1
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Jha T, Jana R, Banerjee S, Baidya SK, Amin SA, Gayen S, Ghosh B, Adhikari N. Exploring different classification-dependent QSAR modelling strategies for HDAC3 inhibitors in search of meaningful structural contributors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:367-389. [PMID: 38757181 DOI: 10.1080/1062936x.2024.2350504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Accepted: 04/28/2024] [Indexed: 05/18/2024]
Abstract
Histone deacetylase 3 (HDAC3), a Zn2+-dependent class I HDACs, contributes to numerous disorders such as neurodegenerative disorders, diabetes, cardiovascular disease, kidney disease and several types of cancers. Therefore, the development of novel and selective HDAC3 inhibitors might be promising to combat such diseases. Here, different classification-based molecular modelling studies such as Bayesian classification, recursive partitioning (RP), SARpy and linear discriminant analysis (LDA) were conducted on a set of HDAC3 inhibitors to pinpoint essential structural requirements contributing to HDAC3 inhibition followed by molecular docking study and molecular dynamics (MD) simulation analyses. The current study revealed the importance of hydroxamate function for Zn2+ chelation as well as hydrogen bonding interaction with Tyr298 residue. The importance of hydroxamate function for higher HDAC3 inhibition was noticed in the case of Bayesian classification, recursive partitioning and SARpy models. Also, the importance of substituted thiazole ring was revealed, whereas the presence of linear alkyl groups with carboxylic acid function, any type of ester function, benzodiazepine moiety and methoxy group in the molecular structure can be detrimental to HDAC3 inhibition. Therefore, this study can aid in the design and discovery of effective novel HDAC3 inhibitors in the future.
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Affiliation(s)
- T Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - R Jana
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S Banerjee
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S K Baidya
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S A Amin
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - S Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - B Ghosh
- Epigenetic Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani, Hyderabad, India
| | - N Adhikari
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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2
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Sengupta A, Singh SK, Kumar R. Support Vector Machine-Based Prediction Models for Drug Repurposing and Designing Novel Drugs for Colorectal Cancer. ACS OMEGA 2024; 9:18584-18592. [PMID: 38680332 PMCID: PMC11044175 DOI: 10.1021/acsomega.4c01195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/28/2024] [Accepted: 03/29/2024] [Indexed: 05/01/2024]
Abstract
Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing demand to identify novel drug therapies to combat CRC. In this study, we addressed this need by utilizing the pharmacological profiles of anticancer drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database and developed QSAR models using the Support Vector Machine (SVM) algorithm for prediction of alternative and promiscuous anticancer compounds for CRC treatment. Our QSAR models demonstrated their robustness by achieving a high correlation of determination (R2) after 10-fold cross-validation. For 12 CRC cell lines, R2 ranged from 0.609 to 0.827. The highest performance was achieved for SW1417 and GP5d cell lines with R2 values of 0.827 and 0.786, respectively. Further, we listed the most common chemical descriptors in the drug profiles of the CRC cell lines and we also further reported the correlation of these descriptors with drug activity. The KRFP314 fingerprint was the predominantly occurring descriptor, with the KRFPC314 fingerprint following closely in prevalence within the drug profiles of the CRC cell lines. Beyond predictive modeling, we also confirmed the applicability of our developed QSAR models via in silico methods by conducting descriptor-drug analyses and recapitulating drug-to-oncogene relationships. We also identified two potential anti-CRC FDA-approved drugs, viomycin and diamorphine, using QSAR models. To ensure the easy accessibility and utility of our research findings, we have incorporated these models into a user-friendly prediction Web server named "ColoRecPred", available at https://project.iith.ac.in/cgntlab/colorecpred. We anticipate that this Web server can be used for screening of chemical libraries to identify potential anti-CRC drugs.
