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All Journal JURNAL KIMIA SAINS DAN APLIKASI Journal of Mathematical and Fundamental Sciences Indonesian Journal of Pharmaceutical Science and Technology Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Chemistry JSFK (Jurnal Sains Farmasi & Klinis) PHARMACY: Jurnal Farmasi Indonesia (Pharmaceutical Journal of Indonesia) JFIOnline Jurnal Ilmu Kefarmasian Indonesia Berkala Ilmiah Mahasiswa Farmasi Charity : Jurnal Pengabdian Masyarakat Jurnal Farmasi dan Sains Indonesia (JFSI) Medical Sains : Jurnal Ilmiah Kefarmasian Pharmaceutical Sciences and Research (PSR) Makara Journal of Science Eduvest - Journal of Universal Studies Borneo Journal of Pharmacy Medical Sains : Jurnal Ilmiah Kefarmasian Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal)
Arry Yanuar
Departemen Farmasi Universitas Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences

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Virtual Screening of the Indonesian Medicinal Plant and Zinc Databases for Potential Inhibitors of the RNA-Dependent RNA Polymerase (RdRp) of 2019 Novel Coronavirus Muhammad Arba; Andry Nur-Hidayat; Ida Usman; Arry Yanuar; Setyanto Tri Wahyudi; Gilbert Fleischer; Dylan James Brunt; Chun Wu
Indonesian Journal of Chemistry Vol 20, No 6 (2020)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijc.56120

Abstract

The novel coronavirus disease 19 (Covid-19) which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a pandemic across the world, which necessitate the need for the antiviral drug discovery. One of the potential protein targets for coronavirus treatment is RNA-dependent RNA polymerase. It is the key enzyme in the viral replication machinery, and it does not exist in human beings, therefore its targeting has been considered as a strategic approach. Here we describe the identification of potential hits from Indonesian Herbal and ZINC databases. The pharmacophore modeling was employed followed by molecular docking and dynamics simulation for 40 ns. 151 and 14480 hit molecules were retrieved from Indonesian herbal and ZINC databases, respectively. Three hits that were selected based on the structural analysis were stable during 40 ns, while binding energy prediction further implied that ZINC1529045114, ZINC169730811, and 9-Ribosyl-trans-zeatin had tighter binding affinities compared to Remdesivir. The ZINC169730811 had the strongest affinity toward RdRp compared to the other two hits including Remdesivir and its binding was corroborated by electrostatic, van der Waals, and nonpolar contribution for solvation energies. The present study offers three hits showing tighter binding to RdRp based on MM-PBSA binding energy prediction for further experimental verification.
Insight on Estrogen Receptor Alpha Modulator from Indonesian Herbal Database: An in-silico analysis Muhammad Arba; Nadhifatul Aslikah; Arfan Arfan; Ruslin Ruslin; Arry Yanuar
PHARMACY: Jurnal Farmasi Indonesia (Pharmaceutical Journal of Indonesia) Jurnal Pharmacy, Vol. 17 No. 02 Desember 2020
Publisher : Pharmacy Faculty, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/pharmacy.v17i2.7592

Abstract

Estrogen receptor α (ERα) is liable for regulating transcription factors which are an important part of hormonal signaling in breast cancer. This study intends to find hit compounds that are considered capable of inhibiting ERα by utilizing structure-based pharmacophores and molecular docking. Pharmacophore of the original ERα ligand (E4D600) has one hydrogen bond acceptor and three hydrogen bond donors which are used to select compounds from the Indonesian herbal database. This pharmacophore model had an Area under Curve of the Receiver Operating Characteristics (AUC-ROC) value is 0.80 and the Goodness of Hits (GH) value is 0.81. The selection process generated 330 compounds which proceed to the molecular docking stage to analyze their binding energy and interactions to ERα. The results indicated potential hit compounds seen from their binding energies in the range -5.42 to -10.01 kcal/mol. four of the best compounds including Lig57/(-)-Bidwillon A, Lig47/Quercetin 3-(6''-galloylgalactoside), Lig197/Multifloroside and Lig83/Erythrabyssin II provide promising information for their detailed analysis as ERα inhibitors.
Computational Studies of Thiourea Derivatives as Anticancer Candidates through Inhibition of Sirtuin-1 (SIRT1) Ruswanto Ruswanto; Richa Mardianingrum; Arry Yanuar
Jurnal Kimia Sains dan Aplikasi Vol 25, No 3 (2022): Volume 25 Issue 3 Year 2022
Publisher : Chemistry Department, Faculty of Sciences and Mathematics, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1667.479 KB) | DOI: 10.14710/jksa.25.3.87-96

