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Prediction of partition and distribution coefficients in various solvent pairs with COSMO-RS

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Abstract

Performance of COSMO-RS method as a tool for partition and distribution modeling in 20 solvent pairs—composed of neutral or acidic aqueous solution and organic solvents of different polarity, ranging from alcohols to toluene and hexane—was evaluated. Experimental partition/distribution data of lignin-related and drug-like compounds (neutral, acidic, moderately basic) were used as reference. Several aspects of partition modeling were addressed: accounting for mutual saturation of aqueous and organic phases, variability of systematic prediction errors across solvent pairs, taking solute ionization into account. COSMO-RS was found to predict extraction outcome for both ligneous and drug-like compounds in various solvent pairs fairly well without any additional empirical input. The solvent-specific systematic errors were found to be moderate, despite being statistically significant, and related to the solvent hydrophobicity. Accounting for mutual solubilities of the two liquids was proven crucial in cases where water was considerably soluble in the organic solvent. The root mean square error of a priori logP prediction varied, depending mainly on the solvent pair, from 0.2 to 0.7, overall value being 0.6 log units. The accuracy was higher in case of hydrophilic than hydrophobic solvents. The logD predictions were less accurate, due to pKa prediction being an additional source of error, and also because of the complexity of modeling the behaviour of ionic species in the two-phase system. A simple correction for partitioning of free ions was found to notably improve logD prediction accuracy in case of the most hydrophilic organic phase (butanol/water).

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Notes

  1. One imaginary frequency was discovered in one of the conformers of compound B17. However, considering the low numerical value of the frequency and extremely low relative abundance of the conformer, it was deemed unlikely to influence the results and was ignored.

  2. Both literature data and computations show that the effect of small (few degrees) temperature variations on solubility is comparable with or lower than the expected error of solubility determinations (mismatch of the values from different literature sources). Therefore the difference between the mutual solubilities of liquids at 25 and ca 23 °C (conditions of logP/logD determinations) was assumed to be negligible. Therefore experimental data from Table 1 was used in logP calculations at 23 °C without alterations.

  3. It was observed earlier [50] that in case of compounds with strongly lipophilic ionized forms the concentration of ions (in the form of ion associates) even in relatively hydrophobic organic phases (octanol, toluene) may be comparable to or exceed the concentration of the neutral species. In such cases Eq. 4 is not adequate and some form of Eq. 5 must be used.

References

  1. Hou TJ, Xu XJ (2003) ADME evaluation in drug discovery. 3. Modeling blood-brain barrier partitioning using simple molecular descriptors. J Chem Inf Comput Sci 43:2137–2152. https://doi.org/10.1021/ci034134i

    Article  CAS  PubMed  Google Scholar 

  2. Hou T, Wang J, Li Y (2007) ADME evaluation in drug discovery. 8. The prediction of human intestinal absorption by a support vector machine. J Chem Inf Model 47:2408–2415. https://doi.org/10.1021/ci7002076

    Article  CAS  PubMed  Google Scholar 

  3. Saiakhov RD, Stefan LR, Klopman G (2000) Multiple computer-automated structure evaluation model of the plasma protein binding affinity of diverse drugs. Perspect Drug Discov Des 19:133–155. https://doi.org/10.1023/A:1008723723679

    Article  CAS  Google Scholar 

  4. Hansch C, Leo A, Mekapati SB, Kurup A (2004) QSAR and ADME. Bioorg Med Chem 12:3391–3400. https://doi.org/10.1016/j.bmc.2003.11.037

    Article  CAS  PubMed  Google Scholar 

  5. Zhao YH, Yuan X, Su LM et al (2009) Classification of toxicity of phenols to Tetrahymena pyriformis and subsequent derivation of QSARs from hydrophobic, ionization and electronic parameters. Chemosphere 75:866–871. https://doi.org/10.1016/j.chemosphere.2009.01.055

    Article  CAS  PubMed  Google Scholar 

  6. Papa E, Villa F, Gramatica P (2005) Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (Fathead Minnow). J Chem Inf Model 45:1256–1266. https://doi.org/10.1021/ci050212l

    Article  CAS  PubMed  Google Scholar 

  7. Voutchkova AM, Kostal J, Steinfeld JB et al (2011) Towards rational molecular design: derivation of property guidelines for reduced acute aquatic toxicity. Green Chem 13:2373–2379. https://doi.org/10.1039/C1GC15651A

    Article  CAS  Google Scholar 

  8. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25. https://doi.org/10.1016/S0169-409X(96)00423-1

    Article  CAS  Google Scholar 

  9. Mannhold R, Poda GI, Ostermann C, Tetko IV (2009) Calculation of molecular lipophilicity: state-of-the-art and comparison of logP methods on more than 96,000 compounds. J Pharm Sci 98:861–893. https://doi.org/10.1002/jps.21494

