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Roterman I, Konieczny L. Protein Is an Intelligent Micelle. ENTROPY (BASEL, SWITZERLAND) 2023; 25:850. [PMID: 37372194 DOI: 10.3390/e25060850] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 03/22/2023] [Accepted: 04/28/2023] [Indexed: 06/29/2023]
Abstract
Interpreting biological phenomena at the molecular and cellular levels reveals the ways in which information that is specific to living organisms is processed: from the genetic record contained in a strand of DNA, to the translation process, and then to the construction of proteins that carry the flow and processing of information as well as reveal evolutionary mechanisms. The processing of a surprisingly small amount of information, i.e., in the range of 1 GB, contains the record of human DNA that is used in the construction of the highly complex system that is the human body. This shows that what is important is not the quantity of information but rather its skillful use-in other words, this facilitates proper processing. This paper describes the quantitative relations that characterize information during the successive steps of the "biological dogma", illustrating a transition from the recording of information in a DNA strand to the production of proteins exhibiting a defined specificity. It is this that is encoded in the form of information and that determines the unique activity, i.e., the measure of a protein's "intelligence". In a situation of information deficit at the transformation stage of a primary protein structure to a tertiary or quaternary structure, a particular role is served by the environment as a supplier of complementary information, thus leading to the achievement of a structure that guarantees the fulfillment of a specified function. Its quantitative evaluation is possible via using a "fuzzy oil drop" (FOD), particularly with respect to its modified version. This can be achieved when taking into account the participation of an environment other than water in the construction of a specific 3D structure (FOD-M). The next step of information processing on the higher organizational level is the construction of the proteome, where the interrelationship between different functional tasks and organism requirements can be generally characterized by homeostasis. An open system that maintains the stability of all components can be achieved exclusively in a condition of automatic control that is realized by negative feedback loops. This suggests a hypothesis of proteome construction that is based on the system of negative feedback loops. The purpose of this paper is the analysis of information flow in organisms with a particular emphasis on the role of proteins in this process. This paper also presents a model introducing the component of changed conditions and its influence on the protein folding process-since the specificity of proteins is coded in their structure.
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Affiliation(s)
- Irena Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University-Medical College, Medyczna 7, 30-688 Kraków, Poland
| | - Leszek Konieczny
- Chair of Medical Biochemistry, Jagiellonian University-Medical College, Kopernika 7, 31-034 Kraków, Poland
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2
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Dafniet B, Cerisier N, Boezio B, Clary A, Ducrot P, Dorval T, Gohier A, Brown D, Audouze K, Taboureau O. Development of a chemogenomics library for phenotypic screening. J Cheminform 2021; 13:91. [PMID: 34819133 PMCID: PMC8611952 DOI: 10.1186/s13321-021-00569-1] [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: 08/14/2021] [Accepted: 11/06/2021] [Indexed: 12/03/2022] Open
Abstract
With the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of novel and safe drugs. However, phenotypic screening does not rely on knowledge of specific drug targets and needs to be combined with chemical biology approaches to identify therapeutic targets and mechanisms of actions induced by drugs and associated with an observable phenotype. In this study, we developed a system pharmacology network integrating drug-target-pathway-disease relationships as well as morphological profile from an existing high content imaging-based high-throughput phenotypic profiling assay known as “Cell Painting”. Furthermore, from this network, a chemogenomic library of 5000 small molecules that represent a large and diverse panel of drug targets involved in diverse biological effects and diseases has been developed. Such a platform and a chemogenomic library could assist in the target identification and mechanism deconvolution of some phenotypic assays. The usefulness of the platform is illustrated through examples.
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Affiliation(s)
- Bryan Dafniet
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Natacha Cerisier
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Batiste Boezio
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Anaelle Clary
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Pierre Ducrot
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Thierry Dorval
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Arnaud Gohier
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - David Brown
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Karine Audouze
- Université de Paris, INSERM UMR S-1124, 75006, Paris, France
| | - Olivier Taboureau
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France.
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3
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Abstract
Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug-target interactions without resorting to 3D modeling. Finally, we present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.
