QUANTITATIVE STRUCTURE–PHARMACOKINETICS RELATIONSHIP FOR PLASMA PROTEIN BINDING OF NEUTRAL DRUGS
DOI:
https://doi.org/10.22159/ijpps.2018v10i4.24612Keywords:
Plasma protein binding (PPB), Nil, In silico prediction, Human serum albumin (HSA), Alpha-1-acid glycoprotein (AGP)Abstract
Objective: Plasma protein binding (PPB) of drugs is important pharmacokinetic (PK) phenomena controlling the free drug concentration in plasma and the overall PK and pharmacodynamic profile. Prediction of PPB at the very early stages of drug development process is of paramount importance for the success of new drug candidates. The study presents a quantitative structure–pharmacokinetics relationship (QSPkR) modelling of PPB for neutral drugs.
Methods: The dataset consists of 117 compounds, described by 138 molecular descriptors. Genetic algorithm and stepwise multiple linear regression are used for variable selection and QSPkR models development. The QSPkRs are evaluated by internal and external validation procedures.
Results: A robust, significant and predictive QSPkR with explained variance r2 0.768, cross-validated q2LOO-CV 0.731,and geometric mean fold error of prediction (GMFEP) 1.79 is generated, which is able to predict the extent of PPB for 67.6% of the drugs in the dataset within the 2-fold error of experimental values. A simple empiric rule is proposed for distinguishing between drugs with different binding affinity, which allowed correct classification of 78% of the high binders and 87.5% of the low binders.
Conclusions: PPB of neutral drugs is favored by lipophilicity, dipole moment, the presence of substituted aromatic and fused rings and a nine-member ring system, and is disfavored by the presence of aromatic N-atoms.
Keywords: Plasma protein binding (PPB), Quantitative structure–pharmacokinetics relationship (QSPkR), In silico prediction, Human serum albumin (HSA), Alpha-1-acid glycoprotein (AGP).
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References
Trainor GL. The importance of plasma protein binding in drug discovery. Exp Opin Drug Discovery 2007;2:51-64.
Bohnert T, Gan LS. Plasma protein binding: from discovery to development. J Pharm Sci 2013;102:2953-94.
Ascenzi P, Fanali G, Fasano M, Pallottini V, Trezza V. Clinical relevance of drug binding to plasma proteins. J Mol Struct 2014;1077:4-13.
Zhang F, Xue J, Shao J, Jia L. Compilation of 222 drug’s plasma protein binding data and guidance for study design. Drug Discovery Today 2012;17:475-85.
Rolan PE. Plasma protein binding displacement interactions–why are they still regarded as clinically important? Br J Clin Pharmacol 1994;37:125-8.
Benet LZ, However BA. Changes in plasma protein binding have little clinical relevance. Clin Pharmacol Therap 2002;71:115-21.
Roberts JA, Pea F, Lipman J. The clinical relevance of plasma protein binding changes. Clin Pharmacokinet 2013;52:1-8.
Schmidt S, Gonzales D, Derendorf H. Significance of protein binding in pharmacokinetics and pharmacodynamics. J Pharm Sci 2010;99:1107-22.
Smith DA, Di L, Kerns EH. The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nat Rev Drug Discovery 2010;9:929-39.
Yamasaki K, Chuang VTG, Maruyama T, Otagiri M. Albumin–drug interaction and its clinical implication. Biochim Biophys Acta 2013;1830:5435-43.
Boobis A, Gundert-Remy U, Kremers P, Macheras P, Pelkonen O. In silico prediction of ADME and pharmacokinetics. Report of an expert meeting organized by COST B15. Eur J Pharm Sci 2002;17:183-93.
Van de Waterbeemd H, Giffold E. ADMET in silico modeling: towards prediction paradise? Nature 2003;2:192-204.
Yamashita F, Hashida M. In silico approaches for predicting ADME properties of drugs. Drug Metab Pharmacokinet 2004;19:327-38.
Butina D, Segall MD, Frankcombe K. Predicting ADME properties in silico: methods and models. DDT 2002;7:S83-8.
Mager D. Quantitative structure-pharmacokinetic/ pharma-codynamics relationships. Adv Drug Delivery Rev 2006; 58:1326-56.
Chohan KK, Paine SW, Waters, S. Advancements in predictive in silico models for ADME. Cur Chem Biol 2008;2:215-28.
Madden JC. In silico approaches for predicting ADME properties. In: Pyzyn T, Leszczynski J, Cronin MTD. editors. Recent advances in QSAR studies. Dordrecht, Heidelberg, London, New York: Springer; 2010. p. 283-304.
