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Empirical versus mechanistic modelling: Comparison of an artificial neural network to a mechanistically based model for quantitative structure pharmacokinetic relationships of a homologous series of barbiturates

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Abstract

The aim of the current study was to compare the predictive performance of a mechanistically based model and an empirical artificial neural network (ANN) model to describe the relationship between the tissue-to-unbound plasma concentration ratios (Kpu's) of 14 rat tissues and the lipophilicity (LogP) of a series of nine 5-n-alkyl-5-ethyl barbituric acids. The mechanistic model comprised the water content, binding capacity, number of the binding sites, and binding association constant of each tissue. A backpropagation ANN with 2 hidden layers (33 neurons in the first layer, 9 neurons in the second) was used for the comparison. The network was trained by an algorithm with adaptive momentum and learning rate, programmed using the ANN Toolbox of MATLAB. The predictive performance of both models was evaluated using a leave-one-out procedure and computation of both the mean prediction error (ME, showing the prediction bias) and the mean squared prediction error (MSE, showing the prediction accuracy). The ME of the mechanistic model was 18% (range, 20 to 57%), indicating a tendency for overprediction; the MSE is 32% (range, 6 to 104%). The ANN had almost no bias: the ME was 2% (range, 36 to 64%) and had greater precision than the mechanistic model, MSE 18% (range, 4 to 70%). Generally, neither model appeared to be a significantly better predictor of the Kpu's in the rat.

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References

  1. Thakur AK, Model mechanistic vs empirical. In: Rescigno A, Thakur AK, eds. New Trends in Pharmacokineties. New York: Plenum Press. 1991; 41–51.

    Chapter  Google Scholar 

  2. Rescigno A, Beck JS (Comments: Thakur AK). The use and abuse of models. I Pharmacokinet Biopharm. 1987;15:327.

    Article  CAS  Google Scholar 

  3. Veng-Pedersen P, Modi NB. Neural networks in pharmacodynamic modeling: is current modeling practice of complex kinetic systems at a dead end? J Pharmacokinet Biopharm. 1992;20:397–412.

    Article  CAS  PubMed  Google Scholar 

  4. Siegel RA. Commentary on “Neural networks in pharmacodynamic modeling: is current modeling practice of complex kinetic systems at a dead end?” J Pharmacokinet Biopharm. 1992;20:413–416.

    Article  Google Scholar 

  5. Veng-Pedersen P. Response to Siegel's commentary. J Pharmacokinet Biopharm. 1992;20:417–418.

    Article  Google Scholar 

  6. Seydel JK, Schaper K-J. Quantitative structure-pharmacokinetic relationships and drug design. In: Rowland M, Tucker G, eds. Pharmacokinetics: Theory and Methodology International Encyclopedia of Pharmacology and Therapeutics, Section 122, Oxford: Pergamon Press, 1986;311–368.

    Google Scholar 

  7. Wasserman PD. Neural Computing, Theory and Practice. New York: Van Nostrand Reinhold, 1989.

    Google Scholar 

  8. Haykin S. Neural Networks. A Comprehensive Foundation. Toronto: Maxwell Macmillan Canada, 1994.

    Google Scholar 

  9. Heicht-Nielsen R. Neurocomputing, Reading, MA: Addison Wesley, 1990.

    Google Scholar 

  10. Jain AK, Mao J, Mohiuddin KM. Artificial neural networks: a tutorial. Computer 1996;29:31–44.

    Article  Google Scholar 

  11. Shang Y, Wah BW. Global optimisation for neural network training. Computer. 1996;29:45–55.

    Article  Google Scholar 

  12. Erb RJ. Introduction to backpropagation neural network computation. Pharm Res. 1993;10:165–170.

    Article  CAS  PubMed  Google Scholar 

  13. Erb RJ. The backpropagation neural network-;a Bayesian classifier. Introduction and applicability to pharmacokinetics. Clin Pharmacokinet. 1995;29:69–79.

    Article  CAS  PubMed  Google Scholar 

  14. Veng-Pedersen P, Modi NB. Neural networks in pharmacodynamic modeling. J Pharm Sci. 1993;82:918–926.

    Article  CAS  PubMed  Google Scholar 

  15. Brier ME. Neural network gentamicin peak and trough predictions from prior dosing data. Clin Pharm Ther. 1995;57:157.

    Article  Google Scholar 

  16. Brier ME, Zurada JM, Aronoff GR. Neural network predicted peak and trough gentamicin concentrations. Pharm Res. 1995;12:406–412.

    Article  CAS  PubMed  Google Scholar 

  17. Brier ME, Aronoff GR. Neural network predictions of cyclosporine A blood concentration. Clin Pharm Ther. 1995;57:157.

    Article  Google Scholar 

  18. Smith BP, Brier ME. Statistical approach to network model building for gentamicin peak predictions. J Pharm Sci. 1996;85:65–69.

    Article  CAS  PubMed  Google Scholar 

  19. Gobburu L, Kumar K, Shelver WH. Prediction of alfentanil plasma concentrations using artificial neural networks (ANN). Pharm Res. 1995;12(Suppl):S329.

    Article  Google Scholar 

  20. Brier ME. Empirical pharmacokinetic predictions for cyclosporine using a time series neural network. Pharm Res. 1995;12(Suppl.):S363.

