Monitoring of Therapy for Mucopolysaccharidosis Type I Using Dysmorphometric Facial Phenotypic Signatures

Increasing Diversity, Equity, Inclusion, and Accessibility in Rare Disease Clinical Trials

Diversity, equity, inclusion, and accessibility (DEIA) are foundational principles for clinical trials and medical research. In rare diseases clinical research, where numbers of participants are already challenged by rarity itself, maximizing inclusion is of particular importance to clinical trial success, as well as ensuring the generalizability and relevance of the trial results to the people affected by these diseases. In this article, we review the medical and gray literature and cite case examples to provide insights into how DEIA can be proactively integrated into rare diseases clinical research. Here, we particularly focus on genetic diversity. While the rare diseases DEIA literature is nascent, it is accelerating as many patient advocacy groups, professional societies, training and educational organizations, researcher groups, and funders are setting intentional strategies to attain DEIA goals moving forward, and to establish metrics to ensure continued improvement. Successful examples in underserved and underrepresented populations are available that can serve as case studies upon which rare diseases clinical research programs can be built. Rare diseases have historically been innovation drivers in basic, translational, and clinical research, and ultimately, all populations benefit from data diversity in rare diseases populations that deliver novel insights and approaches to how clinical research can be performed.

The case for open science: rare diseases

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally.

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.

Advances in computer-assisted syndrome recognition by the example of inborn errors of metabolism

Significant improvements in automated image analysis have been achieved in recent years and tools are now increasingly being used in computer-assisted syndromology. However, the ability to recognize a syndromic facial gestalt might depend on the syndrome and may also be confounded by severity of phenotype, size of available training sets, ethnicity, age, and sex. Therefore, benchmarking and comparing the performance of deep-learned classification processes is inherently difficult. For a systematic analysis of these influencing factors we chose the lysosomal storage diseases mucolipidosis as well as mucopolysaccharidosis type I and II that are known for their wide and overlapping phenotypic spectra. For a dysmorphic comparison we used Smith-Lemli-Opitz syndrome as another inborn error of metabolism and Nicolaides-Baraitser syndrome as another disorder that is also characterized by coarse facies. A classifier that was trained on these five cohorts, comprising 289 patients in total, achieved a mean accuracy of 62%. We also developed a simulation framework to analyze the effect of potential confounders, such as cohort size, age, sex, or ethnic background on the distinguishability of phenotypes. We found that the true positive rate increases for all analyzed disorders for growing cohorts (n = [10...40]) while ethnicity and sex have no significant influence. The dynamics of the accuracies strongly suggest that the maximum distinguishability is a phenotype-specific value, which has not been reached yet for any of the studied disorders. This should also be a motivation to further intensify data sharing efforts, as computer-assisted syndrome classification can still be improved by enlarging the available training sets.

3-Dimensional Facial Analysis-Facing Precision Public Health

Precision public health is a new field driven by technological advances that enable more precise descriptions and analyses of individuals and population groups, with a view to improving the overall health of populations. This promises to lead to more precise clinical and public health practices, across the continuum of prevention, screening, diagnosis, and treatment. A phenotype is the set of observable characteristics of an individual resulting from the interaction of a genotype with the environment. Precision (deep) phenotyping applies innovative technologies to exhaustively and more precisely examine the discrete components of a phenotype and goes beyond the information usually included in medical charts. This form of phenotyping is a critical component of more precise diagnostic capability and 3-dimensional facial analysis (3DFA) is a key technological enabler in this domain. In this paper, we examine the potential of 3DFA as a public health tool, by viewing it against the 10 essential public health services of the “public health wheel,” developed by the US Centers for Disease Control. This provides an illustrative framework to gage current and emergent applications of genomic technologies for implementing precision public health.