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Ji W, She S, Qiao C, Feng Q, Rui M, Xu X, Feng C. A general prediction model for compound-protein interactions based on deep learning. Front Pharmacol 2024; 15:1465890. [PMID: 39295942 PMCID: PMC11408283 DOI: 10.3389/fphar.2024.1465890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Accepted: 08/20/2024] [Indexed: 09/21/2024] Open
Abstract
Background The identification of compound-protein interactions (CPIs) is crucial for drug discovery and understanding mechanisms of action. Accurate CPI prediction can elucidate drug-target-disease interactions, aiding in the discovery of candidate compounds and effective synergistic drugs, particularly from traditional Chinese medicine (TCM). Existing in silico methods face challenges in prediction accuracy and generalization due to compound and target diversity and the lack of largescale interaction datasets and negative datasets for model learning. Methods To address these issues, we developed a computational model for CPI prediction by integrating the constructed large-scale bioactivity benchmark dataset with a deep learning (DL) algorithm. To verify the accuracy of our CPI model, we applied it to predict the targets of compounds in TCM. An herb pair of Astragalus membranaceus and Hedyotis diffusaas was used as a model, and the active compounds in this herb pair were collected from various public databases and the literature. The complete targets of these active compounds were predicted by the CPI model, resulting in an expanded target dataset. This dataset was next used for the prediction of synergistic antitumor compound combinations. The predicted multi-compound combinations were subsequently examined through in vitro cellular experiments. Results Our CPI model demonstrated superior performance over other machine learning models, achieving an area under the Receiver Operating Characteristic curve (AUROC) of 0.98, an area under the precision-recall curve (AUPR) of 0.98, and an accuracy (ACC) of 93.31% on the test set. The model's generalization capability and applicability were further confirmed using external databases. Utilizing this model, we predicted the targets of compounds in the herb pair of Astragalus membranaceus and Hedyotis diffusaas, yielding an expanded target dataset. Then, we integrated this expanded target dataset to predict effective drug combinations using our drug synergy prediction model DeepMDS. Experimental assay on breast cancer cell line MDA-MB-231 proved the efficacy of the best predicted multi-compound combinations: Combination I (Epicatechin, Ursolic acid, Quercetin, Aesculetin and Astragaloside IV) exhibited a half-maximal inhibitory concentration (IC50) value of 19.41 μM, and a combination index (CI) value of 0.682; and Combination II (Epicatechin, Ursolic acid, Quercetin, Vanillic acid and Astragaloside IV) displayed a IC50 value of 23.83 μM and a CI value of 0.805. These results validated the ability of our model to make accurate predictions for novel CPI data outside the training dataset and evaluated the reliability of the predictions, showing good applicability potential in drug discovery and in the elucidation of the bioactive compounds in TCM. Conclusion Our CPI prediction model can serve as a useful tool for accurately identifying potential CPI for a wide range of proteins, and is expected to facilitate drug research, repurposing and support the understanding of TCM.
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Affiliation(s)
- Wei Ji
- School of Pharmacy, Jiangsu University, Zhenjiang, China
- School of Medicine, Jiangsu University, Zhenjiang, China
| | - Shengnan She
- School of Pharmacy, Jiangsu University, Zhenjiang, China
| | - Chunxue Qiao
- School of Pharmacy, Jiangsu University, Zhenjiang, China
| | - Qiuqi Feng
- School of Pharmacy, Jiangsu University, Zhenjiang, China
| | - Mengjie Rui
- School of Pharmacy, Jiangsu University, Zhenjiang, China
| | - Ximing Xu
- School of Pharmacy, Jiangsu University, Zhenjiang, China
| | - Chunlai Feng
- School of Pharmacy, Jiangsu University, Zhenjiang, China
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Thomas M, O'Boyle NM, Bender A, De Graaf C. MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design. J Cheminform 2024; 16:64. [PMID: 38816825 PMCID: PMC11141043 DOI: 10.1186/s13321-024-00861-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Generative models are undergoing rapid research and application to de novo drug design. To facilitate their application and evaluation, we present MolScore. MolScore already contains many drug-design-relevant scoring functions commonly used in benchmarks such as, molecular similarity, molecular docking, predictive models, synthesizability, and more. In addition, providing performance metrics to evaluate generative model performance based on the chemistry generated. With this unification of functionality, MolScore re-implements commonly used benchmarks in the field (such as GuacaMol, MOSES, and MolOpt). Moreover, new benchmarks can be created trivially. We demonstrate this by testing a chemical language model with reinforcement learning on three new tasks of increasing complexity related to the design of 5-HT2a ligands that utilise either molecular descriptors, 266 pre-trained QSAR models, or dual molecular docking. Lastly, MolScore can be integrated into an existing Python script with just three lines of code. This framework is a step towards unifying generative model application and evaluation as applied to drug design for both practitioners and researchers. The framework can be found on GitHub and downloaded directly from the Python Package Index.Scientific ContributionMolScore is an open-source platform to facilitate generative molecular design and evaluation thereof for application in drug design. This platform takes important steps towards unifying existing benchmarks, providing a platform to share new benchmarks, and improves customisation, flexibility and usability for practitioners over existing solutions.
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Affiliation(s)
- Morgan Thomas
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Noel M O'Boyle
- Computational Chemistry, Nxera Pharma, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Chris De Graaf
- Computational Chemistry, Nxera Pharma, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
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3
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Hosseini-Gerami L, Hernansaiz Ballesteros R, Liu A, Broughton H, Collier DA, Bender A. MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny. BMC Bioinformatics 2023; 24:344. [PMID: 37715141 PMCID: PMC10502988 DOI: 10.1186/s12859-023-05416-8] [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: 09/27/2022] [Accepted: 07/18/2023] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from. RESULTS To address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein-protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action. CONCLUSIONS MAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN .
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Affiliation(s)
- Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
- Ignota Labs, London, UK.
| | - Rosa Hernansaiz Ballesteros
- Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg University, Heidelberg, Germany
| | - Anika Liu
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Howard Broughton
- Eli Lilly and Company Centre de Investigacion, Alcobendas, Spain
| | - David Andrew Collier
- Eli Lilly and Company, Bracknell, UK
- King's College London, and Genetics and Genomics Consulting, Surrey, UK
| | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
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4
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Niazi SK. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Des Devel Ther 2023; 17:2691-2725. [PMID: 37701048 PMCID: PMC10493153 DOI: 10.2147/dddt.s424991] [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] [Received: 06/28/2023] [Accepted: 08/24/2023] [Indexed: 09/14/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.
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do Carmo Maquiaveli C, da Silva ER, Hild de Jesus B, Oliveira Monteiro CE, Rodrigues Navarro T, Pereira Branco LO, Souza dos Santos I, Figueiredo Reis N, Portugal AB, Mendes Wanderley JL, Borges Farias A, Correia Romeiro N, de Lima EC. Design and Synthesis of New Anthranyl Phenylhydrazides: Antileishmanial Activity and Structure-Activity Relationship. Pharmaceuticals (Basel) 2023; 16:1120. [PMID: 37631035 PMCID: PMC10458276 DOI: 10.3390/ph16081120] [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: 06/29/2023] [Revised: 07/26/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
Leishmaniasis is a neglected tropical disease affecting millions of people worldwide. A centenary approach to antimonial-based drugs was first initiated with the synthesis of urea stibamine by Upendranath Brahmachari in 1922. The need for new drug development led to resistance toward antimoniates. New drug development to treat leishmaniasis is urgently needed. In this way, searching for new substances with antileishmanial activity, we synthesized ten anthranyl phenylhydrazide and three quinazolinone derivatives and evaluated them against promastigotes and the intracellular amastigotes of Leishmania amazonensis. Three compounds showed good activity against promastigotes 1b, 1d, and 1g, with IC50 between 1 and 5 μM. These new phenylhydrazides were tested against Leishmania arginase, but they all failed to inhibit this parasite enzyme, as we have shown in a previous study. To explain the possible mechanism of action, we proposed the enzyme PTR1 as a new target for these compounds based on in silico analysis. In conclusion, the new anthranyl hydrazide derivatives can be a promising scaffold for developing new substances against the protozoa parasite.
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Affiliation(s)
- Claudia do Carmo Maquiaveli
- Laboratório de Farmacologia e Bioquímica (LFBq), Departamento de Medicina Veterinária, Universidade de São Paulo Faculdade de Zootecnia e Engenharia de Alimentos, Av. Duque de Caxias Norte 225, Pirassununga 13635-900, SP, Brazil
| | - Edson Roberto da Silva
- Laboratório de Farmacologia e Bioquímica (LFBq), Departamento de Medicina Veterinária, Universidade de São Paulo Faculdade de Zootecnia e Engenharia de Alimentos, Av. Duque de Caxias Norte 225, Pirassununga 13635-900, SP, Brazil
| | - Barbara Hild de Jesus
- Laboratório de Farmacologia e Bioquímica (LFBq), Departamento de Medicina Veterinária, Universidade de São Paulo Faculdade de Zootecnia e Engenharia de Alimentos, Av. Duque de Caxias Norte 225, Pirassununga 13635-900, SP, Brazil
| | - Caio Eduardo Oliveira Monteiro
- Laboratório de Farmacologia e Bioquímica (LFBq), Departamento de Medicina Veterinária, Universidade de São Paulo Faculdade de Zootecnia e Engenharia de Alimentos, Av. Duque de Caxias Norte 225, Pirassununga 13635-900, SP, Brazil
| | - Tiago Rodrigues Navarro
- Laboratório de Catálise e Síntese de Substâncias Bioativas, Instituto Multidisciplinar de Química, CM UFRJ-Macaé, Universidade Federal do Rio de Janeiro, Macaé CEP 27971-525, RJ, Brazil
| | - Luiz Octavio Pereira Branco
- Laboratório de Catálise e Síntese de Substâncias Bioativas, Instituto Multidisciplinar de Química, CM UFRJ-Macaé, Universidade Federal do Rio de Janeiro, Macaé CEP 27971-525, RJ, Brazil
| | - Isabela Souza dos Santos
- Laboratório de Catálise e Síntese de Substâncias Bioativas, Instituto Multidisciplinar de Química, CM UFRJ-Macaé, Universidade Federal do Rio de Janeiro, Macaé CEP 27971-525, RJ, Brazil
| | - Nanashara Figueiredo Reis
- Laboratório de Catálise e Síntese de Substâncias Bioativas, Instituto Multidisciplinar de Química, CM UFRJ-Macaé, Universidade Federal do Rio de Janeiro, Macaé CEP 27971-525, RJ, Brazil
| | - Arieli Bernardo Portugal
- Laboratório de Imunoparasitologia, Instituto de Ciências Médicas, Centro Multidisciplinar UFRJ, Macaé CEP 27979-000, RJ, Brazil
- Programa de Pós Graduação em Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes CEP 28013-602, RJ, Brazil
| | - João Luiz Mendes Wanderley
- Laboratório de Imunoparasitologia, Instituto de Ciências Médicas, Centro Multidisciplinar UFRJ, Macaé CEP 27979-000, RJ, Brazil
| | - André Borges Farias
- Unidad Académica de Yucatán, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Mérida 97302, Yucatán, Mexico
- Integrated Laboratory of Scientific Computing (LICC), Federal University of Rio de Janeiro (UFRJ)—Campus Macaé, Aluízio Silva Gomes Avenue 50, Granjas Cavaleiros, Macaé 27930-560, RJ, Brazil
| | - Nelilma Correia Romeiro
- Integrated Laboratory of Scientific Computing (LICC), Federal University of Rio de Janeiro (UFRJ)—Campus Macaé, Aluízio Silva Gomes Avenue 50, Granjas Cavaleiros, Macaé 27930-560, RJ, Brazil
| | - Evanoel Crizanto de Lima
- Laboratório de Catálise e Síntese de Substâncias Bioativas, Instituto Multidisciplinar de Química, CM UFRJ-Macaé, Universidade Federal do Rio de Janeiro, Macaé CEP 27971-525, RJ, Brazil
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6
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Lunghini F, Fava A, Pisapia V, Sacco F, Iaconis D, Beccari AR. ProfhEX: AI-based platform for small molecules liability profiling. J Cheminform 2023; 15:60. [PMID: 37296454 DOI: 10.1186/s13321-023-00728-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/28/2023] [Indexed: 06/12/2023] Open
Abstract
Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R2 determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .
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Affiliation(s)
- Filippo Lunghini
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
| | - Anna Fava
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
| | - Vincenzo Pisapia
- Professional Service Department, SAS Institute, Via Darwin 20/22, 20143, Milan, Italy
| | - Francesco Sacco
- Professional Service Department, SAS Institute, Via Darwin 20/22, 20143, Milan, Italy
| | - Daniela Iaconis
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
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Gupta N, Mohan CD, Shanmugam MK, Jung YY, Chinnathambi A, Alharbi SA, Ashrafizadeh M, Mahale M, Bender A, Kumar AP, Putti TC, Rangappa KS, Zhang X, Ahn KS, Sethi G. CXCR4 expression is elevated in TNBC patient derived samples and Z-guggulsterone abrogates tumor progression by targeting CXCL12/CXCR4 signaling axis in mice model. ENVIRONMENTAL RESEARCH 2023:116335. [PMID: 37290620 DOI: 10.1016/j.envres.2023.116335] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 05/17/2023] [Accepted: 06/05/2023] [Indexed: 06/10/2023]
Abstract
Environmental factors such as exposure to ionizing radiations, certain environmental pollutants, and toxic chemicals are considered as risk factors in the development of breast cancer. Triple-negative breast cancer (TNBC) is a molecular variant of breast cancer that lacks therapeutic targets such as progesterone receptor, estrogen receptor, and human epidermal growth factor receptor-2 which makes the targeted therapy ineffective in TNBC patients. Therefore, identification of new therapeutic targets for the treatment of TNBC and the discovery of new therapeutic agents is the need of the hour. In this study, CXCR4 was found to be highly expressed in majority of breast cancer tissues and metastatic lymph nodes derived from TNBC patients. CXCR4 expression is positively correlated with breast cancer metastasis and poor prognosis of TNBC patients suggesting that suppression of CXCR4 expression could be a good strategy in the treatment of TNBC patients. Therefore, the effect of Z-guggulsterone (ZGA) on the expression of CXCR4 in TNBC cells was examined. ZGA downregulated protein and mRNA expression of CXCR4 in TNBC cells and proteasome inhibition or lysosomal stabilization had no effect on the ZGA-induced CXCR4 reduction. CXCR4 is under the transcriptional control of NF-κB, whereas ZGA was found to downregulate transcriptional activity NF-κB. Functionally, ZGA downmodulated the CXCL12-driven migration/invasion in TNBC cells. Additionally, the effect of ZGA on growth of tumor was investigated in the orthotopic TNBC mice model and ZGA presented good inhibition of tumor growth and liver/lung metastasis in this model. Western blotting and immunohistochemical analysis indicated a reduction of CXCR4, NF-κB, and Ki67 in tumor tissues. Computational analysis suggested PXR agonism and FXR antagonism as targets of ZGA. In conclusion, CXCR4 was found to be overexpressed in majority of patient-derived TNBC tissues and ZGA abrogated the growth of TNBC tumors by partly targeting the CXCL12/CXCR4 signaling axis.
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Affiliation(s)
- Nikita Gupta
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore.
| | | | - Muthu K Shanmugam
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore
| | - Young Yun Jung
- Department of Science in Korean Medicine, Kyung Hee University, 24 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, South Korea
| | - Arunachalam Chinnathambi
- Department of Botany and Microbiology, College of Science, King Saud University, PO Box -2455, Riyadh, 11451, Saudi Arabia
| | - Sulaiman Ali Alharbi
- Department of Botany and Microbiology, College of Science, King Saud University, PO Box -2455, Riyadh, 11451, Saudi Arabia
| | - Milad Ashrafizadeh
- Department of General Surgery and Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Manas Mahale
- Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai, 400 098, India
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
| | - Alan Prem Kumar
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore
| | - Thomas Choudary Putti
- Department of Pathology, National University of Singapore, National University Hospital, Kent Ridge Road, Singapore, 119074, Singapore
| | | | - Xianbin Zhang
- Department of General Surgery and Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Kwang Seok Ahn
- Department of Science in Korean Medicine, Kyung Hee University, 24 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, South Korea.
| | - Gautam Sethi
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117600, Singapore.
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Hosseini-Gerami L, Ficulle E, Humphryes-Kirilov N, Airey DC, Scherschel J, Kananathan S, Eastwood BJ, Bose S, Collier DA, Laing E, Evans D, Broughton H, Bender A. Mechanism of action deconvolution of the small-molecule pathological tau aggregation inhibitor Anle138b. Alzheimers Res Ther 2023; 15:52. [PMID: 36918909 PMCID: PMC10012450 DOI: 10.1186/s13195-023-01182-0] [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: 09/13/2022] [Accepted: 02/06/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND A key histopathological hallmark of Alzheimer's disease (AD) is the presence of neurofibrillary tangles of aggregated microtubule-associated protein tau in neurons. Anle138b is a small molecule which has previously shown efficacy in mice in reducing tau aggregates and rescuing AD disease phenotypes. METHODS In this work, we employed bioinformatics analysis-including pathway enrichment and causal reasoning-of an in vitro tauopathy model. The model consisted of cultured rat cortical neurons either unseeded or seeded with tau aggregates derived from human AD patients, both of which were treated with Anle138b to generate hypotheses for its mode of action. In parallel, we used a collection of human target prediction models to predict direct targets of Anle138b based on its chemical structure. RESULTS Combining the different approaches, we found evidence supporting the hypothesis that the action of Anle138b involves several processes which are key to AD progression, including cholesterol homeostasis and neuroinflammation. On the pathway level, we found significantly enriched pathways related to these two processes including those entitled "Superpathway of cholesterol biosynthesis" and "Granulocyte adhesion and diapedesis". With causal reasoning, we inferred differential activity of SREBF1/2 (involved in cholesterol regulation) and mediators of the inflammatory response such as NFKB1 and RELA. Notably, our findings were also observed in Anle138b-treated unseeded neurons, meaning that the inferred processes are independent of tau pathology and thus represent the direct action of the compound in the cellular system. Through structure-based ligand-target prediction, we predicted the intracellular cholesterol carrier NPC1 as well as NF-κB subunits as potential targets of Anle138b, with structurally similar compounds in the model training set known to target the same proteins. CONCLUSIONS This study has generated feasible hypotheses for the potential mechanism of action of Anle138b, which will enable the development of future molecular interventions aiming to reduce tau pathology in AD patients.
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Affiliation(s)
- Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
- AbsoluteAi Ltd, London, UK
| | - Elena Ficulle
- Eli Lilly and Company, Windlesham, UK
- Zifo RnD Solutions, London, UK
| | | | - David C Airey
- Eli Lilly and Company, Corporate Centre, Indianapolis, IN, USA
| | | | | | - Brian J Eastwood
- Eli Lilly and Company, Windlesham, UK
- Eli Lilly and Company, Bracknell, UK
- Eli Lilly and Company (Retired), Bracknell, UK
| | - Suchira Bose
- Eli Lilly and Company, Windlesham, UK
- Eli Lilly and Company, Bracknell, UK
| | - David A Collier
- Eli Lilly and Company, Windlesham, UK
- Eli Lilly and Company, Bracknell, UK
- Social, Genetic and Developmental Psychiatry Centre, IoPPN, Kings's College London and Genetic and Genomic Consulting Ltd, Farnham, UK
| | - Emma Laing
- Eli Lilly and Company, Windlesham, UK
- GSK, Stevenage, UK
| | - David Evans
- Eli Lilly and Company, Windlesham, UK
- DeepMind, London, UK
| | | | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
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9
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Bocharova OA, Ionov NS, Kazeev IV, Shevchenko VE, Bocharov EV, Karpova RV, Sheychenko OP, Aksyonov AA, Chulkova SV, Kucheryanu VG, Revishchin AV, Pavlova GV, Kosorukov VS, Filimonov DA, Lagunin AA, Matveev VB, Pyatigorskaya NV, Stilidi IS, Poroikov VV. Computer-aided Evaluation of Polyvalent Medications' Pharmacological Potential. Multiphytoadaptogen as a Case Study. Mol Inform 2023; 42:e2200176. [PMID: 36075866 DOI: 10.1002/minf.202200176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 09/08/2022] [Indexed: 01/12/2023]
Abstract
Many human diseases including cancer, degenerative and autoimmune disorders, diabetes and others are multifactorial. Pharmaceutical agents acting on a single target do not provide their efficient curation. Multitargeted drugs exhibiting pleiotropic pharmacological effects have certain advantages due to the normalization of the complex pathological processes of different etiology. Extracts of medicinal plants (EMP) containing multiple phytocomponents are widely used in traditional medicines for multifactorial disorders' treatment. Experimental studies of pharmacological potential for multicomponent compositions are quite expensive and time-consuming. In silico evaluation of EMP the pharmacological potential may provide the basis for selecting the most promising directions of testing and for identifying potential additive/synergistic effects. Multiphytoadaptogen (MPhA) containing 70 major phytocomponents of different chemical classes from 40 medicinal plant extracts has been studied in vitro, in vivo and in clinical researches. Antiproliferative and anti-tumor activities have been shown against some tumors as well as evidence-based therapeutic effects against age-related pathologies. In addition, the neuroprotective, antioxidant, antimutagenic, radioprotective, and immunomodulatory effects of MPhA were confirmed. Analysis of the PASS profiles of the biological activity of MPhA phytocomponents showed that most of the predicted anti-tumor and anti-metastatic effects were consistent with the results of laboratory and clinical studies. Antimutagenic, immunomodulatory, radioprotective, neuroprotective and anti-Parkinsonian effects were also predicted for most of the phytocomponents. Effects associated with positive effects on the male and female reproductive systems have been identified too. Thus, PASS and PharmaExpert can be used to evaluate the pharmacological potential of complex pharmaceutical compositions containing natural products.
