A cascade computer model for mocrobicide diffusivity from mucoadhesive formulations

Abstract

BackgroundThe cascade computer model (CCM) was designed as a machine-learning feature platform for prediction of drug diffusivity from the mucoadhesive formulations. Three basic models (the statistical regression model, the K nearest neighbor model and the modified version of the back propagation neural network) in CCM operate sequentially in close collaboration with each other, employing the estimated value obtained from the afore-positioned base model as an input value to the next-positioned base model in the cascade.The effects of various parameters on the pharmacological efficacy of a female controlled drug delivery system (FcDDS) intended for prevention of women from HIV-1 infection were evaluated using an in vitro apparatus “Simulant Vaginal System” (SVS). We used computer simulations to explicitly examine the changes in drug diffusivity from FcDDS and determine the prognostic potency of each variable for in vivo prediction of formulation efficacy. The results obtained using the CCM approach were compared with those from individual multiple regression model.ResultsCCM significantly lowered the percentage mean error (PME) and enhanced r2 values as compared with those from the multiple regression models. It was noted that CCM generated the PME value of 21.82 at 48169 epoch iterations, which is significantly improved from the PME value of 29.91 % at 118344 epochs by the back propagation network model. The results of this study indicated that the sequential ensemble of the classifiers allowed for an accurate prediction of the domain with significantly lowered variance and considerably reduces the time required for training phase.ConclusionCCM is accurate, easy to operate, time and cost-effective, and thus, can serve as a valuable tool for prediction of drug diffusivity from mucoadhesive formulations. CCM may yield new insights into understanding how drugs are diffused from the carrier systems and exert their efficacies under various clinical conditions.

The Future of Drug Delivery

“…AI can be a robust tool to establish structure–function relationships between formulation parameters and release kinetics and can help tune a drug delivery system for optimal performance . For example, a cascade computer model was designed to predict drug diffusivity from different mucoadhesive formulations . Despite its potential to transform drug delivery, AI still has a long way to go in real-world applications.…”

“…34 For example, a cascade computer model was designed to predict drug diffusivity from different mucoadhesive formulations. 35 Despite its potential to transform drug delivery, AI still has a long way to go in real-world applications. Future efforts to improve interpretability, provide guidance on machine learning model selection, and improve the quality of the data collected are critical to fully realize the potential of AI in drug delivery.…”

Deep Learning in Personalization of Cardiovascular Stents

“…The cascade machine learning model (CMLM) is a classifier made of commercially available data mining models and newly designed (in the author's lab) execution software. 80 As shown in Figure 3, CMLM consists of the 3 component models known as the KNN model, the statistical regression model, and the modified back-propagation neural network. The CMLM works in a series, integrating the estimated value obtained from the base model placed ahead to the base model as an input value placed later in CLML.…”