scholarly journals Envelope Simulation: A New Adaptive Method for Dose Optimization

2020 ◽  
Author(s):  
Steven Piantadosi ◽  
Guohai Zhou
Keyword(s):  
Clinical Trials ◽  
Dose Response ◽  
Human Subjects ◽  
Quantitative Measure ◽  
Adaptive Method ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Continual Reassessment ◽  
Anti Cancer ◽  
Adaptive Experimental Design

AbstractWe present a flexible and general adaptive experimental design for dose finding clinical trials. This method can be applied in sterotypical settings such as determining a maximum tolerated dose, when the dose response relationship is complex, or when the response is an arbitrary quantitative measure. Our design generalizes dose finding methods such as the continual reassessment method (CRM), modified CRM, and estimation with overdose control (EWOC). Similar to those, our design requires a working mathematical model, which assures efficiency, low bias, and a higher fraction of dose selected near the optimum compared to purely operational designs. Unlike typical model based designs that always employ the same model, our method allows individual dose response models tailored to the circumstance. Simulations are also integral to our design allowing it to account for both known and unknown effects of dose on outcome. This method is applicable to general dose finding problems such as those encountered with modern targeted anti-cancer agents, immunotherapies, and titrations against biomarker outcome measures. The method can also support improvements in the design of the experiment being conducted by providing a platform for concurrent simulations to assess the influence of projected design points. Although we present the design in the context of clinical trials, it is equally applicable to experiments with non-human subjects.

scholarly journals A comparison between the continual reassessment method and D-optimum design for dose finding in phase I clinical trials

2016 ◽  
Vol 53 (2) ◽  
pp. 69-82
Author(s):  
M. Iftakhar Alam
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Optimum Design ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Parameter Estimates ◽  
Phase I Clinical Trials ◽  
Continual Reassessment Method ◽  
Continual Reassessment ◽  
Asymptotic Variability

AbstractThe continual reassessment method is a model-based procedure, described in the literature, used to determine the maximum tolerated dose in phase I clinical trials. The maximum tolerated dose can also be found under the framework of D-optimum design, where information is gathered in such a way so that asymptotic variability in the parameter estimates in minimised. This paper investigates the two methods under some realistic settings to explore any potential differences between them. Simulation studies for six plausible dose-response scenarios show that D-optimum design can work well in comparison with the continual reassessment method in many cases. The D-optimum design is also found to allocate doses from the extremes of the design region to the patients in a trial.

scholarly journals A Bayesian dose-finding design for outcomes evaluated with uncertainty

2021 ◽  
pp. 174077452110015
Author(s):  
Matthew J Schipper ◽  
Ying Yuan ◽  
Jeremy MG Taylor ◽  
Randall K Ten Haken ◽  
Christina Tsien ◽  
...  
Keyword(s):  
Phase I ◽  
Phase I Trial ◽  
Data Augmentation ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Continual Reassessment Method ◽  
Number Of Patients ◽  
Continual Reassessment ◽  
Partially Observed ◽  
Dose Limiting Toxicity

Introduction: In some phase I trial settings, there is uncertainty in assessing whether a given patient meets the criteria for dose-limiting toxicity. Methods: We present a design which accommodates dose-limiting toxicity outcomes that are assessed with uncertainty for some patients. Our approach could be utilized in many available phase I trial designs, but we focus on the continual reassessment method due to its popularity. We assume that for some patients, instead of the usual binary dose-limiting toxicity outcome, we observe a physician-assessed probability of dose-limiting toxicity specific to a given patient. Data augmentation is used to estimate the posterior probabilities of dose-limiting toxicity at each dose level based on both the fully observed and partially observed patient outcomes. A simulation study is used to assess the performance of the design relative to using the continual reassessment method on the true dose-limiting toxicity outcomes (available in simulation setting only) and relative to simple thresholding approaches. Results: Among the designs utilizing the partially observed outcomes, our proposed design has the best overall performance in terms of probability of selecting correct maximum tolerated dose and number of patients treated at the maximum tolerated dose. Conclusion: Incorporating uncertainty in dose-limiting toxicity assessment can improve the performance of the continual reassessment method design.

scholarly journals A dynamic stopping rule for phase I clinical trials

2018 ◽  
Vol 55 (1) ◽  
pp. 17-30 ◽  
Author(s):  
M. Iftakhar Alam ◽  
Mohaimen Mansur
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Response ◽  
Parameter Estimate ◽  
Maximum Tolerated Dose ◽  
Stopping Rule ◽  
Bayesian Posterior Probability ◽  
Phase I Clinical Trials ◽  
Response Data ◽  
Number Of Patients

