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He S, Nader K, Abarrategi JS, Bediaga H, Nocedo-Mena D, Ascencio E, Casanola-Martin GM, Castellanos-Rubio I, Insausti M, Rasulev B, Arrasate S, González-Díaz H. NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study. J Nanobiotechnology 2024; 22:435. [PMID: 39044265 PMCID: PMC11267683 DOI: 10.1186/s12951-024-02660-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 06/24/2024] [Indexed: 07/25/2024] Open
Abstract
Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.
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Affiliation(s)
- Shan He
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº 6, Leioa, 48940, Greater Bilbao, Basque Country, Spain
| | - Karam Nader
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
| | - Julen Segura Abarrategi
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
| | - Harbil Bediaga
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº 6, Leioa, 48940, Greater Bilbao, Basque Country, Spain
| | - Deyani Nocedo-Mena
- Faculty of Physical Mathematical Sciences, Autonomous University of Nuevo León, San Nicolás de los Garza, 66455, Nuevo León, México
| | - Estefania Ascencio
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- IKERDATA S.L., ZITEK, UPV/EHU, Rectorate Building, nº 6, Leioa, 48940, Greater Bilbao, Basque Country, Spain
| | - Gerardo M Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
| | - Idoia Castellanos-Rubio
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain.
| | - Maite Insausti
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Leioa, 48940, Spain
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58108, USA
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain.
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa, 48940, Spain
- BIOFISIKA: Basque Center for Biophysics CSIC, University of The Basque Country (UPV/EHU), Barrio Sarriena s/n, Leioa, 48940, Bizkaia, Basque Country, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, 48011, Biscay, Spain
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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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3
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Rémondin C, Mignani S, Rochais C, Dallemagne P. Synthesis and interest in medicinal chemistry of β-phenylalanine derivatives (β-PAD): an update (2010-2022). Future Med Chem 2024; 16:1147-1162. [PMID: 38722231 PMCID: PMC11221601 DOI: 10.1080/17568919.2024.2347063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 04/19/2024] [Indexed: 06/26/2024] Open
Abstract
β-Phenylalanine derivatives (β-PAD) represent a structural family of therapeutic interest, either as components of drugs or as starting materials for access to key compounds. As scaffolds for medicinal chemistry work, β-PAD offer the advantage of great diversity and modularity, a chiral pseudopeptidic character that opens up the capacity to be recognized by natural systems, and greater stability than natural α-amino acids. Nevertheless, their synthesis remains a challenge in drug discovery and numerous methods have been devoted to their preparation. This review is an update of the access routes to β-PAD and their various therapeutic applications.
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Affiliation(s)
| | - Serge Mignani
- Normandie Univ.,
UNICAEN, CERMN,
14000, Caen, France
- UMR 860, Laboratoire de Chimie et de Biochimie
Pharmacologiques et Toxicologique, Université Paris
Descartes, PRES Sorbonne Paris Cité,
CNRS, 45 rue des Saints Pères,
75006, Paris, France
- CQM – Centro de Química da
Madeira, MMRG, Universidad da
Madeira, Campus da Penteada,
9020-105, Funchal,
Portugal
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Kleandrova VV, Speck-Planche A. The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling. Mini Rev Med Chem 2021; 20:1357-1374. [PMID: 32013845 DOI: 10.2174/1389557520666200204123156] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 12/24/2022]
Abstract
Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.
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Affiliation(s)
- 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
| | - Alejandro Speck-Planche
- Department of Chemistry, Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University, Trubetskaya Str., 8, b. 2, 119992, Moscow, Russian Federation
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5
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Sampaio-Dias IE, Rodríguez-Borges JE, Yáñez-Pérez V, Arrasate S, Llorente J, Brea JM, Bediaga H, Viña D, Loza MI, Caamaño O, García-Mera X, González-Díaz H. Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML). ACS Chem Neurosci 2021; 12:203-215. [PMID: 33347281 DOI: 10.1021/acschemneuro.0c00687] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
This work describes the synthesis and pharmacological evaluation of 2-furoyl-based Melanostatin (MIF-1) peptidomimetics as dopamine D2 modulating agents. Eight novel peptidomimetics were tested for their ability to enhance the maximal effect of tritiated N-propylapomorphine ([3H]-NPA) at D2 receptors (D2R). In this series, 2-furoyl-l-leucylglycinamide (6a) produced a statistically significant increase in the maximal [3H]-NPA response at 10 pM (11 ± 1%), comparable to the effect of MIF-1 (18 ± 9%) at the same concentration. This result supports previous evidence that the replacement of proline residue by heteroaromatic scaffolds are tolerated at the allosteric binding site of MIF-1. Biological assays performed for peptidomimetic 6a using cortex neurons from 19-day-old Wistar-Kyoto rat embryos suggest that 6a displays no neurotoxicity up to 100 μM. Overall, the pharmacological and toxicological profile and the structural simplicity of 6a makes this peptidomimetic a potential lead compound for further development and optimization, paving the way for the development of novel modulating agents of D2R suitable for the treatment of CNS-related diseases. Additionally, the pharmacological and biological data herein reported, along with >20 000 outcomes of preclinical assays, was used to seek a general model to predict the allosteric modulatory potential of molecular candidates for a myriad of target receptors, organisms, cell lines, and biological activity parameters based on perturbation theory (PT) ideas and machine learning (ML) techniques, abbreviated as ALLOPTML. By doing so, ALLOPTML shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. To the best of our knowledge, ALLOPTML is the first general-purpose chemoinformatic tool using a PTML-based model for the multioutput and multicondition prediction of allosteric compounds, which is expected to save both time and resources during the early drug discovery of allosteric modulators.