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Affiliation(s)
- O A Bocharova
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - N S Ionov
- Institute of Biomedical Chemistry, 10, Bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - I V Kazeev
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - V E Shevchenko
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - E V Bocharov
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - R V Karpova
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - O P Sheychenko
- All-Russian Scientific Research Institute of Medicinal and Aromatic Plants, 7 Grin Str., Moscow, 117216, Russia
| | - A A Aksyonov
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - S V Chulkova
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - V G Kucheryanu
- Research Institute of General Pathology and Pathophysiology, 8, Baltiyskaya Str., Moscow, 125315, Russia
| | - A V Revishchin
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, 5A Butlerova Str., Moscow, 117485, Russia
| | - G V Pavlova
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, 5A Butlerova Str., Moscow, 117485, Russia.,Sechenov First Moscow State Medical University (Sechenov University), 8, Trubetskaya Str., Moscow, 119991, Russia
| | - V S Kosorukov
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - D A Filimonov
- Institute of Biomedical Chemistry, 10, Bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - A A Lagunin
- Institute of Biomedical Chemistry, 10, Bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - V B Matveev
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - N V Pyatigorskaya
- Sechenov First Moscow State Medical University (Sechenov University), 8, Trubetskaya Str., Moscow, 119991, Russia
| | - I S Stilidi
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - V V Poroikov
- Institute of Biomedical Chemistry, 10, Bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
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10
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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: 7] [Impact Index Per Article: 3.5] [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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11
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Lim S, Lee S, Piao Y, Choi M, Bang D, Gu J, Kim S. On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach. Comput Struct Biotechnol J 2022; 20:4288-4304. [PMID: 36051875 PMCID: PMC9399946 DOI: 10.1016/j.csbj.2022.07.049] [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: 04/02/2022] [Revised: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 11/22/2022] Open
Abstract
A large number of chemical compounds are available in databases such as PubChem and ZINC. However, currently known compounds, though large, represent only a fraction of possible compounds, which is known as chemical space. Many of these compounds in the databases are annotated with properties and assay data that can be used for drug discovery efforts. For this goal, a number of machine learning algorithms have been developed and recent deep learning technologies can be effectively used to navigate chemical space, especially for unknown chemical compounds, in terms of drug-related tasks. In this article, we survey how deep learning technologies can model and utilize chemical compound information in a task-oriented way by exploiting annotated properties and assay data in the chemical compounds databases. We first compile what kind of tasks are trying to be accomplished by machine learning methods. Then, we survey deep learning technologies to show their modeling power and current applications for accomplishing drug related tasks. Next, we survey deep learning techniques to address the insufficiency issue of annotated data for more effective navigation of chemical space. Chemical compound information alone may not be powerful enough for drug related tasks, thus we survey what kind of information, such as assay and gene expression data, can be used to improve the prediction power of deep learning models. Finally, we conclude this survey with four important newly developed technologies that are yet to be fully incorporated into computational analysis of chemical information.
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Affiliation(s)
- Sangsoo Lim
- Bioinformatics Institute, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Sangseon Lee
- Institute of Computer Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Yinhua Piao
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - MinGyu Choi
- Department of Chemistry, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- MOGAM Institute for Biomedical Research, Yong-in 16924, South Korea
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
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12
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Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors. Molecules 2022; 27:molecules27154718. [PMID: 35897894 PMCID: PMC9330098 DOI: 10.3390/molecules27154718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/13/2022] [Accepted: 07/15/2022] [Indexed: 11/19/2022] Open
Abstract
Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20−25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.
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13
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Murali V, Muralidhar YP, Königs C, Nair M, Madhu S, Nedungadi P, Srinivasa G, Athri P. Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning. Chem Biol Drug Des 2022; 100:169-184. [PMID: 35587730 DOI: 10.1111/cbdd.14092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 04/24/2022] [Accepted: 05/15/2022] [Indexed: 11/29/2022]
Abstract
The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of clinical trials have deep implications for costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of the trial with reliable accuracies, using biological activities, physicochemical properties of the compounds, target-related features, and NLP-based compound representation. In the above list, biological activities have never been used as an independent variable towards the prediction of clinical trial outcomes. We have extracted the drug-disease pair from clinical trials and mapped target(s) to that pair using multiple data sources. Empirical results demonstrate that ensemble learning outperforms independently trained, small-data ML models. We report results and inferences derived from a Random forest classifier with an average accuracy of 93%, and an F1 score of 0.96 for the "Pass" class. "Pass" refers to one of the two classes (Pass/Fail) of all clinical trials, and the model performed well in predicting the "Pass" category. Through the analysis of feature contributions to predictive capability, we have demonstrated that bioactivity plays a statistically significant role in predicting clinical trial outcome. A significant effort has gone into the production of the dataset that, for the first time, integrates clinical trial information with protein targets. Cleaned, organized, integrated data and code to map these entities, created as a part of this work, are available open-source. This reproducibility and the freely available code ensure that researchers with access to deep curated and proprietary clinical trial databases (we only use open-source data in this study) can further expand the scope of the results.
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Affiliation(s)
- Vidhya Murali
- Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, India
| | - Y Pradyumna Muralidhar
- PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India
| | - Cassandra Königs
- Bioinformatics and Medical Informatics, Bielefeld University, Northrhine-Westphalia, Germany
| | - Meera Nair
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India
| | - Sethulekshmi Madhu
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India
| | - Prema Nedungadi
- Department of Computer Science and Engineering, Amrita School of Engineering, Kerala, India
| | - Gowri Srinivasa
- PES Center for Pattern Recognition, Department of Computer Science and Engineering, PES University, Bengaluru, India
| | - Prashanth Athri
- Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, India
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14
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López-López E, Fernández-de Gortari E, Medina-Franco JL. Yes SIR! On the structure-inactivity relationships in drug discovery. Drug Discov Today 2022; 27:2353-2362. [PMID: 35561964 DOI: 10.1016/j.drudis.2022.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/09/2022] [Accepted: 05/05/2022] [Indexed: 12/12/2022]
Abstract
In analogy with structure-activity relationships (SARs), which are at the core of medicinal chemistry, studying structure-inactivity relationships (SIRs) is essential to understanding and predicting biological activity. Current computational methods should predict or distinguish 'activity' and 'inactivity' with the same confidence because both concepts are complementary. However, the lack of inactivity data, in particular in the public domain, limits the development of predictive models and its broad application. In this review, we encourage the scientific community to disclose and analyze high-confidence activity data considering both the labeled 'active' and 'inactive' compounds.
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Affiliation(s)
- Edgar López-López
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico; Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07000, Mexico.
| | - Eli Fernández-de Gortari
- Department of Nanosafety, International Iberian Nanotechnology Laboratory, Braga 4715-330, Portugal
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
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15
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Obrezanova O, Martinsson A, Whitehead T, Mahmoud S, Bender A, Miljković F, Grabowski P, Irwin B, Oprisiu I, Conduit G, Segall M, Smith GF, Williamson B, Winiwarter S, Greene N. Prediction of In Vivo Pharmacokinetic Parameters and Time-Exposure Curves in Rats Using Machine Learning from the Chemical Structure. Mol Pharm 2022; 19:1488-1504. [PMID: 35412314 DOI: 10.1021/acs.molpharmaceut.2c00027] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Animal pharmacokinetic (PK) data as well as human and animal in vitro systems are utilized in drug discovery to define the rate and route of drug elimination. Accurate prediction and mechanistic understanding of drug clearance and disposition in animals provide a degree of confidence for extrapolation to humans. In addition, prediction of in vivo properties can be used to improve design during drug discovery, help select compounds with better properties, and reduce the number of in vivo experiments. In this study, we generated machine learning models able to predict rat in vivo PK parameters and concentration-time PK profiles based on the molecular chemical structure and either measured or predicted in vitro parameters. The models were trained on internal in vivo rat PK data for over 3000 diverse compounds from multiple projects and therapeutic areas, and the predicted endpoints include clearance and oral bioavailability. We compared the performance of various traditional machine learning algorithms and deep learning approaches, including graph convolutional neural networks. The best models for PK parameters achieved R2 = 0.63 [root mean squared error (RMSE) = 0.26] for clearance and R2 = 0.55 (RMSE = 0.46) for bioavailability. The models provide a fast and cost-efficient way to guide the design of molecules with optimal PK profiles, to enable the prediction of virtual compounds at the point of design, and to drive prioritization of compounds for in vivo assays.
