known for its and poor performance, continues to be an attractive choice for many trials despite these alternative suggestions. The article seeks to underscore the importance of moving beyond the 3+3 design by highlighting a different key element in trial design: the estimation of sample size and its crucial role in predicting toxicity and determining the MTD. We use simulation studies to compare the performance of the most used phase I approaches: 3+3, Continual Reassessment Method (CRM), Keyboard and Bayesian Optimal Interval (BOIN) designs regarding three key operating characteristics: the percentage of correct selection of the true MTD, the average number of patients allocated per dose level, and the average total sample size. The simulation results consistently show that the 3+3 algorithm underperforms in comparison to model-based and model-assisted designs across all scenarios and metrics. The 3+3 method yields significantly lower (up to three times) probabilities in identifying the correct MTD, often selecting doses one or even two levels below the actual MTD. The 3+3 design allocates significantly fewer patients at the true MTD, assigns higher numbers to lower dose levels, and rarely explores doses above the target dose-limiting toxicity (DLT) rate. The overall performance of the 3+3 method is suboptimal, with a high level of unexplained uncertainty and significant implications for accurately determining the MTD. While the primary focus of the article is to demonstrate the limitations of the 3+3 algorithm, the question remains about the preferred alternative approach. The intention is not to definitively recommend one model-based or model-assisted method over others, as their performance can vary based on parameters and model specifications. However, the presented results indicate that the CRM, Keyboard, and BOIN designs consistently outperform the 3+3 and offer improved efficiency and precision in determining the MTD, which is crucial in early-phase clinical trials.

Accelerating drug development at Bristol Myers Squibb through innovation

This paper focuses on the use of novel technologies and innovative trial designs to accelerate evidence generation and increase pharmaceutical Research and Development (R&D) productivity, at Bristol Myers Squibb. We summarize learnings with case examples, on how we prepared and continuously evolved to address the increasing cost, complexities, and external pressures in drug development, to bring innovative medicines to patients much faster. These learnings were based on review of internal efforts toward accelerating R&D focusing on four key areas: adopting innovative trial designs, optimizing trial designs, leveraging external control data, and implementing novel methods using artificial intelligence and machine learning.

Safety, Tolerability, and Dose-Limiting Toxicity of Lacosamide in Patients With Painful Chronic Pancreatitis: Protocol for a Phase 1 Clinical Trial to Determine Safety and Identify Side Effects

BACKGROUND

Chronic abdominal pain is the hallmark symptom of chronic pancreatitis (CP), with 50% to 80% of patients seeking medical attention for pain control. Although several management options are available, outcomes are often disappointing, and opioids remain a mainstay of therapy. Opioid-induced hyperalgesia is a phenomenon resulting in dose escalation, which may occur partly because of the effects of opioids on voltage-gated sodium channels associated with pain. Preclinical observations demonstrate that the combination of an opioid and the antiseizure drug lacosamide diminishes opioid-induced hyperalgesia and improves pain control.

OBJECTIVE

In this phase 1 trial, we aim to determine the safety, tolerability, and dose-limiting toxicity of adding lacosamide to opioids for the treatment of painful CP and assess the feasibility of performance of a pilot study of adding lacosamide to opioid therapy in patients with CP. As an exploratory aim, we will assess the efficacy of adding lacosamide to opioid therapy in patients with painful CP.

METHODS

Using the Bayesian optimal interval design, we will conduct a dose-escalation trial of adding lacosamide to opioid therapy in patients with painful CP enrolled in cohorts of size 3. The initial dose will be 50 mg taken orally twice a day, followed by incremental increases to a maximum dose of 400 mg/day, with lacosamide administered for 7 days at each dose level. Adverse events will be documented according to Common Terminology Criteria for Adverse Events (version 5.0).

RESULTS

As of December 2023, we have currently enrolled 6 participants. The minimum number of participants to be enrolled is 12 with a maximum of 24. We expect to publish the results by March 2025.

CONCLUSIONS

This trial will test the feasibility of the study design and provide reassurance regarding the tolerability and safety of opioids in treating painful CP. It is anticipated that lacosamide will prove to be safe and well tolerated, supporting a subsequent phase 2 trial assessing the efficacy of lacosamide+opioid therapy in patients with painful CP, and that lacosamide combined with opiates will lower the opioid dose necessary for pain relief and improve the safety profile of opioid use in treating painful CP.

TRIAL REGISTRATION

Clinicaltrials.gov NCT05603702; https://clinicaltrials.gov/study/NCT05603702.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/50513.

