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Murali A, Panwar U, Singh SK. Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach. Methods Mol Biol 2024; 2714:203-213. [PMID: 37676601 DOI: 10.1007/978-1-0716-3441-7_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.
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Affiliation(s)
- Aarthy Murali
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Umesh Panwar
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
- Department of Data Sciences, Centre of Biomedical Research, SGPGIMS Campus, Lucknow, Uttar Pradesh, India
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2
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Wilson BAP, Li N, Martinez Fiesco JA, Dalilian M, Wang D, Smith EA, Wamiru A, Shah R, Goncharova EI, Beutler JA, Grkovic T, Zhang P, O’Keefe BR. Biochemical Discovery, Intracellular Evaluation, and Crystallographic Characterization of Synthetic and Natural Product Adenosine 3',5'-Cyclic Monophosphate-Dependent Protein Kinase A (PKA) Inhibitors. ACS Pharmacol Transl Sci 2023; 6:633-650. [PMID: 37082750 PMCID: PMC10111623 DOI: 10.1021/acsptsci.3c00010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Indexed: 04/22/2023]
Abstract
The recent demonstration that adenosine 3',5'-cyclic monophosphate (cAMP)-dependent protein kinase A (PKA) plays an oncogenic role in a number of important cancers has led to a renaissance in drug development interest targeting this kinase. We therefore have established a suite of biochemical, cell-based, and structural biology assays for identifying and evaluating new pharmacophores for PKA inhibition. This discovery process started with a 384-well high-throughput screen of more than 200,000 substances, including fractionated natural product extracts. Identified active compounds were further prioritized in biochemical, biophysical, and cell-based assays. Priority lead compounds were assessed in detail to fully characterize several previously unrecognized PKA pharmacophores including the generation of new X-ray crystallography structures demonstrating unique interactions between PKA and bound inhibitor molecules.
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Affiliation(s)
- Brice A. P. Wilson
- Molecular
Targets Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Ning Li
- Center
for Structural Biology, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Juliana A. Martinez Fiesco
- Center
for Structural Biology, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Masoumeh Dalilian
- Molecular
Targets Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
- Basic
Science Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Dongdong Wang
- Molecular
Targets Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Emily A. Smith
- Molecular
Targets Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
- Basic
Science Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Antony Wamiru
- Molecular
Targets Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
- Basic
Science Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Rohan Shah
- Molecular
Targets Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Ekaterina I. Goncharova
- Molecular
Targets Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
- Advanced
Biomedical Computational Science, Frederick
National Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - John A. Beutler
- Molecular
Targets Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Tanja Grkovic
- Molecular
Targets Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
- Natural
Products Branch, Developmental Therapeutics Program, Division of Cancer
Treatment and Diagnosis, National Cancer
Institute, Frederick, Maryland 21702, United States
| | - Ping Zhang
- Center
for Structural Biology, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Barry R. O’Keefe
- Molecular
Targets Program, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702, United States
- Natural
Products Branch, Developmental Therapeutics Program, Division of Cancer
Treatment and Diagnosis, National Cancer
Institute, Frederick, Maryland 21702, United States
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3
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Perumal G, Govindan K, Jayaram A, Sundaramoorthi S, Lin W. Preliminary investigation on biological possessions of Saquinavir‐modified quinoline‐derived azadipeptidomimetics. J CHIN CHEM SOC-TAIP 2023. [DOI: 10.1002/jccs.202200504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Gopi Perumal
- Department of Chemistry Vellore Institute of Technology Vellore India
- Department of Medicinal and Applied Chemistry Kaohsiung Medical University Kaohsiung Taiwan, ROC
| | - Karthick Govindan
- Department of Medicinal and Applied Chemistry Kaohsiung Medical University Kaohsiung Taiwan, ROC
| | - Alageswaran Jayaram
- Department of Medicinal and Applied Chemistry Kaohsiung Medical University Kaohsiung Taiwan, ROC
| | | | - Wei‐Yu Lin
- Department of Medicinal and Applied Chemistry Kaohsiung Medical University Kaohsiung Taiwan, ROC
- Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Taiwan, ROC
- Drug Development and Value Creation Research Centre Kaohsiung Medical University Kaohsiung Taiwan, ROC
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Mater AC, Coote ML. Explainable Molecular Sets: Using Information Theory to Generate Meaningful Descriptions of Groups of Molecules. J Chem Inf Model 2021; 61:4877-4889. [PMID: 34636543 DOI: 10.1021/acs.jcim.1c00519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Algorithmically identifying the meaningful similarities between an assortment of molecules is a critical chemical problem, and one which is only gaining in relevance as data-driven chemistry continues to progress. Effectively addressing this challenge can be achieved through a reformulation of the problem into information theory, cluster-based supervised classification, and the implementation of key concepts, particularly information entropy and mutual information. These concepts are combined with unsupervised learning atop learned chemical spaces to generate meaningful labels for arbitrary collections of molecules. An open-source and highly extensible codebase is provided to undertake these experiments, demonstrate the viability of the approach on known clusters, and glean insights into the learned representations of chemical space within message-passing neural networks, an architecture not readily permitting interpretability. This approach facilitates the interoperability between human chemical knowledge and the algorithmically derived insights, which will continue to become more prevalent in the coming years.
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Affiliation(s)
- Adam C Mater
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory 2601, Australia
| | - Michelle L Coote
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory 2601, Australia
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5
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Accurate acid dissociation constant (pK a) calculation for the sulfachloropyridazine and similar molecules. J Mol Model 2021; 27:233. [PMID: 34324066 DOI: 10.1007/s00894-021-04851-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 07/05/2021] [Indexed: 10/20/2022]
Abstract
Accurate calculation of the acid dissociation constant (pKa) has fundamental importance for the description of molecular systems with pharmacological activities. The search for a more appropriate procedure for its determination is always welcome and has aroused increasing interest from the scientific community. In this sense, this work presents a computational study involving the combination of ten DFT functionals (M062X, M06L, B3LYP, BLYP, PBEPBE, BP86, LC-BLYP, SPBE, CAM-B3LYP, LC-PBEPBE) and HF method, eight basis set functions (6-311G, 6-311 + G, 6-311G(d,p), 6-311 + G(d,p), 6-311+ +G(d,p), 6-311(2d,2p), 6-311+ +G(2d,2p), and aug-cc-pVDZ), and three solvation models (SMD, PCM, and CPCM) for an accurate sulfachloropyridazine (SCR) pKa determination. It was found that the smallest deviation (0.02 unit of pKa) between the current study and experimental result was achieved with the BLYP/6-311 + G(d,p)/PCM combination. Therefore, this combination was extended to calculate the pKa of six SCR similar molecules selected through the eletroshape similarity method. For all these molecules, the difference between the obtained results and experimental data ranged between 0.14 and 0.69 units of pKa. This feature suggests that the obtained combination can determine pKa with experimental precision for complexes that are formed by sulfonamide functional group (SO2NHR). Graphical Abstract A computational study involving the combination of different levels of theory, basis sets and solvation models for an accurate sulfanamide pKa determination.