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Affiliation(s)
- Avik Sengupta
- Department
of Biotechnology, Indian Institute of Technology
Hyderabad, Kandi, Telangana 502284, India
| | - Saurabh Kumar Singh
- Department
of Chemistry, Indian Institute of Technology
Hyderabad, Kandi, Telangana 502284, India
| | - Rahul Kumar
- Department
of Biotechnology, Indian Institute of Technology
Hyderabad, Kandi, Telangana 502284, India
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3
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Karampuri A, Perugu S. A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches. FRONTIERS IN BIOINFORMATICS 2024; 3:1328262. [PMID: 38288043 PMCID: PMC10822965 DOI: 10.3389/fbinf.2023.1328262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/21/2023] [Indexed: 01/31/2024] Open
Abstract
Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based on the extent of disease spread, such as surgery, chemotherapy, radiotherapy, and immunotherapy. Combinational therapy is another strategy that has proven to be effective in controlling cancer progression. Administration of Anchor drug, a well-established primary therapeutic agent with known efficacy for specific targets, with Library drug, a supplementary drug to enhance the efficacy of anchor drugs and broaden the therapeutic approach. Our work focused on harnessing regression-based Machine learning (ML) and deep learning (DL) algorithms to develop a structure-activity relationship between the molecular descriptors of drug pairs and their combined biological activity through a QSAR (Quantitative structure-activity relationship) model. 11 popularly known machine learning and deep learning algorithms were used to develop QSAR models. A total of 52 breast cancer cell lines, 25 anchor drugs, and 51 library drugs were considered in developing the QSAR model. It was observed that Deep Neural Networks (DNNs) achieved an impressive R2 (Coefficient of Determination) of 0.94, with an RMSE (Root Mean Square Error) value of 0.255, making it the most effective algorithm for developing a structure-activity relationship with strong generalization capabilities. In conclusion, applying combinational therapy alongside ML and DL techniques represents a promising approach to combating breast cancer.
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Affiliation(s)
| | - Shyam Perugu
- Department of Biotechnology, National Institute of Technology, Warangal, India
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Lyu Z, Xiong M, Mao J, Li W, Jiang G, Zhang W. A dsRNA delivery system based on the rosin-modified polyethylene glycol and chitosan induces gene silencing and mortality in Nilaparvata lugens. PEST MANAGEMENT SCIENCE 2023; 79:1518-1527. [PMID: 36519402 DOI: 10.1002/ps.7322] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/12/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND RNA interference (RNAi) technology has been considered as a promising pest control strategy due to its species selectivity. One of the popular RNAs is exogenous double strand RNA (dsRNA). However, dsRNA is easily degraded by nucleases and is difficult to pass through the insect body walls, and these factors restrict the application of RNAi technology in pest management. Here, the brown planthopper (BPH, Nilaparvata lugens), a major hemipteran pest of rice in Asia countries was used as a model insect, and a dsRNA topical delivery system was constructed. RESULTS The carrier part of the delivery system was composed of rosin-modified polyethylene glycol and chitosan, termed ROPE@C. When the N/P ratio was greater than 1:1.25, the dsRNA/ROPE@C complex encouraged full binding of the dsRNA. The gel electrophoresis results showed that ROPE@C improved the stability of dsRNA in the presence of nucleases in gut and lumen contents for at least 6 h and in the temperature range from 4 °C to 37 °C. The dsNlCHSA/ROPE@C/alkyl polyglycoside (APG) nano-formulation directly penetrated the body walls reaching hemocoel within 6 h, and consequently, the relative expression of chitin synthetase A (CHSA) in BPH was reduced by 54.3% and the mortality rate was 65.8%. CONCLUSION We developed an appropriate delivery method for dsRNA application in BPH, which is helpful for a large-scale application of RNAi pesticides. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Zihao Lyu
- School of Agriculture, State Key Laboratory of Biocontrol, Sun Yat-sen University, Shenzhen, China
| | - Mingxin Xiong
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, China
| | - Jie Mao
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Weixiong Li
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, China
| | - Gangbiao Jiang
- Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials and Energy, South China Agricultural University, Guangzhou, China
| | - Wenqing Zhang
- School of Agriculture, State Key Laboratory of Biocontrol, Sun Yat-sen University, Shenzhen, China