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Affiliation(s)
- Ivo E. Sampaio-Dias
- LAQV/REQUIMTE, Dept. of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - José E. Rodríguez-Borges
- LAQV/REQUIMTE, Dept. of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Víctor Yáñez-Pérez
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Sonia Arrasate
- Dept. of Pharmacology, Faculty of Medicine and Nursing, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Javier Llorente
- Dept. of Pharmacology, Faculty of Medicine and Nursing, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Dept. of Pharmacology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - José M. Brea
- Innopharma Screening Platform, Biofarma Research group, Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Harbil Bediaga
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Dept. of Physical Chemistry, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Dolores Viña
- Dept. of Pharmacology, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - María Isabel Loza
- Innopharma Screening Platform, Biofarma Research group, Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Olga Caamaño
- Dept. of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Xerardo García-Mera
- Dept. of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Humberto González-Díaz
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Basque Center for Biophysics (CSIC UPV/EHU), University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
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Nawrot D, Suchánková E, Janďourek O, Konečná K, Bárta P, Doležal M, Zitko J. N-pyridinylbenzamides: an isosteric approach towards new antimycobacterial compounds. Chem Biol Drug Des 2020; 97:686-700. [PMID: 33068457 DOI: 10.1111/cbdd.13804] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/02/2020] [Accepted: 10/11/2020] [Indexed: 11/27/2022]
Abstract
A series of N-pyridinylbenzamides was designed and prepared to investigate the influence of isosterism and positional isomerism on antimycobacterial activity. Comparison to previously published isosteric N-pyrazinylbenzamides was made as an attempt to draw structure-activity relationships in such type of compounds. In total, we prepared 44 different compounds, out of which fourteen had minimum inhibitory concentration (MIC) values against Mycobacterium tuberculosis H37Ra below 31.25 µg/ml, most promising being N-(5-chloropyridin-2-yl)-3-(trifluoromethyl)benzamide (23) and N-(6-chloropyridin-2-yl)-3-(trifluoromethyl)benzamide (24) with MIC = 7.81 µg/ml (26 µm). Five compounds showed broad-spectrum antimycobacterial activity against M. tuberculosis H37Ra, M. smegmatis and M. aurum. N-(pyridin-2-yl)benzamides were generally more active than N-(pyridin-3-yl)benzamides, indicating that N-1 in the parental structure of N-pyrazinylbenzamides might be more important for antimycobacterial activity than N-4. Marginal antibacterial and antifungal activity was observed for title compounds. The hepatotoxicity of title compounds was assessed in vitro on hepatocellular carcinoma cell line HepG2, and they may be considered non-toxic (22 compounds with IC50 over 200 µm).
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Affiliation(s)
- Daria Nawrot
- Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic
| | - Eliška Suchánková
- Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic
| | - Ondřej Janďourek
- Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic
| | - Klára Konečná
- Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic
| | - Pavel Bárta
- Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic
| | - Martin Doležal
- Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic
| | - Jan Zitko
- Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic
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7
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Kleandrova VV, Speck-Planche A. PTML Modeling for Alzheimer’s Disease: Design and Prediction of Virtual Multi-Target Inhibitors of GSK3B, HDAC1, and HDAC6. Curr Top Med Chem 2020; 20:1661-1676. [DOI: 10.2174/1568026620666200607190951] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/12/2019] [Accepted: 01/05/2020] [Indexed: 01/23/2023]
Abstract
Background:
Alzheimer’s disease is characterized by a progressive pattern of cognitive and
functional impairment, which ultimately leads to death. Computational approaches have played an important
role in the context of drug discovery for anti-Alzheimer's therapies. However, most of the computational
models reported to date have been focused on only one protein associated with Alzheimer's,
while relying on small datasets of structurally related molecules.
Objective:
We introduce the first model combining perturbation theory and machine learning based on
artificial neural networks (PTML-ANN) for simultaneous prediction and design of inhibitors of three
Alzheimer’s disease-related proteins, namely glycogen synthase kinase 3 beta (GSK3B), histone deacetylase
1 (HDAC1), and histone deacetylase 6 (HDAC6).
Methods:
The PTML-ANN model was obtained from a dataset retrieved from ChEMBL, and it relied on
a classification approach to predict chemicals as active or inactive.
Results:
The PTML-ANN model displayed sensitivity and specificity higher than 85% in both training
and test sets. The physicochemical and structural interpretation of the molecular descriptors in the model
permitted the direct extraction of fragments suggested to favorably contribute to enhancing the multitarget
inhibitory activity. Based on this information, we assembled ten molecules from several fragments
with positive contributions. Seven of these molecules were predicted as triple target inhibitors while the
remaining three were predicted as dual-target inhibitors. The estimated physicochemical properties of
the designed molecules complied with Lipinski’s rule of five and its variants.
