Utilizing Large Electronic Medical Record Data Sets to Identify Novel Drug-Gene Interactions for Commonly Used Drugs.

Real-world prescribing of drugs differs from the experimental systems, physiological-pharmacokinetic models, and clinical trials used in drug development and licensing, with drugs often used in patients with multiple comorbidities with resultant polypharmacy. The increasing availability of large biobanks linked to electronic healthcare records enables the potential to identify novel drug-gene interactions in large populations of patients. In this study we used three Scottish cohorts and UK Biobank to identify drug-gene interactions for the 50 most commonly used drugs and 162 variants in genes involved in drug pharmacokinetics. We defined two phenotypes based upon prescribing behavior-drug-stop or dose-decrease. Using this approach, we replicate 11 known drug-gene interactions including, for example, CYP2C9/CYP2C8 variants and sulfonylurea/thiazolidinedione prescribing and ABCB1/ABCG2 variants and statin prescribing. We identify eight novel associations after Bonferroni correction, three of which are replicated or validated in the UK Biobank or have other supporting results: The C-allele at rs4918758 in CYP2C9 was associated with a 25% (15-44%) lower odds of dose reduction of quinine, P = 1.6 × 10-5 ; the A-allele at rs9895420 in ABCC3 was associated with a 46% (24-62%) reduction in odds of dose reduction with doxazosin, P = 1.2 × 10-4 , and altered blood pressure response in the UK Biobank; the CYP2D6*2 variant was associated with a 30% (18-40%) reduction in odds of stopping ramipril treatment, P = 1.01 × 10-5 , with similar results seen for enalapril and lisinopril and with other CYP2D6 variants. This study highlights the scope of using large population bioresources linked to medical record data to explore drug-gene interactions at scale.

The impact of pharmacogenetic testing in patients exposed to polypharmacy: a scoping review.

Polypharmacy poses a significant risk for adverse reactions. While there are clinical decision support tools to assist clinicians in medication management, pharmacogenetic testing to identify potential drug-gene or drug-drug-gene interactions is not widely implemented in the clinical setting. A PRISMA-compliant scoping review was performed to determine if pharmacogenetic testing for absorption, distribution, metabolism, and excretion (ADME)-related genetic variants is associated with improved clinical outcomes in patients with polypharmacy. Six studies were reviewed. Five reported improved clinical outcomes, reduced side effects, reduction in the number of drugs used, or reduced healthcare utilization. The reviewed studies varied in methodological quality, risk of bias, and outcome measures. Age, diet, disease state, and treatment adherence also influence drug response, and may confound the relationship between genetic polymorphisms and treatment outcomes. Further studies using a randomized control design are needed. We conclude that pharmacogenetic testing represents an opportunity to improve health outcomes in patients exposed to polypharmacy, particularly in patients with psychiatric disorders and the elderly.

Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis.

Drug-drug interactions (DDIs) and drug-gene interactions (DGIs) are well known mediators for adverse drug reactions (ADRs), which are among the leading causes of death in many countries. Because physiologically based pharmacokinetic (PBPK) modeling has demonstrated to be a valuable tool to improve pharmacotherapy affected by DDIs or DGIs, it might also be useful for precision dosing in extensive interaction network scenarios. The presented work proposes a novel approach to extend the prediction capabilities of PBPK modeling to complex drug-drug-gene interaction (DDGI) scenarios. Here, a whole-body PBPK network of simvastatin was established, including three polymorphisms (SLCO1B1 (rs4149056), ABCG2 (rs2231142), and CYP3A5 (rs776746)) and four perpetrator drugs (clarithromycin, gemfibrozil, itraconazole, and rifampicin). Exhaustive network simulations were performed and ranked to optimize 10,368 DDGI scenarios based on an exposure marker cost function. The derived dose recommendations were translated in a digital decision support system, which is available at simvastatin.precisiondosing.de. Although the network covers only a fraction of possible simvastatin DDGIs, it provides guidance on how PBPK modeling could be used to individualize pharmacotherapy in the future. Furthermore, the network model is easily extendable to cover additional DDGIs. Overall, the presented work is a first step toward a vision on comprehensive precision dosing based on PBPK models in daily clinical practice, where it could drastically reduce the risk of ADRs.

Drug-Drug Interactions of 257 Antineoplastic and Supportive Care Agents With 7 Anticoagulants: A Comprehensive Review of Interactions and Mechanisms.

