PURPOSE: To determine the outward permeability of retina-choroid-sclera (RCS) layer for different ophthalmic drugs and to develop correlations between drug physicochemical properties and RCS permeability. METHODS: A finite volume model was developed to simulate pharmacokinetics in the eye following drug administration by intravitreal injection. The RCS permeability was determined for 32 compounds by best fitting the drug concentration-time profile obtained by simulation with previously reported experimental data. Multiple linear regression was then used to develop correlations between best fit RCS permeability and drugs physicochemical properties. RESULTS: The RCS drug permeabilities had values that ranged over 3 × 10(-6) m/s. Regression analysis for hydrophilic compounds showed that more than 92% of the variation in permeability values can be explained by correlative models of drug properties that include logarithm of the octanol-water partition coefficient (LogP), protein binding (PB), number of hydrogen bond acceptors (HBA), hydrogen bond donors (HBD), polar surface area (PSA) and dissociation constant (pKa) as independent variables. Regression analysis for lipophilic compounds showed that no significant correlation can be found between just physicochemical properties and RCS permeability. CONCLUSION: Using the RCS permeability obtained from this study for different drugs, one can predict pharmacokinetics of intravitreal drug delivery systems such as solid implants or colloidal systems. Furthermore, the developed correlations between RCS permeability and physicochemical properties of drugs are useful in early drug development by predicting RCS permeability and drug concentration in the vitreous without experimental data.
PURPOSE: To determine the outward permeability of retina-choroid-sclera (RCS) layer for different ophthalmic drugs and to develop correlations between drug physicochemical properties and RCS permeability. METHODS: A finite volume model was developed to simulate pharmacokinetics in the eye following drug administration by intravitreal injection. The RCS permeability was determined for 32 compounds by best fitting the drug concentration-time profile obtained by simulation with previously reported experimental data. Multiple linear regression was then used to develop correlations between best fit RCS permeability and drugs physicochemical properties. RESULTS: The RCS drug permeabilities had values that ranged over 3 × 10(-6) m/s. Regression analysis for hydrophilic compounds showed that more than 92% of the variation in permeability values can be explained by correlative models of drug properties that include logarithm of the octanol-water partition coefficient (LogP), protein binding (PB), number of hydrogen bond acceptors (HBA), hydrogen bond donors (HBD), polar surface area (PSA) and dissociation constant (pKa) as independent variables. Regression analysis for lipophilic compounds showed that no significant correlation can be found between just physicochemical properties and RCS permeability. CONCLUSION: Using the RCS permeability obtained from this study for different drugs, one can predict pharmacokinetics of intravitreal drug delivery systems such as solid implants or colloidal systems. Furthermore, the developed correlations between RCS permeability and physicochemical properties of drugs are useful in early drug development by predicting RCS permeability and drug concentration in the vitreous without experimental data.
Authors: W Liu; Q F Liu; R Perkins; G Drusano; A Louie; A Madu; U Mian; M Mayers; M H Miller Journal: Antimicrob Agents Chemother Date: 1998-06 Impact factor: 5.191
Authors: Marko Lamminsalo; Ella Taskinen; Timo Karvinen; Astrid Subrizi; Lasse Murtomäki; Arto Urtti; Veli-Pekka Ranta Journal: Pharm Res Date: 2018-05-31 Impact factor: 4.200
Authors: Yu Zhang; Hojjat Bazzazi; Raquel Lima E Silva; Niranjan B Pandey; Jordan J Green; Peter A Campochiaro; Aleksander S Popel Journal: Invest Ophthalmol Vis Sci Date: 2018-10-01 Impact factor: 4.799
Authors: Sahar Awwad; Abeer H A Mohamed Ahmed; Garima Sharma; Jacob S Heng; Peng T Khaw; Steve Brocchini; Alastair Lockwood Journal: Br J Pharmacol Date: 2017-10-10 Impact factor: 8.739