Pengyue Zhang

Downregulation of Organic Anion Transporting Polypeptide (OATP) 1B1 Transport Function by Lysosomotropic Drug Chloroquine: Implication in OATP-Mediated Drug-Drug Interactions

Organic anion transporting polypeptide (OATP) 1B1 mediates the hepatic uptake of many drugs including lipid-lowering statins. Decreased OATP1B1 transport activity is often associated with increased systemic exposure of statins and statin-induced myopathy. Antimalarial drug chloroquine (CQ) is also used for long-term treatment of rheumatoid arthritis and systemic lupus erythematosus. CQ is lysosomotropic and inhibits protein degradation in lysosomes. The current studies were designed to determine the effects of CQ on OATP1B1 protein degradation, OATP1B1-mediated transport in OATP1B1-overexpressing cell line, and statin uptake in human sandwich-cultured hepatocytes (SCH). Treatment with lysosome inhibitor CQ increased OATP1B1 total protein levels in HEK293-OATP1B1 cells and in human SCH as determined by OATP1B1 immunoblot. In HEK293-FLAG-tagged OATP1B1 stable cell line, co-immunofluorescence staining indicated that intracellular FLAG-OATP1B1 is colocalized with lysosomal associated membrane glycoprotein (LAMP)-2, a marker protein of late endosome/lysosome. Enlarged LAMP-2-positive vacuoles with FLAG-OATP1B1 protein retained inside were readily detected in CQ-treated cells, consistent with blocking lysosomal degradation of OATP1B1 by CQ. In HEK293-OATP1B1 cells, without pre-incubation, CQ concentrations up to 100 μM did not affect OATP1B1-mediated [(3)H]E217G accumulation. However, pre-incubation with CQ at clinically relevant concentration(s) significantly decreased [(3)H]E217G and [(3)H]pitavastatin accumulation in HEK293-OATP1B1 cells and [(3)H]pitavastatin accumulation in human SCH. CQ pretreatment (25 μM, 2 h) resulted in ∼1.9-fold decrease in Vmax without affecting Km of OATP1B1-mediated [(3)H]E217G transport in HEK293-OATP1B1 cells. Pretreatment with monensin and bafilomycin A1, which also have lysosome inhibition activity, significantly decreased OATP1B1-mediated transport in HEK293-OATP1B1 cells. Pharmacoepidemiologic studies using data from the U.S. Food and Drug Administration Adverse Event Reporting System indicated that CQ plus pitavastatin, rosuvastatin, and pravastatin, which are minimally metabolized by the cytochrome P450 enzymes, led to higher myopathy risk than these statins alone. In summary, the present studies report novel findings that lysosome is involved in degradation of OATP1B1 protein and that pre-incubation with lysosomotropic drug CQ downregulates OATP1B1 transport activity. Our in vitro data in combination with pharmacoepidemiologic studies support that CQ has potential to cause OATP-mediated drug-drug interactions.

Graphic Mining of High-Order Drug Interactions and Their Directional Effects on Myopathy Using Electronic Medical Records

We propose to study a novel pharmacovigilance problem for mining directional effects of high‐order drug interactions on an adverse drug event (ADE). Our goal is to estimate each individual risk of adding a new drug to an existing drug combination. In this proof‐of‐concept study, we analyzed a large electronic medical records database and extracted myopathy‐relevant case control drug co‐occurrence data. We applied frequent itemset mining to discover frequent drug combinations within the extracted data, evaluated directional drug interactions related to these combinations, and identified directional drug interactions with large effect sizes. Furthermore, we developed a novel visualization method to organize multiple directional drug interaction effects depicted as a tree, to generate an intuitive graphical and visual representation of our data‐mining results. This translational bioinformatics approach yields promising results, adds valuable and complementary information to the existing pharmacovigilance literature, and has the potential to impact clinical practice.

