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Zheng JJ, Li QZ, Wang Z, Wang X, Zhao Y, Gao X. Computer-aided nanodrug discovery: recent progress and future prospects. Chem Soc Rev 2024. [PMID: 39148378 DOI: 10.1039/d3cs00575e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.
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Affiliation(s)
- Jia-Jia Zheng
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
| | - Qiao-Zhi Li
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
| | - Zhenzhen Wang
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
| | - Xiaoli Wang
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Yuliang Zhao
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
| | - Xingfa Gao
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
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2
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Rezvantalab S, Mihandoost S, Rezaiee M. Machine learning assisted exploration of the influential parameters on the PLGA nanoparticles. Sci Rep 2024; 14:1114. [PMID: 38212322 PMCID: PMC10784499 DOI: 10.1038/s41598-023-50876-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 12/27/2023] [Indexed: 01/13/2024] Open
Abstract
Poly (lactic-co-glycolic acid) (PLGA)-based nanoparticles (NPs) are widely investigated as drug delivery systems. However, despite the numerous reviews and research papers discussing various physicochemical and technical properties that affect NP size and drug loading characteristics, predicting the influential features remains difficult. In the present study, we employed four different machine learning (ML) techniques to create ML models using effective parameters related to NP size, encapsulation efficiency (E.E.%), and drug loading (D.L.%). These parameters were extracted from the different literature. Least Absolute Shrinkage and Selection Operator was used to investigate the input parameters and identify the most influential features (descriptors). Initially, ML models were trained and validated using tenfold validation methods, and subsequently, next their performances were evaluated and compared in terms of absolute error, mean absolute, error and R-square. After comparing the performance of different ML models, we decided to use support vector regression for predicting the size and E.E.% and random forest for predicting the D.L.% of PLGA-based NPs. Furthermore, we investigated the interactions between these target variables using ML methods and found that size and E.E.% are interrelated, while D.L.% shows no significant relationship with the other targets. Among these variables, E.E.% was identified as the most influential parameter affecting the NPs' size. Additionally, we found that certain physicochemical properties of PLGA, including molecular weight (Mw) and the lactide-to-glycolide (LA/GA) ratio, are the most determining features for E.E.% and D.L.% of the final NPs, respectively.
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Affiliation(s)
- Sima Rezvantalab
- Chemical Engineering Department, Urmia University of Technology, Urmia, 57166‑419, Iran.
| | - Sara Mihandoost
- Electrical Engineering Department, Urmia University of Technology, Urmia, 57166‑419, Iran.
| | - Masoumeh Rezaiee
- Chemical Engineering Department, Urmia University of Technology, Urmia, 57166‑419, Iran
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3
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Yousfan A, Al Rahwanji MJ, Hanano A, Al-Obaidi H. A Comprehensive Study on Nanoparticle Drug Delivery to the Brain: Application of Machine Learning Techniques. Mol Pharm 2024; 21:333-345. [PMID: 38060692 PMCID: PMC10762658 DOI: 10.1021/acs.molpharmaceut.3c00880] [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: 09/23/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 01/02/2024]
Abstract
The delivery of drugs to specific target tissues and cells in the brain poses a significant challenge in brain therapeutics, primarily due to limited understanding of how nanoparticle (NP) properties influence drug biodistribution and off-target organ accumulation. This study addresses the limitations of previous research by using various predictive models based on collection of large data sets of 403 data points incorporating both numerical and categorical features. Machine learning techniques and comprehensive literature data analysis were used to develop models for predicting NP delivery to the brain. Furthermore, the physicochemical properties of loaded drugs and NPs were analyzed through a systematic analysis of pharmacodynamic parameters such as plasma area under the curve. The analysis employed various linear models, with a particular emphasis on linear mixed-effect models (LMEMs) that demonstrated exceptional accuracy. The model was validated via the preparation and administration of two distinct NP formulations via the intranasal and intravenous routes. Among the various modeling approaches, LMEMs exhibited superior performance in capturing underlying patterns. Factors such as the release rate and molecular weight had a negative impact on brain targeting. The model also suggests a slightly positive impact on brain targeting when the drug is a P-glycoprotein substrate.
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Affiliation(s)
- Amal Yousfan
- The
School of Pharmacy, University of Reading, Reading RG6 6AD, U.K.
- Department
of Pharmaceutics and Pharmaceutical Technology, Pharmacy College, Al Andalus University for Medical Sciences, Tartus, AL Kadmous 00000, Syria
| | - Mhd Jawad Al Rahwanji
- Department
of Computer Science, Saarland University, Saarbrücken, Saarbrücken 66123, Germany
| | - Abdulsamie Hanano
- Department
of Molecular Biology and Biotechnology, Atomic Energy Commission of Syria (AECS), P.O. Box 6091, Damascus 00000, Syria
| | - Hisham Al-Obaidi
- The
School of Pharmacy, University of Reading, Reading RG6 6AD, U.K.