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Affiliation(s)
- Olga Obrezanova
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0FZ, U.K
| | - Anton Martinsson
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Tom Whitehead
- Intellegens Ltd., Eagle Labs, Cambridge CB4 3AZ, U.K
| | - Samar Mahmoud
- Optibrium Ltd., Cambridge Innovation Park, Cambridge CB25 9PB, U.K
| | - Andreas Bender
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0FZ, U.K.,Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Filip Miljković
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Piotr Grabowski
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0FZ, U.K
| | - Ben Irwin
- Optibrium Ltd., Cambridge Innovation Park, Cambridge CB25 9PB, U.K
| | - Ioana Oprisiu
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | | | - Matthew Segall
- Optibrium Ltd., Cambridge Innovation Park, Cambridge CB25 9PB, U.K
| | - Graham F Smith
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0FZ, U.K
| | - Beth Williamson
- Drug Metabolism and Pharmacokinetics, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge CB10 1XL, U.K
| | - Susanne Winiwarter
- Drug Metabolism and Pharmacokinetics, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), Biopharmaceutical R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Nigel Greene
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Waltham, Massachusetts 02451, United States
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16
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Parvez MK, Al-Dosari MS, Sinha GP. Machine learning-based predictive models for identifying high active compounds against HIV-1 integrase. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:387-402. [PMID: 35410555 DOI: 10.1080/1062936x.2022.2057588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
HIV-integrase is an important drug target because it catalyzes chromosomal integration of proviral DNA towards establishing latent infection. Computer-aided drug design has immensely contributed to identifying and developing novel antiviral drugs. We have developed various machine learning-based predictive models for identifying high activity compounds against HIV-integrase. Multiclass models were built using support vector machine with reasonable accuracy on the test and evaluation sets. The developed models were evaluated by rigorous validation approaches and the best features were selected by Boruta method. As compared to the model developed from all descriptors set, a slight improvement was observed among the selected descriptors. Validated models were further used for virtual screening of potential compounds from ChemBridge library. Of the six high active compounds predicted from selected models, compounds 9103124, 6642917 and 9082952 showed the most reasonable binding-affinity and stable-interaction with HIV-integrase active-site residues Asp64, Glu152 and Asn155. This was in agreement with previous reports on the essentiality of these residues against a wide range of inhibitors. We therefore highlight the rigorosity of validated classification models for accurate prediction and ranking of high active lead drugs against HIV-integrase.
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Affiliation(s)
- M K Parvez
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - M S Al-Dosari
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - G P Sinha
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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17
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Trapotsi MA, Hosseini-Gerami L, Bender A. Computational analyses of mechanism of action (MoA): data, methods and integration. RSC Chem Biol 2022; 3:170-200. [PMID: 35360890 PMCID: PMC8827085 DOI: 10.1039/d1cb00069a] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/09/2021] [Indexed: 12/15/2022] Open
Abstract
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.
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Affiliation(s)
- Maria-Anna Trapotsi
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
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18
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Shao XX, Chen C, Liang MM, Yu ZY, Zhang FC, Zhou MJ, Wang ZG, Fu XJ. "Efficacy-Nature-Structure" Relationship of Traditional Chinese Medicine Based on Chemical Structural Data and Bioinformatics Analysis. ACS OMEGA 2021; 6:33583-33598. [PMID: 34926906 PMCID: PMC8675060 DOI: 10.1021/acsomega.1c04440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/18/2021] [Indexed: 06/14/2023]
Abstract
Traditional Chinese medicines (TCMs) have wide pharmacological activities, and the ingredients in individual TCMs determine their efficacies. To understand the "efficacy-nature-structure" relationship of TCM, compounds from 2444 kinds of herbs were collected, and the associations between family, structure, nature, and biological activities were mined and analyzed. Bernoulli Naïve Bayes profiling and a data analysis method were used to predict the targets of compounds. The results show that genetic material determined the representation of ingredients from herbs and the nature of TCMs and that the superior scaffolds of compounds of cold nature were 2-phenylochrotinone, anthraquinone, and coumarin, while the compounds of hot nature were cyclohexene. The results of the similarity analysis and distribution for molecular descriptors of compounds show that compounds associated with the same nature were similar and compounds associated with different natures occurred as a transition in part. As for integral compounds from 2-phenylochrotinone, anthraquinone, coumarin, and cyclohexene, the value of the shape index increased, indicating the transition of scaffolds from a spherical structure to a linear structure, with various molecular descriptors decreasing. Three medicines and three recipes prescribed based on "efficacy-nature-structure" had a higher survival rate in the clinic and provided powerful evidence for TCM principles. The research improves the understanding of the "efficacy-nature-structure" relationship and extends TCM applications.
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Affiliation(s)
- Xin-Xin Shao
- Institute
for Literature and Culture of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Key
Laboratory of Classical Theory of Traditional Chinese Medicine, Ministry
of Education, Shandong University of Traditional
Chinese Medicine, Jinan 250355, China
| | - Cong Chen
- Institute
for Literature and Culture of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Key
Laboratory of Classical Theory of Traditional Chinese Medicine, Ministry
of Education, Shandong University of Traditional
Chinese Medicine, Jinan 250355, China
| | - Meng-Meng Liang
- College
of Pharmacy, Shandong University of Traditional
Chinese Medicine, Jinan 250355, China
| | - Zhi-yuan Yu
- College
of Traditional Chinese Medicine, Shandong
University of Traditional Chinese Medicine, Jinan 250355, China
| | - Feng-Cong Zhang
- Institute
for Literature and Culture of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Key
Laboratory of Classical Theory of Traditional Chinese Medicine, Ministry
of Education, Shandong University of Traditional
Chinese Medicine, Jinan 250355, China
| | - Meng-jie Zhou
- College
of Traditional Chinese Medicine, Shandong
University of Traditional Chinese Medicine, Jinan 250355, China
| | - Zhen-guo Wang
- Institute
for Literature and Culture of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Key
Laboratory of Classical Theory of Traditional Chinese Medicine, Ministry
of Education, Shandong University of Traditional
Chinese Medicine, Jinan 250355, China
| | - Xian-Jun Fu
- Key
Laboratory of Classical Theory of Traditional Chinese Medicine, Ministry
of Education, Shandong University of Traditional
Chinese Medicine, Jinan 250355, China
- Marine
Traditional Chinese Medicine Research Center, Qingdao Academy of Traditional
Chinese Medicine, Shandong University of
Traditional Chinese Medicine, Qingdao 266114, China
- Shandong
Engineering and Technology Research Center of Traditional Chinese
Medicine, Jinan 250355, China
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19
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Calixto SD, Simão TLBV, de Almeida FM, Antunes SS, Romeiro NC, de Souza Borges W, das Chagas FO, Seldin L, de Carvalho ECQ, Andrioli WJ, Guimarães DO, Lasunskaia E, Muzitano MF. (R)-(+)-Lasiodiplodin isolated from the endophytic fungus Sordaria tamaensis exhibits potent antimycobacterial and anti-inflammatory activities in vitro and in vivo: a dual approach for the treatment of severe pulmonary tuberculosis. J Pharm Pharmacol 2021; 74:446-457. [PMID: 34850064 DOI: 10.1093/jpp/rgab165] [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: 04/19/2021] [Accepted: 11/03/2021] [Indexed: 12/27/2022]
Abstract
OBJECTIVES This study aimed to evaluate endophytic fungi isolated from Tocoyena bullata and Humiria balsamifera plant species for their antimycobacterial and anti-inflammatory activities, focusing on severe pulmonary tuberculosis cases which are often associated with exacerbated inflammation. METHODS Mycobacterium suspensions were incubated with the samples for 5 days. RAW 264.7 macrophages stimulated with LPS were also incubated with them for 24 h to assess the inhibition of inflammatory mediator production and cytotoxicity. C57BL/6 mice were infected with Mtb M299 and treated for 15 days with lasiodiplodin (Lasio). KEY FINDINGS Endophytic fungus Sordaria tamaensis, obtained from T. bullata, was the most promising. Its ethanolic extract impaired mycobacterial growth with MIC50 (µg/ml): 1.5 ± 0.6 (BCG), 66.8 ± 0.1 (H37Rv) and 80.0 ± 0.1 (M299). (R)-(+)-Lasio showed MIC50 92.2 ± 1.8 µg/ml (M299). In addition, Lasio was able to inhibit NO, IL-1β and TNF-α production and was not cytotoxic for macrophages. M. tuberculosis-infected C57BL/6 animals treated by Lasio reduced the number of acid-fast bacilli, lung pathology, leucocyte influx and proinflammatory cytokine production in the lungs. The class IIa fructose 1,6-bisphosphate aldolase was the predicted hypothetical target of Lasio. CONCLUSIONS (R)-(+)-Lasio stood out as a promising anti-TB compound, exhibiting anti-inflammatory and antimycobacterial effects, as well as low cytotoxicity.
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Affiliation(s)
- Sanderson Dias Calixto
- Laboratório de Biologia do Reconhecer, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, Rio de Janeiro, Brazil.,Laboratório de Produtos Bioativos, Curso de Farmácia, Universidade Federal do Rio de Janeiro, Campus Macaé, Macaé, Rio de Janeiro, Brazil
| | - Thatiana Lopes Biá Ventura Simão
- Laboratório de Produtos Bioativos, Curso de Farmácia, Universidade Federal do Rio de Janeiro, Campus Macaé, Macaé, Rio de Janeiro, Brazil
| | - Fabrício Moreira de Almeida
- Laboratório de Biologia do Reconhecer, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, Rio de Janeiro, Brazil
| | - Stella Schuenck Antunes
- Laboratório Integrado de Computação Científica, Programa de Pós Graduação em Produtos Bioativos e Biociências, Universidade Federal do Rio de Janeiro, Campus Macaé, Macaé, Rio de Janeiro, Brazil
| | - Nelilma Correia Romeiro
- Laboratório Integrado de Computação Científica, Programa de Pós Graduação em Produtos Bioativos e Biociências, Universidade Federal do Rio de Janeiro, Campus Macaé, Macaé, Rio de Janeiro, Brazil
| | - Warley de Souza Borges
- Departamento de Química, Universidade Federal do Espírito Santo, Vitória, Espírito Santo, Brazil
| | - Fernanda Oliveira das Chagas
- Instituto de Pesquisa de Produtos Naturais Walter Mors, Centro de Ciências da Saúde, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Lucy Seldin
- Laboratório de Genética Microbiana CCS, Instituto de Microbiologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Willian Jonis Andrioli
- Laboratório de Produtos Bioativos, Curso de Farmácia, Universidade Federal do Rio de Janeiro, Campus Macaé, Macaé, Rio de Janeiro, Brazil
| | - Denise Oliveira Guimarães
- Laboratório de Produtos Bioativos, Curso de Farmácia, Universidade Federal do Rio de Janeiro, Campus Macaé, Macaé, Rio de Janeiro, Brazil
| | - Elena Lasunskaia
- Laboratório de Biologia do Reconhecer, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, Rio de Janeiro, Brazil
| | - Michelle Frazão Muzitano
- Laboratório de Produtos Bioativos, Curso de Farmácia, Universidade Federal do Rio de Janeiro, Campus Macaé, Macaé, Rio de Janeiro, Brazil
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Natural products from Brazilian biodiversity identified as potential inhibitors of PknA and PknB of M. tuberculosis using molecular modeling tools. Comput Biol Med 2021; 136:104694. [PMID: 34365277 DOI: 10.1016/j.compbiomed.2021.104694] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/07/2021] [Accepted: 07/23/2021] [Indexed: 11/21/2022]
Abstract
Mycobacterium tuberculosis was discovered in 1882 by Robert Koch but, since its discovery, the tuberculosis (TB) epidemic has endured, being one of the top 10 causes of death worldwide. Drug-resistant TB continues to be a public health threat and bioactive compounds with a new mode of action (MoA) are needed to overcome this. Since natural products are described as important sources for the development of new drugs, the objective of this work was to identify potential ligands from Brazilian natural products (NPs) for M. tuberculosis targets using molecular modeling tools. Using chemogenomics we identified the Serine/Threonine Protein Kinase PknB as a putative target for 13 NPs from a database from Brazilian biodiversity (NuBBE). Literature data supported further investigation of NuBBE105, NuBBE598, NuBBE936, NuBBE964, NuBBE1045, and NuBBE1180 by molecular docking and dynamics. Key interactions were observed with PknB and simulations confirmed stability and favorable binding energies. Considering structural similarity with PknB, we further explored binding of the NPs to PknA, critical for M. tuberculosis survival, and all of them resembled important interactions with the enzyme, showing stable and favorable binding energies, whilst van der Waals interactions seem to play a key role for binding to PknA and PknB. NuBBE936 and NuBBE1180 have already had their antimycobacterial activity reported and our results can provide a basis for their MoA. Finally, the other NPs which have not been tested against M. tuberculosis deserve further investigation, aiming at the discovery of antimycobacterial drug candidates with innovative MoA.
