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He S, Segura Abarrategi J, Bediaga H, Arrasate S, González-Díaz H. On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:535-555. [PMID: 38774585 PMCID: PMC11106676 DOI: 10.3762/bjnano.15.47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/23/2024] [Indexed: 05/24/2024]
Abstract
Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood-brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.
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Affiliation(s)
- Shan He
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº6, 48940 Leioa, Greater Bilbao, Basque Country, Spain
| | - Julen Segura Abarrategi
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Harbil Bediaga
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº6, 48940 Leioa, Greater Bilbao, Basque Country, Spain
- Painting Department, Fine Arts Faculty, University of the Basque Country UPV/EHU, 48940, Leioa, Biscay, Basque Country, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- Instituto Biofisika (UPV/EHU-CSIC), 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature. Expert Opin Drug Discov 2023; 18:1231-1243. [PMID: 37639708 DOI: 10.1080/17460441.2023.2251385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
INTRODUCTION Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations. AREAS COVERED The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process. EXPERT OPINION Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Russian Biotechnological University, Moscow, Russian Federation
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
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3
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Shirokii N, Din Y, Petrov I, Seregin Y, Sirotenko S, Razlivina J, Serov N, Vinogradov V. Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2207106. [PMID: 36772908 DOI: 10.1002/smll.202207106] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/09/2023] [Indexed: 05/11/2023]
Abstract
Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q2 = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.
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Affiliation(s)
- Nikolai Shirokii
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Yevgeniya Din
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Ilya Petrov
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Yurii Seregin
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Sofia Sirotenko
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Julia Razlivina
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Nikita Serov
- Advanced Engineering School, Almetyevsk State Oil Institute, Almetyevsk, Russia
| | - Vladimir Vinogradov
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
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Johnston ST, Faria M. Equation learning to identify nano-engineered particle-cell interactions: an interpretable machine learning approach. NANOSCALE 2022; 14:16502-16515. [PMID: 36314284 DOI: 10.1039/d2nr04668g] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Designing nano-engineered particles capable of the delivery of therapeutic and diagnostic agents to a specific target remains a significant challenge. Understanding how interactions between particles and cells are impacted by the physicochemical properties of the particle will help inform rational design choices. Mathematical and computational techniques allow for details regarding particle-cell interactions to be isolated from the interwoven set of biological, chemical, and physical phenomena involved in the particle delivery process. Here we present a machine learning framework capable of elucidating particle-cell interactions from experimental data. This framework employs a data-driven modelling approach, augmented by established biological knowledge. Crucially, the model of particle-cell interactions learned by the framework can be interpreted and analysed, in contrast to the 'black box' models inherent to other machine learning approaches. We apply the framework to association data for thirty different particle-cell pairs. This library of data contains both adherent and suspension cell lines, as well as a diverse collection of particles. We consider hyperbranched polymer and poly(methacrylic acid) particles, from 6 nm to 1032 nm in diameter, with small molecule, monoclonal antibody, and peptide surface functionalisations. Despite the diverse nature of the experiments, the learned models of particle-cell interactions for each particle-cell pair are remarkably consistent: out of 2048 potential models, only four unique models are learned. The models reveal that nonlinear saturation effects are a key feature governing particle-cell interactions. Further, the framework provides robust estimates of particle performance, which facilitates quantitative evaluation of particle design choices.
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Affiliation(s)
- Stuart T Johnston
- School of Mathematics and Statistics, The University of Melbourne, Victoria, Australia.
| | - Matthew Faria
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
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5
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Speck-Planche A, Kleandrova VV. Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles. ACS OMEGA 2022; 7:32119-32130. [PMID: 36120024 PMCID: PMC9476185 DOI: 10.1021/acsomega.2c03363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
Respiratory viruses are infectious agents, which can cause pandemics. Although nowadays the danger associated with respiratory viruses continues to be evidenced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the virus responsible for the current COVID-19 pandemic, other viruses such as SARS-CoV-1, the influenza A and B viruses (IAV and IBV, respectively), and the respiratory syncytial virus (RSV) can lead to globally spread viral diseases. Also, from a biological point of view, most of these viruses can cause an organ-damaging hyperinflammatory response known as the cytokine storm (CS). Computational approaches constitute an essential component of modern drug development campaigns, and therefore, they have the potential to accelerate the discovery of chemicals able to simultaneously inhibit multiple molecular and nonmolecular targets. We report here the first multicondition model based on quantitative structure-activity relationships and an artificial neural network (mtc-QSAR-ANN) for the virtual design and prediction of molecules with dual pan-antiviral and anti-CS profiles. Our mtc-QSAR-ANN model exhibited an accuracy higher than 80%. By interpreting the different descriptors present in the mtc-QSAR-ANN model, we could retrieve several molecular fragments whose assembly led to new molecules with drug-like properties and predicted pan-antiviral and anti-CS activities.
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Affiliation(s)
- Alejandro Speck-Planche
- Grupo
de Química Computacional y Teórica (QCT-USFQ), Departamento
de Ingeniería Química, Universidad
San Francisco de Quito, Diego de Robles y vía Interoceánica, Quito 170901, Ecuador
| | - Valeria V. Kleandrova
- Laboratory
of Fundamental and Applied Research of Quality and Technology of Food
Production, Moscow State University of Food
Production, Volokolamskoe
shosse 11, 125080, Moscow, Russian Federation
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6
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Konstantopoulos G, Koumoulos EP, Charitidis CA. Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives. NANOMATERIALS 2022; 12:nano12152646. [PMID: 35957077 PMCID: PMC9370746 DOI: 10.3390/nano12152646] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 02/05/2023]
Abstract
Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.
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Affiliation(s)
- Georgios Konstantopoulos
- RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, Greece; (G.K.); (C.A.C.)
| | - Elias P. Koumoulos
- Innovation in Research & Engineering Solutions (IRES), Boulevard Edmond Machtens 79/22, 1080 Brussels, Belgium
- Correspondence:
| | - Costas A. Charitidis
- RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, Greece; (G.K.); (C.A.C.)
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7
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Palai D, Tahara H, Chikami S, Latag GV, Maeda S, Komura C, Kurioka H, Hayashi T. Prediction of Serum Adsorption onto Polymer Brush Films by Machine Learning. ACS Biomater Sci Eng 2022; 8:3765-3772. [PMID: 35905395 DOI: 10.1021/acsbiomaterials.2c00441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Using machine learning based on a random forest (RF) regression algorithm, we attempted to predict the amount of adsorbed serum protein on polymer brush films from the films' physicochemical information and the monomers' chemical structures constituting the films using a RF model. After the training of the RF model using the data of polymer brush films synthesized from five different types of monomers, the model became capable of predicting the amount of adsorbed protein from the chemical structure, physicochemical properties of monomer molecules, and structural parameters (density and thickness of the films). The analysis of the trained RF quantitatively provided the importance of each structural parameter and physicochemical properties of monomers toward serum protein adsorption (SPA). The ranking for the significance of the parameters agrees with our general understanding and perception. Based on the results, we discuss the correlation between brush film's physical properties (such as thickness and density) and SPA and attempt to provide a guideline for the design of antibiofouling polymer brush films.
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Affiliation(s)
- Debabrata Palai
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Hiroyuki Tahara
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Shunta Chikami
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Glenn Villena Latag
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Shoichi Maeda
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan
| | - Chisato Komura
- Research Institute for Advanced Materials and Devices, Kyocera Corporation, 3-5-3 Hikaridai, Seika-Cho, Soraku-gun, Kyoto 619-0237, Japan
| | - Hideharu Kurioka
- Research Institute for Advanced Materials and Devices, Kyocera Corporation, 3-5-3 Hikaridai, Seika-Cho, Soraku-gun, Kyoto 619-0237, Japan
| | - Tomohiro Hayashi
- Department of Materials Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta-Cho Midori-Ku, Yokohama, Kanagawa 226-8502, Japan.,The Institute for Solid State Physics, the University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-0882, Japan
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8
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Diéguez-Santana K, Casañola-Martin GM, Torres R, Rasulev B, Green JR, González-Díaz H. Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds. Mol Pharm 2022; 19:2151-2163. [PMID: 35671399 PMCID: PMC9986951 DOI: 10.1021/acs.molpharmaceut.2c00029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Gerardo M Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States.,Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Roldan Torres
- Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Humbert González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, 48940 Leioa, Spain.,IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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9
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Serov N, Vinogradov V. Artificial intelligence to bring nanomedicine to life. Adv Drug Deliv Rev 2022; 184:114194. [PMID: 35283223 DOI: 10.1016/j.addr.2022.114194] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/04/2022] [Accepted: 03/07/2022] [Indexed: 12/13/2022]
Abstract
