Muhammad Salahuddin Haris

Nanoemulsions (NEs) are kinetically stable, thermodynamically unstable colloidal systems composed of nanoscale oil and aqueous domains stabilized primarily by surfactants and, where required, co-surfactants. Their small droplet size, large interfacial area, and tunable physicochemical properties provide opportunities to improve the solubilization, dispersion, permeation, stability, and bioavailability of poorly water-soluble and labile therapeutic agents. This review provides a comprehensive overview of the principles governing NE formulation and their evolving role in pharmaceutical and biomedical applications. Particular emphasis is placed on the selection and functional contribution of formulation components, high- and low-energy preparation approaches, scale-up and manufacturing considerations, critical quality attributes, physicochemical characterization, stability, and nano-bio interactions. The review further summarizes the application of NEs in oral, topical, ocular, pulmonary, parenteral, vaccine, immunological, and disease-specific drug delivery, together with emerging applications in theranostics and imaging. The available evidence indicates that NEs can enhance drug solubilization and delivery performance and can be engineered to incorporate therapeutic and imaging functionalities; however, the extent of targeting and clinical benefit remains dependent on formulation composition, biological barriers, disease heterogeneity, and the stage of translational development. Emerging artificial intelligence (AI) and machine-learning approaches show potential for formulation optimization, prediction of physicochemical and biological behaviour, and interpretation of complex imaging data, but their application to NE systems remains an evolving area constrained by data standardization, model transparency, reproducibility, and regulatory considerations. Major challenges to clinical translation include physical and chemical instability, limitations of passive and active targeting, manufacturing complexity, batch-to-batch reproducibility, scale-up, safety assessment, and regulatory requirements. Overall, NEs represent versatile platforms for pharmaceutical delivery and multifunctional biomedical applications, while further systematic validation, scalable manufacturing, standardized characterization, and clinically relevant evidence are required to translate promising laboratory findings into robust and clinically useful products.

Hormone-dependent cancers, particularly estrogen receptor-positive breast cancer and androgen receptor-driven prostate cancer, contribute substantially to global cancer-related morbidity and mortality. Endocrine therapy has long been the mainstay of treatment for these tumors because of its specific mechanism and good safety profile. The long-term success of common hormone-based treatments is often undermined by resistance, both intrinsic and acquired, disease recurrence, and the activation of compensatory pathways that tumor cells have adapted to. To overcome these challenges, it has been necessary to develop high-quality hormone-based biopharmaceuticals that target the molecular level, enhance pharmacokinetics, and apply precision medicine strategies. The goal of this literature review is to provide an update on progress in hormone-based biopharmaceuticals for cancer management, with a particular focus on preventing endocrine resistance in hormone-dependent cancers. Among the treatments mentioned in the review, most are oral selective estrogen receptor degraders (SERDs) for ER-positive breast cancer, oral gonadotropin-releasing hormone antagonists for prostate cancer, genomically guided combination therapy that integrates inhibitors of the CDK4/6 and PI3K/AKT/mTOR signaling pathways, and next-generation androgen receptor (AR) degraders based on proteolysis-targeting chimera (PROTAC) technology. There is new evidence that these biopharmaceutical methods may be able to treat patients better over longer periods. For those who are less responsive, these approaches are even more effective when paired with proper biopharmaceutical methods, as in the case of hormone therapy. While targeted protein degradation and biomarker-driven combination therapies are repositioning drug development away from receptor blockade, the latter remains the mainstay of treatment. However, these developments do not eliminate concerns related to long-term safety, cost-effectiveness, appropriate patient selection, and resistance to novel agents. The path for hormone-based biopharmaceuticals in personalized cancer therapy requires continuous translational research and clinical validation to determine the best place.