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
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5
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De P, Kumar V, Kar S, Roy K, Leszczynski J. Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection. Struct Chem 2022; 33:1741-1753. [PMID: 35692512 PMCID: PMC9171098 DOI: 10.1007/s11224-022-01975-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 05/25/2022] [Indexed: 12/19/2022]
Abstract
The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. The current research aims to identify potential anti-SARS-CoV-2 drugs using a computational approach and repurpose them if possible. In the present study, we have collected a set of 44 FDA-approved drugs of different classes from a previously published literature with their potential antiviral activity against COVID-19. We have employed both regression- and classification-based quantitative structure–activity relationship (QSAR) modeling to identify critical chemical features essential for anticoronaviral activity. Multiple models with the consensus algorithm were employed for the regression-based approach to improve the predictions. Additionally, we have employed a machine learning-based read-across approach using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home and linear discriminant analysis for the efficient prediction of potential drug candidate for COVID-19. Finally, the quantitative prediction ability of different modeling approaches was compared using the sum of ranking differences (SRD). Furthermore, we have predicted a true external set of 98 pharmaceuticals using the developed models for their probable anti-COVID activity and their prediction reliability was checked employing the “Prediction Reliability Indicator” tool available from https://dtclab.webs.com/software-tools. Though the present study does not target any protein of viral interaction, the modeling approaches developed can be helpful for identifying or screening potential anti-coronaviral drug candidates.
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Affiliation(s)
- Priyanka De
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700032, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700032, India
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217 USA
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata-700032, India
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217 USA
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6
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Halder AK, Moura AS, Cordeiro MNDS. Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next? Int J Mol Sci 2022; 23:ijms23094937. [PMID: 35563327 PMCID: PMC9099502 DOI: 10.3390/ijms23094937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/24/2022] [Accepted: 04/28/2022] [Indexed: 01/27/2023] Open
Abstract
Conventional in silico modeling is often viewed as 'one-target' or 'single-task' computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box-Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool.
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Affiliation(s)
- Amit Kumar Halder
- LAQV@REQUIMTE, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; (A.K.H.); (A.S.M.)
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Dr. Meghnad Saha Sarani, Bidhannagar, Durgapur 713212, West Bengal, India
| | - Ana S. Moura
- LAQV@REQUIMTE, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; (A.K.H.); (A.S.M.)
| | - Maria Natália D. S. Cordeiro
- LAQV@REQUIMTE, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; (A.K.H.); (A.S.M.)
- Correspondence: ; Tel.: +35-12-2040-2502
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7
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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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8
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Cabrera-Andrade A, López-Cortés A, Jaramillo-Koupermann G, González-Díaz H, Pazos A, Munteanu CR, Pérez-Castillo Y, Tejera E. A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing. Pharmaceuticals (Basel) 2020; 13:ph13110409. [PMID: 33266378 PMCID: PMC7700154 DOI: 10.3390/ph13110409] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/11/2020] [Accepted: 11/12/2020] [Indexed: 02/08/2023] Open
Abstract
Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.
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Affiliation(s)
- Alejandro Cabrera-Andrade
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito 170125, Ecuador;
- Carrera de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, Quito 170125, Ecuador
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071 A Coruña, Spain; (A.L.-C.); (A.P.); (C.R.M.)
- Correspondence: (A.C.-A.); (E.T.)
| | - Andrés López-Cortés
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071 A Coruña, Spain; (A.L.-C.); (A.P.); (C.R.M.)
- Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito 170129, Ecuador
- Latin American Network for Implementation and Validation of Clinical Pharmacogenomics Guidelines (RELIVAF-CYTED), 28029 Madrid, Spain
| | - Gabriela Jaramillo-Koupermann
- Laboratorio de Biología Molecular, Subproceso de Anatomía Patológica, Hospital de Especialidades Eugenio Espejo, Quito 170403, Ecuador;
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, and Basque Center for Biophysics CSIC-UPV/EHU, University of the Basque Country UPV/EHU, 48940 Leioa, Spain;
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
| | - Alejandro Pazos
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071 A Coruña, Spain; (A.L.-C.); (A.P.); (C.R.M.)
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), 15006 A Coruña, Spain
| | - Cristian R. Munteanu
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071 A Coruña, Spain; (A.L.-C.); (A.P.); (C.R.M.)
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), 15006 A Coruña, Spain
| | - Yunierkis Pérez-Castillo
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito 170125, Ecuador;
- Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito 170125, Ecuador
| | - Eduardo Tejera
- Grupo de Bio-Quimioinformática, Universidad de Las Américas, Quito 170125, Ecuador;
- Facultad de Ingeniería y Ciencias Agropecuarias, Universidad de Las Américas, Quito 170125, Ecuador
- Correspondence: (A.C.-A.); (E.T.)
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9
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Kleandrova VV, Scotti MT, Scotti L, Nayarisseri A, Speck-Planche A. Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:815-836. [PMID: 32967475 DOI: 10.1080/1062936x.2020.1818617] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 08/31/2020] [Indexed: 06/11/2023]
Abstract
Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.
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Affiliation(s)
- V V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production , Moscow, Russian Federation
| | - M T Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba , João Pessoa, Brazil
| | - L Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba , João Pessoa, Brazil
| | - A Nayarisseri
- In Silico Research Laboratory, Eminent Biosciences , Indore, Madhya Pradesh, India
| | - A Speck-Planche
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba , João Pessoa, Brazil
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10
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Cabrera-Andrade A, López-Cortés A, Munteanu CR, Pazos A, Pérez-Castillo Y, Tejera E, Arrasate S, González-Díaz H. Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds. ACS OMEGA 2020; 5:27211-27220. [PMID: 33134682 PMCID: PMC7594149 DOI: 10.1021/acsomega.0c03356] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 10/06/2020] [Indexed: 06/11/2023]
Abstract
Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple preclinical assays with different experimental conditions. For instance, the ChEMBL database contains outcomes of 37,919 different antisarcoma assays with 34,955 different chemical compounds. Furthermore, the experimental conditions reported in this dataset include 157 types of biological activity parameters, 36 drug targets, 43 cell lines, and 17 assay organisms. Considering this information, we propose combining perturbation theory (PT) principles with machine learning (ML) to develop a PTML model to predict antisarcoma compounds. PTML models use one function of reference that measures the probability of a drug being active under certain conditions (protein, cell line, organism, etc.). In this paper, we used a linear discriminant analysis and neural network to train and compare PT and non-PT models. All the explored models have an accuracy of 89.19-95.25% for training and 89.22-95.46% in validation sets. PTML-based strategies have similar accuracy but generate simplest models. Therefore, they may become a versatile tool for predicting antisarcoma compounds.
