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Journal of Machine Learning for Modeling and Computing

Publication de 4  numéros par an

ISSN Imprimer: 2689-3967

ISSN En ligne: 2689-3975

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A MACHINE LEARNING APPROACH TO QUANTIFY DISSOLUTION KINETICS OF POROUS MEDIA

Volume 2, Numéro 2, 2021, pp. 1-14
DOI: 10.1615/JMachLearnModelComput.2021038529
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RÉSUMÉ

Microspheres are popular drug products comprises of drug particles embedded in a matrix of wax and pore former. Microstructural attributes of these beads affect the overall dissolution release kinetics of the product. Due to the complex geometry and high computational cost associated with pore-scale simulations, the impact of microstructural attributes on the drug release rate is yet to be well studied. In this paper, we propose a machine learning framework to examine the drug release rate by estimating the temporal profile of the effective diffusion coefficient of the dissolved drug through the pores. By incorporating a statistical description of the pore structure via the Minkowski functionals, our model can also provide probabilistic distribution of the effective property at a given time. Leveraging such efficient numerical framework, we conduct sensitivity analysis and rank the geometric parameters according to their impacts on the drug release rate.

RÉFÉRENCES
  1. Alvarez, M.A., Rosasco, L., and Lawrence, N.D., Kernels for Vector-Valued Functions: A Review, Found. Trends Mach. Learn., vol. 4, no. 3,pp. 195-266,2012.

  2. Bartlett, J.A., Oral Multiparticulates as a Pediatric Platform: How to Make Good Medicines Not Taste Bad, AAPS Annual Meeting, Pfizer Inc., 2017.

  3. Beck, R.E. and Schultz, J.S., Hindered Diffusion in Microporous Membranes with Known Pore Geometry, Science, vol. 170, no. 3964, pp. 1302-1305,1970.

  4. Berchtold, M.A., Modelling of Random Porous Media Using Minkowski-Functionals, PhD, ETH Zurich, 2007.

  5. Bhatnagar, P.L., Gross, E.P., and Krook, M., A Model for Collision Processes in Gases. I. Small Amplitude Processes in Charged and Neutral One-Component Systems, Phys. Rev., vol. 94, pp. 511-525,1954.

  6. Blanc, X., Bris, C., and Legoll, F., Some Variance Reduction Methods for Numerical Stochastic Homogenization, 2015. arXiv: 150902389.

  7. Cang, R., Li, H., Yao, H., Jiao, Y., and Ren, Y., Improving Direct Physical Properties Prediction of Heterogeneous Materials from Imaging Data via Convolutional Neural Network and a Morphology-Aware Generative Model, Comput. Mater. Sci., vol. 150, pp. 212-221,2018.

  8. Chen, S. and Doolen, G.D., Lattice Boltzmann Method for Fluid Flows, Ann. Rev. Fluid Mech, vol. 30, no. 1,pp. 329-364,1998. DOI: 10.1146/annurev.fluid.30.1.329.

  9. Conti, S. and O'Hagan, A., Bayesian Emulation of Complex Multi-Output and Dynamic Computer Models, J. Statist. Plann. Inference, vol. 140, pp. 640-651,2010.

  10. Efendiev, Y., Kronsbein, C., and Legoll, F., Multi-Level Monte Carlo Approaches for Numerical Homogenization, 2013. arXiv: 13012798.

  11. Feng, J., He, X., Teng, Q., Ren, C., Chen, H., and Li, Y., Reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Networks, Phys. Rev. E, vol. 100, no. 3, p. 033308,2019.

  12. Girimaji, S., Lattice Boltzmann Method: Fundamentals and Engineering Applications with Computer Codes, AIAAJ, vol. 51, no. 1,pp. 278-279,2013. DOI: 10.2514/1.J051744.

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