Abstract: Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of prediction of DTIs. However, existing studies with multiple similarity measures ignored the global structure information of similarity measures, and required manual extraction features of drug-target pairs, ignoring the non-linear relationship among features. In this paper, we proposed a novel approach MDADTI for DTIs prediction based on MDA. MDADTI applied random walk with restart method and positive pointwise mutual information to calculate the topological similarity matrices of drugs and targets, capturing the global structure information of similarity measures. Then, MDADTI applied multimodal deep autoencoder to fuse multiple topological similarity matrices of drugs and targets, automatically learned the low-dimensional features of drugs and targets, and applied deep neural network to predict DTIs. The results of 5-repeats of 10-fold cross-validation under three different cross-validation settings indicated that MDADTI is superior to the other four baseline methods. In addition, we validated the predictions of the MDADTI in six drug-target interactions reference databases, and the results showed that MDADTI can effectively identify unknown DTIs.

Abstract: The drug target identification is the primary step for drug discovery. Recent development of computational techniques and availability of sequencing data has provided numerous opportunities for target identification but very few of them are fully automated. Here, we have developed a Perl program named Dtar-Finder for drug target identification and its characterization. Dtar-Finder predicts the drug targets which are essential to pathogen and non homologous to human, essential human anti-targets and gut microflora. This program is divided in 6 modules where modules 1-4 extract drug targets while module 5-6 predicts druggability and broad spectrum ability of identified candidates. The performance of this program in terms of sensitivity and specificity is calculated where specificity score was better compare to sensitivity score. Further, we have tested our script on C. botulinum (3572 proteins) and 35 potential drug targets have been identified. Out of which 16 broad spectrums candidates were predicted whereas 8 candidates are found to be druggable whiles remaining are considered to be 'novel'. These drug targets were cross-validated through literature showing 77.14% accuracy. Thus, the idea behind this work was to develop a fast, robust and generic program capable of finding drug targets in bacteria, which has been fulfilled satisfactorily.

Abstract: The high genetic variability of influenza A viruses poses a continual challenge to seasonal and pandemic vaccine development, leaving antiviral drugs as the first line of defense against antigenically different strains or new subtypes. As resistance against drugs targeting viral proteins emerges rapidly, we assessed the antiviral activity of already approved drugs that target cellular proteins involved in the viral life cycle and were orally bioavailable. Out of 15 candidate compounds, four were able to inhibit infection by 10- to 100-fold without causing toxicity, in vitro. Two of the drugs, dextromethorphan and ketotifen, displayed a 50% effective dose between 5 and 50 muM, not only for the classic H1N1 PR8 strain, but also for a pandemic H1N1 and a seasonal H3N2 strain. Efficacy assessment in mice revealed that dextromethorphan consistently resulted in a significant reduction of viral lung titers and also enhanced the efficacy of oseltamivir. Dextromethorphan treatment of ferrets infected with a pandemic H1N1 strain led to a reduction in clinical disease severity, but no effect on viral titer was observed. In addition to identifying dextromethorphan as a potential influenza treatment option, our study illustrates the feasibility of a bioinformatics-driven rational approach for repurposing approved drugs against infectious diseases.

Abstract: In clinical practice, reducing the burden of persistent atrial fibrillation by pharmacological means is challenging. We explored if blocking the background and the acetylcholine-activated inward rectifier potassium currents (IK1 and IKACh) could be antiarrhythmic in persistent atrial fibrillation. We thus tested the hypothesis that blocking IK1 and IKACh with chloroquine decreases the burden of persistent atrial fibrillation. We used patch clamp to determine the IC50 of IK1 and IKACh block by chloroquine and molecular modeling to simulate the interaction between chloroquine and Kir2.1 and Kir3.1, the molecular correlates of IK1 and IKACh. We then tested, as a proof of concept, if oral chloroquine administration to a patient with persistent atrial fibrillation can decrease the arrhythmia burden. We also simulated the effects of chloroquine in a 3D model of human atria with persistent atrial fibrillation. In patch clamp the IC50 of IK1 block by chloroquine was similar to that of IKACh. A 14-day regimen of oral chloroquine significantly decreased the burden of persistent atrial fibrillation in a patient. Mathematical simulations of persistent atrial fibrillation in a 3D model of human atria suggested that chloroquine prolonged the action potential duration, leading to failure of reentrant excitation, and the subsequent termination of the arrhythmia. The combined block of IK1 and IKACh can be a targeted therapeutic strategy for persistent atrial fibrillation.

