Investigational New Drugs

, Volume 30, Issue 6, pp 2263–2273 | Cite as

Screening candidate anticancer drugs for brain tumor chemotherapy: Pharmacokinetic-driven approach for a series of (E)-N-(substituted aryl)-3-(substituted phenyl)propenamide analogues

  • Hua Lv
  • Fan Wang
  • M. V. Ramana Reddy
  • Qingyu Zhou
  • Xiaoping Zhang
  • E. Premkumar Reddy
  • James M. Gallo


A pharmacokinetic [PK]-driven screening process was implemented to select new agents for brain tumor chemotherapy from a series of low molecular weight anticancer agents [ON27x] that consisted of 141 compounds. The screening procedures involved a combination of in silico, in vitro and in vivo mouse studies that were cast into a pipeline of tier 1 and tier 2 failures that resulted in a final investigation of 2 analogues in brain tumor-bearing mice. Tier 1 failures included agents with a molecular weight of > 450 Da, a predicted log P (log P) of either <2 or > 3.5, and a cytotoxicity IC50 value of > 2 uM. Next, 18 compounds underwent cassette dosing studies in normal mice that identified compounds with high systemic clearance, and low blood–brain barrier [BBB] penetration. These indices along with a derived parameter, referred to as the brain exposure index, comprised tier 2 failures that led to the administration of 2 compounds [ON27570, ON27740] as single agents [discrete dosing] to mice bearing intracerebral tumors. Comparison of ON27570’s resultant PK parameters to those obtained in the cassette dosing format suggested a drug-drug interaction most likely at the level of BBB transport, and prompted the use of the in vitro MDCK-MDR1 transport model to help assess the nature of the discrepancy. Overall, the approach was able to identify candidate compounds with suitable PK characteristics yet further revisions to the method, such as the use of in vitro metabolism and transport assays, may improve the PK-directed approach to identify efficacious agents for brain tumor chemotherapy.


Pharmacokinetics Drug development Brain tumor CNS Preclinical 



This work was supported by grants CA127963 and CA127963-S1 from the NIH.

Conflict of interest

Dr. E.P. Reddy is the scientific founder, stock holder, board member and paid consultant of Onconova Therapeutics Inc. He is also one of the inventors of the patents that describe the compounds described here.

Supplementary material

10637_2012_9806_MOESM1_ESM.doc (151 kb)
ESM 1 (DOC 151 kb)


