Pia Abola: Welcome to Countering COVID, a podcast by LGC, Biosearch Technologies, where we speak with clinical lab scientists and industry experts, to understand their perspectives on the current and future state of SARS-CoV-2 testing. My name is Pia Abola, and I'll be your host. On this episode, we'll take a closer look at SARS-CoV-2 testing from a sample point of view. Pros and cons of different sample types and sample prep. Here to discuss this topic, we have Priya Banada- Priya Banada: Hello Pia Abola: An assistant professor at Rutgers Biomedical Health Sciences, who specializes in assays for the detection of infectious diseases. We also have James Grayson. James Grayson: Hi. Pia Abola: James is an expert in the automation of molecular diagnostics workflows and a field application scientist with UgenTec, a company that provides software that automates and standardizes the entire qPCR assay and analysis workflow from molecular diagnostics. So I am very excited to have Priya with us today as her lab is one of the many clinical research labs that pivoted to SARS-CoV-2 testing at the start of the pandemic. I'd like to start by asking you Priya, if you could share with us what your lab was doing before the pandemic, and then the process of pivoting your lab to SARS-CoV-2 testing. Priya Banada: Sure. Thank you so much for having me and our lab is involved in several different research projects with a major emphasis on developing molecular diagnostic testing. And we basically focus on a variety of infectious diseases, such as tuberculosis, sepsis, bio threat agents, other viral diseases including Ebola, chikungunya, and now we have transitioned to COVID-19 as well. So the need of the hour at the time was testing. So my boss and our Director of Public Health Research Institute, Dr. David Alland is the one who activated our team and initiated the work on working with the SARS-CoV-2 in partnership with the companies. At that time it was called NCO. So we were able to quickly set up the workflow and get the virus propagated, evaluate the company's kit. This is a company that worked with was Cepheus. We evaluated their expert express test. Priya Banada: And within two weeks we were able to complete the work file for the FDA UVA application. And it was very quickly approved and all in collaboration with our company. So just as we were wrapping up this and completing this, there was a huge spike in cases in New Jersey and that literally overwhelmed our hospital and the testing labs. So the experience we already had with working with the SARS-CoV-2, we were the first people that they reached out to, and we were ready to extend our services to the much needed testing. It was seriously a Herculean effort. It wasn't easy and coding from a research lab to a clinical lab, I mean, as you might very well understand the culture itself was different. So it wasn't easy for us. And it was a community effort to get the testing done at the time. And we are indebted to all of those people who really helped us. Pia Abola: It does sound like a Herculean effort. How many samples were you guys processing a week? Priya Banada: We had 100s of samples. I mean, per day. Per day we had 100s of samples. The machines were running continuously. Pia Abola: So now that you've done the diagnostic testing and you're doing clinical research testing, are there differences in the approaches that you're taking? Priya Banada: So just to give a kind of a definition. Of diagnostic testing is a part of clinical research. It's integrated. So the data that we produce from testing is what helps us understand the effect on different patient demographics. So with the diagnostic testing probably you wouldn't care whether the patient was immunocompromised or not, for example, or how old was he? Was he young or a child or a person in his 60s and 70s? So for clinical research, we look at every demographic individually. We look at every comorbidity individually, so that we get to understand, what is its effect? What is the result talking about for this particular population? It gives us like a broader view. Pia Abola: Are there any challenges with keeping, keeping all that information together? Priya Banada: Now we have a central patient database here that is secure and managed. So once the patient sample is tagged with a sticker, then it is the same ID that is across the board. Pia Abola: James, do you want to add anything? James Grayson: As we progressed through those early months of the pandemic, sample tracking and workflow monitoring really became a highlight, a really key component or need in the labs we were working with. It became clear that in a lot of these cases, there were significant gaps and being able to identify samples, track samples. And so as a company we tacked towards having and really focusing on workflow software. So how can we help these labs? That in some cases, when I think of our very high end national initiatives, where they're testing over 120,000 samples a day in Europe for a given country, how do you track samples in that situation? How do you review data in that situation? Because these labs were not really equipped for that, you might have some breaks in the chain. James Grayson: You might have missing information and so how do you... In one case in a university here in the US they know they've collected the sample, but they're not always 100% positive it made it from the collection point into the lab. And so just helping to provide tools to say, "Yes, here's the sample. It's been collected." We don't have the linkage right now to verify, to give a little check mark that it's been all the way through, but we can create ways and tools and software support to help them cross reference what's been collected across these disparate softwares and disparate workflows. I think it's interesting just to see how people adapt and how labs adapt and how technology adapts to meet this particular need. Pia Abola: Yeah. So Priya, it sounds like your lab was already set up to handle patient samples. So you didn't have any of these tracking hiccups you guys had to straighten out? Priya Banada: Right. Because we are part of medical school and the university hospital here. And our hospital is