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4
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Li D, Wang X, Han K, Sun Y, Ren T, Sun G, Zhang N, Zhao L, Zhong R. Hypoxia and CD44 receptors dual-targeted nano-micelles with AGT-inhibitory activity for the targeting delivery of carmustine. Int J Biol Macromol 2023; 246:125657. [PMID: 37399878 DOI: 10.1016/j.ijbiomac.2023.125657] [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: 11/26/2022] [Revised: 02/17/2023] [Accepted: 06/30/2023] [Indexed: 07/05/2023]
Abstract
Carmustine (BCNU) is a typical chemotherapy used for treatment of cerebroma and other solid tumors, which exerts antitumor effect by inducing DNA damage at O6 position of guanine. However, the clinical application of BCNU was extremely limited due to the drug resistance mainly mediated by O6-alkylguanine-DNA alkyltransferase (AGT) and absence of tumor-targeting ability. To overcome these limitations, we developed a hypoxia-responsive nanomicelle with AGT inhibitory activity, which was successfully loaded with BCNU. In this nano-system, hyaluronic acid (HA) acts as an active tumor-targeting ligand to bind the overexpressing CD44 receptors on the surface of tumor cells. An azo bond selectively breaks in hypoxic tumor microenvironment to release O6-benzylguanine (BG) as AGT inhibitor and BCNU as DNA alkylating agent. The obtained HA-AZO-BG NPs with shell core structure had an average particle size of 176.98 ± 11.19 nm and exhibited good stability. Meanwhile, HA-AZO-BG NPs possessed a hypoxia-responsive drug release profile. After immobilizing BCNU into HA-AZO-BG NPs, the obtained HA-AZO-BG/BCNU NPs exhibited obvious hypoxia-selectivity and superior cytotoxicity in T98G, A549, MCF-7 and SMMC-7721 cells with IC50 at 189.0, 183.2, 90.1 and 100.1 μm, respectively, under hypoxic condition. Near-infrared imaging in HeLa tumor xenograft models showed that HA-AZO-BG/DiR NPs could effectively accumulate in tumor site at 4 h of post-injection, suggesting its good tumor-targetability. In addition, in vivo anti-tumor efficacy and toxicity evaluation indicated that HA-AZO-BG/BCNU NPs was more effective and less harmful compared to the other groups. After treatment, the tumor weight of HA-AZO-BG/BCNU NPs group was 58.46 % and 63.33 % of the control group and BCNU group, respectively. Overall, HA-AZO-BG/BCNU NPs was expected to be a promising candidate for targeted delivery of BCNU and elimination of chemoresistance.
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Affiliation(s)
- Duo Li
- Beijing Key Laboratory of Environmental & Viral Oncology, Faculty of Environment & Life, Beijing University of Technology, Beijing 100124, China
| | - Xiaoli Wang
- Beijing Key Laboratory of Environmental & Viral Oncology, Faculty of Environment & Life, Beijing University of Technology, Beijing 100124, China
| | - Kaishuo Han
- Beijing Key Laboratory of Environmental & Viral Oncology, Faculty of Environment & Life, Beijing University of Technology, Beijing 100124, China
| | - Yaqian Sun
- Beijing Key Laboratory of Environmental & Viral Oncology, Faculty of Environment & Life, Beijing University of Technology, Beijing 100124, China
| | - Ting Ren
- Beijing Key Laboratory of Environmental & Viral Oncology, Faculty of Environment & Life, Beijing University of Technology, Beijing 100124, China
| | - Guohui Sun
- Beijing Key Laboratory of Environmental & Viral Oncology, Faculty of Environment & Life, Beijing University of Technology, Beijing 100124, China
| | - Na Zhang
- Beijing Key Laboratory of Environmental & Viral Oncology, Faculty of Environment & Life, Beijing University of Technology, Beijing 100124, China
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental & Viral Oncology, Faculty of Environment & Life, Beijing University of Technology, Beijing 100124, China.
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental & Viral Oncology, Faculty of Environment & Life, Beijing University of Technology, Beijing 100124, China
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5
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Wang Y, Zhu L, Wei L, Zhou Y, Yang Y, Zhang L. A bio-orthogonally functionalized chitosan scaffold with esterase-activatable release for nerve regeneration. Int J Biol Macromol 2023; 229:146-157. [PMID: 36528149 DOI: 10.1016/j.ijbiomac.2022.12.113] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/04/2022] [Accepted: 12/11/2022] [Indexed: 12/23/2022]
Abstract
Developing nerve conduits with biological cues is a promising approach for repairing peripheral nerve injuries. Although most biological cues incorporated into conduits generally exert their biological functions at the surface, they could not be released into the on-demand regeneration sites under physiological conditions. Herein, we firstly report a bio-orthogonally functionalized chitosan scaffold with esterase-activatable release for peripheral nerve regeneration. In this study, biological cues are not only selectively conjugated into nerve conduits by bio-orthogonal reaction, but also precisely released in on-demand regeneration sites via esterase-activatable cleavage for peripheral nerve repair. Moreover, this nerve scaffold with esterase-activatable release could promote Schwann cells proliferation. In a rat sciatic nerve defect model, the bio-orthogonally functionalized scaffold with esterase-activatable release significantly increased sciatic nerve function recovery and improved target muscles weight. This strategy of incorporating esterase-activatable bioactive cues into peripheral nerve conduits offers great potential in preclinical studies.
