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All Journal Eksakta : Berkala Ilmiah Bidang MIPA Health Information : Jurnal Penelitian Narra J Pharmaceutical Sciences and Research (PSR) Makara Journal of Science Indonesian Journal of Medical Chemistry and Bioinformatics
Erlina, Linda
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Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Energy Engineering Environmental Science Health Professions Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health

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Virtual Screening and Molecular Modelling Anticancer Molecules Targeting Fibroblast Growth Factor Receptor 4 Rosa, Feby Lilia; Fadilah, Fadilah; Erlina, Linda
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 25 No. 03 (2024): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol25-iss03/464

Abstract

Cancer, characterized by uncontrolled cell proliferation, is a leading global cause of mortality. Targeting the fibroblast growth factor receptor (FGFR), a receptor tyrosine kinase (RTK), holds promise for anticancer drug development. FGFR4, a specific subtype, regulates various cellular processes, making it a valuable target. In-silico methods were employed to screen 20 compounds against FGFR4 (PDB ID 5JKG) using AutoDock Version 4.2.6. The top three potential inhibitors, based on Gibbs energy (ΔG) and inhibition constant (Ki), were identified: epigallocatechin3-O-pcoumarate (ΔG = -10.46 kcal/mol; Ki = 21.37 nM), 6_deoxoteasterone (ΔG = -10.22 kcal/mol; Ki = 32.35 nM), and epigallocatechin3-O-caffeate (ΔG = -9.78 kcal/mol; Ki = 68.16 nM). ADMETOX analysis confirmed compliance with Lipinski's rules, indicating their safety. These compounds show promise as FGFR4 inhibitors, potentially as standalone therapy or in combination with other anticancer drugs.
Therapeutic Options for COVID-19: Drug Repurposing of Serine Protease Inhibitor Against TMPRSS2 Abiyyi, Mohammad Wildan; Dwira, Surya; Bustami, Arleni; Erlina, Linda
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 1, No. 2
Publisher : UI Scholars Hub

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Abstract

The SARS-Coronavirus 2 (SARS-CoV-2) outbreak is a serious global public health threat. Researchers around the world are conducting mass research to control this epidemic, starting from the discovery of vaccines, to new drugs that have specific activities as antivirals. Drug repurposing is a potential method of using drugs with known activity for reuse as COVID-19 therapy. This method has the advantage that it can reduce costs and also the duration in the development of potential drugs. The initial step in drug repurposing can be done computationally to determine the effectiveness and specificity of the drug on the target protein. Molecular docking analysis can see the specific interactions of potential compounds with target proteins by analyzing the energy of the bonds formed. The spike protein of SARS-CoV-2 is a major target in the design and discovery of new drugs for the treatment of Covid-19 disease. In addition, transmembrane protein serine protease (TMPRSS2) from host cells has been shown to have an important role in the proteolytic cleavage of viral spike protein to the ACE2 receptor present in human cells. Based on screening studies, it is known that there are several drugs that have been established that have the potential to inhibit the SARS-CoV-2 transfection mechanism into host cells. 10 potential drug candidates used in this study namely Arbecacin, Bromhexine hydrochloride, Hydroxychloroquine, Camostat mesylate, Darunavir, Dequalinium, Fleroxacin, Lopinavir, Remdesivir, and Octopamine were used in molecular docking. Docking analysis revealed that there were three potential compounds, namely Bromhexine hydrochloride, Camostat mesylate and Octopamine with low binding affinity and inhibition constants. Based on the docking result, Camostat mesylate as the best candidate has a high specific binding affinity for the Ser441 and Asp435 residues present in the TMPRSS2 catalytic triad. Thus, these results reveal the mechanism of inhibition of TMPRSS2 by the known inhibitor Camostat mesylate in detail at the molecular level. Where, Camostat mesylate has a strong bond. This structural information could also be useful for designing and discovering new inhibitors of TMPRSS2, which may be useful for preventing the entry of SARS-CoV 2 into human cells.
Virtual Screening on Indonesian Herbal Compounds as SARS-CoV-2 Spike (S2) Glycoprotein Inhibitors: Pharmacophore Modelling & Molecular Docking Approaches Sugito, Syailendra Karuna; Cristina, Artha Uli; Harimurti, Putri Saskia; Cendani, Gabriella Regita; Insani, Fauzi Azhar; Erlina, Linda; Paramita, Rafika Indah; Fadilah, Fadilah
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 1, No. 2
Publisher : UI Scholars Hub

