Skip to main content
SpringerLink
Log in

Comparison of the Accuracy of Experimental and Predicted pKa Values of Basic and Acidic Compounds

  • Research Paper
  • Published:
Pharmaceutical Research Aims and scope Submit manuscript

Abstract

Purpose

Assessment of the accuracy of experimental and theoretical methods of pKa determination for acids and bases as separate classes.

Methods

Four literature pKa datasets were checked for errors and pKa values assigned unambiguously to a single acidic and/or basic ionisation centre. A new chemically diverse and drug-like dataset was compiled from high-throughput UV–vis spectrophotometry pKa data. Measured pKa values were compared with data obtained by alternative methods and predictions by the Epik, Chemaxon and ACD pKa DB software packages.

Results

The pKa values of bases were considerably less accurately predicted than those of acids, in particular for structurally complex bases. Several new chemical motifs were identified for which pKa values were particularly poorly predicted. Comparison of pKa values obtained by UV–vis spectrophotometry and different literature sources revealed that low aqueous solubility and chromophore strength can affect the accuracy of experimental pKa determination for certain bases but not acids.

Conclusions

The pKa prediction tools Epik, Chemaxon and ACD pKa DB provide significantly less accurate predictions for bases compared to acids. Certain chemical features are underrepresented in currently available pKa data sets and as a result poorly predicted. Acids and bases need to be considered as separate classes during pKa predictor development and validation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Abbreviations

ADMET:

Absorption, distribution, metabolism, excretion and toxicity

DMSO:

Dimethyl sulfoxide

hERG:

Human ether-a-go-go-related gene

HOMO:

Highest occupied molecular orbital

LUMO:

Lowest unoccupied molecular orbital

MAD:

Median absolute deviation

MW:

Molecular weight

References

  1. Abraham MH, Duce PP, Prior DV, Barratt DJ, Morris JJ, Taylor PJ. Hydrogen bonding. Part 9. Solute proton donor and proton acceptor scales for use in drug design. J Chem Soc Perkin Trans. 1989;2:1355–75.

    Article  Google Scholar 

  2. Agouridas V, Laios I, Cleeren A, Kizilian E, Magnier E, Blazejewski JC, et al. Loss of antagonistic activity of tamoxifen by replacement of one N-methyl of its side chain by fluorinated residues. Bioorg Med Chem. 2006;14(22):7531–8.

    Article  CAS  PubMed  Google Scholar 

  3. Mitra R, Shyam R, Mitra I, Miteva MA Alexov E. Calculating the protonation states of proteins and small molecules: Implications to ligand-receptor interactions. Curr Comput-Aided Drug Des. 2008;169–179

  4. Stahl PH. The Pratice of medicinal chemistry. London: Academic; 2003.

    Google Scholar 

  5. WDI. The world Drug Index available from www.derwent.com (Derwent, London, UK).

  6. Tam K, Comer J. Pharmacokinetic optimization in drug research: Biological, physicochemical, and computational strategies. Weinheim: Wiley-VCH; 2001.

    Google Scholar 

  7. Manallack DT. The pK(a) distribution of drugs: application to drug discovery. Perspect Medicin Chem. 2008;1:25–38.

    Google Scholar 

  8. Mitani GM, Steinberg I, Lien EJ, Harrison EC, Elkayam U. The pharmacokinetics of antiarrhythmic agents in pregnancy and lactation. Clin Pharmacokinet. 1987;12(4):253–91.

    Article  CAS  PubMed  Google Scholar 

  9. Xie X, Steiner SH, Bickel MH. Kinetics of distribution and adipose tissue storage as a function of lipophilicity and chemical structure. II. Benzodiazepines. Drug Metab Dispos. 1991;19(1):15–9.

    CAS  PubMed  Google Scholar 

  10. Deak K, Takacs-Novak K, Kapas M, Vastag M, Tihanyi K, Noszal B. Physico-chemical characterization of a novel group of dopamine D(3)/D(2) receptor ligands, potential atypical antipsychotic agents. J Pharm Biomed Anal. 2008;48(3):678–84.

    Article  CAS  PubMed  Google Scholar 

  11. Lombardo F, Obach RS, Shalaeva MY, Gao F. Prediction of human volume of distribution values for neutral and basic drugs. 2. Extended data set and leave-class-out statistics. J Med Chem. 2004;47(5):1242–50.