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Affiliation(s)
- Yuqing Wang
- Key Laboratory of Neuroregeneration of Jiangsu and Ministry of Education, Co-innovation Center of Neuroregeneration, Nantong University, 226001 Nantong, Jiangsu, PR China
| | - Linglin Zhu
- Key Laboratory of Neuroregeneration of Jiangsu and Ministry of Education, Co-innovation Center of Neuroregeneration, Nantong University, 226001 Nantong, Jiangsu, PR China
| | - Le Wei
- Key Laboratory of Neuroregeneration of Jiangsu and Ministry of Education, Co-innovation Center of Neuroregeneration, Nantong University, 226001 Nantong, Jiangsu, PR China
| | - Youlang Zhou
- Hand Surgery Research Center, Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, 226001 Nantong, Jiangsu, PR China
| | - Yumin Yang
- Key Laboratory of Neuroregeneration of Jiangsu and Ministry of Education, Co-innovation Center of Neuroregeneration, Nantong University, 226001 Nantong, Jiangsu, PR China.
| | - Luzhong Zhang
- Key Laboratory of Neuroregeneration of Jiangsu and Ministry of Education, Co-innovation Center of Neuroregeneration, Nantong University, 226001 Nantong, Jiangsu, PR China.
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6
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Tan P, Chen X, Zhang H, Wei Q, Luo K. Artificial intelligence aids in development of nanomedicines for cancer management. Semin Cancer Biol 2023; 89:61-75. [PMID: 36682438 DOI: 10.1016/j.semcancer.2023.01.005] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/28/2022] [Accepted: 01/18/2023] [Indexed: 01/21/2023]
Abstract
Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.
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Affiliation(s)
- Ping Tan
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiaoting Chen
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hu Zhang
- Amgen Bioprocessing Centre, Keck Graduate Institute, Claremont, CA 91711, USA
| | - Qiang Wei
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Kui Luo
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
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7
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The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research. FUTURE INTERNET 2022. [DOI: 10.3390/fi14120356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated.
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8
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Malekjani N, Jafari SM. Intelligent and Probabilistic Models for Evaluating the Release of Food Bioactive Ingredients from Carriers/Nanocarriers. FOOD BIOPROCESS TECH 2022. [DOI: 10.1007/s11947-022-02791-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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9
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Valdivia-Olivares RY, Rodriguez-Fernandez M, Álvarez-Figueroa MJ, Kalergis AM, González-Aramundiz JV. The Importance of Nanocarrier Design and Composition for an Efficient Nanoparticle-Mediated Transdermal Vaccination. Vaccines (Basel) 2021; 9:vaccines9121420. [PMID: 34960166 PMCID: PMC8705631 DOI: 10.3390/vaccines9121420] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 12/13/2022] Open
Abstract
The World Health Organization estimates that the pandemic caused by the SARS-CoV-2 virus claimed more than 3 million lives in 2020 alone. This situation has highlighted the importance of vaccination programs and the urgency of working on new technologies that allow an efficient, safe, and effective immunization. From this perspective, nanomedicine has provided novel tools for the design of the new generation of vaccines. Among the challenges of the new vaccine generations is the search for alternative routes of antigen delivery due to costs, risks, need for trained personnel, and low acceptance in the population associated with the parenteral route. Along these lines, transdermal immunization has been raised as a promising alternative for antigen delivery and vaccination based on a large absorption surface and an abundance of immune system cells. These features contribute to a high barrier capacity and high immunological efficiency for transdermal immunization. However, the stratum corneum barrier constitutes a significant challenge for generating new pharmaceutical forms for transdermal antigen delivery. This review addresses the biological bases for transdermal immunomodulation and the technological advances in the field of nanomedicine, from the passage of antigens facilitated by devices to cross the stratum corneum, to the design of nanosystems, with an emphasis on the importance of design and composition towards the new generation of needle-free nanometric transdermal systems.
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Affiliation(s)
- Rayen Yanara Valdivia-Olivares
- Departamento de Farmacia, Escuela de Química y Farmacia, Facultad de Química y de Farmacia, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile; (R.Y.V.-O.); (M.J.Á.-F.)
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile;
| | - Maria Rodriguez-Fernandez
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile;
| | - María Javiera Álvarez-Figueroa
- Departamento de Farmacia, Escuela de Química y Farmacia, Facultad de Química y de Farmacia, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile; (R.Y.V.-O.); (M.J.Á.-F.)
| | - Alexis M. Kalergis
- Millennium Institute on Immunology and Immunotherapy, Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Av. Libertador Bernardo O’Higgins No. 340, Santiago 7810000, Chile
- Departamento de Endocrinología, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago 7810000, Chile
- Correspondence: (A.M.K.); (J.V.G.-A.)
| | - José Vicente González-Aramundiz
- Millennium Institute on Immunology and Immunotherapy, Departamento de Farmacia, Escuela de Química y Farmacia, Facultad de Química y de Farmacia, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
- Centro de Investigación en Nanotecnología y Materiales Avanzados “CIEN-UC”, Pontificia Universidad Católica de Chile, Santiago 7810000, Chile
- Correspondence: (A.M.K.); (J.V.G.-A.)
