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Zhu H, Wang X, Wang X, Pan G, Zhu Y, Feng Y. The toxicity and safety of Chinese medicine from the bench to the bedside. J Herb Med 2021. [DOI: 10.1016/j.hermed.2021.100450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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2
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Boezio B, Audouze K, Ducrot P, Taboureau O. Network-based Approaches in Pharmacology. Mol Inform 2017; 36. [PMID: 28692140 DOI: 10.1002/minf.201700048] [Citation(s) in RCA: 185] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 06/21/2017] [Indexed: 12/23/2022]
Abstract
In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example.
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Affiliation(s)
- Baptiste Boezio
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
| | - Karine Audouze
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
| | - Pierre Ducrot
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Olivier Taboureau
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
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3
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Miteva MA, Villoutreix BO. Computational Biology and Chemistry in MTi: Emphasis on the Prediction of Some ADMET Properties. Mol Inform 2017; 36. [DOI: 10.1002/minf.201700008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 02/03/2017] [Indexed: 12/21/2022]
Affiliation(s)
- Maria A. Miteva
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques In Silico , Inserm UMR−S 973; 35 rue Helene Brion 75013 Paris France
- INSERM, U973; F-75205 Paris France
| | - Bruno O. Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques In Silico , Inserm UMR−S 973; 35 rue Helene Brion 75013 Paris France
- INSERM, U973; F-75205 Paris France
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4
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Chakraborty N, Meyerhoff J, Jett M, Hammamieh R. Genome to Phenome: A Systems Biology Approach to PTSD Using an Animal Model. Methods Mol Biol 2017; 1598:117-154. [PMID: 28508360 DOI: 10.1007/978-1-4939-6952-4_6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Post-traumatic stress disorder (PTSD) is a debilitating illness that imposes significant emotional and financial burdens on military families. The understanding of PTSD etiology remains elusive; nonetheless, it is clear that PTSD is manifested by a cluster of symptoms including hyperarousal, reexperiencing of traumatic events, and avoidance of trauma reminders. With these characteristics in mind, several rodent models have been developed eliciting PTSD-like features. Animal models with social dimensions are of particular interest, since the social context plays a major role in the development and manifestation of PTSD.For civilians, a core trauma that elicits PTSD might be characterized by a singular life-threatening event such as a car accident. In contrast, among war veterans, PTSD might be triggered by repeated threats and a cumulative psychological burden that coalesced in the combat zone. In capturing this fundamental difference, the aggressor-exposed social stress (Agg-E SS) model imposes highly threatening conspecific trauma on naïve mice repeatedly and randomly.There is abundant evidence that suggests the potential role of genetic contributions to risk factors for PTSD. Specific observations include putatively heritable attributes of the disorder, the cited cases of atypical brain morphology, and the observed neuroendocrine shifts away from normative. Taken together, these features underscore the importance of multi-omics investigations to develop a comprehensive picture. More daunting will be the task of downstream analysis with integration of these heterogeneous genotypic and phenotypic data types to deliver putative clinical biomarkers. Researchers are advocating for a systems biology approach, which has demonstrated an increasingly robust potential for integrating multidisciplinary data. By applying a systems biology approach here, we have connected the tissue-specific molecular perturbations to the behaviors displayed by mice subjected to Agg-E SS. A molecular pattern that links the atypical fear plasticity to energy deficiency was thereby identified to be causally associated with many behavioral shifts and transformations.PTSD is a multifactorial illness sensitive to environmental influence. Accordingly, it is essential to employ the optimal animal model approximating the environmental condition that elicits PTSD-like symptoms. Integration of an optimal animal model with a systems biology approach can contribute to a more knowledge-driven and efficient next-generation care management system and, potentially, prevention of PTSD.
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Affiliation(s)
- Nabarun Chakraborty
- Integrative Systems Biology, Geneva Foundation, USACEHR, 568 Doughten Drive, Fredrick, MD, 21702-5010, USA
| | - James Meyerhoff
- Integrative Systems Biology, Geneva Foundation, USACEHR, 568 Doughten Drive, Fredrick, MD, 21702-5010, USA
| | - Marti Jett
- Integrative Systems Biology, US Army Center for Environmental Health Research, USACEHR, 568 Doughten Drive, Frederick, MD, 21702-5010, USA
| | - Rasha Hammamieh
- Integrative Systems Biology, US Army Center for Environmental Health Research, USACEHR, 568 Doughten Drive, Frederick, MD, 21702-5010, USA.