Summary This paper investigates a stopping rule to be utilised in phase I clinical trials. The motivation is to develop a dynamic rule so that a trial stops early if the maximum tolerated dose lies towards the beginning of a dose region. Also, it will employ many patients if the maximum tolerated dose lies towards the end of a dose region. A two-parameter logistic model is assumed for the dose-response data. A trial is stopped early before reaching the maximum number of patients when the width of the Bayesian posterior probability interval of the slope parameter meets a desired value. Instead of setting a pre-specified width to stop at, we determine it based on the parameter estimate obtained after a reasonable number of steps in a trial. Simulation studies of six plausible dose-response scenarios show that the proposed stopping rule is capable of limiting the number of patients to be recruited depending on the underlying scenario. Although the rule is applied to a D-optimum design here, it will be equally applicable to other model-based designs.

scholarly journals Would the Recommended Dose Have Been Different Using Novel Dose-Finding Designs? Comparing Dose-Finding Designs in Published Trials

2021 ◽  
pp. 1024-1034
Author(s):  
Rebecca B. Silva ◽  
Christina Yap ◽  
Richard Carvajal ◽  
Shing M. Lee
Keyword(s):  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Simulation Studies ◽  
Toxicity Data ◽  
Continual Reassessment Method ◽  
Optimal Interval ◽  
Patient Allocation ◽  
Continual Reassessment ◽  
Continuous Use ◽  
Dose Levels

PURPOSE Simulation studies have shown that novel designs such as the continual reassessment method and the Bayesian optimal interval (BOIN) design outperform the 3 + 3 design by recommending the maximum tolerated dose (MTD) more often, using less patients, and allotting more patients to the MTD. However, it is not clear whether these novel designs would have yielded different results in the context of real-world dose-finding trials. This is a commonly mentioned reason for the continuous use of 3 + 3 designs for oncology trials, with investigators considering simulation studies not sufficiently convincing to warrant the additional design complexity of novel designs. METHODS We randomly sampled 60 published dose-finding trials to obtain 22 that used the 3 + 3 design, identified an MTD, published toxicity data, and had more than two dose levels. We compared the published MTD with the estimated MTD using the continual reassessment method and BOIN using target toxicity rates of 25% and 30% and toxicity data from the trial. Moreover, we compared patient allocation and sample size assuming that these novel designs had been implemented. RESULTS Model-based designs chose dose levels higher than the published MTD in about 40% of the trials, with estimated and observed toxicity rates closer to the target toxicity rates of 25% and 30%. They also assigned less patients to suboptimal doses and permitted faster dose escalation. CONCLUSION This study using published dose-finding trials shows that novel designs would recommend different MTDs and confirms the advantages of these designs compared with the 3 + 3 design, which were demonstrated by simulation studies.

A prospective PGx and PK/PD dose-finding study of irinotecan based on UGT1A1*6 and *28 genotyping (UGT0601)

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e14560-e14560
Author(s):  
T. Esaki ◽  
T. Satoh ◽  
T. Ura ◽  
T. Tsujinaka ◽  
Y. Sasaki ◽  
...  
Keyword(s):  
Initial Dosage ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Hematological Toxicity ◽  
Continual Reassessment Method ◽  
Continual Reassessment ◽  
Finding Study ◽  
Dose Finding Study ◽  
Grade 3 ◽  
Dose Limiting Toxicity

e14560 Background: UGT1A1*6 as well as UGT1A1*28 polymorphisms is associated with decreased glucuronidation of SN-38, the active metabolite of irinotecan (CPT-11). Although the maximum tolerated dose (MTD) and the recommended dose (RD) in Hetero was determined 150 mg/m2 (approval dose in Japan), those of Homo were unknown. Methods: Pts received prior chemotherapies except for CPT-11 for metastatic gastrointestinal cancer were enrolled. UGT1A1 polymorphisms were categorized into Wild(*1/*1), Hetero(*1/*28, *1/*6), and Homo(*28/*28, *6/*6, *28/*6). CPT-11 was administered biweekly. Starting doses were 150 mg/m2 in Wild, 100 mg/m2 in Hetero, and 75 mg/m2 in Homo. DLT was defined as grade 4 hematological, or grade 3 non-hematological toxicity. MTD closest to dose-limiting toxicity (DLT) appearance of 30% was guided by the continual reassessment method in the cohort of Hetero and Homo. DLT and pharmacokinetic (PK) sampling was evaluated during the 1st cycle. Results: Eighty-two pts were enrolled from November 2006 to November 2008 (Wild, Hetero, Homo: 41, 20, and 21, respectively). The dose level reached at 150 mg/m2 in Homo. At 150 mg/m2, DLT was observed in six pts of Homo (grade 4 neutropenia, grade 3 diarrhea: 6 and 1, respectively). The probability of DLTs were 22.2% at 125 mg/m2, and 37.4% at 150 mg/m2. The MTD was determined 150 mg/m2 in pts with Homo group. However, the incidences of grade 3/4 neutropenia at 150 mg/m2 during the 1st cycle were 9.8% (4/41), 18.8% (3/16), and 62.5% (10/16) in Wild, Hetero, and Homo, respectively. And the second administration was delayed 7 days or more in most pts in Homo (63% at 150 mg/m2). In one pt of Homo for *28/*28 died of septic shock during the 2nd cycle. SN-38 AUC (0–24h, ng*hr/mL, median) was 239 in Wild, 237 in Hetero, and 410 in Homo. Pts with Homo showed the different trend of PK/PD compared to those with Wild and Hetero. Conclusions: The MTD was 150 mg/m2 in pts with Homo group and the most frequent DLT was grade 4 neutropenia. However, our findings suggest that 150 mg/m2 q2w is difficult to recommend and the initial dosage and administration should be considered carefully for pts with Homo. [Table: see text]

