Analysis of drug-induced effect patterns to link structure and side effects of medicines

Fliri, Anton; Loging, William; Thadeio, Peter F.; Volkmann, Robert A.

doi:10.1038/nchembio747

Cited by 156 publications

(109 citation statements)

References 23 publications

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“…For example, ADRs can be predicted for a compound on the basis of its profile similarity to other compounds with known ADRs [76], where the profile is determined in ligand-binding assays against a panel of targets [19,40,77].…”

Section: Data Mining On Bioactivity Spectramentioning

confidence: 99%

“…There have also been several attempts to establish relationships between molecular structure and broad biological activity and off-target effects (toxicity) [74]. For example, Fliri et al presented biological spectra for a cross-section of the proteome [40]. Using hierachical clustering of the spectra similarities, they created a relationship between structure and bioactivity.…”

Section: Data Mining On Bioactivity Spectramentioning

confidence: 99%

“…To improve the predictive power of the models, we further expanded the dataset to 5767 compounds tested on up to 79 targets -all selected for their known link with potential safety issues. For comparison, see recent articles that describe in silico approaches to discuss promiscuity and its linkage to side effects (using mainly the Cerep BioPrint dataset) [20,[40][41][42]. In our work, we compared the chemical properties of the compounds showing promiscuous properties to the selective compounds in this panel of assays in order to train a NB model [26,27,43,44] that could predict compound promiscuity or selectivity.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge‐Based and Computational Approaches to In Vitro Safety Pharmacology

Scheiber

Bender

Azzaoui

et al. 2009

Hit and Lead Profiling

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Legitimate estimates suggest that developing a novel chemical entity (NCE) as a drug can cost up to U.S.$ 2 billion [1,2]. Still, about 10% of NCEs show serious adverse drug reactions (ADR) after market launch [3]. The majority of these ADRs can be avoided if possible undesired off-target effects of the compound are understood very early during the drug discovery process, that is, before clinical trials are started.This contribution focuses on computational methods that are used to assist and to guide in vitro preclinical safety pharmacology (PSP), a technology commonly applied in the pharmaceutical industry to evaluate compound selectivity profiles [4][5][6][7][8]. To develop compounds highly selective for a therapeutically relevant target and to avoid side effects or adverse drug reactions are key goals for every small-molecule drug discovery project. To achieve this, preclinical safety pharmacology approaches are commonly employed to screen compounds routinely in comparatively inexpensive, yet predictive assays to generate knowledge about possible polypharmacology. Thereby a comprehensive identification of possible liabilities can be achieved.We outline the currently available environment and approaches that can be applied for thorough computational analyses of in vitro safety pharmacology data. After discussing desirable and necessary prerequisites for the data input from a computational perspective, we address how this data is used to predict a general promiscuity score for a single compound. This approach aims to answer the general question of whether a compound will hit many targets or will be selective. Finally, we demonstrate how to computationally reveal possible single-target liabilities. This has the objective of understanding why a certain compound is active against a defined undesired target on a molecular level.The above approaches are collectively used to triage compounds prior to being screened in an in vitro safety pharmacology panel in order to prioritize compounds for testing and identify those which have the highest likelihood of being selective.Hit and Lead Profiling. Edited by Bernard Faller and Laszlo Urban

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Section: Data Mining On Bioactivity Spectramentioning

confidence: 99%

Section: Data Mining On Bioactivity Spectramentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Knowledge‐Based and Computational Approaches to In Vitro Safety Pharmacology

Scheiber

Bender

Azzaoui

et al. 2009

Hit and Lead Profiling

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“…Fliri et al [1] analyzed the relationships between side effects and molecular target activities from a global perspective by examining data from protein binding assays and corresponding drug side effect information, and ultimately found a strong correlation between binding patterns and side effect patterns.Scheiber et al linked chemical features to side effects using a global perspective. [2] Campillos et al [3] created side effect profiles by computationally extracting adverse effect terms from drug package inserts using natural language processing techniques and then employed the relationships between binding and side effect patterns in order to predict off-target binding activity.…”

Section: Introductionmentioning

confidence: 99%

Drug Side Effect Profiles as Molecular Descriptors for Predictive Modeling of Target Bioactivity

