1
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Zhou Y, Peng S, Wang H, Cai X, Wang Q. Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products. Genes (Basel) 2024; 15:468. [PMID: 38674402 PMCID: PMC11049652 DOI: 10.3390/genes15040468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/11/2023] [Accepted: 05/13/2023] [Indexed: 04/28/2024] Open
Abstract
In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.
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Affiliation(s)
- Yalan Zhou
- Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; (Y.Z.); (S.P.); (H.W.)
| | - Siqi Peng
- Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; (Y.Z.); (S.P.); (H.W.)
| | - Huizhen Wang
- Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; (Y.Z.); (S.P.); (H.W.)
| | - Xinyin Cai
- Shanghai R&D Centre for Standardization of Chinese Medicines, Shanghai 202103, China
| | - Qingzhong Wang
- Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; (Y.Z.); (S.P.); (H.W.)
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2
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Horne R, Wilson-Godber J, González Díaz A, Brotzakis ZF, Seal S, Gregory RC, Possenti A, Chia S, Vendruscolo M. Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity. J Chem Inf Model 2024; 64:590-596. [PMID: 38261763 PMCID: PMC10865343 DOI: 10.1021/acs.jcim.3c01777] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 12/10/2023] [Accepted: 12/12/2023] [Indexed: 01/25/2024]
Abstract
In the early stages of drug development, large chemical libraries are typically screened to identify compounds of promising potency against the chosen targets. Often, however, the resulting hit compounds tend to have poor drug metabolism and pharmacokinetics (DMPK), with negative developability features that may be difficult to eliminate. Therefore, starting the drug discovery process with a "null library", compounds that have highly desirable DMPK properties but no potency against the chosen targets, could be advantageous. Here, we explore the opportunities offered by machine learning to realize this strategy in the case of the inhibition of α-synuclein aggregation, a process associated with Parkinson's disease. We apply MolDQN, a generative machine learning method, to build an inhibitory activity against α-synuclein aggregation into an initial inactive compound with good DMPK properties. Our results illustrate how generative modeling can be used to endow initially inert compounds with desirable developability properties.
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Affiliation(s)
- Robert
I. Horne
- Centre
for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United
Kingdom
| | - Jared Wilson-Godber
- Centre
for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United
Kingdom
| | - Alicia González Díaz
- Centre
for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United
Kingdom
| | - Z. Faidon Brotzakis
- Centre
for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United
Kingdom
| | - Srijit Seal
- Centre
for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United
Kingdom
- Imaging
Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Rebecca C. Gregory
- Centre
for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United
Kingdom
| | - Andrea Possenti
- Centre
for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United
Kingdom
| | - Sean Chia
- Centre
for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United
Kingdom
- Bioprocessing
Technology Institute, Agency for Science, Technology and Research (A*STAR), 138668 Singapore, Singapore
| | - Michele Vendruscolo
- Centre
for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United
Kingdom
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3
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Alnammi M, Liu S, Ericksen SS, Ananiev GE, Voter AF, Guo S, Keck JL, Hoffmann FM, Wildman SA, Gitter A. Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries. J Chem Inf Model 2023; 63:5513-5528. [PMID: 37625010 PMCID: PMC10538940 DOI: 10.1021/acs.jcim.3c00912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Indexed: 08/27/2023]
Abstract
Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM.
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Affiliation(s)
- Moayad Alnammi
- Department
of Computer Sciences, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
- Morgridge
Institute for Research, Madison, Wisconsin 53715, United States
- Department
of Information and Computer Science, King
Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
| | - Shengchao Liu
- Department
of Computer Sciences, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
- Morgridge
Institute for Research, Madison, Wisconsin 53715, United States
| | - Spencer S. Ericksen
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - Gene E. Ananiev
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - Andrew F. Voter
- Department
of Biomolecular Chemistry, University of
Wisconsin−Madison, Madison, Wisconsin 53706, United States
| | - Song Guo
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - James L. Keck
- Department
of Biomolecular Chemistry, University of
Wisconsin−Madison, Madison, Wisconsin 53706, United States
| | - F. Michael Hoffmann
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
- McArdle Laboratory
for Cancer Research, University of Wisconsin−Madison, Madison, Wisconsin 53705, United States
| | - Scott A. Wildman
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - Anthony Gitter
- Department
of Computer Sciences, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
- Morgridge
Institute for Research, Madison, Wisconsin 53715, United States
- Department
of Biostatistics and Medical Informatics, University of Wisconsin−Madison, Madison, Wisconsin 53792, United States
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4
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Young RJ, Flitsch SL, Grigalunas M, Leeson PD, Quinn RJ, Turner NJ, Waldmann H. The Time and Place for Nature in Drug Discovery. JACS AU 2022; 2:2400-2416. [PMID: 36465532 PMCID: PMC9709949 DOI: 10.1021/jacsau.2c00415] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/06/2022] [Indexed: 05/31/2023]
Abstract
The case for a renewed focus on Nature in drug discovery is reviewed; not in terms of natural product screening, but how and why biomimetic molecules, especially those produced by natural processes, should deliver in the age of artificial intelligence and screening of vast collections both in vitro and in silico. The declining natural product-likeness of licensed drugs and the consequent physicochemical implications of this trend in the context of current practices are noted. To arrest these trends, the logic of seeking new bioactive agents with enhanced natural mimicry is considered; notably that molecules constructed by proteins (enzymes) are more likely to interact with other proteins (e.g., targets and transporters), a notion validated by natural products. Nature's finite number of building blocks and their interactions necessarily reduce potential numbers of structures, yet these enable expansion of chemical space with their inherent diversity of physical characteristics, pertinent to property-based design. The feasible variations on natural motifs are considered and expanded to encompass pseudo-natural products, leading to the further logical step of harnessing bioprocessing routes to access them. Together, these offer opportunities for enhancing natural mimicry, thereby bringing innovation to drug synthesis exploiting the characteristics of natural recognition processes. The potential for computational guidance to help identifying binding commonalities in the route map is a logical opportunity to enable the design of tailored molecules, with a focus on "organic/biological" rather than purely "synthetic" structures. The design and synthesis of prototype structures should pay dividends in the disposition and efficacy of the molecules, while inherently enabling greener and more sustainable manufacturing techniques.