Abstract

Cancer is a disease that starts from the uncontrolled growth of abnormal cells in the organs or tissues of the body, which is the second leading cause of death in the world. One of the targets in discovering and developing anticancer drugs is Sirtuin-1. SIRT1 can act as a tumor suppressor or tumor promoter depending on its target in a particular signalling pathway or on particular cancer. This study aimed to study the interaction of a thiourea derivative with SIRT1 (PDB ID:4I5I) through its inhibition of histone deacetylase. Research has been carried out in silico with molecular docking (MGLTools.1.5.6) and molecular dynamics (Desmond 2019) of three thiourea derivatives to the receptor. In addition, pharmacokinetic parameters, toxicity, and selection of Lipinski's Rule of Five were also tested. Molecular docking results showed that compound b ([2-(methylcarbamothioylcarbamoyl)phenyl]benzoate) had the lowest ∆G value of −9.29 kcal/mol with a KI value of 0.156 µM compared to other thiourea derivatives and was proven by molecular dynamics tests for 30 ns and amino acids that play an active role in the interaction include the residue PheA:297. In terms of pharmacokinetics and toxicity, compound b is better than natural ligands. Compound b is predicted to be used as an anticancer candidate through further research.
Eksplorasi dan Karakterisasi berbagai Kristal Ibuprofen Yanuar, Arry; Nursanti, Nursanti; Anwar, Effionora
Majalah Ilmu Kefarmasian Vol. 7, No. 2
Publisher : UI Scholars Hub

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Abstract

Ibuprofen is an analgesic anti-inflammatory nonsteroidal (AINS). Generally, ibuprofen have a bad flowability because a high cohesivity. Another problem in manufacturing is the high tendency for sticking to the punches. Besides these disadvantageous properties, ibuprofen indicates bad dissolution behavior because of its hydrophobic structure. To improve the properties of ibuprofen can be used crystallization method with using variation of solvents. In this experiment observed crystallization method by cooling, evaporation, and water presence, which used methanol, ethanol, and acetone solvents. Of all the crystallization results are produced white prism-shaped crystalline. The selected method is cooling method, which is characterized using Scanning Electron Microscopy (SEM), powder X-ray diffraction, and Differential Scanning Calorimetry (DSC). These three characterizations indicate transformation of crystal form which compared with ibuprofen’s standard. The selected method also produces non cohesive powder which have the size particle is 710-1180µm, compressibility index: IBMD 14.2%, IBED 16.6%, IBAD 17.1%; angles of repose: IBMD 28.1º, IBED 29.7º, IBAD 30.1º, and have higher solubility than the common crystal’s solubility. The result indicates that crystallization method is able to improve flowrate, compressibility index, and dissolution rate properties of ibuprofen’s standard.
Pemanfaatan Maltodekstrin Pati Terigu Sebagai Eksipien Dalam Formula Sediaan Tablet dan Niosom Anwar, Effionora; Djajadisastra, Joshita; Yanuar, Arry; Bahtiar, Anton
Majalah Ilmu Kefarmasian Vol. 1, No. 1
Publisher : UI Scholars Hub

