PARAMETER OPTIMIZATION AND VIRTUAL SCREENING INDONESIAN HERBAL DATABASE AS HUMAN IMMUNODEFICIENCY VIRUS -1 INTEGRASE INHIBITOR USING AUTODOCK AND VINA

Objective: Human immunodeficiency virus (HIV-1) is a virus that causes acquired immunodeficiency syndrome, a disease considered to be one of themost dangerous because of its high mortality, morbidity, and infectivity. The emergence of mutant HIV strains has led treatment to target proteaseas reverse transcriptase and integrase enzyme become less effective. This study aims to provide knowledge about the potential of HIV-1 integraseinhibitors for use as guiding compounds in the development of new anti-HIV drugs.Methods: This study used AutoDock and AutoDock Vina for virtual screening of the Indonesian herbal database for inhibitors of HIV-1 integrase andis validated using a database of the directory of useful decoys. Optimization was accomplished by selecting the grid size, the number of calculations,and the addition of two water molecules and a magnesium atom as cofactor.Results: This study determined that the best grid box size is 21.1725×21.1725×21.1725 in unit space size (1 unit space equals to macromolecules 1Ǻ),using AutoDock Vina with EF and AUC values, 3.93 and 0.693, respectively. Three important water molecules have meaning in molecular dockingaround the binding pocket.Conclusions: This study obtained the top ten ranked compounds using AutoDock Vina. The compounds include: Casuarinin; Myricetin-3-O-(2'',6''-di-O-α-rhamnosyl)-β-glucoside; 5,7,2',4'-tetrahydroxy-6,3'-diprenylisoflavone 5-O-(4''-rhamnosylrhamnoside); myricetin 3-robinobioside; cyanidin3-[6-(6-ferulylglucosyl)-2-xylosylgalactoside]; mesuein, cyanidin 7-(3-glucosyl-6-malonylglucoside)-4'-glucoside; kaempferol 3-[glucosyl-(1→3)-rhamnosyl-(1→6)-galactoside]; 3-O-galloylepicatechin-(4-β→8)-epicatechin-3-O-gallate; and quercetin 4'-glucuronide

ULTRASONIC-ASSISTED EXTRACTION USING A BETAINE-BASED NATURAL DEEP EUTECTIC SOLVENT FOR RESVERATROL EXTRACTION FROM MELINJO (GNETUM GNEMON) SEEDS

Objective: Melinjo (Gnetum gnemon L.) seeds are known to contain resveratrol, which are classified as a phenolic compound of the stilbenoid. Melinjoseeds have high water content, making them unstable to be stored for prolonged periods with open packaging at room temperature. The present studyaimed to explore the use of ultrasonic-assisted extraction with a betaine-based natural deep eutectic solvent (NADES) for resveratrol extraction fromirradiated melinjo seeds.Methods: The best betaine-based NADES component was identified among betaine-urea, betaine-lactic acid, and betaine-malic acid. Optimization ofextraction methods was performed using the best NADES and extraction variables such as time of extraction, water percentage, and sample: solventratio. The outcome of extraction was evaluated by measuring resveratrol content using high-performance liquid chromatography and the results wereanalyzed using response surface methodology.Results: The best betaine-based NADES was found to be betaine-lactic acid, yielding a resveratrol content of 0.3344 mg/g powder. The optimumextraction was achieved in 10 min with 60% water and a sample: solvent ratio of 1:10, yielding a resveratrol content of 0.227 mg/g powder.Conclusion: Betaine-based NADES can be purposed as an alternative solvent for resveratrol extraction from irradiated melinjo seeds

The Role and Development of the Antagonist of Adenosine A_2A in Parkinson’s Disease