Xu C, Mager DE. Quantitative structure-pharmacokinetic relationships. Exp Opin Drug Metab Toxicol 2011;7:63-77.
Wang J, Hou T. Recent advances on in silico ADME modeling. In: Wheeler RA, Spellmeyer D. editors. Annual reports in computational chemistry. Amsterdam, Boston, Heidelberg, etc: Elsevier; 2009. p. 101-27.
Ekins S, Waller CL, Qwaan PW, Cruciani G, Wrighton SA, Wikel JH. Progress in predicting human ADME parameters in silico. J Pharmacol Toxicol Meth 2000;44:251-72.
Huang JH, Cooper MA, Baker MA, Azad MAK, Nation RL, Li J, et al. Drug-binding energetics of human ï¡-1-acid glycoprotein assessed by isothermal titration colorimetry and molecular docking simulations. J Mol Recognit 2012;25:642-56.
Schoenfeld DL, Ravelli RBG, Mueller U, Skerra A. The 1.8A crystal structure of a-1-acid glycoprotein (orosomucoid) solved by UV RIP reveals the broad drug–binding activity of this human plasma lipocalin. J Mol Biol 2008;384:393-405.
Garg A, Manidhar DM, Gokara M, Malleda Ch, Reddy SC, Subramanyam R. Elucidation of the binding mechanism of coumarin derivatives with human serum albumin. PLoS One 2013;8:e63805.
Zaidi N, Ajmal MR, Rabbani G, Ahmad E, Khan RH. A comprehensive insight into the binding of hippuric acid to human serum albumin: a study to uncover its impaired elimination through hemodialysis. PLoS One 2013;8:e71422.
Rehman MT, Shamsi H, Khan AU. Insight into the binding mechanism of imipenem to human serum albumin by the spectroscopic and computational approach. Mol Pharma 2014;11:1785-97.
Dong C, Lu N, Liu Y. Binding of methacycline to human serum albumin at subdomain IIA using multispectroscopic and molecular modeling methods. Luminescence 2013;28:933-41.
Yeggoni DP, Gokara M, Manidhar DM, Rachamallu A, Nakka S, Reedy CS, et al. Binding and molecular dynamics studies of 7–hydroxycoumarin derivatives with human serum albumin and its pharmacological importance. Mol Pharmacol 2014;11:1117-31.
Zhang P, Li Z, Wang X, Shen Z, Wang Y, Yan J, et al. Study of the enantioselective interaction of diclofop and human serum albumin by spectroscopic and molecular modeling approaches in vitro. Chirality 2013;25:719-25.
Lexa KW, Dolghih E, Jacobson MP. A structure-based model for predicting serum albumin binding. PLoS One 2014;9:e93323.
Debnath B, Ganguly S. Molecular docking studies and absorption, distribution, metabolism and excretion prediction of novel isatin analogs as human immunodeficiency virus-1-reverse transcriptase inhibitors with broad spectrum chemotherapeutic properties. Asian J Pharm Clin Res 2014;7:186-94.
Djajadisastra J, Purnama HD, Yanuar A. In silico binding interaction study of mefenamic acid and piroxicam on human serum albumin. Int J Appl Pharmacol 2017;9 Suppl 1:102-6.
Colmenarejo G, Alvarez-Pedraglio A, Lavandera JL. Cheminformatic models to predict binding affinities to human serum albumin. J Med Chem 2001;44:4370-8.
Hall LM, Hall LH, Kier LB. Modeling drug albumin binding affinity with E-state topological structure representation. J Chem Inf Computer Sci 2003;43:2120-8.
Sitarama BG, Ramamurthi N, Akash K. In silico ADME modeling 2: computational models to predict serum albumin binding affinity using ant colony system. Bioorg Med Chem 2006;14:4118-29.
Xue CX, Zhang RS, Liu HX, Yao XJ, Liu MC, Hu ZD, et al. QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine. J Chem Inf Computer Sci 2004;44:1693-700.
Chen L, Chen X. Results of molecular docking as descriptors to predict human serum binding affinity. J Mol Graph Model 2012;33:35-43.
Deeb O, Hemmateenehad B. ANN–QSAR model of drug binding to human serum albumin. Chem Biol Drug Res 2007;70:19-29.
Deeb O. Correlation ranking and stepwise regression procedures in principal components artificial neural networks modeling with application to predict toxic activity and human serum binding affinity. Chem Intel Lab Syst 2010;104:181-94.