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21
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Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer. Pharmaceuticals (Basel) 2021; 14:ph14070699. [PMID: 34358125 PMCID: PMC8308948 DOI: 10.3390/ph14070699] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 07/14/2021] [Accepted: 07/14/2021] [Indexed: 12/22/2022] Open
Abstract
Disruption of epigenetic processes to eradicate tumor cells is among the most promising interventions for cancer control. EZH2 (Enhancer of zeste homolog 2), a catalytic component of polycomb repressive complex 2 (PRC2), methylates lysine 27 of histone H3 to promote transcriptional silencing and is an important drug target for controlling cancer via epigenetic processes. In the present study, we have developed various predictive models for modeling the inhibitory activity of EZH2. Binary and multiclass models were built using SVM, random forest and XGBoost methods. Rigorous validation approaches including predictiveness curve, Y-randomization and applicability domain (AD) were employed for evaluation of the developed models. Eighteen descriptors selected from Boruta methods have been used for modeling. For binary classification, random forest and XGBoost achieved an accuracy of 0.80 and 0.82, respectively, on external test set. Contrastingly, for multiclass models, random forest and XGBoost achieved an accuracy of 0.73 and 0.75, respectively. 500 Y-randomization runs demonstrate that the models were robust and the correlations were not by chance. Evaluation metrics from predictiveness curve show that the selected eighteen descriptors predict active compounds with total gain (TG) of 0.79 and 0.59 for XGBoost and random forest, respectively. Validated models were further used for virtual screening and molecular docking in search of potential hits. A total of 221 compounds were commonly predicted as active with above the set probability threshold and also under the AD of training set. Molecular docking revealed that three compounds have reasonable binding energy and favorable interactions with critical residues in the active site of EZH2. In conclusion, we highlighted the potential of rigorously validated models for accurately predicting and ranking the activities of lead molecules against cancer epigenetic targets. The models presented in this study represent the platform for development of EZH2 inhibitors.
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Pounina TA, Gloriozova TA, Savidov N, Dembitsky VM. Sulfated and Sulfur-Containing Steroids and Their Pharmacological Profile. Mar Drugs 2021; 19:240. [PMID: 33923288 PMCID: PMC8145587 DOI: 10.3390/md19050240] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 04/19/2021] [Accepted: 04/22/2021] [Indexed: 02/06/2023] Open
Abstract
The review focuses on sulfated steroids that have been isolated from seaweeds, marine sponges, soft corals, ascidians, starfish, and other marine invertebrates. Sulfur-containing steroids and triterpenoids are sourced from sedentary marine coelenterates, plants, marine sediments, crude oil, and other geological deposits. The review presents the pharmacological profile of sulfated steroids, sulfur-containing steroids, and triterpenoids, which is based on data obtained using the PASS program. In addition, several semi-synthetic and synthetic epithio steroids, which represent a rare group of bioactive lipids that have not yet been found in nature, but possess a high level of antitumor activity, were included in this review for the comparative pharmacological characterization of this class of compounds. About 140 steroids and triterpenoids are presented in this review, which demonstrate a wide range of biological activities. Therefore, out of 71 sulfated steroids, thirteen show strong antitumor activity with a confidence level of more than 90%, out of 50 sulfur-containing steroids, only four show strong antitumor activity with a confidence level of more than 93%, and out of eighteen epithio steroids, thirteen steroids show strong antitumor activity with a confidence level of 91% to 97.4%.
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Affiliation(s)
- Tatyana A. Pounina
- Far Eastern Geological Institute, Russian Academy of Sciences, 159 Prospect 100-letiya Vladivostoka, 690022 Vladivostok, Russia;
| | - Tatyana A. Gloriozova
- Institute of Biomedical Chemistry, 10 Building 8, Pogodinskaya Street, 119121 Moscow, Russia;
| | - Nick Savidov
- Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, 3000 College Drive South, Lethbridge, AB T1K 1L6, Canada;
| | - Valery M. Dembitsky
- Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, 3000 College Drive South, Lethbridge, AB T1K 1L6, Canada;
- A.V. Zhirmunsky National Scientific Center of Marine Biology, 17 Palchevsky Str., 690041 Vladivostok, Russia
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Understanding structural characteristics of PARP-1 inhibitors through combined 3D-QSAR and molecular docking studies and discovery of new inhibitors by multistage virtual screening. Struct Chem 2021. [DOI: 10.1007/s11224-021-01765-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Trapotsi MA, Mervin LH, Afzal AM, Sturm N, Engkvist O, Barrett IP, Bender A. Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions. J Chem Inf Model 2021; 61:1444-1456. [PMID: 33661004 DOI: 10.1021/acs.jcim.0c00864] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The understanding of the mechanism-of-action (MoA) of compounds and the prediction of potential drug targets play an important role in small-molecule drug discovery. The aim of this work was to compare chemical and cell morphology information for bioactivity prediction. The comparison was performed using bioactivity data from the ExCAPE database, image data (in the form of CellProfiler features) from the Cell Painting data set (the largest publicly available data set of cell images with ∼30,000 compound perturbations), and extended connectivity fingerprints (ECFPs) using the multitask Bayesian matrix factorization (BMF) approach Macau. We found that the BMF Macau and random forest (RF) performance were overall similar when ECFPs were used as compound descriptors. However, BMF Macau outperformed RF in 159 out of 224 targets (71%) when image data were used as compound information. Using BMF Macau, 100 (corresponding to about 45%) and 90 (about 40%) of the 224 targets were predicted with high predictive performance (AUC > 0.8) with ECFP data and image data as side information, respectively. There were targets better predicted by image data as side information, such as β-catenin, and others better predicted by fingerprint-based side information, such as proteins belonging to the G-protein-Coupled Receptor 1 family, which could be rationalized from the underlying data distributions in each descriptor domain. In conclusion, both cell morphology changes and chemical structure information contain information about compound bioactivity, which is also partially complementary, and can hence contribute to in silico MoA analysis.
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Affiliation(s)
- Maria-Anna Trapotsi
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Lewis H Mervin
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Avid M Afzal
- Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Noé Sturm
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Ian P Barrett
- Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
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Dembitsky VM, Ermolenko E, Savidov N, Gloriozova TA, Poroikov VV. Antiprotozoal and Antitumor Activity of Natural Polycyclic Endoperoxides: Origin, Structures and Biological Activity. Molecules 2021; 26:686. [PMID: 33525706 PMCID: PMC7865715 DOI: 10.3390/molecules26030686] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 01/20/2021] [Accepted: 01/25/2021] [Indexed: 02/08/2023] Open
Abstract
Polycyclic endoperoxides are rare natural metabolites found and isolated in plants, fungi, and marine invertebrates. The purpose of this review is a comparative analysis of the pharmacological potential of these natural products. According to PASS (Prediction of Activity Spectra for Substances) estimates, they are more likely to exhibit antiprotozoal and antitumor properties. Some of them are now widely used in clinical medicine. All polycyclic endoperoxides presented in this article demonstrate antiprotozoal activity and can be divided into three groups. The third group includes endoperoxides, which show weak antiprotozoal activity with a reliability of up to 70%, and this group includes only 1.1% of metabolites. The second group includes the largest number of endoperoxides, which are 65% and show average antiprotozoal activity with a confidence level of 70 to 90%. Lastly, the third group includes endoperoxides, which are 33.9% and show strong antiprotozoal activity with a confidence level of 90 to 99.6%. Interestingly, artemisinin and its analogs show strong antiprotozoal activity with 79 to 99.6% confidence against obligate intracellular parasites which belong to the genera Plasmodium, Toxoplasma, Leishmania, and Coccidia. In addition to antiprotozoal activities, polycyclic endoperoxides show antitumor activity in the proportion: 4.6% show weak activity with a reliability of up to 70%, 65.6% show an average activity with a reliability of 70 to 90%, and 29.8% show strong activity with a reliability of 90 to 98.3%. It should also be noted that some polycyclic endoperoxides, in addition to antiprotozoal and antitumor properties, show other strong activities with a confidence level of 90 to 97%. These include antifungal activity against the genera Aspergillus, Candida, and Cryptococcus, as well as anti-inflammatory activity. This review provides insights on further utilization of polycyclic endoperoxides by medicinal chemists, pharmacologists, and the pharmaceutical industry.
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Affiliation(s)
- Valery M. Dembitsky
- Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, 3000 College Drive South, Lethbridge, AB T1K 1L6, Canada;
- A.V. Zhirmunsky National Scientific Center of Marine Biology, 17 Palchevsky Str., 690041 Vladivostok, Russia;
| | - Ekaterina Ermolenko
- A.V. Zhirmunsky National Scientific Center of Marine Biology, 17 Palchevsky Str., 690041 Vladivostok, Russia;
| | - Nick Savidov
- Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, 3000 College Drive South, Lethbridge, AB T1K 1L6, Canada;
| | - Tatyana A. Gloriozova
- Institute of Biomedical Chemistry, 10 Pogodinskaya Str., 119121 Moscow, Russia; (T.A.G.); (V.V.P.)
| | - Vladimir V. Poroikov
- Institute of Biomedical Chemistry, 10 Pogodinskaya Str., 119121 Moscow, Russia; (T.A.G.); (V.V.P.)
