D-Optimal Optimization and Data-Analysis Comparison Between a DoE Software and Artificial Neural Networks of a Chitosan Coating Process onto PLGA Nanoparticles for Lung and Cervical Cancer Treatment

Recent advancements toward the incremsent of drug solubility using environmentally-friendly supercritical CO2: a machine learning perspective

Inadequate bioavailability of therapeutic drugs, which is often the consequence of their unacceptable solubility and dissolution rates, is an indisputable operational challenge of pharmaceutical companies due to its detrimental effect on the therapeutic efficacy. Over the recent decades, application of supercritical fluids (SCFs) (mainly SCCO2) has attracted the attentions of many scientists as promising alternative of toxic and environmentally-hazardous organic solvents due to possessing positive advantages like low flammability, availability, high performance, eco-friendliness and safety/simplicity of operation. Nowadays, application of different machine learning (ML) as a versatile, robust and accurate approach for the prediction of different momentous parameters like solubility and bioavailability has been of great attentions due to the non-affordability and time-wasting nature of experimental investigations. The prominent goal of this article is to review the role of different ML-based tools for the prediction of solubility/bioavailability of drugs using SCCO2. Moreover, the importance of solubility factor in the pharmaceutical industry and different possible techniques for increasing the amount of this parameter in poorly-soluble drugs are comprehensively discussed. At the end, the efficiency of SCCO2 for improving the manufacturing process of drug nanocrystals is aimed to be discussed.

Machine learning assisted exploration of the influential parameters on the PLGA nanoparticles

Poly (lactic-co-glycolic acid) (PLGA)-based nanoparticles (NPs) are widely investigated as drug delivery systems. However, despite the numerous reviews and research papers discussing various physicochemical and technical properties that affect NP size and drug loading characteristics, predicting the influential features remains difficult. In the present study, we employed four different machine learning (ML) techniques to create ML models using effective parameters related to NP size, encapsulation efficiency (E.E.%), and drug loading (D.L.%). These parameters were extracted from the different literature. Least Absolute Shrinkage and Selection Operator was used to investigate the input parameters and identify the most influential features (descriptors). Initially, ML models were trained and validated using tenfold validation methods, and subsequently, next their performances were evaluated and compared in terms of absolute error, mean absolute, error and R-square. After comparing the performance of different ML models, we decided to use support vector regression for predicting the size and E.E.% and random forest for predicting the D.L.% of PLGA-based NPs. Furthermore, we investigated the interactions between these target variables using ML methods and found that size and E.E.% are interrelated, while D.L.% shows no significant relationship with the other targets. Among these variables, E.E.% was identified as the most influential parameter affecting the NPs' size. Additionally, we found that certain physicochemical properties of PLGA, including molecular weight (Mw) and the lactide-to-glycolide (LA/GA) ratio, are the most determining features for E.E.% and D.L.% of the final NPs, respectively.

Review of machine learning algorithms' application in pharmaceutical technology

Machine learning algorithms, and artificial intelligence in general, have a wide range of applications in the field of pharmaceutical technology. Starting from the formulation development, through a great potential for integration within the Quality by design framework, these data science tools provide a better understanding of the pharmaceutical formulations and respective processing. Machine learning algorithms can be especially helpful with the analysis of the large volume of data generated by the Process analytical technologies. This paper provides a brief explanation of the artificial neural networks, as one of the most frequently used machine learning algorithms. The process of the network training and testing is described and accompanied with illustrative examples of machine learning tools applied in the context of pharmaceutical formulation development and related technologies, as well as an overview of the future trends. Recently published studies on more sophisticated methods, such as deep neural networks and light gradient boosting machine algorithm, have been described. The interested reader is also referred to several official documents (guidelines) that pave the way for a more structured representation of the machine learning models in their prospective submissions to the regulatory bodies.

Enhanced Cellular Uptake and Gene Silencing Activity of Survivin-siRNA via Ultrasound-Mediated Nanobubbles in Lung Cancer Cells

PURPOSE

Paclitaxel is a first-line drug for the therapy of lung cancer, however, drug resistance is a serious limiting factor, related to overexpression of anti-apoptotic proteins like survivin. To overcome this phenomenon, developing novel ultrasound responsive nanobubbles - nanosized drug delivery system- for the delivery of paclitaxel and siRNA in order to silence survivin expression in the presence of ultrasound was aimed.

METHODS

Paclitaxel-carrying nanobubble formulation was obtained by modifying the multistep method. Then, the complex formation of the nanobubbles - paclitaxel formulation with survivin-siRNA, was examined in terms of particle size, polydispersity index, zeta potential, and morphology. Furthermore, siRNA binding and protecting ability, cytotoxicity, cellular uptake, gene silencing, and induction of apoptosis studies were investigated in terms of lung cancer cells.

RESULTS

Developed nanobubbles have particle sizes of 218.9-369.6 nm, zeta potentials of 27-34 mV, were able to protect siRNA from degradation and delivered siRNA into the lung cancer cells. Survivin expression was significantly lower compared with the control groups and enhanced apoptosis was induced by the co-delivery of survivin-siRNA and paclitaxel. Furthermore, significantly higher effects were obtained in the presence of ultrasound induction.

CONCLUSION

The ultrasound responsive nanobubble system carrying paclitaxel and survivin-siRNA is a promising and effective approach against lung cancer cells.