Adenosine is a neuromodulator that regulates the body’s response to dopamine and another neurotransmitter in the brain that is responsible for motoric, emotion, learning, and memory function. Adenosine is a G-protein-coupled receptor and has four subtypes, which are A1, A2A, A2B, and A3. Adenosine A2A is located in the striatum of the brain. Antagonist interferes with GABA releasing, modulates acetylcholine and releases dopamine, and also facilitates dopamine receptor’s signaling. Therefore, it can reduce motoric symptoms in Parkinson’s disease. Adenosine A2A antagonist is also believed to have neuroprotective effects. Several compounds have been reported and have undergone clinical test as selective adenosine A2A antagonists, including istradefylline, preladenant, tozadenant, vipadenant, ST-1535, and SYN-115. Nonselective adenosine A2A antagonists from natural compounds are caffeine and theophylline

APPLICATION OF THE MACHINE AND DEEP LEARNING METHODS FOR THE CLASSIFICATION OF CANNABINOID- AND CATHINONE-DERIVED COMPOUNDS

Objective: New psychoactive substances (NPS) have been rapidly developed to avoid legal entanglement. In 2013–2018, the number of cathinonederivedcompounds increased from 30 to 89. In 2016, of 56 NPS compounds, 21 were identified as cannabinoid-derived; only 43 were regulated inthe narcotics law. Artificial intelligence, such as machine and deep learning, is a method of data processing and object recognition, including humanposes and image classifications.Methods: Herein, the machine and deep learning methods for cathinone- and cannabinoid-derived compound classification were compared usingpharmacophore modeling as the reference method. For classifying cathinone-derived compounds, the structure was transformed into fingerprints,which was used as a learning parameter for the machine and deep learning methods. Contrarily, the physicochemical properties and fingerprint shapewere utilized as learning materials for the deep learning method to classify the cannabinoid-derived substances.Results: Consequently, in the cathinone-derived compound classification, the deep learning method produced the accuracy and Cohen kappa valuesof 0.9932 and 0.992, respectively. Furthermore, such values in the pharmacophore modeling method were higher than those in the machine learningmethod (0.911 and 0.708 vs. 0.718 and 0.673, respectively). In the cannabinoid-derived compound classification, the deep learning method with thefingerprint form had the highest accuracy and Cohen kappa values (0.9904 and 0.9876). Such values in this method with the descriptor form werehigher than those in the pharmacophore modeling method (0.8958 and 0.8622 vs. 0.68 and 0.396, respectively).Conclusion: The deep learning method has the potential in the NPS classification

Secondary Metabolites from Marine-Derived Bacteria with Antibiotic and Antibiofilm Activities against Drug-Resistant Pathogens

The search for new antibiotics against drug-resistant microbes has been expanded to marine bacteria. Marine bacteria have been proven to be a prolific source of a myriad of novel compounds with potential biological activities. Therefore, this review highlights novel and bioactive compounds from marine bacteria reported during the period of January 2016 to December 2021. Published articles containing novel marine bacterial secondary metabolites that are active against drug-resistant pathogens were collected. Previously described compounds (prior to January 2016) are not included in this review. Unreported compounds during this period that exhibited activity against pathogenic microbes were discussed and compared in order to find the cue of the structure–bioactivity relationship. The results showed that Streptomyces are the most studied bacteria with undescribed bioactive compounds, followed by other genera in the Actinobacteria. We have categorized the structures of the compounds in the present review into four groups, based on their biosynthetic origins, as polyketide derivatives, amino acid derivatives, terpenoids, as well as compounds with mixed origin. These compounds were active against one or more drug-resistant pathogens, such as methicillin-resistant Staphylococcus aureus (MRSA), methicillin-resistant Staphylococcus epidermidis (MRSE), vancomycin-resistant Enterococci (VRE), multidrug-resistant Mycobacterium tuberculosis (MDR-TB), and amphotericin B-resistant Candida albicans. In addition, some of the compounds also showed activity against biofilm formation of the test bacteria. Some previously undescribed compounds, isolated from marine-derived bacteria during this period, could have a good potential as lead compounds for the development of drug candidates to overcome multidrug-resistant pathogens