Fifty years after thalidomide; what role for drug regulators?

The Challenges of Regulating Artificial Intelligence in Healthcare Comment on "Clinical Decision Support and New Regulatory Frameworks for Medical Devices: Are We Ready for It? - A Viewpoint Paper"

Regulation of health technologies must be rigorous, instilling trust among both healthcare providers and patients. This is especially important for the control and supervision of the growing use of artificial intelligence in healthcare. In this commentary on the accompanying piece by Van Laere and colleagues, we set out the scope for applying artificial intelligence in the healthcare sector and outline five key challenges that regulators face in dealing with these modern-day technologies. Addressing these challenges will not be easy. While artificial intelligence applications in healthcare have already made rapid progress and benefitted patients, these applications clearly hold even more potential for future developments. Yet it is vital that the regulatory environment keep up with this fast-evolving space of healthcare in order to anticipate and, to the extent possible, prevent the risks that may arise.

Associations between uncertainties identified by the European Medicines Agency and national decision making on reimbursement by HTA agencies

We aimed to determine whether uncertainties identified by the European Medicines Agency (EMA) were associated with negative relative effectiveness assessments (REAs) and negative overall reimbursement recommendations by national health technology assessment (HTA) agencies. Therefore, we identified all HTA reports from Haute Autorité de Santé (HAS; France), National Institute for Health and Care Excellence (NICE; England/Wales), Scottish Medicine Consortium (SMC; Scotland), and Zorginstituut Nederland (ZIN; The Netherlands) for a cohort of innovative medicines that the EMA had approved in 2009 to 2010 (excluding vaccines). Uncertainty regarding pivotal trial methodology, clinical outcomes, and their clinical relevance were combined to reflect a low, medium, or high level of uncertainty. We assessed associations by calculating risk ratios (RRs) and 95% confidence intervals (CIs), and agreement between REA and overall reimbursement recommendation outcomes. We identified 36 medicines for which 121 reimbursement recommendations had been issued by the HTA agencies between September 2009 and July 2018. High versus low uncertainty was associated with an increased risk for negative REAs and negative overall reimbursement recommendations: RRs 1.9 (95% CI 0.9-3.9) and 1.6 (95% CI 0.7-3.5), respectively, which was supported by further sensitivity analyses. We identified a lack of agreement between 33 (27%) REA and overall reimbursement recommendation outcomes, which were mostly restricted recommendations that followed on negative REAs in case of low or medium uncertainty. In conclusion, high uncertainty identified by the EMA was associated with negative REAs and negative overall reimbursement recommendations. To reduce uncertainty and ultimately facilitate efficient patient access, regulators, HTA agencies, and other stakeholders should discuss how uncertainties should be weighed and addressed early in the drug life cycle of innovative treatments.

Opportunities and challenges for the inclusion of patient preferences in the medical product life cycle: a systematic review

BACKGROUND

The inclusion of patient preferences (PP) in the medical product life cycle is a topic of growing interest to stakeholders such as academics, Health Technology Assessment (HTA) bodies, reimbursement agencies, industry, patients, physicians and regulators. This review aimed to understand the potential roles, reasons for using PP and the expectations, concerns and requirements associated with PP in industry processes, regulatory benefit-risk assessment (BRA) and marketing authorization (MA), and HTA and reimbursement decision-making.

METHODS

A systematic review of peer-reviewed and grey literature published between January 2011 and March 2018 was performed. Consulted databases were EconLit, Embase, Guidelines International Network, PsycINFO and PubMed. A two-step strategy was used to select literature. Literature was analyzed using NVivo (QSR international).

RESULTS

From 1015 initially identified documents, 72 were included. Most were written from an academic perspective (61%) and focused on PP in BRA/MA and/or HTA/reimbursement (73%). Using PP to improve understanding of patients' valuations of treatment outcomes, patients' benefit-risk trade-offs and preference heterogeneity were roles identified in all three decision-making contexts. Reasons for using PP relate to the unique insights and position of patients and the positive effect of including PP on the quality of the decision-making process. Concerns shared across decision-making contexts included methodological questions concerning the validity, reliability and cognitive burden of preference methods. In order to use PP, general, operational and quality requirements were identified, including recognition of the importance of PP and ensuring patient understanding in PP studies.