Handling Incomplete or Late-Onset Toxicities in Early-Phase Dose-Finding Clinical Trials: Current Practice and Future Prospects

PURPOSE

The way late-onset toxicities are managed can affect trial outcomes and participant safety. Specifically, participants often might not have completed their entire follow-up period to observe any toxicities before new participants would be recruited. We conducted a methodological review of published early-phase dose-finding clinical trials that used designs accounting for partial and complete toxicity information, aiming to understand (1) how such designs were implemented and reported and (2) if sufficient information was provided to enable the replicability of trial results.

METHODS

Until March 26, 2023, we identified 141 trials using the rolling 6 design, the time-to-event continuous reassessment method (TITE-CRM), the TITE-CRM with cycle information, the TITE Bayesian optimal interval design, the TITE cumulative cohort design, and the rapid enrollment design. Clinical settings, design parameters, practical considerations, and dose-limiting toxicity (DLT) information were extracted from these published trials.

RESULTS

The TITE-CRM (61, 43.3%) and the rolling 6 design (76, 53.9%) were most frequently implemented in practice. Trials using the TITE-CRM had longer DLT assessment windows beyond the first cycle compared with the rolling 6 design (52.5% v 6.6%). Most trials implementing the TITE-CRM (91.8%, 56 of 61) failed to describe essential parameters in the protocols or the study result papers. Only five TITE-CRM trials (8.2%, 5 of 61) reported sufficient information to enable replication of the final analysis.

CONCLUSION

When compared with trials using the rolling 6 design, those implementing the TITE-CRM design exhibited notable deficiencies in reporting essential details necessary for reproducibility. Inadequate reporting quality of advanced model-based trial designs hinders their credibility. We provide recommendations that can improve transparency, reproducibility, and accurate interpretation of the results for such designs.

A phase I dose-finding trial of hyperthermic intraperitoneal docetaxel combined with cisplatin in patients with advanced-stage ovarian cancer

OBJECTIVE

To identify the maximum tolerated dose (MTD) of docetaxel combined with a fixed dose of cisplatin (75 mg/m²) delivered as hyperthermic intraperitoneal chemotherapy (HIPEC) in patients with ovarian cancer.

METHODS

In this phase I trial, a time-to-event Bayesian optimal interval design was used. Docetaxel was given at a starting dose of 60 mg/m² and was increased in 5 mg/m² increments until the MTD was determined or the maximum dose level of 75 mg/m² was reached. The dose-limiting toxicity (DLT) rate was set at 25%, with a total sample size of 30 patients. HIPEC was delivered immediately following debulking surgery at a target temperature of 43°C for 90 minutes.

RESULTS

From August 2022 to November 2022, 30 patients were enrolled. Among the patients who received a dose of docetaxel ≤65 mg/m², no DLT was reported. DLTs were observed in one patient who received 70 mg/m² docetaxel (grade 3 anaemia) and in three patients who received 75 mg/m² docetaxel (one case of grade 3 anaemia, one case of grade 3 hepatic impairment and one case of grade 4 thrombocytopenia). Patients treated with docetaxel 75 mg/m² in combination with cisplatin 75 mg/m² had an estimated DLT rate of 25%, which was the closest to the target DLT rate and was therefore chosen as the MTD.

CONCLUSION

Docetaxel, in combination with a fixed dose of cisplatin (75 mg/m²), can be used safely at intraperitoneal doses of 75 mg/m² in ovarian cancer patients who received HIPEC (43°C, 90 minutes) following debulking surgery.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05410483.

Enhancement of Bayesian optimal interval design by accounting for overdose and underdose errors trade-offs

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

A Bayesian optimal interval design for dose optimization with a randomization scheme based on pharmacokinetics outcomes in oncology

The primary objective of an oncology dose-finding trial for novel therapies, such as molecularly targeted agents and immune-oncology therapies, is to identify the optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. Pharmacokinetic (PK) information is considered an appropriate indicator for evaluating the level of drug intervention in humans from a pharmacological perspective. Several novel anticancer agents have been shown to have significant exposure-efficacy relationships, and some PK information has been considered an important predictor of efficacy. This paper proposes a Bayesian optimal interval design for dose optimization with a randomization scheme based on PK outcomes in oncology. A simulation study shows that the proposed design has advantages compared to the other designs in the percentage of correct OD selection and the average number of patients allocated to OD in various realistic settings.

TITE-gBOIN-ET: Time-to-event generalized Bayesian optimal interval design to accelerate dose-finding accounting for ordinal graded efficacy and toxicity outcomes

One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular-targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low or moderate-grade toxicities than dose-limiting toxicities. Besides, for efficacy, evaluating the overall response and long-term stable disease in solid tumors and considering the difference between complete remission and partial remission in lymphoma are preferable. It is also essential to accelerate early-stage trials to shorten the entire period of drug development. However, it is often challenging to make real-time adaptive decisions due to late-onset outcomes, fast accrual rates, and differences in outcome evaluation periods for efficacy and toxicity. To solve the issues, we propose a time-to-event generalized Bayesian optimal interval design to accelerate dose finding, accounting for efficacy and toxicity grades. The new design named "TITE-gBOIN-ET" design is model-assisted and straightforward to implement in actual oncology dose-finding trials. Simulation studies show that the TITE-gBOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment while having comparable or higher performance in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.

