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

NANO.PTML Model for read-across prediction of nanosystems in neurosciences. Computational model and experimental case of study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article NANO.PTML Model for read-across prediction of nanosystems in neurosciences. Computational model and experimental case of study Shan He, Karam Nader, Julen Segura Abarrategi, Harbil Bediaga, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4287147/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jul, 2024 Read the published version in Journal of Nanobiotechnology → Version 1 posted 11 You are reading this latest preprint version 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, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising NDDS and NP candidates and to address the few data in the literature. IF-process was carried out between 4403 NDDS assays and 260 cytotoxicity NP assays conducting a dataset of 500000 cases. The optimal IFPTML identified was the DT algorithm, demonstrating 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 AUROC scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. On the other hand, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (hexadecyltrimethylammonium 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 500000 prediction dataset was created involving n(NP cores)=5 vs. n(cell lines) =53 vs. n(NP shapes) =5 vs. n(NP coats) =16 vs. n(drugs) =123. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. Specifically, the experiment revealed that the cell line Lycopersicon esculentum showed promise for ecotoxicity studies across various coating systems. In contrast, Danio rerio cell lines (embryos, juveniles, and adults) showed lower predictive values, suggesting less favorable candidates. MacGowan volume was notably relevant for CTAB, PS, and PEG as coating agents, excluding PVA. 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. Neurodegenerative disease Nanoparticle Drug Carrier Information Fusion Machine Learning Full Text Additional Declarations No competing interests reported. Supplementary Files SI00.docx SI00Dataset.xlsb SI01ExperimentalCaseofStudyDataset.xlsb Cite Share Download PDF Status: Published Journal Publication published 23 Jul, 2024 Read the published version in Journal of Nanobiotechnology → Version 1 posted Editorial decision: Revision requested 16 May, 2024 Reviews received at journal 11 May, 2024 Reviews received at journal 10 May, 2024 Reviews received at journal 09 May, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviewers invited by journal 23 Apr, 2024 Editor assigned by journal 23 Apr, 2024 Submission checks completed at journal 23 Apr, 2024 First submitted to journal 18 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising NDDS and NP candidates and to address the few data in the literature. IF-process was carried out between 4403 NDDS assays and 260 cytotoxicity NP assays conducting a dataset of 500000 cases. The optimal IFPTML identified was the DT algorithm, demonstrating 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 AUROC scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. On the other hand, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (hexadecyltrimethylammonium 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 500000 prediction dataset was created involving n(NP cores)=5 vs. n(cell lines) =53 vs. n(NP shapes) =5 vs. n(NP coats) =16 vs. n(drugs) =123. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. 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