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Affiliation(s)
- Alejandro Cabrera-Andrade
- Grupo
de Bio-Quimioinformática, Universidad
de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- Carrera
de Enfermería, Facultad de Ciencias de la Salud, Universidad de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
| | - Andrés López-Cortés
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
- Centro
de Investigación Genética y Genómica, Facultad
de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, Quito 170129, Ecuador
| | - Cristian R. Munteanu
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
- Biomedical
Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña 15006, Spain
- Centro de
Investigación en Tecnologías de la Información
y las Comunicaciones (CITIC), Campus de
Elviña s/n, A Coruña 15071, Spain
| | - Alejandro Pazos
- RNASA-IMEDIR,
Computer Sciences Faculty, University of
A Coruña, A Coruña 15071, Spain
- Biomedical
Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña 15006, Spain
| | - Yunierkis Pérez-Castillo
- Grupo
de Bio-Quimioinformática, Universidad
de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- Escuela
de Ciencias Físicas y Matemáticas, Universidad de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
| | - Eduardo Tejera
- Grupo
de Bio-Quimioinformática, Universidad
de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
- Facultad
de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, de los Granados Avenue, Quito 170125, Ecuador
| | - Sonia Arrasate
- Department
of Organic Chemistry II and Basque Center for Biophysics, University of Basque Country UPV/EHU, Leioa 48940, Biscay, Spain
| | - Humbert González-Díaz
- Department
of Organic Chemistry II and Basque Center for Biophysics, University of Basque Country UPV/EHU, Leioa 48940, Biscay, Spain
- Ikerbasque,
Basque Foundation for Science, Bilbao 48011, Biscay, Spain
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11
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Yu S, Jia S, Wang D, Lv Z, Chen Y, Wang N, Yao W, Yuan J. Predicting pungency and understanding the pungency mechanism of capsaicinoids using TOPS-MODE approach. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:527-545. [PMID: 32573260 DOI: 10.1080/1062936x.2020.1777583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 05/31/2020] [Indexed: 06/11/2023]
Abstract
Quantitative structure-property relationship (QSPR) models were developed for predicting the pungency of a set of capsaicinoids. Multiple linear regression (MLR) coupled with topological substructural molecular descriptor (TOPS-MODE) approach was used. The best MLR model based on only five orthogonalized TOPS-MODE variables allowed us to obtain a coefficient of determination of 0.954 on the training set. The predictive power of the model was validated through a test set and several external validation parameters. This showed that the TOPS-MODE descriptors weighted by bond dipole moments, van der Waals atomic radii, and the total solute hydrogen bond basicity affected pungency. The contributions of certain bonds and fragments to pungency were used to understand the pungency mechanism of capsaicinoids. The selected model can more accurately predict pungency of capsaicinoids compared than those found in the literature, and especially bring insights into the structural features and chemical factors related to pungency.
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Affiliation(s)
- S Yu
- Key Laboratory of Natural Medicine and Immune-Engineering of Henan Province, Henan University , Kaifeng, China
| | - S Jia
- Key Laboratory of Natural Medicine and Immune-Engineering of Henan Province, Henan University , Kaifeng, China
| | - D Wang
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
| | - Z Lv
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
| | - Y Chen
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
| | - N Wang
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
| | - W Yao
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
| | - J Yuan
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University , Zhengzhou, China
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12
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Roy J, Kumar Ojha P, Carnesecchi E, Lombardo A, Roy K, Benfenati E. First report on a classification-based QSAR model for chemical toxicity to earthworm. JOURNAL OF HAZARDOUS MATERIALS 2020; 386:121660. [PMID: 31784141 DOI: 10.1016/j.jhazmat.2019.121660] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 11/06/2019] [Accepted: 11/09/2019] [Indexed: 06/10/2023]
Abstract
As the use of the pesticides has increased extensively in the farming fields to have a better agricultural production, the negative impacts of such use have also increased exponentially. Hence, the toxic effects of pesticides along with the targeted organisms affect the non-targeted terrestrial organisms such as earthworm. Therefore, in the present work, we have developed a classification-based quantitative structure-activity relationship (QSAR) model using linear discriminant analysis (LDA) to capture the specific information of pesticides / diverse chemicals in order to determine the structural information responsible for toxicity manifestation towards the non-targeted organism, i.e., earthworm (Eisenia foetida). After variable selection, the model was developed using 2D descriptors only and was subjected to rigorous statistical validation. The best discriminant model obtained with 8 descriptors showed appreciable Wilks' λ value of 0.490, F (Fischer's statistics) value of 14.03, χ2 value of 79.098, canonical regression coefficient (R) value of 0.714 and ρ value of 14.63. The sensitivity, specificity, accuracy, precision and F-measure values of the training set are 90.00, 80.52, 83.76, 70.59 and 79.12 respectively whereas for the test set, these are 58.82, 79.31, 71.74, 62.50 and 60.61 respectively. The insights obtained from the LDA model suggested that lipophilicity, electronrichness, and lower degree of branching of the organic compounds are responsible for earthworm toxicity through various mechanisms. On the other hand, polar and bulky diverse chemicals do not have such toxic effects on earthworm. Hence, this model can be an effective tool to tailor molecular structures of the existing pesticides to develop novel compounds or pesticides which would be less toxic to the non-targeted organisms, specifically earthworm.