Abstract: Computational drug repositioning, designed to identify new indications for existing drugs, significantly reduced the cost and time involved in drug development. Prediction of drug-disease associations is promising for drug repositioning. Recent years have witnessed an increasing number of machine learning-based methods for calculating drug repositioning. In this paper, a novel feature learning method based on Gaussian interaction profile kernel and autoencoder (GIPAE) is proposed for drug-disease association. In order to further reduce the computation cost, both batch normalization layer and the full-connected layer are introduced to reduce training complexity. The experimental results of 10-fold cross validation indicate that the proposed method achieves superior performance on Fdataset and Cdataset with the AUCs of 93.30% and 96.03%, respectively, which were higher than many previous computational models. To further assess the accuracy of GIPAE, we conducted case studies on two complex human diseases. The top 20 drugs predicted, 14 obesity-related drugs, and 11 drugs related to Alzheimer's disease were validated in the CTD database. The results of cross validation and case studies indicated that GIPAE is a reliable model for predicting drug-disease associations.

Abstract: Diffuse gliomas with K27M histone mutations (H3K27M glioma) are generally characterized by a fatal prognosis, particularly affecting the pediatric population. Based on the molecular heterogeneity observed in this tumor type, personalized treatment is considered to substantially improve therapeutic options. Therefore, clinical evidence for therapy, guided by comprehensive molecular profiling, is urgently required. In this study, we analyzed feasibility and clinical outcomes in a cohort of 12 H3K27M glioma cases treated at two centers. Patients were subjected to personalized treatment either at primary diagnosis or disease progression and received backbone therapy including focal irradiation. Molecular analyses included whole-exome sequencing of tumor and germline DNA, RNA-sequencing, and transcriptomic profiling. Patients were monitored with regular clinical as well as radiological follow-up. In one case, liquid biopsy of cerebrospinal fluid (CSF) was used. Analyses could be completed in 83% (10/12) and subsequent personalized treatment for one or more additional pharmacological therapies could be recommended in 90% (9/10). Personalized treatment included inhibition of the PI3K/AKT/mTOR pathway (3/9), MAPK signaling (2/9), immunotherapy (2/9), receptor tyrosine kinase inhibition (2/9), and retinoic receptor agonist (1/9). The overall response rate within the cohort was 78% (7/9) including one complete remission, three partial responses, and three stable diseases. Sustained responses lasting for 28 to 150 weeks were observed for cases with PIK3CA mutations treated with either miltefosine or everolimus and additional treatment with trametinib/dabrafenib in a case with BRAFV600E mutation. Immune checkpoint inhibitor treatment of a case with increased tumor mutational burden (TMB) resulted in complete remission lasting 40 weeks. Median time to progression was 29 weeks. Median overall survival (OS) in the personalized treatment cohort was 16.5 months. Last, we compared OS to a control cohort (n = 9) showing a median OS of 17.5 months. No significant difference between the cohorts could be detected, but long-term survivors (>2 years) were only present in the personalized treatment cohort. Taken together, we present the first evidence of clinical efficacy and an improved patient outcome through a personalized approach at least in selected cases of H3K27M glioma.

