DOCSLIB.ORG

A Systems Pharmacology Approach to Improve Drug Therapy in NSCLC: Establishing a CESAR Network Extended Abstract Ganna V

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Systems Pharmacology Approach to Improve Drug Therapy in NSCLC: Establishing a CESAR Network Extended Abstract Ganna V International Journal of Clinical Pharmacology and Therapeutics, Vol. 52 – No. 1/2014 (89-91) A systems pharmacology approach to improve drug therapy in NSCLC: Establishing a CESAR network Extended Abstract Ganna V. Kalayda1, Martin Michaelis2, Jindrich Cinatl jr.3, Robert M. Mader4, 5 1 6 7 8 ©2014 Dustri-Verlag Dr. K. Feistle Holger Fröhlich , Navin Sarin , Johanna Melin , Florian Engel , Walter Jäger , ISSN 0946-1965 Roland Frötschl7, Ulrich Jaehde1, Charlotte Kloft6, and Christoph A. Ritter9 DOI 10.5414/CPXCES13EA07 e-pub: November 11, 2013 1Institute of Pharmacy, Clinical Pharmacy, University of Bonn, Bonn, Germany, 2Centre for Molecular Processing and School of Biosciences, University of Kent, Canterbury, UK, 3Institut für Medizinische Virologie, Klinikum der Goethe- Universität, Frankfurt/Main, Germany, 4Department of Medicine I, Comprehensive Cancer Center of the Medical University of Vienna, Austria, 5Algorithmic Bioinformatics, University of Bonn, Bonn, 6Department of Clinical Pharmacy and Biochemistry, Freie Universität Berlin, 7Bundesinstitut für Arzneimittel und Medizinprodukte, Bonn, Germany, 8Department of Clinical Pharmacy and Diagnostics, University of Vienna, Austria, and 9Clinical Pharmacy, Institute of Pharmacy, University of Greifswald, Germany Key words Introduction ficial combination therapy to an individual pa- lung cancer – EGFR – tient, knowledge on potential drug resistance drug resistance – sys- tems pharmacology – The epidermal growth factor receptor mechanisms and respective biological mark- network (EGFR) plays an important role in activation ers in a global scale are required. Systems of signaling pathways responsible for prolifer- pharmacology presents an innovative disci- ation of non-small cell lung cancer (NSCLC) pline which combines systematic collections cells. However, EGFR targeting therapy is of experimental data from whole cells, tissues only effective in a subset of EGFR-positive or organisms with the mathematical models of tumors that harbour activating mutations in drug pharmacokinetics and pharmacodynam- the receptor kinase domain occurring in a ics and natural disease progression models frequency of 5 – 20% of EGFR-positive tu- to understand drug action at a global scale mors in Caucasian lung tumor populations. As [3]. Within CESAR a research network was several studies showed that EGFR mutation- established to set up systems pharmacology negative tumors responded better to classical approaches to unravel EGFR-associated drug chemotherapy, regimens based on platinum resistance in NSCLC. complexes or taxanes are still the treatment of choice for those patients [1]. A major draw- back in both regimens, targeted as well as Models and methods chemotherapy treatment, is the development of drug resistance. Mechanisms of drug re- Cell lines of non-small-cell lung can- sistance include alterations in target structure cer origin were selected that express either and function, compensating signaling activa- wild-type EGFR or EGFR harboring ac- Correspondence to tion as well as changes in drug delivery and tivating mutations. The NSCLC cell lines Prof. Dr. Christoph Ritter accumulation within the tumor cell. Typically, HCC4006 and A549 were obtained from Ernst-Moritz-Arndt- Universität Greifswald drug resistance is not restricted to a single ATCC (Manassas, VA, USA). Sub-lines with Institut für Pharmazie, resistance mechanism but is the result of an acquired resistance to cisplatin, gefitinib, or Klinische Pharmazie, accumulation of several mechanisms. There- erlotinib were established as described pre- Friedrich-Ludwig- fore, strategies of combination therapy are viously [4]. The cell lines were designated Jahn-Str. 17, 17487 r 2000 Greifs wald, Germany increasingly investigated to overcome drug as A549 CDDP (cisplatin-resistant), [email protected] resistance [2]. In order to tailor the most bene- HCC4006rGEFI1 (gefitinib-resistant), and Kalayda, Michaelis, Cinatl jr., et al. 90 NSCLC will be studied at three levels of complexity: – 1) The EGFR function will be studied at the molecular level. The EGFR-mediated