Aqueous solubility prediction: Do crystal lattice interactions help?

Molecular Pharmaceutics

Volumn 10, Issue 7, 2013, Pages 2757-2766

  Salahinejad, Maryam ^a,b,c   Le, Tu C ^b   Winkler, David A ^b,c  

^a KHARAZMI UNIVERSITY   (Iran)

^b CSIRO   (Australia)

^c MONASH UNIVERSITY   (Australia)

Author keywords

aqueous solubility; Bayesian neural network; enthalpy of sublimation; lattice energy; optimally sparse models; QSPR; VolSurf descriptors

Indexed keywords

ORGANIC COMPOUND;

ALGORITHM; AQUEOUS SOLUTION; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; CORRELATION ANALYSIS; CRYSTAL STRUCTURE; ENTHALPY; MOLECULAR INTERACTION; MOLECULAR MODEL; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SOLUBILITY; TEMPERATURE;

BAYES THEOREM; MODELS, CHEMICAL; ORGANIC CHEMICALS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOLUBILITY;

EID: 84879698395     PISSN: 15438384     EISSN: 15438392     Source Type: Journal    
DOI: 10.1021/mp4001958     Document Type: Article

References (66)

1. Yalkowsky, S. H. Solubility and Solubilization in Aqueous Media; Oxford University Press: New York, 1999.
2. Huuskonen, J. Estimation of aqueous solubility in drug design Combin. Chem. High Throughput Screen. 2001, 4, 311-6
3. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings Adv. Drug Delivery Rev. 2001, 46, 3-26
4. Klopman, G.; Wang, S.; Balthasar, D. M. Estimation of Aqueous Solubility of Organic-Molecules by the Group Contribution Approach-Application to the Study of Biodegradation J. Chem. Inf. Comput. Sci. 1992, 32, 474-482
5. Klopman, G.; Zhu, H. Estimation of the aqueous solubility of organic molecules by the group contribution approach J. Chem. Inf. Comput. Sci. 2001, 41, 439-45
6. Kuhne, R.; Ebert, R. U.; Kleint, F.; Schmidt, G.; Schuurmann, G. Group-Contribution Methods to Estimate Water Solubility of Organic-Chemicals Chemosphere 1995, 30, 2061-2077
7. Sheikholeslamzadeh, E.; Rohani, S. Solubility Prediction of Pharmaceutical and Chemical Compounds in Pure and Mixed Solvents Using Predictive Models Ind. Eng. Chem. Res. 2012, 51, 464-473
8. Wang, J. M.; Hou, T. J.; Xu, X. J. Aqueous Solubility Prediction Based on Weighted Atom Type Counts and Solvent Accessible Surface Areas J. Chem. Inf. Model. 2009, 49, 571-581
9. Mirmehrabi, M.; Rohani, S.; Perry, L. Thermodynamic mdeling of activity coefficient and prediction of solubility: part 1. Predictive models J. Pharm. Sci. 2006, 95, 790-797
10. Mirmehrabi, M.; Rohani, S.; Perry, L. Thermodynamic modeling of activity coefficient and prediction of solubility: part 2. Semipredictive or semiempirical models J. Pharm. Sci. 2006, 95, 798-809
11. Palmer, D. S.; Llinas, A.; Morao, I.; Day, G. M.; Goodman, J. M.; Glen, R. C.; Mitchell, J. B. O. Predicting intrinsic aqueous solubility by a thermodynamic cycle Mol. Pharmaceutics 2008, 5, 266-279
12. Bergstrom, C. A.; Wassvik, C. M.; Norinder, U.; Luthman, K.; Artursson, P. Global and local computational models for aqueous solubility prediction of drug-like molecules J. Chem. Inf. Comput. Sci. 2004, 44, 1477-88
13. Butina, D.; Gola, J. M. Modeling aqueous solubility J. Chem. Inf. Comput. Sci. 2003, 43, 837-41
