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Toxicogenomics of Drug Candidates

Client

The client is a top-10 pharmaceutical company focused on targeted drugs involved in tumor growth inhibition.

Challenge

The client was studying drug candidates, collecting gene expression profile data in parallel with classical animal PK/TK and histopathology data. The client needed to understand the toxicogenomics of their drug candidates to confirm and extend their knowledge of dose-dependent mechanisms of efficacy and toxicity. Since the primary study design was driven by PK/TK protocols, the samples available for microarray analysis were spotty and incomplete. The data showed a wide range of intra- and inter-treatment variability, and the uses of biological and sampling error replicates were inconsistent. It was important to understand just how much confidence could be assigned to the results, as the PK/TK, histopathology and microarray data left many questions unanswered.

Objectives

AnVil's goal was to find patterns or gene sets in the data in order to help the client understand the dose-dependent mechanisms of toxicity and efficacy.

AnVil would provide the client with clear results, enabling them to make informed decisions earlier in the drug development process. AnVil's strategy was to use statistical overviews to understand the strengths and weaknesses of the data and then use AnVil's high-dimensional data analysis and visualization techniques and expertise to explore the data.

Solutions

Using domain expertise and experience with large, complex sets of data, AnVil developed a global overview of the client’s data and statistical metadata. AnVil used rigorous statistical methods to maintain the integrity of the data without filtering out meaningful biological trends. AnVil then used high-dimensional analysis and visualization to demonstrate clusters in the data surrounding toxic effects of the drug candidates in relationship to the drug dose and other factors. These results were analyzed for both biological and statistical relevance.

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Results

AnVil's results confirmed the client's main conclusions regarding mechanisms of toxicity and drug dose effects. Genes associated with the mechanism of action were also distinguished. By properly balancing statistical rigor, data mining techniques and biological relevance, AnVil expanded gene cluster membership to pathways and biological processes that extended biological confidence in the client's own conclusions. AnVil also detected possible gender effects of the candidate drug that were not well supported by existing epidemiological data. With AnVil's findings in hand, the client was able to build a stronger case for investigating gender contra-indications at an early stage in drug development, averting the likelihood of unexpected findings in the more costly clinical phases of development.

To find out more about how AnVil can help you forge the optimal path to commercial discovery, send e-mail to info@anvilinfo.com.

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