scholarly journals On the coherence of model-based dose-finding designs for drug combination trials

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242561
Author(s):  
Yeonhee Park ◽  
Suyu Liu
Keyword(s):  
Phase I ◽  
Drug Combination ◽  
Treated Patient ◽  
Single Agent ◽  
Sufficient Conditions ◽  
Dose Finding ◽  
Phase I Clinical Trials ◽  
Model Based ◽  
Dose Combination ◽  
Theoretical Results

The concept of coherence was proposed for single-agent phase I clinical trials to describe the property that a design never escalates the dose when the most recently treated patient has toxicity and never de-escalates the dose when the most recently treated patient has no toxicity. It provides a useful theoretical tool for investigating the properties of phase I trial designs. In this paper, we generalize the concept of coherence to drug combination trials, which are substantially different and more challenging than single-agent trials. For example, in the dose-combination matrix, each dose has up to 8 neighboring doses as candidates for dose escalation and de-escalation, and the toxicity orders of these doses are only partially known. We derive sufficient conditions for a model-based drug combination trial design to be coherent. Our results are more general and relaxed than the existing results and are applicable to both single-agent and drug combination trials. We illustrate the application of our theoretical results with a number of drug combination dose-finding designs in the literature.

scholarly journals A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations in the Presence of a Baseline Covariate

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Márcio Augusto Diniz ◽  
Sungjin Kim ◽  
Mourad Tighiouart
Keyword(s):  
Phase I ◽  
Adaptive Design ◽  
Cytotoxic Agents ◽  
Dose Finding ◽  
Model Parameters ◽  
Baseline Characteristic ◽  
Phase I Clinical Trials ◽  
Bayesian Adaptive Design ◽  
Dose Combination ◽  
Toxicity Relationship

A Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility is described. The estimation of the maximum tolerated dose (MTD) curve is a function of a baseline covariate using two cytotoxic agents. A logistic model is used to describe the relationship between the doses, baseline covariate, and the probability of dose limiting toxicity (DLT). Trial design proceeds by treating cohorts of two patients simultaneously using escalation with overdose control (EWOC), where at each stage of the trial, the next dose combination corresponds to the α quantile of the current posterior distribution of the MTD of one of two agents at the current dose of the other agent and the next patient’s baseline covariate value. The MTD curves are estimated as function of Bayes estimates of the model parameters at the end of trial. Average DLT, pointwise average bias, and percent of dose recommendation at dose combination neighborhoods around the true MTD are compared between the design that uses the covariate and the one that ignores the baseline characteristic. We also examine the performance of the approach under model misspecifications for the true dose-toxicity relationship. The methodology is further illustrated in the case of a prespecified discrete set of dose combinations.

scholarly journals The i3+3 design for phase I clinical trials.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3066-3066
Author(s):  
Yuan Ji ◽  
Meizi Liu
Keyword(s):  
Phase I ◽  
Dose Finding ◽  
Operating Characteristics ◽  
Phase I Clinical Trials ◽  
Rule Based ◽  
Model Based ◽  
Simple Rules ◽  
R Shiny ◽  
Statistical Designs ◽  
Immune Oncology

3066 Background: Other than the 3+3 design, new model-based statistical designs like the mTPI design (Ji and Wang, 2013, JCO) are alternative choices for oncology dose-finding trials, including immune oncology dose-finding trials (Atkins et al., 2018, Lancet Oncology). One major criticism of the 3+3 design is that it is based on simple rules, does not depend on statistical models for inference, and leads to unsafe and unreliable operating characteristics. However, the rule-based nature allows 3+3 to be easily understood and implemented in practice, making it practically attractive and friendly. Can friendly rule-based designs achieve great performance seen in model-based designs? For four decades, the answer has been NO. Methods: We propose a new rule-based design called i3+3, where the letter "i" represents the word "interval". The i3+3 design is based on simple but more clever rules that account for the variabilities in the observed data. In short, the i3+3 design simply asks clinicians to compare observed toxicity rates with a prespecified toxicity interval, and make dose escalation decisions according to three simple rules. No sophisticated modeling is needed and the entire design is transparent to clinicians. Results: We compare the operating characteristics for the proposed i3+3 design with other popular phase I designs by simulation. The i3+3 design is far superior than the 3+3 design in trial safety and the ability to identify the true MTD. Compared with model-based phase I designs, i3+3 also demonstrates comparable performances. In other words, the i3+3 design possesses both simplicity and transparency of the rule-based approaches, and the superior operating characteristics seen in model-based approaches. An online R Shiny tool is provided to illustrate the i3+3 design, although in practice it requires no software to design or conduct a dose-finding trial using the design. Conclusions: The i3+3 design could be a practice-altering method for the clinical community. It may increase the safety and efficiency of dose finding trials.