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10
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Mohammadi Y, Saeb MR, Penlidis A, Jabbari E, Stadler FJ, Zinck P, Vivaldo‐Lima E. Toward Olefin Multiblock Copolymers with Tailored Properties: A Molecular Perspective. MACROMOL THEOR SIMUL 2021. [DOI: 10.1002/mats.202100003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Yousef Mohammadi
- Centre for Advanced Macromolecular Design (CAMD) School of Chemical Engineering The University of New South Wales Sydney NSW 2052 Australia
| | | | - Alexander Penlidis
- Department of Chemical Engineering Institute for Polymer Research (IPR) University of Waterloo Waterloo Ontario N2L 3G1 Canada
| | - Esmaiel Jabbari
- Biomimetic Materials and Tissue Engineering Laboratory Department of Chemical Engineering University of South Carolina Columbia SC 29208 USA
| | - Florian J. Stadler
- College of Materials Science and Engineering Shenzhen Key Laboratory of Polymer Science and Technology Guangdong Research Center for Interfacial Engineering of Functional Materials Nanshan District Key Lab for Biopolymers and Safety Evaluation Shenzhen University Shenzhen 518060 China
| | - Philippe Zinck
- Unity of Catalysis and Solid State Chemistry University of Lille, CNRS, Bât C7, Cité Scientifique Villeneuve d'Ascq Cédex 59652 France
| | - Eduardo Vivaldo‐Lima
- Facultad de Química, Departamento de Ingeniería Química Universidad Nacional Autónoma de México, CU México 04510 México
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11
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Egorov E, Pieters C, Korach-Rechtman H, Shklover J, Schroeder A. Robotics, microfluidics, nanotechnology and AI in the synthesis and evaluation of liposomes and polymeric drug delivery systems. Drug Deliv Transl Res 2021; 11:345-352. [PMID: 33585972 PMCID: PMC7882236 DOI: 10.1007/s13346-021-00929-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/26/2021] [Indexed: 01/20/2023]
Abstract
The field of nanotechnology and personalised medicine is undergoing drastic changes in the approach and efficiency of experimentation. The COVID-19 pandemic has spiralled into mass stagnation of major laboratories around the globe and led to increased investment into remote systems for nanoparticle experiments. A significant number of laboratories now operate using automated systems; however, the extension to nanoparticle preparation and artificial intelligence-dependent databases holds great translational promise. The strive to combine automation with artificial intelligence (AI) grants the ability to optimise targeted therapeutic nanoparticles for unique cell types and patients. In this perspective, the current and future trends of automated approaches to nanomedicine synthesis are discussed and compared with traditional methods.
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Affiliation(s)
- Egor Egorov
- Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies, Department of Chemical Engineering, Technion - Israel Institute of Technology, 32000, Haifa, Israel
| | - Calvin Pieters
- Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies, Department of Chemical Engineering, Technion - Israel Institute of Technology, 32000, Haifa, Israel
| | - Hila Korach-Rechtman
- Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies, Department of Chemical Engineering, Technion - Israel Institute of Technology, 32000, Haifa, Israel
| | - Jeny Shklover
- Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies, Department of Chemical Engineering, Technion - Israel Institute of Technology, 32000, Haifa, Israel
| | - Avi Schroeder
- Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies, Department of Chemical Engineering, Technion - Israel Institute of Technology, 32000, Haifa, Israel.
