Current issues in dose-finding designs: A response to the US Food and Drug Adminstrations's Oncology Center of Excellence Project Optimus

With the advent of targeted agents and immunological therapies, the medical research community has become increasingly aware that conventional methods for determining the best dose or schedule of a new agent are inadequate. It has been well established that conventional phase I designs cannot reliably identify safe and effective doses. This problem applies, generally, for cytotoxic agents, radiation therapy, targeted agents, and immunotherapies. To address this, the US Food and Drug Administration's Oncology Center of Excellence initiated Project Optimus, with the goal "to reform the dose optimization and dose selection paradigm in oncology drug development." As a response to Project Optimus, the articles in this special issue of Clinical Trials review recent advances in methods for choosing the dose or schedule of a new agent with an overall objective of informing clinical trialists of these innovative designs. This introductory article briefly reviews problems with conventional methods, the regulatory changes that encourage better dose optimization designs, and provides brief summaries of the articles that follow in this special issue.

Adjusting perioperative methadone dose for elderly and fragile hip fracture patients (MetaHip-trial) - A statistical analysis plan for an adaptive dose-finding trial

Background

The elderly population is expanding globally. This gives numerous challenges especially regarding hip fracture patients. In the US alone over 300.000 hip fracture patients are treated each year, and a large amount of those develop opoid addiction. Hip fractures require surgical intervention within 24 h and is associated with significant pain even at rest. Postoperative analgesic treatment need to be optimized to ensure adequate pain relief and to prevent subsequent opioid addiction. Previous studies have shown that methadone effectively decreases post-operative opioid consumption but the studies focused on younger patients undergoing elective surgery. This study focus on the use of methadone on the elderly, fragile patients undergoing acute surgery, by first determining the maximal tolerable dose.The hypothesis is the maximal tolerable doses of these hip-fracture patients lies between 0.10 mg/kg and 0.20 mg/kg. This trial aims to estimate the maximum tolerable dose of methadone when administered to elderly patients undergoing surgery for a hip fracture.

Method

This project is an adaptive dose-finding trial. The continuous reassessment method will estimate the maximum tolerable dose of methadone. The primary outcome will be respiratory depression. The statistical analysis plan will be published a priori to the closure of patient recruitment and statistical analysis of database results.

Conclusion

The results of this study will give valuable information about the maximally tolerated dose of methadone for postoperative pain relief for elderly patients with hip fractures and potential adverse events.This trial is registered on clinicaltrials.gov with trial registration: NCT05581901. Registered 17 October 2022, https://www.clinicaltrials.gov/ct2/show/NCT05581901?term=methadone&cond = hip&draw = 2&rank = 1.

Bayesian hierarchical model for dose-finding trial incorporating historical data

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

The effect of a multi-component behavior change technique intervention on medication adherence among individuals on primary prevention statin therapy: a dose-finding protocol

BACKGROUND

In the USA, the primary cause of death and morbidity continues to be cardiovascular disease (CVD). Numerous trials have shown that statin medication reduces the likelihood of CVD events; it is a cornerstone of CVD prevention. However, studies have also indicated that up to 60% of the estimated 26.8 million Americans prescribed primary prevention statin treatment are nonadherent during the first year. Multi-component behavioral change technique (BCT) therapies have shown moderate promise in improving medication adherence as well as other positive health behaviors (such as physical activity). However, no research has looked at the duration of multi-component BCT intervention needed to result in a clinically significant improvement in statin adherence behaviors. This study aims to determine the necessary dose of a multi-component BCT intervention (defined as duration in weeks) to promote adherence to statin medication among those on primary prevention statin treatment by utilizing the modified time-to-event continuous reassessment method (TiTE-CRM).

METHODS AND DESIGN

The study will utilize the modified TiTE-CRM in 42 participants, recruited in 14 cohorts of 3 participants each. The goal of this analysis is to identify the minimum effective dose (MED) of a multi-behavior change technique (BCT) intervention required to increase adherence to statins by 20% between baseline and follow-up periods. Using the TiTE-CRM method, the dose of the behavior intervention in weeks will be assigned to each cohort based on the performance of the prior cohort. At the end of the study, the intervention dose that has been found to be associated with a 20% increase in statin adherence among 80% of participants assigned to that dose will be identified as the MED.

DISCUSSION

If successful, the current trial will provide additional guidance to researchers and clinicians seeking to increase statin medication adherence using a BCT intervention by identifying the dose (i.e., the duration) of an intervention required to meaningfully increase adherence.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05273736. Registered on March 10, 2022. https://www.

CLINICALTRIALS

gov/ct2/show/NCT05273736.

The effect of a multi-component behavior change technique intervention on physical activity among individuals on primary prevention statin therapy: A dose-finding trial protocol

BACKGROUND

Statin therapy is a mainstay of cardiovascular disease (CVD) prevention, but research shows that statin therapy alone is insufficient for preventing incident CVD and mortality. Combining statin medication with increased physical activity (PA) can lower mortality risk more than either statin or PA alone. However, PA levels often remain the same and may even decline following statin prescription. Additional information is needed to identify how to increase PA among statin users and determine the minimal length of an intervention (i.e., intervention dose) necessary to increase PA.