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Li Y, Qiao L, Chen C, Wang Z, Fu X. Comparative study of Danshen and Siwu decoction based on the molecular structures of the components and predicted targets. BMC Complement Med Ther 2021; 21:42. [PMID: 33482800 PMCID: PMC7821527 DOI: 10.1186/s12906-021-03209-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 01/07/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND The sentence of "Danshen (Salvia Miltiorrhizae Radix et Rhizoma) and Siwu decoction are similar in function" was first recorded in an ancient Chinese medical book "Fu Ren Ming Li Lun". This theory has far-reaching influence on the clinical practice of Chinese medicine and is highly respected by Chinese medical doctors. However, the theory has limitations and controversial part for there is no in-depth and system comparative study. METHODS We collected the molecular structures of 129 compounds of Danshen and 81 compounds of Siwu decoction from the literatures. MACCS fingerprints and Tanimoto similarity were calculated based on the molecular structures for comparing the structural feature. Molecular descriptors which represent physical and chemical properties were calculated by Discovery Studio. Principal component analysis (PCA) of was performed based on the descriptors. The ADMET properties were predicted by FAF-Drugs4. The effect targets for the compounds with good ADMET properties were confirmed from experimental data and predicted using the algorithm comprising Bernoulli Naive Bayes profiling. RESULTS Based on the molecular structures, the presented study compared the structural feature, physical and chemical properties, ADMET properties, and effect targets of compounds of Danshen and Siwu decoction. It is found that Danshen and Siwu decoction do not have the same main active components. Moreover, the 2D structure of compounds from Danshen and Siwu decoction is not similar. Some of the compounds of Danshen and Siwu decoction are similar in 3D structure. The compounds with good ADMET properties of Danshen and Siwu decoction have same predicted targets, but some have different targets. CONCLUSIONS It can be inferred from the result that Danshen and Siwu decoction have some similarities, but also present differences from each other in the structure of the compounds and predicted targets. This may be the material basis of the similar and different traditional efficacy of Danshen and Siwu decoction. The setence of " Danshen and Siwu decoction are similar in function. " which is used in clinical has its material basis and target connotation to some extent. However, the traditional effects of Danshen and Siwu decoction are not exactly the same.
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Affiliation(s)
- Yang Li
- College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Ji'nan, 250355, Shandong, China
| | - Li Qiao
- Experimental Center, Shandong University of Traditional Chinese Medicine, Ji'nan, 250355, Shandong, China
| | - Cong Chen
- Institute of Traditional Chinese Medicine Literature and Culture, Shandong University of Traditional Chinese Medicine, Ji'nan, 250355, Shandong, China
| | - Zhenguo Wang
- Institute of Traditional Chinese Medicine Literature and Culture, Shandong University of Traditional Chinese Medicine, Ji'nan, 250355, Shandong, China
| | - Xianjun Fu
- Institute of Traditional Chinese Medicine Literature and Culture, Shandong University of Traditional Chinese Medicine, Ji'nan, 250355, Shandong, China.
- Center for Marine Traditional Chinese Medicine Research, Qingdao Academy of Chinese Medical Science, Qingdao, 260000, Shandong, China.
- Laboratory of Traditional Chinese Medicine Network Pharmacology, Shandong University of Traditional Chinese Medicine, Ji'nan, 250355, Shandong, China.
- Shandong Research Center of Engineering and Technology for omics of TCM, Ji'nan, 250355, Shandong, China.
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Liu A, Walter M, Wright P, Bartosik A, Dolciami D, Elbasir A, Yang H, Bender A. Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure. Biol Direct 2021; 16:6. [PMID: 33461600 PMCID: PMC7814730 DOI: 10.1186/s13062-020-00285-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 12/01/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do not yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models. RESULTS As part of the Critical Assessment of Massive Data Analysis (CAMDA) "CMap Drug Safety Challenge" 2019 ( http://camda2019.bioinf.jku.at ), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759 ± 0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, stress response via TP53 and biotransformation. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DYRK2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose. CONCLUSION Using chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint.
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Affiliation(s)
- Anika Liu
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
| | - Moritz Walter
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Peter Wright
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Aleksandra Bartosik
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Daniela Dolciami
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- Department of Pharmaceutical Sciences, University of Perugia, Via del Liceo 1, 06123, Perugia, Italy
| | - Abdurrahman Elbasir
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- ICT Department, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Hongbin Yang
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
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Sakai M, Nagayasu K, Shibui N, Andoh C, Takayama K, Shirakawa H, Kaneko S. Prediction of pharmacological activities from chemical structures with graph convolutional neural networks. Sci Rep 2021; 11:525. [PMID: 33436854 PMCID: PMC7803991 DOI: 10.1038/s41598-020-80113-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 12/17/2020] [Indexed: 01/29/2023] Open
Abstract
Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies.
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Affiliation(s)
- Miyuki Sakai
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan ,Medical Database Ltd., 2-5-5 Sumitomoshibadaimon building, Shibadaimon, Minato-ku, Tokyo, 105-0012 Japan
| | - Kazuki Nagayasu
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Norihiro Shibui
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Chihiro Andoh
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Kaito Takayama
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Hisashi Shirakawa
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Shuji Kaneko
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
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Schmidt F. Computational Toxicology. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11534-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Bender A, Cortés-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discov Today 2020; 26:511-524. [PMID: 33346134 DOI: 10.1016/j.drudis.2020.12.009] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/07/2020] [Accepted: 12/11/2020] [Indexed: 12/30/2022]
Abstract
Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. This article quantifies the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost. Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future.
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Affiliation(s)
- Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road CB2 1EW, UK; Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Isidro Cortés-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK.
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31
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Cáceres EL, Mew NC, Keiser MJ. Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction. J Chem Inf Model 2020; 60:5957-5970. [PMID: 33245237 DOI: 10.1021/acs.jcim.0c00565] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological data sets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios, whose characteristics differ from a random split of conventional training data sets. We developed a pharmacological data set augmentation procedure, Stochastic Negative Addition (SNA), which randomly assigns untested molecule-target pairs as transient negative examples during training. Under the SNA procedure, drug-screening benchmark performance increases from R2 = 0.1926 ± 0.0186 to 0.4269 ± 0.0272 (122%). This gain was accompanied by a modest decrease in the temporal benchmark (13%). SNA increases in drug-screening performance were consistent for classification and regression tasks and outperformed y-randomized controls. Our results highlight where data and feature uncertainty may be problematic and how leveraging uncertainty into training improves predictions of drug-target relationships.
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Affiliation(s)
- Elena L Cáceres
- Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Bakar Computational Health Sciences Institute, Kavli Institute for Fundamental Neuroscience, Institute for Neurodegenerative Diseases, University of California, San Francisco, 675 Nelson Rising Ln NS 416A, San Francisco, California 94143, United States
| | - Nicholas C Mew
- Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Bakar Computational Health Sciences Institute, Kavli Institute for Fundamental Neuroscience, Institute for Neurodegenerative Diseases, University of California, San Francisco, 675 Nelson Rising Ln NS 416A, San Francisco, California 94143, United States
| | - Michael J Keiser
- Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Bakar Computational Health Sciences Institute, Kavli Institute for Fundamental Neuroscience, Institute for Neurodegenerative Diseases, University of California, San Francisco, 675 Nelson Rising Ln NS 416A, San Francisco, California 94143, United States
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32
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Mervin LH, Johansson S, Semenova E, Giblin KA, Engkvist O. Uncertainty quantification in drug design. Drug Discov Today 2020; 26:474-489. [PMID: 33253918 DOI: 10.1016/j.drudis.2020.11.027] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/13/2020] [Accepted: 11/23/2020] [Indexed: 01/03/2023]
Abstract
Machine learning and artificial intelligence are increasingly being applied to the drug-design process as a result of the development of novel algorithms, growing access, the falling cost of computation and the development of novel technologies for generating chemically and biologically relevant data. There has been recent progress in fields such as molecular de novo generation, synthetic route prediction and, to some extent, property predictions. Despite this, most research in these fields has focused on improving the accuracy of the technologies, rather than on quantifying the uncertainty in the predictions. Uncertainty quantification will become a key component in autonomous decision making and will be crucial for integrating machine learning and chemistry automation to create an autonomous design-make-test-analyse cycle. This review covers the empirical, frequentist and Bayesian approaches to uncertainty quantification, and outlines how they can be used for drug design. We also outline the impact of uncertainty quantification on decision making.
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Affiliation(s)
- Lewis H Mervin
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
| | - Simon Johansson
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden; Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Elizaveta Semenova
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Kathryn A Giblin
- Medicinal Chemistry, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden
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33
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Abstract
One of the grand challenges in contemporary chemical biology is the generation of a probe for every member of the human proteome. Probe selection and optimization strategies typically rely on experimental bioactivity data to determine the potency and selectivity of candidate molecules. However, this approach is profoundly limited by the sparsity of the known data, the annotation bias often found in the literature, and the cost of physical screening. Recent advancements in predictive pharmacology, such as the application of multitask and transfer learning, as well as the use of biologically motivated, structure-agnostic features to characterize molecules, should serve to mitigate these issues. Computational modeling likely offers the only cost-effective approach to substantially increasing the bioactivity annotation density both on the local and global scale and thus, we argue, will need to make a substantial contribution if the ambitious goals of probing the human proteome are to be realized in the foreseeable future.
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Affiliation(s)
- Tim James
- Evotec (U.K.) Ltd. 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, U.K
| | - Adam Sardar
- Evotec (U.K.) Ltd. 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, U.K
| | - Andrew Anighoro
- Evotec (U.K.) Ltd. 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire OX14 4RZ, U.K
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34
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Mervin LH, Afzal AM, Engkvist O, Bender A. Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Protein–Ligand Predictions. J Chem Inf Model 2020; 60:4546-4559. [DOI: 10.1021/acs.jcim.0c00476] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Lewis H. Mervin
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Avid M. Afzal
- Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Mölndal SE-431 83, Sweden
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge CB2 1TN, U.K
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35
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Computational Drug Repositioning: Current Progress and Challenges. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155076] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.
