Malacca Pharmaceutics

Evaluating the Effects of Young Areca catechu Seed Extract on FSH and Testosterone Levels in Male Rats: Insights into Natural Anti-Fertility Agents

2026-04-08T22:36:40+07:00

Male participation in contraception remains limited worldwide because few safe and acceptable male contraceptive options exist. Natural products are being explored as potential regulators of male reproductive hormones. Areca catechu (areca nut), traditionally used in Southeast Asia, contains phytochemicals that may influence reproductive function. This study evaluated the effects of the ethanolic extract of young A. catechu seeds on serum follicle-stimulating hormone (FSH) and testosterone levels in male Wistar rats. Twenty-five rats were randomly assigned to five groups (0, 20, 40, 60, and 80 mg/kgbody weight) and treated daily for 48 days. Serum FSH and testosterone were measured by ELISA and analyzed by one-way ANOVA with Duncan’s post-hoc test. Mean FSH and testosterone values varied across doses, with the 40 mg/kg group showing the highest means (10.001 ± 10.413 ng/mL and 2.196 ± 1.254 ng/mL, respectively), but no statistically significant differences were detected for either hormone (FSH p = 0.043; testosterone p = 0.425). The results provide preliminary, hypothesis-generating evidence that the extract may influence the hypothalamic–pituitary–gonadal axis; however, any role in male fertility regulation remains unproven. Larger studies including sperm-quality assessment, mechanistic analyses, and toxicological evaluation are required before considering potential applications in male reproductive health.

Antioxidant and Cytotoxic Activities of Postpartum Herbal Remedies Use in Aceh, Indonesia

2026-04-08T22:36:38+07:00

Traditional medicinal plants continue to play an important role in postpartum care in Aceh, Indonesia, yet their pharmacological properties remain insufficiently documented. This study examined the phytochemical composition, antioxidant capacity, and cytotoxic profile of five commonly used postpartum plants: Glochidion zeylanicum var. zeylanicum, Glochidion obscurum, Mesua ferrea, Vitex pinnata, and Salix babylonica. Ethanolic leaf extracts were prepared by macerating the leaves and analyzed for major secondary metabolites. Antioxidant activity was evaluated using the DPPH radical scavenging assay, while cytotoxicity was assessed through the brine shrimp lethality test (BSLT). All extracts contained flavonoids, phenolics, tannins, and terpenoids, with alkaloids detected in most species. G. obscurum showed the strongest antioxidant activity (IC₅₀ = 2.30 µg/mL), followed by M. ferrea, V. pinnata, and G. zeylanicum, whereas S. babylonica exhibited moderate activity. BSLT results indicated high cytotoxicity in M. ferrea, V. pinnata, and S. babylonica (LC₅₀ < 10 µg/mL), while G. obscurum and G. zeylanicum demonstrated moderate toxicity. These findings provide scientific support for the traditional postpartum use of these plants, particularly regarding antioxidant and restorative functions. However, the strong cytotoxicity observed in several species highlights the need for cautious dosage and further toxicological validation. Additional studies are recommended to isolate active constituents and clarify their safety and therapeutic relevance.

A Systematic Review on the Transformation of Bone Waste into Valuable Dental Biomaterials

2026-04-08T22:36:36+07:00

Bone waste is a sustainable, calcium-rich resource for the production of hydroxyapatite (HA), a biomaterial widely used in dental and bone tissue engineering. This systematic review evaluates recent advances in the extraction, transformation, and biological performance of HA derived from bone waste. A total of 20 records were initially identified, of which 11 full-text articles met the eligibility criteria and were included in the qualitative synthesis. The reviewed studies demonstrate that bone waste can be effectively converted into HA through several routes, including thermal-based extraction (calcination, annealing, and sintering at 600–1000°C), alkaline hydrolysis, and hydrothermal or microwave-assisted methods, enabling the production of micro- and nano-sized HA with high purity. Post-extraction functionalization, such as ion doping (Mg²⁺, Na⁺, Co²⁺), drug loading, and composite formation, further enhances osteogenic, antimicrobial, and mechanical properties. Physicochemical characterization using XRD and FTIR consistently confirmed the formation of non-stoichiometric, ion-substituted HA with Ca/P ratios ranging from 1.6 to 1.9, closely resembling biogenic apatite. The presence of multiscale porosity (25–65%) and nano-scale features promotes protein adsorption, ion exchange, and cellular interactions. In vitro studies confirmed cytocompatibility, while ALP activity and mineralization assays demonstrated strong osteogenic potential. Overall, bone waste–derived HA offers biomimetic, functional, and environmentally sustainable alternatives for dental and maxillofacial applications.

Therapeutic Vaccines in Non-Small Cell Lung Cancer: Immunologic Mechanisms, Therapeutic Platforms, and Barriers to Efficacy

2026-04-08T22:36:35+07:00

Therapeutic vaccines for non-small cell lung cancer (NSCLC) aim to improve treatment outcomes for a disease with high global incidence, mortality, and recurrence risk despite receiving standard multimodal therapy. This field focuses on the use of cancer antigens as vaccine targets in the context of immunology, influenced by immunovigilance, immunoreduction, and the tumor microenvironment, which suppresses the immune system. Mechanistic requirements for effective vaccination include selecting cancer antigens that are highly and homogeneously expressed, functionally linked to oncogenic pathways, and efficiently presented via MHC molecules to coordinate T cell responses. Peptide-based, dendritic cell-based, nucleic acid-based, and microbial vector-based vaccine platforms demonstrate safety and induction of antigen-specific cellular immunity responses. However, survival remains moderate and inconsistent, particularly in advanced-stage patients. Future progress will depend on rigorous, mechanism-based design that integrates data-driven antigen and epitope selection with tailored platform and route selection to shape the desired immune response, while also facilitating personalized and optimized vaccination strategies.

QSAR Modeling of Beta-2 Adrenergic Receptor Ligands Using Molecular Descriptor–Based Machine Learning

2026-04-08T22:36:33+07:00

The Beta-2 Adrenergic Receptor (ADRB2) is a well-characterized G protein–coupled receptor widely studied in pharmacology and drug discovery. In this study, quantitative structure–activity relationship (QSAR) models were developed using molecular descriptor–based machine learning approaches to predict the activity of ADRB2 ligands. A curated dataset of 745 compounds with experimentally determined IC₅₀ values was obtained from the ChEMBL database. Two-dimensional molecular descriptors were calculated and preprocessed to remove low-variance and highly correlated features, resulting in a refined feature set for model development. The dataset was categorized into active and inactive compounds and divided into training and testing subsets. Four machine learning algorithms. Logistic Regression, Support Vector Machine, Gradient Boosting, and Random Forest were implemented and evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Among the models, Random Forest achieved the best performance, with an accuracy of 89.26%, F1-score of 89.87%, and AUC of 0.926, followed by Gradient Boosting with an accuracy of 87.92% and AUC of 0.922. Analysis of physicochemical descriptors indicated that hydrogen-bond donor capacity (nHD) shows a statistically significant association with variations in compound activity toward ADRB2, while lipophilicity (LogP) and hydrogen-bond acceptor count (nHA) do not exhibit statistically significant differences between activity classes. Overall, the results demonstrate that molecular descriptor–based machine learning models, particularly ensemble methods, provide an effective framework for predicting ADRB2-related compound activity and support the prioritization of candidate molecules in computational drug discovery.