Drug repurposing and prediction of multiple interaction types via graph embedding.

BACKGROUND: Finding drugs that can interact with a specific target to induce a desired therapeutic outcome is key deliverable in drug discovery for targeted treatment. Therefore, both identifying new drug-target links, as well as delineating the type of drug interaction, are important in drug repurposing studies. RESULTS: A computational drug repurposing approach was proposed to predict novel drug-target interactions (DTIs), as well as to predict the type of interaction induced. The methodology is based on mining a heterogeneous graph that integrates drug-drug and protein-protein similarity networks, together with verified drug-disease and protein-disease associations. In order to extract appropriate features, the three-layer heterogeneous graph was mapped to low dimensional vectors using node embedding principles. The DTI prediction problem was formulated as a multi-label, multi-class classification task, aiming to determine drug modes of action. DTIs were defined by concatenating pairs of drug and target vectors extracted from graph embedding, which were used as input to classification via gradient boosted trees, where a model is trained to predict the type of interaction. After validating the prediction ability of DT2Vec+, a comprehensive analysis of all unknown DTIs was conducted to predict the degree and type of interaction. Finally, the model was applied to propose potential approved drugs to target cancer-specific biomarkers. CONCLUSION: DT2Vec+ showed promising results in predicting type of DTI, which was achieved via integrating and mapping triplet drug-target-disease association graphs into low-dimensional dense vectors. To our knowledge, this is the first approach that addresses prediction between drugs and targets across six interaction types.

Novel drug-target interactions via link prediction and network embedding.

BACKGROUND: As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures. RESULTS: We report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-drug and protein-protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target embedding vectors as input features. DT2Vec was compared with three top-performing graph similarity-based algorithms on a standard benchmark dataset and achieved competitive results. In order to explore credible novel DTIs, the model was applied to data from the ChEMBL repository that contain experimentally validated positive and negative interactions which yield a strong predictive model. Then, the developed model was applied to all possible unknown DTIs to predict new interactions. The applicability of DT2Vec as an effective method for drug repurposing is discussed through case studies and evaluation of some novel DTI predictions is undertaken using molecular docking. CONCLUSIONS: The proposed method was able to integrate and map chemical and genomic space into low-dimensional dense vectors and showed promising results in predicting novel DTIs.

Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer.

BACKGROUND: Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. MATERIALS AND METHODS: Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. RESULTS: A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. CONCLUSIONS: CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

Spectrofluorimetric method for determination of bupropion in pharmaceutical dosage forms.

OBJECTIVE: Bupropion is a noradrenaline and dopamine reuptake inhibitor which is used as an antidepressant drug. Few HPLC and spectrophotometric methods have been reported before for the determination of bupropion. Most of the previous methods reported for determination of bupropion in pharmaceutical dosage forms are somehow dangerous to health and environment because of using organic solvents. METHOD: In the present method bupropion was determined in pharmaceutical dosage forms by spectrofluorimetry after ion-pair complex formation with eosin Y. The ion-pair complex formation was optimized for reagent amount, buffer pH and time. RESULT: The developed method was linear over the range of 3-120µgmL-1 with an acceptable precision (CV<1.5%) and accuracy (Error<1%). CONCLUSION: The present method is applicable for determination of bupropion in pharmaceutical dosage forms for routine quality control analysis.

A stability indicating HPLC method for determination of mebeverine in the presence of its degradation products and kinetic study of its degradation in oxidative condition.

An HPLC method for determination of mebeverine hydrochloride (MH) in the presence of its degradation products was developed. The degradation of MH was studied under hydrolysis, oxidative and photolysis stress conditions. Under alkaline, acidic and oxidative conditions, degradation of MH was observed. The separation was performed using a Symmetry C18 column and a mixture of 50 mM KH2PO4, acetonitrile and tetrahydrfuran (THF) (63:35:2; v/v/v) as the mobile phase. No interference peaks from degradation products in acidic, alkaline and oxidative conditions were observed. The linearity, accuracy and precision of the method were studied. The method was linear over the range of 1-100 µg/ml MH (r(2)>0.999) and the CV values for intra-day and inter-day variations were in the range of 1.0-1.8%. The limit of quantification (LOQ) and the limit of detection (LOD) of the method were 1.0 and 0.2 µg/ml, respectively. Determination of MH in pharmaceutical dosage forms was performed using the developed method. Furthermore the kinetics of the degradation of MH in the presence of hydrogen peroxide was investigated. The proposed method could be a suitable method for routine quality control studies of mebeverine dosage forms.

