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

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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.

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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. 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):681–690

    Article  PubMed  CAS  Google Scholar 

  2. Winslow WW, Stone WN, Hofling CK (1967) Drug therapy. Prog Neurol Psychiatry 22:509–528

    PubMed  CAS  Google Scholar 

  3. Schoepp DD (2011) Where will new neuroscience therapies come from? Nat Rev Drug Discov 10(10):715–716

    Article  PubMed  CAS  Google Scholar 

  4. Laustsen G, Wimmett L (2005) 2004 Drug approval highlights: FDA update. Nurse Pract 30(2):14–29 quiz 29–31

    Article  PubMed  Google Scholar 

  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):1002–1010

    Article  PubMed  Google Scholar 

  6. Bezprozvanny I (2010) The rise and fall of dimebon. Drug News Perspect 23(8):518–523

    PubMed  Google Scholar 

  7. Geerts H (2009) Of mice and men: bridging the translational disconnect in CNS drug discovery. CNS Drugs 23(11):915–926

    Article  PubMed  CAS  Google Scholar 

  8. Ito K et al (2010) Disease progression meta-analysis model in Alzheimer’s disease. Alzheimers Dement 6(1):39–53

    Article  PubMed  CAS  Google Scholar 

  9. Hurko O, Ryan JL (2005) Translational research in central nervous system drug discovery. NeuroRx 2(4):671–682

    Article  PubMed  Google Scholar 

  10. Geerts H (2011) Modeling and simulation as a tool for improving CNS drug research and development. Drug Dev Res 72:66–73

    Article  CAS  Google Scholar 

  11. Sorger PK, Schoeberl B (2012) An expanding role for cell biologists in drug discovery and pharmacology. Mol Biol Cell 23(21):4162–4164

    Article  PubMed  CAS  Google Scholar 

  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):500–544

    PubMed  CAS  Google Scholar 

  13. Markram H (2012) The human brain project. Sci Am 306(6):50–55

    Article  PubMed  Google Scholar 

  14. Finkel LH (2000) Neuroengineering models of brain disease. Annu Rev Biomed Eng 2:577–606

    Article  PubMed  CAS  Google Scholar 

  15. Erdi P et al (2006) From systems biology to dynamical neuropharmacology: proposal for a new methodology. Syst Biol (Stevenage) 153(4):299–308

    Article  CAS  Google Scholar 

  16. Hines ML, Carnevale NT (1997) The NEURON simulation environment. Neural Comput 9(6):1179–1209

    Article  PubMed  CAS  Google Scholar 

  17. De Schutter E (2008) Why are computational neuroscience and systems biology so separate? PLoS Comput Biol 4(5):e1000078

    Article  PubMed  Google Scholar 

  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 6:589–603

    PubMed  CAS  Google Scholar 

  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):50

    Article  PubMed  CAS  Google Scholar 

  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):1098–1109

    Article  Google Scholar 

  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):59–68

    Article  PubMed  Google Scholar 

  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):114–127

    Article  PubMed  CAS  Google Scholar 

  23. Gabel LA, Nisenbaum ES (1998) Biophysical characterization and functional consequences of a slowly inactivating potassium current in neostriatal neurons. J Neurophysiol 79(4):1989–2002

    PubMed  CAS  Google Scholar 

  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):119–126

    Article  PubMed  CAS  Google Scholar 

  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):1095–1114

    Article  PubMed  Google Scholar 

  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):6650–6661

    PubMed  CAS  Google Scholar 

  27. Bamford NS et al (2004) Dopamine modulates release from corticostriatal terminals. J Neurosci 24(43):9541–9552

    Article  PubMed  CAS  Google Scholar 

  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):6635–6639

    Article  PubMed  CAS  Google Scholar 

  29. Abi-Dargham A et al (2000) Increased baseline occupancy of D2 receptors by dopamine in schizophrenia. Proc Natl Acad Sci USA 97(14):8104–8109