Time-to-event calibration-free odds design: A robust efficient design for phase I trials with late-onset outcomes

Compared with most of the existing phase I designs, the recently proposed calibration-free odds (CFO) design has been demonstrated to be robust, model-free, and easy to use in practice. However, the original CFO design cannot handle late-onset toxicities, which have been commonly encountered in phase I oncology dose-finding trials with targeted agents or immunotherapies. To account for late-onset outcomes, we extend the CFO design to its time-to-event (TITE) version, which inherits the calibration-free and model-free properties. One salient feature of CFO-type designs is to adopt game theory by competing three doses at a time, including the current dose and the two neighboring doses, while interval-based designs only use the data at the current dose and is thus less efficient. We conduct comprehensive numerical studies for the TITE-CFO design under both fixed and randomly generated scenarios. TITE-CFO shows robust and efficient performances compared with interval-based and model-based counterparts. As a conclusion, the TITE-CFO design provides robust, efficient, and easy-to-use alternatives for phase I trials when the toxicity outcome is late-onset.

A comparative study of adaptive trial designs for dose optimization

Recently, the US Food and Drug Administration Oncology Center of Excellence initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. The agency pointed out that the current paradigm for dose selection-based on the maximum tolerated dose (MTD)-is not sufficient for molecularly targeted therapies and immunotherapies, for which efficacy may not increase after the dose reaches a certain level. In these cases, it is more appropriate to identify the optimal biological dose (OBD) that optimizes the risk-benefit tradeoff of the drug. Project Optimus has spurred tremendous interest and urgent need for guidance on designing dose optimization trials. In this article, we review several representative dose optimization designs, including model-based and model-assisted designs, and compare their operating characteristics based on 10,000 randomly generated scenarios with various dose-toxicity and dose-efficacy curves and some fixed representative scenarios. The results show that, compared with model-based designs, model-assisted methods have advantages of easy-to-implement, robustness, and high accuracy to identify OBD. Some guidance is provided to help biostatisticians and clinicians to choose appropriate dose optimization methods in practice.

Incorporating patient-reported outcomes in dose-finding clinical trials with continuous patient enrollment

Dose-finding clinical trials in oncology estimate the maximum tolerated dose (MTD), based on toxicity obtained from the clinician's perspective. While the collection of patient-reported outcomes (PROs) has been advocated to better inform treatment tolerability, there is a lack of guidance and methods on how to use PROs for dose assignments and recommendations. The PRO continual reassessment method (PRO-CRM) has been proposed to formally incorporate PROs into dose-finding trials. In this paper, we propose two extensions of the PRO-CRM, which allow continuous enrollment of patients and longer toxicity observation windows to capture late-onset or cumulative toxicities by using a weighted likelihood to include the partial toxicity follow-up information. The TITE-PRO-CRM uses both the PRO and the clinician's information during the trial for dose assignment decisions and at the end of the trial to estimate the MTD. The TITE-CRM + PRO uses clinician's information solely to inform dose assignments during the trial and incorporates PRO at the end of the trial for the estimation of the MTD. Simulation studies show that the TITE-PRO-CRM performs similarly to the PRO-CRM in terms of dose recommendation and assignments during the trial while almost halving trial duration in case of an accrual of two patients per observation window. The TITE-CRM + PRO slightly underperforms compared to the TITE-PRO-CRM, but similar performance can be attained by requiring larger sample sizes. We also show that the performance of the proposed methods is robust to higher accrual rates, different toxicity hazards, and correlated time-to-clinician toxicity and time-to-patient toxicity data.

Comparison Between Simultaneous and Sequential Utilization of Safety and Efficacy for Optimal Dose Determination in Bayesian Model-Assisted Designs

It has become quite common in recent early oncology trials to include both the dose-finding and the dose-expansion parts within the same study. This shift can be viewed as a seamless way of conducting the trials to obtain information on safety and efficacy hence identifying an optimal dose (OD) rather than just the maximum tolerated dose (MTD). One approach is to conduct a dose-finding part based solely on toxicity outcomes, followed by a dose expansion part to evaluate efficacy outcomes. Another approach employs only the dose-finding part, where the dose-finding decisions are made utilizing both the efficacy and toxicity outcomes of those enrolled patients. In this paper, we compared the two approaches through simulation studies under various realistic settings. The percentage of correct ODs selection, the average number of patients allocated to the ODs, and the average trial duration are reported in choosing the appropriate designs for their early-stage dose-finding trials, including expansion cohorts.