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Affiliation(s)
- Joyita Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
| | - Probir Kumar Ojha
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
| | - Edoardo Carnesecchi
- Institute for Risk Assessment Sciences, Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands; Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy
| | - Anna Lombardo
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India.
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, 20156, Milano, Italy.
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13
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Shiri F, Bakhshayesh S, Ghasemi JB. Computer-aided molecular design of (E)-N-Aryl-2-ethene-sulfonamide analogues as microtubule targeted agents in prostate cancer. ARAB J CHEM 2019. [DOI: 10.1016/j.arabjc.2014.11.063] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
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14
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Halder AK, Giri AK, Cordeiro MNDS. Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents. Molecules 2019; 24:molecules24213909. [PMID: 31671605 PMCID: PMC6864583 DOI: 10.3390/molecules24213909] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 10/21/2019] [Accepted: 10/25/2019] [Indexed: 02/07/2023] Open
Abstract
Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure–activity relationship (mt-QSAR) models based on the Box–Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.
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Affiliation(s)
- Amit Kumar Halder
- Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
| | - Amal Kanta Giri
- Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
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15
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Halder AK, Cordeiro MNDS. Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool. Int J Mol Sci 2019; 20:ijms20174191. [PMID: 31461863 PMCID: PMC6747073 DOI: 10.3390/ijms20174191] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 08/23/2019] [Accepted: 08/24/2019] [Indexed: 12/12/2022] Open
Abstract
The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure–activity relationship (QSAR)-Co tool for predicting the activity of inhibitor compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K inhibitors.
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Affiliation(s)
- Amit Kumar Halder
- Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
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16
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Amin SA, Adhikari N, Gayen S, Jha T. First Report on the Validated Classification-Based Chemometric Modeling of Human Rhinovirus 3C Protease (HRV 3Cpro) Inhibitors. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijqspr.2018070101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Human rhinoviruses (HRVs), a major cause of common cold and upper respiratory infections, may trigger severe respiratory complications like asthma and COPD. To date, no drugs are available in the market which are designed as novel HRV inhibitors despite the involvement of some pharmaceutical companies' due to economical and clinical constraints. HRV 3C protease may be a potential target for drug design as it plays crucial role in viral RNA replication and virion assembly process. Therefore, designing novel HRV 3Cpro inhibitors is necessary and demanding in the field of antiviral drug design. In this article, statistically significant and validated classification-based QSARs of a series of HRV 3Cpro inhibitors were performed for the first time as per the authors' knowledge. Results suggest that oxopyrrolidine and piperidinone rings are favored whereas carboxybenzyl and unsubstituted benzyl functions may be unfavorable. Moreover, this group, along with cyclic alkyl or aryl ring structures may favor HRV 3Cpro inhibition. These observations may be utilized for the design of a higher active anti-HRV agent in future.
