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Quantitative systems pharmacology as an extension of PK/PD modeling in CNS research and development

  • Hugo GeertsEmail author
  • Athan Spiros
  • Patrick Roberts
  • Robert Carr
Original Paper

Abstract

Quantitative systems pharmacology (QSP) is a recent addition to the modeling and simulation toolbox for drug discovery and development and is based upon mathematical modeling of biophysical realistic biological processes in the disease area of interest. The combination of preclinical neurophysiology information with clinical data on pathology, imaging and clinical scales makes it a real translational tool. We will discuss the specific characteristics of QSP and where it differs from PK/PD modeling, such as the ability to provide support in target validation, clinical candidate selection and multi-target MedChem projects. In clinical development the approach can provide additional and unique evaluation of the effect of comedications, genotypes and disease states (patient populations) even before the initiation of actual trials. A powerful property is the ability to perform failure analysis. By giving examples from the CNS R&D field in schizophrenia and Alzheimer’s disease, we will illustrate how this approach can make a difference for CNS R&D projects.

Keywords

Quantitative systems pharmacology CNS diseases Alzheimer’s disease Schizophrenia 

Abbreviations

BOLDfMRI

Blood oxygen level dependent functional magnetic resonance imaging

CNS

Central nervous system

EEG

Electro-encephalography

EPS

Extra-pyramidal symptoms

GPCR

G-protein coupled receptor

GUI

Graphical user interface

MSN

Medium spiny neuron

QSP

Quantitative systems pharmacology

PANSS

Positive and negative symptoms in schizophrenia

References

  1. 1.
    Potkin SG et al (2003) Aripiprazole, an antipsychotic with a novel mechanism of action, and risperidone vs placebo in patients with schizophrenia and schizoaffective disorder. Arch Gen Psychiatry 60(7):681–690PubMedCrossRefGoogle Scholar
  2. 2.
    Winslow WW, Stone WN, Hofling CK (1967) Drug therapy. Prog Neurol Psychiatry 22:509–528PubMedGoogle Scholar
  3. 3.
    Schoepp DD (2011) Where will new neuroscience therapies come from? Nat Rev Drug Discov 10(10):715–716PubMedCrossRefGoogle Scholar
  4. 4.
    Laustsen G, Wimmett L (2005) 2004 Drug approval highlights: FDA update. Nurse Pract 30(2):14–29 quiz 29–31PubMedCrossRefGoogle Scholar
  5. 5.
    Blennow K et al (2012) Effect of immunotherapy with bapineuzumab on cerebrospinal fluid biomarker levels in patients with mild to moderate Alzheimer disease. Arch Neurol 69(8):1002–1010PubMedCrossRefGoogle Scholar
  6. 6.
    Bezprozvanny I (2010) The rise and fall of dimebon. Drug News Perspect 23(8):518–523PubMedGoogle Scholar
  7. 7.
    Geerts H (2009) Of mice and men: bridging the translational disconnect in CNS drug discovery. CNS Drugs 23(11):915–926PubMedCrossRefGoogle Scholar
  8. 8.
    Ito K et al (2010) Disease progression meta-analysis model in Alzheimer’s disease. Alzheimers Dement 6(1):39–53PubMedCrossRefGoogle Scholar
  9. 9.
    Hurko O, Ryan JL (2005) Translational research in central nervous system drug discovery. NeuroRx 2(4):671–682PubMedCrossRefGoogle Scholar
  10. 10.
    Geerts H (2011) Modeling and simulation as a tool for improving CNS drug research and development. Drug Dev Res 72:66–73CrossRefGoogle Scholar
  11. 11.
    Sorger PK, Schoeberl B (2012) An expanding role for cell biologists in drug discovery and pharmacology. Mol Biol Cell 23(21):4162–4164PubMedCrossRefGoogle Scholar
  12. 12.
    Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117(4):500–544PubMedGoogle Scholar
  13. 13.
    Markram H (2012) The human brain project. Sci Am 306(6):50–55PubMedCrossRefGoogle Scholar
  14. 14.
    Finkel LH (2000) Neuroengineering models of brain disease. Annu Rev Biomed Eng 2:577–606PubMedCrossRefGoogle Scholar
  15. 15.
    Erdi P et al (2006) From systems biology to dynamical neuropharmacology: proposal for a new methodology. Syst Biol (Stevenage) 153(4):299–308CrossRefGoogle Scholar
  16. 16.
    Hines ML, Carnevale NT (1997) The NEURON simulation environment. Neural Comput 9(6):1179–1209PubMedCrossRefGoogle Scholar
  17. 17.
    De Schutter E (2008) Why are computational neuroscience and systems biology so separate? PLoS Comput Biol 4(5):e1000078PubMedCrossRefGoogle Scholar
  18. 18.