Data on drug-drug interactions (DDI) of antineoplastic drugs with anticoagulants is scarce. We aim to evaluate factors associated with DDI of antineoplastic and supportive care drugs with anticoagulants resulting in modification of pharmacokinetics of these last mentioned. A literature review on DDI databases and summaries of products characteristics (SmPC) was done. Drug-drug interactions of 257 antineoplastic and supportive care drugs with direct oral anticoagulants (DOACs), warfarin, enoxaparin, or fondaparinux were categorized as no clinically significant expected DDI, potentially weak DDI, potentially clinically significant DDI, and recommendation against coadministration. Logistic regression models were performed to analyze the association between the dependent variable potentially clinically significant interaction/recommendation against coadministration and the mechanisms of DDI. Of the 1799 associations, 84.4% were absence of DDI, 3.6% potentially weak DDI, 10.2% potentially clinically relevant DDI, and 2.0% recommendation against coadministration. Warfarin has higher DDI potential than other anticoagulants. Enoxaparin and fondaparinux have fewer DDI than others. There was no difference between DOACs. Drug-drug interactions with apixaban and rivaroxaban was independently associated with the absence of CYP3A4 competition, P-glycoprotein inhibition, CYP3A4 induction, and drug class of tyrosine kinase inhibitors. Drug-drug interactions with dabigatran and edoxaban was associated with inhibition of P-glycoprotein and tyrosine kinase inhibitors. Warfarin, induction of CYP3A4, and inhibition of CYP2C9. Enoxaparin and fondaparinux, only tyrosine kinase inhibitors. Direct oral anticoagulants did not differ regarding DDI with antineoplastic agents. Warfarin presented more DDI than other anticoagulants. P-glycoprotein inhibition and CYP3A4 induction were independently associated with DDI of antineoplastic agents with DOACs.

Prediction of the impact of CYP2C9 and VKORC1 genotypes on warfarin-sorafenib interactions in whites and Asians.

Aim: To predict the impact of the different CYP2C9 and VKORC1 genotypes on warfarin-sorafenib interactions in whites and Asians. Materials & methods: The influences of the CYP2C9*1/*3 and VKORC1 -1639 A/A genotypes on increases in anticoagulation responses (international normalized ratio [INR]) in the presence of sorafenib were predicted using the population pharmacokinetic/pharmacodynamic (PK/PD) model in whites and Asians. Results: INRs were predicted to be 2.0-2.1- versus 1.8-1.9-times higher in the presence of sorafenib in the CYP2C9 (*1/*1 vs *1/*3) groups than those for warfarin alone in both whites and Asians. INRs were also predicted to be 2.1-2.2- versus 1.9-2.1-times higher in the VKORC1 (GG or GA vs AA) groups. Conclusion: Warfarin-sorafenib interactions might be similar irrespective of CYP2C9 and VKORC1 genotypes or ethnicity.

Severe hyperglycaemia following pazopanib treatment: The role of drug-drug-gene interactions in a patient with metastatic renal cell carcinoma-A case report.

WHAT IS KNOWN AND OBJECTIVE: Pazopanib is a tyrosine kinase inhibitor with hyperglycaemia as a known adverse event, but case reports of severe hyperglycaemia are exceptional. We report a case of severe hyperglycaemia following pazopanib administration in a patient with metastatic renal cell carcinoma. CASE SUMMARY: Severe hyperglycaemia developed in a patient one month following initiation of pazopanib therapy. As drug-drug-gene interactions may lead to hyperglycaemia, pharmacogenetic assessment was requested. The obtained findings indicated intermediate function of both OATP1B1 and P-glycoprotein transporters, which may cause prolonged pazopanib bioavailability and increased toxicity. Pazopanib was discontinued and, following patient recovery, was reintroduced at a lower dose. WHAT IS NEW AND CONCLUSION: The pharmacogenetic profiling of the patient on polypharmacy enabled better management of pazopanib therapy.

A New Strategy for Identifying Mechanisms of Drug-drug Interaction Using Transcriptome Analysis: Compound Kushen Injection as a Proof of Principle.

