Skip to main content
SpringerLink
Log in

Quantitative Systems Pharmacology: Applications and Adoption in Drug Development

  • Chapter
  • First Online:
Systems Pharmacology and Pharmacodynamics

Part of the book series: AAPS Advances in the Pharmaceutical Sciences Series ((AAPS,volume 23))

Abstract

Biopharmaceutical companies have increasingly been exploring Quantitative Systems Pharmacology (QSP) as a potential avenue to address current challenges in drug development. The ability to integrate diverse data into a unified framework provides a promising approach for a systematic, quantitative evaluation and prediction of the complex interaction between potential therapeutics and biological pathways of disease, with application across the research and development pipeline. In this chapter, we discuss the potential for QSP to help address pressing needs in drug development, and present numerous examples of past applications to problems ranging from target identification to in vivo experimental design through clinical trial simulation, patient stratification, and regulatory evaluation. These examples also illustrate the diversity of QSP modeling approaches. Moving forward, the adoption and success of QSP will require a clearly articulated record of impact on drug development decisions, alongside the development of approaches to address current challenges in the implementation and technical evaluation of such efforts.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Agoram B (2014) Evaluating systems pharmacology models is different from evaluating standard pharmacokinetic-pharmacodynamic models. CPT Pharmacomet Syst Pharmacol 3:e101. doi:10.1038/psp.2013.77

    Article  CAS  Google Scholar 

  • Agoram BM, Demin O (2011) Integration not isolation: arguing the case for quantitative and systems pharmacology in drug discovery and development. Drug Discov Today 16(23–24):1031–1036. doi:10.1016/j.drudis.2011.10.001

    Article  CAS  PubMed  Google Scholar 

  • Aldridge BB, Burke JM, Lauffenburger DA, Sorger PK (2006) Physicochemical modelling of cell signalling pathways. Nat Cell Biol 8(11):1195–1203. doi:10.1038/ncb1497

    Article  CAS  PubMed  Google Scholar 

  • Allerheiligen SR (2014) Impact of modeling and simulation: myth or fact? Clin Pharmacol Ther 96(4):413–415. doi:10.1038/clpt.2014.122

    Article  CAS  PubMed  Google Scholar 

  • Arrowsmith J (2011) Trial watch: phase III and submission failures: 2007–2010. Nat Rev Drug Discov 10(2):87. doi:10.1038/nrd3375

    Article  CAS  PubMed  Google Scholar 

  • Arrowsmith J (2012) A decade of change. Nat Rev Drug Discov 11(1):17–18. doi:10.1038/nrd3630

    Article  CAS  PubMed  Google Scholar 

  • Arrowsmith J, Miller P (2013) Trial watch: phase II and phase III attrition rates 2011–2012. Nat Rev Drug Discovery 12(8):569. doi:10.1038/nrd4090

    Article  CAS  PubMed  Google Scholar 

  • Bellido T, Ali AA, Plotkin L, Fu Q, Gubrij I, Roberson PK, Weinstein RS, O’Brien CA, Manolagas SC, Jilka RL (2003) Proteasomal degradation of Runx2 shortens parathyroid hormone-induced anti-apoptotic signaling in osteoblasts. A putative explanation for why intermittent administration is needed for bone anabolism. J Biol Chem 278(50):50259–50272. doi:10.1074/jbc.M307444200

    Article  CAS  PubMed  Google Scholar 

  • Benson AP, Aslanidi OV, Zhang H, Holden AV (2008) The canine virtual ventricular wall: a platform for dissecting pharmacological effects on propagation and arrhythmogenesis. Prog Biophys Mol Biol 96(1–3):187–208. doi:10.1016/j.pbiomolbio.2007.08.002

    Article  CAS  PubMed  Google Scholar 

  • Benson N, Matsuura T, Smirnov S, Demin O, Jones HM, Dua P, van der Graaf PH (2013) Systems pharmacology of the nerve growth factor pathway: use of a systems biology model for the identification of key drug targets using sensitivity analysis and the integration of physiology and pharmacology. Interface Focus 3(2):20120071. doi:10.1098/rsfs.2012.0071

    Article  PubMed  PubMed Central  Google Scholar 

  • Berg JM, Rogers ME et al (2010) Systems biology and pharmacology. Clin Pharmacol Ther 88(1):17–19

