Hasil Pencarian  ::  Simpan CSV :: Kembali

Abstrak :
ABSTRAK
Inhibitor Dipeptidyl Peptidase-4 (DPP-4) menjadi obat yang semakin penting dalam pengobatan diabetes melitus tipe-2, namun beberapa golongan obat ini memiliki efek samping seperti nyeri sendi yang bisa menjadi parah hingga pankreatitis, diperkirakan efek samping ini muncul terkait dengan penghambatannya terhadap enzim DPP-8 dan DPP-9. Untuk mengembangankan inhibitor DPP-4 baru yang memiliki aktivitas penghambatan yang tinggi terhadap DPP-4 dan penghambatan yang rendah terhadap DPP-8 dan DPP-9 maka dilakukan virtual screening pada lebih dari 10 juta molekul, dengan membangun workflow virtual screening menggunakan metode Quantitative Structure-Activity Relationship (QSAR) berbasis artificial intelligence (AI). Lima algoritma machine learning regresi dan empat algoritma machine learning klasifikasi digunakan untuk membangun workflow virtual screening. Algoritma yang memenuhi syarat untuk model QSAR regresi adalah Support Vector regression dengan R2pred 0,78 sedangkan model QSAR klasifikasi adalah Random Forest dengan akurasi 92,21%. Dari hasil virtual screening didapatkan senyawa hit dengan pIC50 diatas 7,5 sebanyak 2.716 senyawa. Hasil penambatan molekul beberapa senyawa hit ke enzim DPP-4, DPP-8 dan DPP-9, didapatkan senyawa hit potensial adalah senyawa CH0002. Senyawa hit ini dapat dikembangkan lebih lanjut sebagai inhibitor DPP-4 dan workflow virtual screening pada penelitian ini dapat diterapkan pada target lainnya.

---

ABSTRACT
Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an important drugs in the treatment of type 2 diabetes mellitus, but some classes of these drugs have side effects such as joint pain that can become severe to pancreatitis, these side effects appear to related with their inhibition against other DPP enzymes. This study aims to find DPP-4 inhibitor hit compounds that are selective against DPP-8 and DPP-9 enzymes. virtual screening is carried out on more than 10 million molecules, by building a virtual screening workflow using Quantitative Structure-Activity Relationship (QSAR) method based on Artificial Intelligence (AI). Five regression algortihms and four classification algorithms machine learning were used to build virtual screening workflows. The algorithm that qualifies for the QSAR regression model was Support Vector regression with R2pred 0,78 while the classification QSAR model was Random Forest with an accuracy of 92,21%. Results of virtual screening obtained hit compounds with pIC50 above 7,5 were 2.716 compounds. Results of molecular docking from several hit compounds to the enzymes DPP-4, DPP-8 and DPP-9, potential hit compound was CH0002. This hit compound can be further developed as a DPP-4 inhibitor and virtual screening workflow in this study can be applied to other targets.

Abstrak :
ABSTRAK
The dichloromethane and methanol extracts, and the essential oil of Persicaria sp. were subjected to in vitro anti rice pathogenic microbial activity tests. The essential oil displayed the most potential antimicrobial activity. GC MS analysis revealed thirteen main compounds as dodecanal (54%), decanal (15%), trans caryophyllene (8%), cyclododecane (7%) and humulene (5%). Strong antimicrobial activities of the oil and dodecanal were found against Rhizoctonia solani (IC50 of 0.066 and 0.851 mg/mL) and Xanthomonas oryzae pv. oryzicola (MIC/MBC of 0.78/12.50 and 0.78/25.00 mg/mL), and potent activities against Bipolaris oryzae (IC50 of 3.047 and 3.341 mg/mL) and X. oryzae pv. oryzae (MIC/MBC of 3.12/12.50 and 3.12/25.00 mg/mL). In terms of structure activity relationship, 2 dodecanone and 2 dodecanol displayed significantly anti fungal activity, while 1 and 2 dodecanols expressed potent anti bacterial activity. The essential oil might be used for new microcides controlling rice pathogenic bacteria and fungi.