Drug-drug interactions (DDIs), especially with herbal medicines, are complex, making it difficult to identify potential molecular mechanisms and targets. We introduce a workflow to carry out DDI research using transcriptome analysis and interactions of a complex herbal mixture, Compound Kushen Injection (CKI), with cancer chemotherapy drugs, as a proof of principle. Using CKI combined with doxorubicin or 5-Fu on cancer cells as a model, we found that CKI enhanced the cytotoxic effects of doxorubicin on A431 cells while protecting MDA-MB-231 cells treated with 5-Fu. We generated and analysed transcriptome data from cells treated with single treatments or combined treatments and our analysis showed that opposite directions of regulation for pathways related to DNA synthesis and metabolism which appeared to be the main reason for different effects of CKI when used in combination with chemotherapy drugs. We also found that pathways related to organic biosynthetic and metabolic processes might be potential targets for CKI when interacting with doxorubicin and 5-Fu. Through co-expression analysis correlated with phenotype results, we selected the MYD88 gene as a candidate major regulator for validation as a proof of concept for our approach. Inhibition of MYD88 reduced antagonistic cytotoxic effects between CKI and 5-Fu, indicating that MYD88 is an important gene in the DDI mechanism between CKI and chemotherapy drugs. These findings demonstrate that our pipeline is effective for the application of transcriptome analysis to the study of DDIs in order to identify candidate mechanisms and potential targets.

The multi-factorial nature of clinical multidrug resistance in cancer.

Curative cancer therapy remains a major challenge particularly in cancers displaying multidrug resistance (MDR). The MDR phenotype is characterized by cross-resistance to a wide array of anticancer drugs harboring distinct structures and mechanisms of action. The multiple factors involved in mediating MDR may include host factors, tumor factors as well as tumor-host interactions. Among the host factors are genetic variants and drug-drug interactions. The plethora of tumor factors involves decreased drug uptake primarily via impaired influx transporters, increased drug efflux predominantly due to the overexpression of MDR efflux transporters of the ATP-binding cassette superfamily or due to drug efflux mediated by extracellular vesicles (EVs) or drug-loaded lysosomes undergoing exocytosis, deregulation of cell death mechanisms (i.e. anti-apoptotic modalities), enhanced DNA damage repair, epigenetic alterations and/or deregulation of microRNAs. The intratumor heterogeneity and dynamics, along with cancer stem cell plasticity, are important tumor factors. Among the tumor-host interactions are the role of the tumor microenvironment, selective pressure of various stressor conditions and agents, acidic pH and the intracellular transfer of traits mediated by EVs. The involvement of these diverse factors in MDR, highlights the need for precision medicine and real-time personalized treatments of individual cancer patients. In this review, written by a group of researchers from COST Action STRATAGEM "New diagnostic and therapeutic tools against multidrug resistant tumors", we aim to bring together these multidisciplinary and interdisciplinary features of MDR cancers. Importantly, it is becoming increasingly clear that deciphering the molecular mechanisms underlying anticancer drug resistance, will pave the way towards the development of novel precision medicine treatment modalities that are able to surmount distinct and well-defined mechanisms of anticancer drug resistance.

Drug interactions between warfarin and lenvatinib in a patient with the CYP2C9*1/*3 and VKORC1-1639G/A genotype.

WHAT IS KNOWN AND OBJECTIVE: Lenvatinib inhibits CYP2C8. (S)-Warfarin is metabolized to (S)-7-hydroxywarfarin by CYP2C9 and (S)-4'-hydroxywarfarin by CYP2C8. Here, we report drug interactions between warfarin and lenvatinib in a patient with CYP2C9*1/*3. CASE SUMMARY: The patient was administered warfarin. His international normalized ratio (INR) was 1.92 before lenvatinib administration. On day 8 after beginning 12 mg/day lenvatinib, plasma trough concentrations of lenvatinib and (S)-warfarin were 33.3 ng/mL and 0.67 µg/mL, respectively. On day 10, his INR increased to 3.48. WHAT IS NEW AND CONCLUSION: Lenvatinib-dependent (S)-warfarin inhibition could involve CYP2C9 and CYP2C8. After initiating warfarin plus lenvatinib, INR assays are necessary.

Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer.

Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.

Projected Prevalence of Actionable Pharmacogenetic Variants and Level A Drugs Prescribed Among US Veterans Health Administration Pharmacy Users.

