Tumor-Agnostic Approvals: Insights and Practical Considerations.

Since the first approval of a tumor-agnostic indication in 2017, a total of seven tumor-agnostic indications involving six drugs have received approval from the FDA. In this paper, the master protocol subteam of the Statistical Methods in Oncology Scientific Working Group, Biopharmaceutical Session, American Statistical Association, provides a comprehensive summary of these seven tumor-agnostic approvals, describing their mechanisms of action; biomarker prevalence; study design; companion diagnostics; regulatory aspects, including comparisons of global regulatory requirements; and health technology assessment approval. Also discussed are practical considerations relating to the regulatory approval of tumor-agnostic indications, specifically (i) recommendations for the design stage to mitigate the risk that exceptions may occur if a treatment is initially hypothesized to be effective for all tumor types and (ii) because drug development continues after approval of a tumor-agnostic indication, recommendations for further development of tumor-specific indications in first-line patients in the setting of a randomized confirmatory basket trial, acknowledging the challenges in this area. These recommendations and practical considerations may provide insights for the future development of drugs for tumor-agnostic indications.

Association of Minimal Residual Disease Negativity Rates With Progression Free Survival in Frontline Therapy Trials for Newly Diagnosed Multiple Myeloma: A Meta-analysis.

Current frontline therapies for newly diagnosed multiple myeloma patients have significantly prolonged progression-free survival (PFS). This has led to interest in minimal residual disease negativity (MRDng) as an efficacy-response biomarker and possible surrogate endpoint. A meta-analysis was conducted to explore the surrogacy of MRD for PFS and quantify the relationship between MRDng rates and PFS at the trial level. A systematic search was conducted on phase II and III trials reporting MRDng rates along with median PFS (mPFS) or PFS hazard ratios (HR). Weighted linear regressions were conducted relating mPFS to MRDng rates, and relating PFS HRs to either odds ratios (OR) or rate differences (RD) for MRDng in comparative trials. A total of 14 trials were available for the mPFS analysis. log(MRDng rate) was moderately associated with log (mPFS), with a slope of ß = 0.37 (95% CI, 0.26 to 0.48) and R2 = 0.62. A total of 13 trials were available for the PFS HR analysis. Treatment effects on MRDng rates were correlated with the corresponding effects on PFS: log (PFS HR) and log (MRDng OR) had a moderate association with ß = -0.36 (95% CI, -0.56 to -0.17) and R2 = 0.53 (95% CI, 0.21 to 0.77); log (PFS HR) and the MRDng RD had a stronger association with slope ß = -0.03 (95% CI, -0.04 to -0.02) and R2 = 0.67 (95% CI, 0.31 to 0.86). MRDng rates moderately associate with PFS outcomes. MRDng RDs are more strongly associated with HRs than MRDng ORs, with evidence suggestive of potential surrogacy.

Efficiency of a randomized confirmatory basket trial design constrained to control the family wise error rate by indication.

Basket trials pool histologic indications sharing molecular pathophysiology, improving development efficiency. Currently, basket trials have been confirmatory only for exceptional therapies. Our previous randomized basket design may be generally suitable in the resource-intensive confirmatory phase, maintains high power even with modest effect sizes, and provides nearly k-fold increased efficiency for k indications, but controls false positives for the pooled result only. Since family wise error rate by indications may sometimes be required, we now simulate a variant of this basket design controlling family wise error rate at 0.025k, the total family wise error rate of k separate randomized trials. We simulated this modified design under numerous scenarios varying design parameters. Only designs controlling family wise error rate and minimizing estimation bias were allowable. Optimal performance results when k=3,4. We report efficiency (expected # true positives/expected sample size) relative to k parallel studies, at 90% power ("uncorrected") or at the power achieved in the basket trial ("corrected," because conventional designs could also increase efficiency by sacrificing power). Efficiency and power (percentage active indications identified) improve with a higher percentage of initial indications active. Up to 92% uncorrected and 38% corrected efficiency improvement is possible. Even under family wise error rate control, randomized confirmatory basket trials substantially improve development efficiency. Initial indication selection is critical.

Bayesian hierarchical model for safety signal detection in multiple clinical trials.