The technology of drug delivery systems (DDSs) has demonstrated an outstanding performance and effectiveness in production of pharmaceuticals, as it is proved by many FDA-approved nanomedicines that have an enhanced selectivity, manageable drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, the rational design and high-throughput development of nanomaterial-based DDSs for specific purposes is far from a routine practice and is still in its infancy, mainly due to the limitations in scientists' capabilities to effectively acquire, analyze, manage, and comprehend complex and ever-growing sets of experimental data, which is vital to develop DDSs with a set of desired functionalities. At the same time, this task is feasible for the data-driven approaches, high throughput experimentation techniques, process automatization, artificial intelligence (AI) technology, and machine learning (ML) approaches, which is referred to as The Fourth Paradigm of scientific research. Therefore, an integration of these approaches with nanomedicine and nanotechnology can potentially accelerate the rational design and high-throughput development of highly efficient nanoformulated drugs and smart materials with pre-defined functionalities. In this Review, we survey the important results and milestones achieved to date in the application of data science, high throughput, as well as automatization approaches, combined with AI and ML to design and optimize DDSs and related nanomaterials. This manuscript mission is not only to reflect the state-of-art in data-driven nanomedicine, but also show how recent findings in the related fields can transform the nanomedicine's image. We discuss how all these results can be used to boost nanomedicine translation to the clinic, as well as highlight the future directions for the development, data-driven, high throughput experimentation-, and AI-assisted design, as well as the production of nanoformulated drugs and smart materials with pre-defined properties and behavior. This Review will be of high interest to the chemists involved in materials science, nanotechnology, and DDSs development for biomedical applications, although the general nature of the presented approaches enables knowledge translation to many other fields of science.
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Affiliation(s)
- Nikita Serov
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, Saint-Petersburg 191002, Russian Federation
| | - Vladimir Vinogradov
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, Saint-Petersburg 191002, Russian Federation.
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10
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PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors. Biomedicines 2022; 10:biomedicines10020491. [PMID: 35203699 PMCID: PMC8962338 DOI: 10.3390/biomedicines10020491] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 02/10/2022] [Accepted: 02/15/2022] [Indexed: 02/07/2023] Open
Abstract
Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwide and exhibits a remarkably poor prognosis. To date, discovering anti-PANC agents remains a very complex and expensive process. Computational approaches can accelerate the search for anti-PANC agents. We report for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules that can simultaneously inhibit multiple PANC cell lines and PANC-related proteins, such as caspase-1, tumor necrosis factor-alpha (TNF-alpha), and the insulin-like growth factor 1 receptor (IGF1R). Both PTML-MLP models exhibited accuracies higher than 78%. Using the interpretation from one of the PTML-MLP models as a guideline, we extracted different molecular fragments desirable for the inhibition of the PANC cell lines and the aforementioned PANC-related proteins and then assembled some of those fragments to form three new molecules. The two PTML-MLP models predicted the designed molecules as potentially versatile anti-PANC agents through inhibition of the three PANC-related proteins and multiple PANC cell lines. Conclusions: This work opens new horizons for the application of the PTML modeling methodology to anticancer research.
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11
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Smart materials: rational design in biosystems via artificial intelligence. Trends Biotechnol 2022; 40:987-1003. [DOI: 10.1016/j.tibtech.2022.01.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/09/2022] [Accepted: 01/10/2022] [Indexed: 12/12/2022]
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12
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Quevedo-Tumailli V, Ortega-Tenezaca B, González-Díaz H. IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds. Int J Mol Sci 2021; 22:ijms222313066. [PMID: 34884870 PMCID: PMC8657696 DOI: 10.3390/ijms222313066] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022] Open
Abstract
The parasite species of genus Plasmodium causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds with novel targets in the proteome of Plasmodium sp. is a very important goal for the pharmaceutical industry. We can expect that the success of the pre-clinical assay depends on the conditions of assay per se, the chemical structure of the drug, the structure of the target protein to be targeted, as well as on factors governing the expression of this protein in the proteome such as genes (Deoxyribonucleic acid, DNA) sequence and/or chromosomes structure. However, there are no reports of computational models that consider all these factors simultaneously. Some of the difficulties for this kind of analysis are the dispersion of data in different datasets, the high heterogeneity of data, etc. In this work, we analyzed three databases ChEMBL (Chemical database of the European Molecular Biology Laboratory), UniProt (Universal Protein Resource), and NCBI-GDV (National Center for Biotechnology Information—Genome Data Viewer) to achieve this goal. The ChEMBL dataset contains outcomes for 17,758 unique assays of potential Antimalarial compounds including numeric descriptors (variables) for the structure of compounds as well as a huge amount of information about the conditions of assays. The NCBI-GDV and UniProt datasets include the sequence of genes, proteins, and their functions. In addition, we also created two partitions (cassayj = caj and cdataj = cdj) of categorical variables from theChEMBL dataset. These partitions contain variables that encode information about experimental conditions of preclinical assays (caj) or about the nature and quality of data (cdj). These categorical variables include information about 22 parameters of biological activity (ca0), 28 target proteins (ca1), and 9 organisms of assay (ca2), etc. We also created another partition of (cprotj = cpj) including categorical variables with biological information about the target proteins, genes, and chromosomes. These variables cover32 genes (cp0), 10 chromosomes (cp1), gene orientation (cp2), and 31 protein functions (cp3). We used a Perturbation-Theory Machine Learning Information Fusion (IFPTML) algorithm to map all this information (from three databases) into and train a predictive model. Shannon’s entropy measure Shk (numerical variables) was used to quantify the information about the structure of drugs, protein sequences, gene sequences, and chromosomes in the same information scale. Perturbation Theory Operators (PTOs) with the form of Moving Average (MA) operators have been used to quantify perturbations (deviations) in the structural variables with respect to their expected values for different subsets (partitions) of categorical variables. We obtained three IFPTML models using General Discriminant Analysis (GDA), Classification Tree with Univariate Splits (CTUS), and Classification Tree with Linear Combinations (CTLC). The IFPTML-CTLC presented the better performance with Sensitivity Sn(%) = 83.6/85.1, and Specificity Sp(%) = 89.8/89.7 for training/validation sets, respectively. This model could become a useful tool for the optimization of preclinical assays of new Antimalarial compounds vs. different proteins in the proteome of Plasmodium.
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Affiliation(s)
- Viviana Quevedo-Tumailli
- Grupo RNASA-IMEDIR, Department of Computer Science, University of A Coruña, 15071 A Coruña, Spain; (V.Q.-T.); (B.O.-T.)
- Research Department, Puyo Campus, Universidad Estatal Amazónica, Puyo 160150, Ecuador
| | - Bernabe Ortega-Tenezaca
- Grupo RNASA-IMEDIR, Department of Computer Science, University of A Coruña, 15071 A Coruña, Spain; (V.Q.-T.); (B.O.-T.)
- Information and Communications Technology Management Department, Puyo Campus, Universidad Estatal Amazónica, Puyo 160150, Ecuador
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, 48940 Leioa, Spain
- BIOFISIKA, Basque Centre for Biophysics, CSIC-UPV/EHU, 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
- Correspondence: ;Tel.: +34-94-601-3547
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13
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Diéguez-Santana K, González-Díaz H. Towards machine learning discovery of dual antibacterial drug-nanoparticle systems. NANOSCALE 2021; 13:17854-17870. [PMID: 34671801 DOI: 10.1039/d1nr04178a] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165 000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%-99.0% and AUROC ≈ 0.94-0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL-1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5-100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain
- Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940 Leioa, Spain.
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning. Int J Mol Sci 2021; 22:ijms222111519. [PMID: 34768951 PMCID: PMC8584266 DOI: 10.3390/ijms222111519] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/08/2021] [Accepted: 10/22/2021] [Indexed: 12/22/2022] Open
Abstract
The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors.
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15
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Gomes SIL, Amorim MJB, Pokhrel S, Mädler L, Fasano M, Chiavazzo E, Asinari P, Jänes J, Tämm K, Burk J, Scott-Fordsmand JJ. Machine learning and materials modelling interpretation of in vivo toxicological response to TiO 2 nanoparticles library (UV and non-UV exposure). NANOSCALE 2021; 13:14666-14678. [PMID: 34533558 DOI: 10.1039/d1nr03231c] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Assessing the risks of nanomaterials/nanoparticles (NMs/NPs) under various environmental conditions requires a more systematic approach, including the comparison of effects across many NMs with identified different but related characters/descriptors. Hence, there is an urgent need to provide coherent (eco)toxicological datasets containing comprehensive toxicity information relating to a diverse spectra of NPs characters. These datasets are test benches for developing holistic methodologies with broader applicability. In the present study we assessed the effects of a custom design Fe-doped TiO2 NPs library, using the soil invertebrate Enchytraeus crypticus (Oligochaeta), via a 5-day pulse via aqueous exposure followed by a 21-days recovery period in soil (survival, reproduction assessment). Obviously, when testing TiO2, realistic conditions should include UV exposure. The 11 Fe-TiO2 library contains NPs of size range between 5-27 nm with varying %Fe (enabling the photoactivation of TiO2 at energy wavelengths in the visible-light range). The NPs were each described by 122 descriptors, being a mixture of measured and atomistic model descriptors. The data were explored using single and univariate statistical methods, combined with machine learning and multiscale modelling techniques. An iterative pruning process was adopted for identifying automatically the most significant descriptors. TiO2 NPs toxicity decreased when combined with UV. Notably, the short-term water exposure induced lasting biological responses even after longer-term recovery in clean exposure. The correspondence with Fe-content correlated with the band-gap hence the reduction of UV oxidative stress. The inclusion of both measured and modelled materials data benefitted the explanation of the results, when combined with machine learning.