14. Dearden, J. C. In silico prediction of aqueous solubility Expert Opin. Drug Discovery 2006, 1, 31-52
15. Delaney, J. S. ESOL: estimating aqueous solubility directly from molecular structure J. Chem. Inf. Comput. Sci. 2004, 44, 1000-5
16. Engkvist, O.; Wrede, P. High-throughput, in silico prediction of aqueous solubility based on one- and two-dimensional descriptors J. Chem. Inf. Comput. Sci. 2002, 42, 1247-9
17. Hughes, L. D.; Palmer, D. S.; Nigsch, F.; Mitchell, J. B. Why are some properties more difficult to predict than others? A study of QSPR models of solubility, melting point, and Log P J. Chem. Inf. Model. 2008, 48, 220-32
18. Mitchell, B. E.; Jurs, P. C. Prediction of aqueous solubility of organic compounds from molecular structure J. Chem. Inf. Comput. Sci. 1998, 38, 489-496
19. Schnieders, M. J.; Baltrusaitis, J.; Shi, Y.; Chattree, G.; Zheng, L. Q.; Yang, W.; Ren, P. Y. The Structure, Thermodynamics, and Solubility of Organic Crystals from Simulation with a Polarizable Force Field J. Chem. Theory Comput. 2012, 8, 1721-1736
20. Faller, B.; Ertl, P. Computational approaches to determine drug solubility Adv. Drug Delivery Rev. 2007, 59, 533-45
21. Johnson, S. R.; Zheng, W. Recent progress in the computational prediction of aqueous solubility and absorption AAPS J. 2006, 8, E27-40
22. Wang, J. M.; Hou, T. J. Recent Advances on Aqueous Solubility Prediction Combin. Chem. High Throughput Screen. 2011, 14, 328-338
23. Votano, J. R.; Parham, M.; Hall, L. H.; Kier, L. B.; Hall, L. M. Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation Chem. Biodivers. 2004, 1, 1829-1841
24. Ali, J.; Camilleri, P.; Brown, M. B.; Hutt, A. J.; Kirton, S. B. Revisiting the general solubility equation: in silico prediction of aqueous solubility incorporating the effect of topographical polar surface area J. Chem. Inf. Model. 2012, 52, 420-8
25. Jain, P.; Yalkowsky, S. H. Prediction of aqueous solubility from SCRATCH Int. J. Pharmaceutics 2010, 385, 1-5
26. Di, L.; Fish, P. V.; Mano, T. Bridging solubility between drug discovery and development Drug Discovery Today 2012, 17, 486-495
27. Clark, M. Generalized fragment-substructure based property prediction method J. Chem. Inf. Model. 2005, 45, 30-38
28. Johnson, S. R.; Chen, X. Q.; Murphy, D.; Gudmundsson, O. A computational model for the prediction of aqueous solubility that includes crystal packing, intrinsic solubility, and ionization effects Mol. Pharmaceutics 2007, 4, 513-523
29. Burden, F. R.; Winkler, D. A. Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods QSAR Combin. Sci. 2009, 28, 645-653
30. Burden, F.; Winkler, D. Bayesian regularization of neural networks Methods Mol. Biol. 2008, 458, 25-44
31. Burden, F. R.; Winkler, D. A. An Optimal Self-Pruning Neural Network and Nonlinear Descriptor Selection in QSAR QSAR Combin. Sci. 2009, 28, 1092-1097
32. PHYSPROP (Physical/Chemical Property database); Syracuse Research Corporation: New York, 2011.
33. Kramer, C.; Heinisch, T.; Fligge, T.; Beck, B.; Clark, T. A Consistent Dataset of Kinetic Solubilities for Early-Phase Drug Discovery ChemMedChem 2009, 4, 1529-1536
