In Vitro Strategies
Developing in vitro systems for toxicology studies can save time and money, according to Eric Blomme, D.V.M., Ph.D., leader of the cellular and molecular toxicology group at Abbott (www.abbott.com).
"Working with in vitro systems early for toxicology profiling is important since it is expensive not only to synthesize compounds, but also to perform animal testing. In vitro systems only require compounds in the milligram and not the gram range to determine specific toxicity endpoints.
"We feel such strategies provide a valid and useful way of evaluating and selecting compounds within the discovery pipeline. Once that is accomplished, in vivo studies can be conducted with the best candidates with a higher chance of success."
In collaboration with Iconix Pharmaceuticals (www.iconixpharm.com), Abbott developed gene expression-based screening assays to generate toxicologic profiles of its early-stage compounds. This collaboration employed Iconix's DrugMatrix system, which catalogs genomic effects of drug and chemical treatments as well as their library of Drug Signatures.
According to Dr. Blomme, their approach involves three basic steps. "First, we select the proper dose for evaluation. A basic principle is that everything is toxic if you go high enough. We use isolated rat hepatocytes to determine the dose needed to produce slight toxicity and, therefore, a reliable readout. We then generate a gene expression profile at that concentration using DNA microarrays.
"Next, we create a large reference database of gene expression profiles induced by positive and negative control compounds for selected toxicologic endpoints, such as DNA damage, mitochondrial damage, phospholipidosis, microvesicular steatosis, and peroxisome proliferation.
"This reference database has allowed us, using various general and proprietary methods, to generate signatures composed of 2080 genes that correlate with these selected toxicologic endpoints. Understanding the performance of these assays is critical, as one wants to avoid false positive results at this stage of the discovery process.
"Finally, we transfer these signatures to more cost-effective platforms amenable to a higher throughput. Our chemists now utilize this in vitro toxicology characterization in SAR studies at the lead optimization stage to improve the toxicologic profile of their compounds."
Deriving accurate and consistent information from volumes of microarray toxicity data is a daunting task, according to Susan Flood, Global Pharma strategist, SAS (www.sas.com). "One of the most common challenges scientists face is correlating their microarray-based toxicological response data to the generated specific biomarkers."
Flood suggested that microarray/genomics technologies need some key refinements to transition arrays from the lab to the clinic. "Part of the problem is a lack of appropriate standardization, such as with experimental standards, data standards, and QA/QC standards.
Other problems include platform variabilities and capability to distinguish adaptive versus toxic reactions. To establish quality analysis procedures it is critical to first decipher variability.
"For example, scientists must evaluate signal-to-noise considerations. In this case, important aspects are appropriate experimental design, careful evaluation of data precision and accuracy, and appropriate statistical analysis of the signal.
"It's also critical to standardize methods from lab to lab and platform to platform. GLP documentation must be made at each of these steps. An emerging theme in cross-platform studies is to expect and deal with considerable biological variation."
According to Flood, to maximize the quality of toxicogenomic data, scientists must develop common standard operating procedures across all collaborations. "It's important to minimize variables in different platforms, sample handling, hybridization, and ontologies, and to maximize throughput controls (in the arena of GLP).
"Accurate analyses require both a statistically driven approach as well as interpretation and visualization skills. Only then will biological themes appear and provide the ability to derive predictive models."