Osteoarthritis is a degenerative joint disease highly prevalent in the elderly, causing chronic pain and physical disability. Although topical Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) are widely prescribed, they frequently trigger adverse cutaneous reactions like hypersensitivity, erythema, and irritation. Nutmeg oil (Myristica fragrans Houtt) serves as a potent natural alternative due to its rich composition of bioactive anti-inflammatory compounds. However, its hydrophobic nature limits skin penetration and efficacy. This study aims to identify the volatile profile of nutmeg oil and optimize a nanoemulsion delivery system using Simplex Lattice Design (SLD). Bioactive components of nutmeg oil were screened using Gas Chromatography-Mass Spectrometry (GC-MS). A pseudo-ternary phase diagram was constructed to determine the nanoemulsion region. Optimization was carried out using SLD by varying Tween 80 and Propylene glycol ratios at a fixed 10% oil load. Droplet size and Polydispersity Index (PdI) were evaluated. GC-MS analysis identified 52 constituents, primarily dominated by Myristicin (23.24%), β-pinene (14.49%), α-pinene (11.92%), and Terpinen-4-ol (10.23%), which possess documented synergistic anti-inflammatory mechanisms. The SLD generated mathematical models for transmittance, droplet size, and PdI. The designated optimum formula consisted of 26.374% Tween 80 and 43.626% Propylene glycol. The experimental verification yields a transmittance of 99.9 ± 1.0%, a droplet size of 183.7 ± 6.5 nm, and PdI 0.564 ± 0.033. The optimized nutmeg oil nanoemulsion remained stable in the centrifugation, freeze-thaw, and dilution stress tests. It demonstrated favorable physicochemical characteristics.
Melasma is a chronic, recurrent hyperpigmentation disorder driven by ultraviolet radiation, hormonal influences, inflammation, vascular changes, and altered keratinocyte–melanocyte signaling. Tranexamic acid (TXA) is an off-label therapeutic option that suppresses melanogenesis primarily through inhibition of the plasminogen–plasmin pathway. Although oral TXA can reduce melasma severity, systemic safety concerns and relapse after discontinuation limit its use. Conventional topical TXA is safer but suffers from poor stratum-corneum penetration, limited epidermal deposition, low local bioavailability, and short cutaneous residence time. Nanostructured lipid carriers (NLCs) offer a promising approach to overcome these limitations through improved drug loading, controlled release, enhanced skin interaction, and prolonged local retention. However, the high aqueous solubility and zwitterionic nature of TXA complicate its incorporation into lipid matrices, making strategies such as hydrophobic ion pairing and double-emulsion systems particularly relevant. This review critically examines the preclinical development of TXA-loaded NLCs for melasma, including disease pathophysiology, TXA mechanisms, limitations of conventional delivery, NLC formulation and preparation, physicochemical characterization, in vitro diffusion, skin tolerability, stability, and translational challenges. Particular attention is given to encapsulation efficiency, skin-deposition evidence, scale-up, sterilization, regulatory considerations, and clinical validation. Current evidence supports TXA-loaded NLCs as a mechanistically rational and preclinically promising dermal delivery platform, but robust skin-deposition studies, long-term stability data, scalable manufacturing, and well-designed clinical trials are still required.
Topical ivermectin (IVM) is limited less by its antiparasitic pharmacology than by its physicochemistry: as a BCS Class II molecule with aqueous solubility below 4 μg/mL, it struggles to cross the stratum corneum in therapeutically useful amounts, and the marketed 1% gel reflects this ceiling, reaching ~68.7% cumulative release at 300 minutes, the last time point sampled. This study set out to test whether a porous nanosponge carrier, optimised through a formal Quality by Design (QbD) workflow, could raise the extent of ivermectin release and control its rate from a topical gel. Nanosponges were fabricated by the quasi-emulsion solvent diffusion (QESD) method using Eudragit RS100 as the polymer and β-cyclodextrin as the cross-linking agent. A two-tier QbD strategy was used: a 12-run Plackett–Burman design (PBD) first screened seven candidate variables, after which the four identified as high-risk were carried into a 27-run Box–Behnken design (BBD) for optimisation. Polymer/crosslinker ratio, stirring speed, homogenisation speed, and homogenisation time emerged as the critical determinants of particle size (Y1) and percentage entrapment efficiency (%EE; Y2). The optimised batch achieved a particle size of 447.05 ± 2.11 nm and %EE of 78.17 ± 1.23%, values that agreed closely with model predictions at checkpoint validation. These nanosponges were then dispersed into an HPMC Gel, which showed a pH of 6.93 ± 0.31, viscosity of 10,230 ± 120.71 cps, spreadability of 9.81 ± 0.95 g·cm/sec, and drug content of 94.21 ± 0.31%. In vitro release reached across a cellulose acetate membrane 94.12% at 480 minutes exceeding the marketed gel up to 240 minutes and matching it at 300 minutes, the last time point sampled for the marketed gel (69.13% vs 68.71%) followed Higuchi diffusion kinetics with an anomalous (non-Fickian) transport signature. Three months of real-time stability testing at room temperature indicated that the gel's physicochemical profile held steady. Together, these findings indicate that pairing a QbD-optimised nanosponge core with a hydrophilic gel shell is a promising route to enhanced, diffusion-controlled in vitro release of ivermectin, with ex vivo skin permeation and in vivo studies still needed to confirm topical performance.