Objective: This study aimed to optimise and evaluate alginate-based microbeads encapsulating ginger oil (GO) and black seed oil (BSO) using a spraying technique. Methods: Formulations were optimised by varying the concentrations of GO and BSO (0–10%), sodium alginate (SA), and Tween 80 (T80) to assess their impact on bead properties. The resulting beads were assessed for particle size, sphericity factor, and physical stability. Rheological analyses were conducted to understand the flow characteristics of the emulsions prior to gelation. Results: Beads containing a balanced mixture of 5% GO and 5% BSO showed favourable properties, with average diameters of approximately 3.8 mm and a sphericity factor of 0.035. All formulations demonstrated non-Newtonian shear-thinning behavior, an essential rheological characteristic that facilitates ease of spray application while maintaining stability at rest. HPLC quantification confirmed successful incorporation of active constituents, with 6-gingerol detected at 0.0236 mg/ml and thymoquinone concentrations reaching 11.10 mg/ml in 10% GBSO beads. Conclusion: These findings support the potential of alginate microbeads as a stable, preformulated platform for future development of natural oil-based topical delivery systems.

The integration of machine learning (ML) and deep learning (DL) into drug discovery and target identification has catalyzed a paradigm shift in pharmaceutical research, enhancing efficiency and translational potential for nano-enabled therapeutics. ML models have demonstrated up to 85% accuracy in predicting drug–target interactions, whereas DL frameworks, such as convolutional neural networks (CNNs), graph neural networks (GNNs), and transformer architectures, can improve molecular property predictions by 40%. AI-driven drug discovery workflows have curtailed drug candidate attrition rates by up to 30% and accelerated discovery timelines by 20%– 40%, accentuating their rising industrial and clinical impact. This critical review evaluates the transformative roles of ML and DL in the drug discovery pipeline, emphasizing their capacity to accelerate development timelines and advance precision nano medicine. We analyzed predictive modelling techniques, including quantitative structure–activity relationship (QSAR) and absorption, distribution, metabolism, and excretion (ADME) predictions, which streamline the identification of viable drug candidates, including nanocarrier-enabled drug systems. Virtual screening and bioactivity prediction further refine candidate prioritization, whereas target identification and validation leverage protein–ligand interaction modelling and biological pathway analysis to ensure therapeutic specificity. Additionally, we discuss the profound impact of DL on medical image analysis, genomic data interpretation, and protein structure prediction (PSP), which collectively advance structural bioinformatics and enable optimized targeted nano medicine. By synergizing ML and DL, multi-modal data fusion, explainable artificial intelligence (XAI), and nanotechnology-driven datasets, the drug discovery process is evolving into a more efficient, predictive, and patient-centric endeavor, paving the way for ground-breaking therapies and improved clinical outcomes. A multi-box infographic titled “Machine learning and deep learning interventions in drug discovery and drug targeting”. A left section shows a human torso silhouette beside a network of computer screens around an Rx symbol, with the text “Machine learning (ML) and deep learning (DL) applications in pharmaceuticals”, plus “T TECHNOLOGY” and a chip icon. Top row, three titled boxes. “Predictive modeling & screening” lists “Quantitative Structure-Activity Relationship”, “Ligand and structure based virtual screening”, “Absorption, distribution, metabolism and excretion”, and “Bioactivity predictions”, illustrated with a QR-like block, a receptor diagram, a blood vessel and a lab scientist with glassware. “Target identification and validation” lists “Protein-ligand interactions”, “Molecular Docking”, “Molecular Dynamics Simulations”, and “Biological pathway analysis”, illustrated with a lab worker, a person at a computer, a gear-like symbol and a pathway graphic. “DL in Pharmaceuticals” lists “Medical diagnoses”, “Microscopy data for cellular analysis”, “Medical imaging for drug delivery optimization”, and “Genomic data analysis for personalized medicine”, illustrated with a joint image, a checklist, a brain with gears and DNA. Bottom row, three titled boxes. “ML and DL in Pharma Industry” lists “Protein structure prediction”, “Text and literature mining process”, “Biopharmaceutical Manufacturing”, and “Personalized Medicine”, illustrated with a protein ribbon, a grid, manufacturing equipment and a laptop with chat bubbles. “ML in biosimilar & drug delivery” lists “Predictive Toxicology”, “Drug Target Identification”, “De Novo Drug Design”, and “Biotechnological products”, illustrated with blood cells, a target-like symbol, a chemical structure and a funnel. “Challenges for ML in Pharma” lists “Ethics & legal issues”, “Regulatory compliance”, “Transparent interpretation”, and “Data availability & quality”, illustrated with a suited figure, a discussion icon, a person at a desk and a group of people. No numeric statistics are shown.An infographic mapping ML and DL uses in pharmaceuticals across modeling, targets, delivery and challenges.

