scholarly journals A web application for evaluating Phase I methods using a non-parametric optimal benchmark

2017 ◽  
Vol 14 (5) ◽  
pp. 553-557 ◽  
Author(s):  
Nolan A Wages ◽  
Nikole Varhegyi
Keyword(s):  
Phase I ◽  
Web Application ◽  
Evaluation Process ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Correct Selection ◽  
Evaluation Tool ◽  
Operating Characteristics ◽  
Dose Selection ◽  
Non Parametric

Background/aims: In evaluating the performance of Phase I dose-finding designs, simulation studies are typically conducted to assess how often a method correctly selects the true maximum tolerated dose under a set of assumed dose–toxicity curves. A necessary component of the evaluation process is to have some concept for how well a design can possibly perform. The notion of an upper bound on the accuracy of maximum tolerated dose selection is often omitted from the simulation study, and the aim of this work is to provide researchers with accessible software to quickly evaluate the operating characteristics of Phase I methods using a benchmark. Methods: The non-parametric optimal benchmark is a useful theoretical tool for simulations that can serve as an upper limit for the accuracy of maximum tolerated dose identification based on a binary toxicity endpoint. It offers researchers a sense of the plausibility of a Phase I method’s operating characteristics in simulation. We have developed an R shiny web application for simulating the benchmark. Results: The web application has the ability to quickly provide simulation results for the benchmark and requires no programming knowledge. The application is free to access and use on any device with an Internet browser. Conclusion: The application provides the percentage of correct selection of the maximum tolerated dose and an accuracy index, operating characteristics typically used in evaluating the accuracy of dose-finding designs. We hope this software will facilitate the use of the non-parametric optimal benchmark as an evaluation tool in dose-finding simulation.

An adaptive design for the identification of the optimal dose using joint modeling of continuous repeated biomarker measurements and time-to-toxicity in phase I/II clinical trials in oncology

2019 ◽  
Vol 29 (2) ◽  
pp. 508-521 ◽  
Author(s):  
Maria-Athina Altzerinakou ◽  
Xavier Paoletti
Keyword(s):  
Phase I ◽  
Adaptive Design ◽  
Random Effect ◽  
Joint Modeling ◽  
Maximum Tolerated Dose ◽  
Small Sample ◽  
Dose Finding ◽  
Operating Characteristics ◽  
Activity Measurements ◽  
Dose Limiting Toxicity

We present a new adaptive dose-finding method, based on a joint modeling of longitudinal continuous biomarker activity measurements and time to first dose limiting toxicity, with a shared random effect. Estimation relies on likelihood that does not require approximation, an important property in the context of small sample sizes, typical of phase I/II trials. We address the important case of missing at random data that stem from unacceptable toxicity, lack of activity and rapid deterioration of phase I patients. The objective is to determine the lowest dose within a range of highly active doses, under the constraint of not exceeding the maximum tolerated dose. The maximum tolerated dose is associated to some cumulative risk of dose limiting toxicity over a predefined number of treatment cycles. Operating characteristics are explored via simulations in various scenarios.

scholarly journals Consequences of Performing Parallel Dose Finding Trials in Heterogeneous Groups of Patients

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Bethany Jablonski Horton ◽  
John O'Quigley ◽  
Mark R Conaway
Keyword(s):  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Operating Characteristics ◽  
Dose Selection ◽  
Partially Ordered ◽  
Optimal Interval ◽  
Multiple Group ◽  
Ordered Groups ◽  
Toxicity Risk ◽  
Dose Recommendations

Abstract Patient heterogeneity, in which patients can be grouped by risk of toxicity, is a design challenge in early phase dose finding trials. Carrying out independent trials for each group is a readily available approach for dose finding. However, this often leads to dose recommendations that violate the known order of toxicity risk by group, or reversals in dose recommendation. In this manuscript, trials for partially ordered groups are simulated using four approaches: independent parallel trials using the continual reassessment method (CRM), Bayesian optimal interval design, and 3 + 3 methods, as well as CRM for partially ordered groups. Multiple group order structures are considered, allowing for varying amounts of group frailty order information. These simulations find that parallel trials in the presence of partially ordered groups display a high frequency of trials resulting in reversals. Reversals occur when dose recommendations do not follow known order of toxicity risk by group, such as recommending a higher dose level in a group of patients known to have a higher risk of toxicity. CRM for partially ordered groups eliminates the issue of reversals, and simulation results indicate improved frequency of maximum tolerated dose selection as well as treating a greater proportion of trial patients at this dose compared with parallel trials. When information is available on differences in toxicity risk by patient subgroup, methods designed to account for known group ordering should be considered to avoid reversals in dose recommendations and improve operating characteristics.