Clinical safety signal detection is of great importance in establishing the safety profile of new drugs and biologics during drug development. Bayesian hierarchical meta-analysis has proven to be a very effective method of identifying potential safety signals by considering the hierarchical structure of clinical safety data from multiple randomized clinical trials conducted under an Investigational New Drug (IND) application or Biological License Application (BLA). This type of model can integrate information across studies, for instance by grouping related adverse events using the MedDRA system-organ-class (SOC) and preferred terms (PT). It therefore improves the precision of parameter estimates compared to models that do not consider the hierarchical structure of the safety data. We propose to extend an existing four-stage Bayesian hierarchical model and consider the exposure adjusted incidence rate, assuming the number of adverse events (AEs) follows a Poisson distribution. The proposed model is applied to a real-world example, using data from three randomized clinical trials of a neuroscience drug and examine in three simulation studies motivated by real-world examples. Comparison is made between the proposed method and other existing methods. The simulation results indicate that our proposed model outperforms other two candidate models in terms of power and false detection rate.

Meta-analysis of blinded and unblinded studies for ongoing aggregate safety monitoring and evaluation.

During the course of clinical development, ongoing aggregate safety monitoring and evaluation are needed to understand the evolving safety profile and to ensure effective risk-management strategies for medicinal products. CIOMS reports and global regulatory guidance (including from ICH, US FDA, and EMA) compel sponsors for assessment of safety based on aggregate data. To identify and characterize the risks of medicinal products at a program level in a more timely and informed manner, aggregate safety evaluations should combine all available information, including from ongoing blinded trials, completed unblinded trials, and other data sources. In this article, we propose two Bayesian meta-analytic approaches for synthesizing blinded and unblinded studies in order to characterize the evolving safety profile of medicinal products at the program level. With the proposed approaches, sponsors can dynamically update knowledge of their product safety profiles as data accrue. Application of the procedures to a real and a hypothetical clinical trial program are provided to illustrate how the proposed approaches can be used to analyze a pre-specified event of interest and to screen for risk-elevated events.

Lessons Learned From Multi-regional Trials With Signals of Treatment Effect Heterogeneity.

Inconsistent results across regions have been reported in a number of recent large trials. In this research, by reviewing results from studies that showed inconsistent treatment effects, and summarizing lessons learned, we provide some recommendations for minimizing the chance of inconsistency and allowing more accurate interpretation when such signs of heterogeneity arise, for example: keep the number of regions for consistency evaluation at a minimum to avoid observing false inconsistency signals; proactively address in the protocol the differences in culture, medical practices, and other factors that are potentially different across regions; closely monitor the blinded data from early-enrolled patients to more effectively identify and address issues such as imbalance of baseline covariates or inconsistency of primary outcome rates across regions. For treatments of life-threatening conditions, the stakes for accurate interpretation of MRCT results are high; the criteria for decisions warrant careful consideration.

Bridging blinded and unblinded analysis for ongoing safety monitoring and evaluation.

In order to better characterize the safety profile of investigational new drugs (INDs) during clinical development, more interest and attention have been paid to ongoing safety monitoring and evaluation. The 2015 US FDA IND safety reporting draft guidance compels sponsors to periodically evaluate unblinded safety data. However, maintaining the trial blind is necessary to avoid jeopardizing the validity of study findings. In this article, we propose an innovative new approach which includes analyzing both blinded and unblinded data. The proposed two-stage framework incorporates periodic analyses of blinded safety data to detect and flag adverse events that may have potential risk elevation related to experimental treatment, as well as planned unblinded analyses to quantify associations between the drug and adverse events, and to determine thresholds for referring adverse events for medical review and safety reporting.

Periodic benefit-risk assessment using Bayesian stochastic multi-criteria acceptability analysis.

Benefit-risk (BR) assessment is essential to ensure the best decisions are made for a medical product in the clinical development process, regulatory marketing authorization, post-market surveillance, and coverage and reimbursement decisions. One challenge of BR assessment in practice is that the benefit and risk profile may keep evolving while new evidence is accumulating. Regulators and the International Conference on Harmonization (ICH) recommend performing periodic benefit-risk evaluation report (PBRER) through the product's lifecycle. In this paper, we propose a general statistical framework for periodic benefit-risk assessment, in which Bayesian meta-analysis and stochastic multi-criteria acceptability analysis (SMAA) will be combined to synthesize the accumulating evidence. The proposed approach allows us to compare the acceptability of different drugs dynamically and effectively and accounts for the uncertainty of clinical measurements and imprecise or incomplete preference information of decision makers. We apply our approaches to two real examples in a post-hoc way for illustration purpose. The proposed method may easily be modified for other pre and post market settings, and thus be an important complement to the current structured benefit-risk assessment (sBRA) framework to improve the transparent and consistency of the decision-making process.