Target Prioritization Research Articles

Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease

Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.

Genetic architecture of asthma in African American patients

Asthma is a chronic inflammatory disorder with a strong genetic inheritance. Although more than 100 loci were reported through the genome-wide association study of European populations, the genetic underpinning of asthma in African American individuals remains largely elusive. We aimed to identify genetic loci associated with asthma in African American individuals. Three cohorts were genotyped at the Children's Hospital of Philadelphia by using the Illumina single-nucleotide polymorphism array platform. Genotype imputation was performed by using the Trans-Omics for Precision Medicine (TOPMed) reference panel, which includes whole genome sequencing data from more than 100,000 individuals. Ameta-analysis of 3 Children's Hospital of Philadelphia cohorts and 10 Consortium on Asthma among African Ancestry Populations in the Americas cohorts, totaling 19,628 subjects, was conducted to identify genetic loci associated with asthma in African American individuals. Our study identified 12 loci surpassing the classical genome-wide significance threshold (5×10-8). Of those loci, 8 reached the stricter significance threshold (3×10-8). The 9p24.1 locus (rs10975467 [P= 1.63×10-8]) has previously been associated with asthma in European individuals. Six loci are associated with enhancer activities, 2 loci are in DNase I-hypersensitive regions, and all of them are associated with regulatory motifs. Moreover, the locus 11q13.4 (rs7480008) isanexpression quantitative trait locus of XRRA1 in lung(P= 9.4×10-10), and the locus 13q14.3 (rs1543525) is a splicing quantitative trait locus of DHRS12 in lung (P= 1.1×10-13). Our findings provide candidate genetic loci for therapeutic target identification and prioritization for African populations.

Social gaze cueing elicits facilitatory and inhibitory effects on movement execution when the model might act on an object.

Social cues, such as eye gaze and pointing fingers, can increase the prioritisation of specific locations for cognitive processing. A previous study using a manual reaching task showed that, although both gaze and pointing cues altered target prioritisation (reaction times [RTs]), only pointing cues affected action execution (trajectory deviations). These differential effects of gaze and pointing cues on action execution could be because the gaze cue was conveyed through a disembodied head; hence, the model lacked the potential for a body part (i.e., hands) to interact with the target. In the present study, the image of a male gaze model, whose gaze direction coincided with two potential target locations, was centrally presented. The model either had his arms and hands extended underneath the potential target locations, indicating the potential to act on the targets (Experiment 1), or had his arms crossed in front of his chest, indicating the absence of potential to act (Experiment 2). Participants reached to a target that followed a nonpredictive gaze cue at one of three stimulus onset asynchronies. RTs and reach trajectories of the movements to cued and uncued targets were analysed. RTs showed a facilitation effect for both experiments, whereas trajectory analysis revealed facilitatory and inhibitory effects, but only in Experiment 1 when the model could potentially act on the targets. The results of this study suggested that when the gaze model had the potential to interact with the cued target location, the model's gaze affected not only target prioritisation but also movement execution.

PWN: enhanced random walk on a warped network for disease target prioritization

BackgroundExtracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks.ResultsWe developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge.ConclusionsWe showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN.

Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases

Prioritization of disease mechanisms, biomarkers, and drug targets in immune-mediated inflammatory diseases (IMIDs) is complicated by altered interactions between thousands of genes. Our multi-organ single-cell RNA sequencing of a mouse IMID model, namely collagen-induced arthritis, shows highly complex and heterogeneous expression changes in all analyzed organs, even though only joints showed signs of inflammation. We organized those into a multi-organ multicellular disease model, which shows predicted molecular interactions within and between organs. That model supports that inflammation is switched on or off by altered balance between pro- and anti-inflammatory upstream regulators (URs) and downstream pathways. Meta-analyses of human IMIDs show a similar, but graded, on/off switch system. This system has the potential to prioritize, diagnose, and treat optimal combinations of URs on the levels of IMIDs, subgroups, and individual patients. That potential is supported by UR analyses in more than 600 sera from patients with systemic lupus erythematosus.

Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization.

Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.

