4.5 Article

Substructure-Based Virtual Screening for Adenosine A2A Receptor Ligands

期刊

CHEMMEDCHEM
卷 6, 期 12, 页码 2302-2311

出版社

WILEY-V C H VERLAG GMBH
DOI: 10.1002/cmdc.201100369

关键词

adenosine A(2A) receptor; antagonists; drug discovery; GPCRs; virtual screening

资金

  1. Dutch Top Institute Pharma (The GPCR Forum) [D1-105]

向作者/读者索取更多资源

A virtual ligand-based screening approach was designed and evaluated for the discovery of new A(2A) adenosine receptor (AR) ligands. For comparison and evaluation, the procedures from a recently published virtual screening study that used the A(2A) AR X-ray crystal structure for the target-based discovery of new A(2A) ligands were largely followed. Several screening models were constructed by deriving the distinguishing structural features from selected sets of A(2A) AR antagonists, so-called frequent substructure mining. The best model in statistical terms was subsequently applied to large-scale virtual screens of a commercial vendor library. This resulted in the selection of 36 candidates for acquisition and testing. Of the selected candidates, eight compounds significantly inhibited radioligand binding at A(2A) AR (> 30%) at 10 mm, corresponding to a hit rate of 22%. This hit rate is quite similar to that of the referenced target-based virtual screening study, while both approaches yield new, non-overlapping sets of ligands.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Ethics

Informal Caregivers of Patients with Disorders of Consciousness: a Qualitative Study of Communication Experiences and Information Needs with Physicians

Karoline Boegle, Marta Bassi, Angela Comanducci, Katja Kuehlmeyer, Philipp Oehl, Theresa Raiser, Martin Rosenfelder, Jaco Diego Sitt, Chiara Valota, Lina Willacker, Andreas Bender, Eva Grill

Summary: Family caregivers of patients with disorders of consciousness emotionally engage in conversations based on the value of the information provided, seeking positive information, comfort, and empathy, and requiring detailed information to gain a deep understanding and clear picture of their loved one's condition.

NEUROETHICS (2022)

Article Medicine, Legal

Retrospective analysis of the potential use of virtual control groups in preclinical toxicity assessment using the eTOX database

Peter S. R. Wright, Graham F. Smith, Katharine A. Briggs, Robert Thomas, Gareth Maglennon, Paulius Mikulskis, Melissa Chapman, Nigel Greene, Benjamin U. Phillips, Andreas Bender

Summary: Virtual Control Groups (VCGs) based on Historical Control Data (HCD) have the potential to reduce animal usage in preclinical toxicity testing. Replacing Concurrent Control Groups (CCGs) with VCGs can improve the consistency of study outcomes. However, some covariates may affect the identification of treatment-relatedness.

REGULATORY TOXICOLOGY AND PHARMACOLOGY (2023)

Article Medicine, Legal

Statistical analysis of preclinical inter-species concordance of histopathological findings in the eTOX database

Peter S. R. Wright, Katharine A. Briggs, Robert Thomas, Graham F. Smith, Gareth Maglennon, Paulius Mikulskis, Melissa Chapman, Nigel Greene, Benjamin U. Phillips, Andreas Bender

Summary: By comparing histopathological findings and target organ toxicities across different preclinical species, this study found that positive concordance is more common than negative concordance in histopathological results, and there is low concordance in target organ toxicities. It provides new statistically significant associations between preclinical species but finds that concordance is rare.

REGULATORY TOXICOLOGY AND PHARMACOLOGY (2023)

Article Pharmacology & Pharmacy

Prediction of inotropic effect based on calcium transients in human iPSC-derived cardiomyocytes and machine learning

Hongbin Yang, Olga Obrezanova, Amy Pointon, Will Stebbeds, Jo Francis, Kylie A. Beattie, Peter Clements, James S. Harvey, Graham F. Smith, Andreas Bender

Summary: Functional changes to cardiomyocytes during drug discovery pose risks of cardiovascular adverse effects. A new approach using calcium transients in hiPSC-CMs has been developed to detect early contractility changes. By deriving 25 parameters from each calcium transient waveform, a modified Random Forest method was able to predict inotropic effects with improved accuracy compared to traditional methods. This study demonstrates the potential of advanced waveform parameters and machine learning techniques in predicting cardiovascular risks associated with inotropic effects.