Investigating the disparate enrollment of older adults on phase I clinical trials: Evolving participation patterns of patients 65 years and older w advanced cancer on phase I trials.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 12044-12044
Author(s):  
Ishwaria Mohan Subbiah ◽  
Aman Buzdar ◽  
Ecaterina Elena Ileana Dumbrava ◽  
Siqing Fu ◽  
Filip Janku ◽  
...  
Keyword(s):  
Older Adults ◽  
Clinical Trials ◽  
Phase I ◽  
Oldest Old ◽  
Phase 1 ◽  
Single Agent ◽  
Primary Objective ◽  
Dose Finding ◽  
Phase I Trials ◽  
Phase I Clinical Trials

12044 Background: While safety and dose-finding remain the primary objective of Phase 1 trials, the potential for clinical benefit has taken a greater meaning in the last decade with the novel therapies. With data from phase I trials being submitted for regulatory approval, the finer details of these studies are under even more scrutiny: in particular, do the trial participants reflect the general patient population for whom the drug may be indicated? To that end, we investigated age-based enrollment on phase I clinical trials over time. Methods: We queried a prospectively maintained database at a major phase I trials center to identify eligible patients and demographic + clinical variables including phase I trial characteristics, age at date of enrollment into 3 age-based cohorts: AYA ages 15-39y, mid-age 40-64y, older adults aged 65y+. We calculated descriptive statistics, and explored correlations (Pearson/Spearman) and associations (linear regression) between age and independent variables. Results: Over a 3-year period (1/1/17 to 12/31/19), we identified 6267 pts enrolled on 338 phase I trials. Median overall age 58.4y (range 15.5-95.1y). 729 (12%, median age 34.8y) were AYA, 3652 (58%, median age 55.4y) mid-age and 1886 (30%, median 70y) older adults, of whom 870 pts were aged 70-79y and 76 pts aged 80y+ (18 being >85y). There was no association b/w senior participation and year of enrollment (2017 31%, 2018 29%, 2019 30%, b/w age and type of therapy (i.e. targeted vs immunotherapy, etc.) or b/w age and # of drugs given on trial (single agent vs combo) (all p > 0.05). Conclusions: Older adults remain underrepresented on phase I trials esp. when compared to incidence of cancer in that age group (30% enrollment vs 60% incidence), a discordance more staggering in the oldest old pts (85y+; only 18 pts enrolled over 3 yrs when compared to 140,690 pts 85y+ w a new cancer dx in just 2019). Once enrolled, older adults received similar types of phase I therapies with comparable number of drugs as compared to middle age patients, i.e. older adults were just as likely to get immunotherapy or targeted therapy as well mono- vs combo therapy as mid-age pts. [Table: see text]

Bayesian optimization design for finding a maximum tolerated dose combination in phase I clinical trials

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ami Takahashi ◽  
Taiji Suzuki
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Optimization Design ◽  
Single Agent ◽  
Maximum Tolerated Dose ◽  
Partial Ordering ◽  
Bayesian Optimization ◽  
Correct Selection ◽  
Phase I Clinical Trials ◽  
Dose Combination

Abstract The development of combination therapies has become commonplace because potential synergistic benefits are expected for resistant patients of single-agent treatment. In phase I clinical trials, the underlying premise is toxicity increases monotonically with increasing dose levels. This assumption cannot be applied in drug combination trials, however, as there are complex drug–drug interactions. Although many parametric model-based designs have been developed, strong assumptions may be inappropriate owing to little information available about dose–toxicity relationships. No standard solution for finding a maximum tolerated dose combination has been established. With these considerations, we propose a Bayesian optimization design for identifying a single maximum tolerated dose combination. Our proposed design utilizing Bayesian optimization guides the next dose by a balance of information between exploration and exploitation on the nonparametrically estimated dose–toxicity function, thereby allowing us to reach a global optimum with fewer evaluations. We evaluate the proposed design by comparing it with a Bayesian optimal interval design and with the partial-ordering continual reassessment method. The simulation results suggest that the proposed design works well in terms of correct selection probabilities and dose allocations. The proposed design has high potential as a powerful tool for use in finding a maximum tolerated dose combination.

scholarly journals Keyboard: An R Package Suite for Early Phase Dose-finding Designs

2021 ◽  
Author(s):  
Hongying Sun ◽  
Chen Li ◽  
Cheng Cheng ◽  
Tang Li ◽  
Haitao Pan
Keyword(s):  
Phase I ◽  
Drug Combination ◽  
Single Agent ◽  
Optimal Dose ◽  
R Package ◽  
Maximum Tolerated Dose ◽  
Cytotoxic Agents ◽  
Dose Finding ◽  
Operating Characteristics ◽  
Background Phase