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Affiliation(s)
| | - Sabine L. Flitsch
- Department
of Chemistry, University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, United Kingdom
| | - Michael Grigalunas
- Department
of Chemical Biology, Max-Planck-Institute
of Molecular Physiology, Otto-Hahn Strasse 11, 44227 Dortmund, Germany
| | - Paul D. Leeson
- Paul
Leeson Consulting Limited, The Malt House, Main Street, Congerstone, Nuneaton, Warwickshire CV13 6LZ, U.K.
| | - Ronald J. Quinn
- Griffith
Institute for Drug Discovery, Griffith University, Nathan, Queensland 4111, Australia
| | - Nicholas J. Turner
- Department
of Chemistry, University of Manchester,
Manchester Institute of Biotechnology, 131 Princess Street, Manchester M1 7DN, United Kingdom
| | - Herbert Waldmann
- Department
of Chemical Biology, Max-Planck-Institute
of Molecular Physiology, Otto-Hahn Strasse 11, 44227 Dortmund, Germany
- Faculty of
Chemistry and Chemical Biology, Technical
University of Dortmund, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany
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5
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Ahn S, Lee SE, Kim MH. Random-forest model for drug-target interaction prediction via Kullbeck-Leibler divergence. J Cheminform 2022; 14:67. [PMID: 36192818 PMCID: PMC9531514 DOI: 10.1186/s13321-022-00644-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 09/11/2022] [Indexed: 12/04/2022] Open
Abstract
Virtual screening has significantly improved the success rate of early stage drug discovery. Recent virtual screening methods have improved owing to advances in machine learning and chemical information. Among these advances, the creative extraction of drug features is important for predicting drug–target interaction (DTI), which is a large-scale virtual screening of known drugs. Herein, we report Kullbeck–Leibler divergence (KLD) as a DTI feature and the feature-driven classification model applicable to DTI prediction. For the purpose, E3FP three-dimensional (3D) molecular fingerprints of drugs as a molecular representation allow the computation of 3D similarities between ligands within each target (Q–Q matrix) to identify the uniqueness of pharmacological targets and those between a query and a ligand (Q–L vector) in DTIs. The 3D similarity matrices are transformed into probability density functions via kernel density estimation as a nonparametric estimation. Each density model can exploit the characteristics of each pharmacological target and measure the quasi-distance between the ligands. Furthermore, we developed a random forest model from the KLD feature vectors to successfully predict DTIs for representative 17 targets (mean accuracy: 0.882, out-of-bag score estimate: 0.876, ROC AUC: 0.990). The method is applicable for 2D chemical similarity.
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Affiliation(s)
- Sangjin Ahn
- Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea.,Department of Artificial Intelligence, Ajou University, Suwon, 16499, Republic of Korea
| | - Si Eun Lee
- Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea
| | - Mi-Hyun Kim
- Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, 191 Hambakmoeiro, Yeonsu-gu, Incheon, Republic of Korea.
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6
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Sánchez-Ruiz A, Colmenarejo G. Systematic Analysis and Prediction of the Target Space of Bioactive Food Compounds: Filling the Chemobiological Gaps. J Chem Inf Model 2022; 62:3734-3751. [PMID: 35938782 DOI: 10.1021/acs.jcim.2c00888] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Food compounds and their molecular interactions are crucial for health and provide new chemotypes and targets for drug and nutraceutic design. Here, we retrieve and analyze the complete set of published interactions of food compounds with human proteins using the FooDB as a compound set and ChEMBL as a source of interactions. The data are analyzed in terms of 19 target classes and 19 compound classes, showing a small fraction of target assignment for the compounds (1.6%) and unraveling multiple gaps in the chemobiological space for these molecules. By using well-established cheminformatic approaches [similarity ensemble approach (SEA) combined with the maximum Tanimoto coefficient to the nearest bioactive, "SEA + TC"], we achieve a much enhanced target assignment (64.2%), filling many of the gaps with target hypothesis for fast focused testing. By publishing these data sets and analyses, we expect to provide a set of resources to speed up the full clarification of the chemobiological space of food compounds, opening new opportunities for drug and nutraceutic design.
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Affiliation(s)
- Andrés Sánchez-Ruiz
- Biostatistics and Bioinformatics Unit, IMDEA Food, CEI UAM+CSIC, E28049 Madrid, Spain
| | - Gonzalo Colmenarejo
- Biostatistics and Bioinformatics Unit, IMDEA Food, CEI UAM+CSIC, E28049 Madrid, Spain
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7
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Discovery of Kinase and Carbonic Anhydrase Dual Inhibitors by Machine Learning Classification and Experiments. Pharmaceuticals (Basel) 2022; 15:ph15020236. [PMID: 35215348 PMCID: PMC8875555 DOI: 10.3390/ph15020236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/11/2022] [Accepted: 02/12/2022] [Indexed: 02/04/2023] Open
Abstract
A multi-target small molecule modulator is advantageous for treating complicated diseases such as cancers. However, the strategy and application for discovering a multi-target modulator have been less reported. This study presents the dual inhibitors for kinase and carbonic anhydrase (CA) predicted by machine learning (ML) classifiers, and validated by biochemical and biophysical experiments. ML trained by CA I and CA II inhibitor molecular fingerprints predicted candidates from the protein-specific bioactive molecules approved or under clinical trials. For experimental tests, three sulfonamide-containing kinase inhibitors, 5932, 5946, and 6046, were chosen. The enzyme assays with CA I, CA II, CA IX, and CA XII have allowed the quantitative comparison in the molecules’ inhibitory activities. While 6046 inhibited weakly, 5932 and 5946 exhibited potent inhibitions with 100 nM to 1 μM inhibitory constants. The ML screening was extended for finding CAs inhibitors of all known kinase inhibitors. It found XMU-MP-1 as another potent CA inhibitor with an approximate 30 nM inhibitory constant for CA I, CA II, and CA IX. Differential scanning fluorimetry confirmed the direct interaction between CAs and small molecules. Cheminformatics studies, including docking simulation, suggest that each molecule possesses two separate functional moieties: one for interaction with kinases and the other with CAs.
