Video Transcription
Jeroen Nieuwenhuis 00:02
Thank you. Good afternoon. Yes. My name is Jeroen Nieuwenhuis, and at Nostics, we're developing a point-of-care diagnostics platform for infectious disease. One of the main problems in infectious disease is when a physician has to make a decision on whether to prescribe antibiotics; they usually have to do this with very limited diagnostic data. As a consequence, you see that many patients receive antibiotics when they actually do not need any antibiotics, and of the patients that do need antibiotics, sometimes they don't get the right antibiotics. As a consequence of this current practice, we see also that our antibiotics are becoming less effective, and that's increasing the burden of infectious disease.
If we look at the workflow, and particularly the workflow in primary care, then you typically see that in the first interaction between the patient and the physician, there's very limited data available, because it takes two to three days to get a result from the lab. That means that the physician will have to prescribe based on empirical diagnosis, and as physicians tend to be on the cautious side, it means quite often, in case of doubt, they decide to prescribe antibiotics, which explains the over-prescription. And of the patients that do need antibiotics, it takes two to three days to get the results from the lab. So that means for those patients, it can take two to three days before they are put on the optimal therapy. And for some patients, that can be quite a long time.
At Nostics, we'd like to change this by taking this test and making it available at the point of care. In this way, during the first consultation, the physician gets all the data to make an informed decision on whether to prescribe antibiotics. With our technology, we can not only detect the presence of bacteria to make the decision whether antibiotics are an appropriate treatment in the first place, but we can also identify the bacteria, ensuring that the optimal antibiotics are being selected for this patient. In this way, the optimal treatment can be started right from the first consultation.
This is made possible by the technology that consists of three main components. The first one is an optical sensing technology known as Raman spectroscopy. It is very specific to recognize certain chemical bonds and can give you very specific information about the composition of the sample that you're looking at. But the main drawback of Raman spectroscopy is that it's not very sensitive, and this is where the second technology comes into play. We add nanophotonic structures to the sample, and in this way, we can amplify the Raman spectrum by up to a billion times. In this way, we can detect the presence of the bacterial signatures without the need to do any culturing. So that means that these results are available directly.
The spectra are quite hard to interpret, particularly for a non-trained user, and this is where the third component comes in. We use a machine learning approach to read the spectra and to map them onto a database of known pathogen signatures. In this way, the software can classify these signatures and tell the physician exactly which pathogen is present in the sample. As a consequence, by combining the features of this technology, we can provide results very quickly, in less than 15 minutes. We can also get signatures only from the viable bacteria. So if we detect bacteria, we know that these are active bacteria that are causing the infection, and because it's a label-free method, we can also expand the library very quickly just by extending our software, so no new chemistry needs to be developed to expand the library.
We've also been putting quite some effort into being able to put this in a small form factor that is portable and can be battery-operated, so it's very suitable for decentralized settings. Now we validated our technology over the last few years on different types of pathogens like bacteria and fungi, and also in looking at different types of sample matrices, like urine or whole blood. As a starting point to commercialize our technology, we selected urinary tract infection because it's one of the most common infections that is seen in primary care, and it's also one of the infections where most of the antibiotics are being prescribed.
Today, our strategy is first to launch in the more advanced healthcare economies, because there the regulatory pathways are most clear, and then to focus on the larger accounts. But even this sub-segment of the market already presents a one-and-a-half billion opportunity. And then we can, of course, expand into further pathogens and other sample matrices. The business model is pretty straightforward. It's like a razor and razor blade model where most of the revenue comes from the sales of the disposables.
If we look at our business case, if we take the reimbursement rates that are now established for the central lab analysis, and look at the cost price predictions that we have for our disposables from our pilot line, we can see that even under those circumstances, we can already get close to the 80% gross margin. So we're confident that when we move to a fully automated line, we can be in excess of 80%, which is a healthy margin for our business case.
Now looking at the competition, there are other technologies that are being deployed for urinary tract infection, and one of them is the dipsticks that are being used at the GP office. But the main problem with those is that they're not very specific and not very sensitive. So despite the frequent use of the dipsticks, still 50% of the antibiotic prescriptions are inappropriate in primary care. We've seen that the gold standard works very well. So when you take a sample, you culture it for a few days, and then you run it on a mass spectrometer, it gives very reliable results. But the main drawback of that approach is that the results are too late. Basically, you have a window of 10 to 15 minutes of the interaction between the physician and the patient where you need this data to make a selection of the appropriate treatment.
We know there's some competition that's looking into molecular diagnostics as a means to detect the pathogens, but the main drawback of that method is that it is also sensitive to very small fragments of the pathogen. I think we've all seen during COVID that even after an infection has been fully recovered, traces of the pathogens can still be found with PCR two to four weeks after the event, and that makes it rather unsuitable for the primary care setting. If we look at rapid culturing, that's another approach that some parties are looking at at the moment, and I think they've convincingly demonstrated that they can accelerate the culturing from days to hours. But the main problem is you only have 10 to 15 minutes, so even if you get it down to a few hours, it's still too late to be available for the first consultation.
So our proposition is really to provide lab-quality results right at the moment when you need it, between the first interaction of patient and physician. We built quite a good proprietary position around this technology. We filed five patents, various on the integration of the photonic substrate in a cost-effective way, on the disposable design. We have some signal processing and also an expansion into AST, so antimicrobial susceptibility testing. Besides that, we built quite some know-how, particularly our pathogen database with bacterial strains from all over the world.
Finally, we also see that our proprietary position is strengthened by being the first; once you move into the market with this technology, you get access to so many bacterial signatures that you can expand your database and further optimize your classifier, making it an even more attractive product. By this, you see that you have a kind of self-reinforcing mechanism that will be very hard to catch up with.
At the moment, we are 25 at Nostics, and in our management team, we have a lot of entrepreneurial experience. Two of our members of the management team had healthcare startups before that were successfully exited. We also have a very strong technical team with many PhDs covering the fields of microbiology, Raman spectroscopy, nanophotonics, and also in the clinical area. And then we have a good team of advisors that advise us on AMR, on microbiology, and also on the business case.
At Nostics, we're currently approaching the end of the feasibility stage, so the technology works well. We validated it in different sample matrices and with different pathogens, and now basically we're raising money to put it in the final form factor where we're going to develop the final system. The final card is, I want to build a pilot line to get us ready for the clinical trials. We think that the clinical trials can be relatively short because we can measure in leftover samples. So we project that it will take about six months, and then, depending on the geography, it might take nine to 12 months to get the clinical approval.
Thank you for your attention. Thank you.
