Abstract
Metabolic stability refers to the susceptibility of compounds to biotransformation in the context of selecting and/or designing drugs with favourable pharmacokinetic properties. Metabolic stability results are usually reported as measures of intrinsic clearance, from which secondary pharmacokinetic parameters such as bioavailability and half-life can be calculated when other data on volume of distribution and fraction absorbed are available. Since these parameters are very important in defining the pharmacological and toxicological profile of drugs as well as patient compliance, the pharmaceutical industry has a particular interest in optimising for metabolic stability during the drug discovery and development process. In the early phases of drug discovery, new chemical entities cannot be administered to humans; hence, predictions of these properties have to be made from in vivo animal, in vitro cellular/subcellular and computational systems. The utility of these systems to define the metabolic stability of compounds that is predictive of the human situation will be reviewed here. The timing of performing the studies in the discovery process and the impact of recent advances in research on drug absorption, distribution, metabolism and excretion (ADME) will be evaluated with respect to the scope and depth of metabolic stability issues.
Quantitative prediction of in vivo clearance from in vitro metabolism data has, for many compounds, been shown to be poor in retrospective studies. One explanation for this may be that there are components used in the equations for scaling that are missing or uncertain and should be an area of more research. For example, as a result of increased biochemical understanding of drug metabolism, old assumptions (e.g. that the liver is the principal site of first-pass metabolism) need revision and new knowledge (e.g. the relationship between transporters and drug metabolising enzymes) needs to be incorporated into in vitro-in vivo correlation models. With ADME parameters increasingly being determined on automated platforms, instead of using results from high throughput screening (HTS) campaigns as simple go/no-go filters, the time saved and the many compounds analysed using the robots should be invested in careful processing of the data. A logical step would be to investigate the potential to construct computational models to understand the factors governing metabolic stability. A rational approach to the use of HTS assays should aim to screen for many properties (e.g. physicochemical parameters, absorption, metabolism, protein binding, pharmacokinetics in animals and pharmacology) in an integrated manner rather than screen against one property on many compounds, since it is likely that the final drug will represent a global average of these properties.
Similar content being viewed by others
References
Obach RS, Baxter JG, Liston TE, et al. The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data. J Pharmacol Exp Ther 1997; 283: 46–58
Obach RS. Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: an examination of in vitro half-life approach and nonspecific binding to microsomes. Drug Metab Dispos 1999; 27: 1350–9
Masimirembwa CM, Thompson R, Andersson TB. In vitro throughput screening of compounds for favourable metabolic properties in drug discovery. Comb Chem High Throughput Screen 2001; 4: 245–63
Thompson TN. Optimization of metabolic stability as a goal of modern drug design. Med Res Rev 2001; 21: 412–49
Kumar GN, Surapaneni S. Role of drug metabolism in drug discovery and development. Med Res Rev 2001; 21: 397–411
White RE. High-throughput screening in drug metabolism and pharmacokinetic support of drug discovery. Annu Rev Pharmacol Toxicol 2000; 40: 133–57
Iwatsubo T, Hirota N, Ooie T, et al. Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data. Pharmacol Ther 1997; 73: 147–71
Rowland M, Benet LZ, Graham GG. Clearance concepts in pharmacokinetics. J Pharmacokinet Biopharm 1973; 1: 123–36
Wilkinson GR, Shand DG. A physiological approach to hepatic clearance. Clin Pharmacol Ther 1975; 18: 377–90
Pang KS, Roland M. Hepatic clearance of drugs I: theoretical considerations of a ‘well-stirred’ and ‘parallel-tube’ model. Influence of hepatic blood flow, plasma and blood cell binding, and the hepatocellular enzymatic activity on hepatic drug clearance. J Pharmacokinet Biopharm 1977; 5: 625–53
Roberts MS, Rowland M. Hepatic elimination dispersion model. J Pharm Sci 1985; 74: 585–7