Hepatocellular carcinoma (HCC) is a critical public health concern due to its rising incidence and mortality. Early diagnosis is challenging due to nonspecific symptoms and limitations of traditional methods. This study explores the potential of three key biomarkers, serum alpha-fetoprotein (AFP), des-γ-carboxyprothrombin (DCP), and glypican-3 (GPC3), in advancing personalized medicine for HCC, focusing on their roles in diagnostics and therapeutics. We conducted a literature review using specific keywords to narrow our search. Our findings indicate that AFP and DCP are primarily used for diagnostics, while GPC3 serves both diagnostic and therapeutic purposes. AFP is commonly used in late-stage detection due to its limited sensitivity and specificity in early stages, which can lead to false diagnoses. DCP plays a significant role as both a diagnostic tool and therapeutic target, while GPC3 is used to differentiate malignant from non-cancerous liver tissues. The review highlights the mechanisms, roles, and personalised treatments associated with these biomarkers, while also addressing challenges such as biopsy issues and funding limitations. In conclusion, the development and improvement of AFP, DCP, and GPC3 as biomarkers for the diagnostics and treatment of HCC are important. Thus, their limitations should be addressed as these biomarkers play a crucial role in modern cancer therapy and are critical for increasing the survival rates of HCC patients.

Sickle cell disease (SCD) is an inherited blood disorder marked by the production of abnormal hemoglobin, leading to the distortion—or sickling—of red blood cells. The SCD arises from a single-point mutation that substitutes glutamic acid with valine at the sixth codon of the β-globin chain in hemoglobin. This substitution promotes deoxyhemoglobin aggregation, elevating red blood cell stiffness, and triggering vaso-occlusive and hemolytic repercussions. To explore therapeutic advances in tackling this disease, this review analyzed articles published from January 2015 to January 2025 using the three databases using relevant keywords focusing on SCD and advancement in therapy. It was found that allogeneic hematopoietic stem cell (HSC) transplantation can alleviate symptoms but is limited by a shortage of well-matched donors and immunological challenges. In contrast, autologous gene-modified HSC transplantation via gene therapy offers comparable therapeutic benefits without associated immunological complications. Clinical trials utilizing lentiviral vector-mediated gene insertion have demonstrated promising therapeutic outcomes by preventing hemoglobin aggregation. Emerging gene editing approaches such as CRISPR/Cas9 are expanding treatment options, marking the transition of SCD gene therapy from theoretical concept to clinical application.

Introduction: Deodorant often contain ingredients like aluminium salts, triclosan, fragrances, propylene glycol, and parabens that are usually associated with skin irritation and other health conditions. This study addresses the problem by formulating and characterising a natural deodorant that was free from these ingredients by using stingless bee honey (SBH) as an antibacterial and antioxidant ingredient. SBH, recognised for its efficacy in inhibiting the proliferation of odour-producing bacteria like Staphylococcus sp, was integrated into a stick deodorant formulation owing to its significant benefits. Methods: The evaluation of SBH began with testing its antioxidant activity, including total phenolic content (TPC), total flavonoid content (TFC) and DPPH assay. Thereafter, the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined. Based on the MIC value, an SBH-stick deodorant was formulated and then characterised based on its pH, softening point, antimicrobial study by using well diffusion method and stability study (30 °C ± 2 °C /75% RH ± 5%) for two weeks. Results: The TPC and TFC in this study were 57.99 ± 0.38 mg GAE/100g and 0.132 ± 0.04 mg QE/mL, respectively. Meanwhile, the DPPH scavenging activity was 66.78 ± 0.45%. The result showed that 20% w/w and 50% w/w of SBH were needed as MIC and MBC, respectively. The formulated stick deodorant was reported to have suitable pH, softening point and exhibit its antibacterial activity towards Staphyloccoccus aureus after being formulated as deodorant. It was also stable during the two weeks of storage. Conclusion: The SBH stick deodorant was successfully formulated and demonstrated potential antibacterial activity against Staphylococcus aureus, a known body-odour causing bacteria. These findings highlight the potential application of SBH as a natural antibacterial agent in personal care products, offering a promising alternative to synthetic deodorants.