one of the big hospitals in the region. So we are fortunate in that way, but still, I mean, in spite of all these things, we had to go through challenges. So imagine smaller hospitals in rural areas. I can imagine. I agree with you, James, that it's pretty challenging to navigate this. Yeah. Pia Abola: Something James said made me think about how a lot of genomics labs were coming in. Was your lab Priya, predominantly doing qPCR assays or were you guys doing a variety of assays? Priya Banada: Yeah. So predominantly qPCR assays. So we are, as I mentioned before, ours is a molecular diagnostic laboratory. So we are already working on a variety of different pathogens and so we have that expertise in hand. And so we are base-acid developers and then from the acid development at the lab stage, so we collaborate with companies and make it a product, ultimately, because that is where we would like to see our research going. It's a very applied research. Pia Abola: Priya, could you tell us a little bit about the different sample types that are used in the diagnostic testing for SARS-CoV-2? Priya Banada: Right now the nasal pharyngeal is what is more common, but catching up on nasal, there is oral, oropharyngeal, saliva. People are explored. There is blood if you are doing antibody tests, then there is a stool and other body fluids are also being explored, like urine. We don't have much data about the application of molecular test for blood stool or other body fluids, other than the respiratory specimens or non-respiratory specimens, because the wider load seem to be extremely low to none in some of these samples. Pia Abola: Just comparing things like nasal pharyngeal samples, nasal samples, oropharyngeal samples, and saliva. I know nasal pharyngeal have been used predominantly, do you think there's a place for these other types of samples and what is their place? Priya Banada: Initially all of us, when we were developing assays, we bagged on influenza, whatever the data we had from influenza. And so we just took that information and nasal pharyngeal sample was what was predominantly used for influenza samples. We know from our experience that virus to virus, the location, the colonization of the viruses change in different sample types, in sample sites, even though it's all in the upper respiratory. It behaves differently. So right now everything is exploratory. We have lots of data now convincing that there is data that says that nasal had equal performance as in pee. Now saliva is catching up to be the most popular sample. So establishing that equalancy to the NP are showing us a superiority to the NP is essential right now in this search. So we are looking for more studies and more data to support that. Priya Banada: So in my opinion, I mean, so far saliva has very popular because saliva is something that self collected. You can just give a cup to a patient and you can collect it at home and drop it off. Saliva is something that it does not need a collection centers that we had set it up all around the town. You don't need those pop-up tents. You don't need technical expertise to collect these specimens. You don't need the swab specialized for the nasal pharyngeal, which is like the thin long shaft, flexible, everything. The more you customize it, the more dirt pops up because it's only one or two companies making that. And so that is where I think we hit that limitation and the NP swabs, and we had to look for alternatives. So it's alive again. None of these requirements are needed. It is just any cup that can collect saliva. It doesn't have to be a specific type and patient collects it and drops it off to the lab and it gets tested very quickly. James Grayson: How are you thinking and your lab thinking of saliva, because you did indicate that the data is starting to be there and there's a lot of interest in it? But is that something that you have built the trust and confidence? And we are from the agentic and the data analysis side. We are starting to see copious amounts of saliva samples processed and results generated. So just curious about your feeling there. Priya Banada: Coming to our lab, obviously we were very much interested, excited, and there was a lot of questions also from different companies and industries and clinicians. Why can't you guys test saliva in your system? And we did take up that challenge. We have so far tested about 8,200 specimens and compared concurrently, and it's a manuscript in preparation, but considering the limitations with NP, I think saliva is a really good alternative choice. Pia Abola: What about from a sample extraction, a nucleic acid extraction point of view? What are the differences between saliva and NP? Priya Banada: So we have had many, many years of sputum extraction for tuberculosis. So we have gained a lot of experience in handling this specimen. So in the basic laboratories, people are not very strange to these specimens. So we already aware of this, we know how to handle these specimens, and we kind of ready to go. James Grayson: Something that I'm seeing that we're seeing at UgenTec is another trend you could say, is a move movement towards extraction free for saliva samples. So spiking like a Proteinase K, and then just incubating and then going from there. And I think about that from two perspectives, time to result, and so there has to be some amount of time saved there in skipping over a significant number of steps in extraction protocol, but also maybe resource availability and cost per test. I think that definitely affects these higher throughput labs, but I'm not sure. Priya Banada: There are going to be challenges, I agree with you when you go to a large scale. But with our experience with the Rutgers test, which is going to go nationwide right now, it's pretty, pretty promising. They are able to handle large numbers of samples and the reserves are within 48 hours right now, because of the amount of samples they receive, frankly. That is the reason. But it's not too bad. Pia Abola: What about limits of detection? Are there any differences between the sample types? Priya Banada: So, as I said, it's exploratory. There are many analytical studies that has been done to establish LOD for different