CONCLUSIONS

Despite the array of opportunities and added value of using PP throughout the different steps of the MPLC identified in this review, their inclusion in decision-making is hampered by methodological challenges and lack of specific guidance on how to tackle these challenges when undertaking PP studies. To support the development of such guidance, more best practice PP studies and PP studies investigating the methodological issues identified in this review are critically needed.

From Individual to Population Preferences: Comparison of Discrete Choice and Dirichlet Models for Treatment Benefit-Risk Tradeoffs

Introduction. The Dirichlet distribution has been proposed for representing preference heterogeneity, but there is limited evidence on its suitability for modeling population preferences on treatment benefits and risks. Methods. We conducted a simulation study to compare how the Dirichlet and standard discrete choice models (multinomial logit [MNL] and mixed logit [MXL]) differ in their convergence to stable estimates of population benefit-risk preferences. The source data consisted of individual-level tradeoffs from an existing 3-attribute patient preference study (N = 560). The Dirichlet population model was fit directly to the attribute weights in the source data. The MNL and MXL population models were fit to the outcomes of a simulated discrete choice experiment in the same sample of 560 patients. Convergence to the parameter values of the Dirichlet and MNL population models was assessed with sample sizes ranging from 20 to 500 (100 simulations per sample size). Model variability was also assessed with coefficient P values. Results. Population preference estimates of all models were very close to the sample mean, and the MNL and MXL models had good fit (McFadden's adjusted R² = 0.12 and 0.13). The Dirichlet model converged reliably to within 0.05 distance of the population preference estimates with a sample size of 100, where the MNL model required a sample size of 240 for this. The MNL model produced consistently significant coefficient estimates with sample sizes of 100 and higher. Conclusion. The Dirichlet model is likely to have smaller sample size requirements than standard discrete choice models in modeling population preferences for treatment benefit-risk tradeoffs and is a useful addition to health preference analyst's toolbox.

The Human Genome Editing Race: Loosening Regulatory Standards for Commercial Advantage?

Medicinal products based on genome editing must undergo rigorous preclinical testing and are subject to regulatory oversight for proper risk assessment prior to first evaluation in humans. We give a European perspective on the regulatory expectations to translate genome editing to the clinic to ensure their timely progress to market.

Quantifying benefit-risk preferences for new medicines in rare disease patients and caregivers

Background

Rare disease patients and caregivers face uncommon, serious, debilitating conditions often characterised by poor prognosis and limited treatment options. This study aimed to explore what they consider of value when choosing between hypothetical therapeutic options and to quantify both their benefit-risk preferences and the influence of disease context.

Methods

A mixed-methods survey with patients and caregivers was conducted in the United Kingdom across a range of rare diseases. Discrete-choice experiments that compared hypothetical treatment profiles of benefits and risks were used to measure respondent preferences across a set of seven attributes related to health outcomes, safety, and process of care. Bespoke questions on current disease management and the joint use of the 12-item WHODAS 2.0 questionnaire and of two Likert scales capturing self- and proxy-assessed disease-induced threat to life and impairment were implemented to describe disease context. Additionally, qualitative insights on the definitions of value and risk were collected from respondents.

Results

Final study sample included 721 patients and 152 informal caregivers, across 52 rare diseases. When choosing between hypothetical novel treatments for rare diseases, respondents attributed most importance to drug response, risk of serious side effects, and the ability to conduct usual activities while on treatment. In contrast, attributes related to treatment modalities were the least important. Respondents expressed a willingness to accept risks in hopes of finding some benefit, such as a higher chance of drug response or greater health improvement potential. Increasing disease severity, impairment or disability, and the lack of effective therapeutic options were shown to raise significantly the willingness to gain benefit through increased risk.

Conclusions

This is the first study performing a quantitative discrete choice experiment amongst patients and caregivers across 52 rare conditions. It enables a more detailed understanding of the relationship between disease context, treatment attributes and the degree of risk respondents are willing to take to gain a specific degree of benefit. Researchers of novel therapeutics for rare diseases should be encouraged to invest in preference elicitation studies to generate rigorous patient evidence and specific regulatory guidance should be issued to acknowledge their importance and their use in marketing authorisations.

Electronic supplementary material

The online version of this article (doi:10.1186/s13023-016-0444-9) contains supplementary material, which is available to authorized users.