Pharmaceutical Research

, Volume 27, Issue 7, pp 1398–1407 | Cite as

Prediction of the Corneal Permeability of Drug-Like Compounds

  • Heidi Kidron
  • Kati-Sisko Vellonen
  • Eva M. del Amo
  • Anita Tissari
  • Arto Urtti
Research Paper

ABSTRACT

Purpose

To develop a computational model for optimisation of low corneal permeability, which is a key feature in ocular drug development.

Methods

We have used multivariate analysis to build corneal permeability models based on a structurally diverse set of 58 drug-like compounds.

Results

According to the models, the most important parameters for permeability are logD at physiologically relevant pH and the number of hydrogen bonds that can be formed. Combining these descriptors resulted in models with Q2 and R2 values ranging from 0.77 to 0.79. The predictive capability of the models was verified by estimating the corneal permeability of an external data set of 11 compounds and by using predicted permeability values to calculate the aqueous humour concentrations in the steady-state of seven compounds. The predicted values correlated well with experimental values.

Conclusion

The developed models are useful in early drug development to predict the corneal permeability and steady-state drug concentration in aqueous humor without experimental data.

KEY WORDS

computational model multivariate analysis ocular absorption ophthalmic drugs QSPR 

ABBREVIATIONS

Css

steady-state concentration

HBA

number of hydrogen bond acceptors

HBD

number of hydrogen bond donors

HBtot

total number of putative hydrogen bonds, i.e. HBD + HBA

logP

the logarithm of the octanol-water partition coefficient of the neutral form

logperm

the logarithm of the corneal permeability

logD7.0, logD7.4 and logD8.0

the logarithm of the octanol-water partition coefficient at pH 7.0, 7.4 and 8.0, respectively

MV

molecular volume

MW

molecular weight

PCA

principal component analysis

PLS

partial least squares

PSA

polar surface area

QSPR

quantitative structure-property relationship

RMSE

root mean squared error

RMSEP

root mean squared error of prediction

VIP

variable importance in the projection

Supplementary material

11095_2010_132_MOESM1_ESM.doc (389 kb)
DOC (389 KB)

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Heidi Kidron
    • 1
  • Kati-Sisko Vellonen
    • 1
    • 2
  • Eva M. del Amo
    • 1
    • 2
  • Anita Tissari
    • 2
  • Arto Urtti
    • 1
  1. 1.Centre for Drug ResearchUniversity of HelsinkiHelsinkiFinland
  2. 2.Division of Biopharmacy and PharmacokineticsUniversity of HelsinkiHelsinkiFinland

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