sample types. The data we have out there is mostly from clinical samples. So people collect from a particular patient, all different sample types and run it concurrently. That's how the data is out there, which demonstrates that, okay, this particular sample seems to have a higher load versus lower load. But there is going to be some effect of inhibition from direct samples like saliva compared to a swab sample, because a swab, if you think about it, there is going to be a very limited amount of the sample that goes in and gets diluted in the media. Priya Banada: And only a small amount is taken from there and used in the test. Whereas with saliva, we are talking about using saliva as is. So it's the complete biological specimen without any dilution we're going to be using. So there's going to be human DNA background that is going to inhibit the assay, that is going to be inhibitors inherent to the sample itself. Sometimes it could be food particles of... Because it's the mouth and there is oral bacteria that could be a competitor. And the mucus itself may not be thin enough and the mucin kind of affected. And the medications the patient might have taken, there are a number of factors which could inhibit and can compromise sensitivity. But the point is when you weigh the overall percent positive rate. If there is a higher load of the virus in general in your saliva compared to nasal pharyngeal area, and you're able to take more samples from your saliva than in the nasal pharyngeal area. So it is always wise to use the sample that gives you a higher percent positive rate. Pia Abola: Well, one of the things that's really interesting to me that you've been talking about James is, how from the software perspective or a vendor perspective, you can see these trends in what's going on in the testing landscape. So how has that changed over time with the samples? I know you said there's a lot of saliva right now, but are there any other trends you're seeing? James Grayson: As time has gone on as resources have become more stable, you see companies or institutions coming into the process a little bit later and really trying to find standardization, trying to find who can be a reliable provider, who can consistently provide all the components they need, because at first it's shopping. It's seeing who's available. It's seeing if I can get tips from over here or swabs from over here. And now you see a lot of trending towards trying to work with companies that can guarantee supply across the board for all of these materials. But that standardization I think, is coming in a lot of the companies and institutions that we serve, kind of in the things I had mentioned before, where you see this really strong move towards saliva. You see the strong move towards extraction free. James Grayson: And I think now that we've had some time and some data, you're seeing people either have trust for these results or move towards them because of maybe supply issues or cost issues or time to result issues. And I think that something that is just starting to come into view is different modalities than maybe molecular diagnostic clinicians are used to looking towards where we're starting to see endpoint assets come into play, not full PCR curve analysis, but endpoint interpretation. Priya Banada: So just add to that, like the CRISPR technology. India brought out a Feluda, which is basically CRISPR based. So that's very exciting because that is what we wanted to make it as point of care as possible because it's easy to read. It is like you see like an antigen, it's like a pregnancy test kit. So it's positive, negative, done, I mean, that's ideal if you think about in surveillance point of view. But for the case management point of view and patient care, personal care, it is a very difficult to do it without knowing the details of it. That is where I was mentioning. Priya Banada: Based on these studies where they do mention the wider load has an impact on mortality. Actually, I propose that not all RT-PCR systems give you the CD values. The final data comes out to be whether it's positive or negative. Nobody prints out the CD values. So they should start printing out CD values in all the tests. And that should come from the clinicians. The clinician should demand for the CD values and say, "Okay, based on the viral load, I'm going to decide how well I'm going to manage this patient." And that will seriously, it's going to help create change in the entire testing chain, from the software to all the way to the clinician. James Grayson: I will sell you UgenTec right now. We do that and we can provide that to you quite easily across all your testing platforms. It's a really interesting point because from my perspective... Yeah. I work for UgenTec now, and it's a European company. But before that, I worked for Siemens Healthineers, also a very European company and I was in a global role then supporting rest of the world. So not the United States. From that historic perspective of mine, you see a lot of difference in what a country or region might accept for visibility into that kind of data. How do you think about that? I mean, in Europe, if you try to not show that data, there would be riots in the streets and in the US it just seems to be accepted. Priya Banada: We have to make a special request to get the CT values. So the system for the particular patient in the broader system with the Epic that I was talking about, for the physicians, they just see whether this particular patient was positive or negative for COVID, that's it. They do not get that information. I mean, if you go back, every instrument does have it stored. That is another step here. Like the physician has to make a special request to look up these patient samples and go to the labs, sit with the technicians and the technicians have to spend half an hour getting the data out of the machines. It shouldn't be that way. That's my point. It should be very straightforward data. So that also brings back to the point of different targets. Priya Banada: So is the call made from a single probe, or single target gene, or from multiple target genes. So maybe I think that is a limitation of certain tests that they can't give you a