Enabling global clinical collaborations on identifiable patient data: The Minerva Initiative

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health

Additional safety risk to exceptionally approved drugs in Europe?

AIMS Regulatory requirements for new drugs have increased. Special approval procedures with priority assessment are possible for drugs with clear 'unmet medical need'. We question whether these Exceptional Circumstances (EC) or Conditional Approval (CA) procedures have led to a higher probability of serious safety issues. METHODS A retrospective cohort study was performed of new drugs approved in Europe between 1999 and 2009. The determinant was EC/CA vs. standard procedure approval. Outcome variables were frequency and timing of a first Direct Healthcare Professional Communication (DHPC). An association between approval procedure and the time from market approval to DHPC was assessed using Kaplan-Meyer survival analysis and Cox-regression to correct for covariates. RESULTS In total 289 new drugs were approved. Forty-six (16.4%) were approved under EC or CA, of which seven received a DHPC (15%). This was similar to the standard approval drugs (243), of which 33 received one or more DHPC (14%, P = 0.77). The probability of acquiring a DHPC for standard approval drugs vs. EC/CA drugs during 11-year follow-up is 22% (95% CI 14%, 29%) and 26% (95% CI 8%, 44%), respectively (log-rank P = 0.726). This difference remained not significant in the Cox-regression model: hazard ratio 0.94 (95% CI 0.40, 2.20). Only drug type was identified as a confounding covariate. CONCLUSION The EC/CA procedure is not associated with a higher probability of DHPCs despite limited clinical development data. These data do not support the view that early drug approval increases the risk of serious safety issues emerging after market approval

"Threshold-crossing": A Useful Way to Establish the Counterfactual in Clinical Trials?

A central question in the assessment of benefit/harm of new treatments is: how does the average outcome on the new treatment (the factual) compare to the average outcome had patients received no treatment or a different treatment known to be effective (the counterfactual)? Randomized controlled trials (RCTs) are the standard for comparing the factual with the counterfactual. Recent developments necessitate and enable a new way of determining the counterfactual for some new medicines. For select situations, we propose a new framework for evidence generation, which we call "threshold-crossing." This framework leverages the wealth of information that is becoming available from completed RCTs and from real world data sources. Relying on formalized procedures, information gleaned from these data is used to estimate the counterfactual, enabling efficacy assessment of new drugs. We propose future (research) activities to enable "threshold-crossing" for carefully selected products and indications in which RCTs are not feasible

Human disease genes website series: An international, open and dynamic library for up-to-date clinical information

Since the introduction of next-generation sequencing, an increasing number of disorders have been discovered to have genetic etiology. To address diverse clinical questions and coordinate research activities that arise with the identification of these rare disorders, we developed the Human Disease Genes website series (HDG website series): an international digital library that records detailed information on the clinical phenotype of novel genetic variants in the human genome (https://humandiseasegenes.info/). Each gene website is moderated by a dedicated team of clinicians and researchers, focused on specific genes, and provides up-to-date—including unpublished—clinical information. The HDG website series is expanding rapidly with 424 genes currently adopted by 325 moderators from across the globe. On average, a gene website has detailed phenotypic information of 14.4 patients. There are multiple examples of added value, one being the ARID1B gene website, which was recently utilized in research to collect clinical information of 81 new patients. Additionally, several gene websites have more data available than currently published in the literature. In conclusion, the HDG website series provides an easily accessible, open and up-to-date clinical data resource for patients with pathogenic variants of individual genes. This is a valuable resource not only for clinicians dealing with rare genetic disorders such as developmental delay and autism, but other professionals working in diagnostics and basic research. Since the HDG website series is a dynamic platform, its data also include the phenotype of yet unpublished patients curated by professionals providing higher quality clinical detail to improve management of these rare disorders