Integrating risk minimization planning throughout the clinical development and commercialization lifecycle: an opinion on how drug development could be improved

Behavioral Science: Enhancing Our Approach to the Development of Effective Additional Risk Minimization Strategies

Additional risk minimization strategies may be required to assure a positive benefit-risk balance for some therapeutic products associated with serious adverse drug reactions/risks of use, without which these products may be otherwise unavailable to patients. The goals of risk minimization strategies are often fundamentally to influence the behavior of healthcare professionals (HCPs) and/or patients and can include appropriate patient selection, provision of education and counselling, appropriate medication use, adverse drug reaction monitoring, and adoption of other elements to assure safe use, such as pregnancy prevention. Current approaches to additional risk minimization strategy development rely heavily on information provision, without full consideration of the contextual factors and multi-level influences on patient and HCP behaviors that impact adoption and long-term adherence to these interventions. Application of evidence-based behavioral science methods are urgently needed to improve the quality and effectiveness of these strategies. Evidence from the fields of adherence, health promotion, and drug utilization research underscores the value and necessity for using established behavioral science frameworks and methods if we are to achieve clinical safety goals for patients. The current paper aims to enhance additional risk minimization strategy development and effectiveness by considering how a behavioral science approach can be applied, drawing from evidence in understanding of engagement with pharmaceutical medicines as well as wider public health interventions for patients and HCPs.

Network model-based screen for FDA-approved drugs affecting cardiac fibrosis

Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.

Linked Pharmacometric-Pharmacoeconomic Modeling and Simulation in Clinical Drug Development

Market access and pricing of pharmaceuticals are increasingly contingent on the ability to demonstrate comparative effectiveness and cost-effectiveness. As such, it is widely recognized that predictions of the economic potential of drug candidates in development could inform decisions across the product life cycle. This may be challenging when safety and efficacy profiles in terms of the relevant clinical outcomes are unknown or highly uncertain early in product development. Linking pharmacometrics and pharmacoeconomics, such that outputs from pharmacometric models serve as inputs to pharmacoeconomic models, may provide a framework for extrapolating from early-phase studies to predict economic outcomes and characterize decision uncertainty. This article reviews the published studies that have implemented this methodology and used simulation to inform drug development decisions and/or to optimize the use of drug treatments. Some of the key practical issues involved in linking pharmacometrics and pharmacoeconomics, including the choice of final outcome measures, methods of incorporating evidence on comparator treatments, approaches to handling multiple intermediate end points, approaches to quantifying uncertainty, and issues of model validation are also discussed. Finally, we have considered the potential barriers that may have limited the adoption of this methodology and suggest that closer alignment between the disciplines of clinical pharmacology, pharmacometrics, and pharmacoeconomics, may help to realize the potential benefits associated with linked pharmacometric-pharmacoeconomic modeling and simulation.

Evaluation of the Effectiveness of Additional Risk Minimization Measures for Voriconazole in the EU: Findings and Lessons Learned from a Healthcare Professional Survey

BACKGROUND

Voriconazole is an extended-spectrum antifungal agent approved for the treatment and prophylaxis of invasive aspergillosis and other serious fungal infections. In 2014, additional risk minimization measures (aRMM) consisting of a Healthcare Professional (HCP) Question and Answer (Q&A) Brochure, HCP Checklist, and Patient Alert Card were implemented on a rolling basis across the European Union (EU) to mitigate three key risks with voriconazole: phototoxicity, squamous cell carcinoma (SCC) of the skin, and hepatotoxicity. The risks of phototoxicity and hepatotoxicity have been documented in the Summary of Product Characteristics (SmPC) since voriconazole was first approved in the EU in 2002. However, the risk of SCC of the skin was a more recent addition to the SmPC (added in 2010).

OBJECTIVES

We evaluated the effectiveness of the aRMM, as per EU Good Pharmacovigilance Practices Module XVI, via a survey of HCPs.

METHODS

An online survey was conducted among specialty care HCPs in 10 EU countries who had received by mail aRMM tools 12 months previously. Survey questions evaluated HCPs' receipt and utilization of aRMM tools, and knowledge of the three risks.

RESULTS

Of 27,396 HCPs invited to participate, 332 eligible respondents completed the survey (response rate: 447/26,735; 1.7%). In total, 19.6% of respondents recalled receiving the HCP Q&A Brochure, 22.6% the HCP Checklist, and 25.9% the Patient Alert Card. HCPs had a high level of knowledge of phototoxicity and hepatotoxicity; however, knowledge of SCC was lower. Knowledge of the three risks and self-reported risk minimization behavior was slightly improved in those who had read the HCP Q&A Brochure compared with those who had not.

CONCLUSION

The effectiveness of the voriconazole aRMM cannot be meaningfully inferred from the results due to the low survey response rate. The assessment indirectly points to the SmPC or other resources being the main source of risk information for HCPs. Engaging HCPs before designing and implementing an aRMM program is crucial to ensure an effective and focused program. (EU PAS registration number: EUPAS12624).

EU postmarket surveillance plans for medical devices

Purpose

Recent public health safety issues involving medical devices have led to a growing demand to improve the current passive‐reactive postmarket surveillance (PMS) system. Various European Union (EU) national competent authorities have started to focus on strengthening the postmarket risk evaluation. As a consequence, the new EU medical device regulation was published; it includes the concept of a PMS Plan.

Methods

This publication reviewed Annex III Technical Documentation on PMS and Annex XIV Part B: Postmarket clinical follow‐up from the new Regulation (EU) 2017/745 of the European Parliament and of the Council on medical devices.

Results

The results of the PMS activities will be described in the PMS plan and will be used to update other related documents. A modular approach to structure the contents of the PMS plan will help to consistently update other PMS information. It is our suggestion that the PMS plan should consist of a PMS plan Core and a PMS plan Supplement. The PMS plan Core document will describe the PMS system, and the PMS plan Supplement will outline the specific activities performed by the manufacturer for a particular medical device.

Conclusions

The PMS plan may serve as a thorough tool for the benefit‐risk evaluation of medical devices. If properly developed and implemented, it will function as a key player in the establishment of a new framework for proactive safety evaluation of medical devices.