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Abstract

The Use of Maltodextrin from Wheat Starch as an Exipient in Formula Tablet and Niosom dosage form. Wheat starch normally can be used as a tablet filler only, because the flow rate and binding capacity are not good enough. The wheat starch should be treated as follows : protein and amine free Bogasari wheat flour starch were hydrolyzed by α-amylase enzyme (Liquezym EX®) at variable temperature and time incubation to produce maltodextrin with different Dextrose Equivalent (DE) value. The maltodextrin could be used as tablet binder on wet granulation, tablet filler and binder on direct compress, a proniosom carrier to prepare niosom, a tablet filler, binder and disintegrator on direct compress tablet, a sugar coated tablet material. All of the product used active compound as amodel and the quality were evaluated according to the 4thed. of Indonesian Pharmacopeae and other valid references. The result shows that maltodextrin DE 1–5 could be used as a tablet binder which was processed by wet granulation on 2-5% concentration, as a tablet binder and filler which was processed by direct compress on 30-35%; maltodextrin DE 10-15 could be used as a proniosom carrier then continued to niosom preparation with surfactant composition of 2 mmol (1 mmol for span 60 and 1 mmol for cholesterol). The surfactant and drug concentration of 100 mmol/lt and 5 mmol/lt subsequently was proved to loading the drug as much as 81.28%. Maltodextrin DE 15-20 could be used as a tablet filler, binder and disintegrator at 84%, and starch hydrolyzed of DE 35-40 as a sugar coating which was more economical than sugar.
Epigenetic Diet to Modulate Immune Response against SARS-CoV-2 Andika, Andika; Ahdyani, Risa; Erlina, Linda; Azminah, Azminah; Yanuar, Arry
Pharmaceutical Sciences and Research Vol. 7, No. 2
Publisher : UI Scholars Hub

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Abstract

The COVID-19 pandemic has spread to various parts of the world and caused many deaths. The victims are infected by SARS-CoV-2, a new type of coronavirus that has appeared since December 2019 and caused respiratory symptoms, fever, coughing, and shortness of breath. In addition to social distancing, wearing masks and washing hands, diet is important as a defense of the body against SARS-CoV-2. In this review, researchers conducted epigenetic diet studies that could potentially inhibit SARS-CoV-2, and can be consumed and used on a daily basis.
Dimensional Reduction of QSAR Features Using a Machine Learning Approach on the SARS-Cov-2 Inhibitor Database Munaya Azizah; Arry Yanuar; Firdayani Firdayani
Jurnal Penelitian Pendidikan IPA Vol 8 No 6 (2022): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v8i6.2432

Abstract

Quantitative Structure-Activity Relationship (QSAR) is a method that relates the chemical composition of a molecule to its biochemical, pharmaceutical and biological activities. The characteristics of a molecule's chemical constituents, such as chemical descriptors and fingerprints, are necessary to create a good QSAR model. Dimensionality reduction can alleviate the issue of several unnecessary and redundant chemical descriptors and chemical fingerprints in a high-dimensional feature-number data set by shrinking the high-dimensional original space to a low-dimensional intrinsic space. There are two categories of dimensional reduction techniques: feature extraction and feature selection. The dimension reduction approach can be utilized as a starting step in running a QSAR Virtual Screening Model on a dataset of SARS-CoV-2 inhibitor medications to create novel treatments for Covid-19 cases based on machine learning (ML) and the idea of medicinal repurposing. Fe extraction and feature selection are crucial to determining which feature sets should be applied to a specific classification process in QSAR modeling to produce reliable virtual screening results. The SARS-Cov-2 inhibitor drug database's chemical descriptor and chemical fingerprint were extracted using a simple, quick, and accurate method in this work. The total number of selected features is 12122 features. PCA, Missing values, and Random Forest are the techniques employed. The Xgboost Tree Ensemble, Naive Bayes, Support Vector Machine, Random Forest, and Deep Learning (Artificial Neural Network/Multilayer Perceptron) were used to classify the QSAR modeling on the training and test data. The Random Forest approach, when applied to all chemical descriptors and chemical fingerprint features, along with the XGBoost algorithm, yields the best feature selection results (accuracy value of 0.845 and AUC of 0.904). There are 233 characteristics for the regression QSAR approach and 273 features for the feature selection-based QSAR method of classification. Next, virtual screening of QSAR modeling of prospective drugs for Covid-19 therapy can be done utilizing the outcomes of the characteristics that have been chosen using the Random Forest approach
REVIEW: NATURAL BIOACTIVE COMPOUNDS POTENTIAL ON INHIBITION OF TRANSMEMBRANE SERINE PROTEASE 2 WITH STRUCTURE-BASED VIRTUAL SCREENING METHOD Haviani Rizka Nurcahyaningtyas; Masteria Yunovilsa Putra; Arry Yanuar
Medical Sains : Jurnal Ilmiah Kefarmasian Vol 8 No 3 (2023)
Publisher : Sekolah Tinggi Farmasi Muhammadiyah Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37874/ms.v8i3.806

