Dundee Cell Proteomics ToxProfileTM
ToxProfileTM is a technology developed to predict the potential toxic effects of drug hits, leads and late stage compounds by providing a high throughput quantitative and unbiased strategy for analyzing how drugs affect the interactions, dynamic properties and/or cellular distributions of proteins in the cell.
Predicting the safety profile of a compound is a major hurdle, which if successful can significantly reduce the attrition rate of the drug discovery process thus substantially reducing costs for the pharmaceutical industry. It requires knowledge of all the cellular processes that might be impacted, and assays to measure that impact. The biological effects of compounds are complex and not completely understood, involving the interplay of multiple cell types and affecting a cascade of signalling networks. Because of this complexity, recapitulating any or all of these events in in vitro assays is difficult. Cellular assays are available that measure a variety of toxicity endpoints, including proliferation, ATP levels, and reactive oxygen species. These provide an important first step in establishing predictive assays, but there is a need to expand the available assay panels to cover even more ‘toxicological space’ in order to better predict compound safety liability. The complexity of potential interactions resulting from exposure of cells to any small molecule, including both indirect as well as direct effects, therefore requires the use of comprehensive and unbiased approaches for characterizing cellular responses and hence for detecting unexpected (and unpredictable) effects that can lead to toxicity.
Major applications of the technology include:
- Analysis of drug mechanism in the system by identifying and validating biochemical pathways affect by drug exposure to cellular proteome.
- Prediction of potential toxic liabilities from information derived from the mechanistic studies
- Selection of clinical candidate drugs by evaluating effects of drug candidates on the cellular proteome and only progressing candidates that show selectivity and specificity for the appropriate cellular targets and with minimum off-target interactions with essential biological processes.
- Analysis of drug-drug interactions on the cellular proteome. Sometimes drugs for different indications have to be administered to patients in combination, creating the potential for toxic effects due to drug-drug interactions. ToxProfileTM provides a model experimental system where multiple drugs can be administered together followed by a comprehensive quantitative analysis of proteomic changes resulting from such treatments. Since the whole proteome is analysed, the data obtained provide information that may be predictive of potential problems at an early stage, before the drugs go into the clinic.
DCP ToxProfile evaluates a drugs effect on a toxicity cell model by analyzing the quantitative proteomic profile linked to interactions between drug and cellular proteins. The technology uses high resolution quantitative mass spectrometry and bioinformatics software tools for protein identification and quantitation, data processing and experienced cell biologists and biochemists for data interpretation.
Early determination of drug toxicity risk profile can lead to a saving of over $10 million per compound in preclinical drug development costs. The FDA estimates that a 10% improvement in the ability to predict drug failures before clinical trials could save $100 million in development costs per drug (FDA 2004, “Innovation or stagnation? Challenge and opportunity on the critical path to new medical products”, FDA Whitepaper) which would benefit the pharmaceutical and biotechnology drug developers as well as patients waiting for new therapies. The cost of discovering and conducting preclinical evaluation of a compound (prior to “first in human” trials) has been estimated to be about $38 million. The pharma industry-wide attrition rate is about 50% and approximately 70% of that is the result of safety-related findings. There is an urgent need to improve the pharmaceutical industry’s ability to identify predictable preclinical liabilities earlier in their pipelines. This would allow pharma companies to select better drug candidates earlier and thus concentrate resources on the most successful programs.