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12
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Nejati S, Karimi‐Soflou R, Karkhaneh A. Influence of process parameters on the characteristics of oxygen‐releasing poly (lactic acid) microparticles: A multioptimization strategy. POLYM ADVAN TECHNOL 2021. [DOI: 10.1002/pat.5134] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Sara Nejati
- Biomedical Engineering Department Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran
| | - Reza Karimi‐Soflou
- Biomedical Engineering Department Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran
| | - Akbar Karkhaneh
- Biomedical Engineering Department Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran
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13
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Saw WS, Anasamy T, Foo YY, Kwa YC, Kue CS, Yeong CH, Kiew LV, Lee HB, Chung LY. Delivery of Nanoconstructs in Cancer Therapy: Challenges and Therapeutic Opportunities. ADVANCED THERAPEUTICS 2021. [DOI: 10.1002/adtp.202000206] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Wen Shang Saw
- Department of Pharmaceutical Chemistry Faculty of Pharmacy University of Malaya Kuala Lumpur 50603 Malaysia
| | - Theebaa Anasamy
- Department of Pharmaceutical Chemistry Faculty of Pharmacy University of Malaya Kuala Lumpur 50603 Malaysia
| | - Yiing Yee Foo
- Department of Pharmacology Faculty of Medicine University of Malaya Kuala Lumpur 50603 Malaysia
| | - Yee Chu Kwa
- Department of Pharmaceutical Chemistry Faculty of Pharmacy University of Malaya Kuala Lumpur 50603 Malaysia
| | - Chin Siang Kue
- Department of Diagnostic and Allied Health Sciences Faculty of Health and Life Sciences Management and Science University Shah Alam Selangor 40100 Malaysia
| | - Chai Hong Yeong
- School of Medicine Faculty of Health and Medical Sciences Taylor's University Subang Jaya Selangor 47500 Malaysia
| | - Lik Voon Kiew
- Department of Pharmacology Faculty of Medicine University of Malaya Kuala Lumpur 50603 Malaysia
| | - Hong Boon Lee
- Department of Pharmaceutical Chemistry Faculty of Pharmacy University of Malaya Kuala Lumpur 50603 Malaysia
- School of Biosciences Faculty of Health and Medical Sciences Taylor's University Subang Jaya Selangor 47500 Malaysia
| | - Lip Yong Chung
- Department of Pharmaceutical Chemistry Faculty of Pharmacy University of Malaya Kuala Lumpur 50603 Malaysia
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14
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Analysis of Dynamics Targeting CNT-Based Drug Delivery through Lung Cancer Cells: Design, Simulation, and Computational Approach. MEMBRANES 2020; 10:membranes10100283. [PMID: 33066362 PMCID: PMC7602173 DOI: 10.3390/membranes10100283] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/25/2020] [Accepted: 10/01/2020] [Indexed: 11/25/2022]
Abstract
Nowadays, carbon nano (CN) structures and specifically carbon nanotubes (CNTs), because of the nanotube’s nanoscale shape, are widely used in carrier and separation applications. The conjugation of CNTs with polysaccharide, proteins, drugs, and magnetic nanoparticles provides a chance for smart targeting and trajectory manipulation, which are used in the crucial field of life science applications, including for cancer disease diagnostics and treatments. Providing an optimal procedure for delivering a drug to a specific area based on mathematical criteria is key in systemic delivery design. Trajectory guidance and applied force control are the main parameters affected by systemic delivery. Moreover, a better understanding of the tissue parameters and cell membrane molecular behaviour are other factors that can be indirectly affected by the targeted delivery. Both sides are an essential part of successful targeting. The lung is one of the challenging organs for drug delivery inside the human body. It has a large surface area with a thin epithelium layer. A few severe diseases directly involve human lung cells, and optimal and successful drug delivery to the lung for the treatment procedure is vital. In this paper, we studied functionalized CNTs’ targeted delivery via crossing through the lung cell membrane. Molecular dynamics (MD) software simulated all the interaction forces. Mathematical modelling of the cell membrane and proposed delivery system based on the relation of velocity and force has been considered. Dynamics equations for CNTs were defined in the time and frequency domain using control theory methods. The proposed delivery system consists of two main parts: crossing through the cell membrane and targeting inside the cell. For both steps, a mathematical model and a proper magnetic field profile have been proposed. The designed system provides criteria for crossing through the cell membrane within 30 s to 5 min and a translocation profile of 1 to 100 Å.
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Neri G, Mion G, Pizzi A, Celentano W, Chaabane L, Chierotti MR, Gobetto R, Li M, Messa P, De Campo F, Cellesi F, Metrangolo P, Baldelli Bombelli F. Fluorinated PLGA Nanoparticles for Enhanced Drug Encapsulation and 19 F NMR Detection. Chemistry 2020; 26:10057-10063. [PMID: 32515857 DOI: 10.1002/chem.202002078] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/08/2020] [Indexed: 12/13/2022]
Abstract
In the continuous search for multimodal systems with combined diagnostic and therapeutic functions, several efforts have been made to develop multifunctional drug delivery systems. In this work, through a covalent approach, a new class of fluorinated poly(lactic-co-glycolic acid) co-polymers (F-PLGA) were designed that contain an increasing number of magnetically equivalent fluorine atoms. In particular, two novel compounds, F3 -PLGA and F9 -PLGA, were synthesized and their chemical structure and thermal stability were analyzed by solution NMR, DSC, and TGA. The obtained F-PLGA compounds were proven to form in aqueous solution colloidal stable nanoparticles (NPs) displaying a strong 19 F NMR signal. The fluorinated NPs also showed an enhanced ability to load hydrophobic drugs containing fluorine atoms compared to analogous pristine PLGA NPs. Preliminary in vitro studies showed high cell viability and the NP ability to intracellularly deliver and release a functioning drug.
