Open Targets: a platform for therapeutic target identification and validation.

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

ADME SARfari: comparative genomics of drug metabolizing systems.

MOTIVATION: ADME SARfari is a freely available web resource that enables comparative analyses of drug-disposition genes. It does so by integrating a number of publicly available data sources, which have subsequently been used to build data mining services, predictive tools and visualizations for drug metabolism researchers. The data include the interactions of small molecules with ADME (absorption, distribution, metabolism and excretion) proteins responsible for the metabolism and transport of molecules; available pharmacokinetic (PK) data; protein sequences of ADME-related molecular targets for pre-clinical model species and human; alignments of the orthologues including information on known SNPs (Single Nucleotide Polymorphism) and information on the tissue distribution of these proteins. In addition, in silico models have been developed, which enable users to predict which ADME relevant protein targets a novel compound is likely to interact with.

Thousands of chemical starting points for antimalarial lead identification.

Malaria is a devastating infection caused by protozoa of the genus Plasmodium. Drug resistance is widespread, no new chemical class of antimalarials has been introduced into clinical practice since 1996 and there is a recent rise of parasite strains with reduced sensitivity to the newest drugs. We screened nearly 2 million compounds in GlaxoSmithKline's chemical library for inhibitors of P. falciparum, of which 13,533 were confirmed to inhibit parasite growth by at least 80% at 2 microM concentration. More than 8,000 also showed potent activity against the multidrug resistant strain Dd2. Most (82%) compounds originate from internal company projects and are new to the malaria community. Analyses using historic assay data suggest several novel mechanisms of antimalarial action, such as inhibition of protein kinases and host-pathogen interaction related targets. Chemical structures and associated data are hereby made public to encourage additional drug lead identification efforts and further research into this disease.

Prevalent polymorphisms in wild-type HIV-1 integrase are unlikely to engender drug resistance to dolutegravir (S/GSK1349572).

The majority of HIV-1 integrase amino acid sites are highly conserved, suggesting that most are necessary to carry out the critical structural and functional roles of integrase. We analyzed the 34 most variable sites in integrase (>10% variability) and showed that prevalent polymorphic amino acids at these positions did not affect susceptibility to the integrase inhibitor dolutegravir (S/GSK1349572), as demonstrated both in vitro (in site-directed mutagenesis studies) and in vivo (in a phase IIa study of dolutegravir monotherapy in HIV-infected individuals). Ongoing clinical trials will provide additional data on the virologic activity of dolutegravir across subject viruses with and without prevalent polymorphic substitutions.

Minipig and beagle animal model genomes aid species selection in pharmaceutical discovery and development.

Improving drug attrition remains a challenge in pharmaceutical discovery and development. A major cause of early attrition is the demonstration of safety signals which can negate any therapeutic index previously established. Safety attrition needs to be put in context of clinical translation (i.e. human relevance) and is negatively impacted by differences between animal models and human. In order to minimize such an impact, an earlier assessment of pharmacological target homology across animal model species will enhance understanding of the context of animal safety signals and aid species selection during later regulatory toxicology studies. Here we sequenced the genomes of the Sus scrofa Göttingen minipig and the Canis familiaris beagle, two widely used animal species in regulatory safety studies. Comparative analyses of these new genomes with other key model organisms, namely mouse, rat, cynomolgus macaque, rhesus macaque, two related breeds (S. scrofa Duroc and C. familiaris boxer) and human reveal considerable variation in gene content. Key genes in toxicology and metabolism studies, such as the UGT2 family, CYP2D6, and SLCO1A2, displayed unique duplication patterns. Comparisons of 317 known human drug targets revealed surprising variation such as species-specific positive selection, duplication and higher occurrences of pseudogenized targets in beagle (41 genes) relative to minipig (19 genes). These data will facilitate the more effective use of animals in biomedical research.

Social contagion process in informal warning networks to understand evacuation timing behavior.

Individual evacuation decisions are often characterized by the influence of one's social network, referred to as informal warning network. In this article, a threshold model of social contagion, originally introduced in the network science literature, is proposed to characterize this social influence in the evacuation decision-making process, in particular the timing of evacuation decision. Simulation models are developed to investigate the effects of community mixing patterns and the strength of ties on timing of evacuation decision.

Privacy and security concerns with passively collected location data for digital contact tracing among U.S. college students.

People continue to use technology in new ways, and how governments harness digital information should consider privacy and security concerns. During COVID19, numerous countries deployed digital contact tracing that collect location data from user's smartphones. However, these apps had low adoption rates and faced opposition. We launched an interdisciplinary study to evaluate smartphone location data concerns among college students in the US. Using interviews and a large survey, we find that college students have higher concerns regarding privacy, and place greater trust in local government with their location data. We discuss policy recommendations for implementing improved contact tracing efforts.

Assessing the crash risks of evacuation: A matched case-control approach applied over data collected during Hurricane Irma.

Recent hurricane experiences have created concerns for transportation agencies and policymakers to find better evacuation strategies, especially after Hurricane Irma-which forced about 6.5 million Floridians to evacuate and caused a significant amount of delay due to heavy congestion. A major concern for issuing an evacuation order is that it may involve a high number of crashes in highways. In this study, we present a matched case-control based approach to understand the factors contributing to the increase in the number of crashes during evacuation. We use traffic data for a period of 5 to 10 min just before the crash occurred. For each crash observation, traffic data are collected from two upstream and two downstream detectors of the crash location. We estimate models for three different conditions: regular period, evacuation period, and combining both evacuation and regular period data. Model results show that, if there exist a high volume of traffic at an upstream station and a high variation of speed at a downstream station, the likelihood of crash occurrence increases. Using a panel mixed binary logit model, we also estimate the effect of evacuation itself on crash risk and find that, after controlling for traffic characteristics, during evacuation the chance of a crash is higher than in a regular period. Our findings have implications for evacuation declarations and highlight the need for better traffic management strategies during evacuation. Future studies may develop advanced real-time crash prediction models which would allow us to deploy proactive countermeasures to reduce crash occurrences during evacuation.

Road to effective data curation for translational research.

Translational research today is data-intensive and requires multi-stakeholder collaborations to generate and pool data together for integrated analysis. This leads to the challenge of harmonization of data from different sources with different formats and standards, which is often overlooked during project planning and thus becomes a bottleneck of the research progress. We report on our experience and lessons learnt about data curation for translational research garnered over the course of the European Translational Research Infrastructure & Knowledge management Services (eTRIKS) program (https://www.etriks.org), a unique, 5-year, cross-organizational, cross-cultural collaboration project funded by the Innovative Medicines Initiative of the EU. Here, we discuss the obstacles and suggest what steps are needed for effective data curation in translational research, especially for projects involving multiple organizations from academia and industry.

Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data.

One major source of uncertainty in accurately estimating human exposure to air pollution is that human subjects move spatiotemporally, and such mobility is usually not considered in exposure estimation. How such mobility impacts exposure estimates at the population and individual level, particularly for subjects with different levels of mobility, remains under-investigated. In addition, a wide range of methods have been used in the past to develop air pollutant concentration fields for related health studies. How the choices of methods impact results of exposure estimation, especially when detailed mobility information is considered, is still largely unknown. In this study, by using a publicly available large cell phone location dataset containing over 35 million location records collected from 310,989 subjects, we investigated the impact of individual subjects' mobility on their estimated exposures for five chosen ambient pollutants (CO, NO2, SO2, O3 and PM2.5). We also estimated exposures separately for 10 groups of subjects with different levels of mobility to explore how increased mobility impacted their exposure estimates. Further, we applied and compared two methods to develop concentration fields for exposure estimation, including one based on Community Multiscale Air Quality (CMAQ) model outputs, and the other based on the interpolated observed pollutant concentrations using the inverse distance weighting (IDW) method. Our results suggest that detailed mobility information does not have a significant influence on mean population exposure estimate in our sample population, although impacts can be substantial at the individual level. Additionally, exposure classification error due to the use of home-location data increased for subjects that exhibited higher levels of mobility. Omitting mobility could result in underestimation of exposures to traffic-related pollutants particularly during afternoon rush-hour, and overestimate exposures to ozone especially during mid-afternoon. Between CMAQ and IDW, we found that the IDW method generates smooth concentration fields that were not suitable for exposure estimation with detailed mobility data. Therefore, the method for developing air pollution concentration fields when detailed mobility data were to be applied should be chosen carefully. Our findings have important implications for future air pollution health studies.

