Furthering Knowledge with Every Experiment
For children, good behavior can lead to extra screen time. For molecules, good behavior can lead to the discovery of a life-saving medicine. And whether you’re a parent or a scientist, the ability to predict this good behavior before it happens can save time, energy and effort.
While parents haven’t been as lucky, scientists are closer to cracking the code: researchers at AbbVie are working on using artificial intelligence that can help predict how a particular biopharmaceutical active substance will behave.
Using a field of mathematical simulations called predictive analytics in combination with a new intelligent laboratory automation solution, researchers will be able to further scientific knowledge with every experiment. Michael Siedler, research fellow and head of High Throughput Screening and Advanced Formulation Sciences at AbbVie’s Ludwigshafen, Germany-based labs, explains the implications of this technology for researchers and ultimately, patients.
Why are predictive analytics so important to biologics research?
Michael Siedler: In the field of biologics research, our scientists are constructing novel, non-natural molecules to hopefully enable better therapies. But non-natural molecules have one disadvantage as compared to natural ones: they have not undergone any "evolutionary" processes, which are important, among other reasons, for sufficient stability. Therefore, we need to conduct numerous experiments in the laboratory to retrospectively stabilize these new molecules. The problem is, modern biological therapies are very complex to develop, and in order to construct stable molecules, we need a lot of data – meaning this takes time and resources that can ultimately delay the development process.
This is where predictive analytics could come in. In general, we can use data to predict certain things, such as Amazon determining which products could be of interest to me based on my consumer behavior. In our case, we want to predict the stability of certain manufactured molecules based on their structure, in order to carry out fewer and more targeted experiments in the laboratory.