Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Equation chapter 1 section 1A new method for predicting human hepatic clearance fromin Vitro experimental data using molecular descriptors

  • 72 Accesses

  • 6 Citations

Abstract

The present study demonstrated that the information of molecular descriptors of drugs increases the accuracy of predicting humanin vivo hepatic clearance fromin vitro experimental data in humans and rats. A new method uses not only the experimental data but also the information of molecular descriptors. Predictions for the datasets from hepatocyte experiments and microsome experiments were made by the present method, and the prediction accuracy was compared with those of the previous methods, such as methods usingin vitro- in vivo scaling factor and multiple linear regression analysis, that use only the experimental data. Results showed that the present method was the most accurate prediction model with the lowest prediction errors and the strongest correlations. These results suggest that the information of molecular descriptors is significant for predicting the humanin vivo pharmacokinetic parameters fromin vitro experimental data. This study also demonstrated thatin vitro experimental data in humans and rats were important information for predicting humanin vivo hepatic clearance, and the additional ratin vivo data were not significant for prediction with the information of molecular descriptors. These results imply that the present method can be useful for high-throughput drug candidate screening by reducing the time and cost in the early stage of the drug discovery process.

This is a preview of subscription content, log in to check access.

References

  1. Boxenbaum, H., Interspecies scaling, allometry, physiological time, and the ground plan of pharmacokinetics.J. Pharmacokinet. Biopharm., 10, 201–227 (1982).

  2. Clark, D. E., In silico prediction of blood-brain barrier permeation.Drug Discov. Today, 8, 927–933 (2003).

  3. Didziapetris, R., Japertas, P., Avdeef, A., and Petrauskas, A., Classification analysis of P-glycoprotein substrate specificity.J, Drug Target, 11, 391–406 (2003).

  4. Doniger, S., Hofmann, T., and Yeh, J., Predicting CNS permeability of drug molecules: comparison of neural network and support vector machine algorithms.J. Comput. Biol., 9, 849–864 (2002).

  5. Ertl, P., Rohde, B., and Selzer, P., Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties.J. Med. Chem., 43, 3714–3717 (2000).

  6. Hayter, A. J., Probability and Statistics for Engineers and Scientists, Duxbury (2002).

  7. Hou, T. J. and Xu, X. J., ADME evaluation in drug discovery. 3. Modeling blood-brain barrier partitioning using simple molecular descriptors.J. Chem. Inf. Comput. Sci., 43, 2137–2152 (2003).

  8. Houston, J. B., Utility of in vitro drug metabolism data in predictingin vivo metabolic clearance.Biochem. Pharmacol., 47,1469–1479(1994).

  9. Ito, K. and Houston, J. B., Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes.Pharm. Res., 21,785–792 (2004).

  10. Ito, K. and Houston, J. B., Prediction of human drug clearance from in vitro and predinical data using physiologically based and empirical approaches.Pharm. Res., 22,103–112 (2005).

  11. Jolivette, L. J. and Ward, K. W., Extrapolation of human pharmacokinetic parameters from rat, dog, and monkey data: Molecular properties associated with extrapolative success or failure.J. Pharm. Sci., 94,1467–1483 (2005).

  12. Kola, I. and Landis, J., Can the pharmaceutical industry reduce attrition rates?Nat. Rev. Drug Discov, 3, 711–715 (2004).

  13. Langowski, J. and Long, A., Computer systems for the prediction of xenobiotic metabolism.Adv. Drug Deliv. Rev., 54, 407–415(2002).

  14. Lave, T., Dupin, S., Schmitt, C, Chou, R. C, Jaeck, D., and Coassolo, P., Integration of in vitro data into allometric scaling to predict hepatic metabolic clearance in man: application to 10 extensively metabolized drugs.J. Pharm. Sci., 86, 584–590(1997).

  15. Leach, A. and Gillet, V, An Introducton to Chemoinformatics, Kluwer Academic Publishers (2003).

  16. Lewis, D. F. and Dickins, M., Substrate SARs in human P450s.Drug Discov. Today, 7, 918–925 (2002).

  17. Lipinski, C. A., Lombardo, F., Dominy, B. W., and Feeney, P. J., Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.Adv. Drug Deliv. Rev., 46, 3–26 (2001).

  18. Nagilla, R. and Ward, K. W., A comprehensive analysis of the role of correction factors in the allometric predictivity of clearance from rat, dog, and monkey to humans.J. Pharm. Sci., 93, 2522–2534 (2004).

  19. Naritomi, Y, Terashita, S., Kimura, S., Suzuki, A., Kagayama, A., and Sugiyama, Y, Prediction of human hepatic clearance from in vivo animal experiments and in vitro metabolic studies with liver microsomes from animals and humans.Drug Metab. Dispos., 29,1316–1324 (2001).

  20. Norinder, U. and Haeberlein, M., Computational approaches to the prediction of the blood-brain distribution.Adv. Drug Deliv. Rev., 54, 291–313(2002).

  21. PreADME, http://preadmet.bmdrc.org/preadmet/index.php

  22. Schneider, G, Coassolo, P., and Lave, T., Combiningin vitro andin vivo pharmacokinetic data for prediction of hepatic drug clearance in humans by artificial neural networks and multivariate statistical techniques.J. Med. Chem., 42, 5072–5076(1999).

  23. Shargel, S. and Yu, A., Applied Biopharmaceutics and Pharma-cokinetics, Prentice-Hall International, lnc(1993).

  24. Shim, C, Pharmacokinetics, Seoul National University Publishers (1994).

  25. Sugawara, M., Takekuma, Y, Yamada, H., Kobayashi, M., Iseki, K., and Miyazaki, K., A general approach for the prediction of the intestinal absorption of drugs: regression analysis using the physicochemical properties and drug-membrane electrostatic interaction.J. Pharm. Sci., 87, 960–966 (1998).

  26. van de Waterbeemd, H. and Gifford, E., ADMET in silico modelling: towards prediction paradise?Nat. Rev. Drug Discov., 2,192–204(2003).

  27. Wajima, T., Fukumura, K., Yano, Y, and Oguma, T., Prediction of human clearance from animal data and molecular structural parameters using multivariate regression analysis.J. Pharm. Sci., 91, 2489–2499 (2002).

Download references

Author information

Correspondence to Soyoung Lee or Dongsup Kim.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lee, S., Kim, D. Equation chapter 1 section 1A new method for predicting human hepatic clearance fromin Vitro experimental data using molecular descriptors. Arch Pharm Res 30, 182–190 (2007). https://doi.org/10.1007/BF02977693

Download citation

Key words

  • Prediction
  • Hepatic clearance
  • In vitro data
  • Molecular descriptors
  • Multiple linear regression