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Affiliation(s)
- Giulia Neri
- Laboratory of Supramolecular and Bio-Nanomaterials (SupraBioNanoLab), Department of Chemistry, Materials, and Chemical Engineering "Giulio Natta", Politecnico di Milano, V. Luigi Mancinelli, 20131, Milan, Italy.,Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, V.le F. Stagno d'Alcontres, 31, 98166, Messina, Italy
| | - Giuliana Mion
- Laboratory of Supramolecular and Bio-Nanomaterials (SupraBioNanoLab), Department of Chemistry, Materials, and Chemical Engineering "Giulio Natta", Politecnico di Milano, V. Luigi Mancinelli, 20131, Milan, Italy
| | - Andrea Pizzi
- Laboratory of Supramolecular and Bio-Nanomaterials (SupraBioNanoLab), Department of Chemistry, Materials, and Chemical Engineering "Giulio Natta", Politecnico di Milano, V. Luigi Mancinelli, 20131, Milan, Italy
| | - Wanda Celentano
- Department of Chemistry, Materials, and Chemical Engineering "Giulio Natta", Politecnico di Milano, V. Luigi Mancinelli, 20131, Milan, Italy
| | - Linda Chaabane
- Institute of Experimental Neurology (INSPE) and Experimental Imaging, Center (CIS), IRCCS San Raffaele Hospital, V. Olgettina, 60, 20132, Milan, Italy
| | - Michele R Chierotti
- Department of Chemistry and NIS Centre, Università di Torino, V. Pietro Giuria, 7, 10125, Turin, Italy
| | - Roberto Gobetto
- Department of Chemistry and NIS Centre, Università di Torino, V. Pietro Giuria, 7, 10125, Turin, Italy
| | - Min Li
- Renal Research Laboratory, Fondazione IRCCS Ca" Granda Ospedale Maggiore Policlinico, V. Francesco Sforza, 35, 20122, Milan, Italy
| | - Piergiorgio Messa
- Renal Research Laboratory, Fondazione IRCCS Ca" Granda Ospedale Maggiore Policlinico, V. Francesco Sforza, 35, 20122, Milan, Italy
| | - Floryan De Campo
- Solvay Specialty Polymers, V. Lombardia, 20, Bollate, 20021, Milan, Italy
| | - Francesco Cellesi
- Department of Chemistry, Materials, and Chemical Engineering "Giulio Natta", Politecnico di Milano, V. Luigi Mancinelli, 20131, Milan, Italy
| | - Pierangelo Metrangolo
- Laboratory of Supramolecular and Bio-Nanomaterials (SupraBioNanoLab), Department of Chemistry, Materials, and Chemical Engineering "Giulio Natta", Politecnico di Milano, V. Luigi Mancinelli, 20131, Milan, Italy
| | - Francesca Baldelli Bombelli
- Laboratory of Supramolecular and Bio-Nanomaterials (SupraBioNanoLab), Department of Chemistry, Materials, and Chemical Engineering "Giulio Natta", Politecnico di Milano, V. Luigi Mancinelli, 20131, Milan, Italy
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Mohammadi Y, Matsuura T, Jansen JC, Esposito E, Fuoco A, Dumée LF, Gallucci F, Drioli E, Soroush M. Optimal Membrane-Process Design (OMPD): A software product for optimal design of membrane gas separation processes. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106724] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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17
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Adir O, Poley M, Chen G, Froim S, Krinsky N, Shklover J, Shainsky-Roitman J, Lammers T, Schroeder A. Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1901989. [PMID: 31286573 PMCID: PMC7124889 DOI: 10.1002/adma.201901989] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 05/17/2019] [Indexed: 05/13/2023]
Abstract
Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine-tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.
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Affiliation(s)
- Omer Adir
- Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
- The Norman Seiden Multidisciplinary Program for Nanoscience and Nanotechnology, Technion - Israel Institute of Technology, Haifa, 32000, Israel
| | - Maria Poley
- Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Gal Chen
- Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Sahar Froim
- Department of Physical Electronics, School of Electrical Engineering, Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Nitzan Krinsky
- Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Jeny Shklover
- Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Janna Shainsky-Roitman
- Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
| | - Twan Lammers
- Institute for Experimental Molecular Imaging, RWTH Aachen University Hospital, Aachen, 52074, Germany
| | - Avi Schroeder
- Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 3200003, Israel
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18
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Babanejad N, Nabid MR, Farhadian A, Dorkoosh F, Zarrintaj P, Saeb MR, Mozafari M. Sustained delivery of olanzapine from sunflower oil-based polyol-urethane nanoparticles synthesised through a cyclic carbonate ring-opening reaction. IET Nanobiotechnol 2020; 13:703-711. [PMID: 31573539 DOI: 10.1049/iet-nbt.2018.5440] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
The forefront horizon of biomedical investigations in recent decades is parcelling-up and delivery of drugs to achieve controlled/targeted release. In this regard, developing green-based delivery systems for a spatiotemporal controlling therapeutic agent have drawn a lot of attention. A facile route based on cyclic carbonate ring-opening reaction has been utilised to synthesise a bio-based polyol-containing urethane bond [polyol-urethane (POU)] as a nanoparticulate drug delivery system of olanzapine in order to enhance its bioavailability. After characterisation, the nanoparticles were also estimated for in vitro release, toxicity, and pharmacokinetic studies. As olanzapine has shown poor bioavailability and permeability in the brain, the sustained release of olanzapine from the designed carriers could enhance pharmacokinetic effectiveness. POU in the aqueous solution formed micelles with a hydrophobic core and embedded olanzapine under the influence of its hydrophobic nature. Drug release from the nanoparticles (90 ± 0.43 nm in diameter) indicated a specific pattern with initial burst release, and then a sustained release behaviour (82 ± 3% after 168 h), by the Higuchi-based release mechanism. Pharmacokinetics assessments of POU-olanzapine nanoparticles were carried in male Wistar rats through intravenous administration. The obtained results paved a way to introduce the POU as an efficient platform to enhance the bioavailability of olanzapine in therapeutic methods.