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
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Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
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6
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Moghadam H, Rahgozar M, Gharaghani S. Scoring multiple features to predict drug disease associations using information fusion and aggregation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:609-628. [PMID: 27455069 DOI: 10.1080/1062936x.2016.1209241] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2016] [Accepted: 06/30/2016] [Indexed: 06/06/2023]
Abstract
Prediction of drug-disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug-disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug-drug level and the drug-disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug-disease interactions. The method was validated against a well-established drug-disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.
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Affiliation(s)
- H Moghadam
- a DBRG, CIPCE, School of Electrical and Computer Engineering, College of Engineering , University of Tehran , Tehran , Iran
| | - M Rahgozar
- a DBRG, CIPCE, School of Electrical and Computer Engineering, College of Engineering , University of Tehran , Tehran , Iran
| | - S Gharaghani
- b LBD, Institute of Biochemistry and Biophysics , University of Tehran , Tehran , Iran
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Te JA, AbdulHameed MDM, Wallqvist A. Systems toxicology of chemically induced liver and kidney injuries: histopathology-associated gene co-expression modules. J Appl Toxicol 2016; 36:1137-49. [PMID: 26725466 PMCID: PMC5064727 DOI: 10.1002/jat.3278] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Revised: 11/18/2015] [Accepted: 11/23/2015] [Indexed: 11/11/2022]
Abstract
Organ injuries caused by environmental chemical exposures or use of pharmaceutical drugs pose a serious health risk that may be difficult to assess because of a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific histopathology outcomes via biomarkers will provide a foundation for designing precise and robust diagnostic tests. We identified co-expressed genes (modules) specific to injury endpoints using the Open Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System (TG-GATEs) - a toxicogenomics database containing organ-specific gene expression data matched to dose- and time-dependent chemical exposures and adverse histopathology assessments in Sprague-Dawley rats. We proposed a protocol for selecting gene modules associated with chemical-induced injuries that classify 11 liver and eight kidney histopathology endpoints based on dose-dependent activation of the identified modules. We showed that the activation of the modules for a particular chemical exposure condition, i.e., chemical-time-dose combination, correlated with the severity of histopathological damage in a dose-dependent manner. Furthermore, the modules could distinguish different types of injuries caused by chemical exposures as well as determine whether the injury module activation was specific to the tissue of origin (liver and kidney). The generated modules provide a link between toxic chemical exposures, different molecular initiating events among underlying molecular pathways and resultant organ damage. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Journal of Applied Toxicology published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Jerez A Te
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, 21702, USA
| | - Mohamed Diwan M AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, 21702, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, 21702, USA
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8
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Verbist BMP, Verheyen GR, Vervoort L, Crabbe M, Beerens D, Bosmans C, Jaensch S, Osselaer S, Talloen W, Van den Wyngaert I, Van Hecke G, Wuyts D, Van Goethem F, Göhlmann HWH. Integrating High-Dimensional Transcriptomics and Image Analysis Tools into Early Safety Screening: Proof of Concept for a New Early Drug Development Strategy. Chem Res Toxicol 2015; 28:1914-25. [DOI: 10.1021/acs.chemrestox.5b00103] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Dirk Wuyts
- Janssen R&D, Turnhoutseweg 30, 2340 Beerse, Belgium
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9
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Audouze K, Taboureau O. Chemical biology databases: from aggregation, curation to representation. DRUG DISCOVERY TODAY. TECHNOLOGIES 2015; 14:25-29. [PMID: 26194584 DOI: 10.1016/j.ddtec.2015.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Revised: 03/19/2015] [Accepted: 03/29/2015] [Indexed: 06/04/2023]
Abstract
Systems chemical biology offers a novel way of approaching drug discovery by developing models that consider the global physiological environment of protein targets and their perturbations by drugs. However, the integration of all these data needs curation and standardization with an appropriate representation in order to get relevant interpretations. In this mini review, we present some databases and services, which integrated together with computational tools and data standardization, could assist scientists in decision making during the different drug development process.
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Affiliation(s)
- Karine Audouze
- Université Paris Diderot - Inserm UMR-S973, MTi, 75013 Paris, France
| | - Olivier Taboureau
- Université Paris Diderot - Inserm UMR-S973, MTi, 75013 Paris, France.
