STATE OF THE ART ANALYSIS
Analysis Enlightens Us
One of the challenges with data of high dimensionality is how to effectively extract and represent the content in a way that enables for more informed decision making. Large data streams from high content, high throughput applications such as those we have developed, require a completely different analytical approach. One of AsedaSciences®’ core principles is that the quality of the ANALYSIS is as important to success as the APPROACH and DESIGN of the biological applications.
Our applications measure 10,000 cells per condition, 10 step dose response and 5-6 parameters simultaneously for multiple profiles. Therefore, simultaneous analysis of all parameters, across an entire cell population, across all profiles, for each measured compound, is critical if the depth and breadth of the data is to be effectively mined. This presents a challenge and is certainly too difficult to analyze manually for a large number of compounds. AsedaSciences has developed a statistical processing pipeline that can embrace the scale and complexity of the data generated.
Our automated approach allows for operator independent analysis – the high content data and the rich information it provides requires an automated analytical approach that matches the throughput of data delivery. The analysis operates in a multivariate format, where compounds are represented in multi-dimensional fingerprints and all parameters are simultaneously analyzed together, cell by cell, using our state-of-the-art algorithm. This ensures that the entire pattern of physiological responses is retained for all correlations within a given compound’s response patterns.
Traditional methods rely on “one measurement, one assay (at a time) and one IC-50”, with comparisons of IC-50 curves being the historical approach. Our profiling method is not limited to a single dimension. We summarize multiple simultaneous biological measurements which allows the creation of a “map” that can indicate the proximity, relationship and similarity in multi-dimensional space of various compound classes based on their biological fingerprints. The sophisticated algorithm can then be used to create new visualization tools that collapse the biological output and mathematical analysis into a form that can be used for more informed decision making.
This provides for a highly robust and sensitive multidimensional analysis, which in turn can rapidly identify similarities between compound classes (such as toxins or on-market therapeutics). When assessing new compounds generated through the “hit to lead” cycle, this knowledge is essential early in the process to avoid costly processing and analysis downstream. The nature of our approach provides new and advanced tools that add significant value and will form an integral part of de-risking your drug discovery process – saving time and optimizing use of both internal and external resources.