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Moreira-Filho JT, Silva AC, Dantas RF, Gomes BF, Souza Neto LR, Brandao-Neto J, Owens RJ, Furnham N, Neves BJ, Silva-Junior FP, Andrade CH. Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence. Front Immunol 2021; 12:642383. [PMID: 34135888 PMCID: PMC8203334 DOI: 10.3389/fimmu.2021.642383] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 04/30/2021] [Indexed: 12/20/2022] Open
Abstract
Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.
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Affiliation(s)
- José T. Moreira-Filho
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Arthur C. Silva
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Rafael F. Dantas
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Barbara F. Gomes
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Lauro R. Souza Neto
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Jose Brandao-Neto
- Diamond Light Source Ltd., Didcot, United Kingdom
- Research Complex at Harwell, Didcot, United Kingdom
| | - Raymond J. Owens
- The Rosalind Franklin Institute, Harwell, United Kingdom
- Division of Structural Biology, The Wellcome Centre for Human Genetic, University of Oxford, Oxford, United Kingdom
| | - Nicholas Furnham
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Bruno J. Neves
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
| | - Floriano P. Silva-Junior
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Carolina H. Andrade
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás – UFG, Goiânia, Brazil
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Tafesse TB, Bule MH, Khan F, Abdollahi M, Amini M. Developing Novel Anticancer Drugs for Targeted Populations: An Update. Curr Pharm Des 2021; 27:250-262. [PMID: 33234093 DOI: 10.2174/1381612826666201124111748] [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: 04/07/2020] [Accepted: 08/16/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Due to higher failure rates, lengthy time and high cost of the traditional de novo drug discovery and development process, the rate of opportunity to get new, safe and efficacious drugs for the targeted population, including pediatric patients with cancer, becomes sluggish. OBJECTIVES This paper discusses the development of novel anticancer drugs focusing on the identification and selection of targeted anticancer drug development for the targeted population. METHODS Information presented in this review was obtained from different databases, including PUBMED, SCOPUS, Web of Science, and EMBASE. Various keywords were used as search terms. RESULTS The pharmaceutical companies currently are executing drug repurposing as an alternative means to accelerate the drug development process that reduces the risk of failure, time and cost, which take 3-12 years with almost 25% overall probability of success as compared to de novo drug discovery and development process (10- 17 years) which has less than 10% probability of success. An alternative strategy to the traditional de novo drug discovery and development process, called drug repurposing, is also presented. CONCLUSION Therefore, to continue with the progress of developing novel anticancer drugs for the targeted population, identification and selection of target to specific disease type is important. Considering the aspects of the age of the patient and the disease stages such as each cancer types are different when we study the disease at a molecular level. Drug repurposing technique becomes an influential alternative strategy to discover and develop novel anticancer drug candidates.
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Affiliation(s)
- Tadesse B Tafesse
- Department of Medicinal Chemistry, School of Pharmacy, Drug Design and Development Research Center and The Institute of Pharmaceutical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammed H Bule
- Department of Medicinal Chemistry, School of Pharmacy, Drug Design and Development Research Center and The Institute of Pharmaceutical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Fazlullah Khan
- Department of Allied Health Sciences, Bashir Institute of Health Sciences, Bhara Kahu Islamabad, Iran
| | - Mohammad Abdollahi
- Toxicology and Diseases Group (TDG), Pharmaceutical Sciences Research Center (PSRC), The Institute of Pharmaceutical Sciences (TIPS), and Department of Toxicology and Pharmacology, School of Pharmacy, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mohsen Amini
- Department of Medicinal Chemistry, School of Pharmacy, Drug Design and Development Research Center and The Institute of Pharmaceutical Sciences, Tehran University of Medical Sciences, Tehran, Iran
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Borba JVVB, Silva AC, Lima MNN, Mendonca SS, Furnham N, Costa FTM, Andrade CH. Chemogenomics and bioinformatics approaches for prioritizing kinases as drug targets for neglected tropical diseases. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2020; 124:187-223. [PMID: 33632465 DOI: 10.1016/bs.apcsb.2020.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Neglected tropical diseases (NTDs) are a group of twenty-one diseases classified by the World Health Organization that prevail in regions with tropical and subtropical climate and affect more than one billion people. There is an urgent need to develop new and safer drugs for these diseases. Protein kinases are a potential class of targets for developing new drugs against NTDs, since they play crucial role in many biological processes, such as signaling pathways, regulating cellular communication, division, metabolism and death. Bioinformatics is a field that aims to organize large amounts of biological data as well as develop and use tools for understanding and analyze them in order to produce meaningful information in a biological manner. In combination with chemogenomics, which analyzes chemical-biological interactions to screen ligands against selected targets families, these approaches can be used to stablish a rational strategy for prioritizing new drug targets for NTDs. Here, we describe how bioinformatics and chemogenomics tools can help to identify protein kinases and their potential inhibitors for the development of new drugs for NTDs. We present a review of bioinformatics tools and techniques that can be used to define an organisms kinome for drug prioritization, drug and target repurposing, multi-quinase inhibition approachs and selectivity profiling. We also present some successful examples of the application of such approaches in recent case studies.
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Affiliation(s)
- Joyce Villa Verde Bastos Borba
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil; Laboratory of Tropical Diseases-Prof. Luiz Jacintho da Silva, Department of Genetics, Evolution and Bioagents, University of Campinas, Campinas, SP, Brazil
| | - Arthur Carvalho Silva
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Marilia Nunes Nascimento Lima
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Sabrina Silva Mendonca
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil
| | - Nicholas Furnham
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Fabio Trindade Maranhão Costa
- Laboratory of Tropical Diseases-Prof. Luiz Jacintho da Silva, Department of Genetics, Evolution and Bioagents, University of Campinas, Campinas, SP, Brazil
| | - Carolina Horta Andrade
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil; Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
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9
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Adeowo FY, Lawal MM, Kumalo HM. Design and Development of Cholinesterase Dual Inhibitors towards Alzheimer's Disease Treatment: A Focus on Recent Contributions from Computational and Theoretical Perspective. ChemistrySelect 2020. [DOI: 10.1002/slct.202003573] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Fatima Y. Adeowo
- Discipline of Medical Biochemistry School of Laboratory Medicine and Medical Science University of KwaZulu-Natal Durban 4001 South Africa
| | - Monsurat M. Lawal
- Discipline of Medical Biochemistry School of Laboratory Medicine and Medical Science University of KwaZulu-Natal Durban 4001 South Africa
| | - Hezekiel M. Kumalo
- Discipline of Medical Biochemistry School of Laboratory Medicine and Medical Science University of KwaZulu-Natal Durban 4001 South Africa
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Scotti MT, Monteiro AFM, de Oliveira Viana J, Bezerra Mendonça Junior FJ, Ishiki HM, Tchouboun EN, De Araújo RSA, Scotti L. Recent Theoretical Studies Concerning Important Tropical Infections. Curr Med Chem 2020; 27:795-834. [DOI: 10.2174/0929867326666190711121418] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 07/20/2018] [Accepted: 04/12/2019] [Indexed: 01/02/2023]
Abstract
Neglected Tropical Diseases (NTDs) form a group of diseases that are strongly associated
with poverty, flourish in impoverished environments, and thrive best in tropical areas,
where they tend to present overlap. They comprise several diseases, and the symptoms
vary dramatically from disease to disease, often causing from extreme pain, and untold misery
that anchors populations to poverty, permanent disability, and death. They affect more than 1
billion people worldwide; mostly in poor populations living in tropical and subtropical climates.
