Topological proteomic: the next step in biomarker ID

A robotic whole-cell imaging technology that can identify combinatorial biomarkers aids decisions from lead selection to patient stratification, resulting in more successful clinical trials.

The major challenge for the drug development industry is to reduce the high attrition rates of drugs entering clinical trials. A better understanding of disease and toxicology mechanisms is urgently needed in order to control the high clinical failure rate among new compounds. Current industry estimates give a new medical compound entering Phase 1 testing only an 8 per cent chance of reaching the market.

The cost of bringing a new drug to market continues to escalate, with current estimates as high as $0.8bn to $1.7bn. Drug development is an exceedingly complicated process and a more comprehensive understanding of biological networks is needed for a rational drug design. Therefore, a biology-based systems understanding of complex organisms is indispensable in the long run. Combinatorial biomarkers represent the first step toward this goal. They are based on systematic approaches and provide a more holistic view of the organism, while not requiring systematic modelling.

The advantage of combinatorial biomarkers is in their broad application, since they can be used even if the action mechanism of a drug is less clear. It can also reveal details about the mechanism and monitor toxic side effects at the same time. As the main components of metabolic pathways, proteins are the preferred drug targets and therefore a logical opportunity for biomarker identification. Cellular function or dysfunction depends upon the protein network environment as a whole. The topology of protein networks that comprise the proteome depends on timing, gene expression patterns and cellular environment. Furthermore, it has been suggested recently that highly specialised individual cells, like cancer stem cells or invasive immune cells, are exceedingly important for diagnosis and therapy of disease, thus creating a strong need for cell-based biomarkers.

A German biotechnology company, MelTec, developed MELK as a solution for combinatorial protein biomarker identification by combining high content analysis with the combinatorial potential of proteomics. MELK is a robotic whole-cell imaging technology that integrates cell biology and biomathematical tools to simultaneously visualise dozens of proteins in a structurally intact cell or tissue. The visualised information generated by MELK is then processed through advanced data analysis software, enabling the identification of protein networks that play a crucial role in biological processes. By identifying which proteins are involved in normal and diseased pathways, these tools simultaneously elucidate disease mechanisms and screen for the effects of compounds on these and toxicology-related pathways in a given tissue. The technology screens for changes in the distribution of all possible combinations of proteins (50 proteins give rise to 1015 different possible combinatorial protein patterns) upon drug treatment or the presence of disease, thereby providing valuable information for the target selection process. At the same time, specific combinatorial biomarkers are identified that aid decisions from lead selection to patient stratification, resulting in more successful clinical trials.

MelTec has already identified novel powerful biomarkers for toxic effects that can be further improved by including customer-specific marker needs. Furthermore, it is possible to monitor multiple markers and pathways from very small blood or tissue samples in their natural biological context, which is useful for patient monitoring and stratification.