Linking Gene Expression Profiles to Clinical Pathology Endpoints
One of the major components of IPA-Tox is the Toxicity Functions, which cover a wide spectrum of well-known drug-related injuries and pathologies usually exhibited as a clinical manifestation. Toxicity Functions are known associations between genes and clinical pathology endpoints and are based on findings published in the toxicology literature. They include specific functions for liver, such as cholestasis, fibrosis, necrosis, proliferation, and steatosis; kidney, such as atrophy, fibrosis, hydronephrosis, and tubular nephrosis; and heart, like dilation, enlargement, hypertrophy, hypoplasia, and inflammation.
These functions are of particular value when analyzing compound-induced gene-expression changes, as they are indicative of the potential for the drug to induce a clinical pathology. Carmustine high-dose (16 mg/kg) and later-time-point (day three and five) treatments are clearly associated with liver toxicities such as hepatomegaly and steatosis (Figure 1). These associations were not observed for any of the other compound treatments analyzed in this study, with the exception of one high-dose thioguanine treatment.
Clinical pathology as measured using traditional histopathology is usually an observed manifestation of the drug treatment. To better understand immediate and/or early response to the drug treatment at the mechanistic level, IPA-Tox utilizes a library of Toxicity Lists manually curated from the scientific literature. The Toxicity Lists consist of sets of genes that are known to be perturbed upon compound treatment and include functional gene groupings based on critical biological processes such as adaptive, defensive, or reparative responses to xenobiotic insult.
Figure 2 shows the results from analyzing drug-induced gene-expression changes in the context of the Toxicity Lists library and provides insight into the mechanism of carmustine-induced hepatotoxicity. This analysis returned several impacted Toxicity Lists, the most significant being the “CAR/RXR Activation”, “Hepatic Cholestasis,” and “LPS & IL-1 Mediated Inhibition of RXR Function” gene lists. These results agree with the previously identified hepatomegaly and steatosis Toxicity Functions results and suggest marked induction of all phases of xenobiotic metabolism.
Hepatomegaly is associated and can be explained with centrilobular hypertrophy, a result of Cyp P450 gene induction. In addition, the significant association of carmustine treatments with the “Hepatic Cholestasis” gene list is in agreement with the bile duct hyperplasia observed at the histopathology level. Cholestasis often occurs either as a result of altered hepatocyte bile formation or disruption of bile flow out of the hepatocyte through intrahepatic bile ductules.
IPA-Tox is fully integrated with IPA’s repository of biological and chemical knowledge, and, as a result, researchers have the ability to explore the biological effects of their compound beyond the context of toxicity, understand the MOA and MOT, and identify potential biomarkers. IPA’s molecular networks, computationally generated from the set of genes perturbed by carmustine_HI_5day treatment, help elucidate carmustine’s mechanism of action by highlighting drug-induced changes in expression of genes involved in xenobiotic metabolism, cell growth, and proliferation (data not shown). This effect was seen across all three high-dose carmustine treatments.
IPA-Tox can identify the number and type of genes associated with carmustine-induced toxicity. In addition, it helps in discriminating carmustine’s induced hepatotoxocity from other anticancer drugs and can be used to build a hypothesis model for how toxicity evolves at the molecular level.
As the field of drug discovery and development continues to adopt molecular toxicology and as new biomarkers of pharmacological effects are discovered and validated, it will be crucial for genomics analysis tools to keep pace with these new discoveries. IPA’s knowledge base of biological and chemical information provides a system for the incorporation of new assay data and information and can adjust to new technology implementations. Its structure enables continuous modeling and incorporation of scientific discoveries as they are published.