OBJECTIVE

The study aims to identify the required dose of a behavioral change technique (BCT) intervention to increase PA among individuals on primary prevention statin therapy who have an elevated risk for cardiovascular disease (CVD).

METHODS

The study will utilize the modified time-to-event continual reassessment method (TiTE-CRM) in 42 participants. We expect insights relating to dose-efficacy models and BCTs (Behavioral Change Techniques) to improve PA in adults at risk for CVD. This trial will also examine potential mechanisms of action (MoAs) for interventions to increase PA, identify any effect a PA intervention may have on medication adherence, and determine whether participants respond uniformly to their respective behavioral interventions.

ETHICS AND DISSEMINATION

This trial was approved by the Northwell Health Institutional Review Board (IRB) and all participants will complete informed consent. The trial results will be published in a peer-reviewed journal. All publications resulting from this series of personalized trials will follow the CONSORT reporting guidelines.

REGISTRATION DETAILS

This trial is registered on www.

CLINICALTRIALS

gov (Number NCT05273723).

Optimizing benefit/risk in oncology: Review of post-marketing dose optimization and reflections on the road ahead

Oncology therapies shifted from chemotherapy to molecularly targeted agents and finally to the era of immune-oncology agents. In contrast to cytotoxic agents, molecularly targeted agents are more selective, exhibit a wider therapeutic window, and may maximally modulate tumor growth at doses lower than the maximum tolerated dose (MTD). However, first-in-patient oncology studies for molecularly targeted agents continued to evaluate escalating doses using limited number of patients per dose cohort assessing dose-limiting toxicities to identify the MTD which is commonly selected for further development adopting a 'more is better' approach that led to several post-marketing requirement (PMR) studies to evaluate alternative, typically lower, doses or dosing frequencies to optimize the benefit-risk profile. In this review, post-marketing dose optimization efforts were reviewed including those required by a regulatory pathway or voluntarily conducted by the sponsor to improve efficacy, safety, or method of administration. Lessons learned and future implications from this deep dive review are discussed considering the evolving regulatory landscape on dose optimization for oncology compounds.

Impact of dose feasibility on the conduct of phase I trials of adoptive cell therapy

BACKGROUND

/Aims: In early-phase cell therapy trials, each dose level being studied is defined by the number of cells infused into the trial participant. The issue of dose feasibility presents itself when the desired number of cells is not reached in the expansion process. Consequently, dose assignments for some patients may deviate from the planned dose according to the chosen design. Widely used algorithmic designs aren't flexible enough to handle this complication and can lead to the exclusion of safety data from the dose assignment algorithm. This article studies the impact of dose feasibility challenges on the behavior of the 3 + 3 decision rule.

METHODS

We conducted a simulation study across six dose-feasibility and dose-toxicity scenarios. Trials are simulated using the 3 + 3 algorithm. We present a novel algorithm for random feasibility curve generation. We used this algorithm to conduct a large-scale simulation study across 100 random scenarios.

RESULTS

We found that the 3 + 3 has problematic characteristics due to the exclusion of safety data from the algorithm. Ignoring toxicity data can complicate the allocation of subsequent patients in the trial and can bias the final maximum tolerated dose recommendation for the next phase of drug development.

CONCLUSION

Our study demonstrates that excluding safety data from the 3 + 3 algorithm can be detrimental to trial conduct. Furthermore, there are existing methods that are flexible enough to include data that is observed away from the planned dose. We recommend that these methods be used in conducting phase I cell therapy trials.

Is more better? An analysis of toxicity and response outcomes from dose-finding clinical trials in cancer

BACKGROUND

The overwhelming majority of dose-escalation clinical trials use methods that seek a maximum tolerable dose, including rule-based methods like the 3+3, and model-based methods like CRM and EWOC. These methods assume that the incidences of efficacy and toxicity always increase as dose is increased. This assumption is widely accepted with cytotoxic therapies. In recent decades, however, the search for novel cancer treatments has broadened, increasingly focusing on inhibitors and antibodies. The rationale that higher doses are always associated with superior efficacy is less clear for these types of therapies.

METHODS

We extracted dose-level efficacy and toxicity outcomes from 115 manuscripts reporting dose-finding clinical trials in cancer between 2008 and 2014. We analysed the outcomes from each manuscript using flexible non-linear regression models to investigate the evidence supporting the monotonic efficacy and toxicity assumptions.

RESULTS

We found that the monotonic toxicity assumption was well-supported across most treatment classes and disease areas. In contrast, we found very little evidence supporting the monotonic efficacy assumption.

CONCLUSIONS

Our conclusion is that dose-escalation trials routinely use methods whose assumptions are violated by the outcomes observed. As a consequence, dose-finding trials risk recommending unjustifiably high doses that may be harmful to patients. We recommend that trialists consider experimental designs that allow toxicity and efficacy outcomes to jointly determine the doses given to patients and recommended for further study.

A benchmark for dose-finding studies with unknown ordering

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

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.