    Article  Google Scholar 

  21. Brier ME. Empirical pharmacokinetic predictions using neural network, generalized linear, and generalized additive models. Pharm Res. 1995;12 Suppl.:S363.

    Article  Google Scholar 

  22. Brier ME, Aronoff GR. Application of artificial neural networks to clinical pharmacology. Int J Clin Pharm Ther. 1996;34:510–514.

    CAS  Google Scholar 

  23. Smith BP, Brier ME. Empirical pharmacodynamic predictions using neural networks: Comparison to NONMEM. Pharm Res. 1995;12 Suppl.:S328.

    Google Scholar 

  24. Gobburu J, Chen EP. Artificial neural networks as a novel approach to integrated pharmacokinetic-; pharmacodynamic analysis. J Pharm Sci. 1996;85:505–510.

    Article  CAS  PubMed  Google Scholar 

  25. Kumar K, Gobburu JVS, Ballantyne JA, Shelver WH. Population pharmacokinetic parameter prediction with artificial neural networks (ANN). Pharm Res. 1995;12 Suppl.:S329.

    Article  Google Scholar 

  26. Chow H-H, Tolle KM, Row DJ, Elsberry V, Chen H. Application of neural networks to population pharmacokinetic data analysis. J Pharm Sci. 1997;86:840–845.

    Article  CAS  PubMed  Google Scholar 

  27. Gobburu J, Shelver WH. Feasibility of artificial neural network application to derive in-vivo characteristics in a congeneric series of compounds. Pharm Res. 1995;12 Suppl.:S364.

    Google Scholar 

  28. Hussain AS. Artificial neural networks based in vitro-in vivo correlations. In: Young D, ed. In Vitro-;In Vivo Correlations. New York: Plenum Press, 1997;149–158.

    Chapter  Google Scholar 

  29. Dowell JA, Hussain AS, Stark P, Devane J, Young D Development of in vitro-;in vivo correlations using various artificial neural network configurations. In: Young D, ed. In vitro-;in vivo correlations. New York: Plenum Press, 1997;225–239.

    Chapter  Google Scholar 

  30. Hussain AS, Johnson RD, Vachharajani NN, Ritschel WA. Feasibility of developing a neural network for prediction of human pharmacokinetic parameters from animal data. Pharm Res. 1993;10:466–469.

    Article  CAS  PubMed  Google Scholar 

  31. Ritschel WA, Akileswaran R, Hussain AS. Application of neural networks for the prediction of human pharmacokinetic parameters. Meth Find Exp Clin Pharmacol. 1995;17:629–643.

    CAS  Google Scholar 

  32. Gobburu J, Shelver WH. Quantitative structure-pharmacokinetic relationship (QSPR) of beta blockers derived using neural networks. J Pharm Sci. 1995;84:862–865.

    Article  CAS  PubMed  Google Scholar 

  33. Blakey GE, Nestorov IA, Arundel PA, Aarons LJ, Rowland M. Quantitative structure-pharmacokinetics relationships: I. Development of a whole-body physiologically based model to characterize changes in pharmacokinetics across a homologous series of barbiturates in the rat. J Pharmacokin Biopharm. 1997;25:277–312.

    Article  CAS  Google Scholar 

  34. Nestorov IA, Aarons LJ, Rowland M. Quantitative structure-pharmacokinetics relationships: II. A mechanistically based model to evaluate the relationship between tissue distribution parameters and compound lipophilicity. J Pharmacokin Biopharm. 1998;26:521–545.

    Article  CAS  Google Scholar 

  35. Sheiner LB, Beal S. Some suggestions for measuring predictive performance. J Pharmacokin Biopharm. 1981;9:503–512.

    Article  CAS  Google Scholar 

  36. ACSL Reference Manual, Edition 11. MGA Software, Concord MA 01742, USA, 1995.

  37. Nestorov I, Aarons L, Rowland M. Physiologically based pharmacokinetic modelling of a homologous series of barbiturates: a sensitivity analysis. J Pharmacokin Biopharm. 1997;25:413–447.

    Article  CAS  Google Scholar 

  38. Hadjitodorov S, Boyanov B, Dalakchieva N. A two-level classifier for text-independent speaker identification. Speech Communication. 1997;21:209–217.

    Article  Google Scholar 

  39. Neural Network Toolbox for use with MATLAB. The Mathworks Inc., Natick MA 01760-1500, USA, 1998.A.

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Authors and Affiliations

  1. Centre for Applied Pharmacokinetic Research, School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Oxford Road, M13 9PL, Manchester, UK

    Ivan Nestorov & Malcolm Rowland

  2. Centre for Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    S. T. Hadjitodorov & I. Petrov

Authors
  1. Ivan Nestorov
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  2. Malcolm Rowland
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  3. S. T. Hadjitodorov
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  4. I. Petrov
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Correspondence to Ivan Nestorov.

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Nestorov, I., Rowland, M., Hadjitodorov, S.T. et al. Empirical versus mechanistic modelling: Comparison of an artificial neural network to a mechanistically based model for quantitative structure pharmacokinetic relationships of a homologous series of barbiturates. AAPS PharmSci 1, 17 (1999). https://doi.org/10.1208/ps010417

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