In this review, several complementary in silico approaches are presented; including
identification of new therapeutic targets, novel mechanisms of activity, high-throughput
screening of small-molecule libraries, as well as in silico quantitative structure-activity relationship
and recent molecular docking studies. Current and active research against Sleeping
Sickness, American trypanosomiasis, Leishmaniasis and Schistosomiasis infections will hopefully
lead to safer, more effective, less costly and more widely available treatments against
these parasitic forms of Neglected Tropical Diseases (NTDs) in the near future.
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Affiliation(s)
- Marcus Tullius Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, Joao Pessoa - PB, Brazil
| | - Alex France Messias Monteiro
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, Joao Pessoa - PB, Brazil
| | - Jéssika de Oliveira Viana
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, Joao Pessoa - PB, Brazil
| | | | - Hamilton M. Ishiki
- University of Western Sao Paulo (Unoeste), Presidente Prudente, SP, Brazil
| | | | - Rodrigo Santos A. De Araújo
- Laboratory of Synthesis and Drug Delivery, Department of Biological Science, State University of Paraiba, Joao Pessoa, PB, Brazil
| | - Luciana Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, Joao Pessoa - PB, Brazil
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Brehm M, Heissler S, Afonin S, Levkin PA. Nanomolar Synthesis in Droplet Microarrays with UV-Triggered On-Chip Cell Screening. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2020; 16:e1905971. [PMID: 31985878 DOI: 10.1002/smll.201905971] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 12/04/2019] [Indexed: 06/10/2023]
Abstract
Miniaturization and parallelization of combinatorial organic synthesis is important to accelerate the process of drug discovery while reducing the consumption of reagents and solvents. This work presents a miniaturized platform for on-chip solid-phase combinatorial library synthesis with UV-triggered on-chip cell screening. The platform is based on a nanoporous polymer coating on a glass slide, which is modified via photolithography to yield arrays of hydrophilic (HL) spots surrounded by superhydrophobic (SH) surface. The combination of HL spots and SH background enables confinement of nanoliter droplets, functioning as miniaturized reactors for the solid-phase synthesis. The polymer serves as support for nanomolar solid-phase synthesis, while a photocleavable linker enables the release of the synthesized compounds into the droplets containing live cells. A 588 compound library of bisamides is synthesized via a four-component Ugi reaction on the chip and products are detected via stamping of the droplet array onto a conductive substrate and subsequent matrix-assisted laser desorption ionization mass spectrometry. The light-induced cleavage shows high flexibility in screening conditions by spatial, temporal, and quantitative control.
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Affiliation(s)
- Marius Brehm
- Karlsruhe Institute of Technology (KIT), Institute of Toxicology and Genetics, Hermann-von Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Stefan Heissler
- Karlsruhe Institute of Technology (KIT), Institute of Functional Interfaces, Hermann-von Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Sergii Afonin
- Karlsruhe Institute of Technology (KIT), Institute of Biological Interfaces (IBG-2), POB 3640, 76021, Karlsruhe, Germany
| | - Pavel A Levkin
- Karlsruhe Institute of Technology (KIT), Institute of Toxicology and Genetics, Hermann-von Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- Karlsruhe Institute of Technology (KIT), Institute of Organic Chemistry, Fritz-Haber-Weg 6, 76131, Karlsruhe, Germany
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12
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Martinez-Mayorga K, Madariaga-Mazon A, Medina-Franco JL, Maggiora G. The impact of chemoinformatics on drug discovery in the pharmaceutical industry. Expert Opin Drug Discov 2020; 15:293-306. [PMID: 31965870 DOI: 10.1080/17460441.2020.1696307] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.
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Affiliation(s)
| | | | - José L Medina-Franco
- Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico
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13
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Medina-Franco JL, Cruz-Lemus Y, Percastre-Cruz Y. Chemoinformatic Resources for Organometallic Drug Discovery. ACTA ACUST UNITED AC 2020. [DOI: 10.4236/cmb.2020.101001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Affiliation(s)
- Hagit Sason
- Faculty of Biomedical Engineering Technion – Israel Institute of Technology Haifa Israel
| | - Yosi Shamay
- Faculty of Biomedical Engineering Technion – Israel Institute of Technology Haifa Israel
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Di Pizio A, Behrens M, Krautwurst D. Beyond the Flavour: The Potential Druggability of Chemosensory G Protein-Coupled Receptors. Int J Mol Sci 2019; 20:E1402. [PMID: 30897734 PMCID: PMC6471708 DOI: 10.3390/ijms20061402] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 03/08/2019] [Accepted: 03/12/2019] [Indexed: 12/21/2022] Open
Abstract
G protein-coupled receptors (GPCRs) belong to the largest class of drug targets. Approximately half of the members of the human GPCR superfamily are chemosensory receptors, including odorant receptors (ORs), trace amine-associated receptors (TAARs), bitter taste receptors (TAS2Rs), sweet and umami taste receptors (TAS1Rs). Interestingly, these chemosensory GPCRs (csGPCRs) are expressed in several tissues of the body where they are supposed to play a role in biological functions other than chemosensation. Despite their abundance and physiological/pathological relevance, the druggability of csGPCRs has been suggested but not fully characterized. Here, we aim to explore the potential of targeting csGPCRs to treat diseases by reviewing the current knowledge of csGPCRs expressed throughout the body and by analysing the chemical space and the drug-likeness of flavour molecules.
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Affiliation(s)
- Antonella Di Pizio
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, 85354, Germany.
| | - Maik Behrens
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, 85354, Germany.
| | - Dietmar Krautwurst
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, 85354, Germany.
