Obesity has become a worldwide challenge with significant health and socioeconomic implications. One of the major implications is its impact on drug therapy. In order to gain a better understanding of this impact, we surveyed the regulatory guidances, the newly approved molecular entity drug products, and drug product labels in the Physician's Desk Reference. This review summarizes the findings of the survey along with the existing knowledge on pharmacokinetic and pharmacodynamic changes associated with obesity.

Evaluation of organic anion transporting polypeptide (OATP) 1B1-mediated drug-drug interactions (DDIs) is an integral part of drug development and is recommended by regulatory agencies. In this study we compared various prediction methods and cutoff criteria based on in vitro inhibition data to assess the potential of a new molecular entity to inhibit OATP1B1 in vivo. In vitro (eg, IC 50 , f u,p ) and in vivo (eg, dose, C max , change in area under the curve [AUC]) data for 11 substrates and 61 inhibitors for OATP1B1 were obtained from literature and Drugs@FDA, which include 107 clinical (in vivo) DDI studies. Substrate dependency and variability of IC 50 values were noted. In addition to the ratio of unbound or total systemic concentration (I max,u and I max ) to IC 50 , maximum unbound inhibitor concentration at the inlet to the liver (I u,in,max ) was used for the estimation of "R value" where R = 1 + I u,in,max /IC 50 . Based on our analyses, I max /K i ࣙ 0.1, R ࣙ 1.04, or R ࣙ 1.1 seem to be appropriate for reducing the false-negative (FN) predictions. However, as compared with R ࣙ 1.1, I max /K i ࣙ 0.1 and R ࣙ 1.04 resulted in higher false positives (FPs) and lower true negatives (TNs). R ࣙ 1.1, I max,u /K i ࣙ 0.02, and R ࣙ 1.25 alone, or combined criterion of I max /K i ࣙ 0.1 and R ࣙ 1.25, were reasonable to determine the need to perform clinical DDI studies with OATP1B1 substrates with similar positive and negative predictive values. Possible reasons of FP or FN from different decision criteria should be considered when interpreting prediction results, and the variability in IC 50 determination needs to be understood and minimized.