Literature DB >> 14971904

Prediction of human volume of distribution values for neutral and basic drugs. 2. Extended data set and leave-class-out statistics.

Franco Lombardo1, R Scott Obach, Marina Y Shalaeva, Feng Gao.   

Abstract

We present an extension and confirmation of our previously published method (J. Med. Chem. 2002, 45, 2867-2876) for the prediction of volume of distribution (VD) in humans for neutral and basic compounds. It is based on two experimentally determined physicochemical parameters, ElogD(7.4) and f(i(7.4)), the latter being the fraction of compound ionized at pH 7.4, and on the fraction of free drug in plasma (fu). By regressing the fraction unbound in tissues, fut, vs the above parameters, we demonstrate the ruggedness of the method in predicting VD through the Oie-Tozer equation, via the use of several testing approaches. A comparison is also presented between several methods based on animal pharmacokinetic data, using the same set of proprietary compounds, and it lends further support for the use of this method, as opposed to methods that require the gathering of pharmacokinetic data in laboratory animals. The reduction in the use of animals and the overall faster and cheaper accessibility of the parameters used make this method highly attractive for prospectively predicting the VD of new chemical entities in humans.

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Year:  2004        PMID: 14971904     DOI: 10.1021/jm030408h

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  28 in total

1.  Molecular interaction fields (MIFs) to predict lipophilicity and ADME profile of antitumor Pt(II) complexes.

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2.  To Apply Microdosing or Not? Recommendations to Single Out Compounds with Non-Linear Pharmacokinetics.

Authors:  Sieto Bosgra; Maria L H Vlaming; Wouter H J Vaes
Journal:  Clin Pharmacokinet       Date:  2016-01       Impact factor: 6.447

Review 3.  Reliability of In Vitro and In Vivo Methods for Predicting the Effect of P-Glycoprotein on the Delivery of Antidepressants to the Brain.

Authors:  Yi Zheng; Xijing Chen; Leslie Z Benet
Journal:  Clin Pharmacokinet       Date:  2016-02       Impact factor: 6.447

4.  Prediction of pK(a) for neutral and basic drugs based on radial basis function Neural networks and the heuristic method.

Authors:  Feng Luan; Weiping Ma; Haixia Zhang; Xiaoyun Zhang; Mancang Liu; Zhide Hu; Botao Fan
Journal:  Pharm Res       Date:  2005-08-24       Impact factor: 4.200

5.  Unified pharmacogenetics-based parent-metabolite pharmacokinetic model incorporating acetylation polymorphism for talampanel in humans.

Authors:  Peter Buchwald; Attila Juhász; Cynthia Bell; Márta Pátfalusi; John Howes; Nicholas Bodor
Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-08       Impact factor: 2.745

Review 6.  Computational methods in drug discovery.

Authors:  Gregory Sliwoski; Sandeepkumar Kothiwale; Jens Meiler; Edward W Lowe
Journal:  Pharmacol Rev       Date:  2013-12-31       Impact factor: 25.468

7.  Comparison of the accuracy of experimental and predicted pKa values of basic and acidic compounds.

Authors:  Luca Settimo; Krista Bellman; Ronald M A Knegtel
Journal:  Pharm Res       Date:  2013-11-19       Impact factor: 4.200

8.  Novel in vitro-in vivo extrapolation (IVIVE) method to predict hepatic organ clearance in rat.

Authors:  Ken-ichi Umehara; Gian Camenisch
Journal:  Pharm Res       Date:  2011-10-20       Impact factor: 4.200

9.  Rank order entropy: why one metric is not enough.

Authors:  Margaret R McLellan; M Dominic Ryan; Curt M Breneman
Journal:  J Chem Inf Model       Date:  2011-08-29       Impact factor: 4.956

10.  Drug Distribution Part 2. Predicting Volume of Distribution from Plasma Protein Binding and Membrane Partitioning.

Authors:  Ken Korzekwa; Swati Nagar
Journal:  Pharm Res       Date:  2016-12-13       Impact factor: 4.200

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