signaling, the EGFR interactome, and the intracellular localization of EGFR in time and space will be analyzed in de- pendence on the receptor genetics, the activation state, and the response to anti- cancer drugs. These data will be used to develop computer-based models of the EGFR subcellular kinetics and the dy- namic EGFR signaling and interaction at a systems level. – 2) Cellular aspects of drug response and drug resistance that are not directly re- lated to EGFR function are studied. This includes the studying of the intracellular accumulation and distribution of anti- cancer drugs, since cancer cell drug re- sistance mechanisms include decreased cellular drug uptake, increased drug ef- flux, and changes in sub-cellular drug Figure 1. Structure of the CESAR research net- location. In addition, transcriptomics work on systems pharmacology approach to im- analyses will reveal regulatory changes prove drug therapy in NSCLC. in response to drug resistance. – 3) A third aspect addresses molecular HCC4006rERLO1 (erlotinib-resistant) and changes outside of the cells, i.e., secreted are part of the Resistant Cancer Cell Line species such as exosomes as well as the (RCCL) collection. The A549 cell lines metabolome. These secreted species play were cultivated in MEM, the HCC4006 in a pivotal role as markers to be extrapo- DMEM/HAM’s F12 medium supplemented lated to the patient situation. Differential with 10% fetal bovine serum at 37 °C in a secretion of metabolites and exosome humidified 5% carbon dioxide atmosphere. content, particularly microRNAs will Data collection will consist of investigating be investigated based on EGFR recep- the EGFR interactome (affinity purification tor genetics, activation state, and drug and high-resolution mass spectrometry), response phenotype, characteristics of characterizing exosomes (microRNA con- exosomes will be correlated with whole tent, protein content, EGFR status), analyz- tumor cells, and specifically identified ing the cellular transcriptomes (microarray metabolites and microRNA species will technologies) and metabolomes (mass spec- be correlated to transcriptome and drug trometry), and determining uptake and dis- response phenotype. tribution of cisplatin, erlotinib, and gefitinib Finally, the data will be integrated into (intracellular accumulation and transwell ex- a multi-level network modeling approach periments) in the investigated cell lines. The to develop predictive models for drug resis- experimental data will be used to develop tance depending on EGFR status and drug predictive, coherent in silico models of the treatment by the core bioinformatics and resistance-associated signaling networks. pharmacometrics units. Resulting concept Conclusions The structure of the research consortium Here, we will develop a systems pharma- is shown in Figure 1. The role of EGFR in cology approach in order to understand the A systems pharmacology approach to improve drug therapy in NSCLC: Establishing a CESAR network 91 role of EGFR in NSCLC 1) as a drug target and 2) in the response to anti-cancer therapy in general which will provide the basis for the development of improved therapies. Acknowledgments The work was supported by the Hilfe für krebskranke Kinder Frankfurt e.V., the Frankfurter Stiftung für krebskranke Kinder, the Kent Cancer Trust, and the Royal Society. References [1] Reungwetwattana T, Weroha SJ, Molina JR. On- cogenic pathways, molecularly targeted therapies, and highlighted clinical trials in non-small-cell lung cancer (NSCLC). Clin Lung Cancer. 2012; 13: 252-266. CrossRef PubMed [2] Belani CP, Goss G, Blumenschein G Jr. Recent clinical developments and rationale for combin- ing targeted agents in non-small cell lung cancer (NSCLC). Cancer Treat Rev. 2012; 38: 173-184. CrossRef PubMed [3] van der Graaf PH, Benson N. Systems pharma- cology: bridging systems biology and pharmaco- kinetics-pharmacodynamics (PKPD) in drug dis- covery and development. Pharm Res. 2011; 28: 1460-1464. CrossRef PubMed [4] Michaelis M, Rothweiler F, Barth S, Cinatl J, van Rikxoort M, Löschmann N, Voges Y, Breitling R, von Deimling A, Rödel F, Weber K, Fehse B, Mack E, Stiewe T, Doerr HW, Speidel D, Cinatl J Jr. Adaptation of cancer cells from different entities to the MDM2 inhibitor nutlin-3 results in the emergence of p53-mutated multi-drug-resistant cancer cells. Cell Death Dis. 2011; 2: e243. CrossRef PubMed.
