Benefit-Risk Analysis for Decision-Making: An Approach

Benefit-Risk Assessment of Vaccines. Part I: A Systematic Review to Identify and Describe Studies About Quantitative Benefit-Risk Models Applied to Vaccines

Introduction

Understanding the balance between the benefits and risks of vaccination is essential to ensure informed and adequate public health decision making. Quantitative benefit–risk models (qBRm) represent useful tools to help decision makers with supporting benefit–risk assessment throughout the lifecycle of a medical product. However, few initiatives have been launched to harmonise qBRm approaches, specifically for vaccines.

Objectives

The aim of this paper was to identify publications about qBRm applied to vaccines through a systematic literature review, and to describe their characteristics.

Methods

Medline, Scopus and Institute for Scientific Information Web of Knowledge databases were searched to identify articles in English, published from database inceptions up to December 2019. The search strategy included the combination of three key concepts: ‘benefit–risk’, ‘modelling’ and ‘vaccines’. Data extracted included the modelling context and the methodological approaches used.

Results

Of 3172 publications screened, 48 original publications were included. Most of the selected studies were published over the past decade and focused on rotavirus (15), dengue (10) and influenza (6) vaccines. The majority (30) of studies reported analyses related to high-income countries. The methodology of the studies differed, particularly in modelling techniques, benefit–risk measures, and sensitivity analyses. The present work also pointed out a high level of variability in the quality of reporting across studies, with particular regard to input parameters and methodological approaches.

Conclusions

This review provides an extensive list of qBRm applied to vaccines. Discrepancies across studies were identified during our review. While the number of published qBRm studies is increasing, no reporting guidance for qBRm applied to vaccines is currently available. This may affect decision makers’ confidence in the results and their benefit–risk assessment(s); therefore, the development of such reporting guidance is highly needed.

Electronic supplementary material

The online version of this article (10.1007/s40264-020-00984-7) contains supplementary material, which is available to authorized users.

Using Discretely Integrated Condition Event Simulation To Construct Quantitative Benefit-Risk Models: The Example of Rotavirus Vaccination in France

PURPOSE

Although quantitative benefit-risk models (qBRms) are indisputably valuable tools for gaining comprehensive assessments of health care interventions, they are not systematically used, probably because they lack an integrated framework that provides methodologic structure and harmonization. An alternative that allows all stakeholders to design operational models starting from a standardized framework was recently developed: the discretely integrated condition event (DICE) simulation. The aim of the present work was to assess the feasibility of implementing a qBRm in DICE, using the example of rotavirus vaccination.

METHODS

A model of rotavirus vaccination was designed using DICE and implemented in spreadsheet software with 3 worksheets: Conditions, Events, and Outputs. Conditions held the information in the model; this information changed at Events, and Outputs were special Conditions that stored the results collected during the analysis. A hypothetical French birth cohort was simulated for the assessment of rotavirus vaccination over time. The benefits were estimated for up to 5 years, and the risks in the 7 days following rotavirus vaccination versus no vaccination were assessed, with the results expressed as benefit-risk ratios.

FINDINGS

This qBRm model required 8 Events, 38 Conditions, and 9 Outputs. Two Events cyclically updated the rates of rotavirus gastroenteritis (RVGE) and intussusception (IS) according to age. Vaccination occurred at 2 additional Events, according to the vaccination scheme applied in France, and affected the occurrence of the other Events. Outputs were the numbers of hospitalizations related to RVGE and to IS, and related deaths. The entire model was specified in a small set of tables contained in a 445-KB electronic workbook. Analyses showed that for each IS-related hospitalization or death caused, 1613 (95% credible interval, 1001-2800) RVGE-related hospitalizations and 787 (95% credible interval, 246-2691) RVGE-related deaths would be prevented by vaccination. These results are consistent with those from a published French study using similar inputs but a very different modeling approach.

IMPLICATIONS

A limitation of the DICE approach was the extended run time needed for completing the sensitivity analyses when implemented in the electronic worksheets. DICE provided a user-friendly integrated framework for developing qBRms and should be considered in the development of structured approaches to facilitate benefit-risk assessment.

Patient-Centered Approach to Benefit-Risk Characterization Using Number Needed to Benefit and Number Needed to Harm: Advanced Non-Small-Cell Lung Cancer

This work summarizes the benefit and risk of the results of clinical trials submitted to the US Food and Drug Administration of therapies for the treatment of non-small cell lung cancer (NSCLC) using number needed to benefit (NNB) and number needed to harm (NNH) metrics. NNB and NNH metrics have been reported as potentially being more patient centric and more intuitive to medical practitioners than more common metrics, such as the hazard ratio, and valuable to medical practitioners in complementing other metrics, such as the median time to event. This approach involved the characterization of efficacy and safety results in terms of NNB and NNH of 30 clinical trials in advanced NSCLC supporting US Food and Drug Administration approval decisions from 2003 to 2017. We assessed trends of NNB over time of treatment (eg, for programmed death 1 inhibitors) and variation of NNB across subpopulations (eg, characterized by epidermal growth factor receptor mutation, programmed death ligand 1 expression, Eastern Cooperative Oncology Group performance status, age, and extent of disease progression). Furthermore, the evolution of NNB of treatments for advanced NSCLC was charted from 2003 to 2017. Across subpopulations, NNB, on average, was 4 patients for approved targeted therapies in molecularly enriched populations, 11 patients for approved therapies in nonmolecularly enriched populations, and 23 patients for withdrawn or unapproved therapies. Furthermore, the NNB analysis showed variation for attributes of epidermal growth factor receptor mutations, level of programmed death 1 expression, Eastern Cooperative Oncology Group performance status, etc. When considering the best-case subpopulations and available drugs, the NNB frontier reduced from an estimated value of 7.7 in 2003 to an estimated value of 2.5 in 2017 at the estimated median overall survival-equal to 6 months-of an untreated patient.

