Keith M. Goldstein

Assessing Concordance of Drug-Induced Transcriptional Response in Rodent Liver and Cultured Hepatocytes

The effect of drugs, disease and other perturbations on mRNA levels are studied using gene expression microarrays or RNA-seq, with the goal of understanding molecular effects arising from the perturbation. Previous comparisons of reproducibility across laboratories have been limited in scale and focused on a single model. The use of model systems, such as cultured primary cells or cancer cell lines, assumes that mechanistic insights derived from the models would have been observed via in vivo studies. We examined the concordance of compound-induced transcriptional changes using data from several sources: rat liver and rat primary hepatocytes (RPH) from Drug Matrix (DM) and open TG-GATEs (TG), human primary hepatocytes (HPH) from TG, and mouse liver / HepG2 results from the Gene Expression Omnibus (GEO) repository. Gene expression changes for treatments were normalized to controls and analyzed with three methods: 1) gene level for 9071 high expression genes in rat liver, 2) gene set analysis (GSA) using canonical pathways and gene ontology sets, 3) weighted gene co-expression network analysis (WGCNA). Co-expression networks performed better than genes or GSA when comparing treatment effects within rat liver and rat vs. mouse liver. Genes and modules performed similarly at Connectivity Map-style analyses, where success at identifying similar treatments among a collection of reference profiles is the goal. Comparisons between rat liver and RPH, and those between RPH, HPH and HepG2 cells reveal lower concordance for all methods. We observe that the baseline state of untreated cultured cells relative to untreated rat liver shows striking similarity with toxicant-exposed cells in vivo, indicating that gross systems level perturbation in the underlying networks in culture may contribute to the low concordance.

Minor compensatory changes in SAGE Mdr1a (P-gp), Bcrp, and Mrp2 knockout rats do not detract from their utility in the study of transporter-mediated pharmacokinetics.

Mdr1a-, Bcrp-, and Mrp2-knockout rats are a more practical species for absorption, distribution, metabolism, and excretion (ADME) studies than murine models and previously demonstrated expected alterations in the pharmacokinetics of various probe substrates. At present, gene expression and pathology changes were systematically studied in the small intestine, liver, kidney, and brain tissue from male SAGE Mdr1a, Bcrp, and Mrp2 knockout rats versus wild-type Sprague-Dawley controls. Gene expression data supported the relevant knockout genotype. As expected, Mrp2 knockout rats were hyperbilirubinemic and exhibited upregulation of hepatic Mrp3. Overall, few alterations were observed within 112 ADME-relevant genes. The two potentially most consequential changes were upregulation of intestinal carboxylesterase in Mdr1a knockouts and catechol-O-methyltransferase in all tissues of Bcrp knockout rats. Previously reported upregulation of hepatic Mdr1b P-glycoprotein in proprietary Wistar Mdr1a knockout rats was not observed in the SAGE counterpart investigated herein. Relative liver and kidney weights were 22–53% higher in all three knockouts, with microscopic increases in hepatocyte size in Mdr1a and Mrp2 knockout rats and glomerular size in Bcrp and Mrp2 knockouts. Increased relative weight of clearing organs is quantitatively consistent with reported increases in the clearance of drugs that are not substrates of the knocked-out transporter. Overall, SAGE knockout rats demonstrated modest compensatory changes, which do not preclude their general application to study transporter-mediated pharmacokinetics. However, until future studies elucidate the magnitude of functional change, caution is warranted in rare instances of extensive metabolism by catechol-O-methyltransferase in Bcrp knockouts and intestinal carboxylesterase in Mdr1a knockout rats, specifically for molecules with free catechol groups and esters subject to gut-wall hydrolysis.

Co-Prescription Trends in a Large Cohort of Subjects Predict Substantial Drug-Drug Interactions

Pharmaceutical prescribing and drug-drug interaction data underlie recommendations on drug combinations that should be avoided or closely monitored by prescribers. Because the number of patients taking multiple medications is increasing, a comprehensive view of prescribing patterns in patients is important to better assess real world pharmaceutical response and evaluate the potential for multi-drug interactions. We obtained self-reported prescription data from NHANES surveys between 1999 and 2010, and confirm the previously reported finding of increasing drug use in the elderly. We studied co-prescription drug trends by focusing on the 2009-2010 survey, which contains prescription data on 690 drugs used by 10,537 subjects. We found that medication profiles were unique for individuals aged 65 years or more, with ≥98 unique drug regimens encountered per 100 subjects taking 3 or more medications. When drugs were viewed by therapeutic class, it was found that the most commonly prescribed drugs were not the most commonly co-prescribed drugs for any of the 16 drug classes investigated. We cross-referenced these medication lists with drug interaction data from Drugs.com to evaluate the potential for drug interactions. The number of drug alerts rose proportionally with the number of co-prescribed medications, rising from 3.3 alerts for individuals prescribed 5 medications to 11.7 alerts for individuals prescribed 10 medications. We found 22% of elderly subjects taking both a substrate and inhibitor of a given cytochrome P450 enzyme, and 4% taking multiple inhibitors of the same enzyme simultaneously. By examining drug pairs prescribed in 0.1% of the population or more, we found low agreement between co-prescription rate and co-discussion in the literature. These data show that prescribing trends in treatment could drive a large extent of individual variability in drug response, and that current pairwise approaches to assessing drug-drug interactions may be inadequate for predicting real world outcomes.

