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Pluchea indica (L.) Less is traditionally utilized to treat postpartum women in Indonesia. The plant has many pharmacological properties, so that it can be further developed as herbal medicine. In that development process, plant authentication is needed to ensure the quality of raw materials. A simple microscopical and thin-layer chromatography (TLC) analysis might be a way to authenticate the plant, but it has never been reported. So, this study evaluates the microscopical and TLC analysis for P. indica authentication in standardized herbal medicines production. Plant microscopic observation, fluorescence analysis, and polyphenol screening were conducted. n-Hexane, ethyl acetate, and methanol extracts of plant organs were then analyzed by TLC. Here, we reported that in microscopical analysis the simplicia of P. indica contains trichomes and tannin-containing cells. In addition, chlorogenic acid as a marker was present in TLC analysis by ethyl acetate-water-formic acid-acetic acid (8.5:1.5:1:1, v/v). The results of this evaluation might provide additional information in the identification, authentication, and quality control of P. indica as a raw material for herbal medicine. [ABSTRACT FROM AUTHOR]

The SIRT1 is overexpressed in a number of cancers. As a result, inhibiting SIRT1 may be used as a cancer treatment technique. Modification of 1-benzoyl-3-methylthiourea derivatives was carried out in this study by changing the aromatic side. The designed compounds (94) were subjected to in silico docking, pharmacokinetics, and preclinical testing. In the docking sample, the (4-decyl-N-(methylcarbamothioyl)benzamide (91), 2-(benzyloxy)-N-(methylcarbamo-thioyl)benzamide (93) and N-(methylcarbamothioyl)-2-naphthamide (94) were predicted to display better inhibition of SIRT1, so they were chosen for subsequent molecular dynamic studies. The compound 93 is proposed as a possible anticancer candidate that inhibits SIRT1 based on the screening results from molecular docking, pharmacokinetic predictions, and molecular dynamics. [ABSTRACT FROM AUTHOR]

Dipeptidyl peptidase-IV (DPP-IV) inhibitor is one of the drug targets for the treatment of diabetes. Some classes of those drugs have dangerous side effects so is critical to develop safer drugs. By using rotation forest methods and in silico, it will be more efficient than conventional methods that require a lot more costs and are more time-consuming. One of in silico methods used in drug design is ligand-based virtual screening (LBVS). The interlocking structure capabilities are identified by the LBVS Process. The fingerprint is one of the structural interpretations. Molecular fingerprints are used as a criterion for LBVS in computational drug discovery. A circular fingerprint is found to improve LBVS performance. In this paper, we used the representation of ECFP and FCFP as a method to extract features, after which we used a Rotation Forest classifier to predict active and inactive compounds. The experiment result shows Rotation Forest has good prediction based on the different circular fingerprint and can successfully better classify with results of MCC being 85% and accuracy 92%. [ABSTRACT FROM AUTHOR]

Several of the DPP-4 inhibitors in the treatment of type 2 diabetes (T2DM) still have unsafe side effects in long-term use. It is necessary to develop a new DPP-4 inhibitor to minimize these unsafe side effects of the drug. QSAR is a model that can be used for the development of DPP-4 inhibitor drugs. The selection of a subset of DPP-4 inhibitor molecules by applying the clustering method can be made to improve the accuracy of the QSAR model. This study aims to select the corresponding DPP-4 inhibitor molecules by using the MiniBatchKMeans algorithm with Levenshtein distance and based on the log P criteria of 'Lipinski's Rule of 5' for QSAR modeling. The research began with the collection of DPP-4 inhibitor molecule data from the ChEMBL database site (https://www.ebi.ac.uk/chembl/) in CSV format. A representation of the molecular structure of the data is obtained from their SMILES features. Before running the clustering process, data in the form of SMILES is extracted into molecular fingerprints using several fingerprint generators, namely MACCS, ECFP, and FCFP. Clustering was carried out on five fingerprint datasets, including ECFP (with 4 and 6 diameters), FCFP (with 4 and 6 diameters), and MACCS (167 structural keys). The clustering process begins by determining the optimal number of clusters evaluated by applying the Davies-Bouldin index, the Silhouette coefficient, and the Calinski Harabasz score. Based on the clustering process, 1540 clusters were obtained from the minimum DBI cluster evaluation values of 0.545311, maximum SCO of 0.302842, and maximum CHS of 331.3942 from the MACCS fingerprint dataset. Based on log P criteria from 'Lipinski's Rule of 5', 1532 molecules were obtained for the molecular selection process that have log P values between -0.205 to 4.95. [ABSTRACT FROM AUTHOR]

