A Machine Learning Approach for PLGA Nanoparticles in Antiviral Drug Delivery

Abstract

In recent years, nanoparticles have been highly investigated in the laboratory. However, only a few laboratory discoveries have been translated into clinical practice. These findings in the laboratory are limited by trial-and-error methods to determine the optimum formulation for successful drug delivery. A new paradigm is required to ease the translation of lab discoveries to clinical practice. Due to their previous success in antiviral activity, it is vital to accelerate the discovery of novel drugs to treat and manage viruses. Machine learning is a subfield of artificial intelligence and consists of computer algorithms which are improved through experience. It can generate predictions from data inputs via an algorithm which includes a method built from inputs and outputs. Combining nanotherapeutics and well-established machine-learning algorithms can simplify antiviral-drug development systems by automating the analysis. Other relationships in bio-pharmaceutical networks would eventually aid in reaching a complex goal very easily. From previous laboratory experiments, data can be extracted and input into machine learning algorithms to generate predictions. In this study, poly (lactic-co-glycolic acid) (PLGA) nanoparticles were investigated in antiviral drug delivery. Data was extracted from research articles on nanoparticle size, polydispersity index, drug loading capacity and encapsulation efficiency. The Gaussian Process, a form of machine learning algorithm, could be applied to this data to generate graphs with predictions of the datasets. The Gaussian Process is a probabilistic machine learning model which defines a prior over function. The mean and variance of the data can be calculated via matrix multiplications, leading to the formation of prediction graphs—the graphs generated in this study which could be used for the discovery of novel antiviral drugs. The drug load and encapsulation efficiency of a nanoparticle with a specific size can be predicted using these graphs. This could eliminate the trial-and-error discovery method and save laboratory time and ease efficiency.

Point biserial correlation symbiotic organism search nanoengineering based drug delivery for tumor diagnosis

“…Here, a machine learning algorithm was employed in generating graphs with predictions of provided datasets. Moreover, the Gaussian Process, a substitute to the probabilistic machine learning model 8 that identified a prior over function, saved time and improved efficiency. Despite the fact that the method was proven to be computationally efficient, the drug delivery rate was not focused.…”

A Comprehensive Study on Nanoparticle Drug Delivery to the Brain: Application of Machine Learning Techniques

“…Additionally, machine learning models heavily rely on the quality and quantity of training data, which can lead to suboptimal performance when faced with data significantly different from those in the training set. Therefore, careful consideration must be given to selecting training data and validating models to ensure both accuracy and generalizability. − …”

“…There have been studies that developed predictive models to optimize drug delivery efficiency. Baghaei et al employed artificial neural networks to predict the NP size and its correlation with the initial burst rate, considering factors such as the molecular weight of polylactic- co -glycolic acid (PLGA), solution concentration, and molecular weight of poly­(vinyl alcohol). − In a different study, Gao et al demonstrated that combining chemical features and clinical phenotypes was more effective in predicting BBB permeability compared to using chemical features alone. , Saini and Srivastava identified important physicochemical properties for predicting the biological activities of nanomaterials, including surface charge, corona, aggregation, and solubility . Another study by Shafaei and Khayati focused on using machine learning to predict the size of NPs, considering parameters such as reaction time, reagent concentration, Au salt-to-stabilizer concentration ratio, intensity, wavelength, and focusing conditions, primarily in the context of in vitro responses …”

Unraveling the Intricacies of Nanoparticle Targeting of Brain Therapeutics for Enhanced Efficacy

“…Additionally, machine learning models heavily rely on the quality and quantity of training data, which can lead to suboptimal performance when faced with significantly different data from the training set. Therefore, careful consideration must be given to selecting training data and validating models to ensure both accuracy and generalizability (19)(20)(21).…”

“…Previous studies have focused on developing predictive models in the areas of nanotechnology and drug delivery. Baghaei et al employed Artificial Neural Networks to predict nanoparticle size and its correlation with the initial burst rate, considering factors such as the molecular weight of PLGA, solution concentration, and molecular weight of poly (vinyl alcohol) (18)(19)(20)(21). Gao et al demonstrated that combining chemical features and clinical phenotypes was more effective in predicting blood-brain barrier (BBB) permeability compared to using chemical features alone (6,22).…”