Abstract
Kinetic profiling of drug binding to its target reveals important mechanistic parameters including drug–target residence time. In this chapter, we focus on global progress curve analysis as a convenient method for kinetic profiling. Detailed guidelines with pros and cons for various experimental designs and data analysis are provided. Kinetic profiling of Boceprevir and Telaprevir is illustrated.
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References
Zhang R, Monsma F (2010) Binding kinetics and mechanism of action: toward the discovery and development of better and best in class drugs. Expert Opin Drug Discov 5:1023–1029
Copeland RA (2010) The dynamics of drug-target interactions: drug-target residence time and its impact on efficacy and safety. Expert Opin Drug Discov 5:305–310
Lu H, Tonge PJ (2010) Drug-target residence time: critical information for lead optimization. Curr Opin Chem Biol 14:467–474
Swinney DC (2009) The role of binding kinetics in therapeutically useful drug action. Curr Opin Drug Discov Dev 12:31–39
Vauquelin G (2010) Rebinding: or why drugs may act longer in vivo than expected from their in vitro target residence time. Expert Opin Drug Discov 5:927–941
Copeland RA, Pompliano DL, Meek TD (2006) Drug-target residence time and its implications for lead optimization. Nat Rev Drug Discov 5:730–739
Tummino PJ, Copeland RA (2008) Residence time of receptor-ligand complexes and its effect on biological function. Biochemistry 47:5481–5492
Zhang R, Monsma F (2009) The importance of drug-target residence time. Curr Opin Drug Discov Dev 12:488–496
Fang Y (2012) Ligand-receptor interaction platforms and their applications for drug discovery. Expert Opin Drug Discov 7:969–988
Vauquelin G (2012) Determination of drug-receptor residence time by radioligand binding and functional assays: experimental strategies and physiological relevance. Med Chem Commun 3:645–651
Andersson K, Karlsson R, Lofas S et al (2006) Label-free kinetic binding data as a decisive element in drug discovery. Expert Opin Drug Discov 1:439–446
Copeland RA (2005) Evaluation of enzyme inhibitors in drug discovery: a guide for medicinal chemists and pharmacologists. Wiley., Hoboken, NJ, pp 141–213
Kwong AD, Kauffman RS, Hurter P, Mueller P (2011) Discovery and development of telaprevir: an NS3-4A protease inhibitor for treating genotype 1 chronic hepatitis C virus. Nat Biotechnol 29:993–1003
Njoroge FG, Chen KX, Shih NY, Piwinski JJ (2008) Challenges in modern drug discovery: a case study of boceprevir, an HCV protease inhibitor for the treatment of hepatitis C virus infection. Acc Chem Res 41:50–59
Taremi SS, Beyer B, Maher M et al (1998) Construction, expression, and characterization of a novel fully activated recombinant single-chain hepatitis C virus protease. Protein Sci 7:2143–2149
Zhang R, Beyer BM, Durkin J et al (1999) A continuous spectrophotometric assay for the hepatitis C virus serine protease. Anal Biochem 270:268–275
Cha S (1976) Tight-binding inhibitors-III. A new approach for the determination of competition between tight-binding inhibitors and substrates-inhibition of adenosine deaminase by coformycin. Biochem Pharmacol 25:2695–2702
Morrison JF, Walsh CT (1988) The behavior and significance of slow-binding enzyme inhibitors. Adv Enzymol Relat Areas Mol Biol 61:201–301
Sculley MJ, Morrison JF, Cleland WW (1996) Slow-binding inhibition: the general case. Biochim Biophys Acta 1298:78–86
Szedlacsek SE, Duggleby RG (1995) Kinetics of slow and tight-binding inhibitors. Methods Enzymol 249:144–180
Williams JW, Morrison JF, Duggleby RG (1979) Methotrexate, a high-affinity pseudosubstrate of dihydrofolate reductase. Biochemistry 18:2567–2573
Murphy DJ (2004) Determination of accurate KI values for tight-binding enzyme inhibitors: an in silico study of experimental error and assay design. Anal Biochem 327:61–67
Copeland RA, Basavapathruni A, Moyer M, Scott MP (2011) Impact of enzyme concentration and residence time on apparent activity recovery in jump dilution analysis. Anal Biochem 416:206–210
Kuzmic P (2008) A steady state mathematical model for stepwise “slow-binding” reversible enzyme inhibition. Anal Biochem 380:5–12
Kuzmic P, Elrod KC, Cregar LM et al (2000) High-throughput screening of enzyme inhibitors: simultaneous determination of tight-binding inhibition constants and enzyme concentration. Anal Biochem 286:45–50
Plesner IW, Bulow A, Bols M (2001) Accurate determination of rate constants of very slow, tight-binding competitive inhibitors by numerical solution of differential equations, independently of precise knowledge of the enzyme concentration. Anal Biochem 295:186–193
Cheng Y, Prusoff WH (1973) Relationship between the inhibition constant (K1) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. Biochem Pharmacol 22:3099–3108
Yang J, Copeland RA, Lai Z (2009) Defining balanced conditions for inhibitor screening assays that target bisubstrate enzymes. J Biomol Screen 14:111–120
Acknowledgment
The authors gratefully acknowledge the technical assistance of Edward DiNunzio, the careful reading of the manuscript by Michael Kavana, and the enthusiastic managerial support from Christine Brideau.
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Zhang, R., Windsor, W.T. (2013). In Vitro Kinetic Profiling of Hepatitis C Virus NS3 Protease Inhibitors by Progress Curve Analysis. In: Gong, E. (eds) Antiviral Methods and Protocols. Methods in Molecular Biology, vol 1030. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-484-5_6
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DOI: https://doi.org/10.1007/978-1-62703-484-5_6
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