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16
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Frohlich KM, Weintraub SF, Bell JT, Todd GC, Väre VYP, Schneider R, Kloos ZA, Tabe ES, Cantara WA, Stark CJ, Onwuanaibe UJ, Duffy BC, Basanta-Sanchez M, Kitchen DB, McDonough KA, Agris PF. Discovery of Small-Molecule Antibiotics against a Unique tRNA-Mediated Regulation of Transcription in Gram-Positive Bacteria. ChemMedChem 2019; 14:758-769. [PMID: 30707489 DOI: 10.1002/cmdc.201800744] [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] [Received: 11/21/2018] [Revised: 01/24/2019] [Indexed: 01/24/2023]
Abstract
The emergence of multidrug-resistant bacteria necessitates the identification of unique targets of intervention and compounds that inhibit their function. Gram-positive bacteria use a well-conserved tRNA-responsive transcriptional regulatory element in mRNAs, known as the T-box, to regulate the transcription of multiple operons that control amino acid metabolism. T-box regulatory elements are found only in the 5'-untranslated region (UTR) of mRNAs of Gram-positive bacteria, not Gram-negative bacteria or the human host. Using the structure of the 5'UTR sequence of the Bacillus subtilis tyrosyl-tRNA synthetase mRNA T-box as a model, in silico docking of 305 000 small compounds initially yielded 700 as potential binders that could inhibit the binding of the tRNA ligand. A single family of compounds inhibited the growth of Gram-positive bacteria, but not Gram-negative bacteria, including drug-resistant clinical isolates at minimum inhibitory concentrations (MIC 16-64 μg mL-1 ). Resistance developed at an extremely low mutational frequency (1.21×10-10 ). At 4 μg mL-1 , the parent compound PKZ18 significantly inhibited in vivo transcription of glycyl-tRNA synthetase mRNA. PKZ18 also inhibited in vivo translation of the S. aureus threonyl-tRNA synthetase protein. PKZ18 bound to the Specifier Loop in vitro (Kd ≈24 μm). Its core chemistry necessary for antibacterial activity has been identified. These findings support the T-box regulatory mechanism as a new target for antibiotic discovery that may impede the emergence of resistance.
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Affiliation(s)
- Kyla M Frohlich
- The RNA Institute and the Department of Biological Sciences, University at Albany - State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA.,Current address: Regeneron Inc., Rensselaer, NY, USA
| | - Spencer F Weintraub
- The RNA Institute and the Department of Biological Sciences, University at Albany - State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA.,Current address: New York Medical College, Valhalla, NY, USA
| | - Janeen T Bell
- The RNA Institute and the Department of Biological Sciences, University at Albany - State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA.,Current address: Albany Medical College, Center for Physician Assistant Studies, Albany, NY, USA
| | - Gabrielle C Todd
- The RNA Institute and the Department of Biological Sciences, University at Albany - State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA
| | - Ville Y P Väre
- The RNA Institute and the Department of Biological Sciences, University at Albany - State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA
| | - Ryan Schneider
- Department of Biomedical Sciences, School of Public Health, University at Albany - State University of New York, P.O. Box 22002, Albany, NY, 12201, USA
| | - Zachary A Kloos
- The RNA Institute and the Department of Biological Sciences, University at Albany - State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA.,Division of Infectious Diseases, Wadsworth Center, New York State Department of Health, P.O. Box 22002, Albany, NY, 12201-2002, USA.,Current address: Molecular, Cellular and Developmental Biology, Yale University, West Haven, CT, USA
| | - Ebot S Tabe
- Division of Infectious Diseases, Wadsworth Center, New York State Department of Health, P.O. Box 22002, Albany, NY, 12201-2002, USA.,Current address: Albany College of Pharmacy and Health Sciences, Albany, NY, USA
| | - William A Cantara
- The RNA Institute and the Department of Biological Sciences, University at Albany - State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA.,Current address: Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Caren J Stark
- The RNA Institute and the Department of Biological Sciences, University at Albany - State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA
| | - Urenna J Onwuanaibe
- The RNA Institute and the Department of Biological Sciences, University at Albany - State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA
| | - Bryan C Duffy
- Albany Molecular Research Incorporated, 26 Corporate Circle, Albany, NY, 12203, USA.,Current address: New York State Department of Health, Albany, NY, USA
| | - Maria Basanta-Sanchez
- The RNA Institute and the Department of Biological Sciences, University at Albany - State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA.,Current address: Waters Corporation, Pleasanton, CA, USA
| | - Douglas B Kitchen
- Albany Molecular Research Incorporated, 26 Corporate Circle, Albany, NY, 12203, USA
| | - Kathleen A McDonough
- Department of Biomedical Sciences, School of Public Health, University at Albany - State University of New York, P.O. Box 22002, Albany, NY, 12201, USA.,Division of Infectious Diseases, Wadsworth Center, New York State Department of Health, P.O. Box 22002, Albany, NY, 12201-2002, USA
| | - Paul F Agris
- The RNA Institute and the Department of Biological Sciences, University at Albany - State University of New York, 1400 Washington Avenue, Albany, NY, 12222, USA.,Current address: Duke University, Medical School, Durham, NC, USA
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17
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Casañola-Martin GM, Pham-The H, Castillo-Garit JA, Le-Thi-Thu H. Atom based linear index descriptors in QSAR-machine learning classifiers for the prediction of ubiquitin-proteasome pathway activity. Med Chem Res 2018. [DOI: 10.1007/s00044-017-2091-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Shanmugam G, Jeon J. Computer-Aided Drug Discovery in Plant Pathology. THE PLANT PATHOLOGY JOURNAL 2017; 33:529-542. [PMID: 29238276 PMCID: PMC5720600 DOI: 10.5423/ppj.rw.04.2017.0084] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Revised: 09/06/2017] [Accepted: 09/06/2017] [Indexed: 05/31/2023]
Abstract
Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides.
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Affiliation(s)
| | - Junhyun Jeon
- Corresponding author. Phone) +82-53-810-3030, FAX) +82-53-810-4769, E-mail)
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19
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The potential role of in silico approaches to identify novel bioactive molecules from natural resources. Future Med Chem 2017; 9:1665-1686. [PMID: 28841048 DOI: 10.4155/fmc-2017-0124] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
In recent years, integration of in silico approaches to natural product (NP) research reawakened the declined interest in NP-based drug discovery efforts. In particular, advancements in cheminformatics enabled comparison of NP databases with contemporary small-molecule libraries in terms of molecular properties and chemical space localizations. Virtual screening and target fishing approaches were successful in recognizing the untold macromolecular targets for NPs to exploit the unmet therapeutic needs. Developments in molecular docking and scoring methods along with molecular dynamics enabled to predict the target-ligand interactions more accurately taking into consideration the remarkable structural complexity of NPs. Hence, innovative in silico strategies have contributed valuably to the NP research in drug discovery processes as reviewed herein. [Formula: see text].