    Google Scholar 

  • Bhattacharya S, Shoda LK, Zhang Q, Woods CG, Howell BA, Siler SQ, Woodhead JL, Yang Y, McMullen P, Watkins PB, Andersen ME (2012) Modeling drug- and chemical-induced hepatotoxicity with systems biology approaches. Front Physiol 3:462. doi:10.3389/fphys.2012.00462

    Article  PubMed  PubMed Central  Google Scholar 

  • Bugatti S, Manzo A, Vitolo B, Benaglio F, Binda E, Scarabelli M, Humby F, Caporali R, Pitzalis C, Montecucco C (2014) High expression levels of the B cell chemoattractant CXCL13 in rheumatoid synovium are a marker of severe disease. Rheumatology (Oxford) 53(10):1886–1895. doi:10.1093/rheumatology/keu163

    Article  Google Scholar 

  • Chassagnole C, Jackson RC, Hussain N, Bashir L, Derow C, Savin J, Fell DA (2006) Using a mammalian cell cycle simulation to interpret differential kinase inhibition in anti-tumour pharmaceutical development. Biosystems 83(2–3):91–97. doi:10.1016/j.biosystems.2005.04.007

    Article  CAS  PubMed  Google Scholar 

  • Chen WW, Schoeberl B, Jasper PJ, Niepel M, Nielsen UB, Lauffenburger DA, Sorger PK (2009) Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data. Mol Syst Biol 5:239. doi:10.1038/msb.2008.74

    PubMed  PubMed Central  Google Scholar 

  • Chen X, Hickling TP, Vicini P (2014a) A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins: part 1—theoretical model. CPT Pharmacom Syst Pharmacol 3:e133. doi:10.1038/psp.2014.30

    Article  CAS  Google Scholar 

  • Chen X, Hickling TP, Vicini P (2014b) A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins: part 2—model applications. CPT Pharmacom Syst Pharmacol 3:e134. doi:10.1038/psp.2014.31

    Article  CAS  Google Scholar 

  • Cohen A (2008) Pharmacokinetic and pharmacodynamic data to be derived from early-phase drug development: designing informative human pharmacology studies. Clin Pharmacokinet 47(6):373–381

    Google Scholar 

  • Davies MR, Mistry HB, Hussein L, Pollard CE, Valentin JP, Swinton J, Abi-Gerges N (2012) An in silico canine cardiac midmyocardial action potential duration model as a tool for early drug safety assessment. Am J Physiol Heart Circ Physiol 302(7):H1466–H1480. doi:10.1152/ajpheart.00808.2011

    Article  CAS  PubMed  Google Scholar 

  • Demin O, Karelina T, Svetlichniy D, Metelkin E, Speshilov G, Demin O Jr, Fairman D, van der Graaf PH, Agoram BM (2013) Systems pharmacology models can be used to understand complex pharmacokinetic-pharmacodynamic behavior: an example using 5-lipoxygenase inhibitors. CPT Pharmacom Syst Pharmacol 2:e74. doi:10.1038/psp.2013.49

    Article  CAS  Google Scholar 

  • Dziuba J, Alperin P, Racketa J, Iloeje U, Goswami D, Hardy E, Perlstein I, Grossman HL, Cohen M (2014) Modeling effects of SGLT-2 inhibitor dapagliflozin treatment versus standard diabetes therapy on cardiovascular and microvascular outcomes. Diab Obes Metab 16(7):628–635. doi:10.1111/dom.12261

    Article  CAS  Google Scholar 

  • Eddy D, Schlessinger L, Kahn R, Peskin B, Schiebinger R (2009) Relationship of insulin resistance and related metabolic variables to coronary artery disease: a mathematical analysis. Diabetes Care 32(2):361–366. doi:10.2337/dc08-0854

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ermakov S, Forster P, Pagidala J, Miladinov M, Wang A, Baillie R, Bartlett D, Reed M, Leil TA (2014) Virtual Systems Pharmacology (ViSP) software for simulation from mechanistic systems-level models. Front Pharmacol 5:232. doi:10.3389/fphar.2014.00232

    Article  PubMed  PubMed Central  Google Scholar 

  • Friedrich CM (2016) A model qualification method for mechanistic physiological QSP models to support model-informed drug development. CPT Pharmacom Syst Pharmacol 5(2):43–53. doi:10.1002/psp4.12056

    Article  CAS  Google Scholar 

  • Gadkar K, Budha N, Baruch A, Davis JD, Fielder P, Ramanujan S (2014) A mechanistic systems pharmacology model for prediction of LDL cholesterol lowering by PCSK9 antagonism in human dyslipidemic populations. CPT Pharmacom Syst Pharmacol 3:e149. doi:10.1038/psp.2014.47