TOXICOLOGY AND APPLIED PHARMACOLOGY (2023)

Article Pharmacology & Pharmacy

In silico prediction and biological assessment of novel angiogenesis modulators from traditional Chinese medicine

Yingli Zhu, Hongbin Yang, Liwen Han, Lewis H. Mervin, Layla Hosseini-Gerami, Peihai Li, Peter Wright, Maria-Anna Trapotsi, Kechun Liu, Tai-Ping Fan, Andreas Bender

Summary: Uncontrolled angiogenesis is a common problem in many deadly and debilitating diseases, and traditional Chinese medicine offers an alternative source for developing drugs to regulate angiogenesis. In this study, 100 traditional Chinese medicine-derived metabolites were investigated, and 51 metabolites were found to have angiogenic activity. The mechanisms of action of these metabolites were analyzed, and a decision tree was generated to predict their poly-pharmacology. In vitro and in vivo experiments were conducted to validate the predictions and identify specific metabolites with pro-angiogenic or anti-angiogenic effects.

FRONTIERS IN PHARMACOLOGY (2023)

Review Biochemical Research Methods

Using chemical and biological data to predict drug toxicity

Anika Liu, Srijit Seal, Hongbin Yang, Andreas Bender

Summary: This review discusses various sources of information, including biological data such as gene expression and cell morphology, for better understanding and predicting compound activity and safety-related endpoints. It introduces different types of chemical, in vitro, and in vivo information that can describe compounds and adverse effects. The review explores how compound descriptors based on chemical structure or biological perturbation response can predict safety-related endpoints, and how biological data can enhance understanding of adverse effects mechanistically. These applications highlight the potential of large-scale biological information in predictive toxicology and drug discovery projects.

SLAS DISCOVERY (2023)

Article Biochemical Research Methods

Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis

Layla Hosseini-Gerami, Ixavier Alonzo Higgins, David A. Collier, Emma Laing, David Evans, Howard Broughton, Andreas Bender

Summary: This study performed a comprehensive evaluation of four causal reasoning algorithms in different networks, and found that the choice of algorithm and network greatly influenced the performance of causal reasoning algorithms. SigNet performed best in recovering direct targets, while CARNIVAL with Omnipath network excelled in recovering informative signaling pathways. The performance of causal reasoning methods was somewhat correlated with the connectivity and biological role of the targets.

BMC BIOINFORMATICS (2023)

Article Biochemistry & Molecular Biology

Deep generative models for 3D molecular structure

Benoit Baillif, Jason Cole, Patrick McCabe, Andreas Bender

Summary: Deep generative models have become popular in chemical design. This article focuses on explicit 3D molecular generative models, which have gained interest recently. Multiple models have been developed to generate molecules in 3D, providing atom types and coordinates. These models can be guided by structural information and produce molecules with similar docking scores to known actives, but they are less efficient and sometimes generate unrealistic conformations. The article advocates for a unified benchmark of metrics and proposes future perspectives to be addressed.