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8
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Green AI, Burslem GM. Photochemical synthesis of an epigenetic focused tetrahydroquinoline library. RSC Med Chem 2021; 12:1780-1786. [PMID: 34778779 DOI: 10.1039/d1md00193k] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 08/24/2021] [Indexed: 12/21/2022] Open
Abstract
Discovery of epigenetic chemical probes is an important area of research with potential to deliver drugs for a multitude of diseases. However, commercially available libraries frequently used in drug discovery campaigns contain molecules that are focused on a narrow range of chemical space primarily driven by ease of synthesis and previously targeted enzyme classes (e.g., kinases) resulting in low hit rates for epigenetic targets. Here we describe the design and synthesis of a compound collection that augments current screening collections by the inclusion of privileged isosteres for epigenetic targets.
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Affiliation(s)
- Adam I Green
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania PA 19104 USA
| | - George M Burslem
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania PA 19104 USA .,Department of Cancer Biology and Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania PA 19104 USA
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9
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Mamun AA, Akter F, Khan M, Ahmed SS, Uddin MG, Tasfia NT, Efaz FM, Ali MA, Sultana MUC, Halim MA. Identification of potent inhibitors against transmembrane serine protease 2 for developing therapeutics against SARS-CoV-2. J Biomol Struct Dyn 2021; 40:13049-13061. [PMID: 34590967 PMCID: PMC8500310 DOI: 10.1080/07391102.2021.1980109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 09/08/2021] [Indexed: 12/27/2022]
Abstract
In viral binding and entry, the Spike(S) protein of SARS-CoV-2 uses transmembrane serine protease 2 (TMPRSS2) for priming to cleavage themselves. In this study, we have screened 'drug-like' 7476 ligands and found that over thirty ligands can effectively inhibit the TMPRSS-2 better than the control ligand. Finally, the three best drug agents L1, L2, and L6 were selected according to their average binding affinities and fitting score. These ligands interact with Asp435, Cys437, Ser436, Trp461, and Cys465 amino acid residues. The three best candidates and a reported drug Nafamostat mesylate (NAM) were selected to run 250 ns molecular dynamics (MD) simulations. Various properties of ligand-protein interactions obtained from MD simulation such as bonds, angle, dihedral, planarity, coulomb, and van der Waals (VdW) were used for principal component analysis (PCA) calculation. PCA discloses the evidence of the structural similarities to the corresponding complexes of L1, L2, and L6 with the complex of TMPRSS2(TM) and Nafamostat mesylate (TM-NAM). Moreover, Quantitative structure-activity relationship (QSAR) pattern recognition was generated using PCA for the investigation of structural similarities among the selected ligands. Multiple Linear Regression (MLR) model was built to predict the binding energy compared to the binding energy obtained from molecular docking. The MLR regression model reveals an accuracy of 80% for the prediction of the binding energy of ligands. ADMET analysis demonstrates that these drug agents are appeared to be safer inhibitors. These three ligands can be used as potential inhibitors against the TMPRSS2.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Abdulla Al Mamun
- Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
- Key Laboratory of Soft Chemistry and Functional Materials of MOE, School of Chemical Engineering, Nanjing University of Science and Technology, Nanjing, P. R. China
| | - Farjana Akter
- Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
| | - Maksud Khan
- Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
| | - Sayeda Samina Ahmed
- Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
| | - Md. Giash Uddin
- Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
- Department of Pharmacy, University of Chittagong, Chittagong, Bangladesh
| | - Nabila Tabassum Tasfia
- Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
| | - Faiyaz Md. Efaz
- Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
| | - Md Ackas Ali
- Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
| | - Mossammad Umme Chand Sultana
- Division of Infectious Diseases and Division of Computer Aided Drug Design, The Red-Green Research Centre, Dhaka, Bangladesh
| | - Mohammad A. Halim
- Department of Chemistry and Biochemistry, Kennesaw State University, Kennesaw, GA, USA
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10
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Marwick JA, Elliott RJR, Longden J, Makda A, Hirani N, Dhaliwal K, Dawson JC, Carragher NO. Application of a High-Content Screening Assay Utilizing Primary Human Lung Fibroblasts to Identify Antifibrotic Drugs for Rapid Repurposing in COVID-19 Patients. SLAS DISCOVERY 2021; 26:1091-1106. [PMID: 34078171 PMCID: PMC8458684 DOI: 10.1177/24725552211019405] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Lung imaging and autopsy reports among COVID-19 patients show elevated lung scarring (fibrosis). Early data from COVID-19 patients as well as previous studies from severe acute respiratory syndrome, Middle East respiratory syndrome, and other respiratory disorders show that the extent of lung fibrosis is associated with a higher mortality, prolonged ventilator dependence, and poorer long-term health prognosis. Current treatments to halt or reverse lung fibrosis are limited; thus, the rapid development of effective antifibrotic therapies is a major global medical need that will continue far beyond the current COVID-19 pandemic. Reproducible fibrosis screening assays with high signal-to-noise ratios and disease-relevant readouts such as extracellular matrix (ECM) deposition (the hallmark of fibrosis) are integral to any antifibrotic therapeutic development. Therefore, we have established an automated high-throughput and high-content primary screening assay measuring transforming growth factor-β (TGFβ)-induced ECM deposition from primary human lung fibroblasts in a 384-well format. This assay combines longitudinal live cell imaging with multiparametric high-content analysis of ECM deposition. Using this assay, we have screened a library of 2743 small molecules representing approved drugs and late-stage clinical candidates. Confirmed hits were subsequently profiled through a suite of secondary lung fibroblast phenotypic screening assays quantifying cell differentiation, proliferation, migration, and apoptosis. In silico target prediction and pathway network analysis were applied to the confirmed hits. We anticipate this suite of assays and data analysis tools will aid the identification of new treatments to mitigate against lung fibrosis associated with COVID-19 and other fibrotic diseases.
