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Gaussian processes modeling for the prediction of polymeric nanoparticle formulation design to enhance encapsulation efficiency and therapeutic efficacy

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Abstract

Conventional drugs have been facing various drug delivery obstacles, including first-pass metabolism for oral medications, drug degradation by cellular enzymes, off-target effects, and cytotoxicity of healthy cells. Nanoparticles (NP) application in drug delivery can compensate for these drawbacks to a great extent. NPs can be fabricated using different materials and structures to achieve desired therapeutic effects. For each type of NP material, its physicochemical properties determine compatibility with specific drugs and other supplemental compositions. The optimized material selection becomes prominent in NP development to improve NP performances. Due to the nature of NP fabrication, the process is long and expensive. To accelerate NP composition optimization, machine learning (ML) techniques are among the most promising methods for efficient data predictions and optimizations.

As a proof-of concept, we created Gaussian Process (GP) models to make predictions for drug encapsulation efficiency (EE%) and therapeutic efficacy of 32 poly (lactic-co-glycolic acid) (PLGA) NPs that are formed with materials with different physicochemical properties. Two model drugs, doxorubicin (DOX) and docetaxel (DTX) were loaded separately. The IC50 values for the various NPs formulations were evaluated using the OVCAR3 epithelial ovarian cancer cell line. EE% GP model has the highest prediction accuracy with the lowest normalized root-mean-squared-error (RMSE) of 0.187. The DOX and DTX IC50 GP models have normalized RMSEs of 0.296 and 0.206, respectively, which are higher than that of the EE% GP model.

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Acknowledgements

We would like to acknowledge Dr. Yannick Traore and Dr. Chen Sun for providing valuable advice in wet lab experiments. We would also like to acknowledge the School of Pharmacy, University of Waterloo, Ontario, Canada, for Science Graduate Award, Graduate Research Scholarship, Graduate Studies Conference Assistantship, and Christine & David Edwards Graduate Studies Travel Award.

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Author notes

  1. Sihan Dong and Haolin Yu contributed equally to this work.

Authors and Affiliations

  1. School of Pharmacy, University of Waterloo, Ontario, N2G 1C5, Canada

    Sihan Dong & Emmanuel A. Ho

  2. David R. Cheriton School of Computer Science, University of Waterloo, Ontario, N2L 3G1, Canada

    Haolin Yu & Pascal Poupart

  3. Waterloo Institute for Nanotechnology, University of Waterloo, Ontario, N2L 3G1, Canada

    Emmanuel A. Ho

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  1. Sihan Dong
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Contributions

All authors contributed to the study conception and design. Wet-lab data collection and all data analysis were performed by Sihan Dong. The vast majority of machine learning programming was performed by Haolin Yu, while a small portion was performed by Sihan Dong. The first draft of the manuscript was written by Sihan Dong. Dr. Emmanuel A. Ho and Dr. Pascal Poupart reviewed and edited the manuscript. All authors read and approved the final manuscript.

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Correspondence to Emmanuel A. Ho.

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Dong, S., Yu, H., Poupart, P. et al. Gaussian processes modeling for the prediction of polymeric nanoparticle formulation design to enhance encapsulation efficiency and therapeutic efficacy. Drug Deliv. and Transl. Res. (2024). https://doi.org/10.1007/s13346-024-01625-7

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