Systems Pharmacology: An Overview

Systems pharmacology has evolved from a discipline that focuses on drug action at the organ level to a discipline that combines traditional pharmacokinetic 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 proteomics 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 combinatorial drug treatment. Traditional pharmacokinetic and pharmacodynamic modeling 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.

[1]  Ryosei Kawai,et al.  Physiologically Based Pharmacokinetic (PBPK) Modeling of Everolimus (RAD001) in Rats Involving Non-Linear Tissue Uptake , 2007, Journal of Pharmacokinetics and Pharmacodynamics.

[2]  Chi-Ying F. Huang,et al.  Ultrasensitivity in the mitogen-activated protein kinase cascade. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Christopher R Myers,et al.  Extracting Falsifiable Predictions from Sloppy Models , 2007, Annals of the New York Academy of Sciences.

[4]  R. Aebersold,et al.  Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions , 2012, Nature Methods.

[5]  A. Bardelli,et al.  Association of KRAS p.G13D mutation with outcome in patients with chemotherapy-refractory metastatic colorectal cancer treated with cetuximab. , 2010, JAMA.

[6]  R. Iyengar,et al.  Merging Systems Biology with Pharmacodynamics , 2012, Science Translational Medicine.

[7]  S. Henikoff,et al.  Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm , 2009, Nature Protocols.

[8]  P. Aloy,et al.  Interactome3D: adding structural details to protein networks , 2013, Nature Methods.

[9]  Christina Kiel,et al.  Structure‐energy‐based predictions and network modelling of RASopathy and cancer missense mutations , 2014, Molecular systems biology.

[10]  Kevan M. Shokat,et al.  Chemical genetic discovery of targets and anti-targets for cancer polypharmacology , 2012, Nature.

[11]  Ursula Pieper,et al.  Protein complex compositions predicted by structural similarity , 2006, Nucleic acids research.

[12]  Avner Schlessinger,et al.  Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET , 2011, Proceedings of the National Academy of Sciences.

[13]  G. Aithal,et al.  Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of bleeding complications , 1999, The Lancet.

[14]  Seth I. Berger,et al.  Systems Pharmacology of Arrhythmias , 2010, Science Signaling.

[15]  Chi-Ren Shyu,et al.  Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning , 2014, PLoS Comput. Biol..

[16]  P. Hirth,et al.  Vemurafenib: the first drug approved for BRAF-mutant cancer , 2012, Nature Reviews Drug Discovery.

[17]  M R Birtwistle,et al.  Mechanistic Vs. Empirical Network Models of Drug Action , 2013, CPT: pharmacometrics & systems pharmacology.

[18]  J. Gallo,et al.  Preclinical pharmacokinetic/pharmacodynamic models of gefitinib and the design of equivalent dosing regimens in EGFR wild-type and mutant tumor models , 2008, Molecular Cancer Therapeutics.

[19]  J. Doyle,et al.  Reverse Engineering of Biological Complexity , 2002, Science.

[20]  Michael J. Keiser,et al.  Predicting new molecular targets for known drugs , 2009, Nature.

[21]  D. A. Harris,et al.  Principles of Biochemistry (2nd edn) , 1993 .

[22]  P. Sanseau,et al.  Computational Drug Repositioning: From Data to Therapeutics , 2013, Clinical pharmacology and therapeutics.

[23]  Jonathan A. Barker,et al.  Kinome Render: a stand-alone and web-accessible tool to annotate the human protein kinome tree , 2013, PeerJ.

[24]  Christopher R. Myers,et al.  Universally Sloppy Parameter Sensitivities in Systems Biology Models , 2007, PLoS Comput. Biol..

[25]  Brian K. Shoichet,et al.  Virtual screening of chemical libraries , 2004, Nature.

[26]  Birgit Schoeberl,et al.  An ErbB3 antibody, MM-121, is active in cancers with ligand-dependent activation. , 2010, Cancer research.

[27]  Malcolm Rowland,et al.  Physiologically-based pharmacokinetics in drug development and regulatory science. , 2011, Annual review of pharmacology and toxicology.

[28]  Laurie E. Grove,et al.  Structural conservation of druggable hot spots in protein–protein interfaces , 2011, Proceedings of the National Academy of Sciences.