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36
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Pearson Z, Singh M, Boskovic Z. Compound collections at KU 1947–2017: cheminformatic analysis and computational protein target prediction. Med Chem Res 2020. [DOI: 10.1007/s00044-020-02571-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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37
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Singh M, Garza N, Pearson Z, Douglas J, Boskovic Z. Broad assessment of bioactivity of a collection of spiroindane pyrrolidines through “cell painting”. Bioorg Med Chem 2020; 28:115547. [DOI: 10.1016/j.bmc.2020.115547] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 04/23/2020] [Accepted: 05/02/2020] [Indexed: 01/16/2023]
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38
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Thafar MA, Olayan RS, Ashoor H, Albaradei S, Bajic VB, Gao X, Gojobori T, Essack M. DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques. J Cheminform 2020; 12:44. [PMID: 33431036 PMCID: PMC7325230 DOI: 10.1186/s13321-020-00447-2] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/16/2020] [Indexed: 12/14/2022] Open
Abstract
In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug–Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.
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Affiliation(s)
- Maha A Thafar
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Collage of Computers and Information Technology, Taif University, Taif, Kingdom of Saudi Arabia
| | - Rawan S Olayan
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Haitham Ashoor
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Somayah Albaradei
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - Vladimir B Bajic
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
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39
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Wong KK. DNMT1: A key drug target in triple-negative breast cancer. Semin Cancer Biol 2020; 72:198-213. [PMID: 32461152 DOI: 10.1016/j.semcancer.2020.05.010] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/04/2020] [Accepted: 05/18/2020] [Indexed: 02/06/2023]
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. Altered epigenetics regulation including DNA hypermethylation by DNA methyltransferase 1 (DNMT1) has been implicated as one of the causes of TNBC tumorigenesis. In this review, the oncogenic functions rendered by DNMT1 in TNBCs, and DNMT1 inhibitors targeting TNBC cells are presented and discussed. In summary, DNMT1 expression is associated with poor breast cancer survival, and it is overexpressed in TNBC subtype. The oncogenic roles of DNMT1 in TNBCs include: (1) Repression of estrogen receptor (ER) expression; (2) Promotion of epithelial-mesenchymal transition (EMT) required for metastasis; (3) Induces cellular autophagy and; (4) Promotes the growth of cancer stem cells in TNBCs. DNMT1 confers these phenotypes by hypermethylating the promoter regions of ER, multiple tumor suppressor genes, microRNAs and epithelial markers involved in suppressing EMT. DNMT1 inhibitors exert anti-tumorigenic effects against TNBC cells. This includes the hypomethylating agents azacitidine, decitabine and guadecitabine that might sensitize TNBC patients to immune checkpoint blockade therapy. DNMT1 represents an epigenetic target for TNBC cells destruction as well as to derail their metastatic and aggressive phenotypes.
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Affiliation(s)
- Kah Keng Wong
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia.
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40
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Norinder U, Spjuth O, Svensson F. Using Predicted Bioactivity Profiles to Improve Predictive Modeling. J Chem Inf Model 2020; 60:2830-2837. [PMID: 32374618 DOI: 10.1021/acs.jcim.0c00250] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Predictive modeling is a cornerstone in early drug development. Using information for multiple domains or across prediction tasks has the potential to improve the performance of predictive modeling. However, aggregating data often leads to incomplete data matrices that might be limiting for modeling. In line with previous studies, we show that by generating predicted bioactivity profiles, and using these as additional features, prediction accuracy of biological endpoints can be improved. Using conformal prediction, a type of confidence predictor, we present a robust framework for the calculation of these profiles and the evaluation of their impact. We report on the outcomes from several approaches to generate the predicted profiles on 16 datasets in cytotoxicity and bioactivity and show that efficiency is improved the most when including the p-values from conformal prediction as bioactivity profiles.
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Affiliation(s)
- Ulf Norinder
- Department of Computer and Systems Sciences, Stockholm University, Box 7003, SE-164 07 Kista, Sweden.,Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-75124 Uppsala, Sweden.,MTM Research Centre, School of Science and Technology, Örebro University, SE-70182 Örebro, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-75124 Uppsala, Sweden.,Science for Life Laboratory, Uppsala University, Box 591, SE-75124 Uppsala, Sweden
| | - Fredrik Svensson
- The Alzheimer's Research UK University College London Drug Discovery Institute, The Cruciform Building, Gower Street, WC1E 6BT London, U.K
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41
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Sturm N, Mayr A, Le Van T, Chupakhin V, Ceulemans H, Wegner J, Golib-Dzib JF, Jeliazkova N, Vandriessche Y, Böhm S, Cima V, Martinovic J, Greene N, Vander Aa T, Ashby TJ, Hochreiter S, Engkvist O, Klambauer G, Chen H. Industry-scale application and evaluation of deep learning for drug target prediction. J Cheminform 2020; 12:26. [PMID: 33430964 PMCID: PMC7169028 DOI: 10.1186/s13321-020-00428-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 03/30/2020] [Indexed: 12/02/2022] Open
Abstract
Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.
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Affiliation(s)
- Noé Sturm
- Clinical Pharmacology and Safety Science, R&D BioPharmaceuticals, AstraZeneca, Pepparedsleden 1, 43183, Mölndal, Sweden.
| | - Andreas Mayr
- LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Altenberger Str. 69, 4040, Linz, Austria
| | - Thanh Le Van
- High-Dimensional Biology & Discovery Data Sciences, Discovery Sciences, Janssen Pharmaceutica, Turnhoutseweg 30, 2349, Beerse, Belgium
| | - Vladimir Chupakhin
- High-Dimensional Biology & Discovery Data Sciences, Discovery Sciences, Janssen R&D, 1400 McKean Rd, Spring House, Pennsylvania, 19002, USA
| | - Hugo Ceulemans
- High-Dimensional Biology & Discovery Data Sciences, Discovery Sciences, Janssen Pharmaceutica, Turnhoutseweg 30, 2349, Beerse, Belgium
| | - Joerg Wegner
- High-Dimensional Biology & Discovery Data Sciences, Discovery Sciences, Janssen Pharmaceutica, Turnhoutseweg 30, 2349, Beerse, Belgium
| | - Jose-Felipe Golib-Dzib
- High-Dimensional Biology & Discovery Data Sciences, Discovery Sciences, Janssen Cilag SA, Calle Río Jarama, 75A, 45007, Toledo, Spain
| | - Nina Jeliazkova
- Ideaconsult Ltd., 4. Angel Kanchev Str., 1000, Sofia, Bulgaria
| | - Yves Vandriessche
- Intel Corporation, Data Center Group, Veldkant 31, 2550, Kontich, Belgium
| | - Stanislav Böhm
- IT4Innovations, VSB - Technical University of Ostrava, 17. Listopadu 2172/15, 70800, Ostrava-Poruba, Czech Republic
| | - Vojtech Cima
- IT4Innovations, VSB - Technical University of Ostrava, 17. Listopadu 2172/15, 70800, Ostrava-Poruba, Czech Republic
| | - Jan Martinovic
- IT4Innovations, VSB - Technical University of Ostrava, 17. Listopadu 2172/15, 70800, Ostrava-Poruba, Czech Republic
| | - Nigel Greene
- Clinical Pharmacology and Safety Science, R&D BioPharmaceuticals, AstraZeneca, Pepparedsleden 1, 43183, Mölndal, Sweden
| | - Tom Vander Aa
- Exascience Lab, Imec, Kapeldreef 75, 3001, Louvain, Belgium
| | - Thomas J Ashby
- Exascience Lab, Imec, Kapeldreef 75, 3001, Louvain, Belgium
| | - Sepp Hochreiter
- LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Altenberger Str. 69, 4040, Linz, Austria
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, R&D BioPharmaceuticals, AstraZeneca, Pepparedsleden 1, 43183, Mölndal, Sweden
| | - Günter Klambauer
- LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz, Altenberger Str. 69, 4040, Linz, Austria.
| | - Hongming Chen
- Hit Discovery, Discovery Sciences, R&D BioPharmaceuticals, AstraZeneca, Pepparedsleden 1, 43183, Mölndal, Sweden.
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42
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Drakakis G, Cortés-Ciriano I, Alexander-Dann B, Bender A. Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account. ACTA ACUST UNITED AC 2020; 11:e73. [PMID: 31483099 DOI: 10.1002/cpch.73] [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] [Indexed: 02/06/2023]
Abstract
The modes of action (MoAs) of drugs frequently are unknown, because many are small molecules initially identified from phenotypic screens, giving rise to the need to elucidate their MoAs. In addition, the high attrition rate for candidate drugs in preclinical studies due to intolerable toxicity has motivated the development of computational approaches to predict drug candidate (cyto)toxicity as early as possible in the drug-discovery process. Here, we provide detailed instructions for capitalizing on bioactivity predictions to elucidate the MoAs of small molecules and infer their underlying phenotypic effects. We illustrate how these predictions can be used to infer the underlying antidepressive effects of marketed drugs. We also provide the necessary functionalities to model cytotoxicity data using single and ensemble machine-learning algorithms. Finally, we give detailed instructions on how to calculate confidence intervals for individual predictions using the conformal prediction framework. © 2019 by John Wiley & Sons, Inc.
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Affiliation(s)
- Georgios Drakakis
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Isidro Cortés-Ciriano
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Ben Alexander-Dann
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
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44
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David L, Arús-Pous J, Karlsson J, Engkvist O, Bjerrum EJ, Kogej T, Kriegl JM, Beck B, Chen H. Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research. Front Pharmacol 2019; 10:1303. [PMID: 31749705 PMCID: PMC6848277 DOI: 10.3389/fphar.2019.01303] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 10/14/2019] [Indexed: 12/21/2022] Open
Abstract
In recent years, the development of high-throughput screening (HTS) technologies and their establishment in an industrialized environment have given scientists the possibility to test millions of molecules and profile them against a multitude of biological targets in a short period of time, generating data in a much faster pace and with a higher quality than before. Besides the structure activity data from traditional bioassays, more complex assays such as transcriptomics profiling or imaging have also been established as routine profiling experiments thanks to the advancement of Next Generation Sequencing or automated microscopy technologies. In industrial pharmaceutical research, these technologies are typically established in conjunction with automated platforms in order to enable efficient handling of screening collections of thousands to millions of compounds. To exploit the ever-growing amount of data that are generated by these approaches, computational techniques are constantly evolving. In this regard, artificial intelligence technologies such as deep learning and machine learning methods play a key role in cheminformatics and bio-image analytics fields to address activity prediction, scaffold hopping, de novo molecule design, reaction/retrosynthesis predictions, or high content screening analysis. Herein we summarize the current state of analyzing large-scale compound data in industrial pharmaceutical research and describe the impact it has had on the drug discovery process over the last two decades, with a specific focus on deep-learning technologies.