Roberts MS, Rowland M. A dispersion model of hepatic elimination. J Pharmacokinet Biopharm 1986; 14: 227–308
Houston JB, Cariile DJ. Prediction of hepatic clearance from microsomes, hepatocytes, and liver slices. Drug Metab Rev 1997; 29: 891–922
Kwon Y, Morris ME. Membrane transport in hepatic clearance of drugs I: extended hepatic clearance models incorporating concentration-dependent transport and elimination processes. Pharm Res 1997; 14: 774–800
Carlile DJ, Zomordi K. Houston JB. Scaling factors to relate drug metabolic clearance in hepatic microsomes, isolated hepatocytes and intact liver. Drug Metab Dispos 1997; 25: 903–11
Cariile DJ, Hakooz N, Bayliss MK, et al. Microsomal prediction of in vivo clearance of CYP2C9 substrates in humans. Br J Clin Pharmacol 1999; 47: 625–35
de Duve C, Pressman BC, Gianetto R, et al. Tissue fractionation studies: 6. intracellular distribution patterns of enzyme in ratliver tissue. Biochem J 1955; 60: 604–717
McLure JA, Miners JO, Birkett DJ. Nonspecific binding of drugs to human liver microsomes. Br J Clin Pharmacol 2000; 49: 453–61
Ito K, Iwatsubo T, Kanamitsu S, et al. Quantitative prediction of in vivo drug clearance and drug interactions from in vitro data on metabolism, together with binding and transport. Annu Rev Pharmacol Toxicol 1998; 38: 199–461
Ludden LK, Ludden TM, Collins JM, et al. Effect of albumin on the estimation, in vitro, of phenytoin Vmax and Km values: implications for clinical correlation. J Pharmacol Exp Ther 1997; 282: 391–6
Venkatakrishnan K, von Moltke LL, Obach RS, et al. Microsomal binding of amitriptyline: effect on estimation of enzyme kinetic parameters in vitro. J Pharmacol Exp Ther 2000, 350
Venkatakrishnan K, von Moltke LL, Greenblatt DJ. Application of the relative activity factor approach in scaling from heterologous expressed cytochromes P450 to human liver microsomes: studies on amitriptyline as a model substrate. J Pharmacol Exp Ther 2001 Apr; 297(1): 326–37
Shibata Y, Takahashi H, Ishii Y. A convenient in vitro screening method for predicting in vivo drug metabolic clearance using isolated hepatocytes suspended in serum. Drug Metab Dispos 2000, 1523
Cross DM, Bayliss MK. A commentary on the use of hepatocytes in drug metabolism studies during drug discovery and development. Drug Metab Rev 2000; 32: 219–40
Li AP, Goycki PD, Hengstler JG, et al. Present status of the application of cryopreserved hepatocytes in the evaluation of xenobiotics: consensus of an external expert panel. Chem Biol Interact 1999; 121: 117–23
Steinberg P, Fischer T, Kiulies S, et al. Drug metabolizing capacity of cryopreserved human, rat, and mouse liver parenchymal cells in suspension. Drug Metab Dispos 1999; 27: 1415–22
De Graaf IAM, Van Der Voort D, Brits JHFG, et al. Increased post-thaw viability and phase I and II biotransformation activity in cryopreserved rat liver slices after improvement of a freezing method. Drug Metab Dispos 2000; 28: 1100–6
Sohlenius-Sternbeck AK, Floby E, Svedling M, et al. High conservation of both phase I and phase II drug-metabolizing activities in cryopreserved rat liver slices. Xenobiotica 2000; 30: 891–903
Houston JB. Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochem Pharmacol 1994; 47: 1469–79
Clarke SE, Jeffrey P. Utility of metabolic stability screening: comparison of in vitro ands in vivo clearance. Xenobiotica 2001; 31: 591–8
Langowski J, Long A. Computer systems for the prediction of xenobiotic metabolism. Adv Drug Deliv Rev 2002; 45: 407–15
Lewis DFV, Dickens M, Eddershaw PJ, et al. Cytochrome P450 substrate specificities, substrate structural templates and enzyme active site geometries. Drug Metab Drug Interact 1999; 15: 1–49
Smith DA, Ackland MJ, Jones BC. Properties of cytochrome P450 isoenzymes and their substrates. Part 1: active site characteristics. Drug Discov Today 1997; 2: 406–14
Smith DA, Ackland MJ, Jones BC. Properties of cytochrome P450 isoenzymes and their substrates. Part 2: properties of cytochrome P450 substrates. Drug Discov Today 1997; 2: 479–85
Halpert JR, Domanski TL, Adali O, et al. Structure-function of cytochromes P450 and flavin-containing monooxygenases: implications for drug metabolism. Drug Metab Dispos 1998; 26: 1223–31
Laak AM, Verleulem NPE. Molecular-modeling approaches to predict metabolism and toxicity. In: Testa AB, van de Waterbeemd H, Folkers G, Guy R, editors. Pharmacokinetic optimization in drug research. Zurich: Verlag Helvetica Chimica Acta, 2001: 551–88