Load more

Recommended publications
  • Proof of Concept: Network and Systems Biology Approaches Aid in the Discovery of Potent Anticancer Drug Combinations
    Published OnlineFirst November 1, 2010; DOI: 10.1158/1535-7163.MCT-10-0642 Molecular Cancer Review Therapeutics Proof of Concept: Network and Systems Biology Approaches Aid in the Discovery of Potent Anticancer Drug Combinations Asfar S. Azmi1, Zhiwei Wang1, Philip A. Philip2, Ramzi M. Mohammad2, and Fazlul H. Sarkar1 Abstract Cancer therapies that target key molecules have not fulfilled expected promises for most common malignancies. Major challenges include the incomplete understanding and validation of these targets in patients, the multiplicity and complexity of genetic and epigenetic changes in the majority of cancers, and the redundancies and cross-talk found in key signaling pathways. Collectively, the uses of single-pathway targeted approaches are not effective therapies for human malignances. To overcome these barriers, it is important to understand the molecular cross-talk among key signaling pathways and how they may be altered by targeted agents. Innovative approaches are needed, such as understanding the global physio- logic environment of target proteins and the effects of modifying them without losing key molecular details. Such strategies will aid the design of novel therapeutics and their combinations against multi- faceted diseases, in which efficacious combination therapies will focus on altering multiple pathways rather than single proteins. Integrated network modeling and systems biology have emerged as powerful tools benefiting our understanding of drug mechanisms of action in real time. This review highlights the significance of the network and systems biology–based strategy and presents a proof of concept recently validated in our laboratory using the example of a combination treatment of oxaliplatin and the MDM2 inhibitor MI-219 in genetically complex and incurable pancreatic adenocarcinoma.
    [Show full text]
  • Chapter 4 Systems Pharmacology: an Overview
    Chapter 4 Systems Pharmacology: An Overview Marc R. Birtwistle, Jens Hansen, James M. Gallo, Sreeharish Muppirisetty, Peter Man-Un Ung, Ravi Iyengar and Avner Schlessinger Abstract Systems pharmacology has evolved from a discipline that focuses on drug action at the organ level to a discipline that combines traditional pharma- cokinetic and pharmacodynamic modeling with recent systems biology approaches. The integration of high-throughput data technologies with computational data analysis and modeling offers new opportunities to overcome the one disease, one target, one drug approach. Whole genomic or transcriptomic sequencing and pro- teomics allow qualitative, and sometimes quantitative, snapshots of the cellular state at any given condition (e.g., during disease or after drug treatment) that can be the basis for the development of whole cell models to predict drug responses. Networks of protein–protein interactions that were confirmed by experimental and computational analysis of the structure of the interaction partners can be combined with graph theory to identify modules regulating the cellular state. Dynamic modeling and sensitivity analysis allow the identification of robust and fragile nodes within these modules to identify putative drug targets for single or combi- natorial drug treatment. Traditional pharmacokinetic and pharmacodynamic mod- eling complements these approaches by predicting drug concentration and target perturbation at the site of drug action. Such general systems pharmacology models are a great leap forward towards the development of patient-specific drug response models as a main component of precision medicine. Keywords Networks-based Á Personalized medicine Á Bioinformatics Á Pharmacogenomics Á Genbank Á Cytochrome P450 Á Protein–protein interaction (PPI) Á Interactome Á Druggability Á Genomic variation Á Nodes M.R.