    Article  CAS  PubMed  Google Scholar 

  10. Bannan CC, Burley KH, Chiu M et al (2016) Blind prediction of cyclohexane–water distribution coefficients from the SAMPL5 challenge. J Comput Aided Mol Des 30:927–944. https://doi.org/10.1007/s10822-016-9954-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Klamt A (1995) Conductor-like screening model for real solvents: a new approach to the quantitative calculation of solvation phenomena. J Phys Chem 99:2224–2235. https://doi.org/10.1021/j100007a062

    Article  CAS  Google Scholar 

  12. Klamt A, Jonas V, Bürger T, Lohrenz JCW (1998) Refinement and parametrization of COSMO-RS. J Phys Chem A 102:5074–5085. https://doi.org/10.1021/jp980017s

    Article  CAS  Google Scholar 

  13. Klamt A, Eckert F, Arlt W (2010) COSMO-RS: an alternative to simulation for calculating thermodynamic properties of liquid mixtures. Annu Rev Chem Biomol Eng 1:101–122. https://doi.org/10.1146/annurev-chembioeng-073009-100903

    Article  CAS  PubMed  Google Scholar 

  14. Eckert F, Klamt A (2002) Fast solvent screening via quantum chemistry: COSMO-RS approach. AIChE J 48:369–385. https://doi.org/10.1002/aic.690480220

    Article  CAS  Google Scholar 

  15. Klamt A (2018) The COSMO and COSMO-RS solvation models. Wiley Interdiscip Rev Comput Mol Sci 8:e1338. https://doi.org/10.1002/wcms.1338

    Article  CAS  Google Scholar 

  16. Blumenthal LC, Jens CM, Ulbrich J et al (2016) Systematic identification of solvents optimal for the extraction of 5-hydroxymethylfurfural from aqueous reactive solutions. ACS Sustain Chem Eng 4:228–235. https://doi.org/10.1021/acssuschemeng.5b01036

    Article  CAS  Google Scholar 

  17. Spieß AC, Eberhard W, Peters M et al (2008) Prediction of partition coefficients using COSMO-RS: solvent screening for maximum conversion in biocatalytic two-phase reaction systems. Chem Eng Process 47:1034–1041. https://doi.org/10.1016/j.cep.2007.02.007

    Article  CAS  Google Scholar 

  18. Preißinger M, Schwöbel JAH, Klamt A, Brüggemann D (2017) Multi-criteria evaluation of several million working fluids for waste heat recovery by means of Organic Rankine Cycle in passenger cars and heavy-duty trucks. Appl Energy 206:887–899. https://doi.org/10.1016/j.apenergy.2017.08.212

    Article  CAS  Google Scholar 

  19. Bezold F, Weinberger ME, Minceva M (2017) Assessing solute partitioning in deep eutectic solvent-based biphasic systems using the predictive thermodynamic model COSMO-RS. Fluid Phase Equilib 437:23–33. https://doi.org/10.1016/j.fluid.2017.01.001

    Article  CAS  Google Scholar 

  20. Lotfi M, Moniruzzaman M, Sivapragasam M et al (2017) Solubility of acyclovir in nontoxic and biodegradable ionic liquids: COSMO-RS prediction and experimental verification. J Mol Liq 243:124–131. https://doi.org/10.1016/j.molliq.2017.08.020

    Article  CAS  Google Scholar 

  21. Jeliński T, Cysewski P (2017) Screening of ionic liquids for efficient extraction of methylxanthines using COSMO-RS methodology. Chem Eng Res Des 122:176–183. https://doi.org/10.1016/j.cherd.2017.04.015

    Article  CAS  Google Scholar 

  22. Song Z, Zeng Q, Zhang J et al (2016) Solubility of imidazolium-based ionic liquids in model fuel hydrocarbons: a COSMO-RS and experimental study. J Mol Liq 224:544–550. https://doi.org/10.1016/j.molliq.2016.10.026

    Article  CAS  Google Scholar 

  23. Liu Y-R, Thomsen K, Nie Y et al (2016) Predictive screening of ionic liquids for dissolving cellulose and experimental verification. Green Chem 18:6246–6254. https://doi.org/10.1039/C6GC01827K

    Article  CAS  Google Scholar 

  24. Garcia-Chavez LY, Hermans AJ, Schuur B, de Haan AB (2012) COSMO-RS assisted solvent screening for liquid–liquid extraction of mono ethylene glycol from aqueous streams. Sep Purif Technol 97:2–10. https://doi.org/10.1016/j.seppur.2011.11.041

    Article  CAS  Google Scholar 

  25. Mokrushina L, Buggert M, Smirnova I et al (2007) COSMO-RS and UNIFAC in prediction of micelle/water partition coefficients. Ind Eng Chem Res 46:6501–6509. https://doi.org/10.1021/ie0704849

