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Malacca Pharmaceutics
Published by PT. Heca Sentra Analitika
ISSN : -     EISSN : 29881064     DOI : https://doi.org/10.60084/mp
Core Subject : Health, Science, Agriculture,
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Immunology & microbiology Medicine & Pharmacology
Malacca Pharmaceutics is a premier interdisciplinary platform dedicated to fostering the exchange of cutting edge research and ideas in the rapidly evolving fields of pharmaceutical science and technology. Our mission is to provide a comprehensive and authoritative forum for scientists, researchers, and practitioners from diverse disciplines to share and advance their knowledge in the development, optimization, and application of innovative therapeutic strategies. The scope of the Malacca Pharmaceutics Journal encompasses a wide range of topics, including, but not limited to:Pharmaceutical formulation, delivery and controlled-release systems for drugs, vaccines, and biopharmaceuticals, pharmaceutical process, engineering, biotechnology, and nanotechnology, devices, cells, molecular biology, and materials science related to drugs and drug delivery pharmacogenetics and pharmacogenomics, biopharmaceutics,nanomedicine, drug targeting, drug design, pharmacokinetics, toxicokinetics, pharmacodynamics, drug discovery, drug design, medicinal chemistry, combinatorial chemistry, SAR, structure-property correlations, molecular modeling, pharmacophore, and bioinformatics
Arjuna Subject : Pharmacology, Toxicology and Pharmaceutics - Ilmu Farmasi
Articles 5 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2024): September 2024" : 5 Documents clear
A Comprehensive Network Pharmacology Study on the Diabetes-Fighting Capabilities of Yacon Leaf Extract Wawo, Arsianita Ester; Simbala, Herny Emma Inonta; Fatimawali, Fatimawali; Tallei, Trina Ekawati
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.161

Abstract

Indonesia ranks fourth in the world for the number of diabetes mellitus (DM) sufferers. DM is a group of metabolic disorders characterized by hyperglycemia due to insulin abnormalities. This research employs Network Pharmacology analysis to examine the target proteins and pharmacological network profiles predicted to be influenced by compounds in the leaves of Smallanthus sonchifolius (yacon) for their anti-diabetic effects. Gas chromatography-mass spectrometry (GC-MS) identified 41 secondary metabolite compounds in yacon leaves, seven of which have a Pa value > 0.5. Compound C28 has the highest Pa value as an insulin promoter, at 0.662. A total of 129 target proteins were found for the secondary metabolite compounds in yacon leaves, and 5,112 target proteins were identified for Type 2 Diabetes Mellitus (T2DM). The intersection analysis between yacon leaves and T2DM revealed 32 common proteins. Network analysis highlighted 10 top proteins: ESR1, PPAR-α, HMGCR, CYP19A1, PPARD, PTP1N, GRIN2B, FYN, AR, and SHBG. Among these, PPAR-α shows great potential and promising prospects as a target for further exploration. Considering several parameters, it can be concluded that PPAR-α is a promising protein and a potential target for new drug candidates for T2DM.
Hybrid Handwash with Silver Nanoparticles from Calotropis gigantea Leaves and Patchouli Oil: Development and Properties Salsabila, Indah; Khairan, Khairan; Kemala, Pati; Idroes, Ghifari Maulana; Isnaini, Nadia; Maulydia, Nur Balqis; El-Shazly, Mohamed; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.206

Abstract

When washing hands, handwashing is one way to prevent diseases caused by bacteria such as Staphylococcus aureus and Escherichia coli, the most common bacteria that can cause infections. The production of handwash utilizing silver nanoparticles as an active antibacterial agent remains a relatively infrequent practice. The synthesis of silver nanoparticles from the leaves of Calotropis gigantea, which grows in the geothermal area of Ie Seu-um Aceh Besar, has been carried out using the green synthesis method and hybrid green synthesis with patchouli oil. Handwash with active ingredients such as silver nanoparticles was successfully formulated, evaluated, and tested against S. aureus and E. coli. The organoleptic characteristics, pH, viscosity, foam height measurements, density, irritation, and antibacterial activity against S. aureus and E. coli were evaluated. The results showed that the organoleptic properties of the handwash with silver nanoparticles were not changed during a 30-day storage period, with pH values in the range of 9.7-10.3, and did not cause irritation upon using silver nanoparticle handwash. The best formula for handwashing with silver nanoparticles in inhibiting the growth of S. aureus and E. coli bacteria was F2, with inhibition zones of 12.9 ± 2.85 mm and 10.95 ± 0.8 mm, respectively. The formulated handwash with silver nanoparticles met the requirements of good liquid soap according to the Indonesian National Standard (SNI) with potent antibacterial activity.
Therapeutic Potential of Aceh's Syzygium polyanthum in Reducing Uric Acid in Rattus Norvegicus Nasrullah, Nasrullah; Siregar, Masra Lena; Suryawati, Suryawati
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.210