Context: Oral cancer is the fourth most common type of cancer among Southeast Asian populations. Despite the existence of numerous anti-cancer drugs, the survival rate of oral cancer patients remains moderate. Furthermore, these drugs often lead to adverse side effects. Southeast Asia has massively underexplored medicinal plants and herbs, and can, therefore, offer a promising avenue for new alternative plant-based drugs against oral cancer. Aims: To review Southeast Asian medicinal plants with in vitro and/or in vivo activities against oral cancer, their bioactive compounds, and their mechanisms of actions using a systematic review approach. Methods: A thorough literature search was conducted in five databases, namely, Google Scholar, PubMed, ScienceDirect, Scopus, and Web of Science, following PRISMA 2020 guidelines and using AXIS quality appraisal tool. Results: From 30 final articles included in this review, 23 plants and 13 bioactive compounds were found to exhibit activities against oral cancer with various anti-cancer mechanisms, most commonly via apoptotic induction. Azadirachta indica with the two bioactive compounds (nimbolide and azadirachtin) showed remarkable in vivo and in vitro anti-cancer properties. Allium sativum (garlic), Zingiber officinale (ginger), and Curcuma longa (turmeric), which are readily available and common in daily cooking, also exhibit anti-cancer activities, which suggest that they can offer good preventive measures against oral cancer. Conclusions: Southeast Asian medicinal plants with remarkable potential, such as A. indica, could be considered for future development of therapeutics for the prevention and treatment of oral cancer.

This study aims to optimize paracetamol-ascorbic acid (PCM-AA) effervescent tablet characteristics through a Quality-by-Design (QbD) approach, investigating the effects of binder concentration, granulation time, and effervescent agents' ratio on hardness, disintegration, and dissolution. QbD approach was implemented by identifying the quality target product profile, critical quality attributes (CQAs), critical material attributes (CMAs), and critical process parameters for formulating PCM-AA effervescent tablets. An Ishikawa diagram identified risk factors for CQAs. A risk estimation matrix evaluated the levels of associated risks. A central composite design-based response surface methodology with 20 experimental runs, including six center points, identified key factors (binder concentration, granulation time, and effervescent agents' ratio) influencing tablet characteristics (hardness, disintegration, dissolution). The optimum formulation, determined by numerical analysis, was characterized for weight uniformity, tablet thickness and diameter, friability, and PCM and AA assay. Optimized PCM (500 mg)-AA(200mg) effervescent tablets with 2.9% PVP concentration, 15 min granulation time, and 1:1.5 sodium bicarbonate-citric acid ratio achieved acceptable characteristics (hardness: 45N ± 20N, disintegration: <5 min, and both PCM and AA dissolution: 0.05), indicating consistent results. The study successfully optimized the hardness, disintegration, and dissolution rate of PCM-AA effervescent tablets via the QbD approach. Granulation time affects hardness and PCM dissolution, binder concentration influences disintegration time, and the effervescent agents' ratio impacts both disintegration time and AA dissolution. This research enhances the understanding of pharmaceutical formulation processes, risk management, and optimization in effervescent tablet development.

Introduction: Poor growth and development can have long-term adverse health impacts, including increased birth defects, impaired cognitive function, and low academic achievement. Essential nutrients, such as omega-3 fatty acids, including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), offer various health benefits. However, the impact of omega-3 on children's growth is under-researched, with studies typically focusing on single domains like cognitive development. This study aims to review the growth-promoting benefits of omega-3 on children across multiple growth domains. Materials and methods: Following PRISMA guidelines and registered with PROSPERO (CRD42023417580), three databases—PubMed, Scopus, and Cochrane Library—were searched for relevant articles from 2018 to 2022. The Cochrane Risk of Bias Tool for randomized trials (RoB2) was used to assess bias risk, and Mendeley was used to manage and deduplicate articles. Results: Seven randomized clinical trials (RCTs) from 2018 to 2020 were reviewed. Only one study found a significant positive impact of omega-3 on children's cognitive development, while five studies did not. One study explored neurodevelopment, and two RCTs from the UK and Spain examined behavioral outcomes. A study from Indonesia assessed physical growth. Conclusion: Overall, omega-3 fatty acids did not show a significant impact on early childhood development. Omega-3 supplements may enhance behavioral development and physical growth in children but do not significantly affect cognitive or neurodevelopment.