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Affiliation(s)
- Niloofar Babanejad
- Department of Pharmaceutical Sciences, College of Pharmacy, Nova Southeastern University, Fort Lauderdale, FL 33328, USA
| | - Mohammad Reza Nabid
- Department of Pharmaceutical Sciences, College of Pharmacy, Nova Southeastern University, Fort Lauderdale, FL 33328, USA.
| | - Abdolreza Farhadian
- Department of Polymer, Faculty of Chemistry and Petroleum Sciences, Shahid Beheshti University, Tehran, Iran
| | - Farid Dorkoosh
- Medical Biomaterial Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Payam Zarrintaj
- Color and Polymer Research Center (CPRC), Amirkabir University of Technology, Tehran, Iran
| | - Mohammad Reza Saeb
- Departments of Resin and Additives, Institute for Color Science and Technology, Tehran, Iran
| | - Masoud Mozafari
- Department of Tissue Engineering & Regenerative Medicine, Iran University of Medical Sciences, Tehran, Iran
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Liang J, Huang Q, Hua C, Hu J, Chen B, Wan J, Hu Z, Wang B. pH‐Responsive Nanoparticles Loaded with Graphene Quantum Dots and Doxorubicin for Intracellular Imaging, Drug Delivery and Efficient Cancer Therapy. ChemistrySelect 2019. [DOI: 10.1002/slct.201803807] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Junlong Liang
- Department of Polymer MaterialsZhejiang Sci-Tech University Hangzhou 310018 China
| | - Qianwei Huang
- Department of Polymer MaterialsZhejiang Sci-Tech University Hangzhou 310018 China
| | - Chenxiang Hua
- Department of Polymer MaterialsZhejiang Sci-Tech University Hangzhou 310018 China
| | - Jinhua Hu
- Department of Polymer MaterialsZhejiang Sci-Tech University Hangzhou 310018 China
| | - Biling Chen
- Department of Polymer MaterialsZhejiang Sci-Tech University Hangzhou 310018 China
| | - Junmin Wan
- Key Laboratory of Advanced Textile Materials and Manufacturing TechnologyMinistry of EducationZhejiang Sci-Tech University Hangzhou 310018 China
| | - Zhiwen Hu
- Key Laboratory of Advanced Textile Materials and Manufacturing TechnologyMinistry of EducationZhejiang Sci-Tech University Hangzhou 310018 China
| | - Bing Wang
- Department of Polymer MaterialsZhejiang Sci-Tech University Hangzhou 310018 China
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20
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Mohammadi Y, Saeb MR, Penlidis A, Jabbari E, J Stadler F, Zinck P, Matyjaszewski K. Intelligent Machine Learning: Tailor-Making Macromolecules. Polymers (Basel) 2019; 11:E579. [PMID: 30960563 PMCID: PMC6523097 DOI: 10.3390/polym11040579] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 03/05/2019] [Accepted: 03/10/2019] [Indexed: 11/16/2022] Open
Abstract
Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and successfully put into practice. The two tools are established through the amalgamation of the Kinetic Monte Carlo simulation approach and machine learning techniques. The former, an intelligent modeling tool, is able to model and visualize the intricate inter-relationships of polymerization recipes/conditions (as input variables) and microstructural features of the produced macromolecules (as responses). The latter is capable of precisely predicting optimal copolymerization conditions to simultaneously satisfy all predefined microstructural features. The effectiveness of the proposed intelligent modeling and optimization techniques for solving this extremely important 'inverse' engineering problem was successfully examined by investigating the possibility of tailor-making the microstructure of Olefin Block Copolymers via chain-shuttling coordination polymerization.
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Affiliation(s)
- Yousef Mohammadi
- Petrochemical Research and Technology Company (NPC-rt), National Petrochemical Company (NPC), P.O. Box 14358-84711, Tehran, Iran.
| | - Mohammad Reza Saeb
- Department of Resin and Additives, Institute for Color Science and Technology, P.O. Box 16765-654, Tehran, Iran.
| | - Alexander Penlidis
- Department of Chemical Engineering, Institute for Polymer Research (IPR), University of Waterloo, Waterloo, ON N2L 3G1, Canada.
| | - Esmaiel Jabbari
- Biomimetic Materials and Tissue Engineering Laboratory, Department of Chemical Engineering, University of South Carolina Columbia, Columbia, SC 29208, USA.
| | - Florian J Stadler
- College of Materials Science and Engineering, Shenzhen Key Laboratory of Polymer Science and Technology, Guangdong Research Center for Interfacial Engineering of Functional Materials, Nanshan District Key Lab for Biopolymers and Safety Evaluation, Shenzhen University, Shenzhen 518055, China.
| | - Philippe Zinck
- Unity of Catalysis and Solid State Chemistry, University of Lille, CNRS, Bât C7, Cité Scientifique, 59652 Villeneuve d'Ascq Cédex, France.