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20
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Computer-aided drug discovery research at a global contract research organization. J Comput Aided Mol Des 2016; 31:309-318. [DOI: 10.1007/s10822-016-9991-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 10/26/2016] [Indexed: 10/20/2022]
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21
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Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R grand challenge. Bioorg Med Chem 2016; 24:4890-4899. [DOI: 10.1016/j.bmc.2016.07.044] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 07/19/2016] [Accepted: 07/20/2016] [Indexed: 01/14/2023]
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22
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Villoutreix B. Combining bioinformatics, chemoinformatics and experimental approaches to design chemical probes: Applications in the field of blood coagulation. ANNALES PHARMACEUTIQUES FRANÇAISES 2016; 74:253-66. [DOI: 10.1016/j.pharma.2016.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 03/21/2016] [Accepted: 03/21/2016] [Indexed: 11/08/2022]
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23
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Zhai Y, Chen K, Zhong Y, Zhou B, Ainscow E, Wu YT, Zhou Y. An Automatic Quality Control Pipeline for High-Throughput Screening Hit Identification. ACTA ACUST UNITED AC 2016; 21:832-41. [PMID: 27313114 DOI: 10.1177/1087057116654274] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 05/19/2016] [Indexed: 01/02/2023]
Abstract
The correction or removal of signal errors in high-throughput screening (HTS) data is critical to the identification of high-quality lead candidates. Although a number of strategies have been previously developed to correct systematic errors and to remove screening artifacts, they are not universally effective and still require fair amount of human intervention. We introduce a fully automated quality control (QC) pipeline that can correct generic interplate systematic errors and remove intraplate random artifacts. The new pipeline was first applied to ~100 large-scale historical HTS assays; in silico analysis showed auto-QC led to a noticeably stronger structure-activity relationship. The method was further tested in several independent HTS runs, where QC results were sampled for experimental validation. Significantly increased hit confirmation rates were obtained after the QC steps, confirming that the proposed method was effective in enriching true-positive hits. An implementation of the algorithm is available to the screening community.
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Affiliation(s)
- Yufeng Zhai
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Kaisheng Chen
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Yang Zhong
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Bin Zhou
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Edward Ainscow
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Ying-Ta Wu
- Genomics Research Center, Academia Sinica, Nankang, Taipei, Taiwan
| | - Yingyao Zhou
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
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24
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Melo-Filho CC, Dantas RF, Braga RC, Neves BJ, Senger MR, Valente WCG, Rezende-Neto JM, Chaves WT, Muratov EN, Paveley RA, Furnham N, Kamentsky L, Carpenter AE, Silva-Junior FP, Andrade CH. QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni. J Chem Inf Model 2016; 56:1357-72. [PMID: 27253773 DOI: 10.1021/acs.jcim.6b00055] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents.
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Affiliation(s)
- Cleber C Melo-Filho
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias , Rua 240, Qd.87, Goiania, GO 74605-510, Brazil
| | - Rafael F Dantas
- Laboratory of Experimental and Computational Biochemistry of Drugs, Oswaldo Cruz Institute , Av. Brasil, 4365, Rio de Janeiro, RJ 21040-900, Brazil
| | - Rodolpho C Braga
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias , Rua 240, Qd.87, Goiania, GO 74605-510, Brazil
| | - Bruno J Neves
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias , Rua 240, Qd.87, Goiania, GO 74605-510, Brazil
| | - Mario R Senger
- Laboratory of Experimental and Computational Biochemistry of Drugs, Oswaldo Cruz Institute , Av. Brasil, 4365, Rio de Janeiro, RJ 21040-900, Brazil
| | - Walter C G Valente
- Laboratory of Experimental and Computational Biochemistry of Drugs, Oswaldo Cruz Institute , Av. Brasil, 4365, Rio de Janeiro, RJ 21040-900, Brazil
| | - João M Rezende-Neto
- Laboratory of Experimental and Computational Biochemistry of Drugs, Oswaldo Cruz Institute , Av. Brasil, 4365, Rio de Janeiro, RJ 21040-900, Brazil
| | - Willian T Chaves
- Laboratory of Experimental and Computational Biochemistry of Drugs, Oswaldo Cruz Institute , Av. Brasil, 4365, Rio de Janeiro, RJ 21040-900, Brazil
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina , Chapel Hill, North Carolina 27599, United States.,Department of Chemical Technology, Odessa National Polytechnic University , 1. Shevchenko Ave., Odessa, 65000, Ukraine
| | - Ross A Paveley
- Department of Pathogen Molecular Biology & Department of Infection and Immunity, London School of Hygiene and Tropical Medicine , London WC1E 7HT, United Kingdom
| | - Nicholas Furnham
- Department of Pathogen Molecular Biology & Department of Infection and Immunity, London School of Hygiene and Tropical Medicine , London WC1E 7HT, United Kingdom
| | - Lee Kamentsky
- Imaging Platform, Broad Institute of Massachusetts Institute of Technology and Harvard , Cambridge, Massachusetts 02142, United States
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of Massachusetts Institute of Technology and Harvard , Cambridge, Massachusetts 02142, United States
| | - Floriano P Silva-Junior
- Laboratory of Experimental and Computational Biochemistry of Drugs, Oswaldo Cruz Institute , Av. Brasil, 4365, Rio de Janeiro, RJ 21040-900, Brazil
| | - Carolina H Andrade
- LabMol-Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias , Rua 240, Qd.87, Goiania, GO 74605-510, Brazil
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25
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Hovlid ML, Winzeler EA. Phenotypic Screens in Antimalarial Drug Discovery. Trends Parasitol 2016; 32:697-707. [PMID: 27247245 DOI: 10.1016/j.pt.2016.04.014] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 04/21/2016] [Indexed: 12/11/2022]
Abstract
Phenotypic high-throughput screens are a valuable tool for identifying new chemical compounds with antimalarial activity. Traditionally, these screens have focused solely on the symptomatic asexual blood stage of the parasite life cycle; however, to discover new medicines for malaria treatment and prevention, robust screening technologies against other parasite life-cycle stages are required. This review highlights recent advances and progress toward phenotypic screening methodologies over the past several years, with a focus on exoerythrocytic stage screens.
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Affiliation(s)
- Marisa L Hovlid
- School of Medicine, Department of Pediatrics, Division of Host-Microbe Systems and Therapeutics, University of California, San Diego (UCSD), 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Elizabeth A Winzeler
- School of Medicine, Department of Pediatrics, Division of Host-Microbe Systems and Therapeutics, University of California, San Diego (UCSD), 9500 Gilman Drive, La Jolla, CA 92093, USA.
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26
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Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method. Int J Mol Sci 2016; 17:ijms17060827. [PMID: 27240346 PMCID: PMC4926361 DOI: 10.3390/ijms17060827] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 05/13/2016] [Accepted: 05/20/2016] [Indexed: 11/17/2022] Open
Abstract
The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties.
