Literature DB >> 15088636

Use of physicochemical calculation of pKa and CLogP to predict phospholipidosis-inducing potential: a case study with structurally related piperazines.

Jan-Peter H T M Ploemen1, Jan Kelder, Theo Hafmans, Han van de Sandt, Johan A van Burgsteden, Paul J M Saleminki, Eric van Esch.   

Abstract

Several cationic amphiphilic compounds are known to induce phospholipidosis, a condition primarily characterized by excessive accumulation of phospholipids in different cell types, giving the affected cells a finely foamy appearance. Excessive storage of lamellar membranous intralysosomal inclusion bodies is the hallmark for phospholipidosis on the electron microscopic level. In case of alveolar phospholipidosis, foamy macrophages accumulate within the alveolar spaces of the lung. Based on such findings in a one-year toxicity study with gepirone in rats, we studied the molecular properties of this compound and compounds suspected of being phospholipidosis inducers by means of physicochemical calculations. Physicochemical molecular calculations of molecular weight, ClogP (partition coefficient octanol/water), logD at pH 7.4, and pKa were performed, for the cationic amphiphilic compounds chlorpromazine, amiodarone, imipramine, propranolol and fluoxetine, and for the structurally related compounds 1-phenylpiperazine (1-PHP), gepirone (and its major metabolites, 3-OH-gepirone and 1-pyrimidinylpiperazine [1-PP]), and buspirone. ClogP and calculated pKa cluster differently for the amphiphilic drugs compared to the chemical series of piperazines. In line with this analysis, lamellar inclusion bodies were found in an in vitro validation experiment in the human monoblastoid cell line U-937, incubated for 96 h at 10 microg/mL with cationic amphiphilic drugs (amiodarone, imipramine, or propranolol). No such lamellar inclusion bodies were seen for any of the compounds from the chemical series of piperazines including gepirone and its metabolites. The data presented support the use of simple physicochemical calculations of ClogP and pKa to discriminate rapidly between compounds suspected of being phospholipidosis inducers. Finally, the discriminative power of these physicochemical ClogP and pKa calculations to predict phospholipidosis-inducing potential was further validated by extension of the set of compounds.

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Year:  2004        PMID: 15088636     DOI: 10.1078/0940-2993-00338

Source DB:  PubMed          Journal:  Exp Toxicol Pathol        ISSN: 0940-2993


  21 in total

1.  Defining desirable central nervous system drug space through the alignment of molecular properties, in vitro ADME, and safety attributes.

Authors:  Travis T Wager; Ramalakshmi Y Chandrasekaran; Xinjun Hou; Matthew D Troutman; Patrick R Verhoest; Anabella Villalobos; Yvonne Will
Journal:  ACS Chem Neurosci       Date:  2010-03-25       Impact factor: 4.418

2.  Structure based model for the prediction of phospholipidosis induction potential of small molecules.

Authors:  Hongmao Sun; Sampada Shahane; Menghang Xia; Christopher P Austin; Ruili Huang
Journal:  J Chem Inf Model       Date:  2012-07-05       Impact factor: 4.956

3.  Simulation-based cheminformatic analysis of organelle-targeted molecules: lysosomotropic monobasic amines.

Authors:  Xinyuan Zhang; Nan Zheng; Gus R Rosania
Journal:  J Comput Aided Mol Des       Date:  2008-03-13       Impact factor: 3.686

4.  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

5.  Identification of hepatic phospholipidosis inducers in sandwich-cultured rat hepatocytes, a physiologically relevant model, reveals altered basolateral uptake and biliary excretion of anionic probe substrates.

Authors:  Brian C Ferslew; Kim L R Brouwer
Journal:  Toxicol Sci       Date:  2014-02-22       Impact factor: 4.849

6.  Cationic amphiphilic drugs cause a marked expansion of apparent lysosomal volume: implications for an intracellular distribution-based drug interaction.

Authors:  Ryan S Funk; Jeffrey P Krise
Journal:  Mol Pharm       Date:  2012-04-06       Impact factor: 4.939

7.  Computer-aided drug design at Boehringer Ingelheim.

Authors:  Ingo Muegge; Andreas Bergner; Jan M Kriegl
Journal:  J Comput Aided Mol Des       Date:  2016-09-20       Impact factor: 3.686

Review 8.  Ligand binding at the protein-lipid interface: strategic considerations for drug design.

Authors:  Jian Payandeh; Matthew Volgraf
Journal:  Nat Rev Drug Discov       Date:  2021-07-13       Impact factor: 84.694

Review 9.  The significance of acid/base properties in drug discovery.

Authors:  David T Manallack; Richard J Prankerd; Elizabeth Yuriev; Tudor I Oprea; David K Chalmers
Journal:  Chem Soc Rev       Date:  2013-01-21       Impact factor: 54.564

10.  Predicting phospholipidosis using machine learning.

Authors:  Robert Lowe; Robert C Glen; John B O Mitchell
Journal:  Mol Pharm       Date:  2010-09-10       Impact factor: 4.939

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