    Article  PubMed  CAS  Google Scholar 

  30. Meyer-Lindenberg A et al (2002) Reduced prefrontal activity predicts exaggerated striatal dopaminergic function in schizophrenia. Nat Neurosci 5(3):267–271

    Article  PubMed  CAS  Google Scholar 

  31. Esmaeilzadeh M et al (2011) Extrastriatal dopamine D(2) receptor binding in Huntington’s disease. Hum Brain Mapp 32(10):1626–1636

    Article  PubMed  Google Scholar 

  32. Geddes J et al (2000) Atypical antipsychotics in the treatment of schizophrenia: systematic overview and meta-regression analysis. BMJ 321(7273):1371–1376

    Article  PubMed  CAS  Google Scholar 

  33. Davis JM, Chen N, Glick ID (2003) A meta-analysis of the efficacy of second-generation antipsychotics. Arch Gen Psychiatry 60(6):553–564

    Article  PubMed  CAS  Google Scholar 

  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):e04

    Article  PubMed  Google Scholar 

  35. Geerts H et al (2012) Blinded prospective evaluation of computer-based mechanistic schizophrenia disease model for predicting drug response. PLoS ONE 7(12):e49732

    Article  PubMed  CAS  Google Scholar 

  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):763–771

    Article  PubMed  CAS  Google Scholar 

  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):317–331

    Article  PubMed  CAS  Google Scholar 

  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):6272–6281

    PubMed  CAS  Google Scholar 

  39. Cragg SJ, Hille CJ, Greenfield SA (2000) Dopamine release and uptake dynamics within nonhuman primate striatum in vitro. J Neurosci 20(21):8209–8217

    PubMed  CAS  Google Scholar 

  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):82–86

    Article  PubMed  CAS  Google Scholar 

  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):1165–1178

    Article  PubMed  Google Scholar 

  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):88–94

    Article  PubMed  CAS  Google Scholar 

  43. Okun I et al (2010) From anti-allergic to anti-Alzheimer’s: molecular pharmacology of dimebon. Curr Alzheimer Res 7(2):97–112

    Article  PubMed  CAS  Google Scholar 

  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):207–215

    Article  PubMed  CAS  Google Scholar 

  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):311

    Google Scholar 

  46. Slifstein M et al (2008) COMT genotype predicts cortical-limbic D1 receptor availability measured with [11C]NNC112 and PET. Mol Psychiatry 13(8):821–827

    Article  PubMed  CAS  Google Scholar 

  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 4:53–61

    CAS  Google Scholar 

  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):414

    Article  Google Scholar 

  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):804–819

    Article  PubMed  CAS  Google Scholar 

  50. Kokel D et al (2012) Behavioral barcoding in the cloud: embracing data-intensive digital phenotyping in neuropharmacology. Trends Biotechnol 30(8):421–425

    Article  PubMed  CAS  Google Scholar 

  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):140–146

    Article  PubMed  CAS  Google Scholar 

  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):173–185

    Article  PubMed  CAS  Google Scholar 

  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):419–425

    PubMed  CAS  Google Scholar 

  54. de Paulis T (2001) M-100907 (Aventis). Curr Opin Investig Drugs 2(1):123–132

    PubMed  Google Scholar 

  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):221–225

    Article  PubMed  CAS  Google Scholar 

  56. Singh SP, Singh V (2011) Meta-analysis of the efficacy of adjunctive NMDA receptor modulators in chronic schizophrenia. CNS Drugs 25(10):859–885

    Article  PubMed  CAS  Google Scholar 

  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):1102–1107

    Article  PubMed  CAS  Google Scholar 

  58. Collins PY et al (2011) Grand challenges in global mental health. Nature 475(7354):27–30

    Article  PubMed  CAS  Google Scholar 

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Correspondence to Hugo Geerts.

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

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  • DOI: https://doi.org/10.1007/s10928-013-9297-1

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