    Article  CAS  Google Scholar 

  26. Klamt A, Eckert F, Reinisch J, Wichmann K (2016) Prediction of cyclohexane-water distribution coefficients with COSMO-RS on the SAMPL5 data set. J Comput Aided Mol Des 30:959–967. https://doi.org/10.1007/s10822-016-9927-y

    Article  CAS  PubMed  Google Scholar 

  27. Wittekindt C, Klamt A (2009) COSMO-RS as a predictive tool for lipophilicity. QSAR Comb Sci 28:874–877. https://doi.org/10.1002/qsar.200810175

    Article  CAS  Google Scholar 

  28. Ingram T, Richter U, Mehling T, Smirnova I (2011) Modelling of pH dependent n-octanol/water partition coefficients of ionizable pharmaceuticals. Fluid Phase Equilib 305:197–203. https://doi.org/10.1016/j.fluid.2011.04.006

    Article  CAS  Google Scholar 

  29. Ikeda H, Chiba K, Kanou A, Hirayama N (2005) Prediction of solubility of drugs by conductor-like screening model for real solvents. Chem Pharm Bull 53:253–255

    Article  CAS  Google Scholar 

  30. Wille S, Buggert M, Mokrushina L et al (2010) Effect of electrolytes on octanol-water partition coefficients: calculations with COSMO-RS. Chem Eng Technol 33:1075–1082. https://doi.org/10.1002/ceat.201000045

    Article  CAS  Google Scholar 

  31. Oleszek-Kudlak S, Grabda M, Shibata E et al (2005) Application of the conductor-like screening model for real solvents for prediction of the aqueous solubility of chlorobenzenes depending on temperature and salinity. Environ Toxicol Chem 24:1368–1375. https://doi.org/10.1897/04-100R1.1

    Article  CAS  PubMed  Google Scholar 

  32. Goral M, Wisniewska-Goclowska B, Skrzecz A et al (2005) IUPAC-NIST solubility data series. 81. Hydrocarbons with water and seawater—revised and updated. Part 4. C6H14 hydrocarbons with water. J Phys Chem Ref Data 34:709–753. https://doi.org/10.1063/1.1796651

    Article  CAS  Google Scholar 

  33. Goral M, Wisniewska-Goclowska B, Skrzecz A et al (2005) IUPAC-NIST solubility data series. 81. Hydrocarbons with water and seawater—revised and updated. Part 5. C7 hydrocarbons with water and heavy water. J Phys Chem Ref Data 34:1399–1487. https://doi.org/10.1063/1.1840737

    Article  CAS  Google Scholar 

  34. Mączyński A, Oracz P, Wiśniewska-Gocłowska B et al (2010) IUPAC-NIST solubility data series. 88. Esters with water—revised and updated. Part 2. C5 and C6 esters. J Phys Chem Ref Data 39:013102. https://doi.org/10.1063/1.3243973

    Article  CAS  Google Scholar 

  35. Horvath AL, Getzen FW (1995) Halogenated methanes with water. Oxford University Press, Oxford

    Google Scholar 

  36. Goral M, Wisniewska-Goclowska B (2007) IUPAC-NIST solubility data series. 82. Alcohols with water—revised and updated: Part 1. C4 alcohols with water. J Phys Chem Ref Data 36:59–132. https://doi.org/10.1063/1.2366707

    Article  CAS  Google Scholar 

  37. Goral M, Wisniewska-Goclowska B (2007) IUPAC-NIST solubility data series. 82. Alcohols with water—revised and updated: Part 5. C8–C17 alcohols with water. J Phys Chem Ref Data 36:685–731. https://doi.org/10.1063/1.2391321

    Article  CAS  Google Scholar 

  38. Goral M, Wisniewska-Goclowska B (2007) IUPAC-NIST solubility data series. 82. Alcohols with water—revised and updated: Part 3. C6 alcohols with water. J Phys Chem Ref Data 36:399–443. https://doi.org/10.1063/1.2383067

    Article  CAS  Google Scholar 

  39. Goral M, Wisniewska-Goclowska B (2007) IUPAC-NIST solubility data series. 82. Alcohols with water—revised and updated: Part 4. C 7 alcohols with water. J Phys Chem Ref Data 36:445–484. https://doi.org/10.1063/1.2389037

    Article  CAS  Google Scholar 

  40. Góral M, Wiśniewska-Gocłowska B (2008) IUPAC-NIST solubility data series. 86. Ethers and ketones with water. Part 1. C2–C5 ethers with water. J Phys Chem Ref Data 37:1119–1146. https://doi.org/10.1063/1.2838022