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Affiliation(s)
| | | | | | - Tarun Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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17
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Kleandrova VV, Ruso JM, Speck-Planche A, Dias Soeiro Cordeiro MN. Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity. ACS COMBINATORIAL SCIENCE 2016; 18:490-8. [PMID: 27280735 DOI: 10.1021/acscombsci.6b00063] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Antimicrobial peptides (AMPs) represent promising alternatives to fight against bacterial pathogens. However, cellular toxicity remains one of the main concerns in the early development of peptide-based drugs. This work introduces the first multitasking (mtk) computational model focused on performing simultaneous predictions of antibacterial activities, and cytotoxicities of peptides. The model was created from a data set containing 3592 cases, and it displayed accuracy higher than 96% for classifying/predicting peptides in both training and prediction (test) sets. The technique known as alanine scanning was computationally applied to illustrate the calculation of the quantitative contributions of the amino acids (in their respective positions of the sequence) to the biological effects of a defined peptide. A small library formed by 10 peptides was generated, where peptides were designed by considering the interpretations of the different descriptors in the mtk-computational model. All the peptides were predicted to exhibit high antibacterial activities against multiple bacterial strains, and low cytotoxicity against various cell types. The present mtk-computational model can be considered a very useful tool to support high throughput research for the discovery of potent and safe AMPs.
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Affiliation(s)
- Valeria V. Kleandrova
- Faculty
of Technology and Production Management, Moscow State University of Food Production, Volokolamskoe shosse 11, Moscow, Russia
| | - Juan M. Ruso
- Department
of Applied Physics, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain
| | - Alejandro Speck-Planche
- Department
of Applied Physics, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain
- LAQV@REQUIMTE,
Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal
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18
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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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19
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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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20
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Ziarek JJ, Liu Y, Smith E, Zhang G, Peterson FC, Chen J, Yu Y, Chen Y, Volkman BF, Li R. Fragment-based optimization of small molecule CXCL12 inhibitors for antagonizing the CXCL12/CXCR4 interaction. Curr Top Med Chem 2013; 12:2727-40. [PMID: 23368099 DOI: 10.2174/1568026611212240003] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2012] [Revised: 10/08/2012] [Accepted: 11/03/2012] [Indexed: 12/21/2022]
Abstract
The chemokine CXCL12 and its G protein-coupled receptor (GPCR) CXCR4 are high-priority clinical targets because of their involvement in metastatic cancers (also implicated in autoimmune disease and cardiovascular disease). Because chemokines interact with two distinct sites to bind and activate their receptors, both the GPCRs and chemokines are potential targets for small molecule inhibition. A number of chemokines have been validated as targets for drug development, but virtually all drug discovery efforts focus on the GPCRs. However, all CXCR4 receptor antagonists with the exception of MSX-122 have failed in clinical trials due to unmanageable toxicities, emphasizing the need for alternative strategies to interfere with CXCL12/CXCR4-guided metastatic homing. Although targeting the relatively featureless surface of CXCL12 was presumed to be challenging, focusing efforts at the sulfotyrosine (sY) binding pockets proved successful for procuring initial hits. Using a hybrid structure-based in silico/NMR screening strategy, we recently identified a ligand that occludes the receptor recognition site. From this initial hit, we designed a small fragment library containing only nine tetrazole derivatives using a fragment-based and bioisostere approach to target the sY binding sites of CXCL12. Compound binding modes and affinities were studied by 2D NMR spectroscopy, X-ray crystallography, molecular docking and cell-based functional assays. Our results demonstrate that the sY binding sites are conducive to the development of high affinity inhibitors with better ligand efficiency (LE) than typical protein-protein interaction inhibitors (LE ≤ 0.24). Our novel tetrazole-based fragment 18 was identified to bind the sY21 site with a K(d) of 24 μM (LE = 0.30). Optimization of 18 yielded compound 25 which specifically inhibits CXCL12-induced migration with an improvement in potency over the initial hit 9. The fragment from this library that exhibited the highest affinity and ligand efficiency (11: K(d) = 13 μM, LE = 0.33) may serve as a starting point for development of inhibitors targeting the sY12 site.