    Article  CAS  PubMed  Google Scholar 

  12. Gleeson MP. Generation of a set of simple, interpretable ADMET rules of thumb. J Med Chem. 2008;51(4):817–34.

    Article  CAS  PubMed  Google Scholar 

  13. Hansch C. Quantitative relationships between lipophilic character and drug metabolism. Drug Metab Rev. 1972;1(1):1–13.

    Article  Google Scholar 

  14. Hansch C, Steward AR, Iwasa J. The use of substituent constants in the correlation of demethylation rates. J Med Chem. 1965;8(6):868–70.

    Article  CAS  PubMed  Google Scholar 

  15. Fermini B, Fossa AA. The impact of drug-induced QT interval prolongation on drug discovery and development. Nat Rev Drug Discov. 2003;2(6):439–47.

    Article  CAS  PubMed  Google Scholar 

  16. Alberati D, Hainzl D, Jolidon S, Krafft EA, Kurt A, Maier A, et al. Discovery of 4-substituted-8-(2-hydroxy-2-phenyl-cyclohexyl)-2,8-diaza-spiro[4.5]decan-1-one as a novel class of highly selective GlyT1 inhibitors with improved metabolic stability. Bioorg Med Chem Lett. 2006;16(16):4311–5.

    Article  CAS  PubMed  Google Scholar 

  17. Jamieson C, Moir EM, Rankovic Z, Wishart G. Medicinal chemistry of hERG optimizations: highlights and hang-ups. J Med Chem. 2006;49(17):5029–46.

    Article  CAS  PubMed  Google Scholar 

  18. Ploemen JP, Kelder J, Hafmans T, van de Sandt H, van Burgsteden JA, van Saleminki PJ, et al. Use of physicochemical calculation of pKa and CLogP to predict phospholipidosis-inducing potential: a case study with structurally related piperazines. Exp Toxicol Pathol. 2004;55(5):347–55.

    CAS  PubMed  Google Scholar 

  19. Lee AC, Crippen GM. Predicting pKa. J Chem Inf Model. 2009;49(9):2013–33.

    Article  CAS  PubMed  Google Scholar 

  20. Luan F, Ma W, Zhang H, Zhang X, Liu M, Hu Z, et al. Prediction of pK(a) for neutral and basic drugs based on radial basis function Neural networks and the heuristic method. Pharm Res. 2005;22(9):1454–60.

    Article  CAS  PubMed  Google Scholar 

  21. Dearden JC, Cronin MTD Lappin DC. A comparison of commercially available software for the prediction of pKa. J Pharm Pharmacol. 2007;59 (Suppl. 1):A–7

    Google Scholar 

  22. Balogh GT, Gyarmati B, Nagy B, Molnar L, Keseru GM. Comparative evaluation of in Silico pKa prediction tools on the gold standard dataset. QSAR Comb Sci. 2009;28(10):1148–55.

    Article  CAS  Google Scholar 

  23. Liao C, Nicklaus MC. Comparison of nine programs predicting pK(a) values of pharmaceutical substances. J Chem Inf Model. 2009;49(12):2801–12.

    Article  CAS  PubMed  Google Scholar 

  24. Manchester J, Walkup G, Rivin O, You Z. Evaluation of pKa estimation methods on 211 druglike compounds. J Chem Inf Model. 2010;50(4):565–71.

    Article  CAS  PubMed  Google Scholar 

  25. Allen RI, Box KJ, Comer JE, Peake C, Tam KY. Multiwavelength spectrophotometric determination of acid dissociation constants of ionizable drugs. J Pharm Biomed Anal. 1998;17(4–5):699–712.

    Article  CAS  PubMed  Google Scholar 

  26. Avdeef A, Box KJ, Comer JE, Gilges M, Hadley M, Hibbert C, et al. PH-metric log P 11. pKa determination of water-insoluble drugs in organic solvent-water mixtures. J Pharm Biomed Anal. 1999;20(4):631–41.

    Article  CAS  PubMed  Google Scholar 

  27. Takács-Novák K, Box KJ, Avdeef A. Potentiometric pKa determination of water-insoluble compounds: validation study in methanol/water mixtures. Intern J Pharm. 1997;151(2):235–48.

    Article  Google Scholar 

  28. Volgyi G, Ruiz R, Box K, Comer J, Bosch E, Takacs-Novak K. Potentiometric and spectrophotometric pKa determination of water-insoluble compounds: validation study in a new cosolvent system. Anal Chim Acta. 2007;583(2):418–28.

    Article  PubMed  Google Scholar 

  29. Albert A, Serjeant E. The determination of ionization constants. 3rd ed. London: Chapman and Hall; 1984.

    Book  Google Scholar 

  30. Ruiz R, Rafols C, Roses M, Bosch E. A potentially simpler approach to measure aqueous pKa of insoluble basic drugs containing amino groups. J Pharm Sci. 2003;92(7):1473–81.

