Unlocking new therapeutic horizons through integrative bioinformatics and transcriptomics for drug repositioning in breast cancer therapy Wirawan Adikusuma, Lalu Muhammad Irham, Rahmat Dani Satria, Ika Nurlaila, Dyah Laksmi Dewi, Firdayani Firdayani, Inna Syafarina Journal of the Egyptian National Cancer Institute, 2026 Background Breast cancer (BRCA) remains one of the most frequently diagnosed malignancies and a leading cause of cancer-related mortality among women worldwide. Its molecular heterogeneity and limited therapeutic options for aggressive subtypes highlight the need for novel treatment strategies. Drug repositioning offers a promising approach by identifying new therapeutic uses for existing drugs with established safety profiles. Methods We applied an integrative transcriptomic and bioinformatics framework to identify candidate drug targets and repurposed drugs for BRCA. Differentially expressed genes (DEGs) were identified from four Gene Expression Omnibus (GEO) microarray datasets using the limma package with thresholds of |log2 fold change| > 1 and false discovery rate (FDR) < 0.05. Overlapping DEGs were expanded through protein–protein interaction analysis using the STRING database. Functional annotation across ten biological evidence categories was performed using WebGestalt to prioritize BRCA risk genes through a multi-criteria scoring approach. Drug–gene interactions were then analyzed using the Drug–Gene Interaction Database (DGIdb), and tissue-specific gene expression was evaluated using the GTEx database. Results Twenty-eight consistently dysregulated genes were identified and expanded into a 77-gene interaction network. Functional prioritization yielded 18 BRCA risk genes, including five druggable targets associated with 11 candidate drugs. ITGB7 emerged as a promising biomarker and therapeutic target, with vedolizumab identified as the top candidate drug. Conclusions This study highlights the potential of integrative transcriptomic analysis to identify biomarkers and drug repositioning candidates in BRCA, providing a foundation for further experimental validation.
Integrative Network Pharmacology, Molecular Docking, and Dynamics Analysis of Rhizomes for Prevention of Sarcopenia Fathir Azzaki Iradata, Ahmad, Firdayani, Irma Ratna Kartika, Danang Waluyo, et al. Biointerface Research in Applied Chemistry, 2026 Sarcopenia is a condition that is characterized by a gradual decline in skeletal muscle mass, strength, and function. Abundant bioactive compounds in jamu, a traditional Indonesian herbal, can reduce oxidative stress and inflammation while improving physical performance and muscle health. The present study aims to uncover the active compounds in Zingiber officinale (red ginger), Kaempferia galanga (aromatic ginger), and Curcuma sp., and elucidate their potential mechanisms for preventing sarcopenia. A total of 41 phytochemicals (15 from Z. officinale, 11 from Curcuma sp., and 15 from K. galanga) were selected from the literature. Network pharmacology analysis identified AKT1, TP53, and BCL2 as the targets with the highest degree values. Molecular docking showed Curcumin exhibited the strongest binding affinity toward AKT1 (re-rank score: -117.897), while 1-dehydro-10-gingerdione showed the highest affinities for both TP53 (re-rank score: -117.595) and BCL2 (re-rank score: -85.1227). The molecular dynamics simulation showed that the Curcumin–AKT1 complex has similar stability to the native–AKT1 complex, with an average RMSD backbone profile of 2.98 Å, SASA of 18,655 Å2, and RMSF of 1.23 Å. This study suggests that the three rhizome-derived compounds may be potential anti-sarcopenia agents, though this will require future experimental validation. The research also establishes a framework for investigating how the three rhizomes might influence the PI3K-Akt pathway and endocrine resistance.
BiGraph-DTA: Predicting drug–target interactions of hepatoprotective agents with graph convolutional networks Arief Sartono, Bambang Riyanto Trilaksono, Sophi Damayanti, Anto Satriyo Nugroho, Firdayani Firdayani Quantitative Biology, 2026 Predicting drug–target affinity (DTA) is critical for discovering and developing hepatoprotective agents that can prevent and treat liver diseases. In this study, we propose BiGraph‐DTA, a new predictive model for identifying DTA score prediction for hepatoprotective compounds by combining graph convolutional networks and bidirectional long short‐term memory networks. This model is based on powerful frameworks that process both graph representations of molecular structures and sequential information from protein sequences to capture complex dependencies and interactions. Leveraging a curated hepatoprotective dataset (from ChEMBL) consisting of 21,421 interactions, the model outperforms traditional machine learning methods (such as random forest and XGBoost) as well as other deep learning methods (such as DeepDTA and GraphDTA) in terms of predictive performance. The BiGraph‐DTA obtained the best mean squared error of 0.7885, R 2 of 0.7208, and concordance index of 0.8508. Our proposed architecture holds potential for accelerating the drug discovery process of hepatoprotective therapy by highlighting the framework through which candidate drugs and their corresponding protein targets can be identified based on robust data‐driven knowledge. This model, therefore, provides a new opportunity for discovering new hepatoprotective compounds, which may also make it possible to speed up finding new liver disease drugs.
