Aim
Repurposing strategies to address the COVID‐19 pandemic have been accelerated. As both pregnant and paediatric patients are likely to be excluded from most planned investigations, the list of ...repurposed options and the available data on these drugs and vaccines provide a baseline risk assessment and identify gaps for targeted investigation.
Methods
Clinical trials have been searched and reviewed; 23 repurposed drugs and drug combinations and nine candidate vaccines have been assessed regarding the availability of relevant data in paediatrics and pregnant women and to evaluate expected or unanticipated risk.
Results
Thirteen of the repurposed drugs or drug combinations are indicated for use in paediatrics in some age category albeit for indications other than COVID‐19; 10 of these are indicated for use in pregnant women. Even in cases where these drugs are indicated in the populations, source data from which safety and or dosing could be extrapolated for use in COVID‐19 is sparse. Vaccine trials are ongoing and generally exclude pregnant women; only in a few instances have paediatric subgroups been planned for enrolment. Data from individual case studies and RWD may suggest that subpopulations of both paediatric patients and pregnant women may be more at risk, particularly those in an increased inflammatory state.
Conclusion
In conjunction with more prospective collaboration, plans are evolving to ensure that we will be better prepared to address similar situations especially in paediatrics and pregnant women where experience is limited and actual practice relies heavily on leveraging data from other populations and indications.
Genome-wide association studies (GWAS) have identified thousands of robust and replicable genetic associations for complex disease. However, the identification of the causal variants that underlie ...these associations has been more difficult. This problem of fine-mapping association signals predates GWAS, but the last few years have seen a surge of studies aimed at pinpointing causal variants using both statistical evidence from large association data sets and functional annotations of genetic variants. Combining these two approaches can often determine not only the causal variant but also the target gene. Recent contributions include analyses of custom genotyping arrays, such as the Immunochip, statistical methods to identify credible sets of causal variants and the addition of functional genomic annotations for coding and non-coding variation to help prioritize variants and discern functional consequence and hence the biological basis of disease risk.
Genome-wide association studies (GWASs) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. In the present study, we present an open ...resource that provides systematic fine mapping and gene prioritization across 133,441 published human GWAS loci. We integrate genetics (GWAS Catalog and UK Biobank) with transcriptomic, proteomic and epigenomic data, including systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues. We identify 729 loci fine mapped to a single-coding causal variant and colocalized with a single gene. We trained a machine-learning model using the fine-mapped genetics and functional genomics data and 445 gold-standard curated GWAS loci to distinguish causal genes from neighboring genes, outperforming a naive distance-based model. Our prioritized genes were enriched for known approved drug targets (odds ratio = 8.1, 95% confidence interval = 5.7, 11.5). These results are publicly available through a web portal ( http://genetics.opentargets.org ), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.
Attempting to classify patients into high or low risk for disease onset or outcomes is one of the cornerstones of epidemiology. For some (but by no means all) diseases, clinically usable risk ...prediction can be performed using classical risk factors such as body mass index, lipid levels, smoking status, family history and, under certain circumstances, genetics (e.g. BRCA1/2 in breast cancer). The advent of genome-wide association studies (GWAS) has led to the discovery of common risk loci for the majority of common diseases. These discoveries raise the possibility of using these variants for risk prediction in a clinical setting. We discuss the different ways in which the predictive accuracy of these loci can be measured, and survey the predictive accuracy of GWAS variants for 18 common diseases. We show that predictive accuracy from genetic models varies greatly across diseases, but that the range is similar to that of non-genetic risk-prediction models. We discuss what factors drive differences in predictive accuracy, and how much value these predictions add over classical predictive tests. We also review the uses and pitfalls of idealized models of risk prediction. Finally, we look forward towards possible future clinical implementation of genetic risk prediction, and discuss realistic expectations for future utility.
Association studies involve accessing, parsing, generating, and analyzing large volumes of data, often carried out in many steps over many months. Large-scale surveys of genetic variation, such as ...the International HapMap Project, and rapidly increasing volumes of single-nucleotide polymorphism (SNP) genotyping data have created exciting opportunities for association studies. However, they have further exacerbated the difficulty of curating and analyzing such data. Haploview is a program developed in Mark Daly's lab at the Broad Institute of MIT and Harvard, which is designed to bundle many everyday analysis tasks into one easy-to-use package. Haploview has several features that are useful throughout different phases of association studies. Several of these features are illustrated in this article by following a hypothetical association study from design to execution. Haploview is used to (1) analyze HapMap data and choose tag-SNPs, (2) evaluate the quality of disease genotype data, (3) test for association, and (4) evaluate a region for follow-up of a positive association.
Blood lipids and metabolites are markers of current health and future disease risk. Here, we describe plasma nuclear magnetic resonance (NMR) biomarker data for 118,461 participants in the UK ...Biobank. The biomarkers cover 249 measures of lipoprotein lipids, fatty acids, and small molecules such as amino acids, ketones, and glycolysis metabolites. We provide an atlas of associations of these biomarkers to prevalence, incidence, and mortality of over 700 common diseases ( nightingalehealth.com/atlas ). The results reveal a plethora of biomarker associations, including susceptibility to infectious diseases and risk of various cancers, joint disorders, and mental health outcomes, indicating that abundant circulating lipids and metabolites are risk markers beyond cardiometabolic diseases. Clustering analyses indicate similar biomarker association patterns across different disease types, suggesting latent systemic connectivity in the susceptibility to a diverse set of diseases. This work highlights the value of NMR based metabolic biomarker profiling in large biobanks for public health research and translation.
Abstract The major inflammatory bowel diseases, Crohn's disease and ulcerative colitis, are both debilitating disorders of the gastrointestinal tract, characterized by a dysregulated immune response ...to unknown environmental triggers. Both disorders have an important and overlapping genetic component, and much progress has been made in the last 20 years at elucidating some of the specific factors contributing to disease pathogenesis. Here we review our growing understanding of the immunogenetics of inflammatory bowel disease, from the twin studies that first implicated a role for the genome in disease susceptibility to the latest genome-wide association studies that have identified hundreds of associated loci. We consider the insight this offers into the biological mechanisms of the inflammatory bowel diseases, such as autophagy, barrier defence and T-cell differentiation signalling. We reflect on these findings in the context of other immune-related disorders, both common and rare. These observations include links both obvious, such as to pediatric colitis, and more surprising, such as to leprosy. As a changing picture of the underlying genetic architecture emerges, we turn to future directions for the study of complex human diseases such as these, including the use of next generation sequencing technologies for the identification of rarer risk alleles, and potential approaches for narrowing down associated loci to casual variants. We consider the implications of this work for translation into clinical practice, for example via early therapeutic hypotheses arising from our improved understanding of the biology of inflammatory bowel disease. Finally, we present potential opportunities to better understand environmental risk factors, such as the human microbiota in the context of immunogenetics.
Pregnant women are still viewed as therapeutic orphans to the extent that they are avoided as participants in mainstream clinical trials and not considered a priority for targeted drug research ...despite the fact that many clinical conditions exist during pregnancy for which pharmacotherapy is warranted. Part of the challenge is the uncertain risk potential that pregnant women represent in the absence of timely and costly toxicology and developmental pharmacology studies, which only partly mitigate such risks. Even when clinical trials are conducted in pregnant women, they are often underpowered and absent biomarkers and exclude evaluation across multiple stages of pregnancy where relevant development risk could have been assessed. Quantitative systems pharmacology model development has been proposed as one solution to fill knowledge gaps, make earlier and perhaps more informed risk assessment, and design more informative trials with better recommendations for biomarker and end point selection including design and sample size optimality. Funding for translational research in pregnancy is limited but will fill some of these gaps, especially when joined with ongoing clinical trials in pregnancy that also fill certain knowledge gaps, especially biomarker and end point evaluation across pregnancy states linked to clinical outcomes. Opportunities exist for further advances in quantitative systems pharmacology model development with the inclusion of real‐world data sources and complimentary artificial intelligence/machine learning approaches. The successful coordination of the approach reliant on these new data sources will require commitments to share data and a diverse multidisciplinary group that seeks to develop open science models that benefit the entire research community, ensuring that such models can be used with high fidelity. New data opportunities and computational resources are highlighted in an effort to project how these efforts can move forward.
PDF
