Current issues in oncology drug development, with a focus on Phase II trials

Evolution of Phase II Oncology Trial Design: from Single Arm to Master Protocol

The recent development of novel anticancer treatments with diverse mechanisms of action has accelerated the detection of treatment candidates tremendously. The rapidly changing drug development landscapes and the high failure rates in Phase III trials both underscore the importance of more efficient and robust phase II designs. The goals of phase II oncology studies are to explore the preliminary efficacy and toxicity of the investigational product and to inform future drug development strategies such as go/no-go decisions for phase III development, or dose/indication selection. These complex purposes of phase II oncology designs call for efficient, flexible, and easy-to-implement clinical trial designs. Therefore, innovative adaptive study designs with the potential of improving the efficiency of the study, protecting patients, and improving the quality of information gained from trials have been commonly used in Phase II oncology studies. Although the value of adaptive clinical trial methods in early phase drug development is generally well accepted, there is no comprehensive review and guidance on adaptive design methods and their best practice for phase II oncology trials. In this paper, we review the recent development and evolution of phase II oncology design, including frequentist multistage design, Bayesian continuous monitoring, master protocol design, and innovative design methods for randomized phase II studies. The practical considerations and the implementation of these complex design methods are also discussed.

A Review of Perspectives on the Use of Randomization in Phase II Oncology Trials

Historically, phase II oncology trials assessed a treatment's efficacy by examining its tumor response rate in a single-arm trial. Then, approximately 25 years ago, certain statistical and pharmacological considerations ignited a debate around whether randomized designs should be used instead. Here, based on an extensive literature review, we review the arguments on either side of this debate. In particular, we describe the numerous factors that relate to the reliance of single-arm trials on historical control data and detail the trial scenarios in which there was general agreement on preferential utilization of single-arm or randomized design frameworks, such as the use of single-arm designs when investigating treatments for rare cancers. We then summarize the latest figures on phase II oncology trial design, contrasting current design choices against historical recommendations on best practice. Ultimately, we find several ways in which the design of recently completed phase II trials does not appear to align with said recommendations. For example, despite advice to the contrary, only 66.2% of the assessed trials that employed progression-free survival as a primary or coprimary outcome used a randomized comparative design. In addition, we identify that just 28.2% of the considered randomized comparative trials came to a positive conclusion as opposed to 72.7% of the single-arm trials. We conclude by describing a selection of important issues influencing contemporary design, framing this discourse in light of current trends in phase II, such as the increased use of biomarkers and recent interest in novel adaptive designs.

Multiscale systems pharmacological analysis of everolimus action in hepatocellular carcinoma

Dysregulation of mTOR pathway is common in hepatocellular carcinoma (HCC). A translational quantitative systems pharmacology (QSP), pharmacokinetic (PK), and pharmacodynamic (PD) model dissecting the circuitry of this pathway was developed to predict HCC patients' response to everolimus, an mTOR inhibitor. The time course of key signaling proteins in the mTOR pathway, HCC cells viability, tumor volume (TV) and everolimus plasma and tumor concentrations in xenograft mice, clinical PK of everolimus and progression free survival (PFS) in placebo and everolimus-treated patients were extracted from literature. A comprehensive and multiscale QSP/PK/PD model was developed, qualified, and translated to clinical settings. Model fittings and simulations were performed using Monolix software. The S6-kinase protein was identified as critical in the mTOR signaling pathway for describing everolimus lack of efficacy in HCC patients. The net growth rate constant (kg) of HCC cells was estimated at 0.02 h^-1 (2.88%RSE). The partition coefficient of everolimus into the tumor (kp) was determined at 0.06 (12.98%RSE). The kg in patients was calculated from the doubling time of TV in naturally progressing HCC patients, and was determined at 0.004 day^-1. Model-predicted and observed PFS were in good agreement for placebo and everolimus-treated patients. In conclusion, a multiscale QSP/PK/PD model elucidating everolimus lack of efficacy in HCC patients was successfully developed and predicted PFS reasonably well compared to observed clinical findings. This model may provide insights into clinical response to everolimus-based therapy and serve as a valuable tool for the clinical translation of efficacy for novel mTOR inhibitors.

The assessment and appraisal of regenerative medicines and cell therapy products: an exploration of methods for review, economic evaluation and appraisal

BACKGROUND

The National Institute for Health and Care Excellence (NICE) commissioned a 'mock technology appraisal' to assess whether changes to its methods and processes are needed. This report presents the findings of independent research commissioned to inform this appraisal and the deliberations of a panel convened by NICE to evaluate the mock appraisal.