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

2020 ◽  
pp. 1393-1402
Author(s):  
Ruitao Lin ◽  
Yanhong Zhou ◽  
Fangrong Yan ◽  
Daniel Li ◽  
Ying Yuan
Keyword(s):  
Phase I ◽  
Trial Design ◽  
Checkpoint Inhibitors ◽  
Optimal Dose ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Operating Characteristics ◽  
T Cell Therapy ◽  
Trade Off ◽  
Optimal Interval

PURPOSE For immunotherapy, such as checkpoint inhibitors and chimeric antigen receptor T-cell therapy, where the efficacy does not necessarily increase with the dose, the maximum tolerated dose may not be the optimal dose for treating patients. For these novel therapies, the objective of dose-finding trials is to identify the optimal biologic dose (OBD) that optimizes patients’ risk-benefit trade-off. METHODS We propose a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit trade-off. The BOIN12 design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocates patients to the dose that optimizes the toxicity-efficacy trade-off. We performed simulation studies to evaluate the performance of the BOIN12 design. RESULTS Compared with existing phase I/II dose-finding designs, the BOIN12 design is simpler to implement, has higher accuracy to identify the OBD, and allocates more patients to the OBD. One of the most appealing features of the BOIN12 design is that its adaptation rule can be pretabulated and included in the protocol. During the trial conduct, clinicians can simply look up the decision table to allocate patients to a dose without complicated computation. CONCLUSION The BOIN12 design is simple to implement and yields desirable operating characteristics. It overcomes the computational and implementation complexity that plagues existing Bayesian phase I/II dose-finding designs and provides a useful design to optimize the dose of immunotherapy and targeted therapy. User-friendly software is freely available to facilitate the application of the BOIN12 design.

scholarly journals Phase I/II dose-finding design for molecularly targeted agent: Plateau determination using adaptive randomization

2016 ◽  
Vol 27 (2) ◽  
pp. 466-479 ◽  
Author(s):  
Marie-Karelle Riviere ◽  
Ying Yuan ◽  
Jacques-Henri Jourdan ◽  
Frédéric Dubois ◽  
Sarah Zohar
Keyword(s):  
Phase I ◽  
Late Onset ◽  
Optimal Dose ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Targeted Agents ◽  
Weighted Likelihood ◽  
Operating Characteristics ◽  
Phase I Clinical Trials ◽  
Molecularly Targeted Agents

Conventionally, phase I dose-finding trials aim to determine the maximum tolerated dose of a new drug under the assumption that both toxicity and efficacy monotonically increase with the dose. This paradigm, however, is not suitable for some molecularly targeted agents, such as monoclonal antibodies, for which efficacy often increases initially with the dose and then plateaus. For molecularly targeted agents, the goal is to find the optimal dose, defined as the lowest safe dose that achieves the highest efficacy. We develop a Bayesian phase I/II dose-finding design to find the optimal dose. We employ a logistic model with a plateau parameter to capture the increasing-then-plateau feature of the dose–efficacy relationship. We take the weighted likelihood approach to accommodate for the case where efficacy is possibly late-onset. Based on observed data, we continuously update the posterior estimates of toxicity and efficacy probabilities and adaptively assign patients to the optimal dose. The simulation studies show that the proposed design has good operating characteristics. This method is going to be applied in more than two phase I clinical trials as no other method is available for this specific setting. We also provide an R package dfmta that can be downloaded from CRAN website.

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 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.

Sequential or combined designs for Phase I/II clinical trials? A simulation study

2019 ◽  
Vol 16 (6) ◽  
pp. 635-644 ◽  
Author(s):  
Caroline Rossoni ◽  
Aurélie Bardet ◽  
Birgit Geoerger ◽  
Xavier Paoletti
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Phase Ii ◽  
Dose Escalation ◽  
Therapeutic Index ◽  
Maximum Tolerated Dose ◽  
Correct Decision ◽  
Operating Characteristics ◽  
In Series ◽  
Expansion Cohort