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10
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Zhang X, Fan HR, Li YZ, Xiao XF, Liu R, Qi JW, Wang J, Zhang ZP, Liu CX, Shen XP. Development and Application of Network Toxicology in Safety Research of Chinese Materia Medica. CHINESE HERBAL MEDICINES 2015. [DOI: 10.1016/s1674-6384(15)60016-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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11
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS IN DRUG DESIGN, PREDICTIVE TOXICOLOGY, AND RISK ASSESSMENT 2015. [DOI: 10.4018/978-1-4666-8136-1.ch001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
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Affiliation(s)
- Maryam Hamzeh-Mivehroud
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Siavoush Dastmalchi
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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12
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Tawa GJ, AbdulHameed MDM, Yu X, Kumar K, Ippolito DL, Lewis JA, Stallings JD, Wallqvist A. Characterization of chemically induced liver injuries using gene co-expression modules. PLoS One 2014; 9:e107230. [PMID: 25226513 PMCID: PMC4165895 DOI: 10.1371/journal.pone.0107230] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Accepted: 08/06/2014] [Indexed: 12/19/2022] Open
Abstract
Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules) specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1) known biochemical pathways associated with liver injuries and 2) clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20%) genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects.
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Affiliation(s)
- Gregory J. Tawa
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
- * E-mail: (AW); (GJT)
| | - Mohamed Diwan M. AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Xueping Yu
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Kamal Kumar
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Danielle L. Ippolito
- U.S. Army Center for Environmental Health Research, Fort Detrick, Maryland, United States of America
| | - John A. Lewis
- U.S. Army Center for Environmental Health Research, Fort Detrick, Maryland, United States of America
| | - Jonathan D. Stallings
- U.S. Army Center for Environmental Health Research, Fort Detrick, Maryland, United States of America
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
- * E-mail: (AW); (GJT)
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13
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Margineanu DG. Systems biology, complexity, and the impact on antiepileptic drug discovery. Epilepsy Behav 2014; 38:131-42. [PMID: 24090772 DOI: 10.1016/j.yebeh.2013.08.029] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2013] [Accepted: 08/26/2013] [Indexed: 12/16/2022]
Abstract
The number of available anticonvulsant drugs increased in the period spanning over more than a century, amounting to the current panoply of nearly two dozen so-called antiepileptic drugs (AEDs). However, none of them actually prevents/reduces the post-brain insult development of epilepsy in man, and in no less than a third of patients with epilepsy, the seizures are not drug-controlled. Plausibly, the enduring limitation of AEDs' efficacy derives from the insufficient understanding of epileptic pathology. This review pinpoints the unbalanced reductionism of the analytic approaches that overlook the intrinsic complexity of epilepsy and of the drug resistance in epilepsy as the core conceptual flaw hampering the discovery of truly antiepileptogenic drugs. A rising awareness of the complexity of epileptic pathology is, however, brought about by the emergence of nonreductionist systems biology (SB) that considers the networks of interactions underlying the normal organismic functions and of SB-based systems (network) pharmacology that aims to restore pathological networks. By now, the systems pharmacology approaches of AED discovery are fairly meager, but their forthcoming development is both a necessity and a realistic prospect, explored in this review.
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Affiliation(s)
- Doru Georg Margineanu
- Department of Neurosciences, Faculty of Medicine and Pharmacy, University of Mons, Ave. Champ de Mars 6, B-7000 Mons, Belgium.
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14
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Kongsbak K, Hadrup N, Audouze K, Vinggaard AM. Applicability of computational systems biology in toxicology. Basic Clin Pharmacol Toxicol 2014; 115:45-9. [PMID: 24528503 DOI: 10.1111/bcpt.12216] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Accepted: 02/05/2014] [Indexed: 12/31/2022]
Abstract
Systems biology as a research field has emerged within the last few decades. Systems biology, often defined as the antithesis of the reductionist approach, integrates information about individual components of a biological system. In integrative systems biology, large data sets from various sources and databases are used to model and predict effects of chemicals on, for instance, human health. In toxicology, computational systems biology enables identification of important pathways and molecules from large data sets; tasks that can be extremely laborious when performed by a classical literature search. However, computational systems biology offers more advantages than providing a high-throughput literature search; it may form the basis for establishment of hypotheses on potential links between environmental chemicals and human diseases, which would be very difficult to establish experimentally. This is possible due to the existence of comprehensive databases containing information on networks of human protein-protein interactions and protein-disease associations. Experimentally determined targets of the specific chemical of interest can be fed into these networks to obtain additional information that can be used to establish hypotheses on links between the chemical and human diseases. Such information can also be applied for designing more intelligent animal/cell experiments that can test the established hypotheses. Here, we describe how and why to apply an integrative systems biology method in the hypothesis-generating phase of toxicological research.