METHODS

Our research included reviews to identify issues, analysis methods and conceptual differences and the relevance of alternative decision frameworks, alongside the development of an exemplar case study of chimeric antigen receptor (CAR) T-cell therapy for treating acute lymphoblastic leukaemia.

RESULTS

An assessment of previous evaluations of regenerative medicines found that, although there were a number of evidential challenges, none was unique to regenerative medicines or was beyond the scope of existing methods used to conceptualise decision uncertainty. Regarding the clinical evidence for regenerative medicines, the issues were those associated with a limited evidence base but were not unique to regenerative medicines: small non-randomised studies, high variation in response and the intervention subject to continuing development. The relative treatment effects generated from single-arm trials are likely to be optimistic unless it is certain that the historical data have accurately estimated the efficacy of the control agent. Pivotal trials may use surrogate end points, which, on average, overestimate treatment effects. To reduce overall uncertainty, multivariate meta-analysis of all available data should be considered. Incorporating indirectly relevant but more reliable (more mature) data into the analysis can also be considered; such data may become available as a result of the evolving regulatory pathways being developed by the European Medicines Agency. For the exemplar case of CAR T-cell therapy, target product profiles (TPPs) were developed, which considered the 'curative' and 'bridging to stem-cell transplantation' treatment approaches separately. Within each TPP, three 'hypothetical' evidence sets (minimum, intermediate and mature) were generated to simulate the impact of alternative levels of precision and maturity in the clinical evidence. Subsequent assessments of cost-effectiveness were undertaken, employing the existing NICE reference case alongside additional analyses suggested within alternative frameworks. The additional exploratory analyses were undertaken to demonstrate how assessments of cost-effectiveness and uncertainty could be impacted by alternative managed entry agreements (MEAs), including price discounts, performance-related schemes and technology leasing. The panel deliberated on the range of TPPs, evidence sets and MEAs, commenting on the likely recommendations for each scenario. The panel discussed the challenges associated with the exemplar and regenerative medicines more broadly, focusing on the need for a robust quantification of the level of uncertainty in the cost-effective estimates and the potential value of MEAs in limiting the exposure of the NHS to high upfront costs and loss associated with a wrong decision.

CONCLUSIONS

It is to be expected that there will be a significant level of uncertainty in determining the clinical effectiveness of regenerative medicines and their long-term costs and benefits, but the existing methods available to estimate the implications of this uncertainty are sufficient. The use of risk sharing and MEAs between the NHS and manufacturers of regenerative medicines should be investigated further.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Regulatory Forum Opinion Piece*: Veterinary Pathologists in Translational Pharmacology and Biomarker Integration in Drug Discovery and Development

This article highlights emerging roles for veterinary pathologists outside of traditional functions and in line with the translational research (TR) approach. Veterinary pathologists offer unique and valuable expertise toward addressing particular TR and associated translational pharmacology questions, identifying gaps and risks in biomarker and pathology strategies, and advancing TR team decision making. Veterinary pathologists' attributes that are integral to the TR approach include (i) well-developed understanding of comparative physiology, pathology, and disease; (ii) extensive experience in interpretation and integration of complex data sets on whole-body responses and utilizing this for deciphering pathogenesis and translating events between laboratory species and man; (iii) proficiency in recognizing differences in disease end points among individuals, animal species and strains, and assessing correlations between these differences and other investigative (including biomarker) findings; and (iv) strong background in a wide spectrum of research technologies that can address pathomechanistic questions and biomarker needs. Some of the more evident roles in which veterinary pathologists can offer their greatest contributions to address questions and strategies of TR and biomarker integration will be emphasized.

The use of phase 2 interim analysis to expedite drug development decisions

PURPOSE

To expedite drug development, we propose a two-step decision-making process that utilizes interim efficacy results from a comparative phase 2 trial to determine whether to accelerate subsequent phase 3 preparations, and final analysis to ultimately determine whether to conduct phase 3 testing.

METHODS

The operational characteristics of this process were evaluated by modeling simulated data of oncology trials and retrospectively analyzing data from historical comparative phase 2 trials. Progression-free survival (PFS) was used as the primary endpoint; the estimated PFS hazard ratios (HRs) of ≤0.60 at interim and of ≤0.65 at final analysis favoring the experimental arm were defined as positive results. The conditional probability of achieving a target PFS HR at final analysis, based on observed interim results, was also estimated by imputing post-interim data with and without the proportional hazard assumption.

