Phosphatase Modulators pp 85-101

Part of the Methods in Molecular Biology book series (MIMB, volume 1053)

Inhibitors of Tissue-Nonspecific Alkaline Phosphatase (TNAP): From Hits to Leads

  • Peter Teriete
  • Anthony B. Pinkerton
  • Nicholas D. P. Cosford


The optimization of active hits, commonly derived from high-throughput screening campaigns (see Chapters 2 and 4), into promising small-molecule lead compounds is one of the fundamental steps in early drug discovery. Directions taken during this stage can have important consequences reaching through lead optimization into preclinical development and beyond. Considering the ever-increasing costs of preclinical as well as clinical development phases (DiMasi et al., J Health Econ 22:151–185, 2003) the choices made at the early stages of drug discovery can have a real impact on the likelihood of the best lead becoming a viable candidate (Bleicher et al., Nat Rev Drug Discov 2:369–378, 2003). Thus it is important to utilize proven and robust methodologies to turn promising hits into suitable lead series with propitious characteristics. Here, we describe such an approach using the example of a tissue-nonspecific alkaline phosphatase (see Chapter 3) inhibitor developed in our group (Sidique et al., Bioorg Med Chem Lett 19:222–225, 2009).

Key words

Absorption, distribution, metabolism, excretion, and toxicity (ADME/T) High-throughput screening (HTS) Hit-to-lead Inhibitor design Medicinal chemistry Pharmacokinetics (PK) Pharmacophore Rational design Structure–activity relationship (SAR) 


