ABC transporters traffic drugs and their metabolites across membranes, making ABC-transporter expression levels a key factor regulating local drug concentrations in different tissues and individuals. Yet, quantification of ABC transporters remains challenging because they are large and low-abundance transmembrane proteins. Here we analyzed 200 samples of crude and membrane-enriched fractions from human liver, kidney, intestine, brain microvessels, and skin, by label-free quantitative mass spectrometry. We identified 32 (out of 48) ABC transporters: ABCD3 was the most abundant in liver, whereas ABCA8, ABCB2/TAP1, and ABCE1 were detected in all tissues. Interestingly, this atlas unveiled that ABCB2/TAP1 may have TAP2-independent functions in the brain, and that biliary atresia and control livers have quite different ABCtransporter profiles. We propose that meaningful biological information can be derived from a direct comparison of these datasets.

In vitro-in vivo extrapolation (IVIVE) linked with physiologically based pharmacokinetic (PBPK) modelling is used to predict the fates of drugs in patients. Ideally, the IVIVE-PBPK models should incorporate "systems" information accounting for characteristics of the specific target population. There is a paucity of such scaling factors in cancer, particularly microsomal protein per gram of liver (MPPGL) and cytosolic protein per gram of liver (CPPGL). In this study, cancerous and histologically normal liver tissue from 16 patients with colorectal liver metastasis (CRLM) were fractionated to microsomes and cytosol.Protein content was measured in homogenates, microsomes and cytosol. The loss of microsomal protein during fractionation was accounted for using corrections based on NADPH cytochrome P450 reductase activity in different matrices. MPPGL was significantly lower in cancerous tissue (24.8 ± 9.8 mg/g) than histologically normal tissue (39.0 ± 13.8 mg/g). CPPGL in cancerous tissue was 42.1 ± 12.9 mg/g compared with 56.2 ± 16.9 mg/g in normal tissue. No correlations between demographics (sex, age and BMI) and MPPGL or CPPGL were apparent in the data. The generated scaling factors together with assumptions regarding the relative volumes of cancerous versus non-cancerous tissue were used to simulate plasma exposure of drugs with different extraction ratios. The PBPK simulations revealed a substantial difference in drug exposure (AUC), up to 3.3-fold, when using typical scaling factors (healthy population) instead of disease-related parameters in cancer population. These indicate the importance of using population-specific scalars in IVIVE-PBPK for different disease states.