In vitro assessment of α-glucosidase inhibitory activity and compound prediction in Phyllanthus niruri from West Java Indonesia Idah Rosidah, Alfan Danny Arbianto, Firdayani Firdayani, Agus Supriyono, Kurnia Agustini, Ngatinem Ngatinem, Sri Ningsih Journal of Applied Pharmaceutical Science, 2025 The aim of this study was to assess the in vitro α-glucosidase inhibitory activity of Phyllanthus niruri fractions (PNFs) and identify the chemicals involved. The ethanolic P. niruri extract was partitioned using medium-pressure liquid chromatography. The PNFs were examined using ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS), the α-glucosidase inhibitory activity was assessed using yeast α-glucosidase, and molecular docking simulations were performed via Molegro Virtual Docker version 6.0. The extract had a water-soluble content of 37.26%, ethanol-soluble content of 17.16%, total phenol concentration of 167.65 mg GAE/g extract, total flavonoid concentration of 10.07 mg QE/g extract, water content of 2.72%, loss on drying of 33.39%, total ash content of 4.1%, an acid-insoluble ash content of 0.42%, and an in vitro α-glucosidase inhibitory activity with an IC50 of 1.48 μg/ml. Also, at concentrations of 1 and 3 μg/ml, PNF-1 to PNF-6 had a much stronger inhibitory effect on α-glucosidase than PNF-7 and PNF-8. The UPLC-QTOF/MS analysis of the active fraction identified significant chemicals, notably corilagin (7) and gallic acid dimethyl ester (8). The docking analysis revealed advantageous docking scores of −152 and −81. This indicates that these chemicals may serve as effective α-glucosidase inhibitors. The results of this study support the use of P. niruri as a good natural product for treating diabetes through the α-glucosidase inhibitory mechanism.
Unveiling hepatitis B biomarkers through genomic network analysis Wirawan Adikusuma, Firdayani Firdayani, Lalu Muhammad Irham, Ichtiarini Nurullita Santri, Yohane Vincent Abero Phiri, Dian Ayu Eka Pitaloka, Rockie Chong, Made Ary Sarasmita, Rahmat Dani Satria Egyptian Journal of Medical Human Genetics, 2025 Background The infection caused by the Hepatitis B virus (HBV) poses a significant challenge to global health. However, identifying and prioritizing functional biomarkers associated with the disease will allow more focused efforts for pre-clinical investigation. This study aimed to employ advanced genomic networks and bioinformatics tools to prioritize biomarkers for HBV. Results Comprehensive curation from six well-known databases yields a set of 495 HBV-associated genes based on gene ontology enrichment, pathway enrichment and protein–protein interaction analyses. Further functional gene network analyses identified 10 hub genes (IL-6, TNF, STAT3, CD4, IL-10, STAT1, IL-1B, AKT1, IL-2, and TLR4), warranting further investigation. Among these genes, IL-6, TNF, and STAT3 emerged as promising biomarkers for HBV through CytoHubba analysis. Conclusions Overall, this study offers a genomic network-centric approach to identify and prioritize potential HBV biomarkers to provide valuable insights into its pathogenesis.