Association between human blood metabolome and the risk of breast cancer

BackgroundBreast cancer is the most common cancer among women with limited treatment options. To identify promising drug targets for breast cancer, we conducted a systematical Mendelian randomization (MR) study to screen blood metabolome for potential causal mediators of breast cancer and further predict target-mediated side effects.MethodsWe selected 112 unique blood metabolites from 3 large-scale European ancestry-based genome-wide association studies (GWASs) with a total of 147,827 participants. Breast cancer data were obtained from a GWAS in the Breast Cancer Association Consortium (BCAC), involving 122,977 cases and 105,974 controls of European ancestry. We conducted MR analyses to systematically assess the associations of blood metabolites with breast cancer, and a phenome-wide MR analysis was further applied to ascertain the potential on-target side effects of metabolite interventions.ResultsTwo blood metabolites were identified as the potential causal mediators for breast cancer, including high-density lipoprotein cholesterol (HDL-C) (odds ratio [OR], 1.09; 95% confidence interval [CI], 1.06–1.12; P = 9.67 × 10−10) and acetate (OR, 1.24; 95% CI, 1.13–1.37; P = 1.35 × 10−5). In the phenome-wide MR analysis, lowering HDL-C might have deleterious effects on the risk of the circulatory system and foreign body injury, while lowering acetate had deleterious effects on mental disorders disease.ConclusionsThe present systematic MR analysis revealed that HDL-C and acetate may be the causal mediators in the risk of developing breast cancer. Side-effect profiles were characterized to help inform drug target prioritization for breast cancer prevention. HDL-C and acetate might be promising drug targets for preventing breast cancer, but they should be applied under weighting advantages and disadvantages.

Identification of potential causal metabolites associated with atopic dermatitis.

Atopic dermatitis is a chronically recurrent dermatologic disease affected by complex pathophysiology with limited therapeutic options. To identify promising biomarkers for atopic dermatitis, we conducted a Mendelian randomization (MR) study to systematically screen blood metabolome for potential causal mediators of atopic dermatitis and further predict target-mediated side effects. We selected 128 unique blood metabolites from three European-descent metabolome genome-wide association studies (GWASs) with a total of 147 827 participants. Atopic dermatitis dataset originated from a large-scale GWAS including 10 788 cases and 30 047 controls of European ancestry. MR analyses were performed to estimate the associations of blood metabolites with atopic dermatitis. We then applied a phenome-wide MR analysis to ascertain potential on-target side effects of metabolite intervention. Three metabolites were identified as potential causal mediators for atopic dermatitis, including docosahexaenoic acid (odds ratio [OR], 0.87; 95% confidence interval [CI], 0.81-0.94; P = 3.45 × 10-4), arachidonate (OR, 0.30; 95% CI, 0.17-0.53; P = 4.09 × 10-5) and 1-arachidonoylglycerophosphoethanolamine (1-arachidonoyl-GPE) (OR, 0.25; 95% CI, 0.12-0.53; P = 2.58 × 10-4). In the phenome-wide MR analysis, docosahexaenoic acid and arachidonate were also identified to have beneficial or detrimental effects on multiple diseases beyond atopic dermatitis, respectively. No adverse side effects were found for 1-arachidonoyl-GPE. In this systematic MR study, docosahexaenoic acid, arachidonate and 1-arachidonoyl-GPE were identified as potential causal and beneficial mediators in the development of atopic dermatitis. Side-effect profiles were characterized to help inform drug target prioritization, and 1-arachidonoyl-GPE was a promising target for prevention and treatment of atopic dermatitis with no predicted adverse side effects.

Chemical Proteomics with Novel Fully Functionalized Fragments and Stringent Target Prioritization Identifies the Glutathione-Dependent Isomerase GSTZ1 as a Lung Cancer Target.

Photoreactive fragment-like probes have been applied to discover target proteins that constitute novel cellular vulnerabilities and to identify viable chemical hits for drug discovery. Through forming covalent bonds, functionalized probes can achieve stronger target engagement and require less effort for on-target mechanism validation. However, the design of probe libraries, which directly affects the biological target space that is interrogated, and effective target prioritization remain critical challenges of such a chemical proteomic platform. In this study, we designed and synthesized a diverse panel of 20 fragment-based probes containing natural product-based privileged structural motifs for small-molecule lead discovery. These probes were fully functionalized with orthogonal diazirine and alkyne moieties and used for protein crosslinking in live lung cancer cells, target enrichment via "click chemistry," and subsequent target identification through label-free quantitative liquid chromatography-tandem mass spectrometry analysis. Pair-wise comparison with a blunted negative control probe and stringent prioritization via individual cross-comparisons against the entire panel identified glutathione S-transferase zeta 1 (GSTZ1) as a specific and unique target candidate. DepMap database query, RNA interference-based gene silencing, and proteome-wide tyrosine reactivity profiling suggested that GSTZ1 cooperated with different oncogenic alterations by supporting survival signaling in refractory non-small cell lung cancer cells. This finding may form the basis for developing novel GSTZ1 inhibitors to improve the therapeutic efficacy of oncogene-directed targeted drugs. In summary, we designed a novel fragment-based probe panel and developed a target prioritization scheme with improved stringency, which allows for the identification of unique target candidates, such as GSTZ1 in refractory lung cancer.