    Article  CAS  Google Scholar 

  • Gadkar K, Kirouac D, Mager DE, Graaf PH, Ramanujan S (2016a) A six-stage workflow for robust application of systems pharmacology. CPT-PSP

    Google Scholar 

  • Gadkar K, Lu J, Sahasranaman S, Davis J, Mazer NA, Ramanujan S (2016b) Evaluation of HDL-modulating interventions for cardiovascular risk reduction using a systems pharmacology approach. J Lipid Res 57(1):46–55. doi:10.1194/jlr.M057943

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gadkar K, Ramanujan S (2015) Workflow and technical methodologies for robust application of quantitative systems pharmacology approaches in model-based drug development. In: ASCPT conference

    Google Scholar 

  • Geerts H, Roberts P, Spiros A (2013a) A quantitative system pharmacology computer model for cognitive deficits in schizophrenia. CPT Pharmacom Syst Pharmacol 2:e36. doi:10.1038/psp.2013.12

    Article  CAS  Google Scholar 

  • Geerts H, Spiros A, Roberts P, Carr R (2013b) Quantitative systems pharmacology as an extension of PK/PD modeling in CNS research and development. J Pharmacokinet Pharmacodyn 40(3):257–265. doi:10.1007/s10928-013-9297-1

    Article  CAS  PubMed  Google Scholar 

  • Ghosh S, Matsuoka Y, Asai Y, Hsin KY, Kitano H (2011) Software for systems biology: from tools to integrated platforms. Nat Rev Genet 12(12):821–832. doi:10.1038/nrg3096

    CAS  PubMed  Google Scholar 

  • Ghosh S, Matsuoka Y, Kitano H (2010) Connecting the dots: role of standardization and technology sharing in biological simulation. Drug Discov Today 15(23–24):1024–1031. doi:10.1016/j.drudis.2010.10.001

    Article  PubMed  Google Scholar 

  • Greisen SR, Schelde KK, Rasmussen TK, Kragstrup TW, Stengaard-Pedersen K, Hetland ML, Horslev-Petersen K, Junker P, Ostergaard M, Deleuran B, Hvid M (2014) CXCL13 predicts disease activity in early rheumatoid arthritis and could be an indicator of the therapeutic ‘window of opportunity’. Arthritis Res Ther 16(5):434. doi:10.1186/s13075-014-0434-z

    Article  PubMed  PubMed Central  Google Scholar 

  • Hansen J, Iyengar R (2013) Computation as the mechanistic bridge between precision medicine and systems therapeutics. Clin Pharmacol Ther 93(1):117–128. doi:10.1038/clpt.2012.199

    Article  CAS  PubMed  Google Scholar 

  • Hendriks BS, Klinz SG, Reynolds JG, Espelin CW, Gaddy DF, Wickham TJ (2013) Impact of tumor HER2/ERBB2 expression level on HER2-targeted liposomal doxorubicin-mediated drug delivery: multiple low-affinity interactions lead to a threshold effect. Mol Cancer Ther 12(9):1816–1828. doi:10.1158/1535-7163.MCT-13-0180

    Article  CAS  PubMed  Google Scholar 

  • Hendriks BS, Reynolds JG, Klinz SG, Geretti E, Lee H, Leonard SC, Gaddy DF, Espelin CW, Nielsen UB, Wickham TJ (2012) Multiscale kinetic modeling of liposomal Doxorubicin delivery quantifies the role of tumor and drug-specific parameters in local delivery to tumors. CPT Pharmacom Syst Pharmacol 1:e15. doi:10.1038/psp.2012.16

    Article  CAS  Google Scholar 

  • Holzhutter HG, Drasdo D, Preusser T, Lippert J, Henney AM (2012) The virtual liver: a multidisciplinary, multilevel challenge for systems biology. Wiley Interdiscip Rev Syst Biol Med 4(3):221–235. doi:10.1002/wsbm.1158

    Article  PubMed  Google Scholar 

  • Honer WG, Thornton AE, Chen EY, Chan RC, Wong JO, Bergmann A, Falkai P, Pomarol-Clotet E, McKenna PJ, Stip E, Williams R, MacEwan GW, Wasan K, Procyshyn R (2006) Clozapine alone versus clozapine and risperidone with refractory schizophrenia. N Engl J Med 354(5):472–482. doi:10.1056/NEJMoa053222