Trial designs and endpoints for immune therapies in multiple myeloma

Immune therapies, including CAR-T cells, bispecific antibodies, and antibody-drug conjugates, are revolutionizing the treatment of multiple myeloma. In this review, we discuss clinical trial design considerations relevant to immune therapies. We first examine issues pertinent to specific populations, including elderly, patients with renal impairment, high-risk/extramedullary disease, and prior immune therapies. We then highlight trial designs to optimize the selection of dose and schedule, explore rational combination therapies based on preclinical data, and evaluate the nuances of commonly used endpoints. By exploiting their pharmacokinetic/pharmacodynamic profiles and utilizing novel translational insights, we can optimize the use of immune therapies in multiple myeloma.

Data-dependent early completion of dose-finding trials for drug-combination

PURPOSE

Model-assisted designs for drug combination trials have been proposed as novel designs with simple and superior performance. However, model-assisted designs have the disadvantage that the sample size must be set in advance, and trials cannot be completed until the number of patients treated reaches the pre-set sample size. Model-assisted designs have a stopping rule that can be used to terminate the trial if the number of patients treated exceeds the predetermined number, there is no statistical basis for the predetermined number. Here, I propose two methods for data-dependent early completion of dose-finding trials for drug combination: (1) an early completion method based on dose retainment probability, and (2) an early completion method in which the dose retainment probability is adjusted by a bivariate isotonic regression.

METHODS

Early completion is determined when the dose retainment probability using both trial data and the number of remaining patients is high. Early completion of a virtual trial was demonstrated. The performances of the early completion methods were evaluated by simulation studies with 12 scenarios.

RESULTS

The simulation studies showed that the percentage of early completion was an average of approximately 70%, and the number of patients treated was 25% less than the planned sample size. The percentage of correct maximum tolerated dose combination selection for the early completion methods was similar to that of non-early completion methods with an average difference of approximately 3%.

CONCLUSION

The performance of the proposed early completion methods was similar to that of the non-early completion methods. Furthermore, the number of patients for determining early completion before the trial starts was determined and a program code for calculating the dose retainment probability was proposed.

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.

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.

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.

A Road Map for Designing Phase I Clinical Trials of Radiotherapy-Novel Agent Combinations

Radiotherapy has proven efficacy in a wide range of cancers. There is growing interest in evaluating radiotherapy-novel agent combinations and a drive to initiate this earlier in the clinical development of the novel agent, where the scientific rationale and preclinical evidence for a radiotherapy combination approach are high. Optimal design, delivery, and interpretation of studies are essential. In particular, the design of phase I studies to determine safety and dosing is critical to an efficient development strategy. There is significant interest in early-phase research among scientific and clinical communities over recent years, at a time when the scrutiny of the trial methodology has significantly increased. To enhance trial design, optimize safety, and promote efficient trial conduct, this position paper reviews the current phase I trial design landscape. Key design characteristics extracted from 37 methodology papers were used to define a road map and a design selection process for phase I radiotherapy-novel agent trials. Design selection is based on single- or dual-therapy dose escalation, dose-limiting toxicity categorization, maximum tolerated dose determination, subgroup evaluation, software availability, and design performance. Fifteen of the 37 designs were identified as being immediately accessible and relevant to radiotherapy-novel agent phase I trials. Applied examples of using the road map are presented. Developing these studies is intensive, highlighting the need for funding and statistical input early in the trial development to ensure appropriate design and implementation from the outset. The application of this road map will improve the design of phase I radiotherapy-novel agent combination trials, enabling a more efficient development pathway.

An overview of the BOIN design and its current extensions for novel early-phase oncology trials

Bayesian Optimal Interval (BOIN) designs are a class of model-assisted dose-finding designs that can be used in oncology trials to determine the maximum tolerated dose (MTD) of a study drug based on safety or the optimal biological dose (OBD) based on safety and efficacy. BOIN designs provide a complete suite for dose finding in early phase trials, as well as a consistent way to explore different scenarios such as toxicity, efficacy, continuous outcomes, delayed toxicity or efficacy and drug combinations in a unified manner with easy access to software to implement most of these designs. Although built upon Bayesian probability models, BOIN designs are operationally simple in general and have good statistical operating characteristics compared to other dose-finding designs. This review paper describes the original BOIN design and its many extensions, their advantages and limitations, the software used to implement them, and the most suitable situation for use of each of these designs. Published examples of the implementation of BOIN designs are provided in the Appendix.

Bayesian Sample Size Planning Tool for Phase I Dose-Finding Trials

PURPOSE

Through Bayesian inference, we propose a method called BayeSize as a reference tool for investigators to assess the sample size and its associated scientific property for phase I clinical trials.