  1. 1.
    Challenges and Opportunities Report (CDER) (2004) Innovation and stagnation: Challenge and opportunity on the critical path to new medicinal products. US Food and Drug AdministrationGoogle Scholar
  2. 2.
    Zerhouni E (2003) Medicine. The NIH Roadmap. Science 302:63–72PubMedCrossRefGoogle Scholar
  3. 3.
    Wang S, Guo P, Wang X, Zhou Q, Gallo JM (2008) Preclinical pharmacokinetic/pharmacodynamic models of gefitinib and the design of equivalent dosing regimens in EGFR wild-type and mutant tumor models. Mol Cancer Ther 7:407–417PubMedCrossRefGoogle Scholar
  4. 4.
    Wang S, Zhou Q, Gallo JM (2009) Demonstration of the equivalent pharmacokinetic/pharmacodynamic dosing strategy in a multiple-dose study of gefitinib. Mol Cancer Ther 8:1438–1447PubMedCrossRefGoogle Scholar
  5. 5.
    He K, Qian M, Wong H, Bai SA, He B, Brogdon B et al (2008) N-in-1 dosing pharmacokinetics in drug discovery: experience, theoretical and practical considerations. J Pharm Sci 97:2568–2580PubMedCrossRefGoogle Scholar
  6. 6.
    Smith NF, Raynaud FI, Workman P (2007) The application of cassette dosing for pharmacokinetic screening in small-molecule cancer drug discovery. Mol Cancer Ther 6:428–440PubMedCrossRefGoogle Scholar
  7. 7.
    Tamvakopoulos CS, Colwell LF, Barakat K, Fenyk-Melody J, Griffin PR, Nargund R et al (2000) Determination of brain and plasma drug concentrations by liquid chromatography/tandem mass spectrometry. Rapid Commun Mass Spectrom 14:1729–1735PubMedCrossRefGoogle Scholar
  8. 8.
    Zhang MY, Kerns E, McConnell O, Sonnenberg-Reines J, Zaleska MM, Steven Jacobsen J et al (2004) Brain and plasma exposure profiling in early drug discovery using cassette administration and fast liquid chromatography-tandem mass spectrometry. J Pharm Biomed Anal 34:359–368PubMedCrossRefGoogle Scholar
  9. 9.
    Hegi ME, Diserens AC, Bady P, Kamoshima Y, Kouwenhoven MC, Delorenzi M et al (2011) Pathway analysis of glioblastoma tissue after preoperative treatment with the EGFR tyrosine kinase inhibitor gefitinib–a phase II trial. Mol Cancer Ther 10:1102–1112PubMedCrossRefGoogle Scholar
  10. 10.
    Heon S, Yeap BY, Britt GJ, Costa DB, Rabin MS, Jackman DM et al (2010) Development of central nervous system metastases in patients with advanced non-small cell lung cancer and somatic EGFR mutations treated with gefitinib or erlotinib. Clin Cancer Res 16:5873–5882PubMedCrossRefGoogle Scholar
  11. 11.
    Geyer JR, Stewart CF, Kocak M, Broniscer A, Phillips P, Douglas JG (2010) A phase I and biology study of gefitinib and radiation in children with newly diagnosed brain stem gliomas or supratentorial malignant gliomas. Eur J Cancer 46:3287–3293PubMedCrossRefGoogle Scholar
  12. 12.
    McKillop D, McCormick AD, Millar A, Miles GS, Phillips PJ, Hutchison M (2005) Cytochrome P450-dependent metabolism of gefitinib. Xenobiotica 35:39–50PubMedCrossRefGoogle Scholar
  13. 13.
    Lemos C, Kathmann I, Giovannetti E, Calhau C, Jansen G, Peters GJ (2009) Impact of cellular folate status and epidermal growth factor receptor expression on BCRP/ABCG2-mediated resistance to gefitinib and erlotinib. Br J Cancer 100:1120–1127PubMedCrossRefGoogle Scholar
  14. 14.
    Kawamura K, Yamasaki T, Yui J, Hatori A, Konno F, Kumata K et al (2009) In vivo evaluation of P-glycoprotein and breast cancer resistance protein modulation in the brain using [(11)C]gefitinib. Nucl Med Biol 36:239–246PubMedCrossRefGoogle Scholar
  15. 15.
    Watanabe T, Schulz D, Morisseau C, Hammock BD (2006) High-throughput pharmacokinetic method: cassette dosing in mice associated with minuscule serial bleedings and LC/MS/MS analysis. Anal Chim Acta 559:37–44PubMedCrossRefGoogle Scholar
  16. 16.
    Smith NF, Hayes A, Nutley BP, Raynaud FI, Workman P (2006) Evaluation of the cassette dosing approach for assessing the pharmacokinetics of geldanamycin analogues in mice. Cancer Chemother Pharmacol 54:475–486CrossRefGoogle Scholar
  17. 17.
    Zhou Q, Gallo JM (2009) Differential effect of sunitinib on the distribution of temozolomide in an orthotopic glioma model. Neuro Oncol 11:301–310PubMedCrossRefGoogle Scholar
  18. 18.
    Boudinot FD, Jusko WJ (1984) Fluid shifts and other factors affecting plasma protein binding of prednisolone by equilibrium dialysis. J Pharm Sci 73:774–780PubMedCrossRefGoogle Scholar
  19. 19.
    Luo S, Kansara VS, Zhu X, Mandava NK, Pal D, Mitra AK (2006) Functional characterization of sodium-dependent multivitamin transporter in MDCK-MDR1 cells and its utilization as a target for drug delivery. Mol Pharm 3:329–339PubMedCrossRefGoogle Scholar
  20. 20.
    Soldner A, Benet LZ, Mutschler E, Christians U (2000) Active transport of the angiotensin-II antagonist losartan and its main metabolite EXP 3174 across MDCK-MDR1 and caco-2 cell monolayers. Br J Pharmacol 129:1235–1243PubMedCrossRefGoogle Scholar
