ADDPLAN DF: Designing More Accurate Dose Escalation Studies in Exploratory Drug Development. An ICON White Paper

Transcription

1 ADDPLAN DF: Designing More Accurate Dose Escalation Studies in Exploratory Drug Development An ICON White Paper

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3 Executive Summary The determination of the maximum tolerated dose (MTD) level is a critical step in oncology drug development. High doses are needed to improve the efficacy of the drug, but can result in an increased number and severity of undesired drug limiting toxic (DLT) reactions. Phase I oncology trials aim to determine the MTD using dose escalation strategies, resulting in the recommendation of a dose or drug combination for Phase II efficacy testing. The selection of the dose escalation strategy has an impact on the recommended dose level and inadequate dose escalation schemes may put the whole drug development program at risk. Oncology Phase I trials differ from most general Phase I trials in that the medication is examined in the target population of diseased patients. Patients included into these trials often suffer from advanced cancer, and have often exhausted the standard treatment options. Therefore in addition to the appropriate detection of the MTD, dose escalation designs should enroll the majority of patients to therapeutic levels of the drug, while controlling the numbers of DLT events. These competing objectives for dose escalation designs make it unlikely that one design will be the best in all circumstances. Although the determination of the MTD is of such critical importance in oncology, in the vast majority of dose escalation trials a simple common method has been used. The 3+3 design is a simple rule-based escalation scheme, and has been used in approximately 95% of the published Phase I oncology trials over the last two decades. 1 During the same period, a number of innovative rule-based and model-based dose escalation designs have been developed and studied, for example the Continual Reassessment Method (CRM) or interval-based escalation schemes. These innovative approaches allow tailored escalation towards the dose level providing the specified maximal tolerable rate of toxic events. innovative dose escalation designs in the fully validated software ADDPLAN DF for designing, simulating and analysing dose-finding trials. The methodology underpinning these innovative dose escalation approaches are described in this paper and their performance is illustrated in a simulation study. The overall scope is to provide insight into the capability of ADDPLAN DF to design, simulate and analyse dose escalation trials. Future versions of ADDPLAN DF will expand the set of available dose escalation methods, and will include approaches targeting efficacy and safety simultaneously, time to event data, and more. The Critical Importance of Accurate Dose Selection The process of defining the dose and dosing regimen has been identified by regulatory authorities and industry as a major factor impacting late-phase attrition. Improved dose selection in both Phase I and Phase II trials is generally considered to increase R&D efficiency and effectiveness. Many groups of methodologists in the pharmaceutical industry and in academia have developed innovative statistical methods for designing and analysing dose-finding trials. The acceptance of innovative dose-finding methodologies was supported this year by the European Medicines Agency (EMA) qualification opinion on the Multiple Comparison Procedure and Modeling (MCP-Mod) approach developed by Novartis for dose-finding under model-uncertainty. 3 The list of innovative design and analysis methods applied in drug development is increasing. Considerable effort is being spent on the development and implementation of innovative approaches for early phase drug development. Particular examples for these novel methodologies are toxicity interval-based dose escalation such as the Novartis version of the CRM 4 and the modified toxicity probability interval (mtpi) approach, which has been utilized by Merck in a number of Phase I oncology trials. 5 In addition to the novel MCP-Mod methodology, 2 ICON has implemented a selection of these 3

4 The inclusion of novel statistical approaches into the standard toolbox of design and analysis techniques will lead to improved decision making in drug development. The consequences of improved dose selection in early phase drug development can amount to billions of dollars of additional value across a product portfolio. Traditional Approach to Dose Escalation The MTD is defined as the maximum dose for which less than a given proportion of the population suffer a DLT. In oncology studies the proportion is often taken to be between 20% and 35%. Dose escalation designs approach this MTD via an adaptive allocation procedure, which either increases or decreases the dose-level for the next cohort of patients based on observed outcomes. As a limited measure of overdose control, many dose escalation algorithms adjust the dose levels to approach the MTD from below. 3+3 Design Approximately 95% of all published Phase I oncology dose escalation trials have used a 3+3 design. The 3+3 design (Figure 1) can be described as follows. Cohorts of 3 patients are entered at given dose level i. If no patients have a DLT, then the dose will be escalated to the next dose level, i+1. If 2 or more patients have a DLT then the previous dose level, i-1, will be considered as MTD. If 1 patient has a DLT an additional 3 patients will be treated at this dose level, i. If no further patients suffer a DLT then the dose level will be escalated to i+1 and if any further patients have a DLT then the previous dose level, i-1, will be considered the MTD. In other words, the MTD level will be the maximum dose level with an observed toxicity rate of 0% or 17%. Depending upon the maximum tolerable toxicity rate for the trial and the numbers of dose levels in the trial, the resulting estimate of the MTD may be much too conservative: the algorithm will reject doses with 20% DLT-rate in at least 29% of the cases. The probability of underestimating the MTD increases with increasing numbers of dose levels below the true but unknown MTD, while additional patients will be treated at sub-therapeutic dose-levels. The 3+3 design is used for its simplicity and the very limited number of patients which are needed to get a decision on the MTD. 4