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Affiliation(s)
- Susana I L Gomes
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
| | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
| | - Suman Pokhrel
- Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany
- Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany
| | - Lutz Mädler
- Department of Production Engineering, University of Bremen, Badgasteiner Str. 1, 28359 Bremen, Germany
- Leibniz Institute for Materials Engineering IWT, Badgasteiner Str. 3, 28359 Bremen, Germany
| | - Matteo Fasano
- Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Eliodoro Chiavazzo
- Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Pietro Asinari
- Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy
- INRIM, Istituto Nazionale di Ricerca Metrologica, Strada delle Cacce 91, Torino 10135, Italy
| | - Jaak Jänes
- Department of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
| | - Kaido Tämm
- Department of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
| | - Jaanus Burk
- Department of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
| | - Janeck J Scott-Fordsmand
- Department of Bioscience, Aarhus University, Vejlsovej 25, PO BOX 314, DK-8600 Silkeborg, Denmark
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16
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Computational Drug Repurposing for Antituberculosis Therapy: Discovery of Multi-Strain Inhibitors. Antibiotics (Basel) 2021; 10:antibiotics10081005. [PMID: 34439055 PMCID: PMC8388932 DOI: 10.3390/antibiotics10081005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 12/13/2022] Open
Abstract
Tuberculosis remains the most afflicting infectious disease known by humankind, with one quarter of the population estimated to have it in the latent state. Discovering antituberculosis drugs is a challenging, complex, expensive, and time-consuming task. To overcome the substantial costs and accelerate drug discovery and development, drug repurposing has emerged as an attractive alternative to find new applications for “old” drugs and where computational approaches play an essential role by filtering the chemical space. This work reports the first multi-condition model based on quantitative structure–activity relationships and an ensemble of neural networks (mtc-QSAR-EL) for the virtual screening of potential antituberculosis agents able to act as multi-strain inhibitors. The mtc-QSAR-EL model exhibited an accuracy higher than 85%. A physicochemical and fragment-based structural interpretation of this model was provided, and a large dataset of agency-regulated chemicals was virtually screened, with the mtc-QSAR-EL model identifying already proven antituberculosis drugs while proposing chemicals with great potential to be experimentally repurposed as antituberculosis (multi-strain inhibitors) agents. Some of the most promising molecules identified by the mtc-QSAR-EL model as antituberculosis agents were also confirmed by another computational approach, supporting the capabilities of the mtc-QSAR-EL model as an efficient tool for computational drug repurposing.
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17
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Zhang H, Barnard AS. Impact of atomistic or crystallographic descriptors for classification of gold nanoparticles. NANOSCALE 2021; 13:11887-11898. [PMID: 34190263 DOI: 10.1039/d1nr02258j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine learning models are known to be sensitive to the features used to train them, but there is currently no way to predict the impact of using different features prior to feature extraction. This is particularly important to fields such as nanotechnology that are highly multi-disciplinary, and samples can be characterised many different ways depending on the preferences of individual researchers. Does it matter if nanomaterials are described using the interatomic coordinations or more complex order parameters? In this study we compare results of supervised and unsupervised learning on a single set of gold nanoparticles that has been characterised by two different descriptors, each with a unique feature space. We find that there are some consistencies, and model selection is descriptor-agnostic, but the level of detail and the type of information that can be extracted from the results is sensitive to the way the particles are described. Unsupervised clustering revealed that an atomistic descriptor provides a finer-grained interpretation and clusters that are sub-clusters of a more sophisticated crystallographic descriptor, which is consistent with both how the features were calculated, and how they are interpreted in the domain. A supervised classifier revealed that the types of features responsible for the separation are related to the bulk structure, regardless of the descriptor, but capture different types of information. For both the atomistic and crystallographic descriptor the gradient boosting decision tree classifier gave superior results of F1-scores of 0.96 and 0.98, respectively, with excellent precision and recall, even though the clustering presented a challenging multi-classification problem.
