Abstract: The pregnane X receptor (PXR) regulates the metabolism of many xenobiotic and endobiotic substances. In consequence, PXR decreases the efficacy of many small-molecule drugs and induces drug-drug interactions. The prediction of PXR activators with theoretical approaches such as machine learning (ML) proves challenging due to the ligand promiscuity of PXR, which is related to its large and flexible binding pocket. In this work we demonstrate, by the example of random forest models and support vector machines, that classifiers generated following classical training procedures often fail to predict PXR activity for compounds that are dissimilar from those in the training set. We present a novel regularization technique that penalizes the gap between a model's training and validation performance. On a challenging test set, this technique led to improvements in Matthew correlation coefficients (MCCs) by up to 0.21. Using these regularized ML models, we selected 31 compounds that are structurally distinct from known PXR ligands for experimental validation. Twelve of them were confirmed as active in the cellular PXR ligand-binding domain assembly assay and more hits were identified during follow-up studies. Comprehensive analysis of key features of PXR biology conducted for three representative hits confirmed their ability to activate the PXR.

Abstract: The clinical efficacy and safety of a drug is determined by its molecular targets in the human proteome. However, proteome-wide evaluation of all compounds in human, or even animal models, is challenging. In this study, we present an unsupervised pre-training deep learning framework, termed ImageMol, from 8.5 million unlabeled drug-like molecules to predict molecular targets of candidate compounds. The ImageMol framework is designed to pretrain chemical representations from unlabeled molecular images based on local- and global-structural characteristics of molecules from pixels. We demonstrate high performance of ImageMol in evaluation of molecular properties (i.e., drugaeuros metabolism, brain penetration and toxicity) and molecular target profiles (i.e., human immunodeficiency virus) across 10 benchmark datasets. ImageMol shows high accuracy in identifying anti-SARS-CoV-2 molecules across 13 high-throughput experimental datasets from the National Center for Advancing Translational Sciences (NCATS) and we re-prioritized candidate clinical 3CL inhibitors for potential treatment of COVID-19. In summary, ImageMol is an active self-supervised image processing-based strategy that offers a powerful toolbox for computational drug discovery in a variety of human diseases, including COVID-19.

Abstract: The coronaviruses belong to the Coronaviridae family, and one such member, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is causing significant destruction around the world in the form of a global pandemic. Although vaccines have been developed, their effectiveness and level of protection is still a major concern, even after emergency approval from the World Health Organisation (WHO). At the community level, no natural medicine is currently available as a cure. In this study, we screened the vast library from Drug Bank and identified Hemi-Babim and Fenoterol as agents that can work against SARS-CoV-2. Furthermore, we performed molecular dynamics (MD) simulation for both compounds with their respective proteins, providing evidence that the said drugs can work against the MPro and papain-like protease, which are the main drug targets. Inhibiting the action of these targets may lead to retaining the virus. Fenoterol is a beta-2 adrenergic agonist used for the symptomatic treatment of asthma as a bronchodilator and tocolytic. In this study, Hemi-Babim and Fenoterol showed good docking scores of -7.09 and -7.14, respectively, and performed well in molecular dynamics simulation studies. Re-purposing the above medications has huge potential, as their effects are already well-proven and under public utilisation for asthma-related problems. Hence, after the comprehensive pipeline of molecular docking, MMGBSA, and MD simulation studies, these drugs can be tested in-vivo for further human utilisation.