    Spiros A, Carr R, Geerts H (2010) Not all partial dopamine D(2) receptor agonists are the same in treating schizophrenia. Exploring the effects of bifeprunox and aripiprazole using a computer model of a primate striatal dopaminergic synapse. Neuropsychiatr Dis Treat 6:589–603PubMedGoogle Scholar
  19. 19.
    Roberts PD, Spiros A, Geerts H (2012) Simulations of symptomatic treatments for Alzheimer’s disease: computational analysis of pathology and mechanisms of drug action. Alzheimers Res Ther 4(6):50PubMedCrossRefGoogle Scholar
  20. 20.
    Spiros A, Roberts P, Geerts H (2012) A quantitative systems pharmacology computer model for schizophrenia efficacy and extrapyramidal side effects. Drug Dev Res 73(4):1098–1109CrossRefGoogle Scholar
  21. 21.
    Kapur S, Mizrahi R, Li M (2005) From dopamine to salience to psychosis-linking biology, pharmacology and phenomenology of psychosis. Schizophr Res 79(1):59–68PubMedCrossRefGoogle Scholar
  22. 22.
    Falk T et al (2008) Over-expression of the potassium channel Kir2.3 using the dopamine-1 receptor promoter selectively inhibits striatal neurons. Neuroscience 155(1):114–127PubMedCrossRefGoogle Scholar
  23. 23.
    Gabel LA, Nisenbaum ES (1998) Biophysical characterization and functional consequences of a slowly inactivating potassium current in neostriatal neurons. J Neurophysiol 79(4):1989–2002PubMedGoogle Scholar
  24. 24.
    Kuzhikandathil EV, Oxford GS (2002) Classic D1 dopamine receptor antagonist R-(+)-7-chloro-8-hydroxy-3-methyl-1-phenyl-2,3,4,5-tetrahydro-1H-3-benzaze pine hydrochloride (SCH23390) directly inhibits G protein-coupled inwardly rectifying potassium channels. Mol Pharmacol 62(1):119–126PubMedCrossRefGoogle Scholar
  25. 25.
    Gruber AJ et al (2003) Modulation of striatal single units by expected reward: a spiny neuron model displaying dopamine-induced bistability. J Neurophysiol 90(2):1095–1114PubMedCrossRefGoogle Scholar
  26. 26.
    Mermelstein PG et al (1998) Inwardly rectifying potassium (IRK) currents are correlated with IRK subunit expression in rat nucleus accumbens medium spiny neurons. J Neurosci 18(17):6650–6661PubMedGoogle Scholar
  27. 27.
    Bamford NS et al (2004) Dopamine modulates release from corticostriatal terminals. J Neurosci 24(43):9541–9552PubMedCrossRefGoogle Scholar
  28. 28.
    Ansanay H et al (1995) cAMP-dependent, long-lasting inhibition of a K+ current in mammalian neurons. Proc Natl Acad Sci USA 92(14):6635–6639PubMedCrossRefGoogle Scholar
  29. 29.
    Abi-Dargham A et al (2000) Increased baseline occupancy of D2 receptors by dopamine in schizophrenia. Proc Natl Acad Sci USA 97(14):8104–8109PubMedCrossRefGoogle Scholar
  30. 30.
    Meyer-Lindenberg A et al (2002) Reduced prefrontal activity predicts exaggerated striatal dopaminergic function in schizophrenia. Nat Neurosci 5(3):267–271PubMedCrossRefGoogle Scholar
  31. 31.
    Esmaeilzadeh M et al (2011) Extrastriatal dopamine D(2) receptor binding in Huntington’s disease. Hum Brain Mapp 32(10):1626–1636PubMedCrossRefGoogle Scholar
  32. 32.
    Geddes J et al (2000) Atypical antipsychotics in the treatment of schizophrenia: systematic overview and meta-regression analysis. BMJ 321(7273):1371–1376PubMedCrossRefGoogle Scholar
  33. 33.
    Davis JM, Chen N, Glick ID (2003) A meta-analysis of the efficacy of second-generation antipsychotics. Arch Gen Psychiatry 60(6):553–564PubMedCrossRefGoogle Scholar
  34. 34.
    Lieberman JA (2007) Effectiveness of antipsychotic drugs in patients with chronic schizophrenia: efficacy, safety and cost outcomes of CATIE and other trials. J Clin Psychiatry 68(2):e04PubMedCrossRefGoogle Scholar
  35. 35.
    Geerts H et al (2012) Blinded prospective evaluation of computer-based mechanistic schizophrenia disease model for predicting drug response. PLoS ONE 7(12):e49732PubMedCrossRefGoogle Scholar
  36. 36.
    Kane JM et al (2002) Efficacy and safety of aripiprazole and haloperidol versus placebo in patients with schizophrenia and schizoaffective disorder. J Clin Psychiatry 63(9):763–771PubMedCrossRefGoogle Scholar
  37. 37.
    Casey DE et al (2008) Efficacy and safety of bifeprunox in patients with an acute exacerbation of schizophrenia: results from a randomized, double-blind, placebo-controlled, multicenter, dose-finding study. Psychopharmacology 200(3):317–331PubMedCrossRefGoogle Scholar
  38. 38.
    Wu Q et al (2002) Concurrent autoreceptor-mediated control of dopamine release and uptake during neurotransmission: an in vivo voltammetric study. J Neurosci 22(14):6272–6281PubMedGoogle Scholar
