Abstract: Prediction of new drug-target interactions is critically important as it can lead the researchers to find new uses for old drugs and to disclose their therapeutic profiles or side effects. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for predictioning new drug-target interactions have gained a tremendous interest in recent times. Here we present iDTI-ESBoost, a prediction model for identification of drug-target interactions using evolutionary and structural features. Our proposed method uses a novel data balancing and boosting technique to predict drug-target interaction. On four benchmark datasets taken from a gold standard data, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under receiver operating characteristic (auROC) curve. iDTI-ESBoost also outperforms the latest and the best-performing method found in the literature in terms of area under precision recall (auPR) curve. This is significant as auPR curves are argued as suitable metric for comparison for imbalanced datasets similar to the one studied here. Our reported results show the effectiveness of the classifier, balancing methods and the novel features incorporated in iDTI-ESBoost. iDTI-ESBoost is a novel prediction method that has for the first time exploited the structural features along with the evolutionary features to predict drug-protein interactions. We believe the excellent performance of iDTI-ESBoost both in terms of auROC and auPR would motivate the researchers and practitioners to use it to predict drug-target interactions. To facilitate that, iDTI-ESBoost is implemented and made publicly available at: http://farshidrayhan.pythonanywhere.com/iDTI-ESBoost/ .

Abstract: BACKGROUND: Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. RESULTS: In this paper, we present a novel measure of drug-drug similarity named "linear neighborhood similarity", which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method "LNSM" and its extension "LNSM-SMI" to predict side effects of new drugs, and propose the method "LNSM-MSE" to predict unobserved side effect of approved drugs. CONCLUSIONS: We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.

Abstract: Population growth has led to increased global discharges of wastewater. Contaminants that are not fully removed during wastewater treatment, such as pharmaceuticals and personal care products (PPCPs), may negatively affect aquatic ecosystems. PPCPs can bioaccumulate causing adverse health effects and behavioural changes in exposed fish. To assess the impact of PPCPs on wild fish, and to assess whether caged fish could be used as a surrogate for resident wild fish in future monitoring, we caged goldfish in a marsh affected by discharges of wastewater effluents (Cootes Paradise, Lake Ontario, Canada). We collected plasma from resident wild goldfish, and from goldfish that we caged in the marsh for three weeks. We analyzed the plasma proteome and metabolome of both wild and caged fish. We also compared proteomic and metabolic responses in caged and wild fish from the marsh to fish caged at a reference site (Jordan Harbour Conservation Area). We identified significant changes in expression of over 250 molecules that were related to liver necrosis, accumulation and synthesis of lipids, synthesis of cyclic AMP, and the quantity of intracellular calcium in fish from the wastewater affected marsh. Our results suggest that PPCPs could be affecting the health of wild fish populations.

Abstract: A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.

Abstract: Nipah virus is an emerging, highly pathogenic, zoonotic virus of the Paramyxoviridae family. Human transmission occurs by close contact with infected animals, the consumption of contaminated food, or, occasionally, via other infected individuals. Currently, we lack therapeutic or prophylactic treatments for Nipah virus. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. This aim led us to perform the present work, in which we identified 101 human-Nipah virus protein-protein interactions (PPIs), most of which (88) are novel. This data set provides a comprehensive view of the host complexes that are manipulated by viral proteins. Host targets include the PRP19 complex and the microRNA (miRNA) processing machinery. Furthermore, we explored the biologic consequences of the interaction with the PRP19 complex and found that the Nipah virus W protein is capable of altering p53 control and gene expression. We anticipate that these data will help in guiding the development of novel interventional strategies to counter this emerging viral threat.IMPORTANCE Nipah virus is a recently discovered virus that infects a wide range of mammals, including humans. Since its discovery there have been yearly outbreaks, and in some of them the mortality rate has reached 100% of the confirmed cases. However, the study of Nipah virus has been largely neglected, and currently we lack treatments for this infection. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. In the present work, we identified 101 human-Nipah virus protein-protein interactions using an affinity purification approach coupled with mass spectrometry. Additionally, we explored the cellular consequences of some of these interactions. Globally, this data set offers a comprehensive and detailed view of the host machinery's contribution to the Nipah virus's life cycle. Furthermore, our data present a large number of putative drug targets that could be exploited for the treatment of this infection.

Abstract: Previous studies have compared the physicochemical properties of allosteric compounds to non-allosteric compounds. Those studies have found that allosteric compounds tend to be smaller, more rigid, more hydrophobic, and more drug-like than non-allosteric compounds. However, previous studies have not properly corrected for the fact that some protein targets have much more data than other systems. This generates concern regarding the possible skew that can be introduced by the inherent bias in the available data. Hence, this study aims to determine how robust the previous findings are to the addition of newer data. This study utilizes the Allosteric Database (ASD v3.0) and ChEMBL v20 to systematically obtain large datasets of both allosteric and competitive ligands. This dataset contains 70,219 and 9,511 unique ligands for the allosteric and competitive sets, respectively. Physically relevant compound descriptors were computed to examine the differences in their chemical properties. Particular attention was given to removing redundancy in the data and normalizing across ligand diversity and varied protein targets. The resulting distributions only show that allosteric ligands tend to be more aromatic and rigid and do not confirm the increase in hydrophobicity or difference in drug-likeness. These results are robust across different normalization schemes.