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Affiliation(s)
- John A Marwick
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.,Centre for Inflammation Research, Queens Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Richard J R Elliott
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - James Longden
- Center for Clinical Brain Sciences, Chancellors Building, University of Edinburgh, Edinburgh, UK
| | - Ashraff Makda
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Nik Hirani
- Centre for Inflammation Research, Queens Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Kevin Dhaliwal
- Centre for Inflammation Research, Queens Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - John C Dawson
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
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11
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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12
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Hughes RE, Elliott RJR, Dawson JC, Carragher NO. High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need. Cell Chem Biol 2021; 28:338-355. [PMID: 33740435 DOI: 10.1016/j.chembiol.2021.02.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/10/2020] [Accepted: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Conventional thinking in modern drug discovery postulates that the design of highly selective molecules which act on a single disease-associated target will yield safer and more effective drugs. However, high clinical attrition rates and the lack of progress in developing new effective treatments for many important diseases of unmet therapeutic need challenge this hypothesis. This assumption also impinges upon the efficiency of target agnostic phenotypic drug discovery strategies, where early target deconvolution is seen as a critical step to progress phenotypic hits. In this review we provide an overview of how emerging phenotypic and pathway-profiling technologies integrate to deconvolute the mechanism-of-action of phenotypic hits. We propose that such in-depth mechanistic profiling may support more efficient phenotypic drug discovery strategies that are designed to more appropriately address complex heterogeneous diseases of unmet need.
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Affiliation(s)
- Rebecca E Hughes
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Richard J R Elliott
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - John C Dawson
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK.
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13
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Xu D, Zhou D, Bum-Erdene K, Bailey BJ, Sishtla K, Liu S, Wan J, Aryal UK, Lee JA, Wells CD, Fishel ML, Corson TW, Pollok KE, Meroueh SO. Phenotypic Screening of Chemical Libraries Enriched by Molecular Docking to Multiple Targets Selected from Glioblastoma Genomic Data. ACS Chem Biol 2020; 15:1424-1444. [PMID: 32243127 PMCID: PMC7919753 DOI: 10.1021/acschembio.0c00078] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Like most solid tumors, glioblastoma multiforme (GBM) harbors multiple overexpressed and mutated genes that affect several signaling pathways. Suppressing tumor growth of solid tumors like GBM without toxicity may be achieved by small molecules that selectively modulate a collection of targets across different signaling pathways, also known as selective polypharmacology. Phenotypic screening can be an effective method to uncover such compounds, but the lack of approaches to create focused libraries tailored to tumor targets has limited its impact. Here, we create rational libraries for phenotypic screening by structure-based molecular docking chemical libraries to GBM-specific targets identified using the tumor's RNA sequence and mutation data along with cellular protein-protein interaction data. Screening this enriched library of 47 candidates led to several active compounds, including 1 (IPR-2025), which (i) inhibited cell viability of low-passage patient-derived GBM spheroids with single-digit micromolar IC50 values that are substantially better than standard-of-care temozolomide, (ii) blocked tube-formation of endothelial cells in Matrigel with submicromolar IC50 values, and (iii) had no effect on primary hematopoietic CD34+ progenitor spheroids or astrocyte cell viability. RNA sequencing provided the potential mechanism of action for 1, and mass spectrometry-based thermal proteome profiling confirmed that the compound engages multiple targets. The ability of 1 to inhibit GBM phenotypes without affecting normal cell viability suggests that our screening approach may hold promise for generating lead compounds with selective polypharmacology for the development of treatments of incurable diseases like GBM.
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Affiliation(s)
- David Xu
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, Indiana 46202, United States
| | - Donghui Zhou
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Khuchtumur Bum-Erdene
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Barbara J Bailey
- Department of Pediatrics, Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Indiana University Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Kamakshi Sishtla
- Eugene and Marilyn Glick Eye Institute, Department of Ophthalmology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Sheng Liu
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Jun Wan
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Uma K Aryal
- Purdue Proteomics Facility, Bindley Bioscience Center, Purdue University, West Lafayette, Indiana 47907, United States
| | - Jonathan A Lee
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Clark D Wells
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Melissa L Fishel
- Department of Pediatrics, Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Indiana University Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Timothy W Corson
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Indiana University Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Eugene and Marilyn Glick Eye Institute, Department of Ophthalmology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Karen E Pollok
- Department of Pediatrics, Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Indiana University Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Samy O Meroueh
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Indiana University Melvin and Bren Simon Cancer Center, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
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14
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Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker. Nat Biotechnol 2020; 38:1087-1096. [PMID: 32440005 DOI: 10.1038/s41587-020-0502-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 03/27/2020] [Indexed: 02/07/2023]
Abstract
Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.