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Affiliation(s)
- Haonan Zhang
- School of Computing, Australian National University, Acton 2601, Australia.
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18
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Ortega-Tenezaca B, González-Díaz H. IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks. NANOSCALE 2021; 13:1318-1330. [PMID: 33410431 DOI: 10.1039/d0nr07588d] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Nanoparticles are useful antimicrobial drug-release systems, but some nanoparticles also exhibit antibacterial activity. However, investigation of their antibacterial activity is a difficult and slow process due to the numerous combinations of nanoparticle size, shape, and composition vs. biological tests, assay organisms, and multiple activity parameters to be measured. Additionally, the overuse of antibiotics has led to the emergence of resistant bacterial strains with different metabolic networks. Computational models may speed up this process, but the models reported to date do not to consider all the previous factors, and the data sources are dispersed and not curated. Thus, herein, we used an information fusion, perturbation-theory machine learning (IFPTML) approach, which is introduced by us for the first time, to fit a model for the discovery of antibacterial nanoparticles. The dataset studied had 15 classes of nanoparticles (1-100 nm) with most cases in the range of 1-50 nm vs. >20 pathogenic bacteria species with different metabolic networks. The nanoparticles studied included metal nanoparticles of Au, Ag, and Cu; oxide nanoparticles of Zn, Cu, La, Al, Fe, Sn, Ti, Cd, and Si; and metal salt nanoparticles of CuI and CdS. We used the SOFT.PTML software (our own application) with a user-friendly interface for the IFPTML calculations and a control statistics package. Using SOFT.PTML, we found a linear logistic regression equation that could model 4 biological activity parameters using only 8 variables with χ2 = 2265.75, p-level <0.05, sensitivity, Sn = 79.4, and specificity, Sp = 99.3, for 3213 cases (nanoparticle-bacteria pairs) in the training series. The model had Sn = 80.8 and Sp = 99.3 for 2114 cases in the external validation series. We also developed a random forest non-linear model with higher values of Sn and Sp = 98-99% in the training/validation series, although it was more complicated to use. SOFT.PTML has been demonstrated to be a useful tool for the analysis of complex data in nanotechnology. We also introduced a new anabolism-catabolism unbalance index of metabolic networks to reveal the biological connotation of the IFPTML predictions for antibacterial nanoparticles. These new models open a new door for the discovery of NPs vs. new bacterial species and strains with different topological structures of their metabolic networks.
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Affiliation(s)
- Bernabé Ortega-Tenezaca
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain and Amazon State University UEA, Puyo, Pastaza, Ecuador and Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain. and Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), 15006 A Coruña, Spain and Center for Investigation on Technologies of Information and Communication (CITIC), University of Coruña (UDC), Campus de Elviña s/n, 15071 A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain. and Basque Center for Biophysics CSIC-UPVEH, University of Basque Country UPV/EHU, 48940 Leioa, Spain and IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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19
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Chan C, Du S, Dong Y, Cheng X. Computational and Experimental Approaches to Investigate Lipid Nanoparticles as Drug and Gene Delivery Systems. Curr Top Med Chem 2021; 21:92-114. [PMID: 33243123 PMCID: PMC8191596 DOI: 10.2174/1568026620666201126162945] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/16/2020] [Accepted: 10/22/2020] [Indexed: 02/06/2023]
Abstract
Lipid nanoparticles (LNPs) have been widely applied in drug and gene delivery. More than twenty years ago, DoxilTM was the first LNPs-based drug approved by the US Food and Drug Administration (FDA). Since then, with decades of research and development, more and more LNP-based therapeutics have been used to treat diverse diseases, which often offer the benefits of reduced toxicity and/or enhanced efficacy compared to the active ingredients alone. Here, we provide a review of recent advances in the development of efficient and robust LNPs for drug/gene delivery. We emphasize the importance of rationally combining experimental and computational approaches, especially those providing multiscale structural and functional information of LNPs, to the design of novel and powerful LNP-based delivery systems.
