Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling.

Journal: ACS applied materials & interfaces
PMID: 39800937

Abstract

Magnetic nanoparticles (NPs) are gaining significant interest in the field of biomedical functional nanomaterials because of their distinctive chemical and physical characteristics, particularly in drug delivery and magnetic hyperthermia applications. In this paper, we experimentally synthesized and characterized new FeO-based NPs, functionalizing its surface with a 5-TAMRA cadaverine modified copolymer consisting of PMAO and PEG. Despite these advancements, many combinations of NP cores and coatings remain unexplored. To address this, we created a new data set of NP systems from public sources. Herein, 11 different AI/ML algorithms were used to develop the predictive AI/ML models. The linear discriminant analysis (LDA) and random forest (RF) models showed high values of sensitivity and specificity (>0.9) in training/validation series and 3-fold cross validation, respectively. The AI/ML models are able to predict 14 output properties (CC (μM), EC (μM), inhibition (%), .) for all combinations of 54 different NP cores classes vs. 25 different coats and vs. 41 different cell lines, allowing the short listing of the best results for experimental assays. The results of this work may help to reduce the cost of traditional trial and error procedures.

Authors

  • Shan He
    Key Laboratory of Applied Marine Biotechnology, Ningbo University, Ningbo 315211, China. Electronic address: heshan@nbu.edu.cn.
  • Ander Barón
    Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Greater Bilbao, Basque Country, Spain.
  • Cristian R Munteanu
    Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruna, Campus de Elviña s/n, 15071, A Coruña, Spain, phone/fax: +34-981167000/+34-981167160. crm.publish@gmail.com.
  • Begoña de Bilbao
    Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Greater Bilbao, Basque Country, Spain.
  • Gerardo M Casañola-Martin
    Department of Systems and Computer Engineering, Carleton University, K1S 5B6, Ottawa, ON, Canada.
  • Mariana Chelu
    "IlieMurgulescu" Institute of Physical Chemistry, 202 Spl. Independentei, 060021 Bucharest, Romania.
  • Adina Magdalena Musuc
    "IlieMurgulescu" Institute of Physical Chemistry, 202 Spl. Independentei, 060021 Bucharest, Romania.
  • Harbil Bediaga
    Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
  • Estefania Ascencio
    Department of Coatings and Polymer Materials, North Dakota State University, Fargo, North Dakota 58102, United States.
  • Idoia Castellanos-Rubio
    Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Greater Bilbao, Basque Country, Spain.
  • Sonia Arrasate
    Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, 48940 Leioa, Spain.
  • Alejandro Pazos
    Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, Universidade da Coruña, A Coruña, Spain.
  • Maite Insausti
    Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Greater Bilbao, Basque Country, Spain.
  • Bakhtiyor Rasulev
    c Department of Coatings and Polymeric Materials , North Dakota State University , Fargo , ND , USA.
  • Humberto Gonzalez-Diaz

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