Targeting the Versatile Wnt/β-Catenin Pathway in Cancer Biology and Therapeutics: From Concept to Actionable Strategy

This expert review offers a critical synthesis of the latest insights and approaches at targeting the Wnt/β-catenin pathway in various cancers such as colorectal cancer, melanoma, leukemia, and breast and lung cancers. Notably, from organogenesis to cancer, the Wnt/β-catenin signaling displays varied and highly versatile biological functions in animals, with virtually all tissues requiring the Wnt/β-catenin signaling in one way or the other. Aberrant expression of the members of the Wnt/β-catenin has been implicated in many pathological conditions, particularly in human cancers. Mutations in the Wnt/β-catenin pathway genes have been noted in diverse cancers. Biochemical and genetic data support the idea that inhibition of Wnt/β-catenin signaling is beneficial in cancer therapeutics. The interaction of this important pathway with other signaling systems is also noteworthy, but remains as an area for further research and discovery. In addition, formation of different complexes by components of the Wnt/β-catenin pathway and the precise roles of these complexes in the cytoplasmic milieu are yet to be fully elucidated. This article highlights the latest medical technologies in imaging, single-cell omics, use of artificial intelligence (e.g., machine learning techniques), genome sequencing, quantum computing, molecular docking, and computational softwares in modeling interactions between molecules and predicting protein-protein and compound-protein interactions pertinent to the biology and therapeutic value of the Wnt/β-catenin signaling pathway. We discuss these emerging technologies in relationship to what is currently needed to move from concept to actionable strategies in translating the Wnt/β-catenin laboratory discoveries to Wnt-targeted cancer therapies and diagnostics in the clinic.

Accelerating anticancer drug development - opportunities and trade-offs

The traditional approach to drug development in oncology, with discrete phases of clinical testing, is becoming untenable owing to expansion of the precision medicine paradigm, whereby patients are stratified into multiple subgroups according to the underlying cancer biology. Seamless approaches to drug development in oncology hold great promise of accelerating the accessibility of novel therapeutic agents to the public but are also accompanied by important trade-offs, including the limited availability of information on the clinical benefit and safety of novel agents at the time of market entry. In this Perspectives article, we describe several opportunities, in the form of novel trial designs or modelling strategies, to improve the efficiency of drug development in oncology, as well as new mechanisms to obtain information about anticancer therapies throughout their life cycle, such as innovative functional imaging techniques or the use of real-world clinical data.

Using a Benefit-Risk Analysis Approach to Capture Regulatory Decision Making: Melanoma

Drug regulators seek to make decisions regarding drug approvals based on analysis of the relevant benefits and risks. In this work, 25 US Food and Drug Administration (FDA) decisions on melanoma drugs were identified and analyzed based on clinical trial results published between 1999 and 2017. In each case, the benefits and risks of the new drug in each clinical trial relative to a comparator (typically the control arm of the same clinical trial) were quantified. The benefits and risks were analyzed using a common scale to allow for direct comparison. A sensitivity analysis was conducted using vemurafenib to explore the magnitude of uncertainty in the quantitative assessments. The associated FDA decision outcomes of the new drugs were consistent with the benefits and risks quantified in this work.

Exposure-Response-Based Product Profile-Driven Clinical Utility Index for Ipatasertib Dose Selection in Prostate Cancer

The aims of this work were to characterize ipatasertib exposure–response (E‐R) relationships in a phase II study and to quantitatively assess benefit‐risk using a clinical utility index approach to support ipatasertib phase III dose selection in patients with metastatic castration‐resistant prostate cancer. Logistic regression and Cox proportional‐hazards models characterized E‐R relationships for safety and efficacy endpoints, respectively. Exposure metrics with and without considering dose interruptions/reductions (modifications) were tested in the E‐R models. Despite a steeper E‐R relationship when accounting for dose modifications, similar dose‐response projections were generated. The clinical utility index analysis assessed important attributes, weights, and clinically meaningful cutoff/tradeoff values based on predefined minimal, target, and optimistic product profiles. Ipatasertib 400 mg daily, showing the highest probability of achieving the minimal product profiles and better benefit‐risk balance than other doses (200–500 mg daily), was selected for further development in metastatic castration‐resistant prostate cancer.

A Benefit-Risk Analysis Approach to Capture Regulatory Decision-Making: Multiple Myeloma

Drug regulators around the world make decisions about drug approvability based on qualitative benefit-risk analysis. In this work, a quantitative benefit-risk analysis approach captures regulatory decision-making about new drugs to treat multiple myeloma (MM). MM assessments have been based on endpoints such as time to progression (TTP), progression-free survival (PFS), and objective response rate (ORR) which are different than benefit-risk analysis based on overall survival (OS). Twenty-three FDA decisions on MM drugs submitted to FDA between 2003 and 2016 were identified and analyzed. The benefits and risks were quantified relative to comparators (typically the control arm of the clinical trial) to estimate whether the median benefit-risk was positive or negative. A sensitivity analysis was demonstrated using ixazomib to explore the magnitude of uncertainty. FDA approval decision outcomes were consistent and logical using this benefit-risk framework.