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27
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Sung YK, Yuan K, de Jesus Perez VA. Novel approaches to pulmonary arterial hypertension drug discovery. Expert Opin Drug Discov 2016; 11:407-14. [PMID: 26901465 PMCID: PMC4933595 DOI: 10.1517/17460441.2016.1153625] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Pulmonary arterial hypertension (PAH) is a rare disorder associated with abnormally elevated pulmonary pressures that, if untreated, leads to right heart failure and premature death. The goal of drug development for PAH is to develop effective therapies that halt, or ideally, reverse the obliterative vasculopathy that results in vessel loss and obstruction of blood flow to the lungs. AREAS COVERED This review summarizes the current approach to candidate discovery in PAH and discusses the currently available drug discovery methods that should be implemented to prioritize targets and obtain a comprehensive pharmacological profile of promising compounds with well-defined mechanisms. EXPERT OPINION To improve the successful identification of leading drug candidates, it is necessary that traditional pre-clinical studies are combined with drug screening strategies that maximize the characterization of biological activity and identify relevant off-target effects that could hinder the clinical efficacy of the compound when tested in human subjects. A successful drug discovery strategy in PAH will require collaboration of clinician scientists with medicinal chemists and pharmacologists who can identify compounds with an adequate safety profile and biological activity against relevant disease mechanisms.
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Affiliation(s)
- Yon K. Sung
- Division of Pulmonary and Critical Care Medicine, The Vera Moulton Wall Center for Pulmonary Vascular Medicine, Stanford Cardiovascular Institute, Stanford, California
| | - Ke Yuan
- Division of Pulmonary and Critical Care Medicine, The Vera Moulton Wall Center for Pulmonary Vascular Medicine, Stanford Cardiovascular Institute, Stanford, California
| | - Vinicio A. de Jesus Perez
- Division of Pulmonary and Critical Care Medicine, The Vera Moulton Wall Center for Pulmonary Vascular Medicine, Stanford Cardiovascular Institute, Stanford, California
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28
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Awale M, Reymond JL. Similarity Mapplet: Interactive Visualization of the Directory of Useful Decoys and ChEMBL in High Dimensional Chemical Spaces. J Chem Inf Model 2015. [PMID: 26207526 DOI: 10.1021/acs.jcim.5b00182] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
An Internet portal accessible at www.gdb.unibe.ch has been set up to automatically generate color-coded similarity maps of the ChEMBL database in relation to up to two sets of active compounds taken from the enhanced Directory of Useful Decoys (eDUD), a random set of molecules, or up to two sets of user-defined reference molecules. These maps visualize the relationships between the selected compounds and ChEMBL in six different high dimensional chemical spaces, namely MQN (42-D molecular quantum numbers), SMIfp (34-D SMILES fingerprint), APfp (20-D shape fingerprint), Xfp (55-D pharmacophore fingerprint), Sfp (1024-bit substructure fingerprint), and ECfp4 (1024-bit extended connectivity fingerprint). The maps are supplied in form of Java based desktop applications called "similarity mapplets" allowing interactive content browsing and linked to a "Multifingerprint Browser for ChEMBL" (also accessible directly at www.gdb.unibe.ch ) to perform nearest neighbor searches. One can obtain six similarity mapplets of ChEMBL relative to random reference compounds, 606 similarity mapplets relative to single eDUD active sets, 30,300 similarity mapplets relative to pairs of eDUD active sets, and any number of similarity mapplets relative to user-defined reference sets to help visualize the structural diversity of compound series in drug optimization projects and their relationship to other known bioactive compounds.
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Affiliation(s)
- Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
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29
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Wójcikowski M, Zielenkiewicz P, Siedlecki P. Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field. J Cheminform 2015; 7:26. [PMID: 26101548 PMCID: PMC4475766 DOI: 10.1186/s13321-015-0078-2] [Citation(s) in RCA: 109] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2014] [Accepted: 05/21/2015] [Indexed: 12/22/2022] Open
Abstract
Background There has been huge progress in the open cheminformatics field in both methods and software development. Unfortunately, there has been little effort to unite those methods and software into one package. We here describe the Open Drug Discovery Toolkit (ODDT), which aims to fulfill the need for comprehensive and open source drug discovery software. Results The Open Drug Discovery Toolkit was developed as a free and open source tool for both computer aided drug discovery (CADD) developers and researchers. ODDT reimplements many state-of-the-art methods, such as machine learning scoring functions (RF-Score and NNScore) and wraps other external software to ease the process of developing CADD pipelines. ODDT is an out-of-the-box solution designed to be easily customizable and extensible. Therefore, users are strongly encouraged to extend it and develop new methods. We here present three use cases for ODDT in common tasks in computer-aided drug discovery. Conclusion Open Drug Discovery Toolkit is released on a permissive 3-clause BSD license for both academic and industrial use. ODDT’s source code, additional examples and documentation are available on GitHub (https://github.com/oddt/oddt). Electronic supplementary material The online version of this article (doi:10.1186/s13321-015-0078-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Maciej Wójcikowski
- Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, 02-106 Warsaw, Poland
| | - Piotr Zielenkiewicz
- Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, 02-106 Warsaw, Poland ; Department of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland
| | - Pawel Siedlecki
- Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, 02-106 Warsaw, Poland ; Department of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland
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30
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Duffy BC, Liu S, Martin GS, Wang R, Hsia MM, Zhao H, Guo C, Ellis M, Quinn JF, Kharenko OA, Norek K, Gesner EM, Young PR, McLure KG, Wagner GS, Lakshminarasimhan D, White A, Suto RK, Hansen HC, Kitchen DB. Discovery of a new chemical series of BRD4(1) inhibitors using protein-ligand docking and structure-guided design. Bioorg Med Chem Lett 2015; 25:2818-23. [PMID: 26022843 DOI: 10.1016/j.bmcl.2015.04.107] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Revised: 04/28/2015] [Accepted: 04/30/2015] [Indexed: 12/12/2022]
Abstract
Bromodomains are key transcriptional regulators that are thought to be druggable epigenetic targets for cancer, inflammation, diabetes and cardiovascular therapeutics. Of particular importance is the first of two bromodomains in bromodomain containing 4 protein (BRD4(1)). Protein-ligand docking in BRD4(1) was used to purchase a small, focused screening set of compounds possessing a large variety of core structures. Within this set, a small number of weak hits each contained a dihydroquinoxalinone ring system. We purchased other analogs with this ring system and further validated the new hit series and obtained improvement in binding inhibition. Limited exploration by new analog synthesis showed that the binding inhibition in a FRET assay could be improved to the low μM level making this new core a potential hit-to-lead series. Additionally, the predicted geometries of the initial hit and an improved analog were confirmed by X-ray co-crystallography with BRD4(1).
