Skip to main content
SpringerLink
Log in

Computational Screening of Drug Solvates

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

Abstract

Purpose

Solvates are mainly undesired by-products during the pharmaceutical development of new drugs. In addition, solvate formation may also distort solubility measurements. The presented study introduces a simple computational approach that allows for the identification of drug solvent pairs which most likely form crystalline solid phases.

Methods

The mixing enthalpy as a measure for drug-solvent complementarity is obtained by computational liquid phase thermodynamics (COSMO-RS theory). In addition a few other simple descriptors were taking into account describing the shape and topology of the drug and the solvent. Using an extensive dataset of drug solvent pairs a simple and statistically robust model is developed which allows for a rough assessment of a solvent’s ability to form a solvate.

Results

Similar to the related issue of cocrystal screening, the mixing (or excess) enthalpy of the subcooled liquid mixture of the drug-solvent pair proves to be an important quantity controlling solvate formation. Due to the fact that many solvates form inclusion compounds, the solvent shape is another important factor influencing solvate formation. Solvates forming channel-like voids in the solid state are predicted less well.

Conclusion

The approach ranks any drug-solvent pair that forms a solvate before any non-solvate by a probability of about 81% (AUC = 0.81), giving a significant advantage over any trial and error approach. Hence it can help to identify suitable solvent candidates early in the drug development process.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Abbreviations

AUC:

Area under the ROC curve

COSMO:

Conductor like screening model

COSMO-RS:

COSMO for realistic solvation

G ex :

Excess free energy

H kmix :

Enthalpy of compound k in mixture

H kpure :

Enthalpy of pure compound k

H ex :

Excess enthalpy

log10(x):

logarithm of mole fraction solubility

n ring , drug :

Number of ring atoms of drug

O solvent :

Ovality index of solvent

p solvate :

Probability of solvate formation

ROC:

Receiver operating characteristic

V COSMO :

Volume of molecular COSMO cavity

x :

Mole fraction

ΔG fus :

Gibbs free energy of fusion

ΔG mix :

Gibbs free energy of mixing

ΔG solvate :

Gibbs free energy of solvate formation

ΔH fus :

Enthalpy of fusion

ΔH mix :

Mixing enthalpy

References

  1. Hilfiker R, editor. Polymorphism in the pharmaceutical industry. Weinheim: Wiley-VCH; 2006.

    Google Scholar 

  2. Korobov MV, Mirakyan AL, Avramenko NV, Olofsson G, Smith AL, Ruoff RS. Calorimetric studies of solvates of C 60 and C 70 with aromatic solvents. J Phys Chem B. 1999;103:1339–46.

    Article  CAS  Google Scholar 

  3. Threlfall TL. Analysis of organic polymorphs. A review. Analyst. 1995;120:2435–60.

    Article  CAS  Google Scholar 

  4. McCrone WC. Physics and chemistry of the organic solid state. Physics and chemistry of the organic solid state. London: Interscience Publishers; 1965. p. 725–67.

    Google Scholar 

  5. Nangia A, Desiraju GR. Pseudopolymorphism: occurrences of hydrogen bonding organic solvents in molecular crystals. Chem Commun. 1999;605–6.

  6. Takieddin K, Khimyak YZ, Fábián L. Prediction of hydrate and solvate formation using statistical models. Cryst Growth Des. 2016;16:70–81.

    Article  CAS  Google Scholar 

  7. Johnston A, Johnston BF, Kennedy AR, Florence AJ. Targeted crystallisation of novel carbamazepine solvates based on a retrospective Random Forest classification. CrystEngComm. 2008;10:23.

    Article  CAS  Google Scholar 

  8. Abramov YA, Loschen C, Klamt A. Rational coformer or solvent selection for pharmaceutical cocrystallization or desolvation. J Pharm Sci. 2012;101:3687.

    Article  CAS  PubMed  Google Scholar 

  9. Loschen C, Klamt A. Solubility prediction, solvate and cocrystal screening as tools for rational crystal engineering: solubility prediction, solvate and cocrystal screening. J Pharm Pharmacol. 2015;67:803–11.

    Article  CAS  PubMed  Google Scholar 

  10. Abramov YA. Virtual hydrate screening and coformer selection for improved relative humidity stability. CrystEngComm. 2015;17:5216–24.

    Article  CAS  Google Scholar 

  11. am Ende D, Klamt A, Loschen C, Anderson S, Salan JS, Dube P, et al. Prediction of energetic-energetic cocrystal forms and their competition with solvate formation [Internet]. Atlanta, GA, USA; 2014. Available from: http://www.aiche.org/conferences/aiche-annual-meeting/2014.

  12. Klamt A. Conductor-like screening model for real solvents: a new approach to the quantitative calculation of solvation phenomena. J Phys Chem. 1995;99:2224–35.

