TY - JOUR T1 - Measurement of Binding of Basic Drugs to Acidic Phospholipids Using Surface Plasmon Resonance and Incorporation of the Data into Mechanistic Tissue Composition Equations to Predict Steady-State Volume of Distribution JF - Drug Metabolism and Disposition JO - Drug Metab Dispos SP - 1789 LP - 1793 DO - 10.1124/dmd.111.040253 VL - 39 IS - 10 AU - Helen Small AU - Iain Gardner AU - Hannah M. Jones AU - John Davis AU - Malcolm Rowland Y1 - 2011/10/01 UR - http://dmd.aspetjournals.org/content/39/10/1789.abstract N2 - Acidic phospholipid binding plays an important role in determining the tissue distribution of basic drugs. This article describes the use of surface plasmon resonance to measure binding affinity (KD) of three basic drugs to phosphatidylserine, a major tissue acidic phospholipid. The data are incorporated into mechanistic tissue composition equations to allow prediction of the steady-state volume of distribution (Vss). The prediction accuracy of Vss using this approach is compared with the original methodology described by Rodgers et al. (J Pharm Sci 94:1259–1276), in which the binding to acidic phospholipids is calculated from the blood/plasma concentration ratio (BPR). The compounds used in this study [amlodipine, propranolol, and 3-dimethylaminomethyl-4-(4-methylsulfanyl-phenoxy)-benzenesulfonamide (UK-390957)] showed higher affinity binding to phosphatidylserine than to phosphatidylcholine. When the binding affinity to phosphatidylserine was incorporated into mechanistic tissue composition equations, the Vss was more accurately predicted for all three compounds by using the surface plasmon resonance measurement than by using the BPR to estimate acidic phospholipid binding affinity. The difference was particularly marked for UK-390957, a sulfonamide that has a high BPR due to binding to carbonic anhydrase. The novel approach described in this article allows the binding affinity of drugs to an acidic phospholipid (phosphatidylserine) to be measured directly and demonstrates the utility of the binding data in the prediction of Vss. ER -