cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ade Arsianti
Contact Email
arsi_ade2002@yahoo.com
Phone
+6285693687726
Journal Mail Official
ijmcb@ui.ac.id
Editorial Address
Jl. Salemba Raya No.4, Kenari, Senen, Jakarta Pusat, DKI Jakarta, 10430
Location
Kota depok,
Jawa barat
INDONESIA
Indonesian Journal of Medical Chemistry and Bioinformatics
Published by Universitas Indonesia
ISSN : -     EISSN : 29633818     DOI : https://doi.org/10.7454/ijmcb
Core Subject : Science,
Chemistry Computer Science & IT
The Indonesian Journal of Medical Chemistry and Bioinformatics (IJMCB) provides a forum for disseminating information on both the theory and the application of in silico, in vitro, and in vivo methods in the analysis and design of molecules, phytochemistry, medicinal chemistry and bioinformatics. Indonesian Journal of Medical Chemistry and Bioinformatics was published by Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia. This peer-reviewed academic open access journal has its first publish in in August 2022 and formerly publish every March and August. The scope of the journal encompasses papers which report new and original research and applications in the following areas: 1. Phytochemical and Medicinal chemistry (identification of targets, design, synthesis and evaluation of biological target) 2. Bioinformatics (genomic profiling, mutation analysis) 3. Molecular modeling (pharmacophore, molecular docking, molecular dynamic simulation) 4. Protein Modeling 5. Network Pharmacology and protein-protein interaction 6. Genomic 7. Metagenomics
Arjuna Subject : Kedokteran - Kedokteran (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 3, No. 1" : 5 Documents clear
Prothrombin Time (PT), Activated Partial Thromboplastin Time (APTT), Fibrinogen, and D-dimer in Coronavirus Disease 2019 Outcome Atmaja, Fredy Wirya; Adiyanti, Sri Suryo; Kristanty, Diyah; Dwira, Surya; Kusmardi, Kusmardi
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 1
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

COVID-19, caused by SARS-CoV-2 has been reported to be associated with coagulopathy and DIC. This study aimed to investigate the profiles and differences of PT, APTT, fibrinogen, and D-dimer in COVID- 19 outcome. This retrospective cohort was conducted at Central Laboratory Clinical Pathology Department of dr. Cipto Mangunkusumo Hospital from July – December 2020. Demographic, clinical, and laboratory data were extracted from EHR and compared between poor and good outcome. Ninety-seven subjects were confirmed positive COVID-19, 45 of whom (46.4%) were in poor outcome group, while 52 subjects (53.6%) were in good outcome group. Median of PT 11.0” (9.7-28.3), APTT 38.4” (23.9-121), fibrinogen 484.8 mg/dL (51.2-940.9), and D-dimer 1,800 µg/L (190-35,200). Longer PT, APTT, and higher D-dimer (p < 0.05), while lower fibrinogen (p > 0.05) was found in poor outcome group. There were significant differences of PT, APTT and D-dimer in COVID-19 outcome.
Molecular Insights into Propylthiouracil as a Thyroid Peroxidase Inhibitor: A Computational Study Approach Suryandari, Dwi Anita; Yunaini, Luluk; Sunaryo, Hadi; Istiadi, Khaerunissa Anbar; Pratomo, Irandi Putra
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 1
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Thyroid peroxidase (TPO) is a crucial enzyme in the biosynthesis of thyroid hormones, catalyzing the iodination of tyrosine residues in thyroglobulin and the coupling of iodotyrosines to form thyroxine (T4) and triiodothyronine (T3). Propylthiouracil (PTU) is an antithyroid drug commonly used to manage hyperthyroidism by inhibiting TPO. Understanding the molecular interactions between TPO and PTU can provide insights into the inhibitory mechanisms and guide the design of more effective antithyroid medications. Objective: This study aims to elucidate the binding interactions between TPO and PTU through molecular docking, providing a detailed understanding of how PTU inhibits TPO activity. Methods: The three-dimensional structure of TPO was obtained from Prosite and modelling by swissmodel and prepared for docking. The structure of PTU was optimized, and molecular docking was performed using AutoDock. The binding affinity, binding poses, and key interactions between TPO and PTU were analyzed. Visualization of the docking results was performed using PyMOL to identify critical residues involved in PTU binding. Results: The docking analysis revealed that PTU binds effectively to the active site of TPO with a binding affinity of -5.45 kcal/mol. The interaction involves coordination with the heme group and several key residues, including His239, which coordinates the heme, and Ser314, which forms hydrogen bonds with PTU. Additionally, hydrophobic interactions with residues Phe241 and Ile399 stabilize the binding of PTU in the active site. Conclusion: The docking study highlights the significant interactions between PTU and TPO, elucidating the molecular basis of TPO inhibition by PTU. The binding affinity and key interactions identified in this study provide a foundation for the design of more potent antithyroid drugs.