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15
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Varela-Rodríguez L, Sánchez-Ramírez B, Hernández-Ramírez VI, Varela-Rodríguez H, Castellanos-Mijangos RD, González-Horta C, Chávez-Munguía B, Talamás-Rohana P. Effect of Gallic acid and Myricetin on ovarian cancer models: a possible alternative antitumoral treatment. BMC Complement Med Ther 2020; 20:110. [PMID: 32276584 PMCID: PMC7149887 DOI: 10.1186/s12906-020-02900-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 03/23/2020] [Indexed: 12/31/2022] Open
Abstract
Background Ovarian cancer is the leading cause of mortality among malignant gynecological tumors. Surgical resection and chemotherapy with intravenous platinum/taxanes drugs are the treatments of choice, with little effectiveness in later stages and severe toxicological effects. Therefore, this study aimed to evaluate the antineoplastic activity of gallic acid (GA) and myricetin (Myr) administrated peritumorally in Nu/Nu mice xenotransplanted with SKOV-3 cells. Methods Biological activity of GA and MYR was evaluated in SKOV-3 and OVCAR-3 cells (ovarian adenocarcinomas) by confocal/transmission electron microscopy, PI-flow cytometry, H2-DCF-DA stain, MTT, and Annexin V/PI assays. Molecular targets of compounds were determined with ACD/I-Labs and SEA. Antineoplastic activity was performed in SKOV-3 cells subcutaneously xenotransplanted into female Nu/Nu mice treated peritumorally with 50 mg/kg of each compound (2 alternate days/week) for 28 days. Controls used were paclitaxel (5 mg/kg) and 20 μL of vehicle (0.5% DMSO in 1X PBS). Tumor lesions, organs and sera were evaluated with NMR, USG, histopathological, and paraclinical studies. Results In vitro studies showed a decrease of cell viability with GA and Myr in SKOV-3 (50 and 166 μg/mL) and OVCAR-3 (43 and 94 μg/mL) cells respectively, as well as morphological changes, cell cycle arrest, and apoptosis induction due to ROS generation (p ≤ 0.05, ANOVA). In silico studies suggest that GA and MYR could interact with carbonic anhydrase IX and PI3K, respectively. In vivo studies revealed inhibitory effects on tumor lesions development with GA and MYR up to 50% (p ≤ 0.05, ANOVA), with decreased vascularity, necrotic/fibrotic areas, neoplastic stroma retraction and apoptosis. However, toxicological effects were observed with GA treatment, such as leukocyte infiltrate and hepatic parenchyma loss, hypertransaminasemia (ALT: 150.7 ± 25.60 U/L), and hypoazotemia (urea: 33.4 ± 7.4 mg/dL), due to the development of chronic hepatitis (p ≤ 0.05, ANOVA). Conclusion GA and Myr (50 mg/kg) administered by peritumoral route, inhibit ovarian tumor lesions development in rodents with some toxicological effects. Additional studies will be necessary to find the appropriate therapeutic dose for GA. Therefore, GA and Myr could be considered as a starting point for the development of novel anticancer agents.
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Affiliation(s)
- Luis Varela-Rodríguez
- Departamento de Infectómica y Patogénesis Molecular, CINVESTAV-IPN. Ave. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, C.P. 07360, Mexico City, Mexico
| | - Blanca Sánchez-Ramírez
- Facultad de Ciencias Químicas, Universidad Autónoma de Chihuahua, Circuito 1, Nuevo Campus Universitario, C.P. 31125, Chihuahua, Chih, Mexico
| | - Verónica Ivonne Hernández-Ramírez
- Departamento de Infectómica y Patogénesis Molecular, CINVESTAV-IPN. Ave. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, C.P. 07360, Mexico City, Mexico
| | - Hugo Varela-Rodríguez
- Laboratorio de Complejidad Molecular y Desarrollo, Unidad de Genómica Avanzada, CINVESTAV-IPN, Libramiento Norte Carretera Irapuato-León Km. 9.6, C.P, 36824, Irapuato, Gto, Mexico
| | - Rodrigo Daniel Castellanos-Mijangos
- Centro Médico ISSEMyM "Arturo Montiel Rojas", Av. Baja Velocidad No. 284, Carretera México-Toluca Km 57.5, Col. San Jerónimo Chicahualco, C.P. 52170, Metepec, Edo. Mex, Mexico
| | - Carmen González-Horta
- Facultad de Ciencias Químicas, Universidad Autónoma de Chihuahua, Circuito 1, Nuevo Campus Universitario, C.P. 31125, Chihuahua, Chih, Mexico
| | - Bibiana Chávez-Munguía
- Departamento de Infectómica y Patogénesis Molecular, CINVESTAV-IPN. Ave. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, C.P. 07360, Mexico City, Mexico
| | - Patricia Talamás-Rohana
- Departamento de Infectómica y Patogénesis Molecular, CINVESTAV-IPN. Ave. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, C.P. 07360, Mexico City, Mexico.
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16
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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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17
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18
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Schroedl S. Current methods and challenges for deep learning in drug discovery. DRUG DISCOVERY TODAY. TECHNOLOGIES 2019; 32-33:9-17. [PMID: 33386100 DOI: 10.1016/j.ddtec.2020.07.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 06/17/2020] [Accepted: 07/24/2020] [Indexed: 12/18/2022]
Abstract
Driven by rapid advances in computer hardware and publicly available datasets over the past decade, deep learning has achieved tremendous success in the transformation of many computational disciplines. These novel technologies have had considerable impact on computer-aided drug design as well, throughout all stages of the development pipeline. A flexible toolbox of neural architectures has been developed that are well-suited to represent the sequential, topological, or geometrical concepts of chemistry and biology; and that are able to either discriminate existing molecules or to generate new ones from scratch. For some biochemical prediction tasks, the state of the art has been advanced; however, for complex and practically relevant projects, the outcomes are less clear-cut. Current deep learning methods rely on massive amounts of labeled examples, but drug discovery data is comparatively limited in quantity and quality. These problems need to be resolved and existing sources used more effectively to demonstrate that deep learning can revolutionize the field in general.