5 Innovative Approaches to Dose Escalation The obvious limitations of the 3+3 design have led to numerous improved dose escalation approaches. Continual Reassessment Method (CRM) 6 An alternative class of dose escalation algorithms is given by the model-based approach known as the continual reassessment method (CRM). The basic procedure is described in the flow chart (Figure 2). is both the strength and weakness of CRM designs. Model-based analysis allows the utilisation of all available data for predicting the probability of toxic events at any dose level. Bayesian modelling allows the inclusion of uncertainty in the model-parameters into the analysis. However, the administration of a dose level to patients based on a set of modelling assumptions may result in doses that are too toxic, if the wrong dose-toxicity curve was selected or if inappropriate parameters were specied. Overdose control options may be adjusted to prevent the CRM from administering these doses. Bayesian Logistic Regression with Overdose Control (BLOC) 4 Overestimating the dose-toxicity curve may lead to suboptimal allocation of patients to sub-therapeutic doses, whereas underestimating the curve may recommend toxic doses. An alternative approach for the estimation of an appropriate dose was proposed by Neuenschwander (2008). The basic idea is very similar to the CRM approach. The toxicity data are analysed using Bayesian modelling on a exible two-parameter dose-toxicity model. However, instead of targeting the MTD rate, the authors recommend that the target dose is selected based on a classication of the drug-limiting toxicity probabilities into four regions: In the design phase of the CRM, a dose-toxicity model, stopping rules and overdose control options need to be dened. Given the toxicity data, a Bayesian dose-toxicity model is tted to the data and the dose with a posterior expected toxicity probability closest to the maximum tolerable toxicity rate is determined. is dose will be administered to the next cohort, unless a stopping rule is met or the dose level is not admissible due to overdose control options. The CRM design stops allocation, if a stopping criterion is met or if the maximum number of patients have been analysed in the trial. The dose with posterior expected toxicity rate closest to the maximum tolerable toxicity rate will be declared the MTD. The estimation is based on the assumed dose-response model and the toxicity data of all dose-levels. Under-dosing: (0,0.2] Target toxicity: (0.2,0.35] Excessive toxicity (0.35,0.6] Unacceptable toxicity (0.6,1.00] The posterior probabilities that the toxicity is located within each of the four toxicity regions can be calculated for each dose level based on Bayesian modelling. A dose may then be recommended based on the observed posterior distribution and some specied considerations on the risks and benets of the dose categories. This Bayesian logistic approach provides a mechanism to control both under- and over-dosing. The assumption of a Bayesian dose-toxicity model 5

