UCSF

Real world data (RWD), data from various sources other than clinical trials, is increasingly being integrated into the research setting. In particular, electronic health records (EHRs), which serve as a clinical record to document a patient’s medical history as well as support administrative functions, have been an invaluable resource rich with patient data. Here we present three projects spanning four chapters where EHRs, in combination with clinical trials and pharmacokinetic and pharmacodynamic (PKPD) modelling, were used to extend and complement studies and findings in the laboratory focusing on transporter-mediated drug interactions. Transporter-mediated drug interactions have the potential to influence both drug efficacy as well as toxicity. During the clinical development of the Janus Kinase 2 (JAK2) inhibitor fedratinib, several patients developed symptoms similar to Wernicke’s encephalopathy, a life-threating disease caused by Vitamin B1 (thiamine) deficiency; subsequent in vitro studies showed that fedratinib is a potent inhibitor of ThTR-2. Motivated by this drug-nutrient interaction (DNI) observed in the fedratinib trial, we investigated if commonly used prescription drugs can inhibit ThTR-2. Using a multifaceted approach, we started with an in vitro high-throughput screen which was further complemented by quantitative structure activity relationship (QSAR) modelling and real world data. Our comprehensive analysis suggested that several marketed drugs inhibit ThTR-2 and may contribute to thiamine deficiency, especially in at-risk populations. In order to further explore the impact of these potential inhibitors in humans, we designed and conducted a clinical study in healthy volunteers. Interestingly, we observed that thiamine concentrations were higher when co-administered with trimethoprim, one of the potent, clinically relevant inhibitors identified in our screen. The maximum concentration achieved (Cmax) and area under the curve from 0 to 24 hours (AUC0-24) were 2.7- and 4.6-fold higher in the combination arm, respectively. We hypothesized that trimethoprim may inhibit OCT1, a hepatic uptake transporter, in addition to ThTR-2, which was supported using EHR data by comparing laboratory values of endogenous OCT1 biomarkers in patients prescribed trimethoprim versus patients not prescribed trimethoprim. Next, we shifted our focus to pharmacogenomics, that is, genetic factors that affect drug response. Response to allopurinol, the first line treatment for gout, is highly variable; the reduced function variant BCRP p.Q141K has been associated with poor response to allopurinol. Thus, we aimed to characterize the relationship between BCRP p.Q141K, allopurinol/oxypurinol, and serum uric acid (SUA) levels by performing a clinical trial, building a PKPD model, and mining EHRs. Our clinical study found that p.Q141K associated with longer half-life of oxypurinol and our PKPD model found that gender affected oxypurinol volume of distribution while BCRP genotype and kidney function were significant covariates for baseline SUA levels. Additionally, using RWD, we found that drugs that were clinical inhibitors of BCRP associated with increased SUA levels, suggesting the potential of these drugs to cause hyperuricemia. Finally, given the ongoing COVID19 pandemic, we conducted extensive in vitro experiments aimed at predicting the potential for 25 small molecule drugs in clinical trials for COVID19 to cause transporter-mediated drug-drug interactions (DDIs). We found that 21 of the drugs were predicted to cause a clinically relevant DDI, and we were able to provide preliminary validation of these in vitro findings using EHR data, including a database representing nearly 120,000 COVID19 patients.

Collectively, my dissertation research demonstrates how the integration of benchwork, clinical trials, and real world data provides us a new approach to translational research, bridging findings from the laboratory to patients.