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19
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Bunally SB, Luscombe CN, Young RJ. Using Physicochemical Measurements to Influence Better Compound Design. SLAS DISCOVERY 2019; 24:791-801. [PMID: 31429385 DOI: 10.1177/2472555219859845] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
During the past decade, the physicochemical quality of molecules under investigation at all stages of the drug discovery process has come under particular scrutiny. The issues associated with excessive lipophilicity and poor solubility in particular are many and varied, ranging from poor outcomes in screening campaigns to promiscuity, limited and/or poorly predictable pharmacokinetic exposure, and, ultimately, greater chances of clinical failure. In this review, contemporary methods to secure key measurements are described along with their relevance to understanding the behavior of molecules in environments pertinent to pharmacological activity. Together, the various measurements contribute to predictive models of both the physicochemical properties themselves and the outcomes they influence.
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Affiliation(s)
| | | | - Robert J Young
- 1 GlaxoSmithKline Medicines Research Centre, Stevenage, UK
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20
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Predicting kinase inhibitors using bioactivity matrix derived informer sets. PLoS Comput Biol 2019; 15:e1006813. [PMID: 31381559 PMCID: PMC6695194 DOI: 10.1371/journal.pcbi.1006813] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 08/15/2019] [Accepted: 07/13/2019] [Indexed: 12/21/2022] Open
Abstract
Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prioritization, initial screening must provide the necessary data. Commonly, such an initial library is selected on the basis of chemical diversity by some pseudo-random process (for example, the first few plates of a larger library) or by selecting an entire smaller library. These approaches may not produce a sufficient number or diversity of actives. An alternative approach is to select an informer set of screening compounds on the basis of chemogenomic information from previous testing of compounds against a large number of targets. We compare different ways of using chemogenomic data to choose a small informer set of compounds based on previously measured bioactivity data. We develop this Informer-Based-Ranking (IBR) approach using the Published Kinase Inhibitor Sets (PKIS) as the chemogenomic data to select the informer sets. We test the informer compounds on a target that is not part of the chemogenomic data, then predict the activity of the remaining compounds based on the experimental informer data and the chemogenomic data. Through new chemical screening experiments, we demonstrate the utility of IBR strategies in a prospective test on three kinase targets not included in the PKIS. In the early stages of drug discovery efforts, computational models are used to predict activity and prioritize compounds for experimental testing. New targets commonly lack the data necessary to build effective models, and the screening needed to generate that experimental data can be costly. We seek to improve the efficiency of the initial screening phase, and of the process of prioritizing compounds for subsequent screening. We choose a small informer set of compounds based on publicly available prior screening data on distinct targets. We then collect experimental data on these informer compounds and use that data to predict the activity of other compounds in the set for the target of interest. Computational and statistical tools are needed to identify informer compounds and to prioritize other compounds for subsequent phases of screening. We find that selection of informer compounds on the basis of bioactivity data from previous screening efforts is superior to the traditional approach of selection of a chemically diverse subset of compounds. We demonstrate the success of this approach in retrospective tests on the Published Kinase Inhibitor Sets (PKIS) chemogenomic data and in prospective experimental screens against three additional non-human kinase targets.
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21
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Koulouridi E, Valli M, Ntie-Kang F, Bolzani VDS. A primer on natural product-based virtual screening. PHYSICAL SCIENCES REVIEWS 2019. [DOI: 10.1515/psr-2018-0105] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Abstract
Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.
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22
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Cheong SL, Federico S, Spalluto G, Klotz KN, Pastorin G. The current status of pharmacotherapy for the treatment of Parkinson's disease: transition from single-target to multitarget therapy. Drug Discov Today 2019; 24:1769-1783. [PMID: 31102728 DOI: 10.1016/j.drudis.2019.05.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Revised: 04/02/2019] [Accepted: 05/10/2019] [Indexed: 12/23/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized by degeneration of dopaminergic neurons. Motor features such as tremor, rigidity, bradykinesia and postural instability are common traits of PD. Current treatment options provide symptomatic relief to the condition but are unable to reverse disease progression. The conventional single-target therapeutic approach might not always induce the desired effect owing to the multifactorial nature of PD. Hence, multitarget strategies have been proposed to simultaneously target multiple proteins involved in the development of PD. Herein, we provide an overview of the pathogenesis of PD and the current pharmacotherapies. Furthermore, rationales and examples of multitarget approaches that have been tested in preclinical trials for the treatment of PD are also discussed.
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Affiliation(s)
- Siew L Cheong
- Department of Pharmaceutical Chemistry, School of Pharmacy, International Medical University, Malaysia.
| | - Stephanie Federico
- Dipartimento di Scienze Chimiche e Farmaceutiche, Università degli Studi di Trieste, Italy
| | - Giampiero Spalluto
- Dipartimento di Scienze Chimiche e Farmaceutiche, Università degli Studi di Trieste, Italy
| | - Karl-Norbert Klotz
- Institut für Pharmakologie und Toxikologie, Universität Würzburg, Germany
| | - Giorgia Pastorin
- Department of Pharmacy, National University of Singapore, Singapore
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23
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Awale M, Reymond JL. Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning. J Chem Inf Model 2018; 59:10-17. [PMID: 30558418 DOI: 10.1021/acs.jcim.8b00524] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Here we report PPB2 as a target prediction tool assigning targets to a query molecule based on ChEMBL data. PPB2 computes ligand similarities using molecular fingerprints encoding composition (MQN), molecular shape and pharmacophores (Xfp), and substructures (ECfp4) and features an unprecedented combination of nearest neighbor (NN) searches and Naı̈ve Bayes (NB) machine learning, together with simple NN searches, NB and Deep Neural Network (DNN) machine learning models as further options. Although NN(ECfp4) gives the best results in terms of recall in a 10-fold cross-validation study, combining NN searches with NB machine learning provides superior precision statistics, as well as better results in a case study predicting off-targets of a recently reported TRPV6 calcium channel inhibitor, illustrating the value of this combined approach. PPB2 is available to assess possible off-targets of small molecule drug-like compounds by public access at http://gdb.unibe.ch .
