Comparative Study for Optimization of Pharmaceutical Self-Emulsifying Pre-concentrate by Design of Experiment and Artificial Neural Network

A Study of Microemulsion Systems for Transdermal Delivery of Risperidone Using Penetration Enhancers

The aim of this study was to develop and characterize microemulsion formulations using penetration enhancers as potential transdermal delivery systems for risperidone. Initially, a simple formulation of risperidone in Propylene Glycol (PG) was prepared as a control formulation, together with formulations incorporating various penetration enhancers, alone and/or in combination, and also microemulsion formulations with various chemical penetration enhancers, were prepared and all were evaluated for risperidone transdermal delivery. An ex-vivo permeation study was carried out using human cadaver skin and vertical glass Franz diffusion cells to compare all the microemulsion formulations. The microemulsion prepared from oleic acid as the oil (15%), Tween 80 (15%) as the surfactant and isopropyl alcohol (20%) as the co-surfactant, and water (50%) showed higher permeation with a flux value of 32.50±3.60 ug/hr/sq.cm, a globule size of 2.96±0.01 nm, a polydispersity index of 0.33±0.02 and pH of 4.95. This novel in vitro research disclosed that an optimized microemulsion formulated using penetration enhancers was able to increase permeation of risperidone by 14-fold compared to the control formulation. The data suggested that microemulsions may be useful in the delivery of risperidone via the transdermal route.

Optimization and characterization of self-nanoemulsifying drug delivery system of iloperidone using box-behnken design and desirability function

PURPOSE

Iloperidone (IP) is an antipsychotic drug which belongs to Biopharmaceutical Classification System (BCS) II exhibiting poor aqueous solubility. The current investigation explores the possibility of enhancement of solubility and dissolution characteristics of IP by formulation of liquid self-nano emulsifying drug delivery system (L-SNEDDS) utilizing Box-Behnken Design (BBD) and desirability function.

METHODS

The oils, surfactants and co-surfactants used in the study were selected based on solubility of the drug and their emulsification ability. Optimization of the formulation was performed using BBD by employing four response variables such as globule size (nm), percentage transmittance (%), self-emulsification time (sec) and percent drug released in 15min. 2D contour plots and 3D response surface plots were constructed using Design Expert software.

RESULTS

The developed optimal L-SNEDDS of IP through BBD approach resulted in improvement of solubility and dissolution rate as compared with the pure drug. Based on desirability function, optimized formulation was prepared and was assessed for response variables (globule size, percentage transmittance, self-emulsification time and percent drug dissolved in 15min). The characterization studies revealed droplet size to be 21.80±2.41nm, 99.584±0.65% transmittance, 24.43±2.12sec emulsification time and 95.31±1.57% cumulative drug release in 15min.

CONCLUSION

The results conclude the potentiality of prepared L-SNEDDS in improving solubility and dissolution rate of IP.

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.

Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective

Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.

Digital Pharmaceutical Sciences

Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, and the continuous developments in machine learning algorithms have resulted in a rapid increase in new machine learning applications in different areas of pharmaceutical sciences. This review summarizes the past, present, and potential future impacts of machine learning technologies on different areas of pharmaceutical sciences, including drug design and discovery, preformulation, and formulation. The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research. AI and machine learning technologies in common day-to-day pharma needs as well as industrial and regulatory insights are reviewed. Beyond traditional potentials of implementing digital technologies using machine learning in the development of more efficient, fast, and economical solutions in pharmaceutical sciences are also discussed.

Topical instillation of triamcinolone acetonide-loaded emulsomes for posterior ocular delivery: statistical optimization and in vitro-in vivo studies

The objective of the present investigation was to formulate and characterize a novel lipid-based carrier-emulsomes loaded with triamcinolone acetonide (TA)/Nile red (NR) for non-invasive delivery to the posterior segment of the eye upon topical application. To optimize and delineate the effect of independent variables on dependent variables, Box-Behnken design (BBD) was adopted. The optimized batch was characterized for size, zeta potential, surface morphology by transmission electron microscopy, drug-excipient interaction by differential scanning calorimetry, osmolarity, pH, ex vivo transcorneal permeation, and stability studies. A short-term exposure (STE) test was performed on Statens Seruminstitut Rabbit Corneal (SIRC) cell lines to evaluate the in vitro ocular irritation. Precorneal retention study was performed in rabbit eyes. Confocal microscopy was used for ocular distribution studies in mice eye by preparing dye (Nile red)-loaded formulations. The surface response and contour plots along with ANOVA results demonstrated an interaction between the factors. The optimized batch had particle size of 131.17 ± 3.17 nm and entrapment efficiency of 71.56 ± 4.19%. TEM image showed unimodal, nano-sized emulsomes. TA-loaded emulsomes exhibited higher transcorneal permeation as compared to drug solution. In vitro irritation studies confirmed the safety of excipients for ophthalmic use. Fluorescence microscopic images obtained after ocular distribution studies showed strong fluorescence in inner and outer plexiform layers of the retina in comparison to dye solution confirming the delivery of dye to the posterior segment of mice eye after topical ocular instillation. Graphical abstract.

Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models

Nano-systems for cancer co-therapy including vitamins or vitamin derivatives have showed adequate results to continue with further research studies to better understand them. However, the number of different combinations of drugs, vitamins, nanoparticle types, coating agents, synthesis conditions, and system types (nanocapsules, micelles, etc.) to be tested is very large generating a high cost in experimentations. In this context, there are reports of large datasets of preclinical assays of compounds (like in the ChEMBL database) and increasing but yet limited reports of experimental measurements of nano-systems per se. On the other hand, Machine Learning is gaining momentum in Nanotechnology and Pharmaceutical Sciences as a tool for rational design of new drugs and drug-release nano-systems. In this work, we propose to combine Perturbation Theory principles and Machine Learning to develop a PTML model for rational selection of the components of cancer co-therapy drug-vitamin release nano-systems (DVRNs). In doing so, we apply information fusion techniques with 2 data sets: (1) a large ChEMBL dataset of >36 000 preclinical assays of vitamin derivatives and a new dataset of >1000 outcomes of DVRNs, collected herein from the literature for the first time. The ChEMBL dataset used covers a considerable number of assay conditions (cjvit) each one with multiple levels. These conditions included >504 biological activity parameters (c0vit), >340 types of proteins (c1vit), >650 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit). Regarding the DVRNs, there are 25 different types of nano-systems (njn), with up to 16 conditions (cjn) including also different levels such as 8 biological activity parameters (c0n), 9 raw nanomaterials (c4n), 15 assay cells (c11n), etc. In the first stage, we used Moving Average operators to quantify the perturbations (deviations) in all input variables with respect to the conditions. After that, we used multiplicative PT operators to carry out data fusion, and dimension reduction, and Linear Discriminant Analysis (LDA) to seek the PTML model. The best PTML model found showed values of specificity, sensitivity, and accuracy in the range of 83-88% in training and external validation series for >130 000 cases (DVRNs vs. ChEMBL data pairs) formed after data fusion. To the best of our knowledge, this is the first general purpose model for the rational design of DVRNs for cancer co-therapy.