Robert F. Murphy, Ph.D., Lane professor of computational biology at Carnegie Mellon University, has embraced an approach that is orthogonal to the traditional compound-target drug screen.
Typically, cell-based assays are used to screen potential drug compounds against a disease target, and targets are chosen one at a time. If you have just one or two targets to screen, then it can be done quite efficiently, but when the group of potential targets is large, then the overall task of screening all available compounds against all targets becomes unmanageable, even with the highest possible throughput.
“The vision of doing a high-content screen to find a drug candidate is premised essentially on the notion that you only have to look at one target and one cell type (or a small number of cell types),” Dr. Murphy says.
But drugs may behave differently in other cell types or on other pathways within the cell. Dr. Murphy, a pioneer in the field of pattern recognition, has developed methods for probing the relationships of many compounds to many proteins in a single experiment. The method is based on the assumption (or hope) that clusters of proteins and clusters of drug compounds have similar behavior, so that the number of total experiments needed is reduced.
“That sounds a little bit like magic,” he concedes, “because you don’t necessarily know in advance what the right combinations are. There are methods that deal with that question.”
Assuming you know nothing, these methods enable you to learn about the dependencies that are in common between targets and compounds and thereby make it possible to measure a much smaller fraction of the total number of combinations.
Early results of simulations where the system must learn a known “correct answer” have been encouraging. This approach would allow scientists to do mega-type drug screens where many drugs, proteins, pathways, or cell types are addressed in a single experiment—a body of work that could take many years to complete the old fashioned way.
Another developing concept in high-throughput and high-content screening is bioassay ontology. This project, inspired by the NIH Roadmap and funded by the National Institutes of Health Genome Research Institute, seeks to create an ontology and software tools for searching, retrieving, and integrating small molecule high-throughput and high-content screen data.
This project addresses the problem of the vast amount of screening experiments that are publicly available, but which are described primarily in the form of free text. Developing an ontology for these experiments will make it easier for scientists to share, analyze, or mine data, without reinventing the wheel every time they do it.
An ontology facilitates searching and integration similar to the semantic web. Creating an ontology involvesformalizing domain knowledge using standardized vocabulary into concepts and relationships with properties. The approach involves top-down domain expert-driven and bottom-up development using automated text mining and natural language processing.
According to Stephan Schuerer, Ph.D., assistant professor of pharmacology at the University of Miami, creating a bioassay ontology not only enables a biologist easier access to the data, but enhances the interpretation of the data. Being aware of relationships that may not be apparent, or giving a name to concepts, aids in the thinking process, leading to new ideas and theories.