    Article  CAS  PubMed  Google Scholar 

  • Howell BA, Siler SQ, Shoda LK, Yang Y, Woodhead JL, Watkins PB (2014) A mechanistic model of drug-induced liver injury AIDS the interpretation of elevated liver transaminase levels in a phase I clinical trial. CPT Pharmacom Syst Pharmacol 3:e98. doi:10.1038/psp.2013.74

    Article  CAS  Google Scholar 

  • Hund TJ, Rudy Y (2004) Rate dependence and regulation of action potential and calcium transient in a canine cardiac ventricular cell model. Circulation 110(20):3168–3174. doi:10.1161/01.CIR.0000147231.69595.D3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Iyengar R, Zhao S, Chung SW, Mager DE, Gallo JM (2012) Merging systems biology with pharmacodynamics. Sci Transl Med 4(126):126ps127. doi:10.1126/scitranslmed.3003563

    Article  Google Scholar 

  • Kadambi K, Young D, Gadkar K (2011) Systems modeling applied to candidate biomarker identification. In: Systems biology in drug discovery and development. Wiley, New York

    Google Scholar 

  • Kirouac DC, Du JY, Lahdenranta J, Overland R, Yarar D, Paragas V, Pace E, McDonagh CF, Nielsen UB, Onsum MD (2013) Computational modeling of ERBB2-amplified breast cancer identifies combined ErbB2/3 blockade as superior to the combination of MEK and AKT inhibitors. Sci Signal 6(288):ra68. doi:10.1126/scisignal.2004008

    Article  PubMed  Google Scholar 

  • Kocher R, Roberts B (2014) The calculus of cures. N Engl J Med 370(16):1473–1475. doi:10.1056/NEJMp1400868

    Article  CAS  PubMed  Google Scholar 

  • Kohl P, Crampin EJ et al (2010) Systems biology: an approach. Clin Pharmacol Ther 88(1):25–33

    Google Scholar 

  • Kuepfer L (2010) Towards whole-body systems pharmacology. Mol Sys Biol 6(409). doi:10.1038/msb.2010.70

  • Leil TA, Bertz R (2014) Quantitative Systems Pharmacology can reduce attrition and improve productivity in pharmaceutical research and development. Front Pharmacol 5:247. doi:10.3389/fphar.2014.00247

    Article  PubMed  PubMed Central  Google Scholar 

  • Lemaire V, Tobin FL, Greller LD, Cho CR, Suva LJ (2004) Modeling the interactions between osteoblast and osteoclast activities in bone remodeling. J Theor Biol 229(3):293–309. doi:10.1016/j.jtbi.2004.03.023

    Article  CAS  PubMed  Google Scholar 

  • Lu J, Hubner K, Nanjee MN, Brinton EA, Mazer NA (2014) An in-silico model of lipoprotein metabolism and kinetics for the evaluation of targets and biomarkers in the reverse cholesterol transport pathway. PLoS Comput Biol 10(3):e1003509. doi:10.1371/journal.pcbi.1003509

    Article  PubMed  PubMed Central  Google Scholar 

  • MacBeath G, Adiwijaya B (2014) A meta-analysis of biomarkers in three randomized, phase 2 studies of MM-121, a ligand-blocking anti-ErbB3 antibody, in patients with ovarian, lung, and breast cancers. In: ESMO 2014

    Google Scholar 

  • Meeuwisse CM, van der Linden MP, Rullmann TA, Allaart CF, Nelissen R, Huizinga TW, Garritsen A, Toes RE, van Schaik R, van der Helm-van Mil AH (2011) Identification of CXCL13 as a marker for rheumatoid arthritis outcome using an in silico model of the rheumatic joint. Arthritis Rheum 63(5):1265–1273. doi:10.1002/art.30273

    Article  CAS  PubMed  Google Scholar 

  • Mirams GR, Davies MR, Cui Y, Kohl P, Noble D (2012) Application of cardiac electrophysiology simulations to pro-arrhythmic safety testing. Br J Pharmacol 167(5):932–945. doi:10.1111/j.1476-5381.2012.02020.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Morgan P, Van Der Graaf PH, Arrowsmith J, Feltner DE, Drummond KS, Wegner CD, Street SD (2012) Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discov Today 17(9–10):419–424. doi:10.1016/j.drudis.2011.12.020