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Affiliation(s)
- Chun Chan
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
| | - Shi Du
- Division of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
| | - Yizhou Dong
- Division of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
- Department of Biomedical Engineering; The Center for Clinical and Translational Science; The Comprehensive Cancer Center; Dorothy M. Davis Heart & Lung Research Institute; Department of Radiation Oncology, The Ohio State University, Columbus, OH 43210, USA
| | - Xiaolin Cheng
- Division of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
- Biophysics Graduate Program, Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
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20
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Santana R, Zuluaga R, Gañán P, Arrasate S, Onieva E, Montemore MM, González-Díaz H. PTML Model for Selection of Nanoparticles, Anticancer Drugs, and Vitamins in the Design of Drug-Vitamin Nanoparticle Release Systems for Cancer Cotherapy. Mol Pharm 2020; 17:2612-2627. [PMID: 32459098 DOI: 10.1021/acs.molpharmaceut.0c00308] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Nanosystems are gaining momentum in pharmaceutical sciences because of the wide variety of possibilities for designing these systems to have specific functions. Specifically, studies of new cancer cotherapy drug-vitamin release nanosystems (DVRNs) including anticancer compounds and vitamins or vitamin derivatives have revealed encouraging results. However, the number of possible combinations of design and synthesis conditions is remarkably high. In addition, a large number of anticancer and vitamin derivatives have been already assayed, but a notably less number of cases of DVRNs were assayed as a whole (with the anticancer compound and the vitamin linked to them). Our approach combines with the perturbation theory and machine learning (PTML) model to predict the probability of obtaining an interesting DVRN by changing the anticancer compound and/or the vitamin present in a DVRN that is already tested for other anticancer compounds or vitamins that have not been tested yet as part of a DVRN. In a previous work, we built a linear PTML model useful for the design of these nanosystems. In doing so, we used information fusion (IF) techniques to carry out data enrichment of DVRN data compiled from the literature with the data for preclinical assays of vitamins from the ChEMBL database. The design features of DVRNs and the assay conditions of nanoparticles (NPs) and vitamins were included as multiplicative PT operators (PTOs) to the system, which indicates the importance of these variables. However, the previous work omitted experiments with nonlinear ML techniques and different types of PTOs such as metric-based PTOs. More importantly, the previous work does not consider the structure of the anticancer drug to be included in the new DVRNs. In this work, we are going to accomplish three main objectives (tasks). In the first task, we found a new model, alternative to the one published before, for the rational design of DVRNs using metric-based PTOs. The most accurate PTML model was the artificial neural network model, which showed values of specificity, sensitivity, and accuracy in the range of 90-95% in training and external validation series for more than 130,000 cases (DVRNs vs ChEMBL assays). Furthermore, in the second task, we used IF techniques to carry out data enrichment of our previous data set. In doing so, we constructed a new working data set of >970,000 cases with the data of preclinical assays of DVRNs, vitamins, and anticancer compounds from the ChEMBL database. All these assays have multiple continuous variables or descriptors dk and categorical variables cj (conditions of the assays) for drugs (dack, cacj), vitamins (dvk, cvj), and NPs (dnk, cnj). These data include >20,000 potential anticancer compounds with >270 protein targets (cac1), >580 assay cell organisms (cac2), and so forth. Furthermore, we include >36,000 assay vitamin derivatives in >6200 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit), and so forth. The enriched data set also contains >20 types of DVRNs (c5n) with 9 NP core materials (c4n), 8 synthesis methods (c7n), and so forth. We expressed all this information with PTOs and developed a qualitatively new PTML model that incorporates information of the anticancer drugs. This new model presents 96-97% of accuracy for training and external validation subsets. In the last task, we carried out a comparative study of ML and/or PTML models published and described how the models we are presenting cover the gap of knowledge in terms of drug delivery. In conclusion, we present here for the first time a multipurpose PTML model that is able to select NPs, anticancer compounds, and vitamins and their conditions of assay for DVRN design.
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Affiliation(s)
- Ricardo Santana
- Department of Chemical and Biomolecular Engineering, Tulane University, 6823 St Charles Avenue, New Orleans, Louisiana 70118, United States.,University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain.,Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1 No. 70-01, 050031 Medellín, Colombia
| | - Robin Zuluaga
- Facultad de Ingeniería Agroindustrial, Universidad Pontificia Bolivariana, Circular 1 No. 70-01, 050031 Medellín, Colombia
| | - Piedad Gañán
- Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1 No. 70-01, 050031 Medellín, Colombia
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Basque Country, Spain
| | - Enrique Onieva
- University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain
| | - Matthew M Montemore
- Department of Chemical and Biomolecular Engineering, Tulane University, 6823 St Charles Avenue, New Orleans, Louisiana 70118, United States
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940 Leioa, Basque Country, Spain.,Basque Center for Biophysics, Spanish National Research Council (CSIC)-University of Basque Country UPV/EHU, 48940 Leioa, Basque Country, Spain.,Ikerbasque, Basque Foundation for Science, 48013 Bilbao, Basque Country, Spain
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21
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Santana R, Zuluaga R, Gañán P, Arrasate S, Onieva E, González-Díaz H. Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models. NANOSCALE 2020; 12:13471-13483. [PMID: 32613998 DOI: 10.1039/d0nr01849j] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
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.
