The long, arduous process of narrowing down millions of chemical compounds to just a select few that can be further developed into mature drugs, may soon be shortened, thanks to new artificial intelligence (AI) software.
Genedata, a bioinformatics solutions company headquartered in Basel, Switzerland, has created Imagence, a high content screening image analysis workflow based on deep learning that cuts image analysis times, while increasing data quality and reproducibility of results.
“We have software systems which can more or less analyze almost every assay that you need there, can construct and organize the data, store the data, federate the data and make a decisions along this process,” said Stephen Steigele, the head of science at Genedata said in an exclusive interview with R&D Magazine “What we have now specifically solved is we developed a software where we use artificial intelligence to make a part of this research process extremely easy.”
The task of analyzing high content screening images is often labor-intensive and time-consuming, involving several different levels of expertise with several manual steps, like the selection of extracted features or correct detection of cells. This process, which can take many weeks, is reduced to only a few hours using the new technology.
The traditional process of analyzing high content screening images needs to be improved, said Steigele. Much more complex phenotypic assays as biologically-relevant model systems are needed in the future for early drug discovery, safety assessment and even to replace more animal models with strong predictive in-vitro assays.
Currently, to develop a small molecule drug, organizations need to first identify which proteins cause the given disease and then find the molecules that can target this protein.
“This is needle in the haystack searching,” Steigele said. “Typically, you have to test millions of compounds to achieve that, following many iterations to refine the chemical molecule with respect to many factors such as bioavailability, toxicity, metabolism, etc. This is a very lengthy process, which can take up to 10 years.”
Imagence helps to speed up this process.
In traditional high-content image analysis, scientists must design the image analysis by handcrafting many hundreds of features, including cell size or fluorescence intensity, when using labeled proteins.
In contrast to this complex procedure, the new deep-learning technology shortens this process by presenting very intuitive maps of the phenotypic space just a few minutes after loading the image data to the system. An assay biologist can then start immediately to define phenotype classes and to review the images of a few hundred cells to generate a tailored deep-learning model for analysis of this assay. This process overall takes just a few hours in total, rather than days or weeks in a classical setup.
Steigele said the technology used to identify different images is similar to the software used by major companies like Facebook and Google to identify whether a given picture is of a dog or a car.
The new software—which was first publicly demonstrated at the SLAS Advanced 3D Human Models and High-Content Analysis Conference in the Netherlands in October 2018—allows biologists to set up and analyze high-content screens without image analysis expertise, reducing the amount of people needed to complete the drug discovery process.
To create the new system, Genedata collaborated with several biopharmaceutical industry leaders who had expressed the need for more efficient ways to analyze high-content screening images. The industry leaders also wanted to eliminate human bias and enable scientists to better understand and examine specific cell biology.
When the system is implemented on a large scale, it will allow drug discovery companies to automate their analysis of phenotypic high content screens and ultimately scale up their operations, while reducing time consuming, labor-intensive work without sacrificing speed on R&D projects.
According to Steigele, Imagence can work on virtually any disease.
“We are quite agnostic, have worked on a dozen examples from our customers, and we’ve worked with a diverse set of diseases,” he said.
“Pharma needs very systematic tests that can be easily repeated, and easily set up in more or less in the same format,” he added.
Steigele also said Imagence could lead to better personalized medicine because it will enable scientists to automatically in just a few seconds adapt and retune the image analysis across sometimes very heterogeneous human sample material such as biopsies in the clinic.
The company plans to license the Genedata Imagence software in 2019.