[29]  U. Bhalla,et al.  Complexity in biological signaling systems. , 1999, Science.

[30]  A. Sali,et al.  Protein Structure Prediction and Structural Genomics , 2001, Science.

[31]  Philip E. Bourne,et al.  Drug Discovery Using Chemical Systems Biology: Weak Inhibition of Multiple Kinases May Contribute to the Anti-Cancer Effect of Nelfinavir , 2011, PLoS Comput. Biol..

[32]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[33]  David S. Goodsell,et al.  The RCSB Protein Data Bank: new resources for research and education , 2012, Nucleic Acids Res..

[34]  B. Chait,et al.  The molecular architecture of the nuclear pore complex , 2007, Nature.

[35]  Gavin MacBeath,et al.  A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks , 2014, Nature Genetics.

[36]  B. Roth,et al.  Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia , 2004, Nature Reviews Drug Discovery.

[37]  Kevan M. Shokat,et al.  K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions , 2013, Nature.

[38]  Sarah L. Kinnings,et al.  Novel computational approaches to polypharmacology as a means to define responses to individual drugs. , 2012, Annual review of pharmacology and toxicology.

[39]  Ben M. Webb,et al.  Putting the Pieces Together: Integrative Modeling Platform Software for Structure Determination of Macromolecular Assemblies , 2012, PLoS biology.

[40]  Jacob D. Durrant,et al.  Molecular dynamics simulations and drug discovery , 2011, BMC Biology.

[41]  Daniel Rauh,et al.  Development of a fluorescent-tagged kinase assay system for the detection and characterization of allosteric kinase inhibitors. , 2009, Journal of the American Chemical Society.

[42]  Ravi Iyengar,et al.  Network analyses in systems pharmacology , 2009, Bioinform..

[43]  Erez Lieberman Aiden,et al.  The expanding scope of DNA sequencing , 2012, Nature Biotechnology.

[44]  Boris N Kholodenko,et al.  Four‐dimensional dynamics of MAPK information‐processing systems , 2009, Wiley interdisciplinary reviews. Systems biology and medicine.

[45]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[46]  Varun Garg,et al.  Comparison of four basic models of indirect pharmacodynamic responses , 1993, Journal of Pharmacokinetics and Biopharmaceutics.

[47]  B. Kholodenko,et al.  Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses , 2007, Molecular systems biology.

[48]  Jeanette J McCarthy,et al.  Genomic Medicine: A Decade of Successes, Challenges, and Opportunities , 2013, Science Translational Medicine.

[49]  H. Kitano.  A robustness-based approach to systems-oriented drug design , 2007, Nature Reviews Drug Discovery.

[50]  Brion W. Murray,et al.  Molecular conformations, interactions, and properties associated with drug efficiency and clinical performance among VEGFR TK inhibitors , 2012, Proceedings of the National Academy of Sciences.

[51]  M. Birtwistle,et al.  A General Network Pharmacodynamic Model–Based Design Pipeline for Customized Cancer Therapy Applied to the VEGFR Pathway , 2014, CPT: pharmacometrics & systems pharmacology.

[52]  J. Gallo,et al.  Multiscale Design of Cell-Type–Specific Pharmacokinetic/Pharmacodynamic Models for Personalized Medicine: Application to Temozolomide in Brain Tumors , 2014, CPT: pharmacometrics & systems pharmacology.

[53]  A. Kwong,et al.  Discovery and development of VX-950, a novel, covalent, and reversible inhibitor of hepatitis C virus NS3.4A serine protease. , 2006, Infectious disorders drug targets.

[54]  Daniel R. Caffrey,et al.  Structure-based maximal affinity model predicts small-molecule druggability , 2007, Nature Biotechnology.

[55]  Adrian Whitty,et al.  The resurgence of covalent drugs , 2011, Nature Reviews Drug Discovery.

[56]  Paolo Vicini,et al.  Predicting Human Tumor Drug Concentrations from a Preclinical Pharmacokinetic Model of Temozolomide Brain Disposition , 2007, Clinical Cancer Research.

[57]  J. Wood,et al.  Structure-based drug design: the discovery of novel nonpeptide orally active inhibitors of human renin. , 2000, Chemistry & biology.

