Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:2.4.1.14 (SPS)
 813 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Mathematical modeling of Quantitative Structure - Property Relationships met great interest in fields of in silico drug design and more recently, pharmaceutical analysis. In our approach we proposed automated method of creation Quantitative Structure-Retention Relationship (QSRR) for analysis of triptans, selective serotonin 5-HT1 receptor agonists used for the treatment of acute headache. The method was created using hybrid machine learning approach, namely Genetic algorithm (GA) coupled with artificial neutral networks (ANN). Performance of proposed hybrid GA-ANN model was evaluated with predicting relative retention times of naratriptan hydrochloride impurities. Several ANN types were coupled with GA and tested: single-layer ANN (SL-ANN), double-layer ANN (D-ANN) and higher order architectures: pi-sigma ANN (PS-ANN) and sigma-pi-sigma ANN (SPS-ANN). Partial Least Squares (PLS) method was used as a reference. The separation of naratriptan hydrochloride and its related products (impurities and degradation products) was obtained by developing a gradient high-performance liquid chromatography method with diode-array detector (HPLC-DAD). Degradation products during acid-basic hydrolysis were identified with an electrospray ionization tandem mass spectrometry (Q-TOF-MS/MS) detector. Independent data for outer validation of QSRR model was obtained from the determination of related products of sumatriptan succinate via an HPLC-DAD method. Accuracy of QSRR was measured by inner-validation on naratriptan data and outer validation on sumatriptan succinate samples. The best performing model were PS-ANN and SPS-ANN with mean errors of 8% (Q2=0.87) and 15% (Q2=0.77) on an inner-validation data set, respectively. Validation on similar samples from an outer validation data set of sumatriptan succinate impurities gave mean errors of 18% (R2pred=0.64) and 17% (R2pred=0.63) for the PS-ANN and SPS-ANN models, respectively.
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PMID:Quantitative structure-retention relationship model for the determination of naratriptan hydrochloride and its impurities based on artificial neural networks coupled with genetic algorithm. 2810 13

Buspirone presents a unique profile of action, which involves activation of 5-HT1A receptors and complex effects on D2-like dopaminergic receptors. This medication is studied in terms of potential clinical repositioning to conditions that are associated with dopaminergic dysfunctions including schizophrenia and substance use disorder. Buspirone antagonizes D3 and D4 receptors, however, depending on the dose it differentially interacts with D2 receptors. Previously, we reported that some of D2/D3 dopaminergic agonists attenuate PTSD-like behavioral symptoms in mice. Here we investigated whether buspirone could also affect PTSD-like symptoms. We used the single prolonged stress (mSPS) protocol to induce PTSD-like behavior in adult male CD-1 mice. Buspirone (0.5, 2, or 10 mg/kg, i.p.) was injected for 15 consecutive days. The subjects were repeatedly examined in a variety of behavioral tests measuring conditioned freezing response, antidepressant-like effects, anxiety, and ultrasonic vocal response to the restraint stress. Mouse SPS resulted in prolonged immobility in the forced swim test and freezing in the fear-conditioning test, and produced symptoms of anxiety. Buspirone dose-dependently decreased the exaggerated freezing response in mice, but only at the dose of 2 mg/kg exhibited the anxiolytic-like effect in the elevated plus maze test. Buspirone reduced the number of ultrasonic calls in mSPS-exposed mice but revealed no antidepressant-like effect in the forced swim test. Present data suggest some positive effects of buspirone in the treatment of selected PTSD-like symptoms and prompt for its further clinical evaluation.
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PMID:Behavioral effects of buspirone in a mouse model of posttraumatic stress disorder. 3176 26