Conclusion:
This work opens new horizons toward the design of multi-target inhibitors for anti- Alzheimer's
therapies.
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Affiliation(s)
- 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
| | - Alejandro Speck-Planche
- Programa Institucional de Fomento a la Investigacion, Desarrollo e Innovacion, Universidad Tecnologica Metropolitana, Ignacio Valdivieso 2409, P.O. Box 8940577, San Joaquin, Santiago, Chile
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Diez-Alarcia R, Yáñez-Pérez V, Muneta-Arrate I, Arrasate S, Lete E, Meana JJ, González-Díaz H. Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [ 35S]GTPγS Binding Assays. ACS Chem Neurosci 2019; 10:4476-4491. [PMID: 31618004 DOI: 10.1021/acschemneuro.9b00302] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
G-protein-coupled receptors (GPCRs), also known as 7-transmembrane receptors, are the single largest class of drug targets. Consequently, a large amount of preclinical assays having GPCRs as molecular targets has been released to public sources like the Chemical European Molecular Biology Laboratory (ChEMBL) database. These data are also very complex covering changes in drug chemical structure and assay conditions like c0 = activity parameter (Ki, IC50, etc.), c1 = target protein, c2 = cell line, c3 = assay organism, etc., making difficult the analysis of these databases that are placed in the borders of a Big Data challenge. One of the aims of this work is to develop a computational model able to predict new GPCRs targeting drugs taking into consideration multiple conditions of assay. Another objective is to perform new predictive and experimental studies of selective 5-HTA2 receptor agonist, antagonist, or inverse agonist in human comparing the results with those from the literature. In this work, we combined Perturbation Theory (PT) and Machine Learning (ML) to seek a general PTML model for this data set. We analyzed 343 738 unique compounds with 812 072 end points (assay outcomes), with 185 different experimental parameters, 592 protein targets, 51 cell lines, and/or 55 organisms (species). The best PTML linear model found has three input variables only and predicted 56 202/58 653 positive outcomes (sensitivity = 95.8%) and 470 230/550 401 control cases (specificity = 85.4%) in training series. The model also predicted correctly 18 732/19 549 (95.8%) of positive outcomes and 156 739/183 469 (85.4%) of cases in external validation series. To illustrate its practical use, we used the model to predict the outcomes of six different 5-HT2A receptor drugs, namely, TCB-2, DOI, DOB, altanserin, pimavanserin, and nelotanserin, in a very large number of different pharmacological assays. 5-HT2A receptors are altered in schizophrenia and represent drug target for antipsychotic therapeutic activity. The model correctly predicted 93.83% (76 of 86) experimental results for these compounds reported in ChEMBL. Moreover, [35S]GTPγS binding assays were performed experimentally with the same six drugs with the aim of determining their potency and efficacy in the modulation of G-proteins in human brain tissue. The antagonist ketanserin was included as inactive drug with demonstrated affinity for 5-HT2A/C receptors. Our results demonstrate that some of these drugs, previously described as serotonin 5-HT2A receptor agonists, antagonists, or inverse agonists, are not so specific and show different intrinsic activity to that previously reported. Overall, this work opens a new gate for the prediction of GPCRs targeting compounds.
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Affiliation(s)
- Rebeca Diez-Alarcia
- Centro de Investigación Biomédica en Red en Salud Mental, 48940 Leioa, Spain
| | | | | | | | | | - J. Javier Meana
- Centro de Investigación Biomédica en Red en Salud Mental, 48940 Leioa, Spain
| | - Humbert González-Díaz
- Biophysics Institute, CSIC-UPV/EHU, University of the Basque Country UPV/EHU, Leioa, 48940, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
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9
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Vásquez-Domínguez E, Armijos-Jaramillo VD, Tejera E, González-Díaz H. Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds. Mol Pharm 2019; 16:4200-4212. [PMID: 31426639 DOI: 10.1021/acs.molpharmaceut.9b00538] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for retroviral infections. For this reason, we propose to develop a new predictive model combining perturbation theory (PT) bases and machine learning (ML) modeling to create a new tool that can take advantage of all the available information. The PTML model proposed in this work for the ChEMBL data set preclinical experimental assays for antiretroviral compounds consists of a linear equation with four variables. The PT operators used are founded on multicondition moving averages, combining different features and simplifying the difficulty to manage all data. More than 140 000 preclinical assays for 56 105 compounds with different characteristics or experimental conditions have been carried out and can be found in ChEMBL database, covering combinations with 359 biological activity parameters (c0), 55 protein accessions (c1), 83 cell lines (c2), 64 organisms of assay (c3), and 773 subtypes or strains. We have included 150 148 preclinical experimental assays for HIV virus, 1188 for HTLV virus, 84 for simian immunodeficiency virus, 370 for murine leukemia virus, 119 for Rous sarcoma virus, 1581 for MMTV, etc. We also included 5277 assays for hepatitis B virus. The developed PTML model reached considerable values in sensibility (73.05% for training and 73.10% for validation), specificity (86.61% for training and 87.17% for validation), and accuracy (75.84% for training and 75.98% for validation). We also compared alternative PTML models with different PT operators such as covariance, moments, and exponential terms. Finally, we made a comparison between literature ML models with our PTML model and also artificial neural network (ANN) nonlinear models. We conclude that this PTML model is the first one to consider multiple characteristics of preclinical experimental antiretroviral assays combined, generating a simple, useful, and adaptable instrument, which could reduce time and costs in antiretroviral drugs research.
