The placenta acts as a barrier, excluding noxious substances whilst actively transferring nutrients to the fetus, mediated by various transporters. This study quantified the expression of key placental transporters in term human placenta (n=5) and BeWo, BeWo b30, and JEG-3 placenta cell lines. Combining these results with pregnancy physiologically-based pharmacokinetic (PBPK) modeling, we demonstrate the utility of proteomic analysis for predicting placental drug disposition and fetal exposure. Using targeted proteomics with QconCAT standards, we found significant expression of P-gp, BCRP, MRP2, MRP4, and MRP6 in the human placenta (0.05 - 0.25 pmol/mg membrane protein) with only regional differences observed for P-gp. Unexpectedly, both P-gp and BCRP were below the limit of quantification in the regularly used BeWo cells, indicating that this cell line may not be suitable for the study of placental P-gp and BCRP-mediated transport. In cellular and vesicular overexpression systems, P-gp and BCRP were detectable as expected. Vesicle batches showed consistent P-gp expression correlating with functional activity (N-methyl-quinidine (NMQ) transport). However, BCRP activity (Estrone 3-sulfate (E1S) transport) did not consistently align with expression levels. Incorporating in vitro transporter kinetic data, along with placental transporter abundance, into a PBPK model enabled the evaluation of fetal exposure. Simulation with a hypothetical drug indicated that estimating fetal exposure relies on the intrinsic clearances of relevant transporters. To minimize interlaboratory discrepancies, expression data was generated using consistent proteomic methodologies in the same lab. Integration of this data in pregnancy-PBPK modeling offers a promising tool to investigate maternal, placental and fetal drug exposure. Significance Statement This study quantified the expression of key transporters in human placenta and various placental cell lines, revealing significant expression variations. By integrating these data with PBPK modeling, the study highlights the importance of transporter abundance data in understanding and predicting placental drug disposition.