34. Hopfinger, A. J.; Esposito, E. X.; Llinas, A.; Glen, R. C.; Goodman, J. M. Findings of the Challenge To Predict Aqueous Solubility J. Chem. Inf. Model. 2009, 49, 1-5
35. Llinas, A.; Glen, R. C.; Goodman, J. M. Solubility challenge: Can you predict solubilities of 32 molecules using a database of 100 reliable measurements? J. Chem. Inf. Model. 2008, 48, 1289-1303
36. Zhao, Y. H.; Le, J.; Abraham, M. H.; Hersey, A.; Eddershaw, P. J.; Luscombe, C. N.; Boutina, D.; Beck, G.; Sherborne, B.; Cooper, I.; Platts, J. A. Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors J. Pharm. Sci. 2001, 90, 749-784
37. Cruciani, G.; Pastor, M.; Guba, W. VolSurf: a new tool for the pharmacokinetic optimization of lead compounds Eur. J. Pharm. Sci. 2000, 11 (Suppl. 2) S29-39
38. Stanton, D. T.; Dimitrov, S.; Grancharov, V.; Mekenyan, O. G. Charged partial surface area (CPSA) descriptors QSAR applications. SAR QSAR Environ Res. 2002, 13, 341-351
39. Stanton, D. T.; Jurs, P. C. Charged Partial Surface-Area Descriptors for Structure-Property Relationship Studies Abstr. Pap. Am. Chem. Soc. 1990, 199, 2
40. Salahinejad, M.; Le, T. C.; Winkler, D. A. Capturing the crystal: prediction of enthalpy of sublimation, crystal lattice energy, and melting points of organic compounds J. Chem. Inf. Model. 2013, 53, 223-229
41. Burden, F. R.; Winkler, D. A. Robust QSAR models using Bayesian regularized neural networks J. Med. Chem. 1999, 42, 3183-3187
42. Tarasova, A.; Burden, F.; Gasteiger, J.; Winkler, D. A. Robust modelling of solubility in supercritical carbon dioxide using Bayesian methods J. Mol. Graph. Model. 2010, 28, 593-7
43. Winkler, D. A. Neural networks as robust tools in drug lead discovery and development Mol. Biotechnol. 2004, 27, 139-167
44. Winkler, D. A.; Burden, F. R. Application of neural networks to large dataset QSAR, virtual screening, and library design Methods Mol. Biol. 2002, 201, 325-67
45. Kiiveri, H. T. A general approach to simultaneous model fitting and variable elimination in response models for biological data with many more variables than observations BMC Bioinformatics 2008, 9, 195
46. Clark, M.; Cramer, R. D. The Probability of Chance Correlation Using Partial Least-Squares (Pls) Quant. Struct.-Act. Relat. 1993, 12, 137-145
47. Le, T. C.; Epa, V. C.; Burden, F. R.; Winkler, D. A. Quantitative structure-property relationship modeling of diverse materials properties Chem. Rev. 2012, 112, 2889-919
48. Katritzky, A. R.; Kuanar, M.; Slavov, S.; Hall, C. D.; Karelson, M.; Kahn, I.; Dobchev, D. A. Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction Chem. Rev. 2010, 110, 5714-5789
49. Hansen, N. T.; Kouskoumvekaki, I.; Jorgensen, F. S.; Brunak, S.; Jonsdottir, S. O. Prediction of pH-dependent aqueous solubility of druglike molecules J. Chem. Inf. Model. 2006, 46, 2601-2609
50. Tetko, I. V.; Oprea, T. I. Early ADMET Prediction: Toy or Tool. In Chemoinformatics Approaches to Virtual Screening; Varnek, A.; Tropsha, A., Eds.; RSC: Cambridge, 2008; pp 250-267.
51. Bennema, P.; van Eupen, J.; van der Wolf, B. M. A.; Los, J. H.; Meekes, H. Solubility of molecular crystals: Polymorphism in the light of solubility theory Int. J. Pharmaceutics 2008, 351, 74-91