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Affiliation(s)
- Emilia Vásquez-Domínguez
- Department of Organic Chemistry II , University of Basque Country UPV/EHU , 48940 Leioa , Spain.,Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Vinicio Danilo Armijos-Jaramillo
- Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador.,Bio-chemioinformatics group , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Eduardo Tejera
- Faculty of Engineering and Applied Sciences-Biotechnology , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador.,Bio-chemioinformatics group , Universidad de Las Américas (UDLA) , 170125 Quito , Ecuador
| | - Humbert González-Díaz
- Department of Organic Chemistry II , University of Basque Country UPV/EHU , 48940 Leioa , Spain.,IKERBASQUE, Basque Foundation for Science , 48011 Bilbao , Spain
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10
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Sampaio-Dias IE, Silva-Reis SC, García-Mera X, Brea J, Loza MI, Alves CS, Algarra M, Rodríguez-Borges JE. Synthesis, Pharmacological, and Biological Evaluation of MIF-1 Picolinoyl Peptidomimetics as Positive Allosteric Modulators of D 2R. ACS Chem Neurosci 2019; 10:3690-3702. [PMID: 31347842 DOI: 10.1021/acschemneuro.9b00259] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
This work describes the synthesis and pharmacological evaluation of picolinoyl-based peptidomimetics of melanocyte stimulating hormone release inhibiting factor 1 (MIF-1) as dopamine modulating agents. Eight novel peptidomimetics were tested for their ability to enhance the maximal effect of tritiated N-propylapomorphine ([3H]-NPA) at dopamine D2 receptors (D2R). Methyl picolinoyl-l-valyl-l-alaninate (compound 6b) produced a statistically significant increase in the maximal [3H]-NPA response at 0.01 nM (11.9 ± 3.7%), which is close to the effect of MIF-1 in this assay at same concentration (18.3 ± 9.1%). Functional assays measuring cAMP mobilization in the presence of dopamine corroborate the activity of peptidomimetic 6b as a positive allosteric modulator (PAM) of D2R. In this assay, 6b produced a typical bell-shaped dose-response curve similar to that of the parent neuropeptide (18.3 ± 7.1% for 6b vs 15.4 ± 5.5% for MIF-1, both at 0.1 nM). Dose-response curves for dopamine in the presence of 6b show EC50 (0.33 ± 0.21 μM for 6b vs 0.17 ± 0.07 μM for MIF-1) and Emax (86.0 ± 5.4% for 6b vs 93.6 ± 4.4% for MIF-1) comparable to those of MIF-1, both at 0.01 nM. Furthermore, peptidomimetic 6b was tested for agonist activity at the human D2R and the results show that it displays no intrinsic agonism effect, endorsing its activity as a PAM of D2R. Cytotoxic and neurotoxic assays were performed for peptidomimetic 6b using HEK 293T cells and cortex neurons from 19 day old Wistar-Kyoto rat embryos, respectively, suggesting this analogue displays no toxicity effect in these assays up to 100 μM. Conformational energy minimization for 6b shows that this peptidomimetic cannot adopt the postulated type-II β-turn bioactive conformation, endorsing the possibility of an extended bioactive conformation as claimed by other researchers as a second bioactive conformation of MIF-1. Overall, the pharmacological and toxicological profile of peptidomimetic 6b together with its favorable druglike properties and structural simplicity makes it a potential lead compound for further development and optimization.