scholarly journals Adaptive Dose-Finding Studies: A Review of Model-Guided Phase I Clinical Trials

2014 ◽  
Vol 32 (23) ◽  
pp. 2505-2511 ◽  
Author(s):  
Alexia Iasonos ◽  
John O'Quigley
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Escalation ◽  
Late Onset ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Design Parameters ◽  
Phase I Trials ◽  
Phase I Clinical Trials ◽  
Trial Duration

Purpose We provide a comprehensive review of adaptive phase I clinical trials in oncology that used a statistical model to guide dose escalation to identify the maximum-tolerated dose (MTD). We describe the clinical setting, practical implications, and safety of such applications, with the aim of understanding how these designs work in practice. Methods We identified 53 phase I trials published between January 2003 and September 2013 that used the continual reassessment method (CRM), CRM using escalation with overdose control, or time-to-event CRM for late-onset toxicities. Study characteristics, design parameters, dose-limiting toxicity (DLT) definition, DLT rate, patient-dose allocation, overdose, underdose, sample size, and trial duration were abstracted from each study. In addition, we examined all studies in terms of safety, and we outlined the reasons why escalations occur and under what circumstances. Results On average, trials accrued 25 to 35 patients over a 2-year period and tested five dose levels. The average DLT rate was 18%, which is lower than in previous reports, whereas all levels above the MTD had an average DLT rate of 36%. On average, 39% of patients were treated at the MTD, and 74% were treated at either the MTD or an adjacent level (one level above or below). Conclusion This review of completed phase I studies confirms the safety and generalizability of model-guided, adaptive dose-escalation designs, and it provides an approach for using, interpreting, and understanding such designs to guide dose escalation in phase I trials.

Maximum Tolerated Dose of Nalmefene in Patients Receiving Epidural Fentanyl and Dilute Bupivacaine for Postoperative Analgesia

2000 ◽  
Vol 92 (4) ◽  
pp. 1010-1016 ◽  
Author(s):  
Thomas B. Dougherty ◽  
Vivian H. Porche ◽  
Peter F. Thall
Keyword(s):  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Epidural Fentanyl ◽  
Opioid Antagonist ◽  
Operating Characteristics ◽  
Continual Reassessment Method ◽  
Statistical Tool ◽  
Anesthetic Agents ◽  
Continual Reassessment ◽  
Postanesthetic Care Unit

Background This study investigated the ability of the modified continual reassessment method (MCRM) to determine the maximum tolerated dose of the opioid antagonist nalmefene, which does not reverse analgesia in an acceptable number of postoperative patients receiving epidural fentanyl in 0.075% bupivacaine. Methods In the postanesthetic care unit, patients received a single intravenous dose of 0.25, 0.50, 0.75, or 1.00 microg/kg nalmefene. Reversal of analgesia was defined as an increase in pain score of two or more integers above baseline on a visual analog scale from 0 through 10 after nalmefene administration. Patients were treated in cohorts of one, starting with the lowest dose. The maximum tolerated dose of nalmefene was defined as that dose, among the four studied, with a final mean probability of reversal of anesthesia (PROA) closest to 0.20 (ie., a 20% chance of causing reversal). The modified continual reassessment method is an iterative Bayesian statistical procedure that, in this study, selected the dose for each successive cohort as that having a mean PROA closest to the preselected target PROA of 0.20. Results The modified continual reassessment method repeatedly updated the PROA of each dose level as successive patients were observed for presence or absence of ROA. After 25 patients, the maximum tolerated dose of nalmefene was selected as 0.50 microg/kg (final mean PROA = 0.18). The 1.00-microg/kg dose was never tried because its projected PROA was far above 0.20. Conclusions The modified continual reassessment method facilitated determination of the maximum tolerated dose ofnalmefene . Operating characteristics of the modified continual reassessment method suggest it may be an effective statistical tool for dose-finding in trials of selected analgesic or anesthetic agents.

