Theerasak Rojanarata’s research while affiliated with Silpakorn University and other places

Early detection of microalbuminuria is essential for preventing the progression of nephropathy and improving patient management. The present research proposes a simple, low-cost dye-binding method based on a standard addition principle for determining urinary albumin at clinically significant levels. The assay principle of the proposed paper-based analytical device is based on utilizing a specific and sensitive dye, bis(3′,3″-diiodo‐4′,4″‐dihydroxy‐5′,5″‐dinitrophenyl) ‐3,4,5,6-tetrabromosulfonphthalein (DIDNTB), which binds to urinary albumin under extreme low pH conditions. The strategy was based on testing with three different albumin concentrations in the urine samples to obtain different color intensity ratios denoted as R1, R2, and R3, and further used to calculate a semi-quantitative amount of albumin. Under the optimized conditions, the linear range from 10 to 200 mg/L albumin and the detection limit 8.2 mg/L (R² = 0.99) was obtained. Interference studies revealed that glucose, ascorbic acid, uric acid, L-histidine, and globulin were in the acceptable recovery range (89–109%). Reproducibility assessments showed that the coefficients of variation (CV) for this method ranged between 3.7% and 7.7%. Additionally, receiver operating characteristic (ROC) plots were generated to establish the cut-off value for instant semi-quantitative interpretation of the results. Results from real sample analysis revealed that the proposed method is comparable to a widely used quantitative immunoturbidity method (r = 0.94, n = 50).

Despite the growing trend of using ethanol as a greener alternative to hazardous solvents like methanol and acetonitrile in high-performance liquid chromatography (HPLC) analysis, no ethanol-based assay has been developed for aspirin tablets, nor has the potential of domestically produced bioethanol as a mobile phase been explored – until now. This study introduces a novel ethanol-based HPLC assay for aspirin and assesses the feasibility of using locally produced bioethanol. The optimized method, featuring a 40% (v/v) ethanol–water mobile phase adjusted to pH 3.6 with glacial acetic acid, with a 15-cm C18 column at 40°C and UV detection at 237 nm, successfully separated aspirin and its degradation product, salicylic acid, in 5 min. It demonstrated excellent linearity (0.1–0.6 mg·mL⁻¹, r² = 0.9997), sensitivity (LOD = 0.01 mg·mL⁻¹, LOQ = 0.03 mg·mL⁻¹), accuracy, and precision, with no interference from excipients. The method’s performance on commercial aspirin tablets aligned with official pharmacopoeial methods. In a case study using fuel-grade bioethanol (>99.5% purity) derived from sugarcane molasses or cassava from local manufacturers in Thailand, chromatographic and analytical results matched those obtained with imported ethanol, validating its effectiveness. This innovative approach not only reduces costs and dependence on imported solvents but also supports local bio-circular-green economies and promotes greener, more sustainable analytical practices.

Currently, fused deposition modeling (FDM) is a 3D printing technology that has been most widely used to develop innovative drug delivery approaches for overcoming the limitations of oral drug administration. Propranolol has a short plasma half-life and is well soluble in acidic environments. Thus, this study aimed to develop a gastro-floating 3D printed device (GFD) to sustain the release of propranolol in the stomach as a gastro-retentive drug delivery system. The polylactic acid (PLA) was selected to fabricate the GFD. An air chamber was included in the interior construction of the GFD design for buoyancy. The number of open channels on the side wall of GFD was modified to regulate release. The propranolol gel formulation was composed of a mixture of propranolol and polyvinylpyrrolidone (PVP) at the weight ratio of 6:5 and was then loaded into GFDs using a syringe. GFD exhibited a floating ability of more than 24 h with low standard deviation (SD) values of weight variation and shape dimension. The propranolol release from GFD shows sustained release properties in the simulated gastric environment without lag time. The 4 and 5 channels of GFD exhibited sustained drug release for 6 h. In addition, the duration of sustained release for 8 h was achieved from the GFD with 2 and 3 channels. The kinetic release of propranolol from GFDs was the best fit with zero-order. Thus, the GFDs could be designed to control the drug release according to each patient, which has the potential for applying personalized gastro-retentive drug delivery in various medications. Graphical abstract

Untreated or poorly managed chronic wounds can progress to skin cancer. Topically applied 5-fluorouracil (5-FU), a nonspecific cytostatic agent, can cause various side effects. Its high polarity also results in low cell membrane affinity and bioavailability. Hydrogel, used for its occlusive effect, is one platform for treating chronic wounds combined with PEGylated liposomes (LPs), developed to increase drug-skin affinity. This research aimed to develop a novel hydrogel forming chitosan-based microneedles (HFM) chemowrap patch containing 5-FU PEGylated LPs, improving 5-FU efficiency for pre-carcinogenic and carcinogenic skin lesions. The results indicated that the 5-FU-PEGylated LPs-loaded HFM chemowrap patch exhibited desirable physical and mechanical characteristics with complete penetration ability. Furthermore, in vivo skin permeation studies demonstrated the highest percentage of 5-FU permeated the skin (42.06 ± 11.82 %) and skin deposition (75.90 ± 1.13 %) compared to the other treatments, with demonstrated superior percentages of complete wound healing in in vivo (47.00 ± 5.77 % wound healing at day 7) and in NHF cells (92.79 ± 7.15 % at 48 h). Furthermore, 5-FU-PEGylated LPs-loaded HFM chemowrap patches exhibit efficient anticancer activity while maintaining safety for normal cells. The results also show that the developed formulation of a 5-FU-PEGylated LPs-loaded HFM chemowrap patch could enhance apoptosis higher than that of the 5-FU solution. Consequently, 5-FU PEGylated LPs-loaded HFM chemowrap patch represented a promising drug delivery approach for treating pre-carcinogenic and carcinogenic skin lesions.

Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.

The application of Artificial Intelligence (AI) has the potential to revolutionize the formulation development of nanomedicine. This study investigated the physicochemical characteristics of progesterone-loaded solid-lipid nanoparticles (PG-SLNs) produced through an emulsification–ultrasonication process, with a focus on demonstrating the efficacy of this controlled preparation method via the Design of Experiments (DoE) and Artificial Neural Networks (ANN). Critical quality factors, including stearic acid, medium chain triglycerides (MCT), Pluronic F-127, and the amount of propylene glycol (PG), were explored using DoE to streamline experimental setups. The concentration of stearic acid was identified as a crucial factor influencing PG-SLN physicochemical properties, impacting particle size (PS), polydispersity index (PDI), zeta potential (ZP), and %drug loading (%DL). Optimal conditions for PS, PDI, ZP, and %DL were identified. DoE revealed acceptable values across multiple runs, and the ANN model demonstrates high prediction accuracy, surpassing Response Surface Methodology (RSM). The selected PG-SLN formulation was tested for transdermal drug delivery, showing improved permeation compared to PG suspension. Loading with limonene further enhances transdermal drug delivery, attributed to limonene’s role as a penetration enhancer. Moreover, the selected PG-SLN formulation was found to be safe and non-toxic to neuronal cells. The combination of DoE and ANN was proposed to enhance predictive ability. This research highlights the potential of PG-SLNs in transdermal drug delivery, emphasizing the role of limonene as a safe and effective enhancer. The study contributes to the growing interest in applying AI tools in pharmaceutical and biomedical fields for improved predictive modeling.