Integrative network pharmacology and experimental validation reveal emodin derivatives as potential therapeutics for hepatocellular carcinoma Wirawan Adikusuma, Firdayani Firdayani, Siska Andrina Kusumastuti, Nuralih Nuralih, Shelvi Listiana, Ayu Masyita, Lalu Muhammad Irham, Siti Hodijah, Suci Zulaikha Hildayani, Eko Mugiyanto Journal of the Egyptian National Cancer Institute, 2025 Background Hepatocellular carcinoma (HCC) is a major global health concern due to its high prevalence and mortality rate. Although emodin, a natural anthraquinone derivative, has demonstrated in vitro anticancer activity against HCC cells, its specific molecular targets in HCC remain unclear. Method This study used an integrated approach combining in silico network pharmacology, molecular docking, molecular dynamics simulations (MDS), and in vitro cytotoxicity assays to evaluate three emodin derivatives: emodin, 3-acetyl emodin (ACE), and 1,3,8-triacetyl emodin (TAEM). Target predictions were performed using the SwissTargetPrediction database, and HCC-related genes were retrieved from cBioPortal. Functional annotations (Gene Ontology and Reactome) identified EGFR and KIT as key targets. Docking simulations were conducted to assess binding affinities, followed by 100 ns MDS to evaluate stability. Cytotoxic effects on HepG2 cells were also assessed. Result TAEM showed the strongest binding affinity to both EGFR and KIT and demonstrated the highest cytotoxicity against HepG2 cells (IC50 = 0.021 mM). MDS results indicated that the KIT-TAEM complex was the most stable among all tested combinations, supported by RMSD, RMSF, Rg, protein–ligand distance, and MM-GBSA binding energy analyses. Conclusion These findings highlight TAEM as a promising therapeutic candidate for HCC. The study demonstrates the value of integrating computational predictions with experimental validation in early-stage drug discovery.
Enhancing the yield of emodin from Cassia alata L. leaves using ultrasound-assisted deep eutectic solvent extraction Prisnu Tirtanirmala, Abdul Mun`im, Firdayani Firdayani Journal of Research in Pharmacy, 2025 Emodin is a bioactive compound found in Cassia alata leaves, which has several pharmacological effects.However, the current extraction methods for these leaves produce a low yield of emodin. Deep eutectic solvent (DES),with their numerous advantages, could be a strategy to increase the yield of emodin during the extraction process. Theobjective of this research was to enhance the yield of emodin from the extraction of Cassia alata leaves using DES. Afterevaluating various DES combinations, the selected DES was found to be lactic acid:choline chloride (2:1). To determineoptimal extraction conditions, response surface methodology with Box Behnken Design was employed. The resultsindicate that the highest total anthraquinone content was obtained at extraction temperature of 53°C, extraction time of19 minutes, and a solid-to-solvent ratio of 1:20 g/mL. Additionally, partial method validation was conducted for thequantification of emodin in Cassia alata leaves using LC-UV instrumentation. The validated method employed thefollowing conditions: isocratic mobile phase of 2% acetic acid:methanol (30:70), flow rate of 0.8 mL/min, wavelength of288 nm, and C-18 column (150 mm x 4.6 mm, 5 µm). The emodin and total anthraquinone content in the Cassia alata leafextract using the selected DES were higher compared to ethanol extract using the same extraction method. In conclusion,the DES solvent (lactic acid:choline chloride in molar ratio 2:1) can be utilized as an alternative solvent in the extractionof Cassia alata leaves, which is more effective and efficient in enhancing emodin yield compared to conventional ethanolsolvents.
Bioinformatics insights into transcriptomic biomarkers for atopic dermatitis Wirawan Adikusuma, Eko Mugiyanto, Lalu Muhammad Irham, Firdayani Firdayani, Shelvi Listiana, Muthia Rahayu Iresha, Ayu Masyita, Maulida Mazaya, Riza Alfian Journal of Research in Pharmacy, 2025 Atopic dermatitis (AD) is a long-term inflammatory skin condition characterized by a complex interplay of genetic and molecular factors. Understanding the underlying transcriptomic changes can aid in identifying biomarkers for diagnosis and therapeutic targets. This study aimed to discover and characterize transcriptomic biomarkers in AD using bioinformatics tools and techniques. Two pre-existing datasets, GSE6012 and GSE16161, were analyzed using the R limma package to identify differentially expressed genes (DEGs). Gene Ontology (GO) and REACTOME pathway enrichment analyses were conducted using WebGestalt 2019 to explore the biological properties and pathways associated with the identified genes. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, with MCODE and CytoHubba plugins used to identify significant gene clusters and hub genes. The analysis identified 352 DEGs (158 upregulated, 194 downregulated) in GSE6012 and 5451 DEGs (2962 upregulated, 2489 downregulated) in GSE16161, with 226 overlapping genes. GO enrichment analysis revealed significant roles in cell proliferation, epidermis development, and immune response. REACTOME pathway analysis highlighted significant modifications in pathways related to skin structure and immune defense, including cornified envelope formation and antimicrobial peptides. The PPI network analysis identified three primary subclusters and pinpointed APOE and STAT1 as key hub genes. This research offers an understanding of the transcriptomic biomarkers of AD. The identified DEGs, enriched biological functions, pathways, and key hub genes offer valuable information for understanding AD's molecular mechanisms and potential therapeutic targets.
