A cascade computer model for mocrobicide diffusivity from mucoadhesive formulations

BMC Bioinformatics

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

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

[2] School of Computing and Engineering, Kansas City, USA

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

Others  :  1229826
DOI  :  10.1186/s12859-015-0684-z

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

【 摘 要 】

Background

The 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.

Results

CCM significantly lowered the percentage mean error (PME) and enhanced r ²values 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.

Conclusion

CCM 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】

   
2015 Lee et al.

【 图 表 】

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