DataSolu takes on CV diseases armed with lab-grown cardiomyocytes and AI

DataSolu wants to speed up preclinical drug development and boost preventive care by revealing and predicting potential heart failures, using a unique cocktail of lab-grown cardiomyocytes, biomimicry and AI.

“Regular screening methods like ECG, exercise stress tests and blood tests may not pick up the very small abnormalities and hidden heart diseases. We want to be the trusted partner that finds even the slightest anomalies accurately,” says Dr. Mika Aho from DataSolu.

DataSolu applications are in cardiac toxicity screening and preventive care. Cardiotoxicity is the main reason for drug withdrawal, and most of the new compounds fail during the late-phase development.

“There are opportunities for reducing the costs and time-to-market significantly in pharmaceutics, but also in chemical and cosmetology companies,” Aho continues.

From the outside, DataSolu service is very straightforward. Pharma companies and CROs send or stream their data to the platform, and in return get back the analysis.

“Why would you use a biologist’s or cardiologist’s valuable time to manually analyze hours of data to detect arrhythmias and classify potential heart diseases, if there are better options?” Aho asks.

Mika Aho (left) and Leo Heinsuo from DataSolu.

Why would you use a biologist’s or cardiologist’s valuable time to manually analyze hours of data to detect arrhythmias and classify potential heart diseases, if there are better options?

Enter: iPSCs!

At the heart of DataSolu innovation, we find the induced pluripotent stem cells (iPSCs), reprogrammed from adult human cells and differentiated into cardiomyocytes. iPSCs are a promising alternative to model human cardiac diseases: they display properties similar to human heart cells, and have the advantage of mass production in the laboratory, having multiple disease-specific and patient-specific lines.

“In addition, they offer the opportunity to study cells that are genetically matched to individual patients with disease, and help overcome the problems associated with animal models. In fact, the European parliament just recently voted in favor of a plan to replace animal experiments with cutting-edge science,” Aho explains.

Meet the MEAs and physiological stimulation

The analysis method deploys microelectrode arrays (MEAs) which are, basically, glass chips printed with multiple microelectrodes, allowing non-invasive long-term measurement of electrically active cells cultured on them. MEAs have been increasingly used iPSC in research for disease modeling, drug testing and screening for early in vitro detection of proarrhythmia risk before clinical use – safe non-arrythmic drugs do not induce arrhythmias.

However, sometimes the anomalies and drug effects are not visible in the data. Research has demonstrated that mimicking human physiology on a dish is an effective stimulus for cardiovascular studies. Which, first of all, mimics the real human physiology, but also stimulates the cells in different conditions, e.g. under physical exertion.

“Physiological stimulation provides us with more valuable data about human response to stress, insight into disease susceptibility, and potentially to understanding individualized treatment response in the future as well,” says Aho.

“Once we have enough data, we can propose which drugs are the best fit for the individual patients,” he adds.

DataSolu’s ultimate goal is to build a rich iPSC data asset, which in the future will enable, for example, rapid personalized drug development and safety testing of new chemicals.

Building the data vault: Data in, analysis out

Using MEA, cardiomyocytes can be monitored during drug testing over longer periods of time.

“MEA devices generate massive amounts of time-series data to feed our deep learning algorithms. We pre-process it, keep the most relevant data and results safe,” says Leo Heinsuo from DataSolu.

DataSolu solution focuses on end-to-end deep learning, omitting traditional feature engineering methods.

“We’ve experimented with countless different types of modern deep learning architectures to tackle the challenge of highly variable iPSC recordings. Unlike ECG waveforms, the field potential (FP) of iPSCs can have very different shapes for each individual recording. The shapes may or may not tell something about an underlying disease or condition,” Heinsuo continues.

DataSolu has been data and AI-driven from the beginning. The company’s ultimate goal is to build a rich iPSC data asset, which in the future will enable, for example, rapid personalized drug development and safety testing of new chemicals.

“Eventually data is the most valuable asset. When you’re utilizing massive amounts of data properly, there is huge business potential involved,” Aho envisions.