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Abstract

Background: There are still no specific treatments for coronavirus disease (COVID-19) until present days. Several studies have been conducted to determine whether herbal medicine can be an option to be used as a definitive medicine for COVID-19. S2 subunit of spike protein which is responsible for SARS-CoV-2 entry to the host cell, is a potential drug target to inhibit the viral infection. In this study, we aimed to find some compounds from the HerbalDB database that have potential as SARS-CoV-2 spike (S2) glycoprotein inhibitor. Methods: The 6LXT protein was used as the target protein. The procedure in this study consisted of these following steps: protein and ligand preparation, pharmacophore modelling and compound screening, molecular docking, ADME, and toxicity analysis. The docking of hit compounds to the target protein were compared to arbidol and quercetin as positive controls. Results: Four hit compounds were screened from HerbalDB compounds. Two of them, octopamine and L-noradrenaline, showed lower binding energies (respectively, -5.19 and -4.98 kcal/mol) than positive controls whereas the other two compounds, mimosine and L-theanine, showed higher binding energies (respectively, -3.99 and -3.62 kcal/mol) compared to positive controls. Mimosine, L-noradrenaline, octopamine, and L-theanine had toxicity classes of IV, II, IV, and IV, respectively. Conclusion: Octopamine shows the best potential as SARS-CoV-2 spike (S2) glycoprotein inhibitor. However, this compound also poses several toxicity risks and therefore, needs a more elaborate considera-tion upon using. There are still no specific treatments for coronavirus disease (COVID-19) until present days. Several studies have been conducted to determine whether herbal medicine can be an option to be used as a definitive medicine for COVID-19. S2 subunit of spike protein which is responsible for SARS-CoV-2 entry to the host cell, is a potential drug target to inhibit the viral infection. In this study, we aimed to find some compounds from the HerbalDB database that have potential as SARS-CoV-2 spike (S2) glycoprotein inhibitor.
Biomarker Metabolite Discovery for Pancreatic Cancer using Machine Learning Kezia, Immanuelle; Erlina, Linda; tedjo, aryo; Fadilah, Fadilah
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 1, No. 2
Publisher : UI Scholars Hub

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Abstract

Pancreatic cancer is one of the deadliest cancers in the world. This cancer is caused by multiple factors and mostly detected at late stadium. Biomarker is a marker that can identify some diseases very specific. For pancreatic cancer, biomarker has been recognized using blood sample known as liquid biopsy, breath, pancreatic secret, and tumor marker CA19-9. Those biomarkers are invasive, so we want to identify the disease using a very convenient method. Metabolite is product from cell metabolism. Metabolites can become a biomarker especially from difficult diseases. In this paper, we want to find biomarker from metabolite using machine learning and enrichment. Metabolites data was obtained from Metabolomic workbench, while the detection and identification is done using in silico. From 106 samples, control and cancer, we found 61 metabolites and analyze them. We got 8 metabolites that play important role in pancreatic cancer and found out 2 of them are the most impactful. From that we found that ethanol is one of the best candidate of biomarker that we provide for pancreatic detection cancer. However, the simulation need to be improved to find another biomarker that provide a better marker for prognosis. Keyword : metabolite, pancreatic, cancer, machine learning
Co-Authors . Andriansjah Abiyyi, Mohammad Wildan Adisti Dwijayanti Ahdyani, Risa Andi Yasmon Andika Andika Arleni Bustami Arry Yanuar Arsianti, Ade Aryo Tedjo Azizah, Norma N. Azminah Azminah Candraningrum, Veronica Hesti Cendani, Gabriella Regita Cristina, Artha Uli Dwira, Surya Fadilah Fadilah Fadilah, Fadilah Fadillah Fadillah Fajarido, Ariski Fatriansyah, Jaka Fajar Frans Dany, Frans Harimurti, Putri Saskia Hashim, Najihah M. Insani, Fauzi Azhar Kezia, Immanuelle Kusmardi Kusmardi Monica, Maria Diyan Nihayah, Silviatun Paramita, Rafika Indah Prawiningrum, Aisyah F Puspitasari, Melya Rahayu, Ratih Ratih Rinendyaputri Ratnoglik, Suratno Lulut Rosa, Feby Lilia SEPTELIA INAWATI WANANDI Sri S. Ningsih, Sri S. Sugito, Syailendra Karuna Syahrani, Resda Akhra Wawaimuli Arozal