Mupirocin is a topical antibiotic that is commonly used for the treatment of superficial skin infections due to Gram-positive bacteria such as Staphylococcus aureus, and methicillin-resistant S. aureus (MRSA). Traditional ointments and creams can however have drawbacks in terms of skin penetration, follicular drug delivery, chemical stability and effectiveness against bacterial biofilms. The nanoemulsions and nanoemulgels as an improvement of topical delivery of mupirocin, antibacterial activity and wound healing are discussed in this review. Literature was reviewed on formulation development, excipient selection, the methods of preparation, skin permeation, anti-bacterial activity, disruption of bacteria associated with biofilms, stability, biocompatibility and regulatory aspects. Nanoemulsions are lipid-based delivery systems in which the droplets are of nanoscale dimensions and might increase the solubility of the drug, increase the contact with the skin and increase the deposition into the skin and in the hair follicles. Additives such as non-ionic surfactant, cosurfactant and certain lipid ingredients could enhance the formulation properties and drug release, meanwhile, suitable gelling agents can increase formulation viscosity, extend skin dwelling time and facilitate the application of the formulation. The literature reviewed indicated that there were promising pre-clinical results of enhanced skin permeation and retention, improved antibacterial activity against MRSA and potential benefits in biofilm management and wound repair. However, these results will vary with formulation formulations, experimental conditions and the biological models used. There are still key points to consider for clinical translation, such as long-term stability, excipient safety, manufacturing reproducibility and regulatory requirements. In conclusion, the mupirocin loaded nanoemulsions and nanoemulgels are effective means to conquer some of the selected limitations of conventional topical formulations. There is a need for additional standardized investigations, thorough safety evaluations and carefully planned clinical trials to demonstrate their therapeutic benefits and clinical utility.

Type 2 Diabetes Mellitus (T2DM) is a prevalent metabolic disorder characterized by chronic hyperglycemia and associated with significant morbidity and mortality worldwide. Metformin is the recommended first-line oral antidiabetic agent owing to its efficacy, safety, cost-effectiveness, and favorable metabolic profile. To enhance therapeutic outcomes and patient convenience, metformin is available as Immediate-Release (IR) and Extended-Release (ER) formulations. Although both formulations contain the same active drug, they differ in release characteristics, dosing frequency, gastrointestinal tolerability, and adherence patterns. This review critically compares IR and ER metformin formulations with respect to pharmacokinetic properties, clinical efficacy, safety, and patient adherence. Evidence from clinical studies demonstrates that both formulations provide comparable glycemic control, including reductions in glycated hemoglobin (HbA1c) and fasting plasma glucose levels. However, ER metformin offers a controlled drug-release profile that minimizes peak plasma concentrations and improves gastrointestinal tolerability. Consequently, ER formulations are associated with fewer adverse gastrointestinal events such as nausea, diarrhea, and abdominal discomfort. The reduced dosing frequency of ER metformin also contributes to improved patient adherence, treatment satisfaction, and long-term persistence with therapy. Despite its higher cost, ER metformin may be particularly beneficial for patients experiencing intolerance or poor compliance with IR formulations. Future research should focus on personalized treatment approaches, innovative delivery technologies, real-world studies evaluating long-term adherence and clinical outcomes, and optimization of formulation selection strategies. Overall, both IR and ER metformin remain effective therapeutic options, with formulation selection depending on individual patient requirements and treatment goals.

Herbal medicines represent a valuable source of therapeutically active compounds and are increasingly incorporated into modern healthcare and drug discovery. However, their chemically complex composition, multi-target pharmacological effects, batch-to-batch variability, metabolic transformation, potential toxicity, and herb–drug interactions present significant challenges for systematic safety assessment. Conventional toxicological approaches are often time-consuming, costly, and dependent on extensive experimental studies, highlighting the need for predictive and mechanism-oriented strategies. Artificial Intelligence (AI), including Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP), has emerged as a promising approach for advancing herbal toxicology and safety evaluation. This review critically examines the application of AI-assisted approaches for toxicity prediction, absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment, herb–drug interaction prediction, mechanistic toxicology, and integrated safety profiling of herbal medicines. Particular attention is given to quantitative structure–activity relationship (QSAR) modelling, molecular docking, virtual screening, pharmacokinetic prediction, multi-omics integration, knowledge graphs, adverse-effect prediction, and explainable AI. These approaches can facilitate the early identification of potentially hazardous phytochemicals, predict organ-specific toxicity, identify toxicophoric structural features, evaluate metabolic liabilities, and characterize interactions with drug-metabolizing enzymes and transporters. Furthermore, integration of chemical, biological, pharmacological, clinical, and toxicological datasets may improve the prioritization of safer herbal candidates and support mechanism-informed risk assessment. Despite these advances, limitations related to data quality, dataset heterogeneity, model interpretability, external validation, uncertainty, and regulatory acceptance remain important challenges. Future integration of explainable AI, standardized toxicological datasets, multi-omics technologies, digital models, and experimental validation could establish more reliable and predictive frameworks for herbal medicine safety evaluation.

Natural products are still vital sources of structurally diverse and biologically active molecules; however, these products are often hard to discover due to the complexity of natural extracts and the isolation of known metabolites. Phytochemical dereplication is an effective tool for identification of known constituents prior to extensive purification and the selection of metabolites to study for possible chemical or biological novelty. The ability for rapid phytochemical investigation has greatly increased in the last few years with the development of liquid chromatography–tandem mass spectrometry (LC–MS/MS), molecular networking (MN), and artificial intelligence (AI). The accurate mass, chromatographic and fragmentation data obtained by LC–MS/MS is then used to correlate similar metabolites into molecular families through molecular networking, and to provide chemical context for unannotated features. Additional support can be provided by machine learning (ML) and AI for spectral classification, chemical-class prediction, candidate-structure generation, similarity assessment and bioactivity based prioritization. The integration then enables metabolite detection, computational metabolite annotation, molecular family analysis, metabolite candidate ranking, targeted isolation and experimental validation to be coupled together in a workflow. However, some important limitations include incomplete spectral libraries, structural isomerism, instrumental variability, training-data bias, limited model interpretability and false annotation. This means that the predictions made from a computer model should be interpreted as a structural hypothesis, unless backed up by suitable experimental evidence. This review explores how LC–MS/MS, molecular networking and AI/ML complement each other in the process of phytochemical dereplication, how they are used in the discovery of natural products, and the challenges and opportunities of explainable AI, multimodal data integration, better spectral databases, and increasingly automated dereplication workflows.