Literature DB >> 12061889

Prediction of volume of distribution values in humans for neutral and basic drugs using physicochemical measurements and plasma protein binding data.

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

Abstract

We present a method for the prediction of volume of distribution 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 (f(u)). The fraction unbound in tissues (f(ut)), determined via a regression analysis from 64 compounds using the parameters described, is then used to predict VD(ss) via the Oie-Tozer equation. Accuracy of this method was determined using a test set of 14 compounds, and it was demonstrated that human VD(ss) values could be predicted, on average, within or very close to 2-fold of the actual value. The present method is as accurate as reported methods based on animal pharmacokinetic data, using a similar set of compounds, and ranges between 1.62 and 2.20 as mean-fold error. This method has the advantage of being amenable to automation, and therefore fast throughput, it is compound and resources sparing, and it offers a rationale for the reduction of the use of animals in pharmacokinetic studies. A discussion of the potential errors that may be encountered, including errors in the determination of f(u), is offered, and the caveats about the use of computed vs experimentally determined logD and pK(a) values are addressed.

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Year:  2002        PMID: 12061889     DOI: 10.1021/jm0200409

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


  41 in total

1.  In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values.

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Journal:  Antimicrob Agents Chemother       Date:  2004-05       Impact factor: 5.191

Review 3.  Coexistence of passive and carrier-mediated processes in drug transport.

Authors:  Kiyohiko Sugano; Manfred Kansy; Per Artursson; Alex Avdeef; Stefanie Bendels; Li Di; Gerhard F Ecker; Bernard Faller; Holger Fischer; Grégori Gerebtzoff; Hans Lennernaes; Frank Senner
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4.  Molecular interaction fields (MIFs) to predict lipophilicity and ADME profile of antitumor Pt(II) complexes.

Authors:  Giulia Caron; Mauro Ravera; Giuseppe Ermondi
Journal:  Pharm Res       Date:  2010-11-17       Impact factor: 4.200

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

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Journal:  Clin Pharmacokinet       Date:  2016-01       Impact factor: 6.447

Review 6.  Modeling kinetics of subcellular disposition of chemicals.

Authors:  Stefan Balaz
Journal:  Chem Rev       Date:  2009-05       Impact factor: 60.622

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.  Higher clearance of micafungin in neonates compared with adults: role of age-dependent micafungin serum binding.

Authors:  Souzan B Yanni; P Brian Smith; Daniel K Benjamin; Patrick F Augustijns; Dhiren R Thakker; Pieter P Annaert
Journal:  Biopharm Drug Dispos       Date:  2011-03-30       Impact factor: 1.627

9.  Data Sets Representative of the Structures and Experimental Properties of FDA-Approved Drugs.

Authors:  Dominique Douguet
Journal:  ACS Med Chem Lett       Date:  2018-01-29       Impact factor: 4.345

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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