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Affiliation(s)
- Maurizio Recanatini
- Department of Pharmacy and
Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | - Chiara Cabrelle
- Department of Pharmacy and
Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
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4
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Duran‐Frigola M, Fernández‐Torras A, Bertoni M, Aloy P. Formatting biological big data for modern machine learning in drug discovery. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2018. [DOI: 10.1002/wcms.1408] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Miquel Duran‐Frigola
- Joint IRB‐BSC‐CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) Barcelona Institute of Science and Technology Barcelona Spain
| | - Adrià Fernández‐Torras
- Joint IRB‐BSC‐CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) Barcelona Institute of Science and Technology Barcelona Spain
| | - Martino Bertoni
- Joint IRB‐BSC‐CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) Barcelona Institute of Science and Technology Barcelona Spain
| | - Patrick Aloy
- Joint IRB‐BSC‐CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona) Barcelona Institute of Science and Technology Barcelona Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain
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5
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Benson HE, Watterson S, Sharman JL, Mpamhanga CP, Parton A, Southan C, Harmar AJ, Ghazal P. Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway. Br J Pharmacol 2017; 174:4362-4382. [PMID: 28910500 PMCID: PMC5715582 DOI: 10.1111/bph.14037] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 08/10/2017] [Accepted: 08/30/2017] [Indexed: 12/22/2022] Open
Abstract
Background and Purpose An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway. Experimental Approach Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment. Key Results We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects. Conclusion and Implications We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses.
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Affiliation(s)
- Helen E Benson
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, UK
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, University of Ulster, C-Tric, Derry, UK
| | - Joanna L Sharman
- Centre for Integrative Physiology, University of Edinburgh, Edinburgh, UK
| | - Chido P Mpamhanga
- Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh, UK
| | - Andrew Parton
- Northern Ireland Centre for Stratified Medicine, University of Ulster, C-Tric, Derry, UK
| | | | - Anthony J Harmar
- Centre for Cardiovascular Science, University of Edinburgh, The Queen's Medical Research Institute, Edinburgh, UK
| | - Peter Ghazal
- Division of Infection and Pathway Medicine, University of Edinburgh Medical School, Edinburgh, UK.,Centre for Synthetic and Systems Biology, CH Waddington Building, King's Buildings, Edinburgh, UK
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6
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Cruz-Monteagudo M, Schürer S, Tejera E, Pérez-Castillo Y, Medina-Franco JL, Sánchez-Rodríguez A, Borges F. Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery. Drug Discov Today 2017; 22:994-1007. [PMID: 28274840 PMCID: PMC5487293 DOI: 10.1016/j.drudis.2017.02.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Revised: 02/02/2017] [Accepted: 02/27/2017] [Indexed: 12/20/2022]
Abstract
Current advances in systems biology suggest a new change of paradigm reinforcing the holistic nature of the drug discovery process. According to the principles of systems biology, a simple drug perturbing a network of targets can trigger complex reactions. Therefore, it is possible to connect initial events with final outcomes and consequently prioritize those events, leading to a desired effect. Here, we introduce a new concept, 'Systemic Chemogenomics/Quantitative Structure-Activity Relationship (QSAR)'. To elaborate on the concept, relevant information surrounding it is addressed. The concept is challenged by implementing a systemic QSAR approach for phenotypic virtual screening (VS) of candidate ligands acting as neuroprotective agents in Parkinson's disease (PD). The results support the suitability of the approach for the phenotypic prioritization of drug candidates.
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Affiliation(s)
- Maykel Cruz-Monteagudo
- CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal.
| | - Stephan Schürer
- Department of Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Eduardo Tejera
- Instituto de Investigaciones Biomédicas (IIB), Universidad de Las Américas, 170513 Quito, Ecuador
| | - Yunierkis Pérez-Castillo
- Sección Físico Química y Matemáticas, Departamento de Química, Universidad Técnica Particular de Loja, San Cayetano Alto S/N, EC1101608 Loja, Ecuador
| | - José L Medina-Franco
- Universidad Nacional Autónoma de México, Departamento de Farmacia, Facultad de Química, Avenida Universidad 3000, Mexico City, 04510, Mexico
| | - Aminael Sánchez-Rodríguez
- Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, Calle París S/N, EC1101608 Loja, Ecuador
| | - Fernanda Borges
- CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal.