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Affiliation(s)
- Ricardo Santana
- University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain. and Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1° N° 70-01, Medellín, Colombia
| | - Robin Zuluaga
- Facultad de Ingeniería Agroindustrial, Universidad Pontificia Bolivariana, Circular 1° N° 70-01, Medellín, Colombia
| | - Piedad Gañán
- Grupo de Investigación Sobre Nuevos Materiales, Facultad de Ingeniería Química, Universidad Pontificia Bolivariana, Circular 1° N° 70-01, Medellín, Colombia
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain.
| | - Enrique Onieva
- University of Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain.
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain. and IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain and Biofisika Institue CSIC-UPVEHU, University of Basque Country UPV/EHU, 48940, Leioa, Spain
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22
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Halder AK, Melo A, Cordeiro MNDS. A unified in silico model based on perturbation theory for assessing the genotoxicity of metal oxide nanoparticles. CHEMOSPHERE 2020; 244:125489. [PMID: 31812055 DOI: 10.1016/j.chemosphere.2019.125489] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/19/2019] [Accepted: 11/26/2019] [Indexed: 06/10/2023]
Abstract
Nanomaterials (NMs) are an ever-increasing field of interest, due to their wide range of applications in science and technology. However, despite providing solutions to many societal problems and challenges, NMs are associated with adverse effects with potential severe damages towards biological species and their ecosystems. Particularly, it has been confirmed that NMs may induce serious genotoxic effects on various biological targets. Given the difficulties of experimental assays for estimating the genotoxicity of many NMs on diverse biological targets, development of alternative methodologies is crucial to establish their level of safety. In silico modelling approaches, such as Quantitative Structure-Toxicity Relationships (QSTR), are now considered a promising solution for such purpose. In this work, a perturbation theory machine learning (PTML) based QSTR approach is proposed for predicting the genotoxicity of metal oxide NMs under various experimental assay conditions. The application of such perturbation approach to 6084 NM-NM pair cases, set up from 78 unique NMs, afforded a final PTML-QSTR model with an accuracy better than 96% for both training and test sets. This model was then used to predict the genotoxicity of some NMs not included in the modelling dataset. The results for this independent data set were in excellent agreement with the experimental ones. Overall, that thus suggests that the derived PTML-QSTR model is a reliable in silico tool to rapidly and cost-efficiently assess the genotoxicity of metal oxide NMs. Finally, and most importantly, the model provides important insights regarding the mechanism of the genotoxicity triggered by these NMs.
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Affiliation(s)
- Amit Kumar Halder
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007, Porto, Portugal.
| | - André Melo
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007, Porto, Portugal
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007, Porto, Portugal.
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23
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Carracedo-Reboredo P, Corona R, Martinez-Nunes M, Fernandez-Lozano C, Tsiliki G, Sarimveis H, Aranzamendi E, Arrasate S, Sotomayor N, Lete E, Munteanu CR, González-Díaz H. MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry. Curr Top Med Chem 2019; 20:305-317. [PMID: 31878856 DOI: 10.2174/1568026620666191226092431] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 09/17/2019] [Accepted: 09/17/2019] [Indexed: 11/22/2022]
Abstract
AIMS Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). BACKGROUND Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). OBJECTIVE Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures. METHODS We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied. RESULTS We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series. CONCLUSION The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071, A Coruña, Spain.,Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain.,Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Ramiro Corona
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Mikel Martinez-Nunes
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071, A Coruña, Spain.,Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain
| | - Georgia Tsiliki
- Institute for the Management of Information Systems, ATHENA Research and Innovation Centre, 15125, Athens, Greece
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Zografou, Campus, 15780, Athens, Greece.,Pharma-Informatics Unit, ATHENA Research and Innovation Centre, 15125, Athens, Greece
| | - Eider Aranzamendi
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Nuria Sotomayor
- Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain
| | - Esther Lete
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Cristian Robert Munteanu
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071, A Coruña, Spain.,Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain
| | - Humbert González-Díaz
- Basque Center for Biophysics, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain.,IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
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