Quantitative structure–permeability relationships at various pH values for neutral and amphoteric drugs and drug-like compoundsFootnote^$

$ Presented at the 17th International Conference on QSAR in Environmental and Health Sciences (QSAR 2016), 13–17 June 2016, Miami Beach, FL, USA.

References

• A. Avdeef, Absorption and Drug Development: Solubility, Permeability, and Charge State, 2nd ed., Wiley, Hoboken, NJ, 2012.
   Google Scholar
• M.B. Brennan, Drug discovery - Filtering out failures early in the game, Chem. Eng. News 78 (2000), pp. 63–73.
   Web of Science ®Google Scholar
• Y. Sambuy, I.D. Angelis, G. Ranaldi, M.L. Scarino, A. Stammati, and F. Zucco, The Caco-2 cell line as a model of the intestinal barrier: Influence of cell and culture-related factors on Caco-2 cell functional characteristics, Cell Biol. Toxicol. 21 (2005), pp. 1–26.
   PubMed Web of Science ®Google Scholar
• P. Artursson, Epithelial transport of drugs in cell culture. I: A model for studying the passive diffusion of drugs over intestinal absorptive (Caco-2) cells, J. Pharm. Sci. 79 (1990), pp. 476–482.
   PubMed Web of Science ®Google Scholar
• M. Kansy, F. Senner, and K. Gubernator, Physicochemical high throughput screening: Parallel artificial membrane permeation assay in the description of passive absorption processes, J. Med. Chem. 41 (1998), pp. 1007–1010.
   PubMed Web of Science ®Google Scholar
• M. Oja and U. Maran, The permeability of an artificial membrane for wide range of pH in human gastrointestinal tract: Experimental measurements and quantitative structure-activity relationship, Mol. Inf. 34 (2015), pp. 493–506.
   PubMed Web of Science ®Google Scholar
• M. Oja and U. Maran, QDB archive #137. QsarDB repository, 2015. http://dx.doi.org/10.15152/QDB.137.
   Google Scholar
• M. Oja and U. Maran, Quantitative structure–permeability relationships at various pH values for acidic and basic drugs and drug-like compounds, SAR QSAR Environ. Res. 26 (2015), pp. 701–719.
   PubMed Web of Science ®Google Scholar
• M. Oja and U. Maran, QDB archive #166. QsarDB repository, 2015. http://dx.doi.org/10.15152/QDB.166
   Google Scholar
• D.T. Manallack, The acid–base profile of a contemporary set of drugs: Implications for drug discovery, SAR QSAR Environ. Res. 20 (2009), pp. 611–655.
   PubMed Web of Science ®Google Scholar
• Wiki-pKa^TM database; available at http://www.in-adme.com/wiki_pka.php.
   Google Scholar
• R.J. Prankerd, Critical compilation of pKa values for pharmaceutical substances, in Profiles of Drug Substances, Excipients, and Related Methodology, Vol. 33, H.G. Brittain, ed., Elsevier, New York, NY, 2007, pp 36–626.
   Google Scholar
• A. Llinàs, R.C. Glen, and J.N. Goodman, Solubility challenge: Can you predict solubilities of 32 molecules using a database of 100 reliable measurements?, J. Chem. Inf. Model. 48 (2008), pp. 1289–1303.
   PubMed Web of Science ®Google Scholar
• S.R. Wegst-Uhrich, D.A.G. Navarro, L. Zimeerman, and D.S. Aga, Assessing antibiotic sorption in soil: A literature review and new case studies on sulfonamides and macrolides, Chem. Cent. J. 8 (2014), p. 5.
   PubMed Web of Science ®Google Scholar
• S. Şanli, Y. Altun, N. Şanli, G. Alsancak, and J.L. Beltran, Solvent effects on pKa values of some substituted sulfonamides in acetonitrile-water binary mixtures by the UV-spectroscopy method, J. Chem. Eng. Data 54 (2009), pp. 3014–3021.
   Web of Science ®Google Scholar
• H. Yang, G. Li, T. An, Y. Gao, and J. Fu, Photocatalytic degradation kinetics and mechanism of environmental pharmaceuticals in aqueous suspension of TiO2: A case of sulfa drugs, Catal. Today 153 (2010), pp. 200–207.
