How Software Based Technologies Help Developing Precise and Effective in vitro Diagnostics

The use of in vitro tests often plays an important role in the diagnosis and treatment of disease. However, many of these tests rely on detecting single biomarkers, which does not always provide a complete picture.

We spoke to Dr Philipp Pagel, CMO at numares AG, to learn about some of the limitations of current in vitro diagnostics, and how software based technologies can help overcome some of these issues.

Anna MacDonald (AM): Can you tell us about some of the limitations of current in vitro diagnostics?

Philipp Pagel (PP): Current laboratory work is focused on individual parameters that are interpreted as indicators of certain conditions. E.g. if your C-reactive protein (CRP) goes up that's a sign of inflammation. Lab results are great because they are quantitative and apparently objective. So for decades, both academia and industry have put a lot of effort into discovering new diagnostic biomarkers hoping to cover each and every disease. However, while there is no shortage of molecules in the human body to study and plenty of candidates have been proposed, the number of successfully introduced novel tests is depressingly low. Apparently, it has become increasingly difficult to develop successful single marker tests that really deliver on their promise. I believe that the reason for this may well be that most of the good single-molecule markers have been found already. Diseases often have complex dynamics and many things happen in the organism - signal transduction, enzyme activity, dys-regulation of biochemical pathways and shifts in physiological states. More often than not hundreds of things are affected making it unlikely that each and every disease actually has a single marker that is both sensitive and specific to that particular condition. I.e. many markers may increase or decrease in concentration in response, but not exclusively in one disease. Additional information is needed to distinguish between diseases.

AM: How can software based technologies help overcome some of these limitations?

PP: Although good single markers may not exist for many diseases that does not necessarily mean that we cannot distinguish them - but we have to look at several pieces of information at the same time. I.e. instead of measuring one marker we can measure e.g. 5 different ones and then let software combine this information for better sensitivity and specificity. The individual markers may not have a very good predictive power, but when combined in the right way the relations between them indicate the disease. We call these metabolic constellations: If I were to show you pictures of the stars Betelgeuse, Rigel, Bellatrix, etc. you will probably not be able to find them in the sky – if I show you a picture of Orion – you’ll find it immediately. It’s the characteristic constellation that carries the useful information.

A chemical assay can only measure one thing – but if your assay is software that opens an entirely new dimension. Now you can analyze many metabolites from a single measurement, or re-analyze a previous measurement with a different question without going back to a stored sample. You are even able to run tests on that stored data that weren’t even developed when the measurement was carried out. This is certainly useful for study cohorts but also when looking into the history of a current patient.

AM: Can you tell us more about how numares is combining NMR technology with AI? 

PP: Those two technologies are a great match! NMR allows us to quantify many metabolites at once in a single measurement. We do not need to know in advance what we are looking for because NMR simply returns a spectrum that we can then analyze. The technology has a great dynamic range, is very precise and allows very simple sample preparation. The last point is important because typically, much of the imprecision found in an assay comes from pre-analytic steps. Artificial intelligence is our development tool of choice: These methods are able to detect the metabolic constellations that indicate health vs. disease or different types of a disease. In order to do so, we put together large data sets of relevant clinical information obtained in studies on one hand and hundreds of metabolites per patient samples measured by NMR on the other hand. The AI then identifies suitable makers as well as the best way to interpret them as a metabolic constellation. Of course, that does not mean that the computer does all the work and no human expertise is required – before the computer can do its magic, it takes a lot of expert work to get the right data, clean it, look for abnormalities, integrate it, understand the biochemistry and pathophysiology of the constellations found etc.

AM: What advantages has this brought to diagnostics?

PP: I think, our approach has the potential to tackle diagnostic questions that are not amenable to single biomarker diagnostics. As mentioned above our tests consist of software rather than antibodies or chemistry so the development process is rapid and we should be able to put together a sizable test menu in a very short period of time giving our customers a new platform with a growing set of test options. Although the instrument is a major investment, we are able to offer the tests for a very competitive price. Unlike many other innovative diagnostic technologies, we do not require price points in the many hundred dollar range or above so market adoption should go much smoother. We believe that we have mastered the technological challenges well at this point so we can focus on growing the menu. I think that numares' NMR-diagnostics will quickly become a staple in the major labs.

Philipp Pagel was speaking to Anna MacDonald, Editor for Technology Networks.