Abstract
An in silico approach is proposed to first predict the partition coefficient of the model drug, paclitaxel, in different biocompatible and biodegradable polymer versus the blood plasma using artificial neural networks (ANNs) and semi-empirical quantitative structure property relationships (QSPRs). A simplified molecular-input line-entry system (SMILES) notation is used to represent the structures of the different polymers and the drug. The SMILES notation is then used to calculate the various structure-based descriptors. These descriptors are then used in the ANNs and semi-empirical QSPRs to predict the properties for a given drug-polymer device. A fluid flow model is subsequently solved to simulate the controlled drug release in the blood plasma. The effects of various parameters are also studied on the drug release profiles from these devices. The proposed approach provides a systematic framework to simulate the controlled release of the drug from the diffusion-controlled drug-polymer release systems. The developed models can be used in a reverse engineer framework to design the controlled delivery devices for a target drug release profile in near future.
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Acknowledgements
The authors would like to thank S. Kale and A. Kumar for their valuable feedback and suggestions during the early stages of this work.
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▪ Developed semi-empirical QSPR models for partition coefficient of the drug in polymer and blood plasma
▪ In silico modeling of the drug release from the controlled drug delivery devices to mimic in vivo drug release
▪ Simulated the effects of different design parameters on the controlled drug release profiles
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Pramanik, A., Garg, S. Prediction of the partition coefficients using QSPR modeling and simulation of paclitaxel release from the diffusion-controlled drug delivery devices. Drug Deliv. and Transl. Res. 8, 1300–1312 (2018). https://doi.org/10.1007/s13346-018-0530-8
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DOI: https://doi.org/10.1007/s13346-018-0530-8