A Mixture Dose–Response Model for Identifying High-Dimensional Drug Interaction Effects on Myopathy Using Electronic Medical Record Databases

Interactions between multiple drugs may yield excessive risk of adverse effects. This increased risk is not uniform for all combinations, although some combinations may have constant adverse effect risks. We developed a statistical model using medical record data to identify drug combinations that induce myopathy risk. Such combinations are revealed using a novel mixture model, comprised of a constant risk model and a dose–response risk model. The dose represents the number of drug combinations. Using an empirical Bayes estimation method, we successfully identified high‐dimensional (two to six) drug combinations that are associated with excessive myopathy risk at significantly low local false‐discovery rates. From the curve of a dose–response model and high‐dimensional drug interaction data, we observed that myopathy risk increases as the drug interaction dimension increases. This is the first time that such a dose–response relationship for high‐dimensional drug interactions was observed and extracted from the medical record database.

Translational High-Dimensional Drug Interaction Discovery and Validation Using Health Record Databases and Pharmacokinetics Models

Polypharmacy increases the risk of drug–drug interactions (DDIs). Combining epidemiological studies with pharmacokinetic modeling, we detected and evaluated high‐dimensional DDIs among 30 frequent drugs. Multidrug combinations that increased the risk of myopathy were identified in the US Food and Drug Administration Adverse Event Reporting System (FAERS) and electronic medical record (EMR) databases by a mixture drug‐count response model. CYP450 inhibition was estimated among the 30 drugs in the presence of 1 to 4 inhibitors using in vitro / in vivo extrapolation. Twenty‐eight three‐way and 43 four‐way DDIs had significant myopathy risk in both databases and predicted increases in the area under the concentration–time curve ratio (AUCR) >2‐fold. The high‐dimensional DDI of omeprazole, fluconazole, and clonidine was associated with a 6.41‐fold (FAERS) and 18.46‐fold (EMR) increased risk of myopathy local false discovery rate (<0.005); the AUCR of omeprazole in this combination was 9.35. The combination of health record informatics and pharmacokinetic modeling is a powerful translational approach to detect high‐dimensional DDIs.

Mixture drug-count response model for the high-dimensional drug combinatory effect on myopathy

Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDAs adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The count indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models parameters and local false discovery rate estimates are evaluated through statistical simulation studies.

Regulation of Organic Anion Transporting Polypeptides (OATP) 1B1- and OATP1B3-Mediated Transport: An Updated Review in the Context of OATP-Mediated Drug-Drug Interactions

Organic anion transporting polypeptides (OATP) 1B1 and OATP1B3 are important hepatic transporters that mediate the uptake of many clinically important drugs, including statins from the blood into the liver. Reduced transport function of OATP1B1 and OATP1B3 can lead to clinically relevant drug-drug interactions (DDIs). Considering the importance of OATP1B1 and OATP1B3 in hepatic drug disposition, substantial efforts have been given on evaluating OATP1B1/1B3-mediated DDIs in order to avoid unwanted adverse effects of drugs that are OATP substrates due to their altered pharmacokinetics. Growing evidences suggest that the transport function of OATP1B1 and OATP1B3 can be regulated at various levels such as genetic variation, transcriptional and post-translational regulation. The present review summarizes the up to date information on the regulation of OATP1B1 and OATP1B3 transport function at different levels with a focus on potential impact on OATP-mediated DDIs.

Three‐Component Mixture Model‐Based Adverse Drug Event Signal Detection for the Adverse Event Reporting System

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is an important source for detecting adverse drug event (ADE) signals. In this article, we propose a three‐component mixture model (3CMM) for FAERS signal detection. In 3CMM, a drug‐ADE pair is assumed to have either a zero relative risk (RR), or a background RR (mean RRu2009=u20091), or an increased RR (mean RR >1). By clearly defining the second component (mean RRu2009=u20091) as the null distribution, 3CMM estimates local false discovery rates (FDRs) for ADE signals under the empirical Bayes framework. Compared with existing approaches, the local FDRs top signals have noninferior or better sensitivities to detect true signals in both FAERS analysis and simulation studies. Additionally, we identify that the top signals of different approaches have different patterns, and they are complementary to each other.

Translational biomedical informatics and pharmacometrics approaches in the drug interactions research

Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.