  39. 39.
    Cragg SJ, Hille CJ, Greenfield SA (2000) Dopamine release and uptake dynamics within nonhuman primate striatum in vitro. J Neurosci 20(21):8209–8217PubMedGoogle Scholar
  40. 40.
    Etievant A et al (2009) Bifeprunox and aripiprazole suppress in vivo VTA dopaminergic neuronal activity via D2 and not D3 dopamine autoreceptor activation. Neurosci Lett 460(1):82–86PubMedCrossRefGoogle Scholar
  41. 41.
    Natesan S et al (2011) Partial agonists in schizophrenia—why some work and others do not: insights from preclinical animal models. Int J Neuropsychopharmacol 14(9):1165–1178PubMedCrossRefGoogle Scholar
  42. 42.
    Wood MD et al (2006) Aripiprazole and its human metabolite are partial agonists at the human dopamine D2 receptor, but the rodent metabolite displays antagonist properties. Eur J Pharmacol 546(1–3):88–94PubMedCrossRefGoogle Scholar
  43. 43.
    Okun I et al (2010) From anti-allergic to anti-Alzheimer’s: molecular pharmacology of dimebon. Curr Alzheimer Res 7(2):97–112PubMedCrossRefGoogle Scholar
  44. 44.
    Doody RS et al (2008) Effect of dimebon on cognition, activities of daily living, behaviour, and global function in patients with mild-to-moderate Alzheimer’s disease: a randomised, double-blind, placebo-controlled study. Lancet 372(9634):207–215PubMedCrossRefGoogle Scholar
  45. 45.
    Geerts H, Roberts P, Spiros A (2012) Failure analysis of dimebon using mechanistic disease modeling: lessons for clinical development of new AD therapies. Alzheimers Dement 8(Suppl):311Google Scholar
  46. 46.
    Slifstein M et al (2008) COMT genotype predicts cortical-limbic D1 receptor availability measured with [11C]NNC112 and PET. Mol Psychiatry 13(8):821–827PubMedCrossRefGoogle Scholar
  47. 47.
    Spiros A, Geerts H (2012) A quantitative way to estimate clinical off-target effects for human membrane brain targets in CNS research and development. J Exp Pharmacol 4:53–61Google Scholar
  48. 48.
    Geerts H, Spiros A, Carr R (2010) Exploring the biology of iloperidone responder profiles in treatment of schizophrenia using a mechanistic disease model. Schizophr Res 117(2–3):414CrossRefGoogle Scholar
  49. 49.
    Lavedan C et al (2009) Association of the NPAS3 gene and five other loci with response to the antipsychotic iloperidone identified in a whole genome association study. Mol Psychiatry 14(8):804–819PubMedCrossRefGoogle Scholar
  50. 50.
    Kokel D et al (2012) Behavioral barcoding in the cloud: embracing data-intensive digital phenotyping in neuropharmacology. Trends Biotechnol 30(8):421–425PubMedCrossRefGoogle Scholar
  51. 51.
    Hayashi-Takagi A, Sawa A (2010) Disturbed synaptic connectivity in schizophrenia: convergence of genetic risk factors during neurodevelopment. Brain Res Bull 83(3–4):140–146PubMedCrossRefGoogle Scholar
  52. 52.
    Wong EH, Tarazi FI, Shahid M (2010) The effectiveness of multi-target agents in schizophrenia and mood disorders: relevance of receptor signature to clinical action. Pharmacol Ther 126(2):173–185PubMedCrossRefGoogle Scholar
  53. 53.
    Truffinet P et al (1999) Placebo-controlled study of the D4/5-HT2A antagonist fananserin in the treatment of schizophrenia. Am J Psychiatry 156(3):419–425PubMedGoogle Scholar
  54. 54.
    de Paulis T (2001) M-100907 (Aventis). Curr Opin Investig Drugs 2(1):123–132PubMedGoogle Scholar
  55. 55.
    Redden L et al (2011) A double-blind, randomized, placebo-controlled study of the dopamine D(3) receptor antagonist ABT-925 in patients with acute schizophrenia. J Clin Psychopharmacol 31(2):221–225PubMedCrossRefGoogle Scholar
  56. 56.
    Singh SP, Singh V (2011) Meta-analysis of the efficacy of adjunctive NMDA receptor modulators in chronic schizophrenia. CNS Drugs 25(10):859–885PubMedCrossRefGoogle Scholar
  57. 57.
    Patil ST et al (2007) Activation of mGlu2/3 receptors as a new approach to treat schizophrenia: a randomized phase 2 clinical trial. Nat Med 13(9):1102–1107PubMedCrossRefGoogle Scholar
  58. 58.
    Collins PY et al (2011) Grand challenges in global mental health. Nature 475(7354):27–30PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Hugo Geerts
    • 1
    • 2
    Email author
  • Athan Spiros
    • 1
  • Patrick Roberts
    • 1
    • 3
  • Robert Carr
    • 1
  1. 1.In Silico BiosciencesBerwynUSA
  2. 2.Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.Oregon Health and Science UniversityPortlandUSA

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