Abstract

The COVID-19 pandemic has led to a global health emergency. Suitable medications are required to prevent severe SARS-CoV-2 infection. Human transmembrane serine protease 2, which is required for viral entry into the host cells, was identified as the target protein. The present study was designed to synthesize, through a systematic review, evidence of phytochemicals found in plants that can inhibit transmembrane serine protease 2 in silico. The databases ScienceDirect, PubMed, Scopus, Nature, and SpringerLink were used for systematic exploration. Among the 113 studies retrieved, 11 were selected for the entire read and 7 studies were deemed appropriate for the qualitative synthesis. Flavonoids, including the bioactive substances luteolin, vicenin 2, naringin, 8-geranylapigenin, phenylethyl-D-rutinoside morusin, sanggenol L, and kaempferol, are the most widely studied classes of secondary metabolites. Other classes that were also evaluated were lactones, terpenoids, and saponins with withanoside-V, 11-hydroxy-2-(3,4-dihydroxybenzoyloxy)abieta-5,7,9(11),13-tetraene-12- one, and licorice as active substances, respectively. This review indicates the most bioactive components of each group of metabolites that demonstrated the greatest binding affinity for the transmembrane serine protease 2 receptor, as an initial point for selecting substances and exploring additional laboratory research and clinical studies to identify novel medication candidates for COVID-19 treatment.  Keywords: Secondary metabolite, SARS-CoV-2, structure-based virtual screening, transmembrane serine protease 2, Plants
SKRINING GEN GLUKOSILTRANSFERASE (GTF) DARI BAKTERI ASAM LAKTAT PENGHASIL EKSOPOLISAKARIDA Malik, Malik; Ariestanti, Donna M; Nurfachtiyani, Anandayu; Yanuar, Arry
Makara Journal of Science Vol. 12, No. 1
Publisher : UI Scholars Hub

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Abstract

Screening for glucosyltransferase gene (gtf) from exopolysaccahride producing lactic acid bacteria. Glucosyltransferase (GTF) is an enzyme involved in exopolysaccharide (EPS) polymer synthesis in microbes. One example of EPS that has been used in pharmaceutical and medical application is dextran. Dextran has been used in conjugated-drug delivery system as matrix. As a group of microbes producing EPS, lactic acid bacteria (LAB) have been well reported carrying sucrase genes glucosyltransferase (gtf), as well as fructosyltransferases (ftf). In an attempt to search for novel gtf genes as the aim of this study, LAB collection isolated from local sources yielded from previous study were screened performing PCR using degenerate primers DegFor and DegRev. An approximately 660 base pairs (bp) amplicons were obtained by using genomic DNAs of those LAB isolates as templates with conserved region of gtf genes catalytic domain as target. Two out of 20 LAB strains were yielded no amplicon as observed on agarose gel, while one strain exhibited non-specific amplicon DNA bands with sizes other than 660 bp. The two negative ones were isolated from soil obtained from dairy product waste field and from waste of soy sauce from previous study, while the latter was isolated from waste of soy sauce.
REVIEW: NATURAL BIOACTIVE COMPOUNDS POTENTIAL ON INHIBITION OF TRANSMEMBRANE SERINE PROTEASE 2 WITH STRUCTURE-BASED VIRTUAL SCREENING METHOD Haviani Rizka Nurcahyaningtyas; Masteria Yunovilsa Putra; Arry Yanuar
Medical Sains : Jurnal Ilmiah Kefarmasian Vol 8 No 3 (2023)
Publisher : Sekolah Tinggi Farmasi Muhammadiyah Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37874/ms.v8i3.806

