The composite solubility versus pH profile and its role in intestinal absorption prediction

AAPS PharmSci - Tập 5 - Trang 1-15 - 2015
Barry A. Hendriksen1, Manuel V. Sanchez Felix1, Michael B. Bolger2,3
1Lilly Research Centre, Eli Lilly and Co, Windlesham, UK
2University of Southern California School of Pharmacy, Los Angeles
3Simulations Plus, Inc, Lancaster

Tóm tắt

The purpose of this study was to examine absorption of basic drugs as a function of the composite solubility curve and intestinally relevant pH by using a gastrointestinal tract (GIT) absorption simulation based on the advanced compartmental absorption and transit model. Absorption simulations were carried out for virtual monobasic drugs having a range of pKa, log D, and dose values as a function of presumed solubility and permeability. Results were normally expressed as the combination that resulted in 25% absorption. Absorption of basic drugs was found to be a function of the whole solubility/pH relationship rather than a single solubility value at pH 7. In addition, the parameter spaces of greatest sensitivity were identified. We compared 3 theoretical scenarios: the GIT pH range overlapping (1) only the salt solubility curve, (2) the salt and base solubility curves, or (3) only the base curve. Experimental solubilities of 32 compounds were determined at pHs of 2.2 and 7.4, and they nearly all fitted into 2 of the postulated scenarios. Typically, base solubilities can be simulated in silico, but salt solubilities at low pH can only be measured. We concluded that quality absorption simulations of candidate drugs in most cases require experimental solubility determination at 2 pHs, to permit calculation of the whole solubility/pH profile.

Tài liệu tham khảo

Simulations Plus, Inc. Available at: http://www.simulationsplus.com. Press WH. Numerical recipes. In: Teukolsky SA, Vetterling WT, Flannery BP, eds. C: The Art of Scientific Computing. New York, NY: Cambridge University Press; 1992: 566–580. Ungell A-L, Nylander S, Bergstrand S, Sjöberg Å, Lemernäs H. Membrane transport of drugs in different regions of the intestinal tract of the rat. J Pharm Sci. 1998; 87(3): 360–366. Adson A, Burton PS, Raub TJ, Barsuhn CL, Audus KL, Ho NF. Passive diffusion of weak organic electrolytes across Caco-2 cell monolayers: uncoupling the contributions of hydrodynamic, transcellular, and paracellular barriers. J Pharm Sci. 1995; 84(10): 1197–1204. Selick HE, Beresford AP, Tarbit MH. The emerging importance of predictive ADME simulation in drug discovery. Drug Discov Today. 2002; 7(2): 109–116. Amidon GL, Lennemas H, Shah VP, Crison JR. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm Res. 1995; 12(3): 423–420. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 1997; 23(1–3): 3–25. Devane J. Oral drug delivery technology: addressing the solubility/permeability paradigm. Pharm Technol. 1998; Nov:68–80. Hussein AS, Lesko LJ, Lo KY, Shah VP, Volpe D, Williams RL. The biopharmaceutics classification system: highlights of the FDA s draft guidance. Dissolution technologies. 1999; May, article 1. Available at: http://www.dissolutiontech.com/DTresour/599articles/Biopharm_Class2_copy.html. Blume HH, Schug BS. The Biopharmaceutics Classification System (BCS): class III drugs—better candidates for BA/BE waiver? Eur J Pharm Sci. 1999; 9: 117–121. Streng WH, His SK, Helms PE, Tan HGH. General treatment of pH-solubility profiles of weak acids and bases and the effects of different acids on the solubility of a weak base. J Pharm Sci. 1984; 73(12): 1679–1684. Kramer SF, Flynn GL. Solubility of organic hydrochlorides. J Pharm Sci. 1972; 61(12): 1896–1904. Jorgensen WL, Duffy EM. Prediction of drug solubility from structure. Adv Drug Deliv Rev. 2002; 54: 355–366. Parshad H, Frydenvang K, Liljefors T, Larsen CS. Correlation of aqueous solubility of salts of benzylamine with experimentally and theoretically derived parameters: a multivariate data analysis approach. Int J Pharm. 2002; 237: 193–207. Bergstrom CAS, Norinder U, Luthman K, Artursson P. Experimental and computational screening models for prediction of aqueous drug solubility. Pharm Res. 2002; 19(2): 182–188. Maeda H, Wu J, Sawa T, Matsumura Y, Hori K. Tumor vascular permeability and the EPR effect in macromolecular therapeutics: a review. J Controlled Rel. 2000; 65: 271–284. Gao H, Shanmugasundaram V, Lee P. Estimation of aqueous solubility of organic compounds with QSPR approach. Pharm Res. 2002; 19(2): 497–503. Yoshida F, Topliss JG. QSAR model for drug human oral bioavailability. J Med Chem. 2000; 43: 2575–2585. Balon K, Riebesehl BU, Muller BW. Drug liposome partioning as a tool for the prediction of human passive intestinal absorption. Pharm Res. 1999; 16(6): 882–888. Wells JI. Pharmaceutical Preformulation. Chichester, England: Ellis Horwell Ltd; 1988: 14. “Clinical Pharmacology,” online at Gold Standard Multimedia. Available at: http://www.gsm.com. ACD Labs, Toronto, Canada.