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Comparative Study
. 2014:2014:957154.
doi: 10.1155/2014/957154. Epub 2014 Aug 4.

Comparison of two methods forecasting binding rate of plasma protein

Liu Hongjiu  1 , Hu Yanrong  1
Affiliations
Comparative Study

Comparison of two methods forecasting binding rate of plasma protein

Liu Hongjiu et al. Comput Math Methods Med. 2014.

Abstract

By introducing the descriptors calculated from the molecular structure, the binding rates of plasma protein (BRPP) with seventy diverse drugs are modeled by a quantitative structure-activity relationship (QSAR) technique. Two algorithms, heuristic algorithm (HA) and support vector machine (SVM), are used to establish linear and nonlinear models to forecast BRPP. Empirical analysis shows that there are good performances for HA and SVM with cross-validation correlation coefficients Rcv(2) of 0.80 and 0.83. Comparing HA with SVM, it was found that SVM has more stability and more robustness to forecast BRPP.

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Figures

Figure 1
Figure 1
Forecasted BRPP and observed BRPP based on HA.
Figure 2
Figure 2
Relation of γ and RMS error on LOO cross-validation.
Figure 3
Figure 3
Relation of ε and RMS error on LOO cross-validation.
Figure 4
Figure 4
Relation of C and RMS error on LOO cross-validation.
Figure 5
Figure 5
Forecasted BRPP and observed BRPP of HA.
See this image and copyright information in PMC

References

    1. Valladares-Mendez A, Hernández-Núñez E, Cedillo-Rivera R, et al. Synthesis, in vitro and in vivo giardicidal activity, and pharmacokinetic profile of a new nitazoxanide analog. Medicinal Chemistry Research. 2014;23:3157–3164.
    1. Stern ST, Zou P, Skoczen S, et al. Prediction of nanoparticle prodrug metabolism by pharmacokinetic modeling of biliary excretion. Journal of Controlled Release. 2013;172(2):558–567. - PMC - PubMed
    1. Mui BL, Tam YK, Jayaraman M, et al. Influence of polyethylene glycol lipid desorption rates on pharmacokinetics and pharmacodynamics of siRNA lipid nanoparticles. Molecular Therapy-Nucleic Acids. 2013;2, article e139 - PMC - PubMed
    1. Jeunesse EC, Schneider M, Woehrle F. Pharmacokinetic/pharmacodynamic modeling for the determination of a cimicoxib dosing regimen in the dog. BMC Veterinary Research. 2013;9, article 250 - PMC - PubMed
    1. Durakovic Z, Vitezic D. Pharmacodynamics and pharmacokinetics in the elderly. Periodicum Biologorum. 2013;115:517–520.

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