A comparative study of Bayesian optimal interval (BOIN) design with interval 3+3 (i3+3) design for phase I oncology dose-finding trials

Bayesian optimal interval (BOIN) design is a model-assisted phase I dose-finding design to find the maximum tolerated dose (MTD). The hallmark of the BOIN design is its concise decision rule - making the decision of dose escalation and de-escalation by simply comparing the observed dose-limiting toxicity (DLT) rate at the current dose with a pair of optimal dose escalation and de-escalation boundaries. The interval 3+3 (i3+3) design is a recently proposed algorithm-based dose-finding design based on a similar decision rule with some modifications. The similarity in the appearance of the two designs has caused confusions among practitioners. In this article, we demystify the i3+3 design by elucidating its links with the BOIN design and compare their similarities and differences, as well as pros and cons. We perform comprehensive simulation studies to compare the operating characteristics of the two designs. Our results show that, compared to the algorithm-based i3+3 design, which are characterized by ad hoc and often scientifically and logically incoherent decision rules, the mode-assisted BOIN design is not only simpler, but also statistically more rigorous with better operating characteristics, thus providing a better design choice for phase I oncology trials.

Fractional design: An alternative paradigm for late-onset toxicities in oncology dose-finding studies

Late-onset (LO) toxicities often arise in the new era of phase I oncology dose-finding trials with targeted agents or immunotherapies. The current LO toxicities modelling is often formulated in a weighted likelihood framework, where the time-to-event continual reassessment method (TITE-CRM) is commonly used. The TITE-CRM uses the patient exposure time as a weight for the censored observation, while there is large uncertainty on which weight function to be used. As an alternative, the fractional scheme formulates an efficient and robust paradigm to address LO toxicity issues in dose finding. We review the fractional continual reassessment method (fCRM) and compare its operating characteristics with those of the TITE-CRM as well as other competitive designs via extensive simulation studies based on both the fixed and randomly generated scenarios. The fCRM is shown to possess desirable operating characteristics in identifying the maximum tolerated dose (MTD) and deliver competitive performances in comparison with other designs. It provides an alternative efficient and robust paradigm for interpreting and addressing LO toxicities in the new era of phase I dose-finding trials in precision oncology. A real trial example is used to illustrate the practical use of the fCRM design.

A surface-free design for phase I dual-agent combination trials

In oncology, there is a growing number of therapies given in combination. Recently, several dose-finding designs for Phase I dose-escalation trials for combinations were proposed. The majority of novel designs use a pre-specified parametric model restricting the search of the target combination to a surface of a particular form. In this work, we propose a novel model-free design for combination studies, which is based on the assumption of monotonicity within each agent only. Specifically, we parametrise the ratios between each neighbouring combination by independent Beta distributions. As a result, the design does not require the specification of any particular parametric model or knowledge about increasing orderings of toxicity. We compare the performance of the proposed design to the model-based continual reassessment method for partial ordering and to another model-free alternative, the product of independent beta design. In an extensive simulation study, we show that the proposed design leads to comparable or better proportions of correct selections of the target combination while leading to the same or fewer average number of toxic responses in a trial.

Bayesian cancer clinical trial designs with subgroup-specific decisions

Two illustrative applications are presented of Bayesian clinical trial designs that make adaptive subgroup-specific decisions based on elicited utilities of patient outcomes to quantify risk-benefit trade-offs. The first design is for a randomized trial to evaluate effects of nutritional prehabilitation on post-operative morbidity in esophageal cancer patients undergoing surgery. The second design is for a dose-finding trial of natural killer cells to treat advanced hematologic malignancies, with five time-to-event outcomes. Each design is based on a Bayesian hierarchical model that borrows strength between subgroups. Computer simulation is used to evaluate each design's properties, including comparison to a simpler design ignoring treatment-subgroup interactions. The simulations show that accounting prospectively for treatment-subgroup interactions yields designs with very desirable properties, is greatly superior to a simplified comparator design that ignores subgroups if treatment-subgroup interactions actually exist, and each design is robust to deviations from the assumed underlying model.

Proportional odds assumption for modeling longitudinal ordinal multiple toxicity outcomes in dose finding studies of targeted agents: A pooled analysis of 54 studies

BACKGROUND

Data generated by phase I trials is richer than the classical binary DLT measured at the first cycle used as primary endpoints. Several works developed designs for more informative endpoints, e.g. ordinal toxicity grades and/or longitudinal data which relied however on strong assumptions, in particular the proportional odds (PO) assumption.

METHODS

We evaluated this PO assumption for the dose and cycle on a large database of individual patient data from 54 phase I clinical trials of molecularly targeted agents. The PO model is a specific case of the continuation ratio logit model (CRLM) with null parameters. We compared the PO and CRLM models using the widely applicable information criterion (WAIC). We considered a longitudinal multivariate ordinal toxicity outcome (cutaneous, digestive, hematological, general disorders, and other toxicities).

RESULTS

WAIC suggested that the CRLM model (WAIC = 30911.58) outperformed the PO model (WAIC = 31432.10). Deviance from PO assumption for dose was observed for digestive and general disorder toxicities. There was moderate cycle effect with slight deviance from PO assumption for the other type of toxicity.

CONCLUSIONS

Designs based on PO for dose should be a useful tool for drug with low expected digestive or general disorder toxicity dose-related incidence.