Hajduk PJ, Mendoza R, Petros A, Huth JR, Bures M, Fesik W, et al. Ligand binding to domain 3 of human serum albumin: a chemometric analysis. J Computer-Aided Mol Des 2003;17:93-102.
Estrada E, Uriarte E, Molina E, Simon-Manco Y, Milne GWA. An integrated in silico analysis of drug binding to human serum albumin. J Chem Inf Model 2006;46:2709-24.
Kratochwil NA, Huber W, Muller F, Kansy M, Gerber PR. Predicting plasma protein binding of drugs: a new approach. Biochem Pharmacol 2002;64:1355-74.
Yamazaki K, Kanaoka M. Computational prediction of the plasma protein binding percent of diverse pharmaceutical compounds. J Pharm Sci 2004;93:1480-94.
Liu J, Yang L, Pan D, Hopfinger A. Constructing plasma protein binding model based on a combination of cluster analysis and 4D-fingerprint molecular similarity analyses. Bioorg Med Chem 2006;14:611-21.
Votano JR, Parham M, Hall LM, Hall LH, Kier LB, Oloff S, et al. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure–information representation. J Med Chem 2006;49:7169-81.
Moda TL, Montanari CA, Andricopulo AD. In silico prediction of human plasma protein binding using hologram QSAR. Lett Drug Des Discovery 2007;4:502-9.
Ghafourian T, Amin Z. QSAR models for the prediction of plasma protein binding. BioImpacts 2013;3:21-7.
Zhivkova Z, Doytchinova I. Quantitative structure–plasma protein binding relationships of acidic drugs. J Pharm Sci 2012;101:4627-41.
Zhivkova Z. Quantitative structure–pharmacokinetics relationships for plasma protein binding of basic drugs. J Pharm Pharm Sci 2017;20:349-59.
Obach RS, Lombardo F, Waters NJ. Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds. Drug Metab Dispos 2008;36:1385-405.
https://www.drugbank.ca/. [Last accessed on 01 Dec 2017]
http://www.chemicalbook.com/. [Last accessed on 01 Dec 2017].
http://www.ebi.ac.uk. [Last accessed on 01 Dec 2017]
Roy K, Kar S, Das RN. Statistical methods in QSAR/QSPR. In: Roy K, Kar S, Das RN. editors. A primer on QSAR/QSPR modeling. Fundamental concepts. Heidelberg, New York, Dordrechs, London: Springer Cham; 2015. p. 37-59.
Berellini G, Waters NJ, Lombardo F. In silico prediction of total plasma clearance. J Chem Inf Model 2012;52:2069-78.
Wasan KM, Brocks DR, Lee SD, Sachs-Barrable K, Thornton SJ. Impact of lipoproteins on the biological activity and disposition of hydrophobic drugs: implications for drug discovery. Nat Rev Drug Discovery 2008;7:84-99.
Sudlov G, Birkett DJ, Wade DN. The characterization of two specific drug binding sites on human serum albumin. Mol Pharmacol 1975;11:824-32.
Sudlov G, Birkett DJ, Wade DN. Further characterization of two specific drug binding sites on human serum albumin. Mol Pharmacol 1976;12:1052-61.
Ghuman J, Zunszain A, Petipas I, Bhattacharya AA, Otagiri M, Curry S. Structural basis of drug-binding specificity of human serum albumin. J Mol Biol 2005;353:38-52.
He XM, Carter DC. Atomic structure and chemistry of human serum albumin. Nature 1992;358:209-15.
Petipas I, Bhattacharya AA, Twine S, East M, Curry S. Crystal structure of warfarin binding to human serum albumin. J Biol Chem 2001;276:22804-9.
Carter CC, He XM, Munson SH, Twigg PD, Gernert KM, Broom MB, et al. Three-dimensional structure of human serum albumin. Science 1989;244:1195-8.
Herve F, Caron G, Duche JC, Gaillard P, Rahman RA, Tsantili-Kakoulidou A, et al. Ligand specificity of the genetic variants of human a-1-acid glycoprotein: generation of a three-dimensional quantitative structure-activity relationship model for drug binding to the A variant. Mol Pharmacol 1998;54:129-38.
Nishi K, Ono T, Nakamura T, Fukunaga N, Izumi M, Watanabe H, et al. Structural insights into differences in drug-binding selectivity between two forms of human alpha1-acid glycoprotein genetic variants, the A and F1*S forms. J Biol Chem 2011;286:14427-34.
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