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7
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Clancy CE, An G, Cannon WR, Liu Y, May EE, Ortoleva P, Popel AS, Sluka JP, Su J, Vicini P, Zhou X, Eckmann DM. Multiscale Modeling in the Clinic: Drug Design and Development. Ann Biomed Eng 2016; 44:2591-610. [PMID: 26885640 PMCID: PMC4983472 DOI: 10.1007/s10439-016-1563-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Accepted: 02/02/2016] [Indexed: 01/30/2023]
Abstract
A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.
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Affiliation(s)
- Colleen E Clancy
- Department of Pharmacology, University of California, Davis, CA, USA.
| | - Gary An
- Department of Surgery, University of Chicago, Chicago, IL, USA
| | - William R Cannon
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yaling Liu
- Department of Mechanical Engineering and Mechanics, Bioengineering Program, Lehigh University, Bethlehem, PA, USA
| | - Elebeoba E May
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Peter Ortoleva
- Department of Chemistry, Indiana University, Bloomington, IN, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - James P Sluka
- Biocomplexity Institute, Indiana University, Bloomington, IN, USA
| | - Jing Su
- Department of Radiology, Wake Forest University, Winston-Salem, NC, USA
| | - Paolo Vicini
- Clinical Pharmacology and DMPK, MedImmune, Cambridge, UK
| | - Xiaobo Zhou
- Department of Radiology, Wake Forest University, Winston-Salem, NC, USA
| | - David M Eckmann
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA, USA.
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8
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Predicting drug target interactions using meta-path-based semantic network analysis. BMC Bioinformatics 2016; 17:160. [PMID: 27071755 PMCID: PMC4830032 DOI: 10.1186/s12859-016-1005-x] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Accepted: 03/31/2016] [Indexed: 12/02/2022] Open
Abstract
Background In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction. Results Supervised machine learning models were constructed based on meta-path topological features of an enriched semantic network, which was derived from Chem2Bio2RDF, and was expanded by adding compound and protein similarity neighboring links obtained from the PubChem databases. The additional semantic links significantly improved the predictive performance of the supervised learning models. The binary classification model built upon the enriched feature space using the Random Forest algorithm significantly outperformed an existing semantic link prediction algorithm, Semantic Link Association Prediction (SLAP), to predict unknown links between compounds and protein targets in an evolving network. In addition to link prediction, Random Forest also has an intrinsic feature ranking algorithm, which can be used to select the important topological features that contribute to link prediction. Conclusions The proposed framework has been demonstrated as a powerful alternative to SLAP in order to predict DTIs using the semantic network that integrates chemical, pharmacological, genomic, biological, functional, and biomedical information into a unified framework. It offers the flexibility to enrich the feature space by using different normalization processes on the topological features, and it can perform model construction and feature selection at the same time. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1005-x) contains supplementary material, which is available to authorized users.
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9
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Abstract
Efforts to compile the phenotypic effects of drugs and environmental chemicals offer the opportunity to adopt a chemo-centric view of human health that does not require detailed mechanistic information. Here, we consider thousands of chemicals and analyze the relationship of their structures with adverse and therapeutic responses. Our study includes molecules related to the etiology of 934 health threatening conditions and used to treat 835 diseases. We first identify chemical moieties that could be independently associated with each phenotypic effect. Using these fragments, we build accurate predictors for approximately 400 clinical phenotypes, finding many privileged and liable structures. Finally, we connect two diseases if they relate to similar chemical structures. The resulting networks of human conditions are able to predict disease comorbidities, as well as identifying potential drug side effects and opportunities for drug repositioning, and show a remarkable coincidence with clinical observations.