Metabolite Biomarker Discovery for Lung Cancer Using Machine Learning Fajarido, Ariski; Erlina, Linda; Tedjo, Aryo; Fadilah, Fadilah; Arozal, Wawaimuli
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 1
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Lung cancer is the leading cause of cancer death worldwide. About 2.1 million lung cancer patients were diagnosed in 2018, accounting for about 11.6% of all newly diagnosed cancer cases. For lung cancer, blood is the first choice as a source of screening biomarker candidates. Blood biomarkers provide a snapshot of the patient's entire body, including the primary tumor, metastatic disease, immune response, and peritumoral stroma. However, sputum sampling, bronchial lavage or aspiration, exhaled breath (EB), and airway epithelial sampling represent unique samples for lung cancer and other airway cancers as potential sources for alternative biomarkers. Metabolites are products of cell metabolism that are unique biomarkers in a disease. In this article, we aim to find metabolite biomarkers using machine learning. Metabolite data were obtained from Metabolomic workbench, while detection and identification were performed in silico. From 82 samples, controls and cancers, we found 158 metabolites and analyzed them. From the analysis, we found 3 metabolites that play an important role in lung cancer and found 1 metabolite that is the most influential. From there we found that glutamic acid is one of the best biomarker candidates we provide for detecting lung cancer. However, this simulation still needs to be improved in order to find other biomarkers that can provide a better detection of lung cancer
The Prospect of Indonesian Herbal as An Alternative Treatment for Covid-19 Patient: A Literature-Based Study SUNARYO, HADI; Hidayati, Wahyu
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 1
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Coronavirus disease 2019 (COVID-19) is a new disease caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease has several symptoms from mild to severe and could lead to death for comorbidities and the elderly. Therefore, the infections and mortality cases keep growing day by day, moreover many authorities are trying to suppress it by applying several health protocols, including medicines. Several studies were conducted on drug discovery from plants to cure the disease. The article aimed to do a narrative review about the prospect of JAMU becoming an alternative medicine to cure COVID-19 by mapping the literature deposited on PubMed which reported medicinal plants as an alternative medicine for COVID-19 by using a text mining program. There are approximately 30,191 articles on PUBMED related to COVID-19 and medicine. Medicinal plants with antiviral and anti-inflammatory activities are the best plants for COVID-19. JAMU, an Indonesian traditional medicine, has an outstanding possibility to be applied in the COVID-19 strategy to recover patients and prevent infections.
In Silico Modelling and Docking Simulation of EGFR-Targeted Diphtheria Toxin Chimera with Various Targeting Moieties Afif Naufal, Muhammad, BMed; Ansell Susanto, Benedictus, BMed; Gunawan, Talitha Dinda, BMed; Ridha Lukman, Azhar, BMed; Rizqina, Alifa Rahma, BMed; Misbahul Fuad, Muhammad, BMed
Indonesian Journal of Medical Chemistry and Bioinformatics Vol. 3, No. 1
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Introduction: Cancer is a major etiology of death worldwide due to high mortality and suboptimal medicine. However, an emerging field, targeted therapy enabled a more selective and effective therapeutic action. This article aims to analyze in-silico the hypothetical targeted therapy agent that is combinations of conjugate consisting of EGFR targeting moieties and diphtheria toxin (DT-390). Method: Our novel peptide is a conjugate of a novel EGFR targeting peptide and DT-390, forming a chimera. The tertiary structure was predicted using AlphaFold 2.0. The best IDDT scoring and stereochemistry profiles were utilized. The HADDOCK2.4 webserver modelled the docking between our model and EGFR dimers, limited to its active residues. Gibbs free energy analysis, dissociation constants, and interfacial contacts are the primary outcomes measured. Results: The confidence of the models ranged from moderate to high. The model conjugated with native hEGF (ΔG -14 kcal/mol) provided the best confidence compared to our novel peptide (ΔG -12.8 kcal/mol). Higher valences of peptides were found to have better confidences (hEGF ΔG -19.3 kcal/mol; EGFR de novo ΔG -14.3 kcal/mol). Our findings correspond to an in vitro study by Qi et al that concludes a bivalent hEGF is more effective than monovalent. However, the linker used also displays considerable bonding to the target. This may be from the linker’s considerable flexibility that allows it to accidentally interact with EGFR active residues. It is to be noted that the interactions formed were nonspecific and therefore unlikely to cause off-target effects. Conclusion: Our novel EGFR targeting peptide is effective in increasing selectivity of DT-390 to EGFR active residues. Our study does not consider the structural changes that might occur due to erroneous binding to other receptors. Further docking and molecular dynamics studies are important to further develop this novel system as a targeted therapy agent.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 4, No. 1 Vol. 3, No. 2 Vol. 3, No. 1 Vol. 2, No. 2 Vol. 2, No. 1 Vol. 1, No. 2 Vol. 1, No. 1