Objective: Previous research found that Adenosine A2A antagonist allows to reduce motor fluctuations, dyskinesia, protect from neurodegenerative disorder in Parkinson's disease in the human brain which is chronic progressive of losing dopaminergic neurons. The aim of this study is to explore Indonesian herbal compounds as Adenosine A2A inhibitor using virtual screening method. Methods: In this study, virtual screening of Indonesian herbal database as Adenosine A2A inhibitor was done by AutoDock and AutoDock Vina and was validated by database from A Directory of Useful Decoys: Enhanced (DUD-E). The method was validated by Enrichment Factor (EF) and Area Under Curve (AUC) of Receiver Operating Characteristics (ROC) curve Results: Based on the validation results, grid box that was used in virtual screening using AutoDock is 60 x 60 x 60 with EF1% 16.5869 and AUC 0.8406. The two compounds Chitranone and 3-O-Methylcalopocarpin with binding energy -10.19 and -9.55 kcal/mol, respectively showing interaction with Adenosine A2A active site at residues ALA63, ILE66, ALA81, LEU85, PHE168, GLU169, MET177, TRP246, LEU249, ASN253 and ILE274. Conclusions: This study concludes that Chitranone and 3-O-Methylcalopocarpin could be proposed to be developed as Adenosine A2A antagonists. [ABSTRACT FROM AUTHOR]

Background: Phyllanthus niruri, in Indonesia, is known as “Meniran” has a long history of use in ethnic or traditional medicine worldwide, mainly as an antihypertensive agent. Objective: The present study was designed to isolate and identify active compounds with angiotensin-converting enzyme (ACE) inhibition activity from P. niruri herb and confirm the mechanism of action, affinity, and domain specificity interactions of the isolated compounds. Materials and Methods: Some fractions of P. niruri methanolic extract were subjected to column chromatography and preparative thin-layer chromatography to get active compounds. Structural elucidation was determined via spectroscopic methods. ACE inhibition activity was measured using hippuryl-L-histidyl-L-leucine as a substrate in vitro assay. Furthermore, confirmation of the mechanism of action, affinity, and domain specificity interaction of the isolated compounds on ACE complex macromolecule (protein database id: 1O86) was performed by in silico molecular docking studies. Results: In this work, four active compounds were isolated from aerial part of P. niruri, including hypophyllantin (50% inhibition concentration [IC50] = 0.180 μg/mL), phyllantin (IC50 = 0.140 μg/mL), methyl gallate (IC50 = 0.015 μg/mL), and quercetin 3-O-β-D-glucopyranosyl-(1'''-6'')-α-rhamnoside (IC50 = 0.086 μg/mL). In silico molecular docking method emphasizes ligand-residue interactions, thereby predicting the inhibitory activity of these compounds. After docking to an ACE complex macromolecule, quercetin 3-O-β-D-glucopyranosyl-(1'''-6'')-α-rhamnoside obtained more interactions than lisinopril. Conclusion: The results were obtained from in silico and in vitro experiments and confirm the potential active compound is an ACE inhibitor and a new antihypertensive agent. Abbreviations Used: P. niruri: Phyllanthus niruri; ACE: Angiotensin-converting enzyme; HHL: Hippuryl-L-histidyl-L-leucine; HA: Hippuric acid; PDB: Protein database; IC50: 50% inhibition concentration; FH: N-hexane fraction; FE: Ethyl acetate fraction; TLC: Thin layer chromatography; UV-VIS: Ultraviolet-visible; NMR: nuclear magnetic resonance; FTIR: Fourier–Transform infrared; MS: Mass spectrometry; HMQC: Heteronuclear Multiple-Quantum Correlation; HMBC: Heteronuclear multiple bond correlation; TADOK: Tugas Akhir Mahasiswa Doktor. [ABSTRACT FROM AUTHOR]