Beam Scheduling of Maritime Multifunctional Radar Based on Binary Integration

This study developed a radar resource management (RRM) framework for maritime multifunctional radar (MFR) that performs surveillance and tracking of unknown targets. The proposed scheme consists of two stages: target prioritization based on fuzzy logic and a beam scheduling approach that transmits surveillance beam and tracking beams for each target appropriately. The proposed beam scheduling scheme also takes into account the imperfect detection of targets in real situation. A conventional heuristic beam scheduling approach was modified to incorporate binary integration in radar theory into the RRM strategy. The resulting scheme efficiently handles the complicated beam scheduling problem by considering the target priority as well as the imperfect detection of targets of a real maritime MFR. Simulation results based on a realistic maritime environment demonstrated that the proposed scheme not only determines the correct order of transmission beams according to the target priority but also maintains good tracking accuracy for the threatening target.

Subtractive sequence analysis aided druggable targets mining in Burkholderia cepacia complex and finding inhibitors through bioinformatics approach.

Burkholderia cepacia complex (BCC) is a group of gram-negative bacteria composed of at least 20 different species that cause diseases in plants, animals as well as humans (cystic fibrosis and airway infection). Here, we analyzed the proteomic data of 47 BCC strains by classifying them in three groups. Phylogenetic analyses were performed followed by individual core region identification for each group. Comparative analysis of the three individual core protein fractions resulted in 1766 ortholog/proteins. Non-human homologous proteins from the core region gave 1680 proteins. Essential protein analyses reduced the target list to 37 proteins, which were further compared to a closely related out-group, Burkholderia gladioli ATCC 10,248 strain, resulting in 21 proteins. 3D structure modeling, validation, and druggability step gave six targets that were subjected to further target prioritization parameters which ultimately resulted in two BCC targets. A library of 12,000 ZINC drug-like compounds was screened, where only the top hits were selected for docking orientations. These included ZINC01405842 (against Chorismate synthase aroC) and ZINC06055530 (against Bifunctional N-acetylglucosamine-1-phosphate uridyltransferase/Glucosamine-1-phosphate acetyltransferase glmU). Finally, dynamics simulation (200ns) was performed for each ligand-receptor complex, followed by ADMET profiling. Of these targets, details of their applicability as drug targets have not yet been elucidated experimentally, hence making our predictions novel and it is suggested that further wet-lab experimentations should be conducted to test the identified BCC targets and ZINC scaffolds to inhibit them.

Validation of an Ergonomics Assessment Method and Identification of Focus Areas through Development of a Novel Statistical Approach for Aggregated Data

Musculoskeletal disorders continue to be a leading source of lost workdays across all industries. Common ergonomics assessment tools may include criteria extraneous to the stresses at specific companies or industries. Therefore, the creation of assessment tools, based on scientifically validated methods, with industry- or company-specific stresses may be of benefit. Privacy regulations (among other factors) may preclude the 1:1 allocation of stresses to health outcomes, prohibiting conventional epidemiological investigation and validation approaches, therefore requiring the use of aggregated data sources.
 Based on data from the ergonomic assessment tool of interest (Safety and Ergonomics Risk Assessment - SERA) and aggregated data derived from internal insurance reports a statistical method is developed to investigate; 1) the validity of the tool, 2) a prioritization of intervention targets, and 3) the derivation of threshold values for monitoring. The method involves statistical tests used in epidemiological investigations and draws on the iterative modelling approach common to machine learning.
 Using the described approach, significant results for known musculoskeletal issues, a prioritization of countermeasure and intervention targets, and threshold values for long-term monitoring were determined. The successful results indicate that the assessment tool investigated is valid. Consequently, the method is generalized such that practitioners can apply it, should they be faced with similarly structured data.

HIV Pre-Exposure Prophylaxis in Central and Eastern Europe-Gains and Challenges in An Ever-Changing World.