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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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24
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Alberga D, Trisciuzzi D, Montaruli M, Leonetti F, Mangiatordi GF, Nicolotti O. A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL). J Chem Inf Model 2018; 59:586-596. [PMID: 30485097 DOI: 10.1021/acs.jcim.8b00698] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
We present MuSSeL, a multifingerprint similarity search algorithm, able to predict putative drug targets for a given query small molecule as well as to return a quantitative assessment of its bioactivity in terms of Ki or IC50 values. Predictions are automatically made exploiting a large collection of high quality experimental bioactivity data available from ChEMBL (version 22.1) combining, in a consensus-like approach, predictions resulting from a similarity search performed using 13 different fingerprint definitions. Importantly, the herein proposed algorithm is also effective in detecting and handling activity cliffs. A calibration set including small molecules present in the last updated version of ChEMBL (version 23) was employed to properly tune the algorithm parameters. Three randomly built external sets were instead challenged for model performances. The potential use of MuSSeL was also challenged by a prospective exercise for the prediction of five bioactive compounds taken from articles published in the Journal of Medicinal Chemistry just few months ago. The paper emphasizes the importance of implementing multifingerprint consensus strategies to increase the confidence in prediction of similarity search algorithms and provides a fast and easy-to-run tool for drug target and bioactivity prediction.
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Affiliation(s)
- Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco , Università degli Studi di Bari "Aldo Moro" , Via E. Orabona, 4 , I-70126 Bari , Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco , Università degli Studi di Bari "Aldo Moro" , Via E. Orabona, 4 , I-70126 Bari , Italy
| | - Michele Montaruli
- Dipartimento di Farmacia-Scienze del Farmaco , Università degli Studi di Bari "Aldo Moro" , Via E. Orabona, 4 , I-70126 Bari , Italy
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco , Università degli Studi di Bari "Aldo Moro" , Via E. Orabona, 4 , I-70126 Bari , Italy
| | - Giuseppe Felice Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco , Università degli Studi di Bari "Aldo Moro" , Via E. Orabona, 4 , I-70126 Bari , Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco , Università degli Studi di Bari "Aldo Moro" , Via E. Orabona, 4 , I-70126 Bari , Italy
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25
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Poirier M, Awale M, Roelli MA, Giuffredi GT, Ruddigkeit L, Evensen L, Stooss A, Calarco S, Lorens JB, Charles RP, Reymond JL. Identifying Lysophosphatidic Acid Acyltransferase β (LPAAT-β) as the Target of a Nanomolar Angiogenesis Inhibitor from a Phenotypic Screen Using the Polypharmacology Browser PPB2. ChemMedChem 2018; 14:224-236. [PMID: 30520265 DOI: 10.1002/cmdc.201800554] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Indexed: 12/11/2022]
Abstract
By screening a focused library of kinase inhibitor analogues in a phenotypic co-culture assay for angiogenesis inhibition, we identified an aminotriazine that acts as a cytostatic nanomolar inhibitor. However, this aminotriazine was found to be completely inactive in a whole-kinome profiling assay. To decipher its mechanism of action, we used the online target prediction tool PPB2 (http://ppb2.gdb.tools), which suggested lysophosphatidic acid acyltransferase β (LPAAT-β) as a possible target for this aminotriazine as well as several analogues identified by structure-activity relationship profiling. LPAAT-β inhibition (IC50 ≈15 nm) was confirmed in a biochemical assay and by its effects on cell proliferation in comparison with a known LPAAT-β inhibitor. These experiments illustrate the value of target-prediction tools to guide target identification for phenotypic screening hits and significantly expand the rather limited pharmacology of LPAAT-β inhibitors.
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Affiliation(s)
- Marion Poirier
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Mahendra Awale
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Matthias A Roelli
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCure, University of Bern, Bühlstrasse 28, 3000, Bern 9, Switzerland
| | - Guy T Giuffredi
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Lars Ruddigkeit
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Lasse Evensen
- Department of Biomedicine, Centre for Cancer Biomarkers (CCBIO), University of Bergen, Jonas Lies vei 91, 5009, Bergen, Norway
| | - Amandine Stooss
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCure, University of Bern, Bühlstrasse 28, 3000, Bern 9, Switzerland
| | - Serafina Calarco
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCure, University of Bern, Bühlstrasse 28, 3000, Bern 9, Switzerland
| | - James B Lorens
- Department of Biomedicine, Centre for Cancer Biomarkers (CCBIO), University of Bergen, Jonas Lies vei 91, 5009, Bergen, Norway
| | - Roch-Philippe Charles
- Institute of Biochemistry and Molecular Medicine, National Center of Competence in Research NCCR TransCure, University of Bern, Bühlstrasse 28, 3000, Bern 9, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, National Center of Competence in Research NCCR TransCure, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
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Saha A, Varghese T, Liu A, Allen SJ, Mirzadegan T, Hack MD. An Analysis of Different Components of a High-Throughput Screening Library. J Chem Inf Model 2018; 58:2057-2068. [PMID: 30204440 DOI: 10.1021/acs.jcim.8b00258] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Since many projects at pharmaceutical organizations get their start from a high-throughput screening (HTS) campaign, improving the quality of the HTS deck can improve the likelihood of discovering a high-quality lead molecule that can be progressed to a drug candidate. Over the past decade, Janssen has implemented several strategies for external compound acquisition to augment the screening deck beyond the chemical space and number of molecules synthesized for internal projects. In this report, we analyzed the performance of each of those compound collections in the screening campaigns performed internally within Janssen during the last five years. We classified the screening library into two broad categories: Internal and External. The comparison of the performance of these sets of libraries was done by considering the primary, confirmation, and dose response hit rates. Our analysis revealed that Internal compounds (resulting from numerous medicinal chemistry efforts against diverse protein targets) have higher average confirmation hit rates than External ones; however, actives from both categories show similar probabilities of hitting multiple distinct targets. We also investigated the property landscape of both sets of libraries to identify the key elements which make a difference in these categories of compounds. From this analysis, Janssen aims to understand the descriptor landscape of the compounds with the highest hit rates and to use them for improving its future acquisition strategies as well as to inform our plating strategy.