    [Show full text]
  • Quantitative and Systems Pharmacology in the Post-Genomic Era: New Approaches to Discovering Drugs and Understanding Therapeutic Mechanisms
    Quantitative and Systems Pharmacology in the Post-genomic Era: New Approaches to Discovering Drugs and Understanding Therapeutic Mechanisms An NIH White Paper by the QSP Workshop Group – October, 2011 Peter K. Sorger (co-chair), Sandra R.B. Allerheiligen (co-chair) Darrell R. Abernethy, Russ B. Altman, Kim L. R. Brouwer, Andrea Califano, David Z. D'Argenio, Ravi Iyengar, William J. Jusko, Richard Lalonde, Douglas A. Lauffenburger, Brian Shoichet, James L. Stevens, Shankar Subramaniam, Piet Van der Graaf and Paolo Vicini Rebecca Ward (editor) This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License Author Affiliations Peter K. Sorger Richard L. Lalonde WAB432, Department of Systems Biology Department of Clinical Pharmacology Harvard Medical School Pfizer Inc. Boston MA 02115 445 Eastern Point Road Groton, CT 06340 Darrell R. Abernethy Office of Clinical Pharmacology/FDA Douglas A. Lauffenburger 10903 New Hampshire Avenue Department of Biological Engineering 16-343 Silver Spring, MD 20993 Massachusetts Institute of Technology Cambridge MA 02139 Sandra R. B. Allerheiligen Merck Research Laboratories Brian Shoichet 351 N. Sumneytown Pike University of California, San Francisco North Wales, PA 19454-1099 Dept. of Pharmaceutical Chemistry San Francisco CA Russ B. Altman Stanford University James L. Stevens 301 Campus Drive, S170 Lilly Research Laboratories Stanford, CA 94025-5444 Indianapolis, IN 46285 Kim L. R. Brouwer Shankar Subramaniam UNC Eshelman School of Pharmacy Department of Bioengineering The University of North Carolina at Chapel Hill 9500 Gilman Drive #0412 Chapel Hill, NC 27599-7569 University of California San Diego La Jolla CA 92093 Andrea Califano Herbert Irving Comprehensive Cancer Center Piet Van der Graaf 1130 St.
    [Show full text]
  • And Poly-Pharmacology Drug Discovery: a Systems Biology View
    Send Orders of Reprints at [email protected] Current Pharmaceutical Design, 2014, 20, 000-000 1 Network-Based Strategies Can Help Mono- and Poly-pharmacology Drug Discovery: A Systems Biology View H. Billur Engin1, Attila Gursoy1, Ruth Nussinov2,3 and Ozlem Keskin1,* 1Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Rumelifeneri Yolu, 34450 Sari- yer Istanbul, Turkey; 2Center for Cancer Research Nanobiology Program, Frederick National Laboratory, National Cancer Institute, Frederick, MD 21702; 3Sackler Inst. Of Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel Abstract: The cellular network and its environment govern cell and organism behavior and are fundamental to the comprehension of function, misfunction and drug discovery. Over the last few years, drugs were observed to often bind to more than one target; thus, poly- pharmacology approaches can be advantageous, complementing the “one drug - one target” strategy. Targeting drug discovery from the systems biology standpoint can help in studies of network effects of mono- and poly-pharmacology. In this mini-review, we provide an overview of the usefulness of network description and tools for mono- and poly-pharmacology, and the ways through which protein in- teractions can help single- and multi-target drug discovery efforts. We further describe how, when combined with experimental data, modeled structural networks which can predict which proteins interact and provide the structures of their interfaces, can model the cellu- lar pathways, and suggest which specific pathways are likely to be affected. Such structural networks may facilitate structure-based drug design; forecast side effects of drugs; and suggest how the effects of drug binding can propagate in multi-molecular complexes and path- ways.
    [Show full text]
  • From Gene Networks to Drugs: Systems Pharmacology Approaches for AUD
    Psychopharmacology https://doi.org/10.1007/s00213-018-4855-2 REVIEW From gene networks to drugs: systems pharmacology approaches for AUD Laura B. Ferguson1,2 & R. Adron Harris1 & Roy Dayne Mayfield1 Received: 2 November 2017 /Accepted: 6 February 2018 # Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.