METHODS

BayeSize applies the concept of effect size in dose finding, assuming that the maximum tolerated dose can be identified on the basis of an interval surrounding its true value because of statistical uncertainty. Leveraging a decision framework that involves composite hypotheses, BayeSize uses two types of priors, the fitting prior (for model fitting) and sampling prior (for data generation), to conduct sample size calculation under the constraints of statistical power and type I error.

RESULTS

Simulation results showed that BayeSize can provide reliable sample size estimation under the constraints of type I/II error rates.

CONCLUSION

BayeSize could facilitate phase I trial planning by providing appropriate sample size estimation. Look-up tables and R Shiny app are provided for practical applications.

Borrowing historical information to improve phase I clinical trials using meta-analytic-predictive priors

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

Adaptive design for identifying maximum tolerated dose early to accelerate dose-finding trial

Purpose

The early identification of maximum tolerated dose (MTD) in phase I trial leads to faster progression to a phase II trial or an expansion cohort to confirm efficacy.

Methods

We propose a novel adaptive design for identifying MTD early to accelerate dose-finding trials. The early identification of MTD is determined adaptively by dose-retainment probability using a trial data via Bayesian analysis. We applied the early identification design to an actual trial. A simulation study evaluates the performance of the early identification design.

Results

In the actual study, we confirmed the MTD could be early identified and the study period was shortened. In the simulation study, the percentage of the correct MTD selection in the early identification Keyboard and early identification Bayesian optimal interval (BOIN) designs was almost same from the non-early identification version. The early identification Keyboard and BOIN designs reduced the study duration by about 50% from the model-assisted designs. In addition, the early identification Keyboard and BOIN designs reduced the study duration by about 20% from time-to-event model-assisted designs.

Conclusion

We proposed the early identification of MTD maintaining the accuracy to be able to short the study period.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01584-y.

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

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

We conclude that the early completion method can be applied unproblematically to the model-assisted design of phase I dose-finding trials.

CUSUMIN: A cumulative sum interval design for cancer phase I dose finding studies

Recently, model-assisted designs, including the Bayesian optimal interval (BOIN) design with optimal thresholds to determine the dose for the next cohort, have been proposed for cancer phase I studies. Model-assisted designs are useful because of their good performance as model-based designs in addition to their algorithm-based simplicity. In BOIN, escalation and de-escalation based on boundaries can be understood as a type of change point detection based on a sequential test procedure. Notably, the sequential test procedure is used in a wide range of fields and is known for its application to control charts, statistical monitoring methods used for detecting abnormalities in manufacturing processes. In control charts, abnormalities are detected if the control chart statistics are observed to be outside of the optimal boundaries. The cumulative sum (CUSUM) statistic, which is developed for control chart applications, derives higher power under the same erroneous judgment rate. Hence, it is expected that a more efficient model-assisted design can be achieved by the application of CUSUM statistics. In this study, a model-assisted design based on the CUSUM statistic is proposed. In the proposed design, the dose for the next cohort is decided by CUSUM statistics calculated from the counts of the dose-limiting toxicity and pre-defined boundaries, based on the CUSUM control chart scheme. Intensive simulation shows that our proposed method performs better than BOIN, and other representative model-assisted designs, including modified toxicity probability interval (mTPI) and Keyboard, in terms of controlling over-dosing rates while maintaining similar performance in the determination of maximum tolerated dose.

TITE-gBOIN: Time-to-event Bayesian optimal interval design to accelerate dose-finding accounting for toxicity grades

The new therapeutic agents, such as molecular targeted agents and immuno-oncology therapies, appear more likely to induce multiple toxicities at different grades than dose-limiting toxicities defined in traditional dose-finding trials. In addition, it is often challenging to make adaptive decisions on dose escalation and de-escalation on time because of the fast accrual rate and/or the late-onset toxicity outcomes, causing the potential suspension of the enrollment and the delay of the trials. To address these issues, we propose a time-to-event Bayesian optimal interval design to accelerate the dose-finding process utilizing toxicity grades based on both cumulative and pending toxicity outcomes. The proposed design, named "TITE-gBOIN" design, is a nonparametric and model-assisted design and has the virtues of robustness, simplicity and straightforward to implement in actual oncology dose-finding trials. A simulation study shows that the TITE-gBOIN design has a higher probability of selecting the MTDs correctly and allocating more patients to the MTDs across various realistic settings while reducing the trial duration significantly, therefore can accelerate early-stage dose-finding trials.