    Article  CAS  Google Scholar 

  13. Klamt A, Krooshof GJP, Taylor R. COSMOSPACE: alternative to conventional activity-coefficient models. AIChE J. 2002;48:2332–49.

    Article  CAS  Google Scholar 

  14. Klamt A. The COSMO and COSMO-RS solvation models. WIREs Comput Mol Sci. 2011;1:699–709.

    Article  CAS  Google Scholar 

  15. U.S. Department of Health and Human Services, Guidance for Industry, Q3C - Tables and List, 2012. [Internet]. U.S. Department of Health and Human Services, Guidance for Industry, Q3C - Tables and List. 2014 [cited 2014 May 19]. Available from: http://www.fda.gov/downloads/Drugs/GuidanceComp lianceRegulatoryInformation/ Guidances/ucm073395.pdf.

  16. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. Springer; 2001.

  17. Herschtal A, Raskutti B. Optimising area under the ROC curve using gradient descent. Proceedings of the Twenty-first International Conference on Machine Learning [Internet]. New York, NY, USA: ACM; 2004. p. 49. Available from: http://doi.acm.org/10.1145/1015330.1015366.

  18. COSMOtherm C3.0, release 15.01. Leverkusen, Germany: COSMOlogic GmbH & Co. KG; 2014. http://www.cosmologic.de.

  19. TURBOMOLE 7.0. Karlsruhe, Germany: University of Karlsruhe and Forschungszentrum Karlsruhe GmbH (1989–2007), TURBOMOLE GmbH (since 2007); 2015. http://www.turbomole.com.

  20. Becke AD. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev A. 1988;38:3098–100.

    Article  CAS  Google Scholar 

  21. Perdew JP. Density-functional approximation for the correlation energy of the inhomogeneous electron gas. Phys Rev B. 1986;33:8822–4.

    Article  Google Scholar 

  22. Klamt A, Eckert F, Diedenhofen M. Prediction of the free energy of hydration of a challenging set of pesticide-like compounds†. J Phys Chem B. 2009;113:4508–10.

    Article  CAS  PubMed  Google Scholar 

  23. R Core Team. R: a language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2013. Available from: http://www.R-project.org/.

  24. Be̅rziņš A, Skarbulis E, Rekis T, Actiņš A. On the formation of droperidol solvates: characterization of structure and properties. Cryst Growth Des. 2014;14:2654–64.

    Article  Google Scholar 

  25. Bingham AL, Hughes DS, Hursthouse MB, Lancaster RW, Tavener S, Threlfall TL. Over one hundred solvates of sulfathiazole. Chem Commun. 2001;603–4.

  26. Nauha E, Saxell H, Nissinen M, Kolehmainen E, Schafer A, Schlecker R. Polymorphism and versatile solvate formation of thiophanate-methyl. CrystEngComm. 2009;11:2536–47.

    Article  CAS  Google Scholar 

  27. Stieger N, Liebenberg W, Wessels J, Samsodien H, Caira M. Channel inclusion of primary alcohols in isostructural solvates of the antiretroviral nevirapine: an X-ray and thermal analysis study. Struct Chem. 2010;21:771–7.

    Article  CAS  Google Scholar 

  28. Wicker JGP, Cooper RI. Will it crystallise? Predicting crystallinity of molecular materials. CrystEngComm. 2015;17:1927–34.

    Article  CAS  Google Scholar 

  29. Hosokawa T, Datta S, Sheth AR, Brooks NR, Young VG, Grant DJW. Isostructurality among five solvates of phenylbutazone. Cryst Growth Des. 2004;4:1195–201.

    Article  CAS  Google Scholar 

  30. Datta S, Grant DJW. Crystal structures of drugs: advances in determination, prediction and engineering. Nat Rev Drug Discov. 2004;3:42–57.

    Article  CAS  PubMed  Google Scholar 

Download references

ACKNOWLEDGMENTS AND DISCLOSURES

We would like to thank Yuriy A. Abramov, Pfizer and David am Ende, Nalas Engineering for fruitful discussions.

The authors declare the following competing financial interest(s): Andreas Klamt is CEO and Christoph Loschen is an employee of COSMOlogic.

COSMOlogic develops and commercially distributes the COSMOtherm software package used in this paper.

Author information

Authors and Affiliations

  1. COSMOlogic GmbH & Co. KG, Imbacher Weg 46, 51379, Leverkusen, Germany

    Christoph Loschen & Andreas Klamt

  2. Institute of Physical and Theoretical Chemistry, University of Regensburg, Regensburg, Germany

    Andreas Klamt

Authors
  1. Christoph Loschen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Klamt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christoph Loschen.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 265 kb)

ESM 2

(XLSX 499 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Loschen, C., Klamt, A. Computational Screening of Drug Solvates. Pharm Res 33, 2794–2804 (2016). https://doi.org/10.1007/s11095-016-2005-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11095-016-2005-2

KEY WORDS

Search

Navigation