An Artificial Neural Network Based Analysis of Factors Controlling Particle Size in a Virgin Coconut Oil-Based Nanoemulsion System Containing Copper Peptide

One-pot synthesis of polysaccharide/gelatin amorphous hydrogels impregnated with a bioflavonoid derived from Elaeis guineensis leaf: wound healing and drug release properties

BACKGROUND

Amorphous hydrogel is a strategic wound healing dressings that comprised of water, polymers and excipients with no shape. The dense cross-linked network of polymer is interspersed by the immobilized water component could rehydrate and promote healing in wound tissue.

OBJECTIVE

In this work, various polysaccharide/gelatin amorphous hydrogels with the impregnation of oil palm leaf derived total flavonoid enriched extract (OPL-TFEE) were fabricated via one-pot synthesis method to provide multiple crosslinking networks.

METHOD

The bioflavonoids (OPL-TFEE) were derived from Elaeis guineensis leaf using an integrated green extraction and enrichment process. Amorphous hydrogels with good wound healing properties were developed by incorporating 0.3% antioxidant agent into the hybrid polymeric gelling system.

RESULT

The formulations appeared as a semi-solid dark yellow translucent hydrogel with good spreading and consistency characteristics and satisfying aesthetic properties. The FTIR analysis indicated that the bioflavonoid was compatible with the matrix, and the hydrogels showed porous morphological structures when observed under SEM. Furthermore, the hydrogels possessed shear thinning, pseudoplastic, and elastic properties. Bioflavonoids-impregnated polysaccharide/gelatin hydrogel release 95-98% bioflavonoids within 24 h, while the drug release profile followed the Korsmeyer-Peppas kinetic model. The hydrogels showed antioxidant and wound healing properties with no sign of cytotoxicity.

CONCLUSION

Overall, the results revealed bioflavonoid-loaded hydrogels exhibited good physicochemical and biological properties, thus could serve as new innovative formulation in the sustainable advancement of wound care product for promoting wound healing.

The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review

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.

Antimicrobial activities of nano-emulsion of virgin coconut oil

This study aimed to determine the nano-emulsion of virgin coconut oil (n-VCO) formula that can produce the best size and zone inhibition of antimicrobial activity. The VCO was formulated with the different percentages of Tween 80 (P1: 24%, P2: 25%, P3: 26%) and sorbitol (P1: 36%, P2: 35%, P3: 34%). The particle size of the n-VCO emulsion was observed under transmission electron microscopy (TEM). The antimicrobial activity test of the n-VCO was determined by a challenge test using Salmonella Typhi (S. Typhi), Staphylococcus aureus (S. aureus), and Escherichia coli (E. coli) bacteria. The data were analysed by a one-way ANOVA (P < 0.05). The significant data were furthermore tested by Duncan's multiple ranges (SPSS v26.0). This study showed that the P3 formulation (26% Tween 80 and 34% sorbitol) produced the best n-VCO among all the treatments showing a particle size of 5-100 nm. Formulas P1 and P2 produced particle sizes of about 500-1 000 nm. The antimicrobial test showed that the P3 formula had a strong inhibitory effect on S. Typhi (7.442 ± 0.52 mm), S. aureus (8.380 ± 0.49 mm), and E. coli (6.490 ± 0.82 mm). This study concluded that the formula of the detergent strongly influences the particle size of the n-VCO. The n-VCO has enormous potential to be used as an alternative antimicrobial.

Artificial Neural Networks to Optimize Oil-in-Water Emulsion Stability with Orange By-Products

The use of artificial neural networks (ANNs) is proposed to optimize the formulation of stable oil-in-water emulsions (oil 6% w/w) with a flour made from orange by-products (OBF), rich in pectins (21 g/100 g fresh matter), in different concentrations (0.95, 2.38, and 3.40% w/w), combined with or without soy proteins (0.3 and 0.6% w/w). Emulsions containing OBF were stable against coalescence and flocculation (with 2.4 and 3.4% OBF) and creaming (3.4% OBF) for 24 h; the droplets' diameter decreased up to 44% and the viscosity increased up to 37% with higher concentrations of OBF. With the protein addition, the droplets' diameter decreased by up to 70%, and flocculation increased. Compared with emulsions produced with purified citrus pectins (0.2 and 0.5% w/w), OBF emulsions exhibited up to 32% lower viscosities, 129% larger droplets, and 45% smaller Z potential values. Optimization solved with ANNs minimizing the droplet size and the emulsion instability resulted in OBF and protein concentrations of 3.16 and 0.14%, respectively. The experimental characteristics of the optimum emulsion closely matched those predicted by ANNs demonstrating the usefulness of the proposed method.