Importance: Implementation of pharmacogenetic testing to guide drug prescribing has potential to improve drug response and prevent adverse events. Robust data exist for more than 30 gene-drug pairs linking genotype to drug response phenotypes; however, it is unclear which pharmacogenetic tests, if implemented, would provide the greatest utility for a given patient population. Objectives: To project the proportion of veterans in the US Veterans Health Administration (VHA) with actionable pharmacogenetic variants and evaluate how testing might be associated with prescribing decisions. Design, Setting, and Participants: This cross-sectional study included veterans who used national VHA pharmacy services from October 1, 2011, to September 30, 2017. Data analyses began April 26, 2018, and were completed February 6, 2019. Exposures: Receipt of level A drugs based on VHA pharmacy dispensing records. Main Outcomes and Measures: Projected prevalence of actionable pharmacogenetic variants among VHA pharmacy users based on variant frequencies from the 1000 Genomes Project and veteran demographic characteristics; incident number of level A prescriptions, and proportion of new level A drug recipients projected to carry an actionable pharmacogenetic variant. Results: During the study, 7 769 359 veterans (mean [SD] age, 58.1 [17.8] years; 7 021 504 [90.4%] men) used VHA pharmacy services. It was projected that 99% of VHA pharmacy users would carry at least 1 actionable pharmacogenetic variant. Among VHA pharmacy users, 4 259 153 (54.8%) received at least 1 level A drug with 1 188 124 (15.3%) receiving 2 drugs, and 912 189 (11.7%) receiving 3 or more drugs. The most common incident prescriptions during the study were tramadol (923 671 new recipients), simvastatin (533 928 new recipients), citalopram (266 952 new recipients), and warfarin (205 177 new recipients). Gene-drug interactions projected to have substantial clinical impacts in the VHA population include the interaction of SLCO1B1 with simvastatin (1 988 956 veterans [25.6%]), CYP2D6 with tramadol (318 544 veterans [4.1%]), and CYP2C9 or VKORC1 with warfarin (7 163 349 veterans [92.2%]). Conclusions and Relevance: Clinically important pharmacogenetic variants are highly prevalent in the VHA population. Almost all veterans would carry an actionable variant, and more than half of the population had been exposed to a drug affected by these variants. These results suggest that pharmacogenetic testing has the potential to affect pharmacotherapy decisions for commonly prescribed outpatient medications for many veterans.

A case of simvastatin-induced myopathy with SLCO1B1 genetic predisposition and co-ingestion of linagliptin and Stevia rebaudiana.

WHAT IS KNOWN AND OBJECTIVE: SLCO1B1 T521>C variant carriers are susceptible to simvastatin-induced myopathy. We report a patient who developed rhabdomyolysis possibly triggered by a drug-drug and/or herb-drug interaction. CASE DESCRIPTION: A 69-year-old man presented with myalgia and weakness progressing to severe rhabdomyolysis. He had been taking 40 mg simvastatin daily for 10 years and recently consumed supplements, including Stevia rebaudiana and linagliptin. Genotyping revealed he carried one copy of SLCO1B1 T521>C and two copies of ABCG2 C421>A. WHAT IS NEW AND CONCLUSION: Despite apparent long-term safe administration, co-ingestion of simvastatin and other CYP3A4 inhibitors may result in severe myopathy in those at increased genetic risk.

Druggability of Coronary Artery Disease Risk Loci.

BACKGROUND: Genome-wide association studies have identified multiple loci associated with coronary artery disease and myocardial infarction, but only a few of these loci are current targets for on-market medications. To identify drugs suitable for repurposing and their targets, we created 2 unique pipelines integrating public data on 49 coronary artery disease/myocardial infarction-genome-wide association studies loci, drug-gene interactions, side effects, and chemical interactions. METHODS: We first used publicly available genome-wide association studies results on all phenotypes to predict relevant side effects, identified drug-gene interactions, and prioritized candidates for repurposing among existing drugs. Second, we prioritized gene product targets by calculating a druggability score to estimate how accessible pockets of coronary artery disease/myocardial infarction-associated gene products are, then used again the genome-wide association studies results to predict side effects, excluded loci with widespread cross-tissue expression to avoid housekeeping and genes involved in vital processes and accordingly ranked the remaining gene products. RESULTS: These pipelines ultimately led to 3 suggestions for drug repurposing: pentolinium, adenosine triphosphate, and riociguat (to target CHRNB4, ACSS2, and GUCY1A3, respectively); and 3 proteins for drug development: LMOD1 (leiomodin 1), HIP1 (huntingtin-interacting protein 1), and PPP2R3A (protein phosphatase 2, regulatory subunit b-double prime, α). Most current therapies for coronary artery disease/myocardial infarction treatment were also rediscovered. CONCLUSIONS: Integration of genomic and pharmacological data may prove beneficial for drug repurposing and development, as evidence from our pipelines suggests.

Repaglinide-irbesartan drug interaction: effects of SLCO1B1 polymorphism on repaglinide pharmacokinetics and pharmacodynamics in Chinese population.

