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Rial R, González-Durruthy M, Liu Z, Ruso JM. Advanced Materials Based on Nanosized Hydroxyapatite. Molecules 2021; 26:3190. [PMID: 34073479 PMCID: PMC8198166 DOI: 10.3390/molecules26113190] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/20/2021] [Accepted: 05/25/2021] [Indexed: 02/02/2023] Open
Abstract
The development of new materials based on hydroxyapatite has undergone a great evolution in recent decades due to technological advances and development of computational techniques. The focus of this review is the various attempts to improve new hydroxyapatite-based materials. First, we comment on the most used processing routes, highlighting their advantages and disadvantages. We will now focus on other routes, less common due to their specificity and/or recent development. We also include a block dedicated to the impact of computational techniques in the development of these new systems, including: QSAR, DFT, Finite Elements of Machine Learning. In the following part we focus on the most innovative applications of these materials, ranging from medicine to new disciplines such as catalysis, environment, filtration, or energy. The review concludes with an outlook for possible new research directions.
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Affiliation(s)
- Ramón Rial
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (R.R.); (M.G.-D.)
| | - Michael González-Durruthy
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (R.R.); (M.G.-D.)
| | - Zhen Liu
- Department of Physics and Engineering, Frostburg State University, Frostburg, MD 21532, USA;
| | - Juan M. Ruso
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; (R.R.); (M.G.-D.)
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2
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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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3
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Ding P, Chen Y, Cao G, Shen H, Ju J, Li W. Solutol ®HS15+pluronicF127 and Solutol ®HS15+pluronicL61 mixed micelle systems for oral delivery of genistein. DRUG DESIGN DEVELOPMENT AND THERAPY 2019; 13:1947-1956. [PMID: 31239645 PMCID: PMC6559771 DOI: 10.2147/dddt.s201453] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 05/10/2019] [Indexed: 12/12/2022]
Abstract
Purpose: We aimed to prepare two oral drug delivery systems consisting of polyoxyl 15 hydroxystearate (HS15) with pluronicF127 (F127) and HS15 with pluronicL61 (L61) to overcome the challenges of genistein’s poor oral bioavailability. This provides a good strategy for enhancing the potential value of genistein. Methods: We designed two binary mixed micelle systems employing the organic solvent evaporation method using surfactants (HS15, L61, and F127). Formulations (GEN-F and GEN-L) were characterized by transmission electron microscopy. Drug content analysis, including entrapment efficiency (EE%), drug loading (DL%), and the cumulative amount of genistein released from the micelles, was performed using HPLC. The permeability of optimum formulation was measured in Caco-2 cell monolayers, and the oral bioavailability was evaluated in SD rats. Results: The solutions of GEN-F and GEN-L were observed to be transparent and colorless. GEN-F had a lower EE% of 80.79±0.55% and a DL% of 1.69±0.24% compared to GEN-L, which had an EE% 83.40±1.36% and a DL% 2.26±0.18%. TEM results showed that the morphology of GEN-F and GEN-L was homogeneous and resembled a spherical shape. The dilution and storage conditions had no significant effect on particle size and EE%. Genistein demonstrated a sustained release behavior when encapsulated in micelles. Pharmacokinetics study showed that the relative oral bioavailability of GEN-F and GEN-L increased by 2.23 and 3.46 fold while also enhancing the permeability of genistein across a Caco-2 cell monolayer compared to that of raw genistein. Conclusion: GEN-F and GEN-L as a drug delivery system provide an effective strategy for enhancing and further realizing the potential value of GEN.