Antibacterial Effect of an Ethyl Acetate Extract of Emericella nidulans Derived from Endophytic Fungus Rhizophora mucronata Against Methicillin-Resistant Staphylococcus aureus Nurhadi Nurhadi, Purwantiningsih Sugita, Novriyandi Hanif, Prasetyawan Yunianto, Agus Supriyono, Firdayani Firdayani Trends in Sciences, 2025 Methicillin-resistant Staphylococcus aureus (MRSA) poses a significant global public health challenge due to its resistance to conventional antibiotics, leading to persistent infections, increased healthcare costs, and high mortality rates. MRSA can cause various diseases, including those affecting the skin, subcutaneous tissues, and internal organs. Its resistance is largely driven by the mecA gene, which encodes Penicillin Binding Protein 2a (PBP2a), which prevents β-lactam antibiotics from effectively targeting the bacterial cell wall. In this study, crude extract and fractions of Emericella nidulans derived from Rhizophora mucronata mangrove were prepared and screened for antibacterial activity against MRSA. In vitro tests on ethyl acetate extracts and fractions of E. nidulans revealed that they can inhibit MRSA growth in a moderate range. The crude extracts were analyzed using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) to determine the compound contained. Forty-eight compounds were screened through molecular docking analysis to identify the bioactive compounds inhibiting the SauPBP2a receptor. It is indicated that E. nidulans shows promise as raw materials for antibacterial against MRSA. HIGHLIGHTS Ethyl acetate extract of nidulans showed antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA) in vitro and contained 47 compounds based on LC-HRMS analysis. In silico studies predicted nine compounds such as 1-linoleoyl glycerol, 9-oxo-10(E),12(E)-octadecadienoic acid, monoolein, methyl ester of 9(Z),11(E),13(E)-octadecadienoic acid, bis(4-ethylbenzylidene) sorbitol, α-linolenic acid, and oleamide have ability to inhibit the SauPBP2a receptor. GRAPHICAL ABSTRACT
Virtual Screening Bioactive Compounds of Rheum Genus in Inhibiting Steatohepatitis: In silico studies Ahmad Ahmad, Firdayani Firdayani, Irma Kartika Turkish Computational and Theoretical Chemistry, 2025 The fatty liver disease known as steatohepatitis is characterized by liver inflammation. De novo lipogenesis and mevalonate pathway have been identified as contributing to the development of fatty liver. Rheum genus has pharmacological properties such as antioxidant, anti-inflammatory, hepatoprotective, and hypolipidemic. Consequently, the current study investigated bioactive compounds from Rheum genus using in-silico method. The 109 compounds and their natural inhibitors against the enzyme targets ATP-citrate lyase (ACLY), Acetyl-CoA carboxylase (ACC), Fatty Acid Synthase (FASN), Stearoyl-CoA desaturase (SCD1), and HMG-CoA reductase (HMGCR) were performed. The results obtained are in the form of a re-rank score, with the best compound results for each enzyme is Catechin 7,3'-di-O-β-D-glucopyranoside (A1) (-148.73), 4' - Methoxy - 3, 3',5 - trihydroxystilbene' - O - β – D (2"-O-p-coumaryl) - glucopyranoside (A2) (-136,808), 2-Cinnamoyl-1,6-digalloyl glucose (A3) (-149,589), 3-O-Galloyl Epicatechin (A4) (-160,018), and 6-C-Glucopyranosyl Procyanidin B2 (A5) (-84,843) as ACLY, ACC, FASN, SCD1 and HMGCR inhibitors, respectively. Analyses using the ADMET and Lipinski rules revealed that A4 has the potential to be a lead compound for oral drug use, but more research is required.
Genetic-driven biomarkers for liver fibrosis through bioinformatic approach Ariza Julia Paulina, Y. Vitriyanna Mutiara, Lalu Muhammad Irham, Darmawi Darmawi, Nurul Qiyaam, Firdayani Firdayani, Dian Ayu Eka Pitaloka, Arfianti Arfianti, Wirawan Adikusuma Egyptian Journal of Medical Human Genetics, 2024
