A cascade computer model for mocrobicide diffusivity from mucoadhesive formulations

BMC Bioinformatics

Research Article

Namita Giri¹  Gayathri Acharya¹  Chi H. Lee¹  Yugyung Lee²  Alok Khemka² 

[1] Division of Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, 64108, Kansas City, MO, USA;School of Computing and Engineering, Kansas City, USA;

关键词: Computer model;    Drug diffusivity;    Microbicides;    Mucoadhesive formulations;   

DOI  :  10.1186/s12859-015-0684-z

 received in 2014-09-22, accepted in 2015-07-24,  发布年份 2015

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Lee et al. 2015