Long-term safety and tolerability of ambrisentan treatment for pediatric patients with pulmonary arterial hypertension: An open-label extension study.

This open-label, extension study assessed long-term safety, tolerability, and efficacy of ambrisentan in a pediatric population (age 8- < 18 years) with pulmonary arterial hypertension (PAH). Following completion of a 6-month, randomized study, participants entered the long-term extension at individualized ambrisentan dosages (2.5/5/7.5 or 10 mg/day). Safety assessments included adverse events (AEs), AEs of special interest, and serious AEs (SAEs); efficacy outcomes included 6-min walking distance (6MWD) and World Health Organization functional class (WHO FC). Thirty-eight of 41 (93%) randomized study participants entered the extension; 21 (55%) completed (reaching age 18 years). Most participants received concomitant phosphodiesterase-5 inhibitors (n = 25/38, 66%). Median ambrisentan exposure was 3.5 years. Most participants experienced ≥ 1 AE (n = 34/38, 89%), and 21 (55%) experienced SAEs, most commonly worsening PAH (n = 3/38, 8%), acute cardiac failure, pneumonia, or anemia (n = 2/38; 5% each); none considered ambrisentan-related. Seven participants (18%) died, with recorded reasons (MedDRA preferred term): cardiac failure (n = 2), PAH (n = 2), COVID-19 (n = 1), acute right ventricular failure (n = 1), and failure to thrive (n = 1); median time to death: 5.2 years. Anemia and hepatotoxicity AEs were generally mild to moderate and did not require ambrisentan dose adjustment. Assessed at study end in 29 participants (76%), mean 6MWD improved by 17% (standard deviation: 34.3%), and all (29/29, 100%) had improved or unchanged WHO FC.    Conclusion: Long-term weight-based ambrisentan dosing, alone or combined with other PAH therapies in children with PAH aged 8- < 18 years, exhibited tolerability and clinical improvements consistent with prior randomized study results.    Trial registration: NCT01342952, April 27, 2011. What is Known: • The endothelin receptor antagonist, ambrisentan, is indicated for treatment of pulmonary arterial hypertension (PAH). Previous studies have shown similar efficacy and tolerability in pediatric patients as in adults. What is New: • This open-label extension study assessed the long-term use of ambrisentan in pediatric patients (8-<18 years) with PAH, most of whom were also receiving recommended background PAH treatment. • Weight-based dosing of ambrisentan, given alone or in combination with other PAH therapies, was well tolerated with clinical improvements consistent with prior randomized study results.

The next-generation Open Targets Platform: reimagined, redesigned, rebuilt.

The Open Targets Platform (https://platform.opentargets.org/) is an open source resource to systematically assist drug target identification and prioritisation using publicly available data. Since our last update, we have reimagined, redesigned, and rebuilt the Platform in order to streamline data integration and harmonisation, expand the ways in which users can explore the data, and improve the user experience. The gene-disease causal evidence has been enhanced and expanded to better capture disease causality across rare, common, and somatic diseases. For target and drug annotations, we have incorporated new features that help assess target safety and tractability, including genetic constraint, PROTACtability assessments, and AlphaFold structure predictions. We have also introduced new machine learning applications for knowledge extraction from the published literature, clinical trial information, and drug labels. The new technologies and frameworks introduced since the last update will ease the introduction of new features and the creation of separate instances of the Platform adapted to user requirements. Our new Community forum, expanded training materials, and outreach programme support our users in a range of use cases.

Polygenic risk scores for schizophrenia and major depression are associated with socio-economic indicators of adversity in two British community samples.

Schizophrenia (SCZ) and major depressive disorder (MDD) are complex psychiatric disorders which contribute substantially to the global burden of disease. Both psychopathologies are heritable with some genetic overlap between them. Importantly, SCZ and MDD have also been found to be associated with environmental risk factors. However, rather than being independent of genetic influences, exposure to environmental risk factors may be under genetic control, known as gene-environment correlation (rGE). In this study we investigated rGE in relation to polygenic risk scores for SCZ and MDD in adults, derived from large genome-wide association studies, across two different British community samples: Understanding Society (USoc) and the National Child Development Study (NCDS). We tested whether established environmental risk factors for SCZ and/or MDD are correlated with polygenic scores in adults and whether these associations differ between the two disorders and cohorts. Findings partially overlapped between disorders and cohorts. In NCDS, we identified a significant correlation between the genetic risk for MDD and an indicator of low socio-economic status, but no significant findings emerged for SCZ. In USoc, we replicated associations between indicators of low socio-economic status and the genetic propensity for MDD. In addition, we identified associations between the genetic susceptibility for SCZ and being single or divorced. Results across both studies provide further evidence that the genetic risk for SCZ and MDD were associated with common environmental risk factors, specifically MDD's association with lower socio-economic status.