scholarly journals Adaptive dose-finding based on safety and feasibility in early-phase clinical trials of adoptive cell immunotherapy

2019 ◽  
Vol 17 (2) ◽  
pp. 157-165
Author(s):  
Nolan A Wages ◽  
Camilo E Fadul
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Safety Data ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Cell Immunotherapy ◽  
Adoptive Cell Immunotherapy ◽  
Early Phase Clinical Trials ◽  
Finding Method ◽  
Number Of Cells

Background/aims: Dose feasibility is a challenge that may arise in the development of adoptive T cell therapies for cancer. In early-phase clinical trials, dose is quantified either by a fixed or per unit body weight number of cells infused. It may not be feasible, however, to administer a patient’s assigned dose due to an insufficient number of cells harvested or functional heterogeneity of the product. The study objective becomes to identify the maximum tolerated dose with high feasibility of being administered. This article describes a new dose-finding method that adaptively accounts for safety and feasibility endpoints in guiding dose allocation. Methods: We propose an adaptive dose-finding method that integrates accumulating feasibility and safety data to select doses for participant cohorts in early-phase trials examining adoptive cell immunotherapy. We sequentially model the probability of dose-limiting toxicity and the probability of feasibility using independent beta-binomial models. The probability model for toxicity borrows information across all dose levels using isotonic regression, allowing participants infused at a lower dose than his or her planned dose to contribute safety data to the dose-finding algorithm. We applied the proposed methodology in a single simulated trial and evaluated its operating characteristics through extensive simulation studies. Results: In simulations conducted for a phase I study of adoptive immunotherapy for newly diagnosed glioblastoma, the proposed method demonstrates the ability to identify accurately the feasible maximum tolerated doses and to treat participants at and around these doses. Over 10 hypothesized scenarios studied, the percentage of correctly selecting the true feasible and maximum tolerated dose ranged from 50% to 90% with sample sizes averaging between 21 and 24 participants. A comparison to the only known existing method accounting for safety and feasibility yields competitive performance. Conclusion: We have developed a new practical adaptive dose-finding method to assess feasibility in early-phase adoptive cell therapy trials. A design that incorporates feasibility, as a function of the quantity and quality of the product manufactured, in addition to safety will have an impact on the recommended phase II doses in studies that evaluate patient outcomes.

Dynamic Programming for Response-Adaptive Dose-Finding Clinical Trials

2021 ◽  
Author(s):  
Amir Ali Nasrollahzadeh ◽  
Amin Khademi
Keyword(s):  
Clinical Trials ◽  
Dose Response ◽  
Operations Research ◽  
Response Curve ◽  
Adaptive Designs ◽  
Dose Response Curve ◽  
Dose Finding ◽  
Nonparametric Model ◽  
Bayesian Nonparametric ◽  
Patient Allocation

Identifying the right dose is one of the most important decisions in drug development. Adaptive designs are promoted to conduct dose-finding clinical trials as they are more efficient and ethical compared with static designs. However, current techniques in response-adaptive designs for dose allocation are complex and need significant computational effort, which is a major impediment for implementation in practice. This study proposes a Bayesian nonparametric framework for estimating the dose-response curve, which uses a piecewise linear approximation to the curve by consecutively connecting the expected mean response at each dose. Our extensive numerical results reveal that a first-order Bayesian nonparametric model with a known correlation structure in prior for the expected mean response performs competitively when compared with the standard approach and other more complex models in terms of several relevant metrics and enjoys computational efficiency. Furthermore, structural properties for the optimal learning problem, which seeks to minimize the variance of the target dose, are established under this simple model. Summary of Contribution: In this work, we propose a methodology to derive efficient patient allocation rules in response-adaptive dose-finding clinical trials, where computational issues are the main concern. We show that our methodologies are competitive with the state-of-the-art methodology in terms of solution quality, are significantly more computationally efficient, and are more robust in terms of the shape of the dose-response curve, among other parameter changes. This research fits in “the intersection of computing and operations research” as it adapts operations research techniques to produce computationally attractive solutions to patient allocation problems in dose-finding clinical trials.

scholarly journals Estimating Similarity of Dose–Response Relationships in Phase I Clinical Trials—Case Study in Bridging Data Package

2021 ◽  
Vol 18 (4) ◽  
pp. 1639
Author(s):  
Adrien Ollier ◽  
Sarah Zohar ◽  
Satoshi Morita ◽  
Moreno Ursino
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Response ◽  
Clinical Trial Data ◽  
Japanese Patients ◽  
Maximum Tolerated Dose ◽  
Phase I Clinical Trials ◽  
Response Curves ◽  
Statistical Tool ◽  
Two Populations

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

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