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Affiliation(s)
- Ivo E. Sampaio-Dias
- LAQV/REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Sara C. Silva-Reis
- LAQV/REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Xerardo García-Mera
- Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, E-15782 Santiago de Compostela, Spain
| | - José Brea
- Innopharma Screening Platform, Biofarma Research Group, Centre of Research in Molecular Medicine and Chronic Diseases (CIMUS), University of Santiago de Compostela, E-15782 Santiago de Compostela, Spain
| | - M. Isabel Loza
- Innopharma Screening Platform, Biofarma Research Group, Centre of Research in Molecular Medicine and Chronic Diseases (CIMUS), University of Santiago de Compostela, E-15782 Santiago de Compostela, Spain
| | - Carla S. Alves
- CQM - Centro de Química da Madeira, Universidade da Madeira, Campus Universitário
da Penteada, 9020-105 Funchal, Portugal
| | - Manuel Algarra
- CQM - Centro de Química da Madeira, Universidade da Madeira, Campus Universitário
da Penteada, 9020-105 Funchal, Portugal
| | - José E. Rodríguez-Borges
- LAQV/REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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11
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Li X, Li X, Li Y, Yu C, Xue W, Hu J, Li B, Wang P, Zhu F. What Makes Species Productive of Anti-Cancer Drugs? Clues from Drugs’ Species Origin, Druglikeness, Target and Pathway. Anticancer Agents Med Chem 2019; 19:194-203. [DOI: 10.2174/1871520618666181029132017] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Revised: 08/22/2017] [Accepted: 03/21/2018] [Indexed: 12/18/2022]
Abstract
Background:Despite the substantial contribution of natural products to the FDA drug approval list, the discovery of anti-cancer drugs from the huge amount of species on the planet remains looking for a needle in a haystack. Objective: Drug-productive clusters in the phylogenetic tree are thus proposed to narrow the searching scope by focusing on much smaller amount of species within each cluster, which enable prioritized and rational bioprospecting for novel drug-like scaffolds. However, the way anti-cancer nature-derived drugs distribute in phylogenetic tree has not been reported, and it is oversimplified to just focus anti-cancer drug discovery on the drug-productive clusters, since the number of species in each cluster remains too large to be managed.Objective:Drug-productive clusters in the phylogenetic tree are thus proposed to narrow the searching scope by focusing on much smaller amount of species within each cluster, which enable prioritized and rational bioprospecting for novel drug-like scaffolds. However, the way anti-cancer nature-derived drugs distribute in phylogenetic tree has not been reported, and it is oversimplified to just focus anti-cancer drug discovery on the drug-productive clusters, since the number of species in each cluster remains too large to be managed.Methods:In this study, 260 anti-cancer drugs approved in the past 70 years were comprehensively analyzed by hierarchical clustering of phylogenetic distribution.Results:207 out of these 260 drugs were derived from or inspired by the natural products isolated from 58 species. Phylogenetic distribution of those drugs further revealed that nature-derived anti-cancer drugs originated mostly from drug-productive families that tend to be clustered rather than scattered on the phylogenetic tree. Moreover, based on their productivity, drug-producing species were categorized into productive (CPS), newly emerging (CNS) and lessproductive (CLS). Statistical significances in druglikeness between drugs from CPS and CLS were observed, and drugs from CNS were found to share similar drug-like properties to those from CPS.Conclusion:This finding indicated a great raise in drug approval standard, which suggested us to focus bioprospecting on the species yielding multiple drugs and keeping productive for long period of time.
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Affiliation(s)
- Xiaofeng Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xiaoxu Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yinghong Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Chunyan Yu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Weiwei Xue
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Jie Hu
- School of International Studies, Zhejiang University, Hangzhou 310058, China
| | - Bo Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Panpan Wang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
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12
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Bellera CL, Talevi A. Quantitative structure-activity relationship models for compounds with anticonvulsant activity. Expert Opin Drug Discov 2019; 14:653-665. [PMID: 31072145 DOI: 10.1080/17460441.2019.1613368] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.
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Affiliation(s)
- Carolina L Bellera
- a Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata, Buenos Aires , Argentina.,b CCT La Plata , Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , Buenos Aires , Argentina
| | - Alan Talevi
- a Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata, Buenos Aires , Argentina.,b CCT La Plata , Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , Buenos Aires , Argentina
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13
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Makhouri FR, Ghasemi JB. In Silico Studies in Drug Research Against Neurodegenerative Diseases. Curr Neuropharmacol 2018; 16:664-725. [PMID: 28831921 PMCID: PMC6080098 DOI: 10.2174/1570159x15666170823095628] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 07/24/2017] [Accepted: 08/16/2017] [Indexed: 01/14/2023] Open
Abstract
Background Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays. Methods In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches. Results Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach. Conclusion Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.
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Affiliation(s)
| | - Jahan B Ghasemi
- Chemistry Department, Faculty of Sciences, University of Tehran, Tehran, Iran
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14
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Liu J, Ming B, Gong GH, Wang D, Bao GL, Yu LJ. Current research on anti-breast cancer synthetic compounds. RSC Adv 2018. [DOI: 10.1039/c7ra12912b] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Breast cancer (BC) is the most common cancer for females and its incidence tends to increase year by year.
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Affiliation(s)
- Jia Liu
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Bian Ming
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Guo-Hua Gong
- First Clinical Medical of Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
| | - Di Wang
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Gui-Lan Bao
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Li-Jun Yu
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
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15
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Computational evaluation of 2-amino-5-sulphonamido-1,3,4-thiadiazoles as human carbonic anhydrase-IX inhibitors: an insight into the structural requirement for the anticancer activity against HEK 293. Med Chem Res 2017. [DOI: 10.1007/s00044-017-1929-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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Speck-Planche A, Cordeiro MNDS. De novo computational design of compounds virtually displaying potent antibacterial activity and desirable in vitro ADMET profiles. Med Chem Res 2017. [DOI: 10.1007/s00044-017-1936-4] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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17
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Kleandrova VV, Luan F, Speck-Planche A, Cordeiro MNDS. QSAR-Based Studies of Nanomaterials in the Environment. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Nanotechnology is a newly emerging field, posing substantial impacts on society, economy, and the environment. In recent years, the development of nanotechnology has led to the design and large-scale production of many new materials and devices with a vast range of applications. However, along with the benefits, the use of nanomaterials raises many questions and generates concerns due to the possible health-risks and environmental impacts. This chapter provides an overview of the Quantitative Structure-Activity Relationships (QSAR) studies performed so far towards predicting nanoparticles' environmental toxicity. Recent progresses on the application of these modeling studies are additionally pointed out. Special emphasis is given to the setup of a QSAR perturbation-based model for the assessment of ecotoxic effects of nanoparticles in diverse conditions. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling are discussed.