    Article  CAS  PubMed  Google Scholar 

  31. Mandic Z, Gabelica V. Ionization, lipophilicity and solubility properties of repaglinide. J Pharm Biomed Anal. 2006;41(3):866–71.

    Article  CAS  PubMed  Google Scholar 

  32. Gobry V, Bouchard G, Carrupt PA, Testa B Girault HH. Physicochemical characterization of sildenafil: ionization, lipophilicity behavior, and ionic-partition diagram studied by two-phase titration and electrochemistry. 2000;83(7):1465–74.

  33. Box K, Ruiz R, Cimpan G, Allen R, Mole J Comer J. A mixed solvent system for use with the ProfilerSGA for rapid measurement of ionisation constants, LogP 2004 The 3rd lipophylicity symposium, Zurich, Switzerland, March 2004.

  34. Fuoss RM. Properties of electrolytic solutions. III. The dissociation constant. J Am Chem Soc. 1933;55(3):1019–28.

    Article  CAS  Google Scholar 

  35. Ramsey J, Colichman E. Dissociation constants of some substituted phenyltrimethylammonium perchlorates in ethylene chloride; Effect of ion asymmetry. J Am Chem Soc. 1947;69(12):3041–5.

    Article  CAS  Google Scholar 

  36. Shedlovsky T. The behaviour of carboxylic acids in mixed solvents. New York: Pergamon Press; 1962.

    Google Scholar 

  37. Prankerd RJ. Profiles of drug substances, excipients, and related methodology. San Diego: Elsevier Academic Press; 2007.

    Google Scholar 

  38. Avdeef A. Absorption and drug development: solubility, permeability, and charge state. New York: Wiley-IEEE; 2003.

    Book  Google Scholar 

  39. Morgenthaler M, Schweizer E, Hoffmann-Roder A, Benini F, Martin RE, Jaeschke G, et al. Predicting and tuning physicochemical properties in lead optimization: amine basicities. ChemMedChem. 2007;2(8):1100–15.

    Article  CAS  PubMed  Google Scholar 

  40. Bolton E, Wang Y, Thiessen PA, Bryant SH. PubChem: integrated platform of small molecules and biological activities. Washington: American Chemical Society; 2008.

    Google Scholar 

  41. Filippov I. http://cactus.nci.nih.gov/osra/, NCI/CADD Group, 2007.

  42. Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M. Epik: a software program for pK(a) prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des. 2007;21(12):681–91.

    Article  CAS  PubMed  Google Scholar 

  43. Szegezdi J Csizmadia F. A Method for Calculating the pK Values of Small and Large Molecules, 233rd ACS National Meeting, CINF41, http://www.chemaxon.com/conf/Calculating_pKa_values_of_small_and_large_molecules.pdf, Chicago, USA. 2007.

  44. Ertl P, Rohde B, Selzer P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem. 2000;43(20):3714–7.

    Article  CAS  PubMed  Google Scholar 

  45. Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem. 1999;1(1):55–68.

    Article  CAS  PubMed  Google Scholar 

  46. Kalsi PS. Spectroscopy of organic compounds. 6th ed. New Delhi: New Age International Pvt Ltd; 2007.

    Google Scholar 

  47. Turro NJ, Ramamurthy V Scaiano JC. Principles of molecular photochemistry. An introduction, University Science Books, USA, 2009

  48. Lombardo F, Obach RS, Shalaeva MY, Gao F. Prediction of volume of distribution values in humans for neutral and basic drugs using physicochemical measurements and plasma protein binding data. J Med Chem. 2002;45(13):2867–76.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vertex Pharmaceuticals (Europe) Ltd, 86-88 Jubilee Avenue Milton Park, Abingdon, OX14 4RW, UK

    Luca Settimo & Ronald M. A. Knegtel

  2. 11010 Torreyana Road, San Diego, California, 92121, USA

    Krista Bellman

Authors
  1. Luca Settimo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krista Bellman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ronald M. A. Knegtel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ronald M. A. Knegtel.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 506 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Settimo, L., Bellman, K. & Knegtel, R.M.A. Comparison of the Accuracy of Experimental and Predicted pKa Values of Basic and Acidic Compounds. Pharm Res 31, 1082–1095 (2014). https://doi.org/10.1007/s11095-013-1232-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11095-013-1232-z

KEY WORDS

Search

Navigation