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Affiliation(s)
- Pinggang Ding
- Department of Pharmaceutical Analysis and Metabolomics, Hospital of Integrated Traditional Chinese and Western Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing, People's Republic of China.,Department of Pharmaceutical Analysis and Metabolomics, Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing, People's Republic of China
| | - Yuxuan Chen
- School of Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, People's Republic of China
| | - Guangshang Cao
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, People's Republic of China
| | - Hongxue Shen
- Department of Pharmaceutical Analysis and Metabolomics, Hospital of Integrated Traditional Chinese and Western Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing, People's Republic of China.,Department of Pharmaceutical Analysis and Metabolomics, Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing, People's Republic of China
| | - Jianming Ju
- Department of Pharmaceutical Analysis and Metabolomics, Hospital of Integrated Traditional Chinese and Western Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing, People's Republic of China.,Department of Pharmaceutical Analysis and Metabolomics, Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing, People's Republic of China
| | - Weiguang Li
- Department of Marine Pharmacy, China Pharmaceutical University, Nanjing, People's Republic of China
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Martínez-Arzate SG, Tenorio-Borroto E, Barbabosa Pliego A, Díaz-Albiter HM, Vázquez-Chagoyán JC, González-Díaz H. PTML Model for Proteome Mining of B-Cell Epitopes and Theoretical–Experimental Study of Bm86 Protein Sequences from Colima, Mexico. J Proteome Res 2017; 16:4093-4103. [DOI: 10.1021/acs.jproteome.7b00477] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Saúl G. Martínez-Arzate
- Molecular
Biology Laboratory, CIESA, FMVZ, Autonomous University of The State of Mexico (UAEM), Toluca, 50200 Mexico State, Mexico
| | - Esvieta Tenorio-Borroto
- Molecular
Biology Laboratory, CIESA, FMVZ, Autonomous University of The State of Mexico (UAEM), Toluca, 50200 Mexico State, Mexico
| | - Alberto Barbabosa Pliego
- Molecular
Biology Laboratory, CIESA, FMVZ, Autonomous University of The State of Mexico (UAEM), Toluca, 50200 Mexico State, Mexico
| | - Héctor M. Díaz-Albiter
- Laboratory
of Biochemistry and Physiology of Insects, Oswaldo Cruz Institute, FIOCRUZ, 4365 Rio de Janeiro, Brazil
- Wellcome
Trust Centre for Molecular Parasitology, University of Glasgow, University Place, Glasgow G12 8TA, United Kingdom
| | - Juan C. Vázquez-Chagoyán
- Molecular
Biology Laboratory, CIESA, FMVZ, Autonomous University of The State of Mexico (UAEM), Toluca, 50200 Mexico State, Mexico
| | - Humbert González-Díaz
- Department
of Organic Chemistry II, University of the Basque Country (UPV/EHU), Bilbao, 48940 Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, 48011 Biscay, Spain
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5
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Liu Y, Munteanu CR, Fernandez-Lozano C, Pazos A, Ran T, Tan Z, Yu Y, Zhou C, Tang S, González-Díaz H. Experimental Study and ANN Dual-Time Scale Perturbation Model of Electrokinetic Properties of Microbiota. Front Microbiol 2017; 8:1216. [PMID: 28713345 PMCID: PMC5491601 DOI: 10.3389/fmicb.2017.01216] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 06/14/2017] [Indexed: 12/18/2022] Open
Abstract
The electrokinetic properties of the rumen microbiota are involved in cell surface adhesion and microbial metabolism. An in vitro study was carried out in batch culture to determine the effects of three levels of special surface area (SSA) of biomaterials and four levels of surface tension (ST) of culture medium on electrokinetic properties (Zeta potential, ξ; electrokinetic mobility, μe), fermentation parameters (volatile fatty acids, VFAs), and ST over fermentation processes (ST-a, γ). The obtained results were combined with previously published data (digestibility, D; pH; concentration of ammonia nitrogen, c(NH3-N)) to establish a predictive artificial neural network (ANN) model. Concepts of dual-time series analysis, perturbation theory (PT), and Box-Jenkins Operators were applied for the first time to develop an ANN model to predict the variations of the electrokinetic properties of microbiota. The best dual-time series Radial Basis Functions (RBR) model for ξ of rumen microbiota predicted ξ for >30,000 cases with a correlation coefficient >0.8. This model provided insight into the correlations between electrokinetic property (zeta potential) of rumen microbiota and the perturbations of physical factors (specific surface area and surface tension) of media, digestibility of substrate, and their metabolites (NH3-N, VFAs) in relation to environmental factors.