Applying machine learning approaches to analyze the vulnerable road-users' crashes at statewide traffic analysis zones.

INTRODUCTION: In this paper, we present machine learning techniques to analyze pedestrian and bicycle crash by developing macro-level crash prediction models. METHODS: We collected the 2010-2012 Statewide Traffic Analysis Zone (STAZ) level crash data and developed rigorous machine learning approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To our knowledge, this is the first application of DTR models in the burgeoning macro-level traffic safety literature. RESULTS: The DTR models uncovered the most significant predictor variables for both response variables (pedestrian and bicycle crash counts) in terms of three broad categories: traffic, roadway, and socio-demographic characteristics. Additionally, spatial predictor variables of neighboring STAZs were considered along with the targeted STAZ in both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the model comparison results discovered that the prediction accuracy of the spatial DTR model performed better than the aspatial DTR model. Finally, the current research effort contributed to the safety literature by applying some ensemble techniques (i.e. bagging, random forest, and gradient boosting) in order to improve the prediction accuracy of the DTR models (weak learner) for macro-level crash count. The study revealed that all the ensemble techniques performed slightly better than the DTR model and the gradient boosting technique outperformed other competing ensemble techniques in macro-level crash prediction models.

On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies.

Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.

The role of positive selection in determining the molecular cause of species differences in disease.

BACKGROUND: Related species, such as humans and chimpanzees, often experience the same disease with varying degrees of pathology, as seen in the cases of Alzheimer's disease, or differing symptomatology as in AIDS. Furthermore, certain diseases such as schizophrenia, epithelial cancers and autoimmune disorders are far more frequent in humans than in other species for reasons not associated with lifestyle. Genes that have undergone positive selection during species evolution are indicative of functional adaptations that drive species differences. Thus we investigate whether biomedical disease differences between species can be attributed to positively selected genes. RESULTS: We identified genes that putatively underwent positive selection during the evolution of humans and four mammals which are often used to model human diseases (mouse, rat, chimpanzee and dog). We show that genes predicted to have been subject to positive selection pressure during human evolution are implicated in diseases such as epithelial cancers, schizophrenia, autoimmune diseases and Alzheimer's disease, all of which differ in prevalence and symptomatology between humans and their mammalian relatives. In agreement with previous studies, the chimpanzee lineage was found to have more genes under positive selection than any of the other lineages. In addition, we found new evidence to support the hypothesis that genes that have undergone positive selection tend to interact with each other. This is the first such evidence to be detected widely among mammalian genes and may be important in identifying molecular pathways causative of species differences. CONCLUSION: Our dataset of genes predicted to have been subject to positive selection in five species serves as an informative resource that can be consulted prior to selecting appropriate animal models during drug target validation. We conclude that studying the evolution of functional and biomedical disease differences between species is an important way to gain insight into their molecular causes and may provide a method to predict when animal models do not mirror human biology.

Recovering motifs from biased genomes: application of signal correction.

A significant problem in biological motif analysis arises when the background symbol distribution is biased (e.g. high/low GC content in the case of DNA sequences). This can lead to overestimation of the amount of information encoded in a motif. A motif can be depicted as a signal using information theory (IT). We apply two concepts from IT, distortion and patterned interference (a type of noise), to model genomic and codon bias respectively. This modeling approach allows us to correct a raw signal to recover signals that are weakened by compositional bias. The corrected signal is more likely to be discriminated from a biased background by a macromolecule. We apply this correction technique to recover ribosome-binding site (RBS) signals from available sequenced and annotated prokaryotic genomes having diverse compositional biases. We observed that linear correction was sufficient for recovering signals even at the extremes of these biases. Further comparative genomics studies were made possible upon correction of these signals. We find that the average Euclidian distance between RBS signal frequency matrices of different genomes can be significantly reduced by using the correction technique. Within this reduced average distance, we can find examples of class-specific RBS signals. Our results have implications for motif-based prediction, particularly with regards to the estimation of reliable inter-genomic model parameters.