    [Show full text]
  • Molecular Networks in Network Medicine: Development and Applications
    Received: 23 November 2019 Revised: 29 February 2020 Accepted: 20 March 2020 DOI: 10.1002/wsbm.1489 ADVANCED REVIEW Molecular networks in Network Medicine: Development and applications Edwin K. Silverman1 | Harald H. H. W. Schmidt2 | Eleni Anastasiadou3 | Lucia Altucci4 | Marco Angelini5 | Lina Badimon6 | Jean-Luc Balligand7 | Giuditta Benincasa8 | Giovambattista Capasso9,10 | Federica Conte11 | Antonella Di Costanzo4 | Lorenzo Farina5 | Giulia Fiscon11 | Laurent Gatto12,13 | Michele Gentili5 | Joseph Loscalzo1,14 | Cinzia Marchese3 | Claudio Napoli8 | Paola Paci5 | Manuela Petti5 | John Quackenbush1,15 | Paolo Tieri16 | Davide Viggiano10,17 | Gemma Vilahur6 | Kimberly Glass1,15 | Jan Baumbach18,19 1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 2Department of Pharmacology and Personalized Medicine, School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands 3Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy 4Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy 5Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy 6Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain 7Pole of Pharmacology and Therapeutics (FATH), Institute for Clinical and Experimental Research
    [Show full text]
  • Systems Biology Michael K. Skinner – Biol 476/576 CUE 418, 10:35-11:50 Am, Tuesdays & Thursdays January 10, 12, 17 & 19, 2017 Weeks 1 and 2
    Spring 2017 – Epigenetics and Systems Biology Lecture Outline – Systems Biology Michael K. Skinner – Biol 476/576 CUE 418, 10:35-11:50 am, Tuesdays & Thursdays January 10, 12, 17 & 19, 2017 Weeks 1 and 2 Systems Biology • History and Definitions • Reductionism/ Genetic Determination • Holism/ Emergentism/ Homeostasis or Robustness • Revolutionary and Evolutionary Systems Biology • Networks and Computational Biology • Basic Molecular and Cellular Components Required Reading Antony PM, Balling R, Vlassis N. (2012) From systems biology to systems biomedicine. Curr Opin Biotechnol. 23(4):604-8. Knepper et al. (2014) Systems biology versus reductionism in cell physiology. Am J Physiol Cell Phisiol 307:C308-C309. Background Book References James A. Marcum (2009) The Conceptual Foundations of Systems Biology, Nova Science Publishers, Inc. Eberhard Voit (2012) A First Course in Systems Biology, Garland Science Capra and Luisi (2014) The Systems View of Life, Cambridge University Press. Literature Nobile MS, Cazzaniga P, Tangherloni A, Besozzi D. (2016) Graphics processing units in bioinformatics, computational biology and systems biology. Brief Bioinform. Jul 8. pii: bbw058. [Epub ahead of print] Turaev D, Rattei T. (2016) High definition for systems biology of microbial communities: metagenomics gets genome-centric and strain-resolved. Curr Opin Biotechnol. Jun;39:174-81. Niu F, Wang DC, Lu J, Wu W, Wang X. (2016) Potentials of single-cell biology in identification and validation of disease biomarkers. J Cell Mol Med. 20(9):1789-95 Petta S, Valenti L, Bugianesi E, Targher G, Bellentani S, Bonino F; Special Interest Group on Personalised Hepatology of the Italian Association for the Study of the Liver (AISF); Special Interest Group on Personalised Hepatology of Italian Association for Study of Liver AISF.
    [Show full text]
  • Proof of Concept: Network and Systems Biology Approaches Aid in the Discovery of Potent Anticancer Drug Combinations
    Published OnlineFirst November 1, 2010; DOI: 10.1158/1535-7163.MCT-10-0642 Molecular Cancer Review Therapeutics Proof of Concept: Network and Systems Biology Approaches Aid in the Discovery of Potent Anticancer Drug Combinations Asfar S. Azmi1, Zhiwei Wang1, Philip A. Philip2, Ramzi M. Mohammad2, and Fazlul H. Sarkar1 Abstract Cancer therapies that target key molecules have not fulfilled expected promises for most common malignancies. Major challenges include the incomplete understanding and validation of these targets in patients, the multiplicity and complexity of genetic and epigenetic changes in the majority of cancers, and the redundancies and cross-talk found in key signaling pathways. Collectively, the uses of single-pathway targeted approaches are not effective therapies for human malignances. To overcome these barriers, it is important to understand the molecular cross-talk among key signaling pathways and how they may be altered by targeted agents. Innovative approaches are needed, such as understanding the global physio- logic environment of target proteins and the effects of modifying them without losing key molecular details. Such strategies will aid the design of novel therapeutics and their combinations against multi- faceted diseases, in which efficacious combination therapies will focus on altering multiple pathways rather than single proteins. Integrated network modeling and systems biology have emerged as powerful tools benefiting our understanding of drug mechanisms of action in real time. This review highlights the significance of the network and systems biology–based strategy and presents a proof of concept recently validated in our laboratory using the example of a combination treatment of oxaliplatin and the MDM2 inhibitor MI-219 in genetically complex and incurable pancreatic adenocarcinoma.
    [Show full text]