Background: Phase I and Phase II clinical trials aim at identifying a dose that is safe and active. Both phases are increasingly combined. For Phase I/II trials, two main types of designs are debated: a dose-escalation stage to select the maximum tolerated dose, followed by an expansion cohort to investigate its activity (dose-escalation followed by an expansion cohort), or a joint modelling to identify the best trade-off between toxicity and activity (efficacy–toxicity). We explore this question in the context of a paediatric Phase I/II platform trial. Methods: In series of simulations, we assessed the operating characteristics of dose-escalation followed by an expansion cohort (DE-EC) designs without and with reassessment of the maximum tolerated dose during the expansion cohort (DE-ECext) and of the efficacy–toxicity (EffTox) design. We investigated the probability to identify an active and tolerable agent, that is, the percentage of correct decision, for various dose-toxicity activity scenarios. Results: For a large therapeutic index, the percentage of correct decision reached 96.0% for efficacy–toxicity versus 76.1% for dose-escalation followed by an expansion cohort versus 79.6% for DE-ECext. Conversely, when all doses were deemed not active, the percentage of correct decision was 47% versus 55.9% versus 69.2%, respectively, for efficacy–toxicity, dose-escalation followed by an expansion cohort and DE-ECext. Finally, in the case of a narrow therapeutic index, the percentage of correct decision was 48.0% versus 64.3% versus 67.2%, respectively, efficacy–toxicity, dose-escalation followed by an expansion cohort and DE-ECext. Conclusion: As narrow indexes are common in oncology, according to the present results, the sequential dose-escalation followed by an expansion cohort is recommended. The importance to re-estimate the maximum tolerated dose during the expansion cohort is confirmed. However, despite their theoretical advantages, Phase I/II designs are challenged by the variations in populations between the Phase I and the Phase II parts and by the lagtime in the evaluation of toxicity and activity.

Innovative Phase I/II Statistical Design in Combination Chemotherapy That Combines Toxicity and Efficacy Parameters to Increase Study Efficiency: Bendamustine and Idarubicin in De Novo Adult AML

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 1552-1552
Author(s):  
Jack M. Lionberger ◽  
Kathleen Shannon Dorcy ◽  
Carol Dean ◽  
Nathan Holm ◽  
Bart Lee Scott ◽  
...  
Keyword(s):  
Phase I ◽  
Phase Ii ◽  
De Novo ◽  
Statistical Design ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Therapeutic Decisions ◽  
Number Of Patients ◽  
Relevant Dose ◽  
Acceptable Probability

Abstract Abstract 1552 Background: Novel drugs or drug combinations are conventionally tested first in Phase I studies (in which therapeutic decisions are based solely on toxicity) with Phase II (efficacy) evaluations following as a separate trial. This process not only slows new drug development, it is challenging for patients during the informed consent process, because they usually enter trials not merely in hope of “no toxicity” but in hope of response. Response rates in Phase I at doses less than the maximum tolerated dose (MTD) may be irrelevant to efficacy, but this common assumption remains unproven. An equally plausible alternative is efficacy failure at these lower doses augurs failure at the MTD in Phase II. This hypothesis prompted development of a Phase I-II Bayesian design that uses both efficacy and toxicity to find a clinically relevant dose (Biometrics 2004;60: 684–93). In the current study, we apply the innovative Bayesian approach to the design of a Phase I-II trial using bendamustine + idarubicin in older patients (>50 yo) with newly-diagnosed AML or high risk MDS (>10% marrow blasts). We then compare and contrast our trial operation with that of the standard 3+3 Phase I design. Methods: The design specifies anticipated probabilities (“priors”) of response (CR or no CR) and toxicity (grade 3–4 or not) at each of 4 doses of bendamustine (45,60,75,90 mg/m2 daily × 5 together with idarubicin 12 mg/m2 daily on day 1 and 2). Patients are entered in groups of 3 beginning at the 45 mg/m2 dose. As response/toxicity data became available for each cohort, Bayes theorem is used to update the priors and derive current probabilities (“posteriors”) of response/toxicity at each dose. The priors are set to be relatively non-informative allowing the posteriors to be primarily influenced by the data from the trial. The posteriors are referred to a minimum acceptable probability of response (here 40%) and a maximum acceptable probability of toxicity (30%). If the posteriors indicate that it is highly unlikely (< 2% chance) that any dose is associated with both of these probabilities the trial stops. Otherwise the next cohort of patients is treated at a dose so associated. This process is repeated iteratively to a maximum sample size of 48 patients. The parameters noted above were chosen to give desirable probabilities of selecting for future study doses meeting the minimum acceptable response and maximum acceptable toxicity rates. Results: Table 1 compares the operation of this trial with a standard 3+3 Phase I trial. Given that 2/3 patients had toxicity at the 75 dose, a Phase I 3+3 design would have declared 60 the MTD. Subsequently, an “expansion cohort” as a Phase II trial would be treated at this dose without any possibility of revisiting the 75 dose. This conclusion flies in the face of basic notions of statistical reliability and ignores the possibility that patients experiencing toxicity may have been particularly old, had significant comorbidities, or have a variable functional reserve for undefined reasons. In contrast, the Phase I-II design allows the trial to continue, and potentially revisit higher doses of therapy depending on the collective outcome of a greater number of patients. Based on our actual data, this trial continued to treat patients at the 60 mg/m2 dose level, and in the next three patients there was no toxicity. In this case response data becomes the determining factor, which improves the efficiency of the trial. If 0/3 patients had a response, the trial would return to 75 mg/m2, however, because 2/3 patients had a response, the trial continues to accrue at 60mg/m2, with the statistical force of twice the number of patients. Conclusion: Accounting for response during dose finding seems to permit more sophisticated/flexible decisions about dosing in addition to improving efficiency. Disclosures: Shannon Dorcy: Cephalon: Consultancy, Honoraria, Speakers Bureau.

