Zoran Antonijevic

Adaptive and Model-Based Dose-Ranging Trials: Quantitative Evaluation and Recommendations. White Paper of the PhRMA Working Group on Adaptive Dose-Ranging Studies

Poor dose-regimen selection remains a key cause of the high attrition rate of investigational drugs in confirmatory trials, being directly related to the escalating costs of drug development. This article is a follow-up to the first white paper put forward by the PhRMA Working Group (WG) on Adaptive Dose-Ranging Studies (Bornkamp et al. 2007). It presents results and conclusions from a new round of simulation-based evaluations conducted by the WG, proposing a new set of recommendations to improve the accuracy and efficiency of dose-finding in clinical drug development.

Impact of Dose Selection Strategies Used in Phase II on the Probability of Success in Phase III

The purpose of this study was to assess the impact of phase II dose-selection strategies on the likelihood of success of phase III clinical programs, comparing both traditional and adaptive approaches. We evaluated the impact of the phase II approach to dose selection (including traditional, design-adaptive, and analysis-adaptive approaches), the sample size used in phase II, the number of doses studied in phase II, and the number of doses selected to advance into phase III on the probability of demonstrating efficacy, of demonstrating a lack of toxicity, of phase III trial success, and on the probability of overall success of the combined phase II/phase III programs. The expected net present value was used to quantify the financial implications of different strategies. We found that adaptive dose allocation approaches (in particular, the Bayesian general adaptive dose allocation method) usually outperformed other fixed dose allocation approaches with respect to both probability of success and dose selection. Design-adaptive approaches were more efficient than analysis-adaptive approaches. The allocation of additional resources into phase II improved the probability of success in phase III and the expected net present value. Bringing two doses forward into phase III testing also increased the probability of success and improved the expected net present value. The overall probability of success in phase III ranged from 35% to 65%, consistent with recent industry experience. This success rate could likely be improved with additional investment in phase II, the use of design-adaptive dose-finding designs when possible, increasing the power of phase III trials, more explicit consideration of toxicity concerns, and better dose selection.

Optimizing Drug Development Programs Type 2 Diabetes Case Study

Recently, consideration was given to the impact of dose selection strategies in phase IIb on the overall success of drug development programs. A natural next step is to simultaneously optimize design aspects of both phase IIB and phase III. We used type 2 diabetes as an example, including realistic regulatory and commercial scenarios for this indication. The expected net present value (eNPV) has been selected as the primary outcome because it naturally accommodates optimization, providing an explicit trade-off between the probability of success (PoS) and time delays and trial costs. Our findings are that larger studies and/or implementation of an adaptive design over a fixed design in phase IIb provide more precise dose selection and reduce the bias of treatment effects and uncertainty in the estimated eNPV within the range of sample sizes that we examined. Developers also have to ensure that dose selection criteria are consistent with development strategy and objectives.

Views on Emerging Issues Pertaining to Data Monitoring Committees for Adaptive Trials

In this paper, the authors express their views on a range of topics related to data monitoring committees (DMCs) for adaptive trials that have emerged recently. The topics pertain to DMC roles and responsibilities, membership, training, and communication. DMCs have been monitoring trials using the group sequential design (GSD) for over 30 years. While decisions may be more complicated with novel adaptive designs, the fundamental roles and responsibilities of a DMC will remain the same, namely, to protect patient safety and ensure the scientific integrity of the trial. It will be the DMC’s responsibility to recommend changes to the trial within the scope of a prespecified adaptation plan or decision criteria and not to otherwise recommend changes to the study design except for serious safety-related concerns. Nevertheless, compared with traditional data monitoring, some additional considerations are necessary when convening DMCs for novel adaptive designs. They include the need to identify DMC members who are familiar with adaptive design and to consider possible sponsor involvement in unique situations. The need for additional expertise in DMC members has prompted some researchers to propose alternative DMC models or alternative governance model. These various options and authors’ views on them are expressed in this article.

Sample Size Re-estimation Designs In Confirmatory Clinical Trials—Current State, Statistical Considerations, and Practical Guidance

A sample size re-estimation (SSR) design is a flexible, adaptive design with the primary purpose of allowing sample size of a study to be reassessed in the mid-course of the study to ensure adequate power. In real world drug product, biologic, and device development, there may be large uncertainty in key factors that drive the sample size estimation for a confirmatory clinical trial. For example, early phase studies may have encouraging results but could be of shorter duration, or use a different endpoint than what is required for confirmatory phase clinical trials. The negative impact of high uncertainty at design stage for a confirmatory trial can be mitigated by an SSR design. Recent surveys have reported an encouraging upward trend in the use of SSR designs in clinical trials since the release of the draft guidance for adaptive design clinical trials for drugs and biologics by the U.S. Food and Drug Administration in 2010 (U.S. Food and Drug Administration (FDA) (February, 2010), Draft Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics). To support broad understanding and acceptance of SSR designs in confirmatory settings, especially unblinded SSR designs, we summarize statistical methods pertaining to SSR designs, including recent development in this field, and discuss design alternatives among blinded SSR, unblinded SSR, and conventional group sequential designs. To support appropriate implementation of SSR designs, we make recommendations on operational logistics for trial conduct based on accumulated experience in recent years, and provide points to consider for final data analysis and reporting for studies where the sample size has been increased following either a blinded or an unblinded SSR algorithm.