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21
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Zarrintaj P, Mostafapoor F, Milan PB, Saeb MR. Theranostic Platforms Proposed for Cancerous Stem Cells: A Review. Curr Stem Cell Res Ther 2019; 14:137-145. [DOI: 10.2174/1574888x13666181002152247] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 08/29/2018] [Accepted: 09/04/2018] [Indexed: 01/06/2023]
Abstract
It is next-to-impossible not to accept that cancer takes a position as the main cause of the global burden of disease, for it is hard to ignore the outnumbered people dying from cancer. Looking at the statistics proves that progress in cancer therapy is always beyond cancer in a race of pessimism about the future; for various kinds of cancers yearly cause death in the world, whereas the conventional and even modern therapies often exhibit lack of reliability in the treatment of cancer. In principle, various reasons are identified for cancer resistance and recurrence. Recognizing the cells/tissue from which cancer takes origin enables its early detection, and optimistically saying, protection of patients against death. It has been recognized that cancer stem cells are responsible for cancer cell proliferation and metastasis. Conventional therapies cannot eradicate the cancer stem cell; therefore, cancer recurrence is unavoidable. In this regards, designing smart platforms with specific properties is an essential step in cancer treatment. Theranostic platforms have facilitated the cancer diagnosis and treatment, simultaneously. In this respect, several types of smart materials have been designed to detect and cure cancer. Cancer stem cell as a root of the cancerous tumor should be eradicated to achieve the complete treatment; hence, cancer stem cell mechanism must be known precisely to design an appropriate platform making possible to encounter with cancer stem cell. In this review paper, various therapeutic and diagnostic techniques of cancerous stem cell are discussed to pave a way for designing proper platforms for cancer eradication.
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Affiliation(s)
- Payam Zarrintaj
- Color and Polymer Research Center (CPRC), Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran
| | - Farnaz Mostafapoor
- Department of Polymer Processing, Iran Polymer and Petrochemical Institute, P.O. Box: 14965-115, Tehran, Iran
| | - Peiman Brouki Milan
- Cellular and Molecular Research Center (CMRC), Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Saeb
- Color and Polymer Research Center (CPRC), Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran
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Mohammadi Y, Penlidis A. “Optimulation” in Chemical Reaction Engineering: Oxidative Coupling of Methane as a Case Study. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b01424] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- Yousef Mohammadi
- Petrochemical Research and Technology Company (NPC-rt), National Petrochemical Company (NPC), P.O. Box 14358-84711, Tehran, Iran
| | - Alexander Penlidis
- Department of Chemical Engineering, Institute for Polymer Research (IPR), University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
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Mohammadi Y, Saeb MR, Penlidis A, Jabbari E, Zinck P, Stadler FJ, Matyjaszewski K. Intelligent Monte Carlo: A New Paradigm for Inverse Polymerization Engineering. MACROMOL THEOR SIMUL 2018. [DOI: 10.1002/mats.201700106] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Yousef Mohammadi
- Petrochemical Research and Technology Company (NPC-rt); National Petrochemical Company (NPC); P.O. Box 14358-84711 Tehran Iran
| | - Mohammad Reza Saeb
- Department of Resin and Additives; Institute for Color Science and Technology; P.O. Box 16765-654 Tehran Iran
| | - Alexander Penlidis
- Department of Chemical Engineering; Institute for Polymer Research (IPR); University of Waterloo; Waterloo Ontario N2L 3G1 Canada
| | - Esmaiel Jabbari
- Biomimetic Materials and Tissue Engineering Laboratory; Department of Chemical Engineering; University of South Carolina; Columbia SC 29208 USA
| | - Philippe Zinck
- Unity of Catalysis and Solid State Chemistry; University of Lille; CNRS; Bât C7, Cité Scientifique 59652 Villeneuve d'Ascq Cédex France
| | - Florian J. Stadler
- College of Materials Science and Engineering; Shenzhen Key Laboratory of Polymer Science and Technology; Guangdong Research Center for Interfacial Engineering of Functional Materials; Nanshan District Key Lab for Biopolymers and Safety Evaluation; Shenzhen University; Shenzhen 518060 China
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Mohammadi Y, Saeb MR, Penlidis A. Heuristic Search Strategy for Transforming Microstructural Patterns to Optimal Copolymerization Recipes. MACROMOL THEOR SIMUL 2018. [DOI: 10.1002/mats.201700088] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yousef Mohammadi
- Petrochemical Research and Technology Company (NPC-rt); National Petrochemical Company (NPC); P.O. Box 14358-84711 Tehran Iran
| | - Mohammad Reza Saeb
- Department of Resin and Additives; Institute for Color Science and Technology; P.O. Box 16765-654 Tehran Iran
| | - Alexander Penlidis
- Department of Chemical Engineering; Institute for Polymer Research (IPR); University of Waterloo; Waterloo Ontario N2L 3G1 Canada
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