  1. 1.
    DiMasi JA, Hansen RW, Grabowski HG (2003) The price of innovation: new estimates of drug development costs. J Health Econ 22:151–185PubMedCrossRefGoogle Scholar
  2. 2.
    Bleicher KH, Bohm HJ, Muller K et al (2003) Hit and lead generation: beyond high-throughput screening. Nat Rev Drug Discov 2:369–378PubMedCrossRefGoogle Scholar
  3. 3.
    Sidique S, Ardecky R, Su Y et al (2009) Design and synthesis of pyrazole derivatives as potent and selective inhibitors of tissue-nonspecific alkaline phosphatase (TNAP). Bioorg Med Chem Lett 19:222–225PubMedCrossRefGoogle Scholar
  4. 4.
    Barh D, Ahmad S, Bhattacharjee A (2012) In silico and ultrahigh-throughput screenings (uHTS) in drug discovery: an overview. Pharmaceutical biotechnology. Wiley-VCH Verlag, Weinheim, Germany, pp 451–490Google Scholar
  5. 5.
    Zoete V, Grosdidier A, Michielin O (2009) Docking, virtual high throughput screening and in silico fragment-based drug design. J Cell Mol Med 13:238–248PubMedCrossRefGoogle Scholar
  6. 6.
    Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53:2719–2740PubMedCrossRefGoogle Scholar
  7. 7.
    Pinkerton AB, Vernier J-M, Cube RV, Hutchinson JH (2006) Inventor Heterocyclic indanone potentiators of metabotropic glutamate receptors. USA WO Patent WO/2006/047237Google Scholar
  8. 8.
    Sergienko E, Su Y, Chan X et al (2009) Identification and characterization of novel tissue-nonspecific alkaline phosphatase inhibitors with diverse modes of action. J Biomol Screen 14:824–837PubMedCrossRefGoogle Scholar
  9. 9.
    Sergienko EA, Millan JL (2010) High-throughput screening of tissue-nonspecific alkaline phosphatase for identification of effectors with diverse modes of action. Nat Protoc 5:1431–1439PubMedCrossRefGoogle Scholar
  10. 10.
    Kerns EH, Di L, Carter GT (2008) In vitro solubility assays in drug discovery. Curr Drug Metab 9:879–885PubMedCrossRefGoogle Scholar
  11. 11.
    John S, Thangapandian S, Arooj M et al (2011) Development, evaluation and application of 3D QSAR Pharmacophore model in the discovery of potential human renin inhibitors. BMC Bioinformatics 12 Suppl 14, S4Google Scholar
  12. 12.
    Binns M, de Visser SP, Theodoropoulos C (2012) Modeling flexible pharmacophores with distance geometry, scoring, and bound stretching. J Chem Inf Model 52:577–588PubMedCrossRefGoogle Scholar
  13. 13.
    Tai W, Lu T, Yuan H et al (2011) Pharmacophore modeling and virtual screening studies to identify new c-Met inhibitors. J Mol Model 1–14Google Scholar
  14. 14.
    Arnold K, Bordoli L, Kopp J et al (2006) The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics 22:195–201PubMedCrossRefGoogle Scholar
  15. 15.
    Kozlenkov A, Le Du MH, Cuniasse P et al (2004) Residues determining the binding specificity of uncompetitive inhibitors to tissue-nonspecific alkaline phosphatase. J Bone Miner Res 19:1862–1872PubMedCrossRefGoogle Scholar
  16. 16.
    Taha MO, Bustanji Y, Al-Bakri AG et al (2007) Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening. J Mol Graph Model 25:870–884PubMedCrossRefGoogle Scholar
  17. 17.
    Zampieri D, Mamolo MG, Laurini E et al (2009) Synthesis, biological evaluation, and three-dimensional in silico pharmacophore model for sigma(1) receptor ligands based on a series of substituted benzo[d]oxazol-2(3H)-one derivatives. J Med Chem 52:5380–5393PubMedCrossRefGoogle Scholar
  18. 18.
    Kariv I, Cao H, Oldenburg KR (2001) Development of a high throughput equilibrium dialysis method. J Pharm Sci 90:580–587CrossRefGoogle Scholar
  19. 19.
    Kansy M, Senner F, Gubernator K (1998) Physicochemical high throughput screening: parallel artificial membrane permeation assay in the description of passive absorption processes. J Med Chem 41:1007–1010CrossRefGoogle Scholar
  20. 20.
    Dahl R, Bravo Y, Sharma V et al (2011) Potent, selective, and orally available benzoisothiazolone phosphomannose isomerase inhibitors as probes for congenital disorder of glycosylation Ia. J Med Chem 54:3661–3668CrossRefGoogle Scholar
  21. 21.
    Lin JH, Lu AY (1997) Role of pharmacokinetics and metabolism in drug discovery and development. Pharmacol Rev 49:403–449PubMedGoogle Scholar
  22. 22.
    Lipinski CA (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 44: 235–249PubMedCrossRefGoogle Scholar
  23. 23.
    Banker MJ, Clark TH et al (2003) Development and validation of a 96-well equilibrium dialysis apparatus for measuring plasma protein binding. J Pharm Sci 92:967–974PubMedCrossRefGoogle Scholar
  24. 24.
    Di L, Kerns EH et al (2008) Applications of high throughput microsomal stability assay in drug discovery. Comb Chem High Throughput Screen 11:469–476CrossRefGoogle Scholar
  25. 25.
    Guengerich FP (1989) Oxidation of halogenated compounds by cytochrome P-450, peroxidases, and model metalloporphyrins. J Biol Chem 264:17098–17205Google Scholar
  26. 26.
    Mosmann T (1983) Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. J Immunol Methods 65:55–63PubMedCrossRefGoogle Scholar
  27. 27.
    Korfmacher WA, Cox KA et al (2001) Cassette-accelerated rapid rat screen: a systematic procedure for the dosing and liquid chromatography/atmospheric pressure ionization tandem mass spectrometric analysis of new chemical entities as part of new drug discovery. Rapid Commun Mass Spectrom 15: 335–340PubMedCrossRefGoogle Scholar
  28. 28.
    Mei H, Korfmacher WA et al. (2006) Rapid in vivo oral screening in rats: reliability, acceptance criteria, and filtering efficiency. AAPS J 8:E493–500PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Peter Teriete
    • 1
  • Anthony B. Pinkerton
    • 1
  • Nicholas D. P. Cosford
    • 1
  1. 1.NCI-Designated Cancer CenterSanford-Burnham Medical Research InstituteLa JollaUSA

Personalised recommendations