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

Application of Design of Experiments® Approach-Driven Artificial Intelligence and Machine Learning for Systematic Optimization of Reverse Phase High Performance Liquid Chromatography Method to Analyze Simultaneously Two Drugs (Cyclosporin A and Etodolac) in Solution, Human Plasma, Nanocapsules, and Emulsions

The objectives of current investigation are (1) to find out wavelength of maximum absorbance (λ_max) for combined cyclosporin A and etodolac solution followed by selection of mobile phase suitable for the RP-HPLC method, (2) to define analytical target profile and critical analytical attributes (CAAs) for the analytical quality by design, (3) to screen critical method parameters with the help of full factorial design followed by optimization with face-centered central composite design (CCD) approach-driven artificial neural network (ANN)-linked with the Levenberg-Marquardt (LM) algorithm for finding the RP-HPLC conditions, (4) to perform validation of analytical procedures (trueness, linearity, precision, robustness, specificity and sensitivity) using combined drug solution, and (5) to determine drug entrapment efficiency value in dual drug-loaded nanocapsules/emulsions, percentage recovery value in human plasma spiked with two drugs and solution state stability analysis at different stress conditions for substantiating the double-stage systematically optimized RP-HPLC method conditions. Through isobestic point and scouting step, 205 nm and ACN:H₂O mixture (74:26) were selected respectively as the λ_max and mobile phase. The ANN topology (3:10:4) indicating the input, hidden and output layers were generated by taking the 20 trials produced from the face-centered CCD model. The ANN-linked LM model produced minimal differences between predicted and observed values of output parameters (or CAAs), low mean squared error and higher correlation coefficient values in comparison to the respective values produced by face-centered CCD model. The optimized RP-HPLC method could be applied to analyze two drugs concurrently in different formulations, human plasma and solution state stability checking.

Recent Advances in Nanomaterials for Dermal and Transdermal Applications

The stratum corneum, the most superficial layer of the skin, protects the body against environmental hazards and presents a highly selective barrier for the passage of drugs and cosmetic products deeper into the skin and across the skin. Nanomaterials can effectively increase the permeation of active molecules across the stratum corneum and enable their penetration into deeper skin layers, often by interacting with the skin and creating the distinct sites with elevated local concentration, acting as reservoirs. The flux of the molecules from these reservoirs can be either limited to the underlying skin layers (for topical drug and cosmeceutical delivery) or extended across all the sublayers of the epidermis to the blood vessels of the dermis (for transdermal delivery). The type of the nanocarrier and the physicochemical nature of the active substance are among the factors that determine the final skin permeation pattern and the stability of the penetrant in the cutaneous environment. The most widely employed types of nanomaterials for dermal and transdermal applications include solid lipid nanoparticles, nanovesicular carriers, microemulsions, nanoemulsions, and polymeric nanoparticles. The recent advances in the area of nanomaterial-assisted dermal and transdermal delivery are highlighted in this review.

An Improved Nanoemulsion Formulation Containing Kojic Monooleate: Optimization, Characterization and In Vitro Studies

Tyrosinase inhibitors have become increasingly important targets for hyperpigmentation disease treatment. Kojic monooleate (KMO), synthesized from the esterification of kojic acid and oleic acid, has shown a better depigmenting effect than kojic acid. In this study, the process parameters include the speed of high shear, the time of high shear and the speed of the stirrer in the production of nanoemulsion containing KMO was optimized using Response Surface Methodology (RSM), as well as evaluated in terms of its physicochemical properties, safety and efficacy. The optimized condition for the formulation of KMO nanoemulsion was 8.04 min (time of high shear), 4905.42 rpm (speed of high shear), and 271.77 rpm (speed of stirrer), which resulted in a droplet size of 103.97 nm. An analysis of variance (ANOVA) showed that the fitness of the quadratic polynomial fit the experimental data with large F-values (148.79) and small p-values (p < 0.0001) and an insignificant lack of fit. The optimized nanoemulsion containing KMO with a pH value of 5.75, showed a high conductivity value (3.98 mS/cm), which indicated that the nanoemulsion containing KMO was identified as an oil-in-water type of nanoemulsion. The nanoemulsion remains stable (no phase separation) under a centrifugation test and displays accelerated stability during storage at 4, 25 and 45 °C over 90 days. The cytotoxicity assay showed that the optimized nanoemulsion was less toxic, with a 50% inhibition of cell viability (IC₅₀) > 500 μg/mL, and that it can inhibit 67.12% of tyrosinase activity. This study reveals that KMO is a promising candidate for the development of a safe cosmetic agent to prevent hyperpigmentation.

The implementation of artificial neural networks for the multivariable optimization of mesoporous NiO nanocrystalline: biodiesel application