The antioxidant activity of some commonly used vegetables in Iranian diet.

Methanol extracts of 26 commonly used vegetables in Iranian diet were monitored for antioxidant activity against linoleic acid peroxidation. Some vegetable extracts including savory, radish leaf, garden-cress, spirmint, leek, chive (aerial part), lettuce and dill showed an antioxidant activity comparable with those of dl-alpha-tocopherol and quercetin.

Muscle relaxant activity of methocarbamol enantiomers in mice.

Documented studies support the emerging idea that drug enantiomers could have different pharmacological activity. Our bibliographical data have shown that so far no report has been published on the pharmacological activity of individual enantiomers of methocarbamol. This study was conducted to characterize the muscle relaxant activity of methocarbamol enantiomers. The rotarod test was used to compare the muscle relaxant activity of racemic methocarbamol and pure enantiomers after intraperitoneal administration of the enantiomers to mice. The results show that (+)-R-methocarbamol has higher muscle relaxant activity compared with racemic methocarbamol or (-)-S-methocarbamol.

A new HPLC technique for the separation of methocarbamol enantiomers.

We have developed a stereoselective high-performance liquid chromatography technique for analytical separation of methocarbamol enantiomers. Precolumn derivatization was performed at room temperature using (-)-menthylchloroformate as a chiral reagent in the presence of pyridine as catalyst. The resulting diastereomers were separated on two Resolve C18 columns connected in series. The mobile phase was phosphate buffer (pH 7.5)-acetonitrile (50: 50, v/v) at a flow rate of 1 mL min(-1). UV detection was set at 274 nm. The optimum amount of reagent and the maximum peak intensity of the diastereomers were determined. The resolution of the diastereomers was satisfactory (alpha = 1.04) under the conditions used.

Stereospecific determination of mefloquine in biological fluids by high-performance liquid chromatography.

A sensitive stereoselective HPLC method was developed for determination of mefloquine (MFQ) enantiomers in plasma, urine and whole blood. The assay involved liquid-liquid extraction of MFQ from biological fluids with a mixture of hexane and isopropanol in the presence of sodium hydroxide and derivatization of the residue by (+)-(S)-naphthylethylisocyanate (NEIC) as chiral derivatizing reagent. Separation of the resulting diastereomers was performed on a silica normal-phase column using chloroform-hexane-methanol (25:74:1) as the mobile phase with a flow-rate of 1 ml/min. Using 200 microl of plasma or whole blood, the limit of determination was 0.2 microg/ml with UV detection for both enantiomers. The limit of determination in 500 microl of urine was 0.08 microg/ml with UV detection.

Ocular effects of oral contraceptives. I. Studies in the dog.

The administration of two oral contraceptives to female dogs for 5 years did not produce ocular lesions. Corneal and lenticular opacities occurred with equal frequency in control and treated groups, and fundic lesions, including papilledema, venous dilatation, and venous or arterial retinal thrombosis, were not produced by doses of Enovid-E or Ovulen 1, 10, and 25 times the human dose.

Casts of chorodial vasculature at physiologic pressures. A new technique.

A method for making latex rubber casts of the ocular vasculature while maintaining physiologic vascular relations has been developed. A series of short-haired domestic ctas, Rhesus monkeys, and albino rabbits were anesthetized and cannulated in the common carotid arteries. The jugular veins were severed, and heparinized saline was infused into the carotid arterial circulation of the animals for complete exsanguination of the head. Throughout the infusion procedure, normal physiologic pressure was maintained. After exsanguination was complete, a solution of latex rubber in distilled water was infused bilaterally and continued until the flow from the jugular veins ceased. The eyes were enucleated and placed in a curing solution. After fixation, the eyes were selectively trimmed and placed into a solution of sodium hydroxide for digestion. The digested tissue was transferred to distilled water for dissection. After dissection, the cast was closely inspected for completeness of capillary filling. Casts such as these are intended for use in observations of ocular vascular anatomy.