Molecular Docking of Natural Product-Derived Compounds: Estimation of Selectivity on Cyclo-oxygenase-2 นิพนธ์ต้นฉบับ Original

Objective: To investigate the binding modes and molecular selectivity onCOX-1 and COX-2 enzymes of 12 natural product-derived compoundsreported as anti-inflammatory agents using in silico prediction. Method:AutoDock 4.2 was employed to determine the free energy of binding (ΔGb)and inhibition constants (Ki). The evaluated inhibition constant (Ki) fromdocking result was used to estimate the calculated selectivity index (ratio ofCOX-2 Ki to COX-1 Ki of each compound). Results: γ-Mangostin gainedthe lowest calculated selectivity index (0.0269) comparable to rofecoxib(0.0407). The calculated selectivity indices of gingerol, [8]-paradol,isorhapontigenin and rutaecarpine were in a range of 0.2-0.5 that could bedefined as preferential COX-2 inhibitors. Conclusion: Binding modes andmolecular selectivity of 12 natural product-derived compounds reported asanti-inflammatory agents were determined. This information from theinhibitor-enzyme interactions and calculated selectivity indices could beuseful in designing NSAIDs with new scaffold and favorable safety profile.Keywords: docking, COX-2 selective inhibitors, natural product-derivedcompounds, Autodoc

3D-QSAR Investigation of Synthetic Antioxidant Chromone Derivatives by Molecular Field Analysis

A series of 7-hydroxy, 8-hydroxy and 7,8-dihydroxy synthetic chromone derivatives was evaluated for their DPPH free radical scavenging activities. A training set of 30 synthetic chromone derivatives was subject to three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using molecular field analysis (MFA). The substitutional requirements for favorable antioxidant activity were investigated and a predictive model that could be used for the design of novel antioxidants was derived. Regression analysis was carried out using genetic partial least squares (G/PLS) method. A highly predictive and statistically significant model was generated. The predictive ability of the developed model was assessed using a test set of 5 compounds (r2pred = 0.924). The analyzed MFA model demonstrated a good fit, having r2 value of 0.868 and cross-validated coefficient r2cv value of 0.771

Basic Chemistry Calculation Achievement Enhancement for Pharmacy Students Using Chemistry Calculation Exercise

Objective: To develop basic chemistry-calculation achievement for thepharmacy students by using chemistry-calculation exercise. Method: Theexercise consisted of 4 basic learning units including 1) chemical unit andunit conversion, 2) chemical equation (balancing chemical equations), 3)solution and expression of concentration, and 4) chemical equilibrium.Subjects were 52 3rd year pharmacy students enrolling in thePharmaceutical Quality Control I (QC I) course in the 1st semester of theacademic year 2009, voluntarily participating in the study. With one-grouppretest-posttest design, 4 learning units were given sequentially to studentswithin a 3-month period. Chemistry calculation achievement was testedbefore and after the 3-month learning. Students’ average score of thecalculation parts from the midterm and final examination of the QC I coursecalculation was also tested whether at least 50% of such total score wasachieved. Results: Posttest score was significantly higher than the pretestone (from 2.46 to 4.26, P < 0.001, Wilcoxon signed rank test). Score fromcalculation part from the QC I course was significantly higher than 50%(70.0%, P < 0.001, t-test). Conclusion: Learning by means of chemistrycalculationexercise effectively enhanced the calculation achievementreflected by test scores among pharmacy students in the PharmaceuticalQuality Control I course. The exercise and its concept could be furtherapplied in other courses, but with some caution since there was no controlgroup and some other factors potentially contributing to such achievementcould not be fully controlled.Keywords: chemistry calculation, pharmacy students, chemistry calculationexercis

เจตคติและประสิทธิผลของกระบวนการเรียนรู้ด้วยตนเองด้วยการทำรายงาน: กรณีศึกษารายวิชาเคมีของยา 2 Attitude towards and Effectiveness of Report-based Self-directed Learning Process: A Case Study in Medicinal Chemistry-2 Course

ABSTRACTObjective: To evaluate attitude towards and effectiveness of a reportbasedself-directed learning process, in addition to traditional classroomlearning. Methods: In this descriptive classroom research, we enrolledstudents registering the medicinal chemistry-2 of the Faculty of Pharmacy,Srinakharinwirot University, in the 2nd semester, academic year 2008. Inevaluating the report-based self-directed learning, 1) self-directed learningbehavior, 2) attitude towards the learning process were self-assessed bythe students, 3) effectiveness of the learning process was assessed by theinstructors grading 8 reports. Results: The majority of students (50.00%)spent 2 – 4 hours per week for self-directed learning, while 94.10% learnedat their residence, 72.10% learned alone, and 92.60% learned from coursematerials. Structure-activity relationship was the topic the majority ofstudents learned (73.50%). Most students (83.90%) reported that thelearning helped them understand more, 79.40% agreed that the learningprocess helped them realize their problems and obstacles in the classroomlearning, and 70.60% felt enthusiastic in the learning. In terms ofeffectiveness, assignment scores ranged from 8 – 9 points (out of 10) forall topics. Conclusion: Report-based self-directed learning process helpedstudents review course materials, realized their shortcomings and unclearcontents for further self-directed learning. The process could help studentsimprove their life-long learning process.Keywords: self-directed learning, self-directed learning report, medicinalchemistry cours

Current Mathematical Methods Used in QSAR/QSPR Studies

This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future