CURRENT OPINION IN STRUCTURAL BIOLOGY (2023)

Article Chemistry, Medicinal

Proteochemometric Modeling Identifies Chemically Diverse Norepinephrine Transporter Inhibitors

Brandon J. Bongers, Huub J. Sijben, Peter B. R. Hartog, Andrey Tarnovskiy, Adriaan P. IJzerman, Laura H. Heitman, Gerard J. P. van Westen

Summary: In this study, a computational screening pipeline was developed to find new inhibitors for the NET protein. A data-driven approach was used to diversify the chemical space and select optimal proteins to model for NETs. A proteochemometric model was created and applied to an extensive compound database, resulting in the identification of five potential hit compounds with promising inhibitory potencies toward NET.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2023)

Article Medicine, Research & Experimental

Prediction of Compound Plasma Concentration-Time Profiles in Mice Using Random Forest

Koichi Handa, Peter Wright, Saki Yoshimura, Michiharu Kageyama, Takeshi Iijima, Andreas Bender

Summary: This study developed machine learning models to predict plasma concentration-time profiles of drugs after intravenous and oral administration. The predictive accuracy of different models was investigated, and random forest showed the best performance. The importance of in vitro pharmacokinetic parameters was also explored and found to be well-reflected in the model.

MOLECULAR PHARMACEUTICS (2023)

Article Chemistry, Medicinal

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

Lavinia-Lorena Pruteanu, Andreas Bender

Summary: Gene expression and cell morphology data are important for drug discovery. They can describe different biological states and the effects of compound treatment, and are useful for drug repurposing and compound characterization. This paper discusses recent advances in this area, and emphasizes the need for better understanding the applicability domain of readouts and their relevance for decision-making.

ACS MEDICINAL CHEMISTRY LETTERS (2023)

Article Chemistry, Multidisciplinary

Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data

Srijit Seal, Hongbin Yang, Maria-Anna Trapotsi, Satvik Singh, Jordi Carreras-Puigvert, Ola Spjuth, Andreas Bender

Summary: In this study, similarity-based merger models were developed to combine outputs of individual models trained on cell morphology and chemical structure, as well as the structural and morphological similarities of compounds in the test dataset. Logistic regression models were used to apply these similarity-based merger models on predictions and similarities from 177 assays, achieving better performance compared to using structural or cell painting models alone. These results demonstrate that similarity-based merger models combining structure and cell morphology can accurately predict a wide range of biological assay outcomes and expand the applicability domain.

JOURNAL OF CHEMINFORMATICS (2023)

Article Chemistry, Medicinal

Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice

Lavinia-Lorena Pruteanu, Andreas Bender

Summary: Gene expression and cell morphology data are valuable in drug discovery, providing insight into biological systems in different states and after compound treatment. This article discusses recent advances in using these data for drug repurposing and highlights the need for further understanding of the applicability domain and relevance of the readouts for decision making.

ACS MEDICINAL CHEMISTRY LETTERS (2023)

Article Chemistry, Medicinal

Proteochemometric Modeling Identifies Chemically Diverse Norepinephrine Transporter Inhibitors

Brandon J. Bongers, Huub J. Sijben, Peter B. R. Hartog, Andrey Tarnovskiy, Adriaan P. IJzerman, Laura H. Heitman, Gerard J. P. van Westen

Summary: This study developed a computational screening pipeline to identify new inhibitors for the high-affinity norepinephrine transporter (NET). By using the chemical space of related proteins, a data-driven approach was used to diversify the known chemical space for NET modeling. The final model, created through a two-step approach, predicted 46 chemically diverse candidates, of which five compounds showed promising inhibitory potency towards NET in experimental assays.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2023)

Article Biochemistry & Molecular Biology

Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature

Maria-Anna Trapotsi, Elizabeth Mouchet, Guy Williams, Tiziana Monteverde, Karolina Juhani, Riku Turkki, Filip Miljkovic, Anton Martinsson, Lewis Mervin, Kenneth R. Pryde, Erik Mullers, Ian Barrett, Ola Engkvist, Andreas Bender, Kevin Moreau

Summary: This study used unbiased high-content imaging method Cell Painting to identify phenotypic signatures of PROTACs and discovered mitochondrial toxicity signatures that could not be expected by screening individual PROTAC components. The results highlight the potential of unbiased phenotypic methods in identifying toxic signatures and impacting drug design.

ACS CHEMICAL BIOLOGY (2022)

暂无数据