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Affiliation(s)
- Bryan C Duffy
- Albany Molecular Research (AMRI), 26 Corporate Circle, PO Box 15098, Albany, NY 12212-5098, USA
| | - Shuang Liu
- Albany Molecular Research (AMRI), 26 Corporate Circle, PO Box 15098, Albany, NY 12212-5098, USA
| | - Gregory S Martin
- Albany Molecular Research (AMRI), 26 Corporate Circle, PO Box 15098, Albany, NY 12212-5098, USA
| | - Ruifang Wang
- Albany Molecular Research (AMRI), 26 Corporate Circle, PO Box 15098, Albany, NY 12212-5098, USA
| | - Ming Min Hsia
- Albany Molecular Research (AMRI), 26 Corporate Circle, PO Box 15098, Albany, NY 12212-5098, USA
| | - He Zhao
- Albany Molecular Research (AMRI), 26 Corporate Circle, PO Box 15098, Albany, NY 12212-5098, USA
| | - Cheng Guo
- Albany Molecular Research (AMRI), 26 Corporate Circle, PO Box 15098, Albany, NY 12212-5098, USA
| | - Michael Ellis
- Albany Molecular Research (AMRI), 26 Corporate Circle, PO Box 15098, Albany, NY 12212-5098, USA
| | - John F Quinn
- JFQuinn Consulting, 113 Jay St., Albany, NY 12210, USA
| | - Olesya A Kharenko
- Zenith Epigenetics Corp., Suite 300, 4820 Richard Road SW, Calgary, Alberta T3E 6L1, Canada
| | - Karen Norek
- Zenith Epigenetics Corp., Suite 300, 4820 Richard Road SW, Calgary, Alberta T3E 6L1, Canada
| | - Emily M Gesner
- Zenith Epigenetics Corp., Suite 300, 4820 Richard Road SW, Calgary, Alberta T3E 6L1, Canada
| | - Peter R Young
- Zenith Epigenetics Corp., Suite 300, 4820 Richard Road SW, Calgary, Alberta T3E 6L1, Canada
| | - Kevin G McLure
- Zenith Epigenetics Corp., Suite 300, 4820 Richard Road SW, Calgary, Alberta T3E 6L1, Canada
| | - Gregory S Wagner
- Zenith Epigenetics Corp., Suite 300, 4820 Richard Road SW, Calgary, Alberta T3E 6L1, Canada
| | | | - Andre White
- Xtal BioStructures, Inc., 12 Michigan Dr., Natick, MA 01760, USA
| | - Robert K Suto
- Xtal BioStructures, Inc., 12 Michigan Dr., Natick, MA 01760, USA
| | - Henrik C Hansen
- Zenith Epigenetics Corp., Suite 300, 4820 Richard Road SW, Calgary, Alberta T3E 6L1, Canada
| | - Douglas B Kitchen
- Albany Molecular Research (AMRI), 26 Corporate Circle, PO Box 15098, Albany, NY 12212-5098, USA.
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31
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Caldwell GW. In silico tools used for compound selection during target-based drug discovery and development. Expert Opin Drug Discov 2015; 10:901-23. [DOI: 10.1517/17460441.2015.1043885] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Gary W Caldwell
- Janssen Research & Development LLC, Discovery Sciences, Spring House, PA, USA
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32
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Nybond S, Ghemtio L, Nawrot DA, Karp M, Xhaard H, Tammela P. Integrated In Vitro–In Silico Screening Strategy for the Discovery of Antibacterial Compounds. Assay Drug Dev Technol 2015; 13:25-33. [DOI: 10.1089/adt.2014.625] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Affiliation(s)
- Susanna Nybond
- Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Leo Ghemtio
- Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Dorota A. Nawrot
- Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Matti Karp
- Department of Chemistry and Bioengineering, Tampere University of Technology, Tampere, Finland
| | - Henri Xhaard
- Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
- Division of Pharmaceutical Chemistry and Technology, University of Helsinki, Helsinki, Finland
| | - Päivi Tammela
- Centre for Drug Research, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
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33
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Bujak A, Stefaniak F, Zdzalik D, Grygielewicz P, Dymek B, Zagozda M, Gunerka P, Lamparska-Przybysz M, Dubiel K, Wieczorek M, Dzwonek K. Discovery of TRAF-2 and NCK-interacting kinase (TNIK) inhibitors by ligand-based virtual screening methods. MEDCHEMCOMM 2015. [DOI: 10.1039/c5md00090d] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
TRAF-2 and NCK-interacting kinase (TNIK) is a serine–threonine kinase with a proposed role in Wnt/β-catenin and JNK pathways.
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34
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Gan S, Cosgrove DA, Gardiner EJ, Gillet VJ. Investigation of the use of spectral clustering for the analysis of molecular data. J Chem Inf Model 2014; 54:3302-19. [PMID: 25379955 DOI: 10.1021/ci500480b] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Spectral clustering involves placing objects into clusters based on the eigenvectors and eigenvalues of an associated matrix. The technique was first applied to molecular data by Brewer [J. Chem. Inf. Model. 2007, 47, 1727-1733] who demonstrated its use on a very small dataset of 125 COX-2 inhibitors. We have determined suitable parameters for spectral clustering using a wide variety of molecular descriptors and several datasets of a few thousand compounds and compared the results of clustering using a nonoverlapping version of Brewer's use of Sarker and Boyer's algorithm with that of Ward's and k-means clustering. We then replaced the exact eigendecomposition method with two different approximate methods and concluded that Singular Value Decomposition is the most appropriate method for clustering larger compound collections of up to 100,000 compounds. We have also used spectral clustering with the Tversky coefficient to generate two sets of clusters linked by a common set of eigenvalues and have used this novel approach to cluster sets of fragments such as those used in fragment-based drug design.
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Affiliation(s)
- Sonny Gan
- Information School, University of Sheffield , Regent Court, 211 Portobello Street, Sheffield S1 4DP, United Kingdom
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35
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Dahlin JL, Walters MA. The essential roles of chemistry in high-throughput screening triage. Future Med Chem 2014; 6:1265-90. [PMID: 25163000 PMCID: PMC4465542 DOI: 10.4155/fmc.14.60] [Citation(s) in RCA: 95] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
It is increasingly clear that academic high-throughput screening (HTS) and virtual HTS triage suffers from a lack of scientists trained in the art and science of early drug discovery chemistry. Many recent publications report the discovery of compounds by screening that are most likely artifacts or promiscuous bioactive compounds, and these results are not placed into the context of previous studies. For HTS to be most successful, it is our contention that there must exist an early partnership between biologists and medicinal chemists. Their combined skill sets are necessary to design robust assays and efficient workflows that will weed out assay artifacts, false positives, promiscuous bioactive compounds and intractable screening hits, efforts that ultimately give projects a better chance at identifying truly useful chemical matter. Expertise in medicinal chemistry, cheminformatics and purification sciences (analytical chemistry) can enhance the post-HTS triage process by quickly removing these problematic chemotypes from consideration, while simultaneously prioritizing the more promising chemical matter for follow-up testing. It is only when biologists and chemists collaborate effectively that HTS can manifest its full promise.