Probability-of-decision interval 3+3 (POD-i3+3) design for phase I dose finding trials with late-onset toxicity

Late-onset toxicities often occur in phase I trials investigating novel immunotherapy and molecular targeted therapies. For trials with cohort based designs (such as modified toxicity probability interval, Bayesian optimal interval, and i3+3), patients are often turned away since the current cohort are still being followed without definite dose-limiting toxicities, which results in prolonged trial duration and waste of patient resources. In this paper, we incorporate a probability-of-decision framework into the i3+3 design and allow real-time dosing inference when the next patient becomes available. Both follow-up time for the pending patients and time to dose-limiting toxicities for the observed patients are used in calculating the posterior probability of each possible dosing decision. An intensive simulation study is conducted to evaluate the operating characteristics of the newly proposed probability-of-decision-i3+3 design under various dosing scenarios and patient accrual settings. Results show that the probability-of-decision-i3+3 design achieves comparable safety and reliability performances but much shorter trial duration compared to the complete designs.

BOIN: a novel Bayesian design platform to accelerate early phase brain tumor clinical trials

Despite decades of extensive research, the progress in developing effective treatments for primary brain tumors lags behind that of other cancers, largely due to the unique challenges of brain tumors (eg, the blood-brain barrier and high heterogeneity) that limit the delivery and efficacy of many therapeutic agents. One way to address this issue is to employ novel trial designs to better optimize the treatment regimen (eg, dose and schedule) in early phase trials to improve the success rate of subsequent phase III trials. The objective of this article is to introduce Bayesian optimal interval (BOIN) designs as a novel platform to design various types of early phase brain tumor trials, including single-agent and combination regimen trials, trials with late-onset toxicities, and trials aiming to find the optimal biological dose (OBD) based on both toxicity and efficacy. Unlike many novel Bayesian adaptive designs, which are difficult to understand and complicated to implement by clinical investigators, the BOIN designs are self-explanatory and user friendly, yet yield more robust and powerful operating characteristics than conventional designs. We illustrate the BOIN designs using a phase I clinical trial of brain tumor and provide software (freely available at www.trialdesign.org) to facilitate the application of the BOIN design.

Contemporary dose-escalation methods for early phase studies in the immunotherapeutics era

Phase 1 dose-escalation trials are crucial to drug development by providing a framework to assess the toxicity of novel agents in a stepwise and monitored fashion. Despite widely adopted, rule-based dose-escalation methods (such as 3 + 3) are limited in finding the maximum tolerated dose (MTD) and tend to treat a significant number of patients at subtherapeutic doses. Newer methods of dose escalation, such as model-based and model-assisted designs, have emerged and are more accurate in finding MTD. However, these designs have not yet been broadly embraced by investigators. In this review, we summarise the advantages and disadvantages of contemporary dose-escalation methods, with emphasis on model-assisted designs, including time-to-event designs and hybrid methods involving optimal biological dose (OBD). The methods reviewed include mTPI, keyboard, BOIN, and their variations. In addition, the challenges of drug development (and dose-escalation) in the era of immunotherapeutics are discussed, where many of these agents typically have a wide therapeutic window. Fictional examples of how the dose-escalation method chosen can alter the outcomes of a phase 1 study are described, including the number of patients enrolled, the trial's timeframe, and the dose level chosen as MTD. Finally, the recent trends in dose-escalation methods applied in phase 1 trials in the immunotherapeutics era are reviewed. Among 856 phase I trials from 2014 to 2019, a trend towards the increased use of model-based and model-assisted designs over time (OR = 1.24) was detected. However, only 8% of the studies used non-rule-based dose-escalation methods. Increasing familiarity with such dose-escalation methods will likely facilitate their uptake in clinical trials.

Moving Beyond 3+3: The Future of Clinical Trial Design

Misgivings have been raised about the operating characteristics of the canonical 3+3 dose-escalation phase I clinical trial design. Yet, the traditional 3+3 design is still the most commonly used. Although it has been implied that adhering to this design is due to a stubborn reluctance to adopt change despite other designs performing better in hypothetical computer-generated simulation models, the continued adherence to 3+3 dose-escalation phase I strategies is more likely because these designs perform the best in the real world, pinpointing the correct dose and important side effects with an acceptable degree of precision. Beyond statistical simulations, there are little data to refute the supposed shortcomings ascribed to the 3+3 method. Even so, to address the unique nuances of gene- and immune-targeted compounds, a variety of inventive phase 1 trial designs have been suggested. Strategies for developing these therapies have launched first-in-human studies devised to acquire a breadth of patient data that far exceed the size of a typical phase I design and blur the distinction between dose selection and efficacy evaluation. Recent phase I trials of promising cancer therapies assessed objective tumor response and durability at various doses and schedules as well as incorporated multiple expansion cohorts spanning a variety of histology or biomarker-defined tumor subtypes, sometimes resulting in U.S. Food and Drug Administration approval after phase I. This article reviews recent innovations in phase I design from the perspective of multiple stakeholders and provides recommendations for future trials.