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Affiliation(s)
- Laurianne David
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
- Department of Life Science Informatics, B-IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Josep Arús-Pous
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
- Department of Chemistry and Biochemistry, University of Bern, Bern, Switzerland
| | - Johan Karlsson
- Quantitative Biology, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Esben Jannik Bjerrum
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Thierry Kogej
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - Jan M. Kriegl
- Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Bernd Beck
- Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
| | - Hongming Chen
- Hit Discovery, Discovery Sciences, Biopharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
- Chemistry and Chemical Biology Centre, Guangzhou Regenerative Medicine and Health – Guangdong Laboratory, Guangzhou, China
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45
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Yang G, Ma A, Qin ZS. An Integrated System Biology Approach Yields Drug Repositioning Candidates for the Treatment of Heart Failure. Front Genet 2019; 10:916. [PMID: 31608126 PMCID: PMC6773955 DOI: 10.3389/fgene.2019.00916] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 08/29/2019] [Indexed: 12/20/2022] Open
Abstract
Identifying effective pharmacological treatments for heart failure (HF) patients remains critically important. Given that the development of drugs de novo is expensive and time consuming, drug repositioning has become an increasingly important branch. In the present study, we propose a two-step drug repositioning pipeline and investigate the novel therapeutic effects of existing drugs approved by the US Food and Drug Administration to discover potential therapeutic drugs for HF. In the first step, we compared the gene expression pattern of HF patients with drug-induced gene expression profiles to obtain preliminary candidates. In the second step, we performed a systems biology approach based on the known protein–protein interaction information and targets of drugs to narrow down preliminary candidates to obtain final candidates. Drug set enrichment analysis and literature search were applied to assess the performance of our repositioning approach. We also constructed a mode of action network for each candidate and performed pathway analysis for each candidate using genes contained in their mode of action network to uncover pathways that potentially reflect the mechanisms of candidates’ therapeutic efficacy to HF. We discovered numerous preliminary candidates, some of which are used in clinical practice and supported by the literature. The final candidates contained nearly all of the preliminary candidates supported by previous studies. Drug set enrichment analysis and literature search support the validity of our repositioning approach. The mode of action network for each candidate not only displayed the underlying mechanisms of drug efficacy but also uncovered potential biomarkers and therapeutic targets for HF. Our two-step drug repositioning approach is efficient to find candidates with potential therapeutic efficiency to HF supported by the literature and might be of particular use in the discovery of novel effective pharmacological therapies for HF.
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Affiliation(s)
- Guodong Yang
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
| | - Aiqun Ma
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zhaohui S Qin
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States
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Matsuzaka Y, Uesawa Y. Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library. Int J Mol Sci 2019; 20:ijms20194855. [PMID: 31574921 PMCID: PMC6801383 DOI: 10.3390/ijms20194855] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 09/23/2019] [Accepted: 09/27/2019] [Indexed: 12/30/2022] Open
Abstract
The constitutive androstane receptor (CAR) plays pivotal roles in drug-induced liver injury through the transcriptional regulation of drug-metabolizing enzymes and transporters. Thus, identifying regulatory factors for CAR activation is important for understanding its mechanisms. Numerous studies conducted previously on CAR activation and its toxicity focused on in vivo or in vitro analyses, which are expensive, time consuming, and require many animals. We developed a computational model that predicts agonists for the CAR using the Toxicology in the 21st Century 10k library. Additionally, we evaluate the prediction performance of novel deep learning (DL)-based quantitative structure-activity relationship analysis called the DeepSnap-DL approach, which is a procedure of generating an omnidirectional snapshot portraying three-dimensional (3D) structures of chemical compounds. The CAR prediction model, which applies a 3D structure generator tool, called CORINA-generated and -optimized chemical structures, in the DeepSnap-DL demonstrated better performance than the existing methods using molecular descriptors. These results indicate that high performance in the prediction model using the DeepSnap-DL approach may be important to prepare suitable 3D chemical structures as input data and to enable the identification of modulators of the CAR.
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Affiliation(s)
- Yasunari Matsuzaka
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo 204-8588, Japan.
| | - Yoshihiro Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo 204-8588, Japan.
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Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform 2019; 20:1878-1912. [PMID: 30084866 PMCID: PMC6917215 DOI: 10.1093/bib/bby061] [Citation(s) in RCA: 237] [Impact Index Per Article: 47.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 05/25/2018] [Indexed: 01/16/2023] Open
Abstract
The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.
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Affiliation(s)
- Ahmet Sureyya Rifaioglu
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
- Department of Computer Engineering, İskenderun Technical University, Hatay, Turkey
| | - Heval Atas
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
| | - Maria Jesus Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
| | - Rengul Cetin-Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Volkan Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Tunca Doğan
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey and European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
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El-Atawneh S, Hirsch S, Hadar R, Tam J, Goldblum A. Prediction and Experimental Confirmation of Novel Peripheral Cannabinoid-1 Receptor Antagonists. J Chem Inf Model 2019; 59:3996-4006. [PMID: 31433190 DOI: 10.1021/acs.jcim.9b00577] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Small molecules targeting peripheral CB1 receptors have therapeutic potential in a variety of disorders including obesity-related, hormonal, and metabolic abnormalities, while avoiding the psychoactive effects in the central nervous system. We applied our in-house algorithm, iterative stochastic elimination, to produce a ligand-based model that distinguishes between CB1R antagonists and random molecules by physicochemical properties only. We screened ∼2 million commercially available molecules and found that about 500 of them are potential candidates to antagonize the CB1R. We applied a few criteria for peripheral activity and narrowed that set down to 30 molecules, out of which 15 could be purchased. Ten out of those 15 showed good affinity to the CB1R and two of them with nanomolar affinities (Ki of ∼400 nM). The eight molecules with top affinities were tested for activity: two compounds were pure antagonists, and five others were inverse agonists. These molecules are now being examined in vivo for their peripheral versus central distribution and subsequently will be tested for their effects on obesity in small animals.
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Baltatu OC, Senar S, Campos LA, Cipolla-Neto J. Cardioprotective Melatonin: Translating from Proof-of-Concept Studies to Therapeutic Use. Int J Mol Sci 2019; 20:ijms20184342. [PMID: 31491852 PMCID: PMC6770816 DOI: 10.3390/ijms20184342] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 08/29/2019] [Accepted: 09/04/2019] [Indexed: 12/30/2022] Open
Abstract
In this review we summarized the actual clinical data for a cardioprotective therapeutic role of melatonin, listed melatonin and its agonists in different stages of development, and evaluated the melatonin cardiovascular target tractability and prediction using machine learning on ChEMBL. To date, most clinical trials investigating a cardioprotective therapeutic role of melatonin are in phase 2a. Selective melatonin receptor agonists Tasimelteon, Ramelteon, and combined melatonergic-serotonin Agomelatine, and other agonists with registered structures in CHEMBL were not yet investigated as cardioprotective or cardiovascular drugs. As drug-able for these therapeutic targets, melatonin receptor agonists have the benefit over melatonin of well-characterized pharmacologic profiles and extensive safety data. Recent reports of the X-ray crystal structures of MT1 and MT2 receptors shall lead to the development of highly selective melatonin receptor agonists. Predictive models using machine learning could help to identify cardiovascular targets for melatonin. Selecting ChEMBL scores > 4.5 in cardiovascular assays, and melatonin scores > 4, we obtained 284 records from 162 cardiovascular assays carried out with 80 molecules with predicted or measured melatonin activity. Melatonin activities (agonistic or antagonistic) found in these experimental cardiovascular assays and models include arrhythmias, coronary and large vessel contractility, and hypertension. Preclinical proof-of-concept and early clinical studies (phase 2a) suggest a cardioprotective benefit from melatonin in various heart diseases. However, larger phase 3 randomized interventional studies are necessary to establish melatonin and its agonists’ actions as cardioprotective therapeutic agents.
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Affiliation(s)
- Ovidiu Constantin Baltatu
- Center of Innovation, Technology and Education (CITE), School of Health Sciences at Anhembi Morumbi University, Laureate International Universities, Sao Jose dos Campos 12247-016, Brazil.
| | | | - Luciana Aparecida Campos
- Center of Innovation, Technology and Education (CITE), School of Health Sciences at Anhembi Morumbi University, Laureate International Universities, Sao Jose dos Campos 12247-016, Brazil.
| | - José Cipolla-Neto
- Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo 05508-900, Brazil.
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Durán C, Daminelli S, Thomas JM, Haupt VJ, Schroeder M, Cannistraci CV. Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory. Brief Bioinform 2019; 19:1183-1202. [PMID: 28453640 PMCID: PMC6291778 DOI: 10.1093/bib/bbx041] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Indexed: 01/03/2023] Open
Abstract
The bipartite network representation of the drug–target interactions (DTIs) in a biosystem enhances understanding of the drugs’ multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared—using standard and innovative validation frameworks—with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory—initially detected in brain-network topological self-organization and afterwards generalized to any complex network—is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug–target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.
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Affiliation(s)
| | - Simone Daminelli
- Corresponding authors: Carlo Cannistraci, Biomedical Cybernetics Group at Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Department of Physics, Technische Universität Dresden (TUD), Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40080; E-mail: ; Simone Daminelli, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular Cellular Bioengineering (CMCB), TUD, Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40060; E-mail: ; Michael Schroeder, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular and Cellular Bioengineering (CMCB), TUD Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40062; E-mail:
| | | | | | - Michael Schroeder
- Corresponding authors: Carlo Cannistraci, Biomedical Cybernetics Group at Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Department of Physics, Technische Universität Dresden (TUD), Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40080; E-mail: ; Simone Daminelli, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular Cellular Bioengineering (CMCB), TUD, Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40060; E-mail: ; Michael Schroeder, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular and Cellular Bioengineering (CMCB), TUD Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40062; E-mail:
| | - Carlo Vittorio Cannistraci
- Corresponding authors: Carlo Cannistraci, Biomedical Cybernetics Group at Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Department of Physics, Technische Universität Dresden (TUD), Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40080; E-mail: ; Simone Daminelli, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular Cellular Bioengineering (CMCB), TUD, Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40060; E-mail: ; Michael Schroeder, Bioinformatics Group at Biotechnology Center (BIOTEC), Center of Molecular and Cellular Bioengineering (CMCB), TUD Tatzberg 47-49, 01307 Dresden, Germany, Tel.: +49 (0)351 463 40062; E-mail:
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