Abstract: Aim: Achyranthes bidentata is one of the most commonly used Chinese herbal medicines (CHM) that is currently considered for the treatment of osteoarthritis. The purpose of this study was to reveal the mechanism of Achyranthes bidentata in osteoarthritis treatment based on the network pharmacology. Methods: The effective components of Achyranthes bidentata were firstly screened out from the TCMSP database with ADME property parameters. Then, osteoarthritis-related proteins targeted by the effective components were predicted based on the DrugBank and CTD databases. Subsequently, enrichment analysis and interaction network between targets of effective components and pathways were also studied. In addition, the differentially expressed genes (DEGs) of GSE55457 were used for validation of the osteoarthritis-related target proteins. Finally, the effective components-target molecular docking models were predicted. Results: A total of 10 effective components were identified, of which kaempferol and quercetin had 1 and 29 targets, respectively. There were 26 target proteins of quercetin related to the osteoarthritis. These targets were mainly enriched in mitochondrial ATP synthesis coupled proton transport, cellular response to estradiol stimulus, and nitric oxide biosynthetic process. In addition, there were three common proteins, PIM1, CYP1B1, and HSPA2 based on the DEGs of GSE55457, which were considered as the key targeted proteins of the quercetin. Conclusion: The docking of PIM1-quercetin, CYP1B1-quercetin, and HSPA2-quercetin may play important roles during the treatment of osteoarthritis by Achyranthes bidentata.

Abstract: Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies.

Abstract: Rossmann folds are ancient, frequently diverged domains found in many biological reaction pathways where they have adapted for different functions. Consequently, discernment and classification of their homologous relations and function can be complicated. We define a minimal Rossmann-like structure motif (RLM) that corresponds for the common core of known Rossmann domains and use this motif to identify all RLM domains in the Protein Data Bank (PDB), thus finding they constitute about 20% of all known 3D structures. The Evolutionary Classification of protein structure Domains (ECOD) classifies RLM domains in a number of groups that lack evidence for homology (X-groups), which suggests that they could have evolved independently multiple times. Closely related, homologous RLM enzyme families can diverge to bind different ligands using similar binding sites and to catalyze different reactions. Conversely, non-homologous RLM domains can converge to catalyze the same reactions or to bind the same ligand with alternate binding modes. We discuss a special case of such convergent evolution that is relevant to the polypharmacology paradigm, wherein the same drug (methotrexate) binds to multiple non-homologous RLM drug targets with different topologies. Finally, assigning proteins with RLM domain to the Enzyme Commission classification suggest that RLM enzymes function mainly in metabolism (and comprise 38% of reference metabolic pathways) and are overrepresented in extant pathways that represent ancient biosynthetic routes such as nucleotide metabolism, energy metabolism, and metabolism of amino acids. In fact, RLM enzymes take part in five out of eight enzymatic reactions of the Wood-Ljungdahl metabolic pathway thought to be used by the last universal common ancestor (LUCA). The prevalence of RLM domains in this ancient metabolism might explain their wide distribution among enzymes.

Abstract: PURPOSE: The population of Chile has aged, and in 2017, cancer became the leading cause of death. Since 2005, a national health program has expanded coverage of drugs for 13 types of cancer and related palliative care. We describe the trends in public and private oncology drug expenditures in Chile and consider how increasing expenditures might be addressed. METHODS: We analyzed total quarterly drug expenditures for 131 oncology drugs from quarter (Q)3 2012 until Q1 2017, including public and private insurance payments and patient out-of-pocket spending. The data were analyzed by drug-mix, sources of funding, growth, and intellectual property status. The Laspeyres Price Index was used to analyze expenditure growth. RESULTS: We found 131 oncology drugs associated with 87,129 observations. Spending on drugs rose 120% from the first period, spanning from the first 3 quarters (Q3, Q4, Q1 2012-2013) to the last period (Q3, Q4, Q1 2016-2017), corresponding to an annualized rate of 19.2% and totaling US$398 million (in 2017 dollars). The public sector accounted for 84.2% of spending, which included 50 drugs in the official treatment protocols, whereas private insurance accounted for 7.3% in on-protocol drugs. The remaining 8.5% was paid out of pocket. In the public sector, more than 90% of growth resulted from increased use. Seven drugs, including 3 with nonexpired patents, accounted for 50% of total expenditures. CONCLUSION: Increased use and access enabled by expanded public expenditures drove most of the growth in oncology drug expenditures. However, the rate of public expenditure growth may be fiscally unsustainable. Policies are urgently needed to promote the use of generic drugs, the appropriate mix of on-protocol versus off-protocol drugs, and the curbing of off-label prescribing.