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Affiliation(s)
| | - Feng Luan
- Yantai University, China & University of Porto, Portugal
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18
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Cao Z, Zhu H, Meng X, Guan J, Zhang Q, Tian L, Sun X, Chen G, You J. Metal-Free Reaction ofortho-Carbonylated Alkynyl-Substituted Arylaldehydes with Common Amines: Selective Access to Functionalized Isoindolinone and Indenamine Derivatives. Chemistry 2016; 22:16979-16985. [DOI: 10.1002/chem.201603045] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2016] [Indexed: 12/17/2022]
Affiliation(s)
- Ziping Cao
- School of Chemistry and Chemical Engineering; Qufu Normal University; Shandong Key Laboratory of Life-Organic Analysis; Key Laboratory of Pharmaceutical Intermediates and Analysis of Natural Medicine, Qufu; 273165 Shandong P. R. China
| | - Hongbo Zhu
- School of Chemistry and Chemical Engineering; Qufu Normal University; Shandong Key Laboratory of Life-Organic Analysis; Key Laboratory of Pharmaceutical Intermediates and Analysis of Natural Medicine, Qufu; 273165 Shandong P. R. China
| | - Xin Meng
- School of Chemistry and Chemical Engineering; Qufu Normal University; Shandong Key Laboratory of Life-Organic Analysis; Key Laboratory of Pharmaceutical Intermediates and Analysis of Natural Medicine, Qufu; 273165 Shandong P. R. China
| | - Jun Guan
- School of Chemistry and Chemical Engineering; Qufu Normal University; Shandong Key Laboratory of Life-Organic Analysis; Key Laboratory of Pharmaceutical Intermediates and Analysis of Natural Medicine, Qufu; 273165 Shandong P. R. China
| | - Qiang Zhang
- School of Chemistry and Chemical Engineering; Qufu Normal University; Shandong Key Laboratory of Life-Organic Analysis; Key Laboratory of Pharmaceutical Intermediates and Analysis of Natural Medicine, Qufu; 273165 Shandong P. R. China
| | - Laijin Tian
- School of Chemistry and Chemical Engineering; Qufu Normal University; Shandong Key Laboratory of Life-Organic Analysis; Key Laboratory of Pharmaceutical Intermediates and Analysis of Natural Medicine, Qufu; 273165 Shandong P. R. China
| | - Xuejun Sun
- School of Chemistry and Chemical Engineering; Qufu Normal University; Shandong Key Laboratory of Life-Organic Analysis; Key Laboratory of Pharmaceutical Intermediates and Analysis of Natural Medicine, Qufu; 273165 Shandong P. R. China
| | - Guang Chen
- School of Chemistry and Chemical Engineering; Qufu Normal University; Shandong Key Laboratory of Life-Organic Analysis; Key Laboratory of Pharmaceutical Intermediates and Analysis of Natural Medicine, Qufu; 273165 Shandong P. R. China
| | - Jinmao You
- School of Chemistry and Chemical Engineering; Qufu Normal University; Shandong Key Laboratory of Life-Organic Analysis; Key Laboratory of Pharmaceutical Intermediates and Analysis of Natural Medicine, Qufu; 273165 Shandong P. R. China
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Romero-Durán FJ, Alonso N, Yañez M, Caamaño O, García-Mera X, González-Díaz H. Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives. Neuropharmacology 2016; 103:270-8. [DOI: 10.1016/j.neuropharm.2015.12.019] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2015] [Revised: 11/22/2015] [Accepted: 12/18/2015] [Indexed: 01/22/2023]
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20
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Shar PA, Tao W, Gao S, Huang C, Li B, Zhang W, Shahen M, Zheng C, Bai Y, Wang Y. Pred-binding: large-scale protein-ligand binding affinity prediction. J Enzyme Inhib Med Chem 2016; 31:1443-50. [PMID: 26888050 DOI: 10.3109/14756366.2016.1144594] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand-protein pairs to predict DTIs that were quantified by Ki values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for Ki predictions. These models provide a new opportunity for the prediction of ligand-receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.