    Article  CAS  Google Scholar 

  41. Horvath AL, Getzen FW (1985) Halogenated benzenes, toluenes and phenols with water. Pergamon Press, Oxford

    Google Scholar 

  42. Tshepelevitsh S, Hernits K, Jenčo J et al (2017) Systematic optimization of liquid–liquid extraction for isolation of unidentified components. ACS Omega 2:7772–7776. https://doi.org/10.1021/acsomega.7b01445

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. TURBOMOLE V6.2 (2010) A development of University of Karlsruhe and Forschungszentrum Karlsruhe GmbH, 1989–2007, TURBOMOLE GmbH, since 2007. http://www.turbomole.com. Accessed 27 May 2018

  44. TURBOMOLE V6.5 (2013) A development of University of Karlsruhe and Forschungszentrum Karlsruhe GmbH, 1989–2007, TURBOMOLE GmbH, since 2007. http://www.turbomole.com. Accessed 27 May 2018

  45. Eckert F, Klamt A, COSMOtherm, Version C3.0, release 17.01; COSMOlogic GmbH & Co. KG. http://www.cosmologic.de. Accessed 27 May 2018

  46. Klamt A (2003) Prediction of the mutual solubilities of hydrocarbons and water with COSMO-RS. Fluid Phase Equilib 206:223–235. https://doi.org/10.1016/S0378-3812(02)00322-9

    Article  CAS  Google Scholar 

  47. Freire MG, Ventura SPM, Santos LMNBF. et al (2008) Evaluation of COSMO-RS for the prediction of LLE and VLE of water and ionic liquids binary systems. Fluid Phase Equilib 268:74–84. https://doi.org/10.1016/j.fluid.2008.04.009

    Article  CAS  Google Scholar 

  48. Fukasawa T, Tominaga Y, Wakisaka A (2004) Molecular association in binary mixtures of tert-butyl alcohol–water and tetrahydrofuran–heavy water studied by mass spectrometry of clusters from liquid droplets. J Phys Chem A 108:59–63. https://doi.org/10.1021/jp031011s

    Article  CAS  Google Scholar 

  49. Isele-Holder RE, Rabideau BD, Ismail AE (2012) Definition and computation of intermolecular contact in liquids using additively weighted voronoi tessellation. J Phys Chem A 116:4657–4666. https://doi.org/10.1021/jp3021886

    Article  CAS  PubMed  Google Scholar 

  50. Selberg S, Rodima T, Lõkov M et al (2017) Synthesis and properties of highly lipophilic phosphazene bases. Tetrahedron Lett 58:2098–2102. https://doi.org/10.1016/j.tetlet.2017.04.039

    Article  CAS  Google Scholar 

  51. Abraham MH, Acree WE (2011) Hydrogen bond descriptors and other properties of ion pairs. New J Chem 35:1740. https://doi.org/10.1039/c1nj20324j

    Article  CAS  Google Scholar 

  52. Zamora WJ, Curutchet C, Campanera JM, Luque FJ (2017) Prediction of pH-dependent hydrophobic profiles of small molecules from Miertus–Scrocco–Tomasi continuum solvation calculations. J Phys Chem B 121:9868–9880. https://doi.org/10.1021/acs.jpcb.7b08311

    Article  CAS  PubMed  Google Scholar 

  53. Chen C-S, Lin S-T (2016) Prediction of pH effect on the octanol–water partition coefficient of ionizable pharmaceuticals. Ind Eng Chem Res 55:9284–9294. https://doi.org/10.1021/acs.iecr.6b02040

    Article  CAS  Google Scholar 

  54. Avdeef A (2012) Absorption and drug development: solubility, permeability, and charge state, 2nd edn. Wiley, Hoboken

    Book  Google Scholar 

  55. Eckert F, Klamt A (2006) Accurate prediction of basicity in aqueous solution with COSMO-RS. J Comput Chem 27:11–19. https://doi.org/10.1002/jcc.20309

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by the Institutional Funding IUT20-14 from the Estonian Research Council and by the EU through the European Regional Development Fund (TK141 “Advanced materials and high-technology devices for energy recuperation systems”). Authors thank Dr. Jens Reinisch for helpful discussions. Authors also thank Dr. Joel Hawkins and Pfizer Inc. for helpful discussions and help in obtaining chemicals.

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  1. Institute of Chemistry, University of Tartu, Ravila 14a, Tartu, 50411, Estonia

    Sofja Tshepelevitsh, Kertu Hernits & Ivo Leito

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  1. Sofja Tshepelevitsh
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Tshepelevitsh, S., Hernits, K. & Leito, I. Prediction of partition and distribution coefficients in various solvent pairs with COSMO-RS. J Comput Aided Mol Des 32, 711–722 (2018). https://doi.org/10.1007/s10822-018-0125-y

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