Controlled release of chlorhexidine from a HEMA-UDMA resin using a magnetic field.

OBJECTIVES: To functionalize novel chlorhexidine (CHX) particles with iron oxide (Fe3O4) nanoparticles and control their release kinetics in a dental resin using an external magnetic field. METHODS: Fe3O4 nanoparticles were synthesized and incorporated into spherical CHX particles and the powder was freeze dried. Resin disc specimens were produced using a UDMA-HEMA resin mixed with freeze dried spherical Fe3O4-CHX particles (5wt.%), which were placed into a Teflon mould (10mm diameter×1mm depth) and covered with a Mylar strip. A MACS magnet was left in contact for 0min (Group 1), 5min (Group 2) or 10min (Group 3) and the resin discs subsequently light cured (Bluedent LED pen, Bulgaria) for 60s per side. The resin discs were immersed in deionized water at various time points up to 650h. UV-Vis absorbance was used to determine the CHX content. CHX released for each time point was determined. The functionalized CHX particles and resin discs were characterized using TEM, TGA, EDX and SEM. RESULTS: Fe3O4 nanoparticles (20nm) incorporated into the spherical CHX particles led to a mean (SD) particle size reduction from 17.15 (1.99)µm to 10.39 (2.61)µm. The presence of Fe3O4 nanoparticles in the spherical CHX particles was confirmed with SEM, EDX, and TGA. SEM of Group 1 resin discs (no magnetic exposure) showed functionalized CHX spheres were homogeneously distributed within the resin discs. For resin discs which had magnetic exposure (5 or 10min) the particles started to cluster nearer the surface (Group 2: 43.7%, Group 3: 57.3%), to a depth of 94µm. UV-Vis absorbance revealed Group 1 resin discs had a cumulative CHX release of 4.4% compared to 5.9% for Group 2 and 7.4% for Group 3 resin discs, which had magnetic exposure (5, 10min). SIGNIFICANCE: Fe3O4 nanoparticle functionalized CHX spheres demonstrated a magnetic field responsive property. A magnetic field responsive release of CHX may be useful in clinical situations where the drug can be directed to give a tailored release at the site of infection.

Prioritizing genomic drug targets in pathogens: application to Mycobacterium tuberculosis.

We have developed a software program that weights and integrates specific properties on the genes in a pathogen so that they may be ranked as drug targets. We applied this software to produce three prioritized drug target lists for Mycobacterium tuberculosis, the causative agent of tuberculosis, a disease for which a new drug is desperately needed. Each list is based on an individual criterion. The first list prioritizes metabolic drug targets by the uniqueness of their roles in the M. tuberculosis metabolome ("metabolic chokepoints") and their similarity to known "druggable" protein classes (i.e., classes whose activity has previously been shown to be modulated by binding a small molecule). The second list prioritizes targets that would specifically impair M. tuberculosis, by weighting heavily those that are closely conserved within the Actinobacteria class but lack close homology to the host and gut flora. M. tuberculosis can survive asymptomatically in its host for many years by adapting to a dormant state referred to as "persistence." The final list aims to prioritize potential targets involved in maintaining persistence in M. tuberculosis. The rankings of current, candidate, and proposed drug targets are highlighted with respect to these lists. Some features were found to be more accurate than others in prioritizing studied targets. It can also be shown that targets can be prioritized by using evolutionary programming to optimize the weights of each desired property. We demonstrate this approach in prioritizing persistence targets.

Ectopic Scrotum with VACTERL Association.

Scrotal ectopia is a rare condition. Associated anomalies are common. We describe a neonate with ectopic scrotum with VACTERL association. This combination of anomalies is very rare.

Accessibility and socio-economic development of human settlements.