Data-driven analysis approach for biomarker discovery using molecular-profiling technologies

Abstract High-throughput molecular-profiling technologies provide rapid, efficient and systematic approaches to search for biomarkers. Supervised learning algorithms are naturally suited to analyse a large amount of data generated using these technologies in biomarker discovery efforts. The study demonstrates with two examples a data-driven analysis approach to analysis of large complicated datasets collected in high-throughput technologies in the context of biomarker discovery. The approach consists of two analytic steps: an initial unsupervised analysis to obtain accurate knowledge about sample clustering, followed by a second supervised analysis to identify a small set of putative biomarkers for further experimental characterization. By comparing the most widely applied clustering algorithms using a leukaemia DNA microarray dataset, it was established that principal component analysis-assisted projections of samples from a high-dimensional molecular feature space into a few low dimensional subspaces provides a more effective and accurate way to explore visually and identify data structures that confirm intended experimental effects based on expected group membership. A supervised analysis method, shrunken centroid algorithm, was chosen to take knowledge of sample clustering gained or confirmed by the first step of the analysis to identify a small set of molecules as candidate biomarkers for further experimentation. The approach was applied to two molecular-profiling studies. In the first study, PCA-assisted analysis of DNA microarray data revealed that discrete data structures exist in rat liver gene expression and correlated with blood clinical chemistry and liver pathological damage in response to a chemical toxicant diethylhexylphthalate, a peroxisome-proliferator-activator receptor agonist. Sixteen genes were then identified by shrunken centroid algorithm as the best candidate biomarkers for liver damage. Functional annotations of these genes revealed roles in acute phase response, lipid and fatty acid metabolism and they are functionally relevant to the observed toxicities. In the second study, 26 urine ions identified from a GC/MS spectrum, two of which were glucose fragment ions included as positive controls, showed robust changes with the development of diabetes in Zucker diabetic fatty rats. Further experiments are needed to define their chemical identities and establish functional relevancy to disease development.

The Inhibin B (InhB) Response to the Testicular Toxicants Mono-2-Ethylhexyl Phthalate (MEHP), 1,3 Dinitrobenzene (DNB), or Carbendazim (CBZ) Following Short-term Repeat Dosing in the Male Rat

OBJECTIVE The objective of this study was to evaluate the utility of plasma Inhibin B (InhB) as a biomarker of testicular injury in adult rats following short-term exposure to the known Sertoli cell toxicants mono-2-ethylhexyl phthalate (MEHP), 1,3 dinitrobenzene (DNB), or carbendazim (CBZ). METHODS Following oral gavage administration of the compounds for 2 or 7 days, the rats were evaluated for clinical signs, body weight, food consumption, organ weights, plasma hormone levels, and gross and microscopic pathology. RESULTS MEHP, DNB, and CBZ produced a range of testicular toxicity characterized by minimal exfoliation of germ cells as demonstrated by increased cellular debris in the epididymis (MEHP) to more severe and dose/duration responsive Sertoli cell vacuolation, germ cell degeneration, and multinucleated giant cells of germ cell origin (DNB and CBZ). The slight to moderate Sertoli and germinal cell injuries did not correlate with significant changes in plasma InhB levels following 2- or 7-day exposures. However, more severe injury to germinal epithelium following up to 7 days of exposure, but not after 2 days of exposure, correlated with decreased plasma InhB levels and less consistently with increases in plasma follicle stimulating hormone. CONCLUSION In conclusion, under the conditions of these studies, changes in InhB were not an effective early onset marker of testicular toxicity or an effective marker for slight to moderate levels of acute injury, and only reflected more severe disruption of spermatogenesis. Changes in plasma InhB and follicle stimulating hormone were poorly correlated except in some instances of moderate to marked testicular toxicity.

Use of a rat ex-vivo testis culture method to assess toxicity of select known male reproductive toxicants

Due to the complex physiology of the testes, in vitro models have been largely unsuccessful at modeling testicular toxicity in vivo. We conducted a pilot study to evaluate the utility of the Durand ex vivo rat seminiferous tubule culture model [1-3] that supports spermatogenesis through meiosis II, including the formation of round spermatids. We used this system to evaluate the toxicity of four known testicular toxicants: 1,3-dinitrobenzene (DNB), 2-methoxyacetic acid (MAA), bisphenol A (BPA), and lindane over 21 days of culture. This organotypic culture system demonstrated the ability to successfully model in vivo testicular toxicity (Sertoli cell toxicity and disruption of meiosis) for all four compounds. These findings support the application of this system to study molecules and evaluate mechanisms of testicular toxicity.

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.