PURPOSE: On account of the potential inhibition of OATP1B1 (organic anion transporting polypeptide) by angiotensin II receptor blockers (ARBs) and the effects of SLCO1B1 (solute carrier organic anion transporter family member) polymorphism, the aim of current study is to assess the impact of ARBs on the pharmacokinetics (PK) and pharmacodynamics (PD) of repaglinide in Chinese healthy volunteers with different SLCO1B1 genotypes. METHODS: The in vitro study was conducted on irbesartan, valsartan, olmesartan, and losartan by using HEK293 cells transfected with OATP1B1. Data on drug interactions between repaglinide and irbesartan from 21 healthy Chinese-Han male volunteers were collected and analyzed. RESULTS: IC50 from in vitro study suggested irbesartan was the most potent inhibitor of OATP1B1 transporter. Clinical data from single dose of repaglinide indicated SLCO1B1 c.521 T>C polymorphism influenced the PK and PD of repaglinide in healthy Chinese-Han male volunteers. In subjects with SLCO1B1 c.521 TT genotype, irbesartan comedication increased the exposure of repaglinide. In details, the peak plasma concentration [Cmax] increased 84% (P = 0.003) and the area under the curve of plasma concentration 0-8 h [AUC0-8] increased 34% (P = 0.004), while the minimum blood glucose concentration [Cmin] decreased 33.8% (P = 0.005). No significant change was observed in repaglinide exposure in subjects with SLCO1B1 c.521 TC genotype in presence or absence of irbesartan. CONCLUSION: SLCO1B1 c.521 T>C polymorphism affects the PK of repaglinide in Chinese population. Irbesartan increased repaglinide exposure in subjects with SLCO1B1 c.521 TT genotype, but not SLCO1B1 c.521 TC genotype.

A Phase Ib Study of Sorafenib (BAY 43-9006) in Patients with Kaposi Sarcoma.

LESSONS LEARNED: Oral targeted agents are desirable for treatment of Kaposi sarcoma (KS); however, in patients with HIV, drug-drug interactions must be considered. In this study to treat KS, sorafenib was poorly tolerated at doses less than those approved by the U.S. Food and Drug Administration for hepatocellular carcinoma and other cancers, and showed only modest activity.Sorafenib's metabolism occurs via the CYP3A4 pathway, which is inhibited by ritonavir, a commonly used antiretroviral agent used by most patients in this study. Strong CYP3A4 inhibition by ritonavir may contribute to the observed sorafenib toxicity.Alternate antiretroviral agents without predicted interactions are preferred for co-administration in patients with HIV and cancers for which sorafenib is indicated. BACKGROUND: We conducted a phase Ib study of sorafenib, a vascular epithelial growth factor receptor (VEGFR), c-kit, and platelet derived growth factor receptor (PDGFR)-targeted treatment in Kaposi sarcoma (KS). We evaluated drug-drug interactions between sorafenib and ritonavir, an HIV medication with strong CYP3A4 inhibitory activity. METHODS: Two cohorts were enrolled: HIV-related KS on ritonavir (Cohort R) and HIV-related or classical KS not receiving ritonavir (Cohort NR). Sorafenib dose level 1 in cohort R (R1) was 200 mg daily and 200 mg every 12 hours in cohort NR (NR1). Steady-state pharmacokinetics were evaluated at cycle 1, day 8. KS responses and correlative factors were assessed. RESULTS: Ten patients (nine HIV+) were enrolled: R1 (eight), NR1 (two). Median CD4+ count (HIV+) was 500 cells/µL. Dose-limiting toxicities (DLTs) were grade 3 elevated lipase (R1), grade 4 thrombocytopenia (R1), and grade 3 hand-foot syndrome (NR1). Two of seven evaluable patients had a partial response (PR; 29%; 95% CI 4%-71%). Steady-state area under the curve of the dosing interval (AUCTAU) of sorafenib was not significantly affected by ritonavir; however, a trend for decreased AUCTAU of the CYP3A4 metabolite sorafenib-N-oxide (3.8-fold decrease; p = .08) suggests other metabolites may be increased. CONCLUSION: Sorafenib was poorly tolerated, and anti-KS activity was modest. Strong CYP3A4 inhibitors may contribute to sorafenib toxicity, and ritonavir has previously been shown to be a CYP3A4 inhibitor. Alternate antiretroviral agents without predicted interactions should be used when possible for concurrent administration with sorafenib. The Oncologist 2017;22:505-e49.

LDL cholesterol counteracts the antitumour effect of tyrosine kinase inhibitors against renal cell carcinoma.