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Affiliation(s)
- Jayme L Dahlin
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
- Medical Scientist Training Program, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Michael A Walters
- Institute for Therapeutics Discovery & Development, University of Minnesota, Minneapolis, MN 55414, USA
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36
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Ekins S, Casey AC, Roberts D, Parish T, Bunin BA. Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis. Tuberculosis (Edinb) 2014; 94:162-9. [PMID: 24440548 PMCID: PMC4394018 DOI: 10.1016/j.tube.2013.12.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Revised: 12/04/2013] [Accepted: 12/09/2013] [Indexed: 12/19/2022]
Abstract
The search for compounds active against Mycobacterium tuberculosis is reliant upon high-throughput screening (HTS) in whole cells. We have used Bayesian machine learning models which can predict anti-tubercular activity to filter an internal library of over 150,000 compounds prior to in vitro testing. We used this to select and test 48 compounds in vitro; 11 were active with MIC values ranging from 0.4 μM to 10.2 μM, giving a high hit rate of 22.9%. Among the hits, we identified several compounds belonging to the same series including five quinolones (including ciprofloxacin), three molecules with long aliphatic linkers and three singletons. This approach represents a rapid method to prioritize compounds for testing that can be used alongside medicinal chemistry insight and other filters to identify active molecules. Such models can significantly increase the hit rate of HTS, above the usual 1% or lower rates seen. In addition, the potential targets for the 11 molecules were predicted using TB Mobile and clustering alongside a set of over 740 molecules with known M. tuberculosis target annotations. These predictions may serve as a mechanism for prioritizing compounds for further optimization.
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Affiliation(s)
- Sean Ekins
- Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA; Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA.
| | - Allen C Casey
- Infectious Disease Research Institute, Seattle, WA, USA
| | - David Roberts
- Infectious Disease Research Institute, Seattle, WA, USA
| | - Tanya Parish
- Infectious Disease Research Institute, Seattle, WA, USA
| | - Barry A Bunin
- Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA
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37
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Ekins S. Progress in computational toxicology. J Pharmacol Toxicol Methods 2013; 69:115-40. [PMID: 24361690 DOI: 10.1016/j.vascn.2013.12.003] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2013] [Accepted: 12/08/2013] [Indexed: 01/02/2023]
Abstract
INTRODUCTION Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed. METHODS A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods. RESULTS The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data. DISCUSSION Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC 27526, USA; Department of Pharmaceutical Sciences, University of Maryland, 20 Penn Street, Baltimore, MD 21201, USA; Department of Pharmacology, Rutgers University-Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ 08854, USA; Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, NC 27599-7355, USA.
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38
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Somvanshi PR, Venkatesh KV. A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics. SYSTEMS AND SYNTHETIC BIOLOGY 2013; 8:99-116. [PMID: 24592295 DOI: 10.1007/s11693-013-9125-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 09/10/2013] [Indexed: 12/28/2022]
Abstract
Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.
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Affiliation(s)
- Pramod Rajaram Somvanshi
- Biosystems Engineering, Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 Maharashtra India
| | - K V Venkatesh
- Biosystems Engineering, Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 Maharashtra India
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39
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Ponder EL, Freundlich JS, Sarker M, Ekins S. Computational models for neglected diseases: gaps and opportunities. Pharm Res 2013; 31:271-7. [PMID: 23990313 DOI: 10.1007/s11095-013-1170-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Accepted: 07/28/2013] [Indexed: 01/22/2023]
Abstract
Neglected diseases, such as Chagas disease, African sleeping sickness, and intestinal worms, affect millions of the world's poor. They disproportionately affect marginalized populations, lack effective treatments or vaccines, or existing products are not accessible to the populations affected. Computational approaches have been used across many of these diseases for various aspects of research or development, and yet data produced by computational approaches are not integrated and widely accessible to others. Here, we identify gaps in which computational approaches have been used for some neglected diseases and not others. We also make recommendations for the broad-spectrum integration of these techniques into a neglected disease drug discovery and development workflow.
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Affiliation(s)
- Elizabeth L Ponder
- Center for Emerging and Neglected Diseases, Berkeley, 444A Li Ka Shing Center, Berkeley, California, 94720-3370, USA,
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40
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Schaal W, Hammerling U, Gustafsson MG, Spjuth O. Automated QuantMap for rapid quantitative molecular network topology analysis. ACTA ACUST UNITED AC 2013; 29:2369-70. [PMID: 23828784 PMCID: PMC3753568 DOI: 10.1093/bioinformatics/btt390] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Summary: The previously disclosed QuantMap method for grouping chemicals by biological activity used online services for much of the data gathering and some of the numerical analysis. The present work attempts to streamline this process by using local copies of the databases and in-house analysis. Using computational methods similar or identical to those used in the previous work, a qualitatively equivalent result was found in just a few seconds on the same dataset (collection of 18 drugs). We use the user-friendly Galaxy framework to enable users to analyze their own datasets. Hopefully, this will make the QuantMap method more practical and accessible and help achieve its goals to provide substantial assistance to drug repositioning, pharmacology evaluation and toxicology risk assessment. Availability:http://galaxy.predpharmtox.org Contact:mats.gustafsson@medsci.uu.se or ola.spjuth@farmbio.uu.se Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wesley Schaal
- Department of Pharmaceutical Biosciences, Uppsala University, SE-751 24 Uppsala, Sweden
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41
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Medina-Franco JL, Edwards BS, Pinilla C, Appel JR, Giulianotti MA, Santos RG, Yongye AB, Sklar LA, Houghten RA. Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches. J Chem Inf Model 2013; 53:1475-85. [PMID: 23705689 DOI: 10.1021/ci400192y] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
We present a general approach to describe the structure-activity relationships (SAR) of combinatorial data sets with activity for two biological endpoints with emphasis on the rapid identification of substitutions that have a large impact on activity and selectivity. The approach uses dual-activity difference (DAD) maps that represent a visual and quantitative analysis of all pairwise comparisons of one, two, or more substitutions around a molecular template. Scanning the SAR of data sets using DAD maps allows the visual and quantitative identification of activity switches defined as specific substitutions that have an opposite effect on the activity of the compounds against two targets. The approach also rapidly identifies single- and double-target R-cliffs, i.e., compounds where a single or double substitution around the central scaffold dramatically modifies the activity for one or two targets, respectively. The approach introduced in this report can be applied to any analogue series with two biological activity endpoints. To illustrate the approach, we discuss the SAR of 106 pyrrolidine bis-diketopiperazines tested against two formylpeptide receptors obtained from positional scanning deconvolution methods of mixture-based libraries.
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Affiliation(s)
- José L Medina-Franco
- Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida 34987, USA.
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42
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Anighoro A, Rastelli G. BEAR, a Molecular Docking Refinement and Rescoring Method. ACTA ACUST UNITED AC 2013. [DOI: 10.4236/cmb.2013.32004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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