Efficient dose-finding for drug combination studies involving a shift in study populations

This paper describes the design of an early phase, prospective trial evaluating the safety and tolerability of the combination of the histone deacetylase inhibitor, entinostat, in combination with capecitabine. The study consists of two parts; an initial phase evaluating the safety of the combination in participants with metastatic breast cancer, followed by a second phase assessing the safety of the combination in participants with residual disease after neo-adjuvant chemotherapy for breast cancer. We describe the adaptation of a model-based design for identifying the maximum tolerated dose combination that efficiently moves from the initial phase in an advanced disease population to the second phase in the target population. Operating characteristics demonstrate the ability of the method to accurately predict true maximum tolerated dose combinations in a high percentage of trials with reasonable sample sizes, while treating participants at and around desirable combinations. The proposed design is a practical, early-phase, adaptive method for use with drug combination dose finding in the presence of shifting patient populations. More challenging research questions are being investigated in early-phase trials, which has created the need to implement more flexible designs that can meet the objectives of current studies, such as those exploring drug combinations while addressing patient heterogeneity. Our goal is to facilitate acceptance and application of more novel designs in contemporary early-phase studies.

Pragmatic dose-escalation methods incorporating relative dose intensity assessment for molecularly targeted agents in phase I trials

The recommended phase 2 doses of molecularly targeted agents, determined by using an ordinal dose-finding method that only uses toxicity data at first cycle, may not be optimal. Some researchers have proposed the use of relative dose intensity that can account for late-onset, cumulative, and low-grade toxicities to determine the recommended phase 2 dose (RP2D). In this study, we proposed two dose escalation methods based on the observed relative dose intensities (RDIs) between the pre-specified intervals (cycles) for toxicity evaluation used in combination with DLT evaluation in the first cycle. First, we propose the modified 3 + 3 design that incorporates longitudinal RDI assessment. Second, we propose the sequential assessment method for longitudinal RDI (SARDI) to achieve faster dose escalation compared to that of the modified 3 + 3 design. Simulation studies demonstrated that the SARDI was, in many cases, superior to the ordinal and modified 3 + 3 designs in respect to the selection rate of true RP2D and study period. The two proposed methods could also in some cases decrease the average number of patients enrolled in the trial compared to that of the ordinary 3 + 3 design. Incorporation of the RDI assessment into the 3 + 3 design is not difficult and does not require the use of complex statistical techniques. Therefore, we believe that investigators who routinely use the 3 + 3 design in practice can easily use our proposed methods.

Optimizing Hippocampal-Cortical Network Modulation via Repetitive Transcranial Magnetic Stimulation: A Dose-Finding Study Using the Continual Reassessment Method

OBJECTIVE

Repetitive transcranial magnetic stimulation (rTMS) can cause potentially useful changes in brain functional connectivity (FC), but the number of treatment sessions required is unknown. We applied the continual reassessment method (CRM), a Bayesian, adaptive, dose-finding procedure to a rTMS paradigm in an attempt to answer this question.

MATERIALS AND METHODS

The sample size was predetermined at 15 subjects and the cohort size was set with three individuals (i.e., five total cohorts). In a series of consecutive daily sessions, we delivered rTMS to the left posterior parietal cortex and measured resting-state FC with fMRI in a predefined hippocampal network in the left hemisphere. The session number for each successive cohort was determined by the CRM algorithm. We set a response criterion of a 0.028 change in FC between the hippocampus and the parietal cortex, which was equal to the increase seen in 87.5% of participants in a previous study using five sessions.

RESULTS

A ≥criterion change was observed in 9 of 15 participants. The CRM indicated that greater than four sessions are required to produce the criterion change reliably in future studies.

CONCLUSIONS

The CRM can be adapted for rTMS dose finding when a reliable outcome measure, such as FC, is available. The minimum effective dose needed to produce a criterion increase in FC in our hippocampal network of interest at 87.5% efficacy was estimated to be greater than four sessions. This study is the first demonstration of a Bayesian, adaptive method to explore a rTMS parameter.

Evaluating the effects of design parameters on the performances of phase I trial designs

Numerous designs have been proposed for phase I clinical trials. Although studies have compared their performances, few have considered the effects of changing design parameters. In this article, we review a few popular designs, including the 3 + 3, continuous reassessment method (CRM), Bayesian optimal interval (BOIN) design, and Keyboard design, and evaluate how varying design parameters (such as number of dose levels, target toxicity rate, maximum sample size, and cohort size) could impact the performances of each design through simulations. Excluded from our analysis is the mTPI-2 design, which operates in the same way as the Keyboard. Our results suggest that regardless of the choices of design parameters, the 3 + 3 design performs worse than the other ones, and BOIN and Keyboard have comparable performance to CRM. For any design, the performance varies with the choice of parameters. In particular, it improves as sample sizes increase, but the magnitude of benefit from increasing sample sizes varies substantially across scenarios. The impact of cohort size on design performances seems to have no clear direction. Therefore, BOIN and Keyboard designs are generally recommended due to their simplicity and good performance. With regard to choices of sample size and cohort size in designing a trial, it is recommend that simulations be performed for the particular clinical settings to aid decision making.