RESULTS

Simulations of phase 2 trials showed that estimated interim PFS HRs correlated with estimated final PFS HRs, with reasonably low false-positive rates for supporting phase 3 "go" decisions at interim. Using observed historical data, decisions based on interim PFS analyses also matched final phase 3 "go" and "no-go" decisions with a false-positive rate of 16.7% (2 of 12 trials) and a false-negative rate of 9.4% (3 of 32 trials). Analytical modeling accurately predicted final PFS HRs from observed interim data when accounting for appropriate underlying assumptions.

CONCLUSIONS

The results support the usefulness of a two-step decision-making process that utilizes interim phase 2 results to reduce the interval between phase 2 completion and phase 3 initiation.

Targeted therapies in neuroendocrine tumors (NET): clinical trial challenges and lessons learned

In the past 3 years, we have witnessed the completion of four randomized phase III studies in neuroendocrine tumors and the approval of two new drugs, everolimus and sunitinib, for the treatment of patients with well-differentiated pancreatic neuroendocrine tumors. These studies demonstrate a shift from case series and single-arm studies toward prospective, randomized controlled clinical trials and evidence-based therapy in the neuroendocrine tumor field. However, the clinical development of these agents also highlights the potential challenges awaiting other new drugs in this area. Herein, we discuss the strengths and weaknesses of the most recent phase II and phase III neuroendocrine tumor studies and discuss how limitations inherent in current trial design can lead to potential pitfalls. We also discuss how trial design can be improved, with the hope of increasing the number of drugs successfully developed to treat patients with neuroendocrine tumors.

Smaller sample sizes for phase II trials based on exact tests with actual error rates by trading-off their nominal levels of significance and power

Background:

Sample sizes for single-stage phase II clinical trials in the literature are often based on exact (binomial) tests with levels of significance (alpha (α) <5% and power >80%). This is because there is not always a sample size where α and power are exactly equal to 5% and 80%, respectively. Consequently, the opportunity to trade-off small amounts of α and power for savings in sample sizes may be lost.

Methods:

Sample-size tables are presented for single-stage phase II trials based on exact tests with actual levels of significance and power. Trade-off in small amounts of α and power allows the researcher to select from several possible designs with potentially smaller sample sizes compared with existing approaches. We provide SAS macro coding and an R function, which for a given treatment difference, allow researchers to examine all possible sample sizes for specified differences are provided.

Results:

In a single-arm study with P₀ (standard treatment)=10% and P₁ (new treatment)=20%, and specified α=5% and power=80%, the A’Hern approach yields n=78 (exact α=4.53%, power=80.81%). However, by relaxing α to 5.67% and power to 77.7%, a sample size of 65 can be used (a saving of 13 patients).

Interpretation:

The approach we describe is especially useful for trials in rare disorders, or for proof-of-concept studies, where it is important to minimise the trial duration and financial costs, particularly in single-arm cancer trials commonly associated with expensive treatment options.

Quantitative evaluation of single-arm versus randomized phase II cancer clinical trials

BACKGROUND

There is a debate among cancer researchers about the use of single-arm or randomized phase II clinical trial designs; however, there is limited published objective evaluation of this issue.

PURPOSE

To objectively quantify the impact on phase III clinical trials of a policy of all single-arm versus all randomized phase II trials.

METHODS

A simulation study was performed comparing optimal single-arm and randomized phase II trial designs with a variety of commonly used α and β error rates. Parameters modeled included: between-institution variability in the standard of care response rate, between-institution variability in the treatment effect, between-institution variability in the estimate of historical control rate (for selecting H0), presence of historical bias, and proportion of phase II trials conducted using active agents.

RESULTS

Using single-arm phase II trials resulted in a higher percentage of phase III trials conducted using active agents when there was minimal standard of care activity, or in the presence of a positive historical bias (H0 estimated to be greater than truth). Randomized phase II trials performed better in the presence of a negative historical bias, in the presence of high variability, and were more consistent across variation of historical bias. The proportion of phase III trials conducted using active agents was increased by reducing the α error. Presence of historical bias and the proportion of active agents studied in phase II had the greatest influence on results.

LIMITATIONS

It was estimated that between 5% and 20% of agents studied in phase II trials are active; however, the conclusions could change if this estimate is incorrect. This study did not account for the possibility of a new drug application submission immediately following phase II. The primary outcome looked at was response rate, although some investigators have suggested that time-to-event outcomes should be used in phase II, particularly for randomized phase II trials.

CONCLUSIONS

Both single-arm and randomized phase II trials appear warranted in certain situations. Investigators should increase consideration of the potential impact on phase III trials to optimally select the proper trial design prior to phase II study implementation.