Multidrug resistance (MDR) is a primary barrier to successful cancer treatment with small molecule drugs. One mechanism of resistance is through transporters which pump drug out of cancer cells before they exert any therapeutic effect. The most studied transporter is P-glycoprotein (Pgp), a member of the ATP Binding Cassette (ABC) superfamily of drug pumps. Prior research has focused on determining whether drugs are substrates of Pgp. Pgp specificity for FDA approved drugs is currently unclear due to technical variability in assays that quantify enzyme kinetics using non-cellular experimental models. Other approaches for overcoming MDR yielded selective Pgp inhibitors to increase intracellular drug accumulation. However, these selective inhibitors failed clinically. A quantitative, multifaceted approach is needed to characterize the MDR phenotype in cancer and optimize drug selection. Studying Pgp in the context of MDR, we improved Pgp specificity scores by leveraging new Pgp expression (cell lines, tissues) and function (drug screening) datasets. We experimentally and computationally integrated functional dataset information to better understand Pgp specificity using an approach based on underlying Michaelis-Menten enzyme kinetics. We obtained consensus scores for Pgp specificity across ~150 FDA approved oncology drugs and validated them experimentally in a subset of 76 substrates selected to represent the spectrum of drugs for Pgp specificity. These scores can be used to calibrate clinical diagnostics (Pgp expression), and our experimental platform can be used to quantify Pgp function in clinical samples. Overall, we have developed a parallel computational and experimental procedure to estimate Pgp selectivity in live cells. This procedure can be expanded to other drug transporters which contribute to MDR to further characterize this phenotype quantitatively.