Gene-Environment Correlation over Time: A Longitudinal Analysis of Polygenic Risk Scores for Schizophrenia and Major Depression in Three British Cohorts Studies.

Research suggests that both genetic and environmental risk factors are involved in the aetiology of schizophrenia (SCZ) and major depressive disorder (MDD). Importantly, environmental and genetic risk factors are often related as evidenced in gene-environment correlation (rGE), which describes the observation that genetic and environmental factors are associated with each other. It is understood that rGE gets stronger over time as individuals select their environments more actively based on their genetic propensities. However, little is known whether rGEs remain stable over time or change across different development periods. Using data from three British longitudinal cohorts, we investigated whether rGE patterns of polygenic risk scores (PRS) for SCZ and MDD changed over time across childhood and adulthood, as well as across both from birth to age 55 and whether results differed between SCZ and MDD. Overall, the majority of rGEs remained stable across the investigated development periods. Furthermore, the few detected rGE changes which did differ between SCZ and MDD, could not be explained by the confounding of clinical cases and are therefore likely the result of actual changes in environmental and cultural risk factors with genetic susceptibility to SCZ and MDD likely playing a less significant role.

Polygenic scores for schizophrenia and major depression are associated with psychosocial risk factors in children: evidence of gene-environment correlation.

BACKGROUND: Whilst genetic and environmental risk factors for schizophrenia (SCZ) and major depressive disorder (MDD) have been established, it is unclear whether exposure to environmental risk factors is genetically confounded by passive, evocative or active gene-environment correlation (rGE). STUDY OBJECTIVE: This study aims to investigate: (a) whether the genetic risk for SCZ/MDD in children is correlated with established environmental and psychosocial risk factors in two British community samples, the 1958 National Child Development Study (NCDS) and the Millennium Cohort Study (MCS), (b) whether these associations vary between both psychopathologies, and (c) whether findings differ across the two cohorts which were born 42 years apart. METHODS: Polygenic risk scores (PRS) from existing large genome-wide associations studies (GWAS) were applied to test the correlation between the child genetic risk for SCZ/MDD and known environmental risk factors. In addition, parental and child genetic data from MCS were used to distinguish between passive and evocative rGE. RESULTS: The child polygenic risk for SCZ and MDD was correlated with single parenthood in MCS. Moreover, the lack of father's involvement in child care was associated with the genetic risk for SCZ in NCDS. However, we also found associations between several indicators of low socioeconomic status and heightened genetic risk for MDD in children in both cohorts. Further, the genetic risk for MDD was associated with parental lack of interest in the child's education in NCDS as well as more maternal smoking and less maternal alcohol consumption during childhood in MCS. According to sensitivity analyses in MCS (controlling for parental genotype), more than half of our significant correlations reflected passive rGE. CONCLUSIONS: Findings suggest that several established environmental and psychosocial risk factors for SCZ and MDD are at least partially associated with children's genetic risk for these psychiatric disorders.

Open Targets Platform: supporting systematic drug-target identification and prioritisation.

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target-disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.

Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics.

Open Targets Genetics (https://genetics.opentargets.org) is an open-access integrative resource that aggregates human GWAS and functional genomics data including gene expression, protein abundance, chromatin interaction and conformation data from a wide range of cell types and tissues to make robust connections between GWAS-associated loci, variants and likely causal genes. This enables systematic identification and prioritisation of likely causal variants and genes across all published trait-associated loci. In this paper, we describe the public resources we aggregate, the technology and analyses we use, and the functionality that the portal offers. Open Targets Genetics can be searched by variant, gene or study/phenotype. It offers tools that enable users to prioritise causal variants and genes at disease-associated loci and access systematic cross-disease and disease-molecular trait colocalization analysis across 92 cell types and tissues including the eQTL Catalogue. Data visualizations such as Manhattan-like plots, regional plots, credible sets overlap between studies and PheWAS plots enable users to explore GWAS signals in depth. The integrated data is made available through the web portal, for bulk download and via a GraphQL API, and the software is open source. Applications of this integrated data include identification of novel targets for drug discovery and drug repurposing.