BACKGROUND: Treatment with tyrosine kinase inhibitors (TKIs) significantly improves survival of patients with renal cell carcinoma (RCC). However, about one-quarter of the RCC patients are primarily refractory to treatment with TKIs. METHODS: We examined viability of RCC and endothelial cells treated with low-density lipoprotein (LDL) and/or TKIs. Next, we validated the potential role of PI3K/AKT signalling in LDL-mediated TKI resistance. Finally, we examined the effect of a high-fat/high-cholesterol diet on the response of RCC xenograft tumours to sunitinib. RESULTS: The addition of LDL cholesterol increases activation of PI3K/AKT signalling and compromises the antitumour efficacy of TKIs against RCC and endothelial cells. Furthermore, RCC xenograft tumours resist TKIs in mice fed a high-fat/high-cholesterol diet. CONCLUSIONS: The ability of renal tumours to maintain their cholesterol homoeostasis may be a critical component of TKI resistance in RCC patients.

Avoidable drug-gene conflicts and polypharmacy interactions in patients participating in a personalized medicine program.

AIM: Determine the ability of a pharmacogenetic service, PRIMER, to identify drug-gene (DGI) and drug-drug interactions (DDI) in patients across multiple conditions. PRIMER consists of patient selection criteria, a gene panel and actionable guidance for DGIs and DDIs. RESULTS: The average patient was prescribed 12 medications. PRIMER identified significant DGIs in 73% of patients tested, with 43% having more than one DGI. DDIs were found in 87% of patients. The most common actionable DGIs were for opioid, psychotropic and cardiovascular medications. CONCLUSION: The pairing of patient selection criteria, a multigene panel with evidence-based interpretation and review of DDIs maximizes the patients tested who have actionable benefit and alerts physicians to potentially critical adjustments needed for the patient's medication regimen.

Identification of gene-drug interactions that impact patient survival in TCGA.

BACKGROUND: With the advent of large scale biological data collection for various diseases, data analysis pipelines and workflows need to be established to build frameworks for integrative analysis. Here the authors present a pipeline for identifying disease specific gene-drug interactions using CNV (Copy Number Variation) and clinical data from the TCGA (The Cancer Genome Atlas) project. Two cancer types were selected for analysis, LGG (Brain lower grade glioma) and GBM (Glioblastoma multiforme), due to the possible progression from LGG to GBM in some cases. The copy number and clinical data were then used to preform survival analysis on a gene by gene basis on sub-populations of patients exposed to a given drug. RESULTS: Several gene-drug interactions are identified, where the copy number of a gene is associated to survival of a patient exposed to a certain drug. Both Irinotecan/HAS2 (Hyaluronan synthase 2) and Bevacizumab/PGAM1 (Phosphoglycerate mutase 1) are interactions found in this study with independent confirmation. Independent work in colon, breast cancer and leukemia (Györffy, Breast Cancer Res Treat 123:725-731, 2010; Mueller, Mol Cancer Ther 11:3024-3032, 2010; Hitosugi, Cancer Cell 13:585-600, 2012) showed these two interactions can lead to increased survival. CONCLUSION: While the pipeline produced several possible interactions where increased survival is linked to normal or increased copy number of a given gene for patients treated with a given drug, no instance of low copy number or full deletion was linked to increased survival. The development of this pipeline shows a promising utility to identify possible beneficial gene-drug interactions that could improve patient survival and may illustrate some of the problems inherent in this kind of analysis on these data.

Metabolomic and Genome-wide Association Studies Reveal Potential Endogenous Biomarkers for OATP1B1.

Transporter-mediated drug-drug interactions (DDIs) are a major cause of drug toxicities. Using published genome-wide association studies (GWAS) of the human metabolome, we identified 20 metabolites associated with genetic variants in organic anion transporter, OATP1B1 (P < 5 × 10-8 ). Of these, 12 metabolites were significantly higher in plasma samples from volunteers dosed with the OATP1B1 inhibitor, cyclosporine (CSA) vs. placebo (q-value < 0.2). Conjugated bile acids and fatty acid dicarboxylates were among the metabolites discovered using both GWAS and CSA administration. In vitro studies confirmed tetradecanedioate (TDA) and hexadecanedioate (HDA) were novel substrates of OATP1B1 as well as OAT1 and OAT3. This study highlights the use of multiple datasets for the discovery of endogenous metabolites that represent potential in vivo biomarkers for transporter-mediated DDIs. Future studies are needed to determine whether these metabolites can serve as qualified biomarkers for organic anion transporters. Quantitative relationships between metabolite levels and modulation of transporters should be established.