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Affiliation(s)
- Kristine Kongsbak
- Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Søborg, Denmark; Department for Systems Biology, Centre for Biological Sequence Analysis, Technical University of Denmark, Kgs. Lyngby, Denmark
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15
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Liu X, Wu WY, Jiang BH, Yang M, Guo DA. Pharmacological tools for the development of traditional Chinese medicine. Trends Pharmacol Sci 2013; 34:620-8. [PMID: 24139610 DOI: 10.1016/j.tips.2013.09.004] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Revised: 08/25/2013] [Accepted: 09/16/2013] [Indexed: 02/08/2023]
Abstract
Pharmacology as a modern science was introduced in China approximately 150 years ago, and has been used since then to study traditional Chinese medicine (TCM). Pharmacology has experienced its own development over this time and continues to provide new tools for the study of TCM. In the present review, three models for the pharmacological study of TCM are considered: (i) chemistry-focused study; (ii) target-directed study; and (iii) systems-biology-based study. These approaches correspond to recent developments in pharmacology, and in particular to new tools available to the field. Representative achievements and the pharmacological tools used to study TCM are reviewed. Pharmacology has played, and will continue to play, an indispensable role in elucidating the chemical basis, biological targets, and mechanisms of action of TCM medicines, and in developing a scientific basis for the theory of TCM.
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Affiliation(s)
- Xuan Liu
- Shanghai Research Center for TCM Modernization, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, P.R. China
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16
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Gautier L, Taboureau O, Audouze K. The effect of network biology on drug toxicology. Expert Opin Drug Metab Toxicol 2013; 9:1409-18. [PMID: 23937336 DOI: 10.1517/17425255.2013.820704] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
INTRODUCTION The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity. AREAS COVERED This review describes the strengths and limitations of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity. EXPERT OPINION There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network biology has the opportunity to contribute to a better understanding of a drug's safety profile. The authors believe that considering a drug action and protein's function in a global physiological environment may benefit our understanding of the impact some chemicals have on human health and toxicity. The next step for network biology will be to better integrate differential and quantitative data.
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Affiliation(s)
- Laurent Gautier
- Technical University of Denmark, Center for Biological Sequence Analysis, Department of Systems Biology , Lyngby , Denmark
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García-Sosa AT, Maran U. Drugs, non-drugs, and disease category specificity: organ effects by ligand pharmacology. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013; 24:319-331. [PMID: 23534612 DOI: 10.1080/1062936x.2013.773373] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Important understanding can be gained from using molecular biology-based and chemistry-based techniques together. Bayesian classifiers have thus been developed in the present work using several statistically significant molecular properties of compiled datasets of drugs and non-drugs, including their disease category or organ. The results show they provide a useful classification and simplicity of several different ligand efficiencies and molecular properties. Early recall of drugs among non-drugs using the classifiers as a ranking tool is also provided. As the chemical space of compounds is addressed together with their anatomical characterization, chemical libraries can be improved to select for specific organ or disease. Eventually, by including even finer detail, the method may help in designing libraries with specific pharmacological or toxicological target chemical space. Alternatively, a lack of statistically significant differences in property density distributions may help in further describing compounds with possibility of activity on several organs or disease groups, and given their very similar or considerably overlapping chemical space, therefore wanted or unwanted side-effects. The overlaps between densities for several properties of organs or disease categories were calculated by integrating the area under the curves where they intersect. The naïve Bayesian classifiers are readily built, fast to score, and easily interpretable.
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Affiliation(s)
- A T García-Sosa
- Institute of Chemistry, University of Tartu, Tartu, Estonia.
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18
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The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so. Drug Discov Today 2012. [PMID: 23207804 DOI: 10.1016/j.drudis.2012.11.008] [Citation(s) in RCA: 117] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).
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