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10
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May EE, Leitão A, Tropsha A, Oprea TI. A systems chemical biology study of malate synthase and isocitrate lyase inhibition in Mycobacterium tuberculosis during active and NRP growth. Comput Biol Chem 2013; 47:167-80. [PMID: 24121675 DOI: 10.1016/j.compbiolchem.2013.07.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 07/24/2013] [Indexed: 01/30/2023]
Abstract
The ability of Mycobacterium tuberculosis (Mtb) to survive in low oxygen environments enables the bacterium to persist in a latent state within host tissues. In vitro studies of Mtb growth have identified changes in isocitrate lyase (ICL) and malate synthase (MS) that enable bacterial persistence under low oxygen and other environmentally limiting conditions. Systems chemical biology (SCB) enables us to evaluate the effects of small molecule inhibitors not only on the reaction catalyzed by malate synthase and isocitrate lyase, but the effect on the complete tricarboxylic acid cycle (TCA) by taking into account complex network relationships within that system. To study the kinetic consequences of inhibition on persistent bacilli, we implement a systems-chemical biology (SCB) platform and perform a chemistry-centric analysis of key metabolic pathways believed to impact Mtb latency. We explore consequences of disrupting the function of malate synthase (MS) and isocitrate lyase (ICL) during aerobic and hypoxic non-replicating persistence (NRP) growth by using the SCB method to identify small molecules that inhibit the function of MS and ICL, and simulating the metabolic consequence of the disruption. Results indicate variations in target and non-target reaction steps, clear differences in the normal and low oxygen models, as well as dosage dependent response. Simulation results from singular and combined enzyme inhibition strategies suggest ICL may be the more effective target for chemotherapeutic treatment against Mtb growing in a microenvironment where oxygen is slowly depleted, which may favor persistence.
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Affiliation(s)
- Elebeoba E May
- Biomedical Engineering Department, University of Houston, 3605 Cullen Blvd., Houston, TX 77204, USA.
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11
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Azzaoui K, Jacoby E, Senger S, Rodríguez EC, Loza M, Zdrazil B, Pinto M, Williams AJ, de la Torre V, Mestres J, Pastor M, Taboureau O, Rarey M, Chichester C, Pettifer S, Blomberg N, Harland L, Williams-Jones B, Ecker GF. Scientific competency questions as the basis for semantically enriched open pharmacological space development. Drug Discov Today 2013; 18:843-52. [DOI: 10.1016/j.drudis.2013.05.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2012] [Revised: 04/17/2013] [Accepted: 05/14/2013] [Indexed: 10/26/2022]
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Abstract
Several complex diseases are caused by the malfunction of human metabolism, and deciphering the underlying molecular mechanisms can elucidate their aetiology. Systems biology is an integrative approach combining experimental and computational biology to identify and describe the molecular mechanisms of complex biological systems. Systems medicine has the potential to elucidate the onset and progression of complex metabolic diseases through the use of computational approaches. Advances in biotechnology have resulted in the provision of high-throughput data, which provide information about different metabolic processes. The systems medicine approach can utilize such data to reconstruct genome-scale metabolic models that can be used to study the function of specific enzymes and pathways in the context of the complete metabolic network. In this review, we outline the importance of genome-scale models in systems medicine and discuss how they may contribute towards the development of personalized medicine.
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Affiliation(s)
- A Mardinoglu
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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13
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Oprea TI, Taboureau O, Bologa CG. Of possible cheminformatics futures. J Comput Aided Mol Des 2011; 26:107-12. [PMID: 22207193 DOI: 10.1007/s10822-011-9535-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2011] [Accepted: 12/14/2011] [Indexed: 10/14/2022]
Abstract
For over a decade, cheminformatics has contributed to a wide array of scientific tasks from analytical chemistry and biochemistry to pharmacology and drug discovery; and although its contributions to decision making are recognized, the challenge is how it would contribute to faster development of novel, better products. Here we address the future of cheminformatics with primary focus on innovation. Cheminformatics developers often need to choose between "mainstream" (i.e., accepted, expected) and novel, leading-edge tools, with an increasing trend for open science. Possible futures for cheminformatics include the worst case scenario (lack of funding, no creative usage), as well as the best case scenario (complete integration, from systems biology to virtual physiology). As "-omics" technologies advance, and computer hardware improves, compounds will no longer be profiled at the molecular level, but also in terms of genetic and clinical effects. Among potentially novel tools, we anticipate machine learning models based on free text processing, an increased performance in environmental cheminformatics, significant decision-making support, as well as the emergence of robot scientists conducting automated drug discovery research. Furthermore, cheminformatics is anticipated to expand the frontiers of knowledge and evolve in an open-ended, extensible manner, allowing us to explore multiple research scenarios in order to avoid epistemological "local information minimum trap".