Abstract: Neurological and psychiatric disorders have overlapped phenotypic profiles, but the underlying tissue-specific functional processes remain largely unknown. In this study, we explore the shared tissue-specificity among 14 neuropsychiatric disorders through the disrupted long-range gene regulations by GWAS-identified regulatory SNPs. Through Hi-C interactions, averagely 38.0% and 17.2% of the intergenic regulatory SNPs can be linked to target protein-coding genes in brain and non-brain tissues, respectively. Interestingly, while the regulatory target genes in the brain tend to enrich in nervous system development related processes, those in the non-brain tissues are inclined to interfere with synapse and neuroinflammation related processes. Compared to psychiatric disorders, neurological disorders present more prominently the neuroinflammatory processes in both brain and non-brain tissues, indicating an intrinsic difference in mechanisms. Through tissue-specific gene regulatory networks, we then constructed disorder similarity networks in two brain and three non-brain tissues, highlighting both known disorder clusters (e.g. the neurodevelopmental disorders) and unexpected disorder clusters (e.g. Parkinson's disease is consistently grouped with psychiatric disorders). We showcase the potential pharmaceutical applications of the small bowel and its disorder clusters, illustrated by the known drug targets NR1I3 and NFACT1, and their small bowel-specific regulatory modules. In conclusion, disrupted long-range gene regulations in both brain and non-brain tissues contribute to the similarity among distinct clusters of neuropsychiatric disorders, and the tissue-specifically shared functions and regulators for disease clusters may provide insights for future therapeutic investigations.

Abstract: Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as beta-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from mus-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.

Abstract: BACKGROUND: Despite the prevalence and negative impact of posttraumatic stress disorder (PTSD), there are few medications approved by the U.S. Food and Drug Administration for treatment, and approved medications do not work well enough. We leveraged large-scale electronic health record data to identify existing medications that may be repurposed as PTSD treatments. METHODS: We constructed a mechanistic tree of all Food and Drug Administration-approved medications and used the tree-based scan statistic to identify medications associated with greater than expected levels of clinically meaningful improvement in PTSD symptoms using electronic health record data from the U.S. Department of Veterans Affairs. Our cohort included patients with a diagnosis of PTSD who had repeated symptom measurements using the PTSD Checklist over a 20-year period (N = 168,941). We calculated observed numbers based on patients taking each drug or mechanistically related class of drugs and the expected numbers based on the tree as a whole. RESULTS: Medications typically used to treat PTSD, such as the Food and Drug Administration-approved agent sertraline, were associated with improvement in PTSD symptoms, but the effects were small. Several, but not all, direct-acting antivirals used in the treatment of hepatitis C virus demonstrated a strong association with PTSD improvement. The finding was robust to a sensitivity analysis excluding patients who received established PTSD treatments, including trauma-focused psychotherapy, concurrent with hepatitis treatment. CONCLUSIONS: Our exploratory approach both demonstrated findings that are consistent with what is known about pharmacotherapy for PTSD and uncovered a novel class of medications that may improve PTSD symptoms.

Abstract: Single chain antibody fragments (scFvs) are favored in diagnostic and therapeutic fields thanks to their small size and the availability of various engineering approaches. Linker between variable heavy (VH) and light (VL) chains of scFv covalently links these domains and it can affect scFv's bio-physical/chemical properties and in vivo activity. Thus, scFv linker design is important for a successful scFv construction, and flexible linkers are preferred for a proper pairing of VH-VL. The flexibility of the linker is determined by length and sequence content and glycine-serine (GS) linkers are commonly preferred for scFvs based on their highly flexible profiles. Despite the advantage of this provided flexibility, GS linkers carry repeated sequences which can cause problems for PCR-based engineering approaches and immunogenicity. Here, two different linkers, a repetitive GS linker and an alternative non-repetitive linker with similar flexibility but lower immunogenicity are employed to generate anti-Vascular Endothelial Growth Factor scFvs derived from bevacizumab. Our findings highlight a better in vitro profile of the non-repetitive linker such as a higher monomer ratio, higher thermal stability while there was no significant difference in in vivo efficacy in a zebrafish embryonic angiogenesis model. This is the first study to compare in vivo efficacy of scFvs with different linkers in a zebrafish model.