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Affiliation(s)
- Piar Ali Shar
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Weiyang Tao
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Shuo Gao
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Chao Huang
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Bohui Li
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Wenjuan Zhang
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Mohamed Shahen
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Chunli Zheng
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Yaofei Bai
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
| | - Yonghua Wang
- a Bioinformatics Center, College of Life Sciences, Northwest A & F University , Yangling , Shaanxi , China
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Halder A, Goodarzi M. Recent Advances in Multi-Task QSAR Modeling for Drug Design. PHARMACEUTICAL SCIENCES 2015. [DOI: 10.15171/ps.2015.33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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22
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Uhlig C, Krause H, Koch T, Gama de Abreu M, Spieth PM. Anesthesia and Monitoring in Small Laboratory Mammals Used in Anesthesiology, Respiratory and Critical Care Research: A Systematic Review on the Current Reporting in Top-10 Impact Factor Ranked Journals. PLoS One 2015; 10:e0134205. [PMID: 26305700 PMCID: PMC4549323 DOI: 10.1371/journal.pone.0134205] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Accepted: 07/07/2015] [Indexed: 11/19/2022] Open
Abstract
RATIONALE This study aimed to investigate the quality of reporting of anesthesia and euthanasia in experimental studies in small laboratory mammals published in the top ten impact factor journals. METHODS A descriptive systematic review was conducted and data was abstracted from the ten highest ranked journals with respect to impact factor in the categories 'Anesthesiology', 'Critical Care Medicine' and 'Respiratory System' as defined by the 2012 Journal Citation Reports. Inclusion criteria according to PICOS criteria were as follows: 1) population: small laboratory mammals; 2) intervention: any form of anesthesia and/or euthanasia; 3) comparison: not specified; 4) primary outcome: type of anesthesia, anesthetic agents and type of euthanasia; secondary outcome: animal characteristics, monitoring, mechanical ventilation, fluid management, postoperative pain therapy, animal care approval, sample size calculation and performed interventions; 5) study: experimental studies. Anesthesia, euthanasia, and monitoring were analyzed per performed intervention in each article. RESULTS The search yielded 845 articles with 1,041 interventions of interest. Throughout the manuscripts we found poor quality and frequency of reporting with respect to completeness of data on animal characteristics as well as euthanasia, while anesthesia (732/1041, 70.3%) and interventions without survival (970/1041, 93.2%) per se were frequently reported. Premedication and neuromuscular blocking agents were reported in 169/732 (23.1%) and 38/732 (5.2%) interventions, respectively. Frequency of reporting of analgesia during (117/610, 19.1%) and after painful procedures (38/364, 10.4%) was low. Euthanasia practice was reported as anesthesia (348/501, 69%), transcardial perfusion (37/501, 8%), carbon dioxide (26/501, 6%), decapitation (22/501, 5%), exsanguination (23/501, 5%), other (25/501, 5%) and not specified (20/501, 4%, respectively. CONCLUSIONS The present systematic review revealed insufficient reporting of anesthesia and euthanasia methods throughout experimental studies in small laboratory mammals. Specific guidelines for anesthesia and euthanasia regimens should be considered to achieve comparability, quality of animal experiments and animal welfare. These measures are of special interest when translating experimental findings to future clinical applications.
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Affiliation(s)
- Christopher Uhlig
- Pulmonary Engineering Group, Department of Anesthesiology and Intensive Care Therapy, University Hospital Dresden, Dresden, Technische Universität Dresden, Germany
| | - Hannes Krause
- Pulmonary Engineering Group, Department of Anesthesiology and Intensive Care Therapy, University Hospital Dresden, Dresden, Technische Universität Dresden, Germany
| | - Thea Koch
- Pulmonary Engineering Group, Department of Anesthesiology and Intensive Care Therapy, University Hospital Dresden, Dresden, Technische Universität Dresden, Germany
| | - Marcelo Gama de Abreu
- Pulmonary Engineering Group, Department of Anesthesiology and Intensive Care Therapy, University Hospital Dresden, Dresden, Technische Universität Dresden, Germany
| | - Peter Markus Spieth
- Pulmonary Engineering Group, Department of Anesthesiology and Intensive Care Therapy, University Hospital Dresden, Dresden, Technische Universität Dresden, Germany
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23
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Casañola-Martin GM, Le-Thi-Thu H, Pérez-Giménez F, Marrero-Ponce Y, Merino-Sanjuán M, Abad C, González-Díaz H. Multi-output model with Box–Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin–proteasome pathway. Mol Divers 2015; 19:347-56. [DOI: 10.1007/s11030-015-9571-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2014] [Accepted: 02/14/2015] [Indexed: 12/29/2022]
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24
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Speck-Planche A, Cordeiro MNDS. Multitasking models for quantitative structure–biological effect relationships: current status and future perspectives to speed up drug discovery. Expert Opin Drug Discov 2015; 10:245-56. [DOI: 10.1517/17460441.2015.1006195] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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25
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Speck-Planche A, Cordeiro MNDS. A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents. Methods Mol Biol 2015; 1260:45-64. [PMID: 25502375 DOI: 10.1007/978-1-4939-2239-0_4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions.
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Affiliation(s)
- A Speck-Planche
- Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal
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26
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Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents. Future Med Chem 2014; 6:2013-28. [DOI: 10.4155/fmc.14.136] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Background: Gram-positive cocci are increasingly antibiotic-resistant bacteria responsible for causing serious diseases. Chemoinformatics can help to rationalize the discovery of more potent and safer antibacterial drugs. We have developed a chemoinformatic model for simultaneous prediction of anti-cocci activities, and profiles involving absorption, distribution, metabolism, elimination and toxicity (ADMET). Results: A dataset containing 48,874 cases from many different chemicals assayed under dissimilar experimental conditions was created. The best model displayed accuracies around 93% in both training and prediction (test) sets. Quantitative contributions of several fragments to the biological effects were calculated and analyzed. Multiple biological effects of the investigational drug JNJ-Q2 were correctly predicted. Conclusion: Our chemoinformatic model can be used as powerful tool for virtual screening of promising anti-cocci agents.