A Practical Guide to Data Monitoring Committees in Adaptive Trials

Adaptive clinical trials require access to interim data to carry out trial modification as allowed by a prespecified adaptation plan. A data monitoring committee (DMC) is a group of experts that is charged with monitoring accruing trial data to ensure the safety of trial participants and that in adaptive trials may also play a role in implementing a preplanned adaptation. In this paper, we summarize current practices and viewpoints and provide guidance on evolving issues related to the use of DMCs in adaptive trials. We describe the common types of adaptive designs and point out some DMC-related issues that are unique to this class of designs. We include 3 examples of DMCs in late-stage adaptive trials that have been implemented in practice. We advocate training opportunities for researchers who may be interested in serving on a DMC for an adaptive trial since qualified DMC members are fundamental to the successful execution of DMC responsibilities.

Practical Considerations for a Two-Stage Confirmatory Adaptive Clinical Trial Design and Its Implementation: ADVENT Trial

In this chapter, we provide the details of an innovative two-stage, seamless adaptive clinical trial called ADVENT. This trial was conducted as a “final phase 3” clinical trial to establish the safety and efficacy of a first-in-class antidiarrheal agent, crofelemer, for the symptomatic relief of diarrhea in HIV patients receiving anti-retroviral therapy. Given that this was a trial with two-stage design that included a dose selection, it was necessary to demonstrate the strong control of Type 1 error. This was accomplished with a close testing procedure applied to combination tests that utilized the inverse normal combination function. We developed a one-sided significance testing procedure that ensures strong control of the Type 1 error at level 0.025. Using appropriate statistical methodology for combining the results from the two stages, a statistically significant outcome was obtained for the primary efficacy endpoint and crofelemer received marketing approval based on the ADVENT trial. While the authors acknowledge the importance of statistical methodology required to analyze the data from the ADVENT trial, this chapter also provides significant details on the clinical and regulatory challenges that were demanded for the conduct of this innovative, two-stage, adaptive clinical trial.

Drug Development and the Cost of Capital

In the pharmaceutical industry today, R & D often struggles to generate returns on capital above its cost of capital, leading to low to no creation of financial value for investors. Declining returns on R & D investment is a trend that has been unfolding for years given increasingly expensive, long, risky development programs required by regulators and payers for new therapeutics. Once they reach the market, new products face increasingly competitive pressures and decreasing willingness to pay by commercial and government-sponsored insurers. Given these difficulties, funding, particularly of early-stage drugs, has slowed and large pharmaceutical companies and financial sponsors of biotechnology companies have begun questioning the sustainability of the current R & D model. The industry is looking for alternative development and funding options to ensure much-needed new therapies can be brought to market efficiently.

Need for Optimal Design of Pharmaceutical Programs and Portfolios in Modern Medical Product Development

In drug development decisions regarding various development options should be defined such that that the expected value of a product or a portfolio is maximized. This needs to be done through the process of quantitative optimization of development decisions. This process involves development of multiple scenarios that are best compared by using simulations. While this optimization could focus on individual development programs, it is preferable that these decisions are made at the portfolio level, as individual programs are interdependent. The value of a product depends on quality of product itself, and quality of development program. The value of a portfolio depends on the value of individual products and the strategy for portfolio optimization. Key concepts that are addressed here are: 1. Drug development decisions should be made at the portfolio level. Budgets are limited and are determined at the portfolio level; therefore decisions within individual programs should be interrelated. 2. Study design and development strategy have a major impact on the success of drug development at: Trial level by application of adaptive design. Program level. More effective dose-finding leads to higher success rates in Phase 3 and an improved efficacy/safety profile Portfolio level. Improved allocation of a fixed budget into individual trials leads to an improved value of the portfolio. 3. An integrated, R&D and commercial approach is necessary for optimizing pharmaceutical portfolios.

Impact of Phase 2b Strategies on Optimization of Drug Development Programs

Selecting the right dose is critical for the success of any drug development program, and for maximizing the value of a product. A well selected dose will have a better chance to demonstrate a desirable risk/benefit profile and thus increase the chance of regulatory success and reimbursement by payors. It will also result in improved patient care and greater benefit to society. Multiple papers have been published within industry’s adaptive design working groups, and these are the key findings.