Kidney Cortical Transporter Expression across Species Using Quantitative Proteomics.

Limited understanding of species differences in kidney transporters is a critical knowledge gap for prediction of drug-induced acute kidney injury, drug interaction, and pharmacokinetics in humans. Here, we report protein abundance data of 19 transporters in the kidney cortex across five species (human, monkey, dog, rat, and mouse). In general, the abundance of all of the 19 membrane transporters was higher in preclinical species compared with human except for multidrug resistance protein 1 (MDR1), organic cation transporter (OCT) 3, and OCTN1. In nonhuman primate, the total abundance of 12 transporters for which absolute data were available was 2.1-fold higher (P = 0.025) relative to human but the percentage of distribution of these transporters was identical in both species. Multidrug resistance-associated protein (MRP) 4, OCTN2, organic anion transporter (OAT) 2, sodium/potassium-transporting ATPase, MRP3, SGLT2, OAT1, MRP1, MDR1, and OCT2 were expressed differently with cross-species variabilities of 8.2-, 7.4-, 6.1-, 5.9-, 5.4-, 5.2-, 4.1-, 3.3-, and 2.8-fold, respectively. Sex differences were only significant in rodents and dog. High protein-protein correlation was observed in OAT1 versus MRP2/MRP4 as well as OCT2 versus MATE1 in human and monkey. The cross-species and sex-dependent protein abundance data are important for animal to human scaling of drug clearance as well as for mechanistic understanding of kidney physiology and derisking of kidney toxicity for new therapeutic candidates in drug development.

Ultrasensitive Quantification of Drug-metabolizing Enzymes and Transporters in Small Sample Volume by Microflow LC-MS/MS.

Protein abundance data of drug-metabolizing enzymes and transporters (DMETs) are broadly applicable to the characterization of in vitro and in vivo models, in vitro to in vivo extrapolation (IVIVE), and interindividual variability prediction. However, the emerging need of DMET quantification in small sample volumes such as organ-on a chip effluent, organoids, and biopsies requires ultrasensitive protein quantification methods. We present an ultrasensitive method that relies on an optimized sample preparation approach involving acetone precipitation coupled with a microflow-based liquid chromatography-tandem mass spectrometry (µLC-MS/MS) for the DMET quantification using limited sample volume or protein concentration, i.e., liver tissues (1-100 mg), hepatocyte counts (~4000 to 1 million cells), and microsomal protein concentration (0.01-1 mg/ml). The method was applied to quantify DMETs in differential tissue S9 fractions (liver, intestine, kidney, lung, and heart) and cryopreserved human intestinal mucosa (i.e., CHIM). The method successfully quantified >75% of the target DMETs in the trypsin digests of 1 mg tissue homogenate, 15,000 hepatocytes, and 0.06 mg/ml microsomal protein concentration. The precision of DMET quantification measured as the coefficient of variation across different tissue weights, cell counts, or microsomal protein concentration was within 30%. The method confirmed significant extrahepatic abundance of non-cytochrome P450 enzymes such as dihydropyridine dehydrogenase (DPYD), epoxide hydrolases (EPXs), arylacetamide deacetylase (AADAC), paraoxonases (PONs), and glutathione S-transferases (GSTs). The ultrasensitive method developed here is applicable to characterize emerging miniaturized in vitro models and small volume biopsies. In addition, the differential tissue abundance data of the understudied DMETs will be important for physiologically-based pharmacokinetic (PBPK) modeling of drugs.

P-glycoprotein Substrate Assessment in Drug Discovery: Application of Modeling to Bridge Differential Protein Expression Across In Vitro Tools.

P-glycoprotein (P-gp) efflux assay is an integral part of discovery screening, especially for drugs requiring brain penetration as P-gp efflux ratio (ER) inversely correlates with brain exposure. However, significant variability in P-gp ER generated across cell lines can lead to misclassification of a P-gp substrate and subsequently disconnect with brain exposure data. We hypothesized that the ER depends on P-gp protein expression level in the in vitro assay. Quantitative proteomics and immunofluorescence staining were utilized to characterize P-gp protein expression and localization in four recombinant cell lines, over-expressing human or mouse P-gp isoforms, followed by functional evaluation. Efflux data generated in each cell line was compared against available rodent brain distribution data. The results suggested that the cell line with highest P-gp expression (hMDCK-MDR1 sourced from NIH) led to greatest dynamic range for efflux; thus, proving to be the most sensitive model to predict brain penetration. Cell lines with lower P-gp expression exhibited the greatest tendency for compound-dependent in vitro efflux saturation leading to false negative results. Ultimately, P-gp kinetics were characterized using a compartmental model to generate system-independent parameters to resolve such discrepancy. This study highlights the need for careful choice of well characterized P-gp in vitro tools and utility of modeling techniques to enable appropriate interpretation of the data.