Lewis DFV. Modelling human cytochrome P450 for evaluating drug metabolism: an update. Drug Metab Drug Interact 2000; 16: 307–18
Ekins S, De Groot MJ, Jones JP. Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome P450 active sites. Drug Metab Dispos 2001; 29: 936–44
Lewis DFV. On the recognition of mammalian microsomal cytochrome P450 substrates and their characteristics: towards the prediction of human P450 substrate specificity and metabolism. Biochem Pharmacol 2000; 60: 293–306
Groot MJ, Ekins S. Pharmacophore modeling of cytochrome P450. Adv Drug Deliv Rev 2002; 54: 367–83
Hansch C, Zhang L. Quantitative structure-activity relationship of cytochrome P450. Drug Metab Rev 1993; 25: 1–48
Ekins S, Erickson JA. A pharmacophore for human pregnane X receptor ligands. Drug Metab Dispos 2002; 30: 96–9
Riley RJ, Parker AJ, Trigg S, et al. Development of a generalized, quantitative physiocochemical model of CYP3A4 inhibition for use in early drug discovery. Pharm Res 2001; 18: 652–5
Upthagrove AL, Nelson WL. Importance of amine pKa and distribution coefficient in the metabolism of fluorinated propanolol analogs: metabolism by CYP1A2. Drug Metab Dispos 2001; 29: 1389–95
Afzelius L, Zamora I, Ridderström M, et al. Competitive CYP2C9 inhibitors: enzyme inhibition studies, protein homology modeling, and three-dimensional quantitative structure-activity relationship analysis. Mol Pharmacol 2001; 59: 909–19
Williams PA, Cosme J, Sridhar V, et al. Mammalian microsomal cytochrome P450 monooxygenase: structural adaptations for membrane binding and functional diversity. Mol Cell 2000; 5: 121–31
Astex™ Technology. Astex determines structure of the key drug metabolising enzyme -human cytochrome P450 3A4 [press release]. Cambridge, UK: Astex Technology, 2002 Oct 28. Available from: http://www.astex-technology.com/one/29_37.html [accessed 2003 Apr 08]
Jones JP. Predicting the regioselectivity and reactivity of cytochrome P450 mediated reactions [abstact]. 10th North American International Society for the Study of Xenobiotics meeting; Indianapolis. Drug Metab Rev. 2000 Oct; 32: 139
Zamora I, Afzelius L, Cruciani G. Predicting Drug Metabolism: a cytochrome P450 2C9 case study. J Med Chem 2003; In press.
De Groot MJ, Ackland MJ, Horne VA, et al. A novel approach to predict P450 mediated drug metabolism. CYP2D6 catalysed N-dealkylation reactions and qualitative metabolite predictions using a combined protein and pharmachore model for CYP2D6. J Med Chem 1999; 42: 4062–70
Onderwater RCA, Venhorst J, Commandeur J, et al. Design, synthesis, and characterisation of 7-methoxy-4-(aminomethyl) coumarin as a novel and selective cytochrome P450 2D6 substrate suitable for high-throughput screening. Chem Res Toxicol 1999; 12: 555–9
Jones JP, Korzekwa KR. Predicting the rates and regioselectivity of reactions mediated by the cytochrome P450 family. Methods Enzymol 1996; 72: 326–35
Waller CL, Evans MV, McKinney JD. Modelling the cytochrome P450-mediated metabolism of chlorinated volatile organic compounds. Drug Metab Dispos 1996; 24: 203–10
Yin H, Anders MW, Korzekwa KR, et al. Designing safer chemicals: predicting the rates of metabolism of halogenated alkanes. Proc Natl Acad Sci U S A 1995; 92: 11076–80
Buchwald P, Bodor N. Quantitative structure-metabolism relationships: steric and nonsteric effects in the enzymatic hydrolysis of noncongener carboxylic esters. J Med Chem 1999; 42: 5160–8
Takenaga N, Ishii M, Kamei T, et al. Structure-activity relationship in O-glucuronidation of indolocarbazole analogs. Drug Metab Dispos 2002; 30: 494–7
Ekins S, Obach RS. Three-dimensional quantitative structure activity relationship computational approaches for prediction of human in vitro intrinsic clearance. J Pharmacol Exp Ther 2000; 295: 463–73
Yoshida F, Topliss JG. QSAR model for drug human oral bioavailability. J Med Chem 2000; 43: 2575–85
Basak SC, Grunwald GD, Gute BD, et al. Use of statistical and neural approaches in predicting toxicity of chemicals. J Chem Inf Comput Sci 2000; 40: 885–90
Amedis Pharmaceuticals Ltd. Artificial intelligence software: ADMET prediction [web page]. Available from: http://www.amedis.co.uk/aisoftware.htm# [accessed 2003 Apr 08]
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Masimirembwa, C.M., Bredberg, U. & Andersson, T.B. Metabolic Stability for Drug Discovery and Development. Clin Pharmacokinet 42, 515–528 (2003). https://doi.org/10.2165/00003088-200342060-00002
Published:
Issue Date:
DOI: https://doi.org/10.2165/00003088-200342060-00002