Baker

Fourches

Tropsha

2015

Molecular Informatics

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We have explored the potential of using side effect profiles of drugs to predict their bioactivities at the receptor level. Serotonin 5-HT6 binding and dopamine antagonism were investigated in separate studies. A set of 5-HT6 binders and non-binders was retrieved from the PDSP Ki database, whereas dopamine antagonists were retrieved from the MeSH Pharmaceutical Action file. The side effect data was extracted from ChemoText, a data repository containing MeSH annotations pulled from MEDLINE records. These side effects profiles were treated as molecular descriptors enabling a QSAR-like approach to build models that could reliably discriminate different classes of molecules, e.g., binders versus non-binders, and dopamine antagonists versus non-antagonists. Selected models with the best external prediction performances were applied to a library of ca. 1000 chemicals with known side effects profiles in order to predict their potential 5-HT6 binding and/or dopamine antagonism. In each case the virtual screening process was able to identify putatively active compounds that through subsequent literature-based validation were found to be likely or known 5-HT6 binders or dopamine antagonists. These results demonstrate that side effect profiles can be utilized to predict a drug's unknown molecular activity, thus representing a valuable opportunity in repositioning the drug for a new indications.

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“…Whereas conventional strategies by and large rely on assay panels of 20 -100 purified enzymes to address compound potency, selectivity, and potential off-target liabilities (77), the recent developments in affinity-based proteomics techniques have enabled the direct determination of protein binding profiles of small molecule drugs under more physiological conditions. These techniques are in principle based on affinity chromatography, typically using immobilized drugs or tool compounds (for reviews, see Refs.…”

Section: Advances In Lc-ms Technologies: Quantification and Analysis mentioning

confidence: 99%

Chemical and Pathway Proteomics

Kruse¹,

Bantscheff²,

Drewes³

et al. 2008

Molecular & Cellular Proteomics

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In recent years mass spectrometry-based proteomics has moved beyond a mere quantitative description of protein expression levels and their possible correlation with disease or drug action. Impressive progress in LC-MS instrumentation together with the availability of new enabling tools and methods for quantitative proteome analysis and for identification of posttranslational modifications has triggered a surge of chemical and functional proteomics studies dissecting mechanisms of action of cancer drugs and molecular mechanisms that modulate signal transduction pathways. Despite the tremendous progress that has been made in the field, major challenges, relating to sensitivity, dynamic range, and throughput of the described methods, remain. In this review we summarize recent advances in LC-MS-based approaches and their application to cancer drug discovery and to studies of cancer-related pathways in cell culture models with particular emphasis on mechanistic studies of drug action in these systems. Moreover we highlight the emerging utility of pathway and chemical proteomics techniques for translational research in patient tissue. Molecular & Cellular Proteomics 7:1887-1901, 2008. LC-MS-BASED INVESTIGATION OF CANCER PATHWAYS AND ONCOLOGY DRUG ACTIONCancers share a restricted set of capabilities crucial for the development of the tumor phenotype: proliferation in the absence of growth factors, insensitivity to growth-suppressive mechanisms, avoidance of apoptosis, limitless replication, angiogenesis, tissue invasion, and metastasis. These characteristics have been referred to as the hallmarks of cancer (1). They are acquired by cancer cells over time and are caused by the accumulation of mostly somatic gene mutations and by overexpression of key causative "driver" proteins. These pivotal proteins, which are differentially expressed between cancer patients and healthy individuals, are being sought after as possible drug targets or biomarkers for early diagnosis of disease, for clinical trial management, and for personalized health care. Consequently in the classical expression proteomics paradigm these signature proteins are identified by LC-MS profiling methods in increasingly sophisticated ways that have recently been reviewed elsewhere (2).In addition to cellular changes in protein expression, it has been hypothesized that different peptidomes might be present in serum of cancer patients and healthy individuals as a result of cancer-associated changes in proteolytic processing. Based on this assumption proteomic peptide patterns (as opposed to identified peptide sequences), recorded by high resolution mass spectrometers and interpreted by suitable pattern analysis software, could serve as diagnostic tools (3). For example, proteomic MS patterns can distinguish between masked sets of serum samples from (known) ovarian cancer patients and those from healthy controls (4). Since this initial report, proteomics methods for exploiting the information content of the blood peptidome have evolved significantly (5). Recent...

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Analysis of drug-induced effect patterns to link structure and side effects of medicines

Cited by 156 publications

References 23 publications

Knowledge‐Based and Computational Approaches to In Vitro Safety Pharmacology

Knowledge‐Based and Computational Approaches to In Vitro Safety Pharmacology

Drug Side Effect Profiles as Molecular Descriptors for Predictive Modeling of Target Bioactivity

Chemical and Pathway Proteomics

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