In the present research, artificial neural network (ANN) modelling was utilized to determine the relative importance of effective variables to achieve optimum specific surface areas of a synthesized catalyst. Initially, carbonaceous nanocrystalline mesoporous NiO core–shell solid sphere composites were produced by applying incomplete carbonized glucose (ICG) as the pore directing agent and polyethylene glycol (PEG; 4000) as the surfactant via a hydrothermal-assisted method. The Brunauer–Emmett–Teller (BET) model was applied to ascertain the textural characteristics of the as-prepared mesoporous NiO catalyst. The effects of several key parameters such as metal ratio, surfactant and template concentrations, and calcination temperature on the prediction of the surface areas of the as-synthesized catalyst were evaluated. In order to verify the optimum hydrothermal fabrication conditions, ANN was trained over five different algorithms (QP, BBP, IBP, LM, and GA). Among five different algorithms, LM-4-7-1 representing 4 nodes in the input layer, 7 nodes in the hidden layer, and 1 node in the output layer was verified as the optimum model due to its optimum numerical properties. According to the modelling study, the calcination temperature demonstrated the most effective parameter, while the ICG concentration indicated the least effect. By verifying the optimum hydrothermal fabrication conditions, the thermal decomposition of ammonium sulphate (TDAS) was applied to the functionalized surface areas and mesoporous walls by –SO₃H functional groups. In addition, the catalytic performance and reusability of the produced mesoporous SO₃H–NiO catalyst were evaluated via the transesterification of waste cooking palm oil, resulting in a methyl ester content of 97.4% and excellent stability for nine consecutive transesterification reactions without additional treatments.

In the present research, artificial neural network (ANN) modelling was utilized to determine the relative importance of effective variables to achieve optimum specific surface areas of a synthesized catalyst.

Development of MART for the Rapid Production of Nanostructured Lipid Carriers Loaded with All-Trans Retinoic Acid for Dermal Delivery

All-trans retinoic acid (ATRA) has been regarded as a wonder drug for many dermatological complications; however, its application is limited due to the extreme irritation, and toxicity seen once it has sufficiently concentrated into the bloodstream from the skin. Thus, the present study was aimed to increase the entrapment of ATRA and minimize its transdermal permeation. ATRA incorporated within nanostructured lipid carriers (NLCs) were produced by a green and facile thin lipid-film based microwave-assisted rapid technique (MART). The optimization was carried out using the response surface methodology (RSM)-driven artificial neural network (ANN) coupled with genetic algorithm (GA). The liquid lipid and surfactants were seen to play a very crucial role culminating in the particle size (< 70 nm), zeta potential (< - 32 mV), and entrapment of ATRA (> 98%). ANN-GA-optimized NLCs required a minimal quantity of the surfactants, formed within 2 min and were stable for 1 year at different storage conditions. The optimized NLC-loaded creams showed a skin retention (ex vivo) to an extent of 87.42% with no detectable drug in the receptor fluid (24 h) in comparison to the marketed cream which released 47.32% (12 h) of ATRA. The results were in good correlation with the in vivo skin deposition studies. The NLCs were biocompatible and non-skin irritant based on the primary irritation index. In conclusion, the NLCs were seen to have a very high potential in overcoming the drawbacks of ATRA for dermal delivery and could be produced conveniently by the MART.

Development of a kojic monooleate-enriched oil-in-water nanoemulsion as a potential carrier for hyperpigmentation treatment

Introduction

Kojic monooleate (KMO) is an ester derived from a fungal metabolite of kojic acid with monounsaturated fatty acid, oleic acid, which contains tyrosinase inhibitor to treat skin disorders such as hyperpigmentation. In this study, KMO was formulated in an oil-in-water nanoemulsion as a carrier for better penetration into the skin.

Methods

The nanoemulsion was prepared by using high and low energy emulsification technique. D-optimal mixture experimental design was generated as a tool for optimizing the composition of nanoemulsions suitable for topical delivery systems. Effects of formulation variables including KMO (2.0%–10.0% w/w), mixture of castor oil (CO):lemon essential oil (LO; 9:1) (1.0%–5.0% w/w), Tween 80 (1.0%–4.0% w/w), xanthan gum (0.5%–1.5% w/w), and deionized water (78.8%–94.8% w/w), on droplet size as a response were determined.

Results

Analysis of variance showed that the fitness of the quadratic polynomial fits the experimental data with F-value (2,479.87), a low P-value (P<0.0001), and a nonsignificant lack of fit. The optimized formulation of KMO-enriched nanoemulsion with desirable criteria was KMO (10.0% w/w), Tween 80 (3.19% w/w), CO:LO (3.74% w/w), xanthan gum (0.70% w/w), and deionized water (81.68% w/w). This optimum formulation showed good agreement between the actual droplet size (110.01 nm) and the predicted droplet size (111.73 nm) with a residual standard error <2.0%. The optimized formulation with pH values (6.28) showed high conductivity (1,492.00 µScm⁻¹) and remained stable under accelerated stability study during storage at 4°C, 25°C, and 45°C for 90 days, centrifugal force as well as freeze–thaw cycles. Rheology measurement justified that the optimized formulation was more elastic (shear thinning and pseudo-plastic properties) rather than demonstrating viscous characteristics. In vitro cytotoxicity of the optimized KMO formulation and KMO oil showed that IC₅₀ (50% inhibition of cell viability) value was >100 µg/mL.

Conclusion

The survival rate of 3T3 cell on KMO formulation (54.76%) was found to be higher compared to KMO oil (53.37%) without any toxicity sign. This proved that the KMO formulation was less toxic and can be applied for cosmeceutical applications.

Detecting event-related changes in organizational networks using optimized neural network models

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques.