Incorporating Human Dosimetry and Exposure into the Utilization of ToxCast In Vitro Screening Data for Chemical Prioritization

Humans are frequently exposed to chemicals that have undergone limited safety testing. To reduce the number of untested chemicals and prioritize limited testing resources, multiple governmental programs are using high throughput in vitro toxicity screens for assessing effects across multiple cellular pathways. In this study, metabolic clearance and plasma protein binding were experimentally measured for 39 of the 309 ToxCast Phase I chemicals. The experimental data was modeled using pharmacokinetics for estimating human oral equivalent doses that would produce steady state in vivo concentrations equivalent to ToxCast in vitro AC[50] values. The range of oral equivalent doses for the ToxCast assays was compared with human oral exposure estimates to assess whether in vitro bioactivity could occur at human exposure levels. Two of the 39 chemicals had overlapping oral equivalent doses and human exposures and would not have been identified using the rank potencies of the AC50 values. These results demonstrate the importance of incorporating dosimetry and exposure when using high throughput in vitro data to identify the highest priorities for further testing and risk management.Master of Science in Public Healt

Common and rare genetic markers of lipid variation in subjects with type 2 diabetes from the ACCORD clinical trial

Background Individuals with type 2 diabetes are at an increased risk of cardiovascular disease. Alterations in circulating lipid levels, total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TG) are heritable risk factors for cardiovascular disease. Here we conduct a genome-wide association study (GWAS) of common and rare variants to investigate associations with baseline lipid levels in 7,844 individuals with type 2 diabetes from the ACCORD clinical trial. Methods DNA extracted from stored blood samples from ACCORD participants were genotyped using the Affymetrix Axiom Biobank 1 Genotyping Array. After quality control and genotype imputation, association of common genetic variants (CV), defined as minor allele frequency (MAF) ≥ 3%, with baseline levels of TC, LDL, HDL, and TG was tested using a linear model. Rare variant (RV) associations (MAF < 3%) were conducted using a suite of methods that collapse multiple RV within individual genes. Results Many statistically significant CV ( p  < 1 × 10 −8 ) replicate findings in large meta-analyses in non-diabetic subjects. RV analyses also confirmed findings in other studies, whereas significant RV associations with CNOT2 , HPN-AS1 , and SIRPD appear to be novel ( q  < 0.1). Discussion Here we present findings for the largest GWAS of lipid levels in people with type 2 diabetes to date. We identified 17 statistically significant ( p  < 1 × 10 −8 ) associations of CV with lipid levels in 11 genes or chromosomal regions, all of which were previously identified in meta-analyses of mostly non-diabetic cohorts. We also identified 13 associations in 11 genes based on RV, several of which represent novel findings

Incorporating Concomitant Medications into Genome-Wide Analyses for the Study of Complex Disease and Drug Response

Given the high costs of conducting a drug-response trial, researchers are now aiming to use retrospective analyses to conduct genome-wide association studies (GWAS) to identify underlying genetic contributions to drug-response variation. To prevent confounding results from a GWAS to investigate drug response, it is necessary to account for concomitant medications, defined as any medication taken concurrently with the primary medication being investigated. We use data from the Action to Control Cardiovascular Disease (ACCORD) trial in order to implement a novel scoring procedure for incorporating concomitant medication information into a linear regression model in preparation for GWAS. In order to accomplish this, two primary medications were selected: thiazolidinediones and metformin because of the wide-spread use of these medications and large sample sizes available within the ACCORD trial. A third medication, fenofibrate, along with a known confounding medication, statin, were chosen as a proof-of-principle for the scoring procedure. Previous studies have identified SNP rs7412 as being associated with statin response. Here we hypothesize that including the score for statin as a covariate in the GWAS model will correct for confounding of statin and yield a change in association at rs7412. The response of the confounded signal was successfully diminished from p = 3.19 × 10−7 to p = 1.76 × 10−5, by accounting for statin using the scoring procedure presented here. This approach provides the ability for researchers to account for concomitant medications in complex trial designs where monotherapy treatment regimens are not available

Tumor exome sequencing and copy number alterations reveal potential predictors of intrinsic resistance to multi-targeted tyrosine kinase inhibitors

Multi-targeted tyrosine kinase inhibitors (TKIs) have broad efficacy and similar FDA-approved indications, suggesting shared molecular drug targets across cancer types. Irrespective of tumor type, 20-30% of patients treated with multi-targeted TKIs demonstrate intrinsic resistance, with progressive disease as a best response. We conducted a retrospective cohort study to identify tumor (somatic) point mutations, insertion/deletions, and copy number alterations (CNA) associated with intrinsic resistance to multi-targeted TKIs. Using a candidate gene approach (n=243), tumor next-generation sequencing and CNA data was associated with resistant and non-resistant outcomes. Resistant individuals (n=11) more commonly harbored somatic point mutations in NTRK1, KDR, TGFBR2, and PTPN11 and CNA in CDK4, CDKN2B, and ERBB2 compared to non-resistant (n=26, p<0.01). Using a random forest classification model for variable reduction and a decision tree classification model, we were able to differentiate intrinsically resistant from non-resistant patients. CNA in CDK4 and CDKN2B were the most important analytical features, implicating the cyclin D pathway as a potentially important factor in resistance to multi-targeted TKIs. Replication of these results in a larger, independent patient cohort has potential to inform personalized prescribing of these widely utilized agents

Common and rare genetic markers of lipid variation in subjects with type 2 diabetes from the ACCORD clinical trial

Background Individuals with type 2 diabetes are at an increased risk of cardiovascular disease. Alterations in circulating lipid levels, total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TG) are heritable risk factors for cardiovascular disease. Here we conduct a genome-wide association study (GWAS) of common and rare variants to investigate associations with baseline lipid levels in 7,844 individuals with type 2 diabetes from the ACCORD clinical trial. Methods DNA extracted from stored blood samples from ACCORD participants were genotyped using the Affymetrix Axiom Biobank 1 Genotyping Array. After quality control and genotype imputation, association of common genetic variants (CV), defined as minor allele frequency (MAF) ≥ 3%, with baseline levels of TC, LDL, HDL, and TG was tested using a linear model. Rare variant (RV) associations (MAF < 3%) were conducted using a suite of methods that collapse multiple RV within individual genes. Results Many statistically significant CV (p < 1 × 10−8) replicate findings in large meta-analyses in non-diabetic subjects. RV analyses also confirmed findings in other studies, whereas significant RV associations with CNOT2, HPN-AS1, and SIRPD appear to be novel (q < 0.1). Discussion Here we present findings for the largest GWAS of lipid levels in people with type 2 diabetes to date. We identified 17 statistically significant (p < 1 × 10−8) associations of CV with lipid levels in 11 genes or chromosomal regions, all of which were previously identified in meta-analyses of mostly non-diabetic cohorts. We also identified 13 associations in 11 genes based on RV, several of which represent novel findings

Diagnostic and Prognostic Significance of Complement in Patients with Alcohol-associated Hepatitis

BACKGROUND and AIMS: Given the lack of effective therapies and high mortality in acute alcohol-associated hepatitis (AH), it is important to develop rationally-designed biomarkers for effective disease management. Complement, a critical component of the innate immune system, contributes to uncontrolled inflammatory responses leading to liver injury, but is also involved in hepatic regeneration. Here we investigated if a panel of complement proteins and activation products would provide useful biomarkers for severity of AH and aid in predicting 90 days mortality. APPROACH and RESULTS: Plasma samples collected at time of diagnosis from 254 patients with moderate and severe AH recruited from four medical centers and 31 healthy individuals were used to quantify complement proteins by ELISA and Luminex arrays. Components of the classical and lectin pathways, including complement factors C2, C4b and C4d, as well as complement factor I (CFI) and C5, were reduced in AH patients compared to healthy individuals. In contrast, components of the alternative pathway, including complement factor Ba (CFBa) and factor D (CFD), were increased. Markers of complement activation were also differentially evident, with C5a increased and the soluble terminal complement complex (sC5b9) decreased in AH. Mannose binding lectin (MBL), C4b, CFI, C5 and sC5b9 were negatively correlated with model for end-stage liver disease (MELD) score, while CFBa and CFD were positively associated with disease severity. Lower CFI and sC5b9 were associated with increased 90-day mortality in AH. CONCLUSIONS: Taken together, these data indicate that AH is associated with a profound disruption of complement. Inclusion of complement, especially CFI and sC5b9, along with other laboratory indicators, could improve diagnostic and prognostic indications of disease severity and risk of mortality for AH patients

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High Throughput Screening Assays for the Estrogen Receptor

We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform (“assay interference”). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available

A genome-wide study of lipid response to fenofibrate in Caucasians: a combined analysis of the GOLDN and ACCORD studies

Fibrates are commonly prescribed for hypertriglyceridemia but also lower low-density lipoprotein cholesterol (LDL-C) and raise high-density lipoprotein cholesterol (HDL-C). Large inter-individual variation in lipid response suggests that some persons may benefit more than others and genetic studies could help identify those persons