   Web of Science ®Google Scholar
• N.W. Jacobsen, B. Halling-Sørensen, and F.K. Birkved, Inhibition of human aromatase complex (CYP19) by antiepileptic drugs, Toxicol. In Vitro 22 (2008), pp. 146–153.
   PubMed Web of Science ®Google Scholar
• SciFinder Scholar, American Chemical Society. Calculated using Advanced Chemistry Development (ACD/Labs) Software V11.02.
   Google Scholar
• MacroModel version 9, Schrödinger, Inc., Portland, OR, USA, 2000; software available at http://www.schrodinger.com.
   Google Scholar
• J.J.P. Stewart, MOPAC Program Package 7.0, QCPE Bloomiagton, IN, USA, 1993.
   Google Scholar
• CODESSA PRO User’s manual, University of Florida, Gainsville, FL, USA, 2005.
   Google Scholar
• Syracuse research corporation. Physical/chemical Property Database–(PHYSPROP), SRC Environmental Science Centre: Syracuse, NY, USA, 1994.
   Google Scholar
• T. Cheng, Y. Zhao, X. Li, F. Lin, Y. Xu, X. Zhang, Y. Li, R. Wang, and L. Lai, Computation of octanol-water partition coefficients by guiding an additive model with knowledge, J. Chem. Inf. Model. 47 (2007), pp. 2140–2148.
   PubMed Web of Science ®Google Scholar
• E. Bolton, Y. Wang, P.A. Thiessen, and S.H. Bryant, PubChem: Integrated Platform of Small Molecules and Biological Activities, in Annual Reports in Computational Chemistry, Vol. 4, American Chemical Society, Washington, DC, 2008, Chapter 12 .
   Google Scholar
• PubChem database, available at http://pubchem.ncbi.nlm.nih.gov.
   Google Scholar
• JChem for Excel 14.7.2800.480, 2014, ChemAxon; available at http://www.chemaxon.com.
   Google Scholar
• A.C. Atkinson, Plots, Transformation, Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis, Oxford University Press, Oxford, UK, 1985.
   Google Scholar
• J. Jaworska, N. Nikolova-Jeliazkova, and T. Aldenberg, QSAR applicability domain estimation by projection of the training set in descriptor space: A review, ATLA 33 (2005), pp. 445–459.
   Google Scholar
• A. Tropsha, P. Gramatica, and V.K. Gombar, The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci. 22 (2003), pp. 69–77.
   Google Scholar
• T.I. Netzeva, A.P. Worth, T. Aldenberg, R. Benigni, M.T.D. Cronin, P. Gramatica, J.S. Jaworska, S. Kahn, G. Klopman, C.A. Marchant, G. Myatt, N. Nikolova-Jeliazkova, G.Y. Patlewicz, R. Perkins, D.W. Roberts, T.W. Schultz, D.T. Stanton, J.J.M. van de Sandt, W. Tong, G. Veith, and C. Yang, Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships, Altern. Lab. Anim. 33 (2005), pp. 155–173.
   PubMed Web of Science ®Google Scholar
• V. Ruusmann, S. Sild, and U. Maran, QSAR DataBank - an approach for the digital organization and archiving of QSAR model information, J. Cheminf. 6 (2014), p. 25.
   PubMed Web of Science ®Google Scholar
• V. Ruusmann, S. Sild, and U. Maran, QSAR DataBank repository: Open and linked qualitative and quantitative structure–activity relationship models, J. Cheminf. 7 (2015), p. 32.
   PubMed Web of Science ®Google Scholar
• QsarDB repository, available at http://qsardb.org/.
   Google Scholar
• R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics, 2nd ed., WILEY-VCH, Weinheim, Germany, 2009.
   Google Scholar
• R.H. Rohrbaugh and P.C. Jurs, Molecular shape and the prediction of high-performance liquid chromatographic retention indexes of polycyclic aromatic hydrocarbons, Anal. Chem. 59 (1987), pp. 1048–1054.
   PubMed Web of Science ®Google Scholar
• U. Maran, S. Sild, I. Tulp, K. Takkis, and M. Moosus, Molecular Descriptors from Two-Dimensional Chemical Structure, in In Silico Toxicology: Principles and Applications, M.T.D. Cronin and J.C. Madden, eds., Royal Society of Chemistry, 2010, pp. 148–192.
   Google Scholar