R-TPI: rolling toxicity probability interval design to shorten the duration and maintain safety of phase I trials

To shorten trial duration and improve safety of Phase I trials, we propose R-TPI, a rolling enrollment design that combines the features in model-based designs such as mTPI-2 and rule-based designs such as rolling six. R-TPI employs a novel rolling enrollment scheme, which allows concurrent patient enrollment that is faster than cohort-based enrollment. Bench-marking against rolling six, we find that the R-TPI design is as fast in completing clinical trials but with fewer toxicity events and higher chance of finding the maximum tolerated dose (MTD) in the single scenario laid out in the 2008 rolling six publication. We also find that in a broad setting involving multiple scenarios, R-TPI is generally faster, safer, and more reliable than standard designs. R-TPI is a general design that can be applied to adult and pediatric Phase I trials. It reduces the length of trial duration, leads to safer trials with fewer toxicity events, and maintains relatively a high chance of identifying the MTD.

An information theoretic phase I-II design for molecularly targeted agents that does not require an assumption of monotonicity

For many years phase I and phase II clinical trials have been conducted separately, but there has been a recent shift to combine these phases. Although a variety of phase I-II model-based designs for cytotoxic agents have been proposed in the literature, methods for molecularly targeted agents (TAs) are just starting to develop. The main challenge of the TA setting is the unknown dose-efficacy relationship that can have either an increasing, plateau or umbrella shape. To capture these, approaches with more parameters are needed or, alternatively, more orderings are required to account for the uncertainty in the dose-efficacy relationship. As a result, designs for more complex clinical trials, e.g. trials looking at schedules of a combination treatment involving TAs, have not been extensively studied yet. We propose a novel regimen finding design which is based on a derived efficacy-toxicity trade-off function. Because of its special properties, an accurate regimen selection can be achieved without any parametric or monotonicity assumptions. We illustrate how this design can be applied in the context of a complex combination-schedule clinical trial. We discuss practical and ethical issues such as coherence, delayed and missing efficacy responses, safety and futility constraints.

Oral administration of irinotecan in patients with solid tumors: an open-label, phase I, dose escalating study evaluating safety, tolerability and pharmacokinetics

BACKGROUND

Oral drug formulations have several advantages compared to intravenous formulation. Apart from patient convenience and favorable pharmacoeconomics, they offer the possibility of frequent drug administration at home. In this study, we present a new oral irinotecan formulation designed as an enteric coated immediate release tablet which in pre-clinical studies has shown good exposure with low variability.

METHODS

A phase I, dose escalating study to assess safety, tolerability, pharmacokinetics and efficacy of an oral irinotecan formulation and to establish the maximum tolerated dose (MTD). Each treatment cycle was once-daily irinotecan for 14 days followed by 1 week rest.

RESULTS

25 patients were included across four cohorts; 3 patients were included in cohort 1 (20 mg/m²), 7 patients were included in cohort 2 (30 mg/m²), 3 patients were included in cohort 3 (25 mg/m²) and 12 patients were included in cohort 4 (21 mg/m²). Median age was 67 years, 52% were performance status (PS) 0 while 48% were PS 1. Median number of prior therapies was 3 (range 1-6). MTD was established at 21 mg/m². No responses were observed. Nine patients (36%) had stable disease (SD), lasting median 19 weeks (range 7-45 weeks). Among these five patients had previously received irinotecan. No grade 3/4 hematologic toxicities were reported. Totally six patients experienced grade 1/2 anemia, three patients had grade 1/2 leucopenia and 1 patient had grade 1 thrombocytopenia. Most common non-hematological grade 1 and 2 adverse events were nausea, fatigue, diarrhea, vomiting and cholinergic syndrome. Grade 3 toxicities included diarrhea, fatigue, nausea and vomiting, no grade 4 events were reported. PK data showed consistent daily exposures during treatment at days 1 and 14 and no drug accumulation. SN-38 interpatient variability was in the same range as after infusion.

CONCLUSIONS

Oral irinotecan was generally well tolerated; side effects were manageable and similar in type to those observed with intravenous irinotecan. Hematological toxicities were few and only grade 1/2. In this heavily pre-treated patient population, oral irinotecan demonstrated activity even among patients previously treated with irinotecan.

A placebo-controlled Bayesian dose finding design based on continuous reassessment method with application to stroke research

Traditional dose-finding designs do not require assignment of patients to a control group. Motivated by SHRINC (Safety of Pioglitazone for hematoma resolution in intracerebral hemorrhage), we developed a placebo-controlled dose-finding study to identify the maximum tolerated dose for pioglitazone in stroke patients with spontaneous intracerebral hemorrhage. We designed an extension of the continuous reassessment method that allowed to incorporate information from the control group (i.e., the standard of care), and utilized it to determine the maximum tolerated dose in the SHRINC trial. We evaluated the operating characteristics of our design by conducting extensive simulation studies. Our findings from the simulation studies demonstrate that our proposed design is robust and performs well. By estimating the toxicity rate in the control group, we were able to obtain more accurate information about the natural history of the disease and identify appropriate dose for the next phase of this study. The proposed design provides a tool to incorporate the information from the control group into the dose-finding framework for trials with similar objectives.

Phase II dose-response trials: A simulation study to compare analysis method performance under design considerations

Phase II trials are intended to provide information about the dose-response relationship and to support the choice of doses for a pivotal phase III trial. Recently, new analysis methods have been proposed to address these objectives, and guidance is needed to select the most appropriate analysis method in specific situations. We set up a simulation study to evaluate multiple performance measures of one traditional and three more recent dose-finding approaches under four design options and illustrate the investigated analysis methods with an example from clinical practice. Our results reveal no general recommendation for a particular analysis method across all design options and performance measures. However, we also demonstrate that the new analysis methods are worth the effort compared to the traditional ANOVA-based approach.