Pharmacodynamic-guided, modified continuous reassessment method (mCRM)-based, dose finding study of rapamycin in adult patients with solid tumors

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 3020-3020 ◽  
Author(s):  
A. Jimeno ◽  
P. Kulesza ◽  
G. Cusatis ◽  
A. Howard ◽  
Y. Khan ◽  
...  
Keyword(s):  
Phase I ◽  
Solid Tumors ◽  
Dose Level ◽  
Dose Escalation ◽  
Dose Finding ◽  
Phase I Trials ◽  
Dose Selection ◽  
Normal Tissues ◽  
Tumor Biopsies ◽  
Dose Levels

3020 Background: Pharmacodynamic (PD) studies, using either surrogate or tumor tissues, are frequently incorporated in Phase I trials. However, it has been less common to base dose selection, the primary endpoint in Phase I trials, in PD effects. We conducted a PD-based dose selection study with rapamycin (Rap). Methods: We used the modified continuous reassessment method (mCRM), a computer-based dose escalation algorithm, and adapted the logit function from its classic toxicity-based input data to a PD-based input. We coupled this design to a Phase I trial of Rap with 2 parts: a dose estimation phase where PD endpoints are measured in normal tissues and a confirmation phase where tumor tissue is assessed. Patients (pts) had solid tumors refractory to standard therapy. Rap was given starting at 2 mg/day continuously in 3-pt cohorts. The PD endpoint was pP70S6K in skin and tumor. Biopsies were done on days 0 and 28 of cycle 1, and a PD effect was defined as ≥ 80% inhibition from baseline. The first 2 dose levels (2 and 3 mgs) were evaluated before implementing the mCRM. The data was then fed to the computer that based on the PD effect calculated the next dose level. The mCRM was set so escalation continued until a dose level elicited a PD effect and the mCRM assigned the same dose to 8 consecutive pts, at which point the effect of that dose will be confirmed in tumor biopsies. Other correlates were PET-CT and pharmacokinetics. Results: Ten pts were enrolled at doses of 2 mg (n = 4), 3 mg (n = 3) and 6 mg (n = 3). Toxicity was anemia (4 G1, 1 G2), leucopenia (1 G1, 2 G2), low ANC (2 G2), hyperglycemia (2 G1, 1 G2), hyperlipidemia (4 G1), and mucositis (1 G1, 1 G2). PD responses were seen in 2 and 1 pt at 2 and 3 mg dose levels. Input of data to the mCRM selected a dose of 6 mg for the third cohort, where PD effect was seen in 1 pt, and thus a fourth dose around 9 mg will be tested. No responses by RECIST occurred, but 2 pts had a response by PET. The PK was consistent with prior data (t1/2 24.6 ± 10.2 h, CL 31.4 ± 12.0 L/h, vol of distribution 235 ± 65 L), and exposure increased with dose. Steady-state concentration were in the 5–20 nM range. Conclusions: mCRM-based dose escalation based on real-time PD assessment is feasible and permits the exploitation of PD effects for dose selection in a rational manner. No significant financial relationships to disclose.

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