6 Modified Toxicity Probability Interval (mtpi) Approach 7 A compromise between model-based and rule-based dose escalation methods is given by the modied toxicity probability interval approach (mtpi). Similar to Neuenschwander s BLOC approach, mtpi provides a recommendation to escalate, de-escalate or stay at the current dose level, based on the posterior probability of toxicity regions. The equivalence interval species a range of toxicity probabilities, close to the maximum tolerable toxicity, which is equivalent to the target toxicity range of Neuenschwander. One difference compared to CRM is that there is no formal dose-toxicity model linking responses at the various doses tested. For each dose level, a Beta-prior on the probability of toxic events is used to model the uncertainty on the true toxicity probability. Given observations at this dose level, the posterior probabilities of under-dosing, target-dosing or over-dosing are calculated. The dose-level to be used at the next stage is determined from the unit posterior probability mass of the toxicity ranges. The resulting dose escalation scheme can easily be specied for a range of toxicity outcomes as described in Ji and Wang.5 Additionally, Bayesian modelling allows the inclusion of an overdose control into the mtpi based on the posterior probability of the toxicity rate. When the trial is stopped, the MTD is estimated in this approach using isotonic regression based on the posterior mean toxicity probabilities at each acceptable dose level. The resulting estimate of the MTD is therefore based on a monotonic non-parametric estimation of the dose-toxicity curve and may be regarded as robust. The fully validated dose escalation functionality of ADDPLAN DF allows the design and analyses of innovative Phase I oncology trials. Operating characteristics of 3+3 designs, the classical CRM, BLOC and the mtpi approach may be studied under different dose-toxicity proles. is allows the selection of an appropriate dose escalation design. ADDPLAN DF - A Tool to Design, Simulate, and Analyse Innovative Dose Escalation Studies The characteristics of dose escalation designs can be studied using the simulation engine of ADDPLAN DF, which provides four different dose escalation methods and allows the specication of additional options. Classical 3+3 designs may be simulated to give a reliable insight into the characteristics of the approach. The classical CRM is implemented for two different one-parameter probability models and a range of prior distributions. There are different options to enable the selection of the next stage dose which are based on a range of estimates of the probability of toxicity. The inuence of three dierent stopping rules, which may be combined, can be studied using the CRM simulations. The Bayesian logistic regression with overdose control (BLOC) follows the approach to dose escalation proposed by Neuenschwander. The toxicity probabilities may be changed and an overdose control option may be used to prevent excessive toxicity. The mtpi approach can be simulated for dierent selections of the Beta-prior and equivalence interval. Active dose escalation may be analysed using the dose escalation functionality in the analysis engine of ADDPLAN DF. The analysis functionality provides recommendations of the dose-level for the next cohort of patients and the nal target dose. Additionally, information on the posterior distribution is provided when using any of the CRM approaches. The posterior probability of the toxicity ranges and the posterior probability for the doses being the MTD is displayed after clicking the compute button. ADDPLAN DF supports the implementation of innovative dose escalation trials, from design to analysis in a fully validated dose finding software suite. 6

7 Example: Identification of the MTD 8 The continual reassessment method (CRM) was used in to identify the MTD of an agent, intravenously administered to subjects with solid tumours. The study was separated into two parts. The first part aimed at escalating to the MTD with about 50 subjects, whereas the second part of the study aimed at confirming this estimated MTD and to assess safety, tolerability and preliminary efficacy in a group of 20 subjects. The CRM design was used, since the targeted MTD dose range was not well dened, implying that the application of the standard 3+3 approach might result in an inefficient dose escalation strategy. The dose range considered during the planning stage of the trial consisted of 20 doses, ranging from 10mg to 319mg in increments of 20%. The target toxicity range for the new compound was set at 18% to 33% with a target toxicity of 25% being used for simulating the operating characteristics of the CRM design. In designing the dose escalation trial, a set of six different dose-toxicity scenarios was considered. In the current discussion, we focus on three scenarios, representing target toxicity either at the low end, in the middle or at the high end of the dose range (Figure 3). Figure 3: Candidate shapes Independent standard normal priors for the two parameters of the logistic model were used in the simulation of the BLOC. The toxicity regions were specied with a target toxicity range of 18% to 33%. The next stage cohort will generally be allocated to the dose level having the highest posterior probability of the DLT rate to be located within the target toxicity range. A safety rule will additionally limit the number of allocations to toxic doses. Cohorts will not be allocated to dose levels with posterior probabilities of the DLT rate exceeding the target toxicity range above some safety boundary. The maximum acceptable posterior probability of overdosing is set in the simulations at 25%. Increasing the overdose threshold would increase the number of DLTs per patient, as the probability of allocating cohorts to toxic doses will be increased. The DLT probabilities using the CRM dose escalation design were modelled using a one-parametric logistic dose-toxicity model with normal prior on the logarithm of the slope parameter. The probability of observing DLTs is, in the CRM settings, assumed to follow a straight line from 10% DLT probability at the minimum dose to 70% DLT probability at the maximum dose. If the posterior probability that a dose is the MTD exceeds 70% after a run-in phase of 5 cohorts, the corresponding dose will be claimed as the MTD. Alternative prior distributions and stopping rules may be studied using ADDPLAN DF. Figure 4 displays the simulated probability of selecting appropriate dose-levels for the MTD in the considered scenarios. Throughout all three scenarios, the tendency of the 3+3 designs to underestimate the target dose is evident. In the first scenario with a target toxicity range at the very beginning of the dose range, the 3+3 designs lead in about 70% of the simulations to an under-estimated MTD. About 60% of the patients are treated with doses, which may be regarded as sub-therapeutic. Similar relations resulted for mtpi. However, the estimates of the MTD were more reliable in scenario 1 using mtpi. In about 50% of the cases, the target toxicity region was reached, compared to 30% when using the 3+3 design. The mtpi provided the minimum number of DLTs per patient throughout all methods. 7

