Graph Neural Network-Based Prediction of Protein - Protein Interactions with Molecular Docking Validation in Tuberculosis Immune Pathways
Targeting Parkinson's Disease through Systems Biology: Multitarget Insights into Demethoxycurcumin from Network Pharmacology and Docking Analysis
Single-Time-Point Surface Bioburden Study at 96 Hour Dirty-Hold Time on Amlodipine Manufacturing Equipment in 'Class D' Facility (Non-Sterile)
Castleman Disease of Hyaline Vascular Type: The Ignored Lymphoproliferative Condition
Pediatric Leukemia: A Clinical and Therapeutic Perspective on Challenges and Progress
Advertising and Marketing within the Pharmaceutical industry: Navigating ethical and regulatory-demanding situations
Efficacy Expectations and Adherence: Evidence of Consumer Biases and Heuristics in Pharmaceutical Marketing
A Review Based on the Synthesis of Isoniazid Derivatives and their Pharmacological Activities
Onychomycosis: An Updated Review
Complementary and Alternative Medicine
An Overview on Brain Targeted Drug Delivery Systems
Targeting Parkinson's Disease through Systems Biology: Multitarget Insights into Demethoxycurcumin from Network Pharmacology and Docking Analysis
Graph Neural Network-Based Prediction of Protein - Protein Interactions with Molecular Docking Validation in Tuberculosis Immune Pathways
Single-Time-Point Surface Bioburden Study at 96 Hour Dirty-Hold Time on Amlodipine Manufacturing Equipment in 'Class D' Facility (Non-Sterile)
Efficacy Expectations and Adherence: Evidence of Consumer Biases and Heuristics in Pharmaceutical Marketing
Protein–protein interactions (PPIs) are essential for cellular functions such as signal transduction, immune responses, and metabolism. Experimental methods for detecting PPIs are typically costly, time-consuming, and limited in scale. Computational prediction methods offer an efficient alternative. Graph Neural Networks (GNNs) have emerged as a promising deep learning framework for modeling PPIs due to their ability to learn from graph-structured biological data. This paper explores GNN-based PPI prediction methods, reviews data representations and architectures, and demonstrates a computational workflow including molecular docking validation of tuberculosis-associated immune signaling proteins. Example docking analyses and Ramachandran plots illustrate the approach. The results support the use of GNNs for scalable and accurate PPI prediction to guide drug discovery and systems biology research.
Parkinson's disease (PD) is a multifactorial neurodegenerative disorder characterized by dopaminergic neuronal loss and complex pathogenic mechanisms, including oxidative stress, mitochondrial dysfunction, neuroinflammation, protein misfolding, and autophagic impairment. Current therapeutic regimens primarily offer symptomatic relief and lack disease-modifying efficacy. In this study, a network pharmacology framework was employed to elucidate the multi-target mechanisms of demethoxycurcumin, a bioactive curcuminoid derived from the Ayurvedic herb Curcuma longa. Potential protein targets of demethoxycurcumin were predicted through SwissTargetPrediction and mapped against PD-associated genes retrieved from the GeneCards database, yielding 83 overlapping targets. Protein–protein interaction (PPI) network analysis, conducted using STRING and visualized in Cytoscape, identified key hub genes including AKT1, TNF, EP300, APP, EGFR, MTOR, STAT3, NFE2L2, GSK3B, and BRAF. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed significant involvement of these targets in biological processes such as protein phosphorylation, amyloid-β response, and tyrosine kinase signaling, as well as pathways related to neuroinflammation and intracellular signaling. Molecular docking analysis using AutoDock Vina demonstrated a lowest binding affinity of demethoxycurcumin to AKT1 (–7.557 kcal/mol), supported by hydrogen bonding and hydrophobic interactions. Collectively, these findings suggest that demethoxycurcumin may exert neuroprotective effects in PD through modulation of multiple molecular targets and signaling pathways, thus supporting its potential as a multitarget therapeutic candidate for neurodegenerative diseases.
The objective of dirty equipment hold time study (DEHT) was to find the load of microorganism on the equipment's which were used for manufacturing of amlodipine tablets 5 mg in class D (non- sterile) manufacturing facility. Amlodipine tablets contains starch and microcrystalline cellulose as major excipients which may serve as excellent growth medium for growth as well as proliferation of microbes on dirty equipment's. The dirty equipment hold time study was carried out for single product, amlodipine 5mg tablet and hold time of a 96 hours was selected for hold time study on dirty equipment. From 9 equipment's, altogether 18 samples were sampled using sterile swab from 25cm2 on single time point (96 hours) for study purpose. Microbial limit 100 cfu/25cm2 was set as predefined pass/fail limit in analogy to the class D area of environment monitoring in pharmaceutical manufacturing facility. The results of microbial sample were found below 100 cfu/25cm2. Standard deviation of total aerobic microbial count was found to be 20.61(n=9) and total yeast and mold count 0.44 (n=9) respectively. The result of study suggest that the load of microorganism were in predefined limit on amlodipine tablets manufactured dirty equipment in controlled class D Environment. The load of microorganism was found to be very low, this might be due to controlled environment, low water activity on surface of equipment's or low nutrient viability. Further multi time point studies are needed to establish hold time justification.
Hyaline vascular type Castleman disease (HV-CD) is a rare but distinct benign lymphoproliferative disorder that is a subtype of unicentric Castleman disease (UCD). Its lack of symptoms, unusual histopathological patterns, and localized appearance frequently make it hard to tell apart from lymphomas or other reactive lymphadenopathies. To avoid overtreatment, it is important to recognize and correctly differentiate quickly, since complete surgical excision is typically curative. Castleman disease (CD) is a rare, non-clonal lymphoproliferative disorder that shows up as a group of different types of lymph node growths. The hyaline vascular type (HV-CD) is the most common histological variant found in unicentric CD (UCD). It usually has a benign, asymptomatic course.
Leukemia is a malignancy affecting the blood-forming tissues, including the bone marrow, and represents the most frequently diagnosed cancer in the pediatric population, accounting for over one-quarter of childhood cancer cases. The disease disrupts normal white blood cell production, leading to immune suppression and increased vulnerability to infections. Impaired immunity frequently necessitates intensive management of both opportunistic infections and the malignancy itself. This review examines the complete clinical spectrum of pediatric leukemia, encompassing diagnostic methods, disease progression, physiological alterations, and therapeutic strategies. Advances over recent decades, supported by large-scale clinical trials and cohort analyses, have introduced novel treatment agents that improve survival outcomes while reducing treatment-related complications. Overall, the review highlights contemporary best practices in managing childhood leukemia.