Pre-exposure prophylaxis (PrEP) is an effective prevention tool for controlling the HIV epidemic. Since its approval in the United States in 2012 and Europe in 2016, it has become available on a global scale offered as a registered strategy in clinical studies or demonstration projects with a slow and steady increase. In the second quarter of 2022, PrEP became available in 78 countries globally, with around 3 million people having started using PrEP. Europe has been much slower than the rest of the world to roll out PrEP; nevertheless, currently, PrEP is nationally available and reimbursed in 21 countries; generics are available but not reimbursed in 14 countries. PrEP is not formally implemented in 20 countries, which are mostly Central and Eastern European countries. There are significant disparities between countries in terms of PrEP availability, accessibility, and usage, most likely due to social, cultural, and political differences. The major barriers to PrEP use are reported to be lack of knowledge of people in need, not being reimbursed, and low perception of HIV. PrEP uptake globally and regionally still lacks the power to have an impact on controlling the epidemic. High prioritization of PrEP targets will offer us a realistic chance of reaching the Joint United Nations Programme on HIV/AIDS (UNAIDS) goal of a 90% reduction in HIV infections by 2030 compared to 2010.

The module triad: a novel network biology approach to utilize patients’ multi-omics data for target discovery in ulcerative colitis

Tumor necrosis factor-alpha inhibitors (TNFi) have been a standard treatment in ulcerative colitis (UC) for nearly 20 years. However, insufficient response rate to TNFi therapies along with concerns around their immunogenicity and inconvenience of drug delivery through injections calls for development of UC drugs targeting alternative proteins. Here, we propose a multi-omic network biology method for prioritization of protein targets for UC treatment. Our method identifies network modules on the Human Interactome—a network of protein-protein interactions in human cells—consisting of genes contributing to the predisposition to UC (Genotype module), genes whose expression needs to be modulated to achieve low disease activity (Response module), and proteins whose perturbation alters expression of the Response module genes to a healthy state (Treatment module). Targets are prioritized based on their topological relevance to the Genotype module and functional similarity to the Treatment module. We demonstrate utility of our method in UC and other complex diseases by efficiently recovering the protein targets associated with compounds in clinical trials and on the market . The proposed method may help to reduce cost and time of drug development by offering a computational screening tool for identification of novel and repurposing therapeutic opportunities in UC and other complex diseases.

Identification and Prioritization of PET Neuroimaging Targets for Microglial Phenotypes Associated with Microglial Activity in Alzheimer's Disease.

Activation of microglial cells accompanies the progression of many neurodegenerative disorders, including Alzheimer's disease (AD). Development of molecular imaging tools specific to microglia can help elucidate the mechanism through which microglia contribute to the pathogenesis and progression of neurodegenerative disorders. Through analysis of published genetic, transcriptomic, and proteomic data sets, we identified 19 genes with microglia-specific expression that we then ranked based on association with the AD characteristics, change in expression, and potential druggability of the target. We believe that the process we used to identify and rank microglia-specific genes is broadly applicable to the identification and evaluation of targets in other disease areas and for applications beyond molecular imaging.

Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease

AbstractBackgroundPerivascular space (PVS) burden is an emerging MRI‐marker of cerebral small vessel disease (cSVD), a leading cause of stroke and dementia. Underlying mechanisms of PVS are unknown. PVS are thought to be related to the glymphatic system, involved in brain clearance of molecules such as amyloid beta. We aimed to decipher the genetic underpinnings of PVS burden.MethodWe conducted genome‐wide and whole‐exome association studies in N = 39,823 participants for white matter (WM) PVS, N = 40,000 for basal ganglia (BG) PVS and N = 40,095 for hippocampal (HIP) PVS (21 population‐based cohorts, 66.3±8.6 years). As PVS were rated with different scales across cohorts, we tested association of genetic variants with the top quartile of PVS burden distribution in each cohort followed by a sample‐size weighted meta‐analysis. We searched for shared genetic variation with related vascular and neurological phenotypes using linkage disequilibrium‐score regression, explored causality of associations with putative risk factors using Mendelian randomization and conducted extensive functional exploration of identified PVS loci using multiple bioinformatics approaches, including transcriptome‐wide association studies.ResultWe identified 24 genome‐wide significant PVS risk loci. These showed association with WM PVS already at age 20 in 1,748 young healthy adults, suggesting an important role of early‐life factors. PVS loci were enriched in genes causing early‐onset leukodystrophies and genes expressed in fetal brain endothelial cells. Mendelian randomization analyses supported causal associations of high blood pressure with BG and HIP PVS, and of BG PVS with stroke. Transcriptome‐wide association and colocalization analyses suggest causal implication of 11 genes, that could be prioritized for experimental follow‐up. Two‐thirds of PVS loci point to novel pathways, involving extracellular matrix, membrane transport, and developmental processes, with enrichment in targets of existing drugs for vascular cognitive, and infectious disorders.ConclusionIn this first gene‐mapping study of PVS, one of the earliest MRI‐markers of cSVD, we describe 24 genome‐wide significant risk loci. Our findings provide completely novel insight into the biology of PVS across the adult lifespan and its contribution to cSVD pathophysiology, with potential for genetically informed prioritization of drug targets for prevention trials of cSVD, a major cause of stroke and dementia worldwide.