52. Pudipeddi, M.; Serajuddin, A. T. M. Trends in solubility of polymorphs J. Pharm. Sci. 2005, 94, 929-939
53. Cheng, T.; Li, Q.; Wang, Y.; Bryant, S. H. Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection J. Chem. Inf. Model. 2011, 51, 229-36
54. Muratov, E. N.; Kuz'min, V. E.; Artemenko, A. G.; Kovdienko, N. A.; Gorb, L.; Hill, F.; Leszczynski, J. New QSPR equations for prediction of aqueous solubility for military compounds Chemosphere 2010, 79, 887-90
55. Fatemi, M. H.; Heidari, A.; Ghorbanzade, M. Prediction of Aqueous Solubility of Drug-Like Compounds by Using an Artificial Neural Network and Least-Squares Support Vector Machine Bull. Chem. Soc. Jpn. 2010, 83, 1338-1345
56. Shayanfar, A.; Fakhree, M. A. A.; Jouyban, A. A simple QSPR model to predict aqueous solubility of drugs J. Drug. Delivery Sci. Technol. 2010, 20, 467-476
57. Cao, D. S.; Xu, Q. S.; Liang, Y. Z.; Chen, X. A.; Li, H. D. Prediction of aqueous solubility of druglike organic compounds using partial least squares, back-propagation network and support vector machine J. Chemomet. 2010, 24, 584-595
58. Duchowicz, P. R.; Talevi, A.; Bruno-Blanch, L. E.; Castro, E. A. New QSPR study for the prediction of aqueous solubility of drug-like compounds Bioorg. Med. Chem. 2008, 16, 7944-7955
59. Zhang, Y.; Ray, A.; Rohani, S. Measurement and prediction of phase diagrams of the enantiomeric 3-chloromandelic acid system Chem. Eng. Sci. 2009, 64, 192-197
60. Crea, F.; Cucinotta, D.; De Stefano, C.; Milea, D.; Sammartano, S.; Vianelli, G. Modelling solubility, acid-base properties and activity coefficients of amoxicillin, ampicillin and (+)6-aminopenicillanic acid, in NaCl((aq)) at different ionic strengths and temperatures Eur. J. Pharm. Sci. 2012, 47, 661-77
61. Lin, M.; Tesconi, M.; Tischler, M. Use of H-1 NMR to facilitate solubility measurement for drug discovery compounds Int. J. Pharmaceutics 2009, 369, 47-52
62. Wassvik, C. M.; Holmen, A. G.; Draheim, R.; Artursson, P.; Bergstrom, C. A. S. Molecular characteristics for solid-state limited solubility J. Med. Chem. 2008, 51, 3035-3039
63. Hsieh, Y.; Ilevbare, G. A.; Van Eerdenbrugh, B.; Box, K. J.; Sanchez-Felix, M. V.; Taylor, L. S. pH-Induced Precipitation Behavior of Weakly Basic Compounds: Determination of Extent and Duration of Supersaturation Using Potentiometric Titration and Correlation to Solid State Properties Pharm. Res. 2012, 29, 2738-2753
64. Wu, Z.; Li, X. X.; Hou, C. Y.; Qian, Y. Solubility of Folic Acid in Water at pH Values between 0 and 7 at Temperatures (298.15, 303.15, and 313.15) K J. Chem. Eng. Data 2010, 55, 3958-3961
65. Murdande, S. B.; Pikal, M. J.; Shanker, R. M.; Bogner, R. H. Solubility Advantage of Amorphous Pharmaceuticals: II. Application of Quantitative Thermodynamic Relationships for Prediction of Solubility Enhancement in Structurally Diverse Insoluble Pharmaceuticals Pharm. Res. 2010, 27, 2704-2714
66. Martinez, F.; Pena, M. A.; Bustamante, P. Thermodynamic analysis and enthalpy-entropy compensation for the solubility of indomethacin in aqueous and non-aqueous mixtures Fluid Phase Equilib. 2011, 308, 98-106