Abstract: BACKGROUND: Anticholinergic (AC) adverse drug events (ADEs) are caused by inhibition of muscarinic receptors as a result of designated or off-target drug-receptor interactions. In practice, AC toxicity is assessed primarily based on clinician experience. The goal of this study was to evaluate a novel concept of integrating big pharmacological and healthcare data to assess clinical AC toxicity risks. METHODS: AC toxicity scores (ATSs) were computed using drug-receptor inhibitions identified through pharmacological data screening. A longitudinal retrospective cohort study using medical claims data was performed to quantify AC clinical risks. ATS was compared with two previously reported toxicity measures. A quantitative structure-activity relationship (QSAR) model was established for rapid assessment and prediction of AC clinical risks. RESULTS: A total of 25 common medications, and 575,228 exposed and unexposed patients were analyzed. Our data indicated that ATS is more consistent with the trend of AC outcomes than other toxicity methods. Incorporating drug pharmacokinetic parameters to ATS yielded a QSAR model with excellent correlation to AC incident rate (R(2) = 0.83) and predictive performance (cross validation Q(2) = 0.64). Good correlation and predictive performance (R(2) = 0.68/Q(2) = 0.29) were also obtained for an M2 receptor-specific QSAR model and tachycardia, an M2 receptor-specific ADE. CONCLUSIONS: Albeit using a small medication sample size, our pilot data demonstrated the potential and feasibility of a new computational AC toxicity scoring approach driven by underlying pharmacology and big data analytics. Follow-up work is under way to further develop the ATS scoring approach and clinical toxicity predictive model using a large number of medications and clinical parameters.

Abstract: Experimental evidence has shown that some of the human endogenous hormones significantly affect drug efficacy. Since hormone status varies with individual physiological states, it is essential to understand the interplay of hormones and drugs for precision medicine. Here, we developed an in silico method to predict interactions between 283 human endogenous hormones and 590 drugs for 20 diseases including cancers and non-cancer diseases. We extracted hormone effect paths and drug effect paths from a large-scale molecular network that contains protein interactions, transcriptional regulations, and signaling interactions. If two kinds of effect paths for a hormone-drug pair intersect closely, we expect that the influence of the hormone on the drug efficacy is significant. It has been shown that the proposed method correctly distinguishes hormone-drug pairs with known interactions from random pairs in blind experiments. In addition, the method can suggest underlying interaction mechanisms at the molecular level so that it helps us to better understand the interplay of hormones and drugs.

Abstract: Conventional genetic approaches and computational strategies have converged on immune-inflammatory pathways as key events in the pathogenesis of late onset sporadic Alzheimer's disease (LOAD). Mutations and/or differential expression of microglial specific receptors such as TREM2, CD33, and CR3 have been associated with strong increased risk for developing Alzheimer's disease (AD). DAP12 (DNAX-activating protein 12)/TYROBP, a molecule localized to microglia, is a direct partner/adapter for TREM2, CD33, and CR3. We and others have previously shown that TYROBP expression is increased in AD patients and in mouse models. Moreover, missense mutations in the coding region of TYROBP have recently been identified in some AD patients. These lines of evidence, along with computational analysis of LOAD brain gene expression, point to DAP12/TYROBP as a potential hub or driver protein in the pathogenesis of AD. Using a comprehensive panel of biochemical, physiological, behavioral, and transcriptomic assays, we evaluated in a mouse model the role of TYROBP in early stage AD. We crossed an Alzheimer's model mutant APP (KM670/671NL) /PSEN1 (Deltaexon9) (APP/PSEN1) mouse model with Tyrobp (-/-) mice to generate AD model mice deficient or null for TYROBP (APP/PSEN1; Tyrobp (+/-) or APP/PSEN1; Tyrobp (-/-)). While we observed relatively minor effects of TYROBP deficiency on steady-state levels of amyloid-beta peptides, there was an effect of Tyrobp deficiency on the morphology of amyloid deposits resembling that reported by others for Trem2 (-/-) mice. We identified modulatory effects of TYROBP deficiency on the level of phosphorylation of TAU that was accompanied by a reduction in the severity of neuritic dystrophy. TYROBP deficiency also altered the expression of several AD related genes, including Cd33. Electrophysiological abnormalities and learning behavior deficits associated with APP/PSEN1 transgenes were greatly attenuated on a Tyrobp-null background. Some modulatory effects of TYROBP on Alzheimer's-related genes were only apparent on a background of mice with cerebral amyloidosis due to overexpression of mutant APP/PSEN1. These results suggest that reduction of TYROBP gene expression and/or protein levels could represent an immune-inflammatory therapeutic opportunity for modulating early stage LOAD, potentially leading to slowing or arresting the progression to full-blown clinical and pathological LOAD.

Abstract: The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions. The ability to accurately capture both semantic and syntactic structures in clinical narratives becomes increasingly critical to enable efficient and accurate ADE detection. Significant progress has been made in algorithm development and resource construction since 2000. Since 2012, statistical analysis and machine learning methods have gained traction in automation of ADE mining from EHR narratives. Current state-of-the-art methods for NLP-based ADE detection from EHRs show promise regarding their integration into production pharmacovigilance systems. In addition, integrating multifaceted, heterogeneous data sources has shown promise in improving ADE detection and has become increasingly adopted. On the other hand, challenges and opportunities remain across the frontier of NLP application to EHR-based pharmacovigilance, including proper characterization of ADE context, differentiation between off- and on-label drug-use ADEs, recognition of the importance of polypharmacy-induced ADEs, better integration of heterogeneous data sources, creation of shared corpora, and organization of shared-task challenges to advance the state-of-the-art.