Systematic comparison of the statistical operating characteristics of various Phase I oncology designs

Dose finding Phase I oncology designs can be broadly categorized as rule based, such as the 3 + 3 and the accelerated titration designs, or model based, such as the CRM and Eff-Tox designs. This paper systematically reviews and compares through simulations several statistical operating characteristics, including the accuracy of maximum tolerated dose (MTD) selection, the percentage of patients assigned to the MTD, over-dosing, under-dosing, and the trial dose-limiting toxicity (DLT) rate, of eleven rule-based and model-based Phase I oncology designs that target or pre-specify a DLT rate of ∼0.2, for three sets of true DLT probabilities. These DLT probabilities are generated at common dosages from specific linear, logistic, and log-logistic dose-toxicity curves. We find that all the designs examined select the MTD much more accurately when there is a clear separation between the true DLT rate at the MTD and the rates at the dose level immediately above and below it, such as for the DLT rates generated using the chosen logistic dose-toxicity curve; the separations in these true DLT rates depend, in turn, not only on the functional form of the dose-toxicity curve but also on the investigated dose levels and the parameter set-up. The model based mTPI, TEQR, BOIN, CRM and EWOC designs perform well and assign the greatest percentages of patients to the MTD, and also have a reasonably high probability of picking the true MTD across the three dose-toxicity curves examined. Among the rule-based designs studied, the 5 + 5 a design picks the MTD as accurately as the model based designs for the true DLT rates generated using the chosen log-logistic and linear dose-toxicity curves, but requires enrolling a higher number of patients than the other designs. We also find that it is critical to pick a design that is aligned with the true DLT rate of interest. Further, we note that Phase I trials are very small in general and hence may not provide accurate estimates of the MTD. Thus our work provides a map for planning Phase I oncology trials or developing new ones.

How can we optimise inhaled beta2 agonist dose as 'reliever' medicine for wheezy pre-school children? Study protocol for a randomised controlled trial

Background

Asthma is a common problem in children and, if inadequately controlled, may seriously diminish their quality of life. Inhaled short-acting beta2 agonists such as salbutamol are usually prescribed as ‘reliever’ medication to help control day-to-day symptoms such as wheeze. As with many medications currently prescribed for younger children (defined as those aged 2 years 6 months to 6 years 11 months), there has been no pre-licensing age-specific pharmacological testing; consequently, the doses currently prescribed (200–1000 μg) may be ineffective or likely to induce unnecessary side effects. We plan to use the interrupter technique to measure airway resistance in this age group, allowing us for the first time to correlate inhaled salbutamol dose with changes in clinical response. We will measure urinary salbutamol levels 30 min after dosing as an estimate of salbutamol doses in the lungs, and also look for genetic polymorphisms linked to poor responses to inhaled salbutamol.

Methods

This is a phase IV, randomised, controlled, observer-blinded, single-centre trial with four parallel groups (based on a sparse sampling approach) and a primary endpoint of the immediate bronchodilator response to salbutamol so that we can determine the most appropriate dose for an individual younger child. Simple randomisation will be used with a 1:1:1:1 allocation.

Discussion

The proposed research will exploit simple, non-invasive and inexpensive tests that can mostly be performed in an outpatient setting in order to help develop the evidence for the correct dose of salbutamol in younger children with recurrent wheeze who have been prescribed salbutamol by their doctor.

Trial registration

EudraCT2014-001978-33, ISRCTN15513131. Registered on 8 April 2015.

Integrating the escalation and dose expansion studies into a unified Phase I clinical trial

We focus on Phase I dose finding studies as they are currently undertaken. The design and analysis of these trials have changed over the last years and, in particular, it is now rare for a Phase I study to not include one or more dose-expansion cohorts (DEC). It is common to see DEC involving several hundred patients, building on an initial dose escalation study that may have no >20 to 30 patients. There has been recent focus by researchers on the design of DEC and the analysis of DEC data. It is reasonable to explicitly account for the uncertainty in the estimation of the MTD, the dose upon which the whole of the DEC is currently based. In this paper, we focus on the dose escalation phase prior to the DEC, with the purpose of adapting it to the needs of DEC. Specifically, before beginning the DEC phase, we need to identify those dose levels that will be taken into the DEC. We define a useful concept for this purpose, the co-MTD, and the results support that the estimated MTD and co-MTD contain the true MTD with high probability. We also provide stopping rules for when the data support that the dose escalation can end and the dose expansion can begin. Simulated trials support the use of the proposed approach and provide additional information on how this approach compares with current practice.

Implementation of a two-group likelihood time-to-event continual reassessment method using SAS

BACKGROUND AND OBJECTIVES

Dose finding trials using model-based methods have the ability to handle the increasingly complex landscape being seen in clinical trials. Issues such as patient heterogeneity in trial populations are important to address in the designing of a trial in addition to the inclusion/exclusion criteria. Designs accommodating patient heterogeneity have been described using the continual reassessment method (CRM) and time-to-event CRM (TITE-CRM), yet, the implementation of these trials in practice have been limited. These methods and other model-based methods generally need statisticians to help design and conduct these trials. However, the statistical programs which facilitate the use of these methods, currently available focus on estimation in the one-sample case.