    Article  CAS  PubMed  Google Scholar 

  • Orrell D, Fernandez E (2010) Using predictive mathematical models to optimise the scheduling of anti-cancer drugs. Biopharma

    Google Scholar 

  • Peskin BR, Shcheprov AV, Boye KS, Bruce S, Maggs DG, Gaebler JA (2011) Cardiovascular outcomes associated with a new once-weekly GLP-1 receptor agonist vs. traditional therapies for type 2 diabetes: a simulation analysis. Diabetes Obes Metab 13(10):921–927. doi:10.1111/j.1463-1326.2011.01430.x

    Article  CAS  PubMed  Google Scholar 

  • Peterson MC, Riggs MM (2010) A physiologically based mathematical model of integrated calcium homeostasis and bone remodeling. Bone 46(1):49–63. doi:10.1016/j.bone.2009.08.053

    Article  CAS  PubMed  Google Scholar 

  • Peterson MC, Riggs MM (2012) Predicting nonlinear changes in bone mineral density over time using a multiscale systems pharmacology model. CPT Pharmacom Syst Pharmacol 1:e14. doi:10.1038/psp.2012.15

    Article  CAS  Google Scholar 

  • Peterson MC, Riggs MM (2015) FDA advisory meeting clinical pharmacology review utilizes a quantitative systems pharmacology (QSP) model: a watershed moment? CPT Pharmacom Syst Pharmacol 4(3):e00020. doi:10.1002/psp4.20

    Article  CAS  Google Scholar 

  • Raposo JF, Sobrinho LG, Ferreira HG (2002) A minimal mathematical model of calcium homeostasis. J Clin Endocrinol Metab 87(9):4330–4340. doi:10.1210/jc.2002-011870

    Article  CAS  PubMed  Google Scholar 

  • Raterman HG, Vosslamber S, de Ridder S, Nurmohamed MT, Lems WF, Boers M, van de Wiel M, Dijkmans BA, Verweij CL, Voskuyl AE (2012) The interferon type I signature towards prediction of non-response to rituximab in rheumatoid arthritis patients. Arthritis Res Ther 14(2):R95. doi:10.1186/ar3819

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Riggs MM, Peterson MC, Gastonguay MR (2012) Multiscale physiology-based modeling of mineral bone disorder in patients with impaired kidney function. J Clin Pharmacol 52(Suppl 1):45S–53S. doi:10.1177/0091270011412967

    Article  PubMed  Google Scholar 

  • Rostami-Hodjegan A (2012) Physiologically based pharmacokinetics joined with in vitro-in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin Pharmacol Ther 92(1):50–61. doi:10.1038/clpt.2012.65

    Article  CAS  PubMed  Google Scholar 

  • Rowland M, Peck C, Tucker G (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45–73. doi:10.1146/annurev-pharmtox-010510-100540

    Article  CAS  PubMed  Google Scholar 

  • Rullmann JA, Struemper H, Defranoux NA, Ramanujan S, Meeuwisse CM, van Elsas A (2005) Systems biology for battling rheumatoid arthritis: application of the Entelos PhysioLab platform. Syst Biol (Stevenage) 152(4):256–262

    Article  CAS  Google Scholar 

  • Schmidt BJ, Casey FP, Paterson T, Chan JR (2013) Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis. BMC Bioinform 14:221. doi:10.1186/1471-2105-14-221

    Article  CAS  Google Scholar 

  • Schoeberl B, Pace EA, Fitzgerald JB, Harms BD, Xu L, Nie L, Linggi B, Kalra A, Paragas V, Bukhalid R, Grantcharova V, Kohli N, West KA, Leszczyniecka M, Feldhaus MJ, Kudla AJ, Nielsen UB (2009) Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis. Sci Signal 2(77):ra31. doi:10.1126/scisignal.2000352

    Article  PubMed  Google Scholar 

  • Shoda LK, Woodhead JL, Siler SQ, Watkins PB, Howell BA (2014) Linking physiology to toxicity using DILIsym(R), a mechanistic mathematical model of drug-induced liver injury. Biopharm Drug Dispos 35(1):33–49. doi:10.1002/bdd.1878

    Article  CAS  PubMed  Google Scholar 

  • Sonesson C, Johansson PA, Johnsson E, Gause-Nilsson I (2016) Cardiovascular effects of dapagliflozin in patients with type 2 diabetes and different risk categories: a meta-analysis. Cardiovasc Diabetol 15(1):37. doi:10.1186/s12933-016-0356-y