Rolling continual reassessment method with overdose control: An efficient and safe dose escalation design

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

A novel framework of Bayesian optimal interval design for phase I trials with late-onset toxicities

As molecularly targeted agents (MTAs) and immunotherapies have widely demonstrated delayed toxicity profile after multiple treatment cycles, the traditional phase I dose-finding designs may not be appropriate anymore because they just account for the acute toxicities occurring in the early period of treatment. When the dose-limiting toxicity (DLT) assessment window is prolonged to account for late-onset DLTs, it will cause logistic issues if the enrollment is suspended until all the DLT information is collected. We propose a novel framework to estimate the toxicity probability in the scenarios where some patients' DLT information are not complete and then implement the Bayesian optimal interval (BOIN) design to make decisions on dose escalation/de-escalation. Our proposed approach maintains BOIN's transparency by simply comparing the estimated toxicity probability with the escalation/de-escalation boundaries to decide the next dose level. The numerical studies show that our proposed framework can achieve comparable operating characteristics as other dose-finding designs considering late-onset DLTs, thus providing an attractive option of phase I dose-finding clinical trials for MTAs and immunotherapies.

SPA: Single patient acceleration in oncology dose-escalation trials

Efficient identification of the optimal dose and dosing scheme is one of the most critical and challenging tasks in early-phase oncology trials. The results are far-reaching because advancing a sub-optimal dose to late-stage development may not only jeopardize patients' safety or fail to deliver desired efficacy, but also be costly to sponsors as refined doses must be evaluated further before seeking regulatory approval. A good dose-escalation design is anticipated to yield high accuracy of selecting the correct dose while using fewer patients and keeping the trial duration short. Recently, treating a single patient at each lower dose level until certain events are triggered to switch to larger cohorts has gained much popularity. We name this approach "Single Patient Acceleration" (SPA), which is essentially a variant of the Accelerated Titration Design (ATD) by Simon et al. [25]. Although literature on novel dose-escalation methods is abundant in the past decade, there is a surprisingly lack of research on evaluating the ATD/SPA framework. In this article, we conduct comprehensive simulations to evaluate the performance of dose-escalation designs with or without SPA, and show that SPA improves design efficiency with similar or better accuracy to those without the "single patient" component under certain circumstances (e.g., slow initial enrollment, or the true maximum tolerated dose is at higher candidate dose levels). Potential safety concerns as a cost of efficiency improvement are also investigated in a quantitative manner to illustrate a comprehensive benefit-risk profile of SPA. Practical considerations and recommendations in using SPA are also discussed.

Advancing Effective Clinical Trial Designs for Myelofibrosis

Design features of phase I, II, and III clinical trials of pharmaceutical interventions in myelofibrosis (MF) are discussed. Model-assisted and model-based designs for phase I trials are useful for maximizing therapeutic benefit and include novel approaches to dose escalation. Trials in MF have shifted to accommodate new challenges following approval of JAK inhibitor therapies. Standardized response criteria exist; however, alternative measures of response when evaluating newer agents may be needed. Noninferiority and other adaptive designs can be used to incorporate design changes over time. Patient-reported outcomes, including quality-of-life and symptom assessment, should be included as outcome measures.

Optimal two-stage design of single arm Phase II clinical trials based on median event time test

The Phase II clinical trials aim to assess the therapeutic efficacy of a new drug. The therapeutic efficacy has been often quantified by response rate such as overall response rate or survival probability in the Phase II setting. However, there is a strong desire to use survival time, which is the gold standard endpoint for the confirmatory Phase III study, when investigators set the primary objective of the Phase II study and test hypotheses based on the median survivals. We propose a method for median event time test to provide the sample size calculation and decision rule of testing. The decision rule is simple and straightforward in that it compares the observed median event time to the identified threshold. Moreover, it is extended to optimal two-stage design for practice, which extends the idea of Simon’s optimal two-stage design for survival endpoint. We investigate the performance of the proposed methods through simulation studies. The proposed methods are applied to redesign a trial based on median event time for trial illustration, and practical strategies are given for application of proposed methods.

Designing Dose-Finding Phase I Clinical Trials: Top 10 Questions That Should Be Discussed With Your Statistician

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

BOIN Suite: A Software Platform to Design and Implement Novel Early-Phase Clinical Trials

PURPOSE

Using novel Bayesian adaptive designs has great potential to improve the efficiency of early-phase clinical trials. A major barrier for clinical researchers to adopt novel designs is the lack of easy-to-use software. Our purpose is to develop a user-friendly software platform to implement novel clinical trial designs that address various challenges in early-phase dose-finding trials.

METHODS

We used R Shiny to develop a web-based software platform to facilitate the use of recent novel adaptive designs.