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Affiliation(s)
- Yong Liu
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, Hunan Research Center of Livestock and Poultry Sciences, South-Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, Chinese Academy of SciencesChangsha, China
- RNASA-IMEDIR, Computer Science Faculty, University of A CorunaA Coruña, Spain
| | | | - Carlos Fernandez-Lozano
- RNASA-IMEDIR, Computer Science Faculty, University of A CorunaA Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña, Complexo Hospitalario Universitario de A CoruñaA Coruña, Spain
| | - Alejandro Pazos
- RNASA-IMEDIR, Computer Science Faculty, University of A CorunaA Coruña, Spain
- Instituto de Investigación Biomédica de A Coruña, Complexo Hospitalario Universitario de A CoruñaA Coruña, Spain
| | - Tao Ran
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, Hunan Research Center of Livestock and Poultry Sciences, South-Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, Chinese Academy of SciencesChangsha, China
| | - Zhiliang Tan
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, Hunan Research Center of Livestock and Poultry Sciences, South-Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, Chinese Academy of SciencesChangsha, China
- Hunan Co-Innovation Center of Animal Production Safety, CICAPSChangsha, China
| | - Yizun Yu
- Institute of Biological Resources, Jiangxi Academy of SciencesJiangxi, China
| | - Chuanshe Zhou
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, Hunan Research Center of Livestock and Poultry Sciences, South-Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, Chinese Academy of SciencesChangsha, China
- Hunan Co-Innovation Center of Animal Production Safety, CICAPSChangsha, China
| | - Shaoxun Tang
- Key Laboratory for Agro-Ecological Processes in Subtropical Region, Hunan Research Center of Livestock and Poultry Sciences, South-Central Experimental Station of Animal Nutrition and Feed Science in the Ministry of Agriculture, Institute of Subtropical Agriculture, Chinese Academy of SciencesChangsha, China
- Hunan Co-Innovation Center of Animal Production Safety, CICAPSChangsha, China
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHULeioa, Spain
- IKERBASQUE, Basque Foundation for ScienceBilbao, Spain
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6
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Gastrointestinal Spatiotemporal mRNA Expression of Ghrelin vs Growth Hormone Receptor and New Growth Yield Machine Learning Model Based on Perturbation Theory. Sci Rep 2016; 6:30174. [PMID: 27460882 PMCID: PMC4962052 DOI: 10.1038/srep30174] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 06/28/2016] [Indexed: 12/16/2022] Open
Abstract
The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract (GIT) of kid goats under housing and grazing systems. The experiments show that the feeding system and age affected the expression of either Ghrelin or GHR with different mechanisms. Furthermore, the experimental data are used to build new Machine Learning models based on the Perturbation Theory, which can predict the effects of perturbations of Ghrelin and GHR mRNA expression on the growth yield. The models consider eight longitudinal GIT segments (rumen, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum), seven time points (0, 7, 14, 28, 42, 56 and 70 d) and two feeding systems (Supplemental and Grazing feeding) as perturbations from the expected values of the growth yield. The best regression model was obtained using Random Forest, with the coefficient of determination R2 of 0.781 for the test subset. The current results indicate that the non-linear regression model can accurately predict the growth yield and the key nodes during gastrointestinal development, which is helpful to optimize the feeding management strategies in ruminant production system.
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Blázquez-Barbadillo C, Aranzamendi E, Coya E, Lete E, Sotomayor N, González-Díaz H. Perturbation theory model of reactivity and enantioselectivity of palladium-catalyzed Heck–Heck cascade reactions. RSC Adv 2016. [DOI: 10.1039/c6ra08751e] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
A new multi-output PT-QSRR model to correlate and predict the enantioselectivity and yield of Heck–Heck cascade reactions has been developed.
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Affiliation(s)
- C. Blázquez-Barbadillo
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- 48080 Bilbao
- Spain
| | - E. Aranzamendi
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- 48080 Bilbao
- Spain
| | - E. Coya
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- 48080 Bilbao
- Spain
| | - E. Lete
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- 48080 Bilbao
- Spain
| | - N. Sotomayor
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- 48080 Bilbao
- Spain
| | - H. González-Díaz
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- 48080 Bilbao
- Spain
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