Abstract Background: Phase I and/or I/II oncology trials are conducted to find the maximum tolerated dose (MTD) and/or optimal biological dose (OBD) of a new drug or treatment. In these trials, for cytotoxic agents, the primary aim of the single-agent or drug-combination is to find the MTD with a certain target toxicity rate, while for the cytostatic agents, a more appropriate target is the OBD, which is often defined by consideration of toxicity and efficacy simultaneously. However, there still lacks accessible software packages to achieve both yet. Results: Objective of this work is to develop a software package that can provide tools for both MTD- and OBD-finding trials, which implements the Keyboard design for single-agent MTD-finding trials by Yan et al., the Keyboard design for drug-combination MTD-finding trials by Pan et al., and phase I/II OBD-finding method by Li et al., in a single R package, called Keyboard. For each of the designs, the Keyboard package provides corresponding functions that begins with get.boundary( . . . ) to determine the optimal dose escalation and de-escalation boundaries, that begins with select.mtd( . . . ) to select the MTD when the trial is completed, that begins with select.obd( . . . ) to select the OBD at the end of a trial, and that begins with get.oc( . . . ) to generate the operating characteristics. Conclusions: The developed Keyboard R package provides convenient tools for designing, conducting and analyzing single-agent, drug-combination and phase I/II dose-finding trials, which supports Bayesian designs of innovative dose-finding studies.

scholarly journals Revisiting isotonic phase I design in the era of model-assisted dose-finding

2018 ◽  
Vol 15 (5) ◽  
pp. 524-529 ◽  
Author(s):  
Nolan A. Wages ◽  
Mark R Conaway
Keyword(s):  
Phase I ◽  
Web Application ◽  
Current Model ◽  
Single Agent ◽  
Drug Combinations ◽  
Dose Finding ◽  
Operating Characteristics ◽  
Simulation Performance ◽  
Model Based ◽  
User Friendly

Background/aims In the conduct of phase I trials, the limited use of innovative model-based designs in practice has led to an introduction of a class of “model-assisted” designs with the aim of effectively balancing the trade-off between design simplicity and performance. Prior to the recent surge of these designs, methods that allocated patients to doses based on isotonic toxicity probability estimates were proposed. Like model-assisted methods, isotonic designs allow investigators to avoid difficulties associated with pre-trial parametric specifications of model-based designs. The aim of this work is to take a fresh look at an isotonic design in light of the current landscape of model-assisted methods. Methods The isotonic phase I method of Conaway, Dunbar, and Peddada was proposed in 2004 and has been regarded primarily as a design for dose-finding in drug combinations. It has largely been overlooked in the single-agent setting. Given its strong simulation performance in application to more complex dose-finding problems, such as drug combinations and patient heterogeneity, as well as the recent development of user-friendly software to accompany the method, we take a fresh look at this design and compare it to a current model-assisted method. We generated operating characteristics of the Conaway–Dunbar–Peddada method using a new web application developed for simulating and implementing the design and compared it to the recently proposed Keyboard design that is based on toxicity probability intervals. Results The Conaway–Dunbar–Peddada method has better performance in terms of accuracy of dose recommendation and safety in patient allocation in 17 of 20 scenarios considered. The Conaway–Dunbar–Peddada method also allocated fewer patients to doses above the maximum tolerated dose than the Keyboard method in many of scenarios studied. Overall, the performance of the Conaway–Dunbar–Peddada method is strong when compared to the Keyboard method, making it a viable simple alternative to the model-assisted methods developed in recent years. Conclusion The Conaway–Dunbar–Peddada method does not rely on the specification and fitting of a parametric model for the entire dose-toxicity curve to estimate toxicity probabilities as other model-based designs do. It relies on a similar set of pre-trial specifications to toxicity probability interval-based methods, yet unlike model-assisted methods, it is able to borrow information across all dose levels, increasing its efficiency. We hope this concise study of the Conaway–Dunbar–Peddada method, and the availability of user-friendly software, will augment its use in practice.

scholarly journals Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study

2021 ◽  
Vol 18 (1) ◽  
pp. 345
Author(s):  
Pavel Mozgunov ◽  
Rochelle Knight ◽  
Helen Barnett ◽  
Thomas Jaki
Keyword(s):  
Phase I ◽  
Interaction Parameter ◽  
Simulation Study ◽  
Small Sample Size ◽  
Small Sample ◽  
Dose Finding ◽  
Phase I Trials ◽  
Model Based ◽  
Combination Toxicity ◽  
Toxicity Relationship

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.

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