Access to facilities, services and socio-economic opportunities plays a critical role in the growth and decline of cities and human settlements. Previous attempts to explain changes in socio-economic indicators by differences in accessibility have not been convincing as countries with highly developed transport infrastructure have only seen marginal benefits of infrastructure improvements. Australia offers an ideal case for investigating the effects of accessibility on development since it is seen as home to some of the most liveable cities in the world while, at the same time, it also has some of the most isolated settlements. We investigate herein the connectivity and accessibility of all 1814 human settlements (population centers exceeding 200 persons) in Australia, and how they relate to the socio-economic characteristics of, and opportunities in, each population center. Assuming population as a proxy indicator of available opportunities, we present a simple ranking metric for a settlement using the number of population and the distance required to access all other settlements (and the corresponding opportunities therein). We find a strikingly unequal distribution of access to opportunities in Australia, with a marked prominence of opportunities in capital cities in four of the eight states. The two largest cities of Sydney and Melbourne have a dominant position across all socio-economic indicators, compared to all the other cities. In general, we observe across all the settlements that a decrease in access to opportunities is associated with relatively greater socio-economic disadvantage including increased median age and unemployment rate and decreased median household income. Our methodology can be used to better understand the potential benefits of improved accessibility based on infrastructure development, especially for remote areas and for cities and towns with many socio-economically disadvantaged population.

Uncovering novel repositioning opportunities using the Open Targets platform.

The recently developed Open Targets platform consolidates a wide range of comprehensive evidence associating known and potential drug targets with human diseases. We have harnessed the integrated data from this platform for novel drug repositioning opportunities. Our computational workflow systematically mines data from various evidence categories and presents potential repositioning opportunities for drugs that are marketed or being investigated in ongoing human clinical trials, based on evidence strength on target-disease pairing. We classified these novel target-disease opportunities in several ways: (i) number of independent counts of evidence; (ii) broad therapy area of origin; and (iii) repositioning within or across therapy areas. Finally, we elaborate on one example that was identified by this approach.

Linking rare and common disease: mapping clinical disease-phenotypes to ontologies in therapeutic target validation.

BACKGROUND: The Centre for Therapeutic Target Validation (CTTV - https://www.targetvalidation.org/) was established to generate therapeutic target evidence from genome-scale experiments and analyses. CTTV aims to support the validity of therapeutic targets by integrating existing and newly-generated data. Data integration has been achieved in some resources by mapping metadata such as disease and phenotypes to the Experimental Factor Ontology (EFO). Additionally, the relationship between ontology descriptions of rare and common diseases and their phenotypes can offer insights into shared biological mechanisms and potential drug targets. Ontologies are not ideal for representing the sometimes associated type relationship required. This work addresses two challenges; annotation of diverse big data, and representation of complex, sometimes associated relationships between concepts. METHODS: Semantic mapping uses a combination of custom scripting, our annotation tool 'Zooma', and expert curation. Disease-phenotype associations were generated using literature mining on Europe PubMed Central abstracts, which were manually verified by experts for validity. Representation of the disease-phenotype association was achieved by the Ontology of Biomedical AssociatioN (OBAN), a generic association representation model. OBAN represents associations between a subject and object i.e., disease and its associated phenotypes and the source of evidence for that association. The indirect disease-to-disease associations are exposed through shared phenotypes. This was applied to the use case of linking rare to common diseases at the CTTV. RESULTS: EFO yields an average of over 80% of mapping coverage in all data sources. A 42% precision is obtained from the manual verification of the text-mined disease-phenotype associations. This results in 1452 and 2810 disease-phenotype pairs for IBD and autoimmune disease and contributes towards 11,338 rare diseases associations (merged with existing published work [Am J Hum Genet 97:111-24, 2015]). An OBAN result file is downloadable at http://sourceforge.net/p/efo/code/HEAD/tree/trunk/src/efoassociations/. Twenty common diseases are linked to 85 rare diseases by shared phenotypes. A generalizable OBAN model for association representation is presented in this study. CONCLUSIONS: Here we present solutions to large-scale annotation-ontology mapping in the CTTV knowledge base, a process for disease-phenotype mining, and propose a generic association model, 'OBAN', as a means to integrate disease using shared phenotypes. AVAILABILITY: EFO is released monthly and available for download at http://www.ebi.ac.uk/efo/.