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Affiliation(s)
- Tudor I Oprea
- Division of Biocomputing, Department of Biochemistry and Molecular Biology, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA.
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14
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Cavero I. Promises and partnership: FDA's Critical Path Initiative and its intersection with pharmacology: an ASPET 2011 annual meeting symposium. Expert Opin Drug Saf 2011. [DOI: 10.1517/14740338.2011.608065] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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15
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Benet LZ, Broccatelli F, Oprea TI. BDDCS applied to over 900 drugs. AAPS JOURNAL 2011; 13:519-47. [PMID: 21818695 DOI: 10.1208/s12248-011-9290-9] [Citation(s) in RCA: 450] [Impact Index Per Article: 34.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2011] [Accepted: 06/22/2011] [Indexed: 11/30/2022]
Abstract
Here, we compile the Biopharmaceutics Drug Disposition Classification System (BDDCS) classification for 927 drugs, which include 30 active metabolites. Of the 897 parent drugs, 78.8% (707) are administered orally. Where the lowest measured solubility is found, this value is reported for 72.7% (513) of these orally administered drugs and a dose number is recorded. The measured values are reported for percent excreted unchanged in urine, LogP, and LogD (7.4) when available. For all 927 compounds, the in silico parameters for predicted Log solubility in water, calculated LogP, polar surface area, and the number of hydrogen bond acceptors and hydrogen bond donors for the active moiety are also provided, thereby allowing comparison analyses for both in silico and experimentally measured values. We discuss the potential use of BDDCS to estimate the disposition characteristics of novel chemicals (new molecular entities) in the early stages of drug discovery and development. Transporter effects in the intestine and the liver are not clinically relevant for BDDCS class 1 drugs, but potentially can have a high impact for class 2 (efflux in the gut, and efflux and uptake in the liver) and class 3 (uptake and efflux in both gut and liver) drugs. A combination of high dose and low solubility is likely to cause BDDCS class 4 to be underpopulated in terms of approved drugs (N = 53 compared with over 200 each in classes 1-3). The influence of several measured and in silico parameters in the process of BDDCS category assignment is discussed in detail.
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Affiliation(s)
- Leslie Z Benet
- Department of Bioengineering & Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, 94143-0912, USA.
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16
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Chen ML, Amidon GL, Benet LZ, Lennernas H, Yu LX. The BCS, BDDCS, and regulatory guidances. Pharm Res 2011; 28:1774-8. [PMID: 21491148 DOI: 10.1007/s11095-011-0438-1] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2011] [Accepted: 03/22/2011] [Indexed: 11/29/2022]
Affiliation(s)
- Mei-Ling Chen
- Office of Pharmaceutical Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, Maryland 20993-0002, USA.
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17
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Glick M, Jacoby E. The role of computational methods in the identification of bioactive compounds. Curr Opin Chem Biol 2011; 15:540-6. [PMID: 21411361 DOI: 10.1016/j.cbpa.2011.02.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2011] [Revised: 02/01/2011] [Accepted: 02/21/2011] [Indexed: 10/18/2022]
Abstract
Computational methods play an ever increasing role in lead finding. A vast repertoire of molecular design and virtual screening methods emerged in the past two decades and are today routinely used. There is increasing awareness that there is no single best computational protocol and correspondingly there is a shift recommending the combination of complementary methods. A promising trend for the application of computational methods in lead finding is to take advantage of the vast amounts of HTS (High Throughput Screening) data to allow lead assessment by detailed systems-based data analysis, especially for phenotypic screens where the identification of compound-target pairs is the primary goal. Herein, we review trends and provide examples of successful applications of computational methods in lead finding.
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Affiliation(s)
- Meir Glick
- Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
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