Evaluation of cynomolgus monkeys for the identification of endogenous biomarkers for hepatic transporter inhibition and as a translatable model to predict pharmacokinetic interactions with statins in humans.

Inhibition of hepatic transporters such as organic anion transporting polypeptides (OATPs) 1B can cause drug-drug interactions (DDIs). Determining the impact of perpetrator drugs on the plasma exposure of endogenous substrates for OATP1B could be valuable to assess the risk for DDIs early in drug development. As OATP1B orthologs are well conserved between human and monkey, we assessed in cynomolgus monkeys the endogenous OATP1B substrates that are potentially suitable to assess DDI risk in humans. The effect of rifampin (RIF), a potent inhibitor for OATP1B, on plasma exposure of endogenous substrates of hepatic transporters was measured. From the 18 biomarkers tested, RIF (18 mg/kg, oral) caused significant elevation of plasma unconjugated and conjugated bilirubin, which may be attributed to inhibition of cOATP1B1 and cOATP1B3 based on in vitro to in vivo extrapolation analysis. To further evaluate whether cynomolgus monkeys are a suitable translational model to study OATP1B-mediated DDIs, we determined the inhibitory effect of RIF on in vitro transport and pharmacokinetics of rosuvastatin (RSV) and atorvastatin (ATV). RIF strongly inhibited the uptake of RSV and ATV by cOATP1B1 and cOATP1B3 in vitro. In agreement with clinical observations, RIF (18 mg/kg, oral) significantly decreased plasma clearance and increased the area under the plasma concentration curve (AUC) of intravenously administered RSV by 2.8- and 2.7-fold, and increased the AUC and maximum plasma concentration of orally administered RSV by 6- and 10.3-fold, respectively. In contrast to clinical findings, RIF did not significantly increase plasma exposure of either intravenous or orally administered ATV, indicating species differences in the rate-limiting elimination pathways.

Statistical and graphical methods for quality control determination of high-throughput screening data.

High-throughput screening (HTS) is used in modern drug discovery to screen hundreds of thousands to millions of compounds on selected protein targets. It is an industrial-scale process relying on sophisticated automation and state-of-the-art detection technologies. Quality control (QC) is an integral part of the process and is used to ensure good quality data and mini mize assay variability while maintaining assay sensitivity. The authors describe new QC methods and show numerous real examples from their biologist-friendly Stat Server HTS application, a custom-developed software tool built from the commercially available S-PLUS and Stat Server statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables. It allows users to visualize HTS data and examine assay performance during the HTS campaign to quickly react to or avoid quality problems.

Improved statistical methods for hit selection in high-throughput screening.

High-throughput screening (HTS) plays a central role in modern drug discovery, allowing the rapid screening of large compound collections against a variety of putative drug targets. HTS is an industrial-scale process, relying on sophisticated automation, control, and state-of-the art detection technologies to organize, test, and measure hundreds of thousands to millions of compounds in nano- to microliter volumes. Despite this high technology, hit selection for HTS is still typically done using simple data analysis and basic statistical methods. The authors discuss in this article some shortcomings of these methods and present alternatives based on modern methods of statistical data analysis. Most important, they describe and show numerous real examples from the biologist-friendly Stat Server HTS application (SHS), a custom-developed software tool built on the commercially available S-PLUS and StatServer statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables.