An Algorithm for Evaluating Potential Tissue Drug Distribution in Toxicology Studies from Readily Available Pharmacokinetic Parameters. Issue 10 (22nd July 2013)

• Record Type: Journal Article
• Authors: Poulin, Patrick; Dambach, Donna M.; Hartley, Dylan H.; Ford, Kevin; Theil, Frank‐Peter; Harstad, Eric; Halladay, Jason; Choo, Edna; Boggs, Jason; Liederer, Bianca M.; Dean, Brian; Diaz, Dolores
• Is Part Of: Journal of pharmaceutical sciences. Volume 102:Issue 10(2013:Oct.)
• Page Start: 3816
• Page End: 3829
• Publication Date: 2013-07-22
• Subjects: Pharmacy -- Periodicals; 615.1
• DOI: 10.1002/jps.23670
• Languages: English
• ISSNs: 0022-3549

Abstract

<abstract abstract-type="main" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Having an understanding of drug tissue accumulation can be informative in the assessment of target organ toxicities; however, obtaining tissue drug levels from toxicology studies by bioanalytical methods is labor‐intensive and infrequently performed. Additionally, there are no described methods for predicting tissue drug distribution for the experimental conditions in toxicology studies, which typically include non‐steady‐state conditions and very high exposures that may saturate several processes. The aim was the development of an algorithm to provide semiquantitative and quantitative estimates of tissue‐to‐plasma concentration ratios (<italic>K</italic><sub>p</sub>) for several tissues from readily available parameters of pharmacokinetics (PK) such as volume of distribution (<italic>V</italic><sub>d</sub>) and clearance of each drug, without performing tissue measurement <italic>in vivo</italic>. The computational approach is specific for the oral route of administration and non‐steady‐state conditions and was applied for a dataset of 29 Genentech small molecules such as neutral compounds as well as weak and strong organic bases. The maximum success rate in predicting <italic>K</italic><sub>p</sub> values within 2.5‐fold error of observed <italic>K</italic><sub>p</sub> values was 82% at low doses (<100 mg/kg) in preclinical species. Prediction accuracy was relatively lower with saturation at high doses (≥100 mg/kg); however, an approach to perform low‐to‐high dose extrapolations of <italic>K</italic><sub>p</sub> values was presented and applied successfully in most cases. An approach for the interspecies scaling was also applied successfully. Finally, the proposed algorithm was used in a case study and successfully predicted differential tissue distribution of two small‐molecule MET kinase inhibitors, which had different toxicity profiles in mice. This newly developed algorithm can be used to predict the partition coefficients <italic>K</italic><sub>p</sub> for small molecules in toxicology studies, which can be leveraged to optimize the PK drivers of tissue distribution in an attempt to decrease drug tissue level, and improve safety margins. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:3816–3829, 2013</p> </abstract>