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Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models.
Nanoscale. 2020 Jul 02; 12(25):13471-13483.N

Abstract

Nanoparticles (NPs) decorated with coating agents (polymers, gels, proteins, etc.) form Nanoparticle Drug Delivery Systems (DDNS), which are of high interest in nanotechnology and biomaterials science. There have been increasing reports of experimental data sets of biological activity, toxicity, and delivery properties of DDNS. However, these data sets are still dispersed and not as large as the datasets of DDNS components (NP and drugs). This has prompted researchers to train Machine Learning (ML) algorithms that are able to design new DDNS based on the properties of their components. However, most ML models reported up to date predictions of the specific activities of NP or drugs over a determined target or cell line. In this paper, we combine Perturbation Theory and Machine Learning (PTML algorithm) to train a model that is able to predict the best components (NP, coating agent, and drug) for DDNS design. In so doing, we downloaded a dataset of >30 000 preclinical assays of drugs from ChEMBL. We also downloaded an NP data set formed by preclinical assays of coated Metal Oxide Nanoparticles (MONPs) from public sources. Both the drugs and NP datasets of preclinical assays cover multiple conditions of assays that can be listed as two arrays, namely, cjdrug and cjNP. The cjdrug array includes >504 biological activity parameters (c0drug), >340 target proteins (c1drug), >650 types of cells (c2drug), >120 assay organisms (c3drug), and >60 assay strains (c4drug). On the other hand, the cjNP array includes 3 biological activity parameters (c0NP), 40 types of proteins (c1NP), 10 shapes of nanoparticles (c2NP), 6 assay media (c3NP), and 12 coating agents (c4NP). After downloading, we pre-processed both the data sets by separate calculation PT operators that are able to account for changes (perturbations) in the drug, coating agents, and NP chemical structure and/or physicochemical properties as well as for the assay conditions. Next, we carry out an information fusion process to form a final dataset of above 500 000 DDNS (drug + MONP pairs). We also trained other linear and non-linear PTML models using R studio scripts for comparative purposes. To the best of our knowledge, this is the first multi-label PTML model that is useful for the selection of drugs, coating agents, and metal or metal-oxide nanoparticles to be assembled in order to design new DDNS with optimal activity/toxicity profiles.

Authors+Show Affiliations

University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain. ricardo.santana@opendeusto.es and Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1° N° 70-01, Medellín, Colombia.Facultad de Ingeniería Agroindustrial, Universidad Pontificia Bolivariana, Circular 1° N° 70-01, Medellín, Colombia.Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1° N° 70-01, Medellín, Colombia.Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain. humberto.gonzalezdiaz@ehu.es.University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain. ricardo.santana@opendeusto.es.Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain. humberto.gonzalezdiaz@ehu.es and IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain and Biofisika Institue CSIC-UPVEHU, University of Basque Country UPV/EHU, 48940, Leioa, Spain.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32613998

Citation

Santana, Ricardo, et al. "Predicting Coated-nanoparticle Drug Release Systems With Perturbation-theory Machine Learning (PTML) Models." Nanoscale, vol. 12, no. 25, 2020, pp. 13471-13483.
Santana R, Zuluaga R, Gañán P, et al. Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models. Nanoscale. 2020;12(25):13471-13483.
Santana, R., Zuluaga, R., Gañán, P., Arrasate, S., Onieva, E., & González-Díaz, H. (2020). Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models. Nanoscale, 12(25), 13471-13483. https://doi.org/10.1039/d0nr01849j
Santana R, et al. Predicting Coated-nanoparticle Drug Release Systems With Perturbation-theory Machine Learning (PTML) Models. Nanoscale. 2020 Jul 2;12(25):13471-13483. PubMed PMID: 32613998.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models. AU - Santana,Ricardo, AU - Zuluaga,Robin, AU - Gañán,Piedad, AU - Arrasate,Sonia, AU - Onieva,Enrique, AU - González-Díaz,Humbert, PY - 2020/7/3/entrez PY - 2020/7/3/pubmed PY - 2020/7/3/medline SP - 13471 EP - 13483 JF - Nanoscale JO - Nanoscale VL - 12 IS - 25 N2 - Nanoparticles (NPs) decorated with coating agents (polymers, gels, proteins, etc.) form Nanoparticle Drug Delivery Systems (DDNS), which are of high interest in nanotechnology and biomaterials science. There have been increasing reports of experimental data sets of biological activity, toxicity, and delivery properties of DDNS. However, these data sets are still dispersed and not as large as the datasets of DDNS components (NP and drugs). This has prompted researchers to train Machine Learning (ML) algorithms that are able to design new DDNS based on the properties of their components. However, most ML models reported up to date predictions of the specific activities of NP or drugs over a determined target or cell line. In this paper, we combine Perturbation Theory and Machine Learning (PTML algorithm) to train a model that is able to predict the best components (NP, coating agent, and drug) for DDNS design. In so doing, we downloaded a dataset of >30 000 preclinical assays of drugs from ChEMBL. We also downloaded an NP data set formed by preclinical assays of coated Metal Oxide Nanoparticles (MONPs) from public sources. Both the drugs and NP datasets of preclinical assays cover multiple conditions of assays that can be listed as two arrays, namely, cjdrug and cjNP. The cjdrug array includes >504 biological activity parameters (c0drug), >340 target proteins (c1drug), >650 types of cells (c2drug), >120 assay organisms (c3drug), and >60 assay strains (c4drug). On the other hand, the cjNP array includes 3 biological activity parameters (c0NP), 40 types of proteins (c1NP), 10 shapes of nanoparticles (c2NP), 6 assay media (c3NP), and 12 coating agents (c4NP). After downloading, we pre-processed both the data sets by separate calculation PT operators that are able to account for changes (perturbations) in the drug, coating agents, and NP chemical structure and/or physicochemical properties as well as for the assay conditions. Next, we carry out an information fusion process to form a final dataset of above 500 000 DDNS (drug + MONP pairs). We also trained other linear and non-linear PTML models using R studio scripts for comparative purposes. To the best of our knowledge, this is the first multi-label PTML model that is useful for the selection of drugs, coating agents, and metal or metal-oxide nanoparticles to be assembled in order to design new DDNS with optimal activity/toxicity profiles. SN - 2040-3372 UR - https://www.unboundmedicine.com/medline/citation/32613998/Predicting_coated-nanoparticle_drug_release_systems_with_perturbation-theory_machine_learning_(PTML)_models L2 - https://doi.org/10.1039/d0nr01849j DB - PRIME DP - Unbound Medicine ER -
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