METHODS

A SAS program to accommodate two groups using the TITE-CRM and likelihood estimation has been developed. The program consists of macros that assist with the planning and implementation of a trial accounting for patient heterogeneity.

RESULTS

Description of the program is given as well as examples using the programs. For planning purposes, an example will be provided showing how the program can be used to guide sample size estimates for the trial.

CONCLUSIONS

This program provides researchers with a valuable tool for designing dose-finding studies to account for the presence of patient heterogeneity and conduct a trial using a hypothetical example.

Two-group time-to-event continual reassessment method using likelihood estimation

The presence of patient heterogeneity in dose finding studies is inherent (i.e. groups with different maximum tolerated doses). When this type of heterogeneity is not accounted for in the trial design, subjects may be exposed to toxic or suboptimal doses. Options to handle patient heterogeneity include conducting separate trials or splitting the trial into arms. However, cost and/or lack of resources may limit the feasibility of these options. If information is shared between the groups, then both of these options do not benefit from using the shared information. Extending current dose finding designs to handle patient heterogeneity maximizes the utility of existing methods within a single trial. We propose a modification to the time-to-event continual reassessment method to accommodate two groups using a two-parameter model and maximum likelihood estimation. The operating characteristics of the design are investigated through simulations under different scenarios including the scenario where one conducts two separate trials, one for each group, using the one-sample time-to-event continual reassessment method.

Randomized, Double-Blind, Dose-Finding Study for Tiotropium when Added to Olodaterol, Administered via the Respimat® Inhaler in Patients with Chronic Obstructive Pulmonary Disease

Introduction

Combining long-acting muscarinic antagonists (LAMAs) and long-acting β₂-agonists (LABAs) is beneficial in chronic obstructive pulmonary disease (COPD), as the two classes of bronchodilator have complementary modes of action. The optimal dose for the fixed-dose combination of the LAMA tiotropium and the LABA olodaterol needed to be determined. In this phase II trial, the dose response of tiotropium on top of olodaterol was investigated in a free-dose combination, while other phase II studies have explored different doses of olodaterol on top of tiotropium, with both drugs delivered using the Respimat^® inhaler.

Methods

This was a double-blind incomplete crossover trial in which 233 patients with moderate or severe COPD were randomized to receive four out of eight free-dose combinations of olodaterol (5 or 10 µg) and tiotropium (1.25, 2.5, or 5 µg) or placebo for 4 weeks each. Primary end point was trough forced expiratory volume in 1 s (FEV₁) change from baseline (response) after 4 weeks.

Results

Addition of tiotropium 1.25, 2.5, and 5 µg to olodaterol 5 µg increased mean trough FEV₁ response by 0.054, 0.065, and 0.084 L, respectively; addition of tiotropium 1.25, 2.5, and 5 µg to olodaterol 10 µg increased mean trough FEV₁ response by 0.051, 0.083, and 0.080 L, respectively. All treatments were well tolerated and incidence of adverse events was similar with all treatments.

Conclusions

Overall, a dose response for tiotropium on top of both doses of olodaterol was observed, with increasing improvements in trough FEV₁ compared to olodaterol alone as the tiotropium dose was increased.

Funding

Boehringer Ingelheim.

Trial registration: ClinicalTrials.gov number, NCT01040403.

Electronic supplementary material

The online version of this article (doi:10.1007/s12325-015-0239-8) contains supplementary material, which is available to authorized users.

Phase I study of iniparib concurrent with monthly or continuous temozolomide dosing schedules in patients with newly diagnosed malignant gliomas

Iniparib is a prodrug that converts to highly reactive cytotoxic metabolites intracellularly with activity in preclinical glioma models. We investigated the maximum tolerated dose (MTD) of iniparib with monthly (m) and continuous (c) temozolomide (TMZ) dosing schedules in patients with malignant gliomas (MG). Adults with newly diagnosed MG who had successfully completed ≥80% of radiation (RT) and TMZ without toxicity received mTMZ dosing (150-200 mg/m(2) days 1-5/28 days) or cTMZ dosing (75 mg/m(2)/days × 6 weeks) in conjunction with iniparib (i.v. 2 days/week) in the adjuvant setting. Iniparib was dose escalated using a modified continual reassessment method (mCRM). 43 patients (32 male; 34 GBM, 8 AA, 1 gliosarcoma; median age 54 years; median KPS 90) were enrolled across 4 dose levels. In the mTMZ group, 2/4 patients had dose limiting toxicities (DLT) at 19 mg/kg/week (rash/hypersensitivity). At 17.2 mg/kg/week, 1/9 patients had a DLT (grade 3 fatigue). Additional grade 3 toxicities were neutropenia, lymphopenia, and nausea. In the cTMZ group, one DLT (thromboembolic event) occurred at 10.2 mg/kg/week. Dose escalation stopped at 16 mg/kg/week based on mCRM. The mean maximum plasma concentration of iniparib increased with dose. Concentration of the two major circulating metabolites, 4-iodo-3-aminobenzamide and 4-iodo-3-aminobenzoic acid, was ≤5% of the corresponding iniparib concentration. Iniparib is well tolerated, at doses higher than previously investigated, in combination with TMZ after completion of RT + TMZ in patients with MG. Recommended phase 2 dosing of iniparib based on mCRM is 17.2 mg/kg/week with mTMZ and 16 mg/kg/week with cTMZ. An efficacy study of TMZ/RT + iniparib followed by TMZ + iniparib in newly diagnosed GBM using these doses has completed enrollment. Survival assessment is ongoing.