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Affiliation(s)
- Joshua J Ziarek
- Department of Biochemistry, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA
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21
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Kar S, Roy K. Prediction of Milk/Plasma Concentration Ratios of Drugs and Environmental Pollutants Using In Silico Tools: Classification and Regression Based QSARs and Pharmacophore Mapping. Mol Inform 2013; 32:693-705. [DOI: 10.1002/minf.201300018] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2013] [Accepted: 04/17/2013] [Indexed: 11/12/2022]
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22
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Speck-Planche A, Kleandrova VV, Cordeiro MND. New insights toward the discovery of antibacterial agents: Multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs. Eur J Pharm Sci 2013; 48:812-8. [DOI: 10.1016/j.ejps.2013.01.011] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Revised: 01/05/2013] [Accepted: 01/23/2013] [Indexed: 01/11/2023]
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23
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Kar S, Roy K. First report on predictive chemometric modeling, 3D-toxicophore mapping and in silico screening of in vitro basal cytotoxicity of diverse organic chemicals. Toxicol In Vitro 2013; 27:597-608. [DOI: 10.1016/j.tiv.2012.10.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2012] [Revised: 10/19/2012] [Accepted: 10/24/2012] [Indexed: 12/12/2022]
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24
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Prediction of hERG Potassium Channel Blocking Actions Using Combination of Classification and Regression Based Models: A Mixed Descriptors Approach. Mol Inform 2012; 31:879-94. [DOI: 10.1002/minf.201200039] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2012] [Accepted: 11/15/2012] [Indexed: 11/07/2022]
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25
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Mor S, Pahal P, Narasimhan B. Synthesis, characterization, biological evaluation and QSAR studies of 11-p-substituted phenyl-12-phenyl-11a,12-dihydro-11H-indeno[2,1-c][1,5]benzothiazepines as potential antimicrobial agents. Eur J Med Chem 2012; 57:196-210. [DOI: 10.1016/j.ejmech.2012.09.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2012] [Revised: 08/31/2012] [Accepted: 09/03/2012] [Indexed: 10/27/2022]
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26
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Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MND. Rational drug design for anti-cancer chemotherapy: Multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. Bioorg Med Chem 2012; 20:4848-55. [DOI: 10.1016/j.bmc.2012.05.071] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2012] [Revised: 05/21/2012] [Accepted: 05/25/2012] [Indexed: 12/23/2022]
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27
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Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MND. Chemoinformatics in anti-cancer chemotherapy: Multi-target QSAR model for the in silico discovery of anti-breast cancer agents. Eur J Pharm Sci 2012; 47:273-9. [DOI: 10.1016/j.ejps.2012.04.012] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2012] [Revised: 03/22/2012] [Accepted: 04/09/2012] [Indexed: 12/25/2022]
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28
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Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS. A ligand-based approach for the in silico discovery of multi-target inhibitors for proteins associated with HIV infection. MOLECULAR BIOSYSTEMS 2012; 8:2188-96. [PMID: 22688327 DOI: 10.1039/c2mb25093d] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Acquired immunodeficiency syndrome (AIDS) is a dangerous disease, which damages the immune system cells to the point that the immune system can no longer fight against other infections that it would usually be able to prevent. The causal agent is the human immunodeficiency virus (HIV), and for this reason, the search for more effective chemotherapies against HIV is a challenge for the scientific community. Chemoinformatics and Quantitative Structure-Activity Relationship (QSAR) studies have played an essential role in the design of potent inhibitors for proteins associated with the HIV infection. However, all previous studies took into consideration the discovery of future drug candidates using homogeneous series of compounds against only one protein. This fact limits the use of more efficient anti-HIV chemotherapies. In this work, we develop the first ligand-based approach for the in silico design of multi-target (mt) inhibitors for seven key proteins associated with the HIV infection. Two mt-QSAR models were constructed from a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors. The second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted and their contributions to anti-HIV activity through inhibition of the different proteins were calculated using the mt-QSAR-LDA model. New molecules designed from fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-HIV agents.
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Affiliation(s)
- Alejandro Speck-Planche
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
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