[58]  R. Iyengar,et al.  Systems approaches to polypharmacology and drug discovery. , 2010, Current opinion in drug discovery & development.

[59]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[60]  B. Rost,et al.  SNAP: predict effect of non-synonymous polymorphisms on function , 2007, Nucleic acids research.

[61]  J. Gallo,et al.  Physiologically Based Pharmacokinetic Models of Tyrosine Kinase Inhibitors: A Systems Pharmacological Approach to Drug Disposition , 2013, Clinical pharmacology and therapeutics.

[62]  Andrej Sali,et al.  Comparative Protein Structure Modeling and its Applications to Drug Discovery , 2004 .

[63]  A. Sali,et al.  Structure-based ligand discovery for the Large-neutral Amino Acid Transporter 1, LAT-1 , 2013, Proceedings of the National Academy of Sciences.

[64]  Philip M. Kim,et al.  Relating Three-Dimensional Structures to Protein Networks Provides Evolutionary Insights , 2006, Science.

[65]  Walter Kolch,et al.  Cell fate decisions are specified by the dynamic ERK interactome , 2009, Nature Cell Biology.

[66]  J. Stelling,et al.  Robustness of Cellular Functions , 2004, Cell.

[67]  Lewis B. Sheiner,et al.  Simultaneous modeling of pharmacokinetics and pharmacodynamics: Application to d‐tubocurarine , 1979 .

[68]  T. Blundell,et al.  Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.

[69]  Christopher L. McClendon,et al.  Reaching for high-hanging fruit in drug discovery at protein–protein interfaces , 2007, Nature.

[70]  V. Grantcharova,et al.  Therapeutically Targeting ErbB3: A Key Node in Ligand-Induced Activation of the ErbB Receptor–PI3K Axis , 2009, Science Signaling.

[71]  Stéphanie Pérot,et al.  Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. , 2010, Drug discovery today.

[72]  P. Vicini,et al.  Pharmacokinetic Model-Predicted Anticancer Drug Concentrations in Human Tumors , 2004, Clinical Cancer Research.

[73]  Avner Schlessinger,et al.  Molecular modeling and ligand docking for solute carrier (SLC) transporters. , 2013, Current topics in medicinal chemistry.

[74]  John G. Albeck,et al.  Systematic calibration of a cell signaling network model , 2010, BMC Bioinformatics.

[75]  P. Bork,et al.  Human non-synonymous SNPs: server and survey. , 2002, Nucleic acids research.

[76]  R. Iyengar,et al.  Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. , 2012, Annual review of pharmacology and toxicology.

[77]  A. Hopkins,et al.  The druggable genome , 2002, Nature Reviews Drug Discovery.

[78]  N Benson,et al.  Model‐Based Drug Development: A Rational Approach to Efficiently Accelerate Drug Development , 2013, Clinical pharmacology and therapeutics.

[79]  Ching-Hon Pui,et al.  Modeling Mechanisms of In Vivo Variability in Methotrexate Accumulation and Folate Pathway Inhibition in Acute Lymphoblastic Leukemia Cells , 2010, PLoS Comput. Biol..

[80]  G Levy,et al.  Kinetics of pharmacologic effects , 1966, Clinical pharmacology and therapeutics.

[81]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[82]  M. Sabathie,et al.  [Use of diarginine alpha-ketoglutarate following abdominal surgery. Apropos of 30 cases]. , 1975, Semaine des hopitaux. Therapeutique.

[83]  Ravi Iyengar,et al.  Systems Pharmacology of Adverse Event Mitigation by Drug Combinations , 2013, Science Translational Medicine.

[84]  J. Moult,et al.  SNPs, protein structure, and disease , 2001, Human mutation.

[85]  B. Honig,et al.  Structure-based prediction of protein-protein interactions on a genome-wide scale , 2012, Nature.

[86]  D. Baker,et al.  A simple physical model for binding energy hot spots in protein–protein complexes , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[87]  François Stricher,et al.  The FoldX web server: an online force field , 2005, Nucleic Acids Res..

[88]  N. Benson,et al.  The rise of systems pharmacology in drug discovery and development. , 2014, Future medicinal chemistry.