8 The simulations used in the model-based approach, demonstrated the most promising results in terms of accurately estimating the MTD. Both CRM and BLOC allocated fewer patients to sub-therapeutic dose levels and identified the MTD in the target toxicity range in scenario 1 and 2 in more than 50% of the cases. It is well known, that overdosing might be an issue when using the CRM approach. The relative number of DLTs per patient was in all considered scenarios at the maximum when using the CRM approach. An alternative implementation using escalation designs with overdose control may minimise these shortcomings of CRM, as displayed by the promising simulation results for BLOC. ICON is working closely with international academic and industrial dose-finding methodology specialists to further enhance and extend adaptive and innovative approaches to dose-finding. Future versions of ADDPLAN DF will include additional sets of innovative dose escalation designs, targeting both safety and efficacy endpoints. Adaptive components for Phase II studies using multiple comparison procedures, dose-response modelling and MCP-Mod 1 are in development for future software releases. The total number of observations is at minimum when using the 3+3 design and the trial will stop early when using 3+3 designs, if there is a high probability of observing toxicities at low doses, as given in scenario 1. For scenarios 2 and 3, the sample size among all procedures was similar. Using the ADDPLAN DF simulation results, the dose escalation design options may be adjusted to obtain a design, which fits well in all scenarios. Different assumptions on the dose-toxicity model and prior distributions, additional stopping rules and options limiting the probability of overdosing may be verified to further optimise the trial design. Conclusion The accurate estimation of the MTD is of critical importance in the drug development process. This white paper discusses innovative dose escalation designs and their implementation in ADDPLAN DF. The aim of the software is to support the process of decision making in the design and analysis of dose Finding trials using these innovative methods. Uncertainty in the true underlying dose-toxicity profile needs to be taken into account when designing efficient and effective dose escalation trials. ADDPLAN DF enables drug developers to study the operating characteristics of standard and innovative dose escalation methods under different scenarios, allowing the selection of the appropriate methods for successful Phase I dose escalation trials. 8

9 ICON - A Leader in Adaptive Trials ICON offers design, simulation and execution of adaptive clinical trials. We are the only CRO that offers the knowledge, software, systems and global footprint to make global adaptive trials a reality. More than a decade of experience in successfully planning and managing nearly 200 adaptive clinical trials for over 30 sponsors Experts with direct involvement in regulatory agency adoption of adaptive design trials and subsequent agency guidance Operational teams and technologies to apply the power of adaptive techniques to drug and medical device trials Additionally, you have access to the ICON Adaptive Trial Innovation Centre, a group of world leading experts in adaptive design and execution, providing leadership in these key areas: Design, simulation and execution of adaptive trials across all phases of development Development of innovative trial methodologies Customized training in adaptive trial statistical methodology Advice and guidance on the logistical and operational requirements for successful adaptive trial execution References 1. Le Tourneau, C., Jack Lee, J., Siu, L.L. (2009) Dose Escalation Methods in Phase I Cancer Clinical Trials. Journal of the National Cancer Institute, 101, ADDPLAN DF An Advanced Tool for Optimizing Dose Selection in Exploratory Drug Development. An Aptiv Solutions White Paper. addplan-df-landing-page/ 3. EMA-CHMP (2014) Qualification Opinion of MCP-Mod as an efficient statistical methodology for model-based design and analysis of Phase II dose finding studies under model uncertainty. EMA/CHMP/ SAWP/757052/ Neuenschwander, B., Branson, M., Gsponer, T. (2008) Critical Aspects of the Bayesian Approach to Phase I Cancer Trials. Statistics in Medicine, 27, Ji, Y., Wang, S.-J. (2013) Modified Toxicity Probability Interval Design: A Safer and More Reliable Method Than the 3+3 Design for Practical Phase I Trials. Journal of Clinical Oncology 31, O Quigley, H., Pepe, M., Fisher, L. (1990) Continual Reassessment Method: A Practical Design for Phase 1 Clinical Trials in Cancer, Biometrics, 46, Ji, Y., Liu, P., Li, Y., Bekele, B.N. (2010) A Modified Toxicity Probability Interval Method for Dose-Finding Trials, Clinical Trials, 7, Perevozskaya, I., Han, L., Pierce, K. (2014) Continual Reassessment Method For First-in-Human Trial: From Design to Trial Implementation. KOL lecture series on adaptive designs ; Friday, March 14, Additional ICON White Papers Using Surrogates for Decision Making in Confirmatory Adaptive Clinical Trials Using Adaptive Design to Optimize Product Development at the Program and Portfolio Level ADDPLAN PE: Population Enrichment Designs for Adaptive Clinical Trials 9

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