RESULTS

We developed a web-based software suite, called Bayesian optimal interval (BOIN) suite, which includes R Shiny applications to handle various clinical settings, including single-agent phase I trials with and without prior information, trials with late-onset toxicity, trials to find the optimal biological dose based on risk-benefit trade-off, and drug combination trials to find a single maximum tolerated dose (MTD) or the MTD contour. The applications are built using the same software architecture to ensure the best and a uniform user experience, and they are developed using a proven software development standard operating procedure to ensure accuracy, robustness, and reproducibility. The suite is freely available with internet access and a web browser without the need of installing any other software.

CONCLUSION

The BOIN suite allows clinical researchers to design various types of early-phase clinical trials under a unified framework. This work is extremely important because it not only advances the clinical research and drug development by facilitating the use of novel trial designs with optimal performance but also enhances collaborations between biostatisticians and clinicians by disseminating novel statistical methodology to broader scientific communities through user-friendly software. The BOIN suite establishes a KISS principle: keep it simple, but smart.

CWL: A conditional weighted likelihood method to account for the delayed joint toxicity-efficacy outcomes for phase I/II clinical trials

The delayed outcome issue is common in early phase dose-finding clinical trials. This problem becomes more intractable in phase I/II clinical trials because both toxicity and efficacy responses are subject to the delayed outcome issue. The existing methods applying for the phase I trials cannot be used directly for the phase I/II trial due to a lack of capability to model the joint toxicity-efficacy distribution. In this paper, we propose a conditional weighted likelihood (CWL) method to circumvent this issue. The key idea of the CWL method is to decompose the joint probability into the product of marginal and conditional probabilities and then weight each probability based on each patient's actual follow-up time. The CWL method makes no parametric model assumption on either the dose-response curve or the toxicity-efficacy correlation and therefore can be applied to any existing phase I/II trial design. Numerical trial applications show that the proposed CWL method yields desirable operating characteristics.

BOIN12: Bayesian Optimal Interval Phase I/II Trial Design for Utility-Based Dose Finding in Immunotherapy and Targeted Therapies

PURPOSE

For immunotherapy, such as checkpoint inhibitors and chimeric antigen receptor T-cell therapy, where the efficacy does not necessarily increase with the dose, the maximum tolerated dose may not be the optimal dose for treating patients. For these novel therapies, the objective of dose-finding trials is to identify the optimal biologic dose (OBD) that optimizes patients' risk-benefit trade-off.

METHODS

We propose a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit trade-off. The BOIN12 design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocates patients to the dose that optimizes the toxicity-efficacy trade-off. We performed simulation studies to evaluate the performance of the BOIN12 design.

RESULTS

Compared with existing phase I/II dose-finding designs, the BOIN12 design is simpler to implement, has higher accuracy to identify the OBD, and allocates more patients to the OBD. One of the most appealing features of the BOIN12 design is that its adaptation rule can be pretabulated and included in the protocol. During the trial conduct, clinicians can simply look up the decision table to allocate patients to a dose without complicated computation.

CONCLUSION

The BOIN12 design is simple to implement and yields desirable operating characteristics. It overcomes the computational and implementation complexity that plagues existing Bayesian phase I/II dose-finding designs and provides a useful design to optimize the dose of immunotherapy and targeted therapy. User-friendly software is freely available to facilitate the application of the BOIN12 design.

Phase 1 study of the HSP90 inhibitor onalespib in combination with AT7519, a pan-CDK inhibitor, in patients with advanced solid tumors

PURPOSE

We conducted a phase 1 trial of the HSP90 inhibitor onalespib in combination with the CDK inhibitor AT7519, in patients with advanced solid tumors to determine the safety profile and maximally tolerated dose, pharmacokinetics, preliminary antitumor activity, and to assess the pharmacodynamic (PD) effects on HSP70 expression in patient-derived PBMCs and plasma.

METHODS

This study followed a 3 + 3 trial design with 1 week of intravenous (IV) onalespib alone, followed by onalespib/AT7519 (IV) on days 1, 4, 8, and 11 of a 21-days cycle. PK and PD samples were collected at baseline, after onalespib alone, and following combination therapy.

RESULTS

Twenty-eight patients were treated with the demonstration of downstream target engagement of HSP70 expression in plasma and PBMCs. The maximally tolerated dose was onalespib 80 mg/m² IV + AT7519 21 mg/m² IV. Most common drug-related adverse events included Grade 1/2 diarrhea (79%), fatigue (54%), mucositis (57%), nausea (46%), and vomiting (50%). Partial responses were seen in a palate adenocarcinoma and Sertoli-Leydig tumor; a colorectal and an endometrial cancer patient both remained on study for ten cycles with stable disease as the best response. There were no clinically relevant PK interactions for either drug.

CONCLUSIONS

Combined onalespib and AT7519 is tolerable, though below monotherapy RP2D. Promising preliminary clinical activity was seen. Further benefit may be seen with the incorporation of molecular signature pre-selection. Further biomarker development will require the assessment of the on-target impact on relevant client proteins in tumor tissue.

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.