The Emory‐Sage‐SGC TREAT‐AD Center: Tool and probe development for emerging targets in Alzheimer’s Disease

AbstractBackgroundAlzheimer’s disease is the most common form of dementia and there is currently no effective therapy for either treatment or prevention. With repeated failure in DRUG DEVELOPMENT across industry and demonstrated heterogeneity in biological and genetic components of the disease, there is a need to evaluate a broad range of therapeutic hypotheses for target prioritization. The Emory‐Sage‐SGC TREAT‐AD Center (https://treatad.org) is generating and openly distributing validated experimental tools necessary to test target predictions generated through sequence‐based characterization of human disease state. We believe that these tools and reagents, including chemical and biological probes that target the multifaceted dysregulation in the brains of AD patients will advance the discovery of potential drug targets for AD.MethodThe Emory‐Sage‐SGC TREAT‐AD Center uses integrated computational approaches to identify target predictions from a set of prioritized therapeutic hypotheses. Emerging AD targets derived from systems biology studies within the Accelerating Medicines Partnership in AD (AMP‐AD) consortium and additional NIA‐supported AD consortia were mapped to 15 biological domains (BDs) and prioritized based on an unbiased bioinformatic assessment across multiple lines of evidence for overall AD‐risk. Targets are then evaluated to identify a set of experimental reagents necessary for hypothesis testing, termed a “target enablement package (TEP).ResultWithin these 15 biological domains, we prioritized more than 30 targets for target enabling package (TEP) development. TEPs include expression constructs, knockout cell lines, assays, antibody validation, and crystal structures. All reagents are developed to meet established quality criteria. For a subset of tractable targets (MSN, SYK, SFRP1, SDC4 and CAPN2), chemical probe development is underway to provide tools to test these therapeutic hypotheses in cellular and animal systems. TREAT‐AD investigators place all data, knowledge, reagents, and tools into the open domain with no intellectual property claims.ConclusionThe open drug discovery approach of TREAT‐AD is aimed to de‐risk potential AD therapeutics to catalyze robust and independent evaluation of a diverse portfolio of promising yet untested AD therapeutic hypotheses. All data, protocols, reagent sets, and chemical tools will be made widely available on the AD Knowledge Portal. For more information see www.treatad.org.

Transcriptome and proteome profiling of activated cardiac fibroblasts supports target prioritization in cardiac fibrosis.

Activated cardiac fibroblasts (CF) play a central role in cardiac fibrosis, a condition associated with most cardiovascular diseases. Conversion of quiescent into activated CF sustains heart integrity upon injury. However, permanence of CF in active state inflicts deleterious heart function effects. Mechanisms underlying this cell state conversion are still not fully disclosed, contributing to a limited target space and lack of effective anti-fibrotic therapies. To prioritize targets for drug development, we studied CF remodeling upon activation at transcriptomic and proteomic levels, using three different cell sources: primary adult CF (aHCF), primary fetal CF (fHCF), and induced pluripotent stem cells derived CF (hiPSC-CF). All cell sources showed a convergent response upon activation, with clear morphological and molecular remodeling associated with cell-cell and cell-matrix interactions. Quantitative proteomic analysis identified known cardiac fibrosis markers, such as FN1, CCN2, and Serpine1, but also revealed targets not previously associated with this condition, including MRC2, IGFBP7, and NT5DC2. Exploring such targets to modulate CF phenotype represents a valuable opportunity for development of anti-fibrotic therapies. Also, we demonstrate that hiPSC-CF is a suitable cell source for preclinical research, displaying significantly lower basal activation level relative to primary cells, while being able to elicit a convergent response upon stimuli.

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.

Identifying and working with appropriate treatment targets with people who are sexually attracted to children

Research has shown that people within society experience sexual attractions to children, and a substantial number of these seek support related to this. However, professional practices around working with minor-attracted persons (MAPs) are variable. Clinicians possess low levels of knowledge about this population and are unclear about the correct treatment goals. In this work we explored the prioritization of different treatment goals by MAPs (n = 150), before investigating the demographic, sexuality-related, and psychological predictors of treatment target prioritization. Self-compassion drove many treatment targets among MAPs. We offer recommendations about how professionals might work collaboratively and effectively with this population.