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27
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Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates. Int J Mol Sci 2014; 15:17035-64. [PMID: 25255029 PMCID: PMC4200850 DOI: 10.3390/ijms150917035] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Revised: 08/19/2014] [Accepted: 08/21/2014] [Indexed: 11/25/2022] Open
Abstract
In a multi-target complex network, the links (Lij) represent the interactions between the drug (di) and the target (tj), characterized by different experimental measures (Ki, Km, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (cj). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%–90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.
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28
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Ambure P, Roy K. Advances in quantitative structure–activity relationship models of anti-Alzheimer’s agents. Expert Opin Drug Discov 2014; 9:697-723. [DOI: 10.1517/17460441.2014.909404] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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29
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Speck-Planche A, Cordeiro MNDS. Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening. ACS COMBINATORIAL SCIENCE 2014; 16:78-84. [PMID: 24383958 DOI: 10.1021/co400115s] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.
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Affiliation(s)
- Alejandro Speck-Planche
- REQUIMTE/Department of Chemistry
and Biochemistry, University of Porto, 4169-007 Porto, Portugal
| | - M. N. D. S. Cordeiro
- REQUIMTE/Department of Chemistry
and Biochemistry, University of Porto, 4169-007 Porto, Portugal
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30
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Alonso N, Caamaño O, Romero-Duran FJ, Luan F, D. S. Cordeiro MN, Yañez M, González-Díaz H, García-Mera X. Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. ACS Chem Neurosci 2013; 4:1393-403. [PMID: 23855599 DOI: 10.1021/cn400111n] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The disappointing results obtained in recent clinical trials renew the interest in experimental/computational techniques for the discovery of neuroprotective drugs. In this context, multitarget or multiplexing QSAR models (mt-QSAR/mx-QSAR) may help to predict neurotoxicity/neuroprotective effects of drugs in multiple assays, on drug targets, and in model organisms. In this work, we study a data set downloaded from CHEMBL; each data point (>8000) contains the values of one out of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. In this work, we introduce the first mx-QSAR model for neurotoxicity/neuroprotective effects of drugs based on the MARCH-INSIDE (MI) method. First, we used MI to calculate the stochastic spectral moments (structural descriptors) of all compounds. Next, we found a model that classified correctly 2955 out of 3548 total cases in the training and validation series with Accuracy, Sensitivity, and Specificity values>80%. The model also showed excellent results in Computational-Chemistry simulations of High-Throughput Screening (CCHTS) experiments, with accuracy=90.6% for 4671 positive cases. Next, we reported the synthesis, characterization, and experimental assays of new rasagiline derivatives. We carried out three different experimental tests: assay (1) in the absence of neurotoxic agents, assay (2) in the presence of glutamate, and assay (3) in the presence of H2O2. Compounds 11 with 27.4%, 8 with 11.6%, and 9 with 15.4% showed the highest neuroprotective effects in assays (1), (2), and (3), respectively. After that, we used the mx-QSAR model to carry out a CCHTS of the new compounds in >400 unique pharmacological tests not carried out experimentally. Consequently, this model may become a promising auxiliary tool for the discovery of new drugs for the treatment of neurodegenerative diseases.
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Affiliation(s)
- Nerea Alonso
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Olga Caamaño
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Francisco J. Romero-Duran
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Feng Luan
- REQUIMTE/Department of Chemistry
and Biochemistry, University of Porto,
4169-007, Porto, Portugal
- Department of Applied Chemistry, Yantai University, Yantai 264005, People’s Republic
of China
| | | | - Matilde Yañez
- Department of
Pharmacology,
Faculty of Pharmacy, USC, 15782, Santiago
de Compostela, Spain
| | - Humberto González-Díaz
- Departament
of Organic Chemistry
II, University of the Basque Country UPV/EHU, 48940, Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
| | - Xerardo García-Mera
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
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31
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Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals. Bioorg Med Chem 2013; 21:2727-32. [PMID: 23582445 DOI: 10.1016/j.bmc.2013.03.015] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2012] [Revised: 03/03/2013] [Accepted: 03/04/2013] [Indexed: 11/21/2022]
Abstract
Streptococci are a group of Gram-positive bacteria which are responsible for causing many diverse diseases in humans and other animals worldwide. The high prevalence of resistance of these bacteria to current antibacterial drugs is an alarming problem for the scientific community. The battle against streptococci by using antimicrobial chemotherapies will depend on the design of new chemicals with high inhibitory activity, having also as low toxicity as possible. Multi-target approaches based on quantitative-structure activity relationships (mt-QSAR) have played a very important role, providing a better knowledge about the molecular patterns related with the appearance of different pharmacological profiles including antimicrobial activity. Until now, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking (mtk) QSAR model for the simultaneous prediction of anti-streptococci activity and toxic effects against biological models like Mus musculus and Rattus norvegicus. The mtk-QSAR model was created by using artificial neural networks (ANN) analysis for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under diverse sets of experimental conditions. Our mtk-QSAR model, correctly classified more than 97% of the cases in the whole database (more than 11,500 cases), serving as a promising tool for the virtual screening of potent and safe anti-streptococci drugs.
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