Abstract

The COVID-19 pandemic has led to a global health emergency. Suitable medications are required to prevent severe SARS-CoV-2 infection. Human transmembrane serine protease 2, which is required for viral entry into the host cells, was identified as the target protein. The present study was designed to synthesize, through a systematic review, evidence of phytochemicals found in plants that can inhibit transmembrane serine protease 2 in silico. The databases ScienceDirect, PubMed, Scopus, Nature, and SpringerLink were used for systematic exploration. Among the 113 studies retrieved, 11 were selected for the entire read and 7 studies were deemed appropriate for the qualitative synthesis. Flavonoids, including the bioactive substances luteolin, vicenin 2, naringin, 8-geranylapigenin, phenylethyl-D-rutinoside morusin, sanggenol L, and kaempferol, are the most widely studied classes of secondary metabolites. Other classes that were also evaluated were lactones, terpenoids, and saponins with withanoside-V, 11-hydroxy-2-(3,4-dihydroxybenzoyloxy)abieta-5,7,9(11),13-tetraene-12- one, and licorice as active substances, respectively. This review indicates the most bioactive components of each group of metabolites that demonstrated the greatest binding affinity for the transmembrane serine protease 2 receptor, as an initial point for selecting substances and exploring additional laboratory research and clinical studies to identify novel medication candidates for COVID-19 treatment.  Keywords: Secondary metabolite, SARS-CoV-2, structure-based virtual screening, transmembrane serine protease 2, Plants
Co-Authors . Hayun Abdul Mun’im Ahdyani, Risa Amarila Malik Ambarsari, Christy Anandayu Nurfachtiyani Andika Andika Andry Nur Hidayat Andry Nur-Hidayat Anton Bahtiar Anton Bahtiar Arba, Muhammad Arfan Arfan Ari Wibisono Ari Wibisono Ariestanti, Donna M Arif Arrahman Arif Arrahman Asmiyenti Djaliasrin Djalil Azminah Azminah Bambang Wispriyono Bustamam, Alhadi Cahaya Azzahra Rahmadhani Chun Wu Claus, Matheus Prayoga Daryono Hadi Tjahjono Donna M. Ariestanti Donny Risnanda Herdien Dylan James Brunt Effionora Anwar Erlina, Linda Firdayani Firdayani Gilbert Fleischer Hariyanti Hariyanti Haviani Rizka Nurcahyaningtyas Hayun Hayun Hayun Hayun Hayun Herman Suryadi Hertono, Gatot Fatwanto Heru Suhartanto Ida Usman Illahi, Adha Dastu Joko Tri Wibowo, Joko Tri Joshita Djajadisastra Joshita Djajadisastra, Joshita Kusmadi Kurmardi Laila Fitria LARASATI, ANNISA LAZUARDI Leonardus Broto Sugeng Kardono Maksum Radji Malik Malik, Malik Masteria Yunovilsa Putra Maulana, Irvan Mohamad Kashuri Muhammad Hanafi Muhammad Sulaiman Zubair Munaya Azizah Nadhifatul Aslikah Nur Hasanah Nurfachtiyani, Anandayu Nursanti Nursanti Nursanti Nursanti Putra, Masteria Yunovilsa Rani Sauriasari, Rani Retnosari Andrajati Richa Mardianingrum Rizna Triana Dewi Ruslin Ruslin Ruswanto, Ruswanto Sanang Nur Safitri Septia Nurmala Setiajid, Muhammad Aditya Setiajid, Muhammad Aditya Setyanto Tri Wahyudi Silvia Surini Siwi, Inas Priasti Sutriyo Sutriyo, Sutriyo Teni Ernawati Wening Lestari Yohanes Gultom Yona Harianti Putri YUDI SRIFIANA