Abstract

This research aims to evaluate the anti-hyperuricemic activity of Syzygium polyanthum ethanolic extract in hyperuricemic male rats (Rattus norvegicus) induced by liver juice.  A total of 25 animals were divided into five groups: a negative control group, a positive control group, and three treatment groups receiving S. polyanthum extract at doses of 150, 200, and 250 mg/kg body weight, respectively. The result showed that the dose of 250 mg/kg body weight resulted in the highest decrease of uric acid plasma, measuring 3.44 ± 2.03 mg/dL. This reduction is comparable to the effect of allopurinol, which showed a decrease of 3.70 ± 1.54 mg/dL. A minimum dose-dependent activity was observed. To conclude, the administration of ethanolic extract of S. polyanthum for 14 days significantly reduced uric acid levels. Further exploration of higher doses or a long-term treatment period to enhance its effectiveness is needed.
Application of Ensemble Machine Learning Methods for QSAR Classification of Leukotriene A4 Hydrolase Inhibitors in Drug Discovery Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Mohd Fauzi, Fazlin; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.217

Abstract

Inflammatory diseases such as asthma, rheumatoid arthritis, and cardiovascular conditions are driven by overproduction of leukotriene B4 (LTB4), a potent inflammatory mediator. Leukotriene A4 hydrolase (LTA4H) plays a critical role in converting leukotriene A4 into LTB4, making it a prime target for drug discovery. Despite ongoing efforts, developing effective LTA4H inhibitors has been challenging due to the complex binding properties of the enzyme and the structural diversity of potential inhibitors. Traditional drug discovery methods, like high-throughput screening (HTS), are often time-consuming and inefficient, prompting the need for more advanced approaches. Quantitative Structure-Activity Relationship (QSAR) modeling, enhanced by ensemble machine learning techniques, provides a promising solution by enabling accurate prediction of compound bioactivity based on molecular descriptors. In this study, six ensemble machine learning methods—AdaBoost, Extra Trees, Gradient Boosting, LightGBM, Random Forest, and XGBoost—were employed to classify LTA4H inhibitors. The dataset, comprising 636 compounds labeled as active or inactive based on pIC50 values, was processed to extract 450 molecular descriptors after feature engineering. The results show that the LightGBM model achieved the highest classification accuracy (83.59%) and Area Under the Curve (AUC) value (0.901), outperforming other models. XGBoost and Random Forest also demonstrated strong performance, with AUC values of 0.890 and 0.895, respectively. The high sensitivity (95.24%) of the XGBoost model highlights its ability to accurately identify active compounds, though it exhibited slightly lower specificity (61.36%), indicating a higher false-positive rate. These findings suggest that ensemble machine learning models, particularly LightGBM, are highly effective in predicting bioactivity, offering valuable tools for early-stage drug discovery. The results indicate that ensemble methods significantly enhance QSAR model accuracy, making them viable for identifying promising LTA4H inhibitors, potentially accelerating the development of anti-inflammatory therapies.
QSAR Modeling for Predicting Beta-Secretase 1 Inhibitory Activity in Alzheimer's Disease with Support Vector Regression Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Tallei, Trina Ekawati; Handayani, Dian; Idroes, Rinaldi
Malacca Pharmaceutics Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/mp.v2i2.226

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, with the accumulation of β-amyloid (Aβ) plaques playing a key role in its progression. Beta-Secretase 1 (BACE1) is a crucial enzyme in Aβ production, making it a prime therapeutic target for AD treatment. However, designing effective BACE1 inhibitors has been challenging due to poor selectivity and limited blood-brain barrier permeability. To address these challenges, we employed a machine learning approach using Support Vector Regression (SVR) in a Quantitative Structure-Activity Relationship (QSAR) model to predict the inhibitory activity of potential BACE1 inhibitors. Our model, trained on a dataset of 7,298 compounds from the ChEMBL database, accurately predicted pIC50 values using molecular descriptors, achieving an R² of 0.690 on the testing set. The model's performance demonstrates its utility in prioritizing drug candidates, potentially accelerating drug discovery. This study highlights the effectiveness of computational approaches in optimizing drug discovery and suggests that further refinement could enhance the model’s predictive power for AD therapeutics.

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