  21. 21.
    Wang Q, Rager JD, Weinstein K, Kardos PS, Dobson GL, Li J et al (2005) Evaluation of the MDR-MDCK cell line as a permeability screen for the blood–brain barrier. Int J Pharm 288:349–359PubMedCrossRefGoogle Scholar
  22. 22.
    van de Waterbeemd H, Gifford E (2003) ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2:192–204PubMedCrossRefGoogle Scholar
  23. 23.
    Waterhouse RN (2003) Determination of lipophilicity and its use as a predictor of blood–brain barrier penetration of molecular imaging agents. Mol Imaging Biol 5:376–389PubMedCrossRefGoogle Scholar
  24. 24.
    Levin VA (1980) Relationship of octanol/water partition coefficient and molecular weight to rat brain capillary permeability. J Med Chem 23:682–684PubMedCrossRefGoogle Scholar
  25. 25.
    Agarwal S, Sane R, Gallardo JL, Ohlfest JR, Elmquist WF (2010) Distribution of gefitinib to the brain is limited by P-glycoprotein (ABCB1) and breast cancer resistance protein (ABCG2)-mediated active efflux. J Pharmacol Exp Ther 334:147–155PubMedCrossRefGoogle Scholar
  26. 26.
    Hofer S, Frei K, Rutz HP (2006) Gefitinib accumulation in glioblastoma tissue. Cancer Biol Ther 5:483–484PubMedGoogle Scholar
  27. 27.
    Ozvegy-Laczka C, Hegedus T, Várady G, Ujhelly O, Schuetz JD, Váradi A et al (2004) High-affinity interaction of tyrosine kinase inhibitors with the ABCG2 multidrug transporter. Mol Pharmacol 65:1485–1495PubMedCrossRefGoogle Scholar
  28. 28.
    Carcaboso AM, Elmeliegy MA, Shen J, Juel SJ, Zhang ZM, Calabrese C et al (2010) Tyrosine kinase inhibitor gefitinib enhances topotecan penetration of gliomas. Cancer Res 70:4499–4508PubMedCrossRefGoogle Scholar
  29. 29.
    Nagilla R, Nord M, McAtee JJ, Jolivette LJ (2011) Cassette dosing for pharmacokinetic screening in drug discovery: comparison of clearance, volume of distribution, half-life, mean residence time, and oral bioavailability obtained by cassette and discrete dosing in rats. J Pharm Sci 100:3862–3874PubMedCrossRefGoogle Scholar
  30. 30.
    Li J, Brahmer J, Messersmith W, Hidalgo M, Baker SD (2006) Binding of gefitinib, an inhibitor of epidermal growth factor receptor-tyrosine kinase, to plasma proteins and blood cells: in vitro and in cancer patients. Invest New Drugs 24:291–297PubMedCrossRefGoogle Scholar
  31. 31.
    McKillop D, Hutchison M, Partridge EA, Bushby N, Cooper CM, Clarkson-Jones JA et al (2004) Metabolic disposition of gefitinib, an epidermal growth factor receptor tyrosine kinase inhibitor, in rat, dog and man. Xenobiotica 34:917–934PubMedCrossRefGoogle Scholar
  32. 32.
    Kerns EH (2001) High throughput physicochemical profiling for drug discovery. J Pharm Sci 90:1838–1858PubMedCrossRefGoogle Scholar
  33. 33.
    Hop CE, Cole MJ, Davidson RE, Duignan DB, Federico J, Janiszewski JS et al (2008) High throughput ADME screening: practical considerations, impact on the portfolio and enabler of in silico ADME models. Curr Drug Metab 9:847–853PubMedCrossRefGoogle Scholar
  34. 34.
    Carlson TJ, Fisher MB (2008) Recent advances in high throughput screening for ADME properties. Comb Chem High Throughput Screen 11:258–264PubMedCrossRefGoogle Scholar
  35. 35.
    Ebinger M, Uhr M (2006) ABC drug transporter at the blood–brain barrier: effects on drug metabolism and drug response. Eur Arch Psychiatry Clin Neurosci 256:294–298PubMedCrossRefGoogle Scholar
  36. 36.
    Tamai I, Tsuji A (2000) Transporter-mediated permeation of drugs across the blood–brain barrier. J Pharm Sci 89:1371–1388PubMedCrossRefGoogle Scholar
  37. 37.
    Wan H, Rehngren M, Giordanetto F, Bergström F, Tunek A (2007) High-throughput screening of drug-brain tissue binding and in silico prediction for assessment of central nervous system drug delivery. J Med Chem 50:4606–4615PubMedCrossRefGoogle Scholar
  38. 38.
    Hammarlund-Udenaes M, Fridén M, Syvänen S, Gupta A (2008) On the rate and extent of drug delivery to the brain. Pharm Res 25:1737–1750PubMedCrossRefGoogle Scholar
  39. 39.
    Taub ME, Podila L, Ely D, Almeida I (2005) Functional assessment of multiple P-glycoprotein (P-gp) probe substrates: influence of cell line and modulator concentration on P-gp activity. Drug Metab Dispos 33:1679–1687PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Hua Lv
    • 1
    • 3
  • Fan Wang
    • 1
    • 3
  • M. V. Ramana Reddy
    • 2
  • Qingyu Zhou
    • 1
  • Xiaoping Zhang
    • 1
  • E. Premkumar Reddy
    • 2
  • James M. Gallo
    • 1
    • 3
  1. 1.Department of Pharmacology and Systems TherapeuticsMount Sinai School of MedicineNew YorkUSA
  2. 2.Department of Oncological SciencesMount Sinai School of MedicineNew YorkUSA
  3. 3.Department of Pharmacology and System TherapeuticsMount Sinai School of MedicineNew YorkUSA

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