    Article  PubMed  PubMed Central  Google Scholar 

  • Sorger P, Allerheiligen S (2011) 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

    Google Scholar 

  • Thalhauser CJ, Schmidt BJM M, Leil TA (2015) Mechanistic predictions of response to combinations of biologic agents in a quantitative systems pharmacology model of rheumatoid arthritis. J Pharmacokinet Pharmacodyn 42(1):S11–S107

    Google Scholar 

  • van der Graaf PH, Benson N (2011) Systems pharmacology: bridging systems biology and pharmacokinetics-pharmacodynamics (PKPD) in drug discovery and development. Pharm Res 28(7):1460–1464. doi:10.1007/s11095-011-0467-9

    Article  PubMed  Google Scholar 

  • van Herick A, Schuetz CA, Alperin P, Bullano MF, Balu S, Gandhi S (2012) The impact of initial statin treatment decisions on cardiovascular outcomes in clinical care settings: estimates using the Archimedes Model. Clinicoecon Outcomes Res 4:337–347. doi:10.2147/CEOR.S35487

    Article  PubMed  PubMed Central  Google Scholar 

  • Vicini P, van der Graaf PH (2013) Systems pharmacology for drug discovery and development: paradigm shift or flash in the pan? Clin Pharmacol Ther 93(5):379–381. doi:10.1038/clpt.2013.40

    Article  CAS  PubMed  Google Scholar 

  • Visser SA, de Alwis DP, Kerbusch T, Stone JA, Allerheiligen SR (2014) Implementation of quantitative and systems pharmacology in large pharma. CPT Pharmacom Syst Pharmacol 3:e142. doi:10.1038/psp.2014.40

    Article  CAS  Google Scholar 

  • Woodhead JL, Yang K, Siler SQ, Watkins PB, Brouwer KL, Barton HA, Howell BA (2014) Exploring BSEP inhibition-mediated toxicity with a mechanistic model of drug-induced liver injury. Front Pharmacol 5:240. doi:10.3389/fphar.2014.00240

    Article  PubMed  PubMed Central  Google Scholar 

  • Xing H, McDonagh PD, Bienkowska J, Cashorali T, Runge K, Miller RE, Decaprio D, Church B, Roubenoff R, Khalil IG, Carulli J (2011) Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis. PLoS Comput Biol 7(3):e1001105. doi:10.1371/journal.pcbi.1001105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhao P, Zhang L, Grillo JA, Liu Q, Bullock JM, Moon YJ, Song P, Brar SS, Madabushi R, Wu TC, Booth BP, Rahman NA, Reynolds KS, Gil Berglund E, Lesko LJ, Huang SM (2011) Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin Pharmacol Ther 89(2):259–267. doi:10.1038/clpt.2010.298

    Article  CAS  PubMed  Google Scholar 

  • Zhao S, Nishimura T, Chen Y, Azeloglu EU, Gottesman O, Giannarelli C, Zafar MU, Benard L, Badimon JJ, Hajjar RJ, Goldfarb J, Iyengar R (2013) Systems pharmacology of adverse event mitigation by drug combinations. Sci Transl Med 5(206):206ra140. doi:10.1126/scitranslmed.3006548

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Genentech Inc., 1 DNA Way, South San Francisco, CA, 94080, USA

    Saroja Ramanujan & Kapil Gadkar

  2. Rosa & Co. LLC, 751 Laurel St., Ste. 127, San Carlos, CA, 94070, USA

    Ananth Kadambi

Authors
  1. Saroja Ramanujan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kapil Gadkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ananth Kadambi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saroja Ramanujan .

Editor information

Editors and Affiliations

  1. Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, New York, USA

    Donald E. Mager

  2. Global Clinical Pharmacology, Janssen Research and Development, LLC,, Spring House, Pennsylvania, USA

    Holly H.C. Kimko

Rights and permissions

Reprints and permissions

Copyright information

© 2016 American Association of Pharmaceutical Scientists

About this chapter

Cite this chapter

Ramanujan, S., Gadkar, K., Kadambi, A. (2016). Quantitative Systems Pharmacology: Applications and Adoption in Drug Development. In: Mager, D., Kimko, H. (eds) Systems Pharmacology and Pharmacodynamics. AAPS Advances in the Pharmaceutical Sciences Series, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-44534-2_3

Download citation

Publish with us

Policies and ethics

Search

Navigation