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Affiliation(s)
- Arjun Saha
- Janssen Pharmaceutical Research and Development , 3210 Merryfield Row , La Jolla , California 92121 , United States
| | - Teena Varghese
- Janssen Pharmaceutical Research and Development , 3210 Merryfield Row , La Jolla , California 92121 , United States
| | - Annie Liu
- Janssen Pharmaceutical Research and Development , 3210 Merryfield Row , La Jolla , California 92121 , United States
| | - Samantha J Allen
- Janssen Pharmaceutical Research and Development , 3210 Merryfield Row , La Jolla , California 92121 , United States
| | - Taraneh Mirzadegan
- Janssen Pharmaceutical Research and Development , 3210 Merryfield Row , La Jolla , California 92121 , United States
| | - Michael D Hack
- Janssen Pharmaceutical Research and Development , 3210 Merryfield Row , La Jolla , California 92121 , United States
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Yi F, Li L, Xu LJ, Meng H, Dong YM, Liu HB, Xiao PG. In silico approach in reveal traditional medicine plants pharmacological material basis. Chin Med 2018; 13:33. [PMID: 29946351 PMCID: PMC6006786 DOI: 10.1186/s13020-018-0190-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 06/12/2018] [Indexed: 02/07/2023] Open
Abstract
In recent years, studies of traditional medicinal plants have gradually increased worldwide because the natural sources and variety of such plants allow them to complement modern pharmacological approaches. As computer technology has developed, in silico approaches such as virtual screening and network analysis have been widely utilized in efforts to elucidate the pharmacological basis of the functions of traditional medicinal plants. In the process of new drug discovery, the application of virtual screening and network pharmacology can enrich active compounds among the candidates and adequately indicate the mechanism of action of medicinal plants, reducing the cost and increasing the efficiency of the whole procedure. In this review, we first provide a detailed research routine for examining traditional medicinal plants by in silico techniques and elaborate on their theoretical principles. We also survey common databases, software programs and website tools that can be used for virtual screening and pharmacological network construction. Furthermore, we conclude with a simple example that illustrates the whole methodology, and we present perspectives on the development and application of this in silico methodology to reveal the pharmacological basis of the effects of traditional medicinal plants.
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Affiliation(s)
- Fan Yi
- Key Laboratory of Cosmetic, China National Light Industry, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People’s Republic of China
- Beijing Key Laboratory of Plant Resources Research and Development, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People’s Republic of China
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 151 Malianwa North Road, Haidian District, Beijing, 100193 People’s Republic of China
| | - Li Li
- Key Laboratory of Cosmetic, China National Light Industry, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People’s Republic of China
- Beijing Key Laboratory of Plant Resources Research and Development, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People’s Republic of China
| | - Li-jia Xu
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 151 Malianwa North Road, Haidian District, Beijing, 100193 People’s Republic of China
| | - Hong Meng
- Key Laboratory of Cosmetic, China National Light Industry, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People’s Republic of China
- Beijing Key Laboratory of Plant Resources Research and Development, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People’s Republic of China
| | - Yin-mao Dong
- Key Laboratory of Cosmetic, China National Light Industry, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People’s Republic of China
- Beijing Key Laboratory of Plant Resources Research and Development, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People’s Republic of China
| | - Hai-bo Liu
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 151 Malianwa North Road, Haidian District, Beijing, 100193 People’s Republic of China
| | - Pei-gen Xiao
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 151 Malianwa North Road, Haidian District, Beijing, 100193 People’s Republic of China
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Kooistra AJ, Vass M, McGuire R, Leurs R, de Esch IJP, Vriend G, Verhoeven S, de Graaf C. 3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery. ChemMedChem 2018; 13:614-626. [PMID: 29337438 PMCID: PMC5900740 DOI: 10.1002/cmdc.201700754] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 01/11/2018] [Indexed: 01/06/2023]
Abstract
eScience technologies are needed to process the information available in many heterogeneous types of protein-ligand interaction data and to capture these data into models that enable the design of efficacious and safe medicines. Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i) structure-based bioactivity data mapping, ii) structure-based identification of scaffold replacement strategies for ligand design, iii) ligand-based target prediction, iv) protein sequence-based binding site identification and ligand repurposing, and v) structure-based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well-established standards allows the re-use of these protocols and facilitates the design of customized computer-aided drug discovery workflows.
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Affiliation(s)
- Albert J. Kooistra
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Márton Vass
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Ross McGuire
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
- BioAxis Research, Pivot ParkOssThe Netherlands
| | - Rob Leurs
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Iwan J. P. de Esch
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Gert Vriend
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
| | | | - Chris de Graaf
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
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