Multivariate Markov models for the conditional probability of toxicity in phase II trials

In addition to getting a preliminary assessment of efficacy, phase II trials can also help to determine dose(s) that have an acceptable toxicity profile over repeated cycles as well as identify subgroups with particularly poor toxicity profiles. Correct modeling of the dose-toxicity relationship in patients receiving multiple cycles of the same dose in oncology trials is crucial. A major challenge lies in taking advantage of the conditional nature of data collection, that is each cycle is observed conditional on having no previous toxicities on earlier cycles. We develop a novel and parsimonious model for the probability of toxicity during a kth cycle of therapy, conditional on not seeing toxicity in any of the k-1 previous cycles using a Markov model, hereafter we refer to these probabilities as conditional probabilities of toxicity. Our model allows the conditional probability of toxicity to depend on randomized dose group, cumulative dose from prior cycles, a measure of how consistently a patient responds to the same dose exposure and individual risk factors influencing the ability to tolerate the treatment regimen. Simulations studying finite sample properties of the model are given. Finally, the approach is demonstrated in a phase II trial studying two dose levels of ifosfamide plus doxorubicin and granulocyte colony-stimulating factor in soft tissue sarcoma patients over four cycles. The Markov model provides correct estimates of the probabilities of toxicity in finite sample simulations. It also correctly models the data from the phase II clinical trial, and identifies particularly high cumulative toxicity in females.

Bayesian optimal interval design for dose finding in drug-combination trials

Interval designs have recently attracted enormous attention due to their simplicity and desirable properties. We develop a Bayesian optimal interval design for dose finding in drug-combination trials. To determine the next dose combination based on the cumulative data, we propose an allocation rule by maximizing the posterior probability that the toxicity rate of the next dose falls inside a prespecified probability interval. The entire dose-finding procedure is nonparametric (model-free), which is thus robust and also does not require the typical "nonparametric" prephase used in model-based designs for drug-combination trials. The proposed two-dimensional interval design enjoys convergence properties for large samples. We conduct simulation studies to demonstrate the finite-sample performance of the proposed method under various scenarios and further make a modication to estimate toxicity contours by parallel dose-finding paths. Simulation results show that on average the performance of the proposed design is comparable with model-based designs, but it is much easier to implement.

Seamless Phase I/II Adaptive Design for Oncology Trials of Molecularly Targeted Agents

In dose-finding trials of chemotherapeutic agents, the goal of identifying the maximum tolerated dose is usually determined by considering information on toxicity only, with the assumption that the highest safe dose also provides the most promising outlook for efficacy. Trials of molecularly targeted agents challenge accepted dose-finding methods because minimal toxicity may arise over all doses under consideration and higher doses may not result in greater response. In this article, we propose a new early-phase method for trials investigating targeted agents. We provide simulation results illustrating the operating characteristics of our design.

A hybrid method to estimate the minimum effective dose for monotone and non-monotone dose-response relationships

This article proposes a new multiple-testing approach for estimation of the minimum effective dose allowing for non-monotonous dose-response shapes. The presented approach combines the advantages of two commonly used methods. It is shown that the new approach controls the error rate of underestimating the true minimum effective dose. Monte Carlo simulations indicate that the proposed method outperforms alternative methods in many cases and is only marginally worse in the remaining situations.

A Bayesian Dose-finding Design for Oncology Clinical Trials of Combinational Biological Agents

Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which efficacy and toxicity monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a dose-finding design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships.

pocrm: an R-package for phase I trials of combinations of agents

This paper presents the R package pocrm for implementing and simulating the partial order continual reassessment method (PO-CRM; [1,2]) in Phase I trials of combinations of agents. The aim of this article is to illustrate, through examples of the pocrm package, how the PO-CRM works and how its operating characteristics can inform clinical trial investigators. This should promote the use of the PO-CRM in designing and conducting dose-finding Phase I trials of combinations of agents.

Up-and-down designs for phase I clinical trials

Various up-and-down designs have been proposed to improve the operating characteristics of the traditional "3 + 3" design, but they have been of limited use in practice. A major impediment to the adoption of the improved up-and-down designs is a lack of general guidance and a comprehensive assessment of the operating characteristics of these designs under practical clinical settings. To fill this gap, we review six up-and-down designs: the "3 + 3" design, accelerated titration design, biased coin design, k-in-a-row design, group up-and-down design and cumulative group up-and-down design. We conduct comprehensive simulation studies to evaluate their operating characteristics under various practical settings, and compare their performance to a theoretical optimal bound of nonparametric designs. The results show that the cumulative group up-and-down design has the best overall performance in terms of selecting the maximum tolerated dose (MTD), assigning patients to the MTD and patient safety. Its performance is generally close to the upper bound of nonparametric designs, but improvement seems possible in some cases.