NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

He, Shan; Nader, Karam; Abarrategi, Julen Segura; Bediaga, Harbil; Nocedo-Mena, Deyani; Ascencio, Estefania; Casanola-Martin, Gerardo M.; Castellanos-Rubio, Idoia; Insausti, Maite; Rasulev, Bakhtiyor; Arrasate, Sonia; González-Díaz, Humberto

doi:10.1186/s12951-024-02660-9

Title: NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

Journal Article · 23 July 2024 · Journal of Nanobiotechnology

DOI: https://doi.org/10.1186/s12951-024-02660-9 · OSTI ID:2406090

He, Shan; Nader, Karam; Abarrategi, Julen Segura; Bediaga, Harbil; Nocedo-Mena, Deyani; Ascencio, Estefania; Casanola-Martin, Gerardo M.; Castellanos-Rubio, Idoia; Insausti, Maite; Rasulev, Bakhtiyor; Arrasate, Sonia; González-Díaz, Humberto

Abstract Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe ₃ O ₄ _A and Fe ₃ O ₄ _B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe ₃ O ₄ _A and Fe ₃ O ₄ _B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0022239

OSTI ID:: 2406090

Journal Information:: Journal of Nanobiotechnology, Journal Name: Journal of Nanobiotechnology Journal Issue: 1 Vol. 22; ISSN 1477-3155

Publisher:: Springer Science + Business MediaCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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