Background: Docetaxel (DTX) is a potent chemotherapeutic agent whose clinical utility is limited by poor aqueous solubility, low bioavailability, and significant off-target toxicity. Nanoparticle-based delivery systems offer a promising solution, yet the complex, multi-parametric relationships governing pH-sensitive drug release and selective cytotoxicity remain incompletely understood. Methods: A Physics-Informed Neural Network (PINN) framework was developed integrating experimental data from 90 DTX nanocarrier formulations spanning eight platform categories with governing ordinary differential equations (ODEs) for pH-dependent drug release kinetics. The dual-output architecture simultaneously predicted cumulative release profiles at physiological (pH 7.4) and tumor-mimetic (pH 5.5-6.5) conditions alongside selectivity indices. Results: The PINN achieved R 2 = 0.94 ± 0.03 for release prediction and R 2 = 0.89 ± 0.04 for selectivity prediction, representing an approximately 23% reduction (evaluated on a held-out test set of ~14 formulations; larger independent datasets are needed to confirm this magnitude) in prediction error relative to conventional neural networks. Feature importance analysis identified pH sensitivity as the dominant predictor (40.5% relative importance), followed by zeta potential (28.3%) and nanoparticle size (18.7%). The HA-Ag-PB@DTX hybrid nanocarrier emerged as the optimal formulation, demonstrating a selectivity index of 18.2 and pH release ratio of 3.85. Conclusion: This work establishes a robust computational framework for accelerating rational design of smart nanomedicines with predictable release kinetics and therapeutic selectivity, providing hypothesis-generating guidance for tumor microenvironment-responsive DTX delivery, pending wet-lab and in vivo validation of the top-ranked formulations.
Nonviral nanocarrier delivery remains a major translational bottleneck for Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based genome editing because functional outcome emerges from coupled losses across biodistribution, cell uptake, intracellular trafficking, endosomal escape, access to the relevant cellular compartment, editing and tolerability. This Perspective develops three complementary conceptual tools. The Delivery-Specificity-Toxicity (DST) framework treats formulation design as a constrained multi-objective problem. The Composite Nanobio Delivery Efficiency (CNBDE) framework represents functional delivery as a chain of conditional probabilities rather than a product of statistically independent barriers, with shared formulation and biological variables allowed to alter multiple stages simultaneously. We explicitly distinguish the chain-rule formulation from the simpler independence approximation and clarify that the novelty of CNBDE lies in CRISPR-specific mechanistic decomposition and its linkage to measurable state variables, not in multiplication itself. Heterogeneous literature values are reclassified by evidence maturity instead of being treated as universal cross-platform constants. An explicitly hypothetical in silico stress test is added to demonstrate uncertainty propagation and structural sensitivity; importantly, it shows that the bare multiplicative form cannot by itself prove that any single stage, including endosomal escape, is universally dominant. A worked hepatic Lipid-Nanoparticle (LNP) example, contrasted with lung-targeted LNP studies, illustrates why organ-specific parameterization is required. Finally, the Nanobio Digital Twin System (NB-DTS) is defined as a digital-twin-ready multiscale architecture requiring a biological counterpart, iterative data assimilation, uncertainty-aware state updating, prediction and feedback. CNBDE and NB-DTS remain hypothesis-generating frameworks and require prospective experimental and external validation before predictive or clinical use.