Abstract: Chemicals, including some systemically administered xenobiotics and their biotransformations, can be detected noninvasively using skin swabs and untargeted metabolomics analysis. We sought to understand the principal drivers that determine whether a drug taken orally or systemically is likely to be observed on the epidermis by using a random forest classifier to predict which drugs would be detected on the skin. A variety of molecular descriptors describing calculated properties of drugs, such as measures of volume, electronegativity, bond energy, and electrotopology, were used to train the classifier. The mean area under the receiver operating characteristic curve was 0.71 for predicting drug detection on the epidermis, and the SHapley Additive exPlanations (SHAP) model interpretation technique was used to determine the most relevant molecular descriptors. Based on the analysis of 2561 US Food and Drug Administration (FDA)-approved drugs, we predict that therapeutic drug classes, such as nervous system drugs, are more likely to be detected on the skin. Detecting drugs and other chemicals noninvasively on the skin using untargeted metabolomics could be a useful clinical advancement in therapeutic drug monitoring, adherence, and health status.

Abstract: Identifying new disease indications for existing drugs can help facilitate drug development and reduce development cost. The previous drug-disease association prediction methods focused on data about drugs and diseases from multiple sources. However, they did not deeply integrate the neighbor topological information of drug and disease nodes from various meta-path perspectives. We propose a prediction method called NAPred to encode and integrate meta-path-level neighbor topologies, multiple kinds of drug attributes, and drug-related and disease-related similarities and associations. The multiple kinds of similarities between drugs reflect the degrees of similarity between two drugs from different perspectives. Therefore, we constructed three drug-disease heterogeneous networks according to these drug similarities, respectively. A learning framework based on fully connected neural networks and a convolutional neural network with an attention mechanism is proposed to learn information of the neighbor nodes of a pair of drug and disease nodes. The multiple neighbor sets composed of different kinds of nodes were formed respectively based on meta-paths with different semantics and different scales. We established the attention mechanisms at the neighbor-scale level and at the neighbor topology level to learn enhanced neighbor feature representations and enhanced neighbor topological representations. A convolutional-autoencoder-based module is proposed to encode the attributes of the drug-disease pair in three heterogeneous networks. Extensive experimental results indicated that NAPred outperformed several state-of-the-art methods for drug-disease association prediction, and the improved recall rates demonstrated that NAPred was able to retrieve more actual drug-disease associations from the top-ranked candidates. Case studies on five drugs further demonstrated the ability of NAPred to identify potential drug-related disease candidates.

Abstract: The pandemic situation of novel coronavirus disease 2019 (COVID-19) is a global threat on our current planet, with its rapid spread and high mortality rate. Sarcoidosis patients are at high risk to COVID-19 severity for having lung injuries as well as treating with immunosuppressive agents. So, physicians are in dilemma whether they should use immunosuppressive agents or not for the patients with sarcoidosis history and COVID-19 infection. Therefore, common factors should be identified to provide effective treatment. For determining the common genes between COVID-19 and sarcoidosis, GSE164805 and GSE18781 were retrieved from the Gene Expression Omnibus (GEO) database. Common upregulated genes were identified by using R language to investigate their involved pathways and gene ontologies (GO). With the aid of the STRING Cytoscape plugin tool, protein-protein interactions (PPIs) network was constructed. From the PPIs network, Hub genes and essential modules were detected by using Cytohubba, and MCODE respectively. For hub genes, TFs, TFs-miRNA, and drug, interaction networks were built through the NetworkAnalyst web platform. A total of 34 common upregulated genes were identified and among them, five hub genes, including TET2, MUC5AC, VDR, NFE2L2, and BCL6 were determined. In addition, a cluster having VDR and NFE2L2 was detected from the PPIs network. Moreover, 32 transcription factors and 9 miRNA were recognized for hub genes. Furthermore, vitamin D and some of its analogous compounds were obtained from the drug interaction network. In conclusion, hub genes identified in this study might have potential roles in modulating COVID-19 infection and sarcoidosis. However, further studies are required to corroborate this study.