The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug–target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug–target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug–target interactions and repurpose existing drugs.

Apolipoprotein E4 (apoE4) is the major genetic risk factor for Alzheimer's disease. However, the underlying mechanisms are unclear. We found that female apoE4 knock-in (KI) mice had an age-dependent decrease in hilar GABAergic interneurons that correlated with the extent of learning and memory deficits, as determined in the Morris water maze, in aged mice. Treating apoE4-KI mice with daily peritoneal injections of the GABA A receptor potentiator pentobarbital at 20 mg/kg for 4 weeks rescued the learning and memory deficits. In neurotoxic apoE4 fragment transgenic mice, hilar GABAergic interneuron loss was even more pronounced and also correlated with the extent of learning and memory deficits. Neurodegeneration and tauopathy occurred earliest in hilar interneurons in apoE4 fragment transgenic mice; eliminating endogenous Tau prevented hilar GABAergic interneuron loss and the learning and memory deficits. The GABA A receptor antagonist picrotoxin abolished this rescue, while pentobarbital rescued learning deficits in the presence of endogenous Tau. Thus, apoE4 causes age-and Tau-dependent impairment of hilar GABAergic interneurons, leading to learning and memory deficits in mice. Consequently, reducing Tau and enhancing GABA signaling are potential strategies to treat or prevent apoE4-related Alzheimer's disease.

Significance This manuscript describes a previously unidentified mechanism for organic cation transporter 1 (OCT1), the major hepatic metformin transporter, in hepatic steatosis. Here we show that OCT1, long thought to function primarily as a transporter for drugs, functions as a major thiamine transporter in the liver, which has profound implications in cellular metabolism. Collectively, our results point to an important role of thiamine (through OCT1) in hepatic steatosis and suggest that the modulation of thiamine disposition by metformin may contribute to its pharmacologic effects.

High levels of heparanase are an indicator of poor prognosis in myeloma patients, and up-regulation of the enzyme enhances tumor growth, angiogenesis, and metastasis in animal models. At least part of the impact of heparanase in driving the aggressive tumor phenotype is due to its effect on increasing the expression and shedding of the heparan sulfate proteoglycan syndecan-1, a molecule known to promote myeloma progression. The present work demonstrated that elevation in heparanase expression in myeloma cells stimulates sustained ERK phosphorylation that in turn drives MMP-9 expression. In addition, urokinase-type plasminogen activator (uPA) and uPA receptor expression levels increased, and blocking the proteolytic activation of either MMP-9 or uPA inhibited the heparanase-induced increase in syndecan-1 shedding. Together these data provide a mechanism for heparanase-induced syndecan-1 shedding and, more importantly, demonstrate that heparanase activity in myeloma cells can lead to increased levels of proteases that are known to play important roles in the aggressive behavior of myeloma tumors. This in addition to its other known biological roles, indicates that heparanase acts as a master regulator of the aggressive tumor phenotype by up-regulating protease expression and activity within the tumor microenvironment.

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Objective The goal of this study was to determine the effect of a genetic variant in the organic cation transporter (OCT2), OCT2-808G/T, which results in an amino acid change, A270S, on the pharmacokinetics of the anti-diabetic drug, metformin. Methods The uptake of metformin was performed in stably transfected HEK-293 cells expressing the empty vector (MOCK), the reference OCT2-808G and the variant OCT2-808T. Healthy individuals with known OCT2 genotypes [fourteen homozygous for the OCT2 reference allele (808G/G) and nine heterozygous for the variant allele (808G/T, *3D)] were recruited into this study. Metformin concentrations in plasma and urine were measured by liquid chromatography-tandem mass spectrometry method. Creatinine levels were also measured in plasma and urine. Pharmacokinetic parameters were evaluated for both groups. Results We observed that in HEK-293 stably transfected cells, OCT2-808T had a greater capacity to transport metformin than did the reference OCT2. Metformin pharmacokinetics were characterized in twenty-three healthy volunteers of Caucasian and African American ancestries. We observed that the renal clearance (CLR) and the net secretion (SrCLR) of metformin were significantly different between the volunteers heterozygous for the variant allele (808G/T), and the volunteers homozygous for the reference allele (808G/G) (p<0.005). Multivariate analysis revealed that OCT2 genotype was a significant predictor of CLR and SrCLR of metformin (p<0.01). Conclusion We conclude that genetic variation in OCT2 plays an important role in the CLR and SrCLR of metformin in healthy volunteers.

The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. The retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that our machine learning based method can successfully predict effective drug candidates : bioRxiv preprint against a specific coronavirus. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to the prevention immunopathology induced by SARS-CoV-2 infection. Further pharmacokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infection. Moreover, molecular docking simulation suggested that CVL218 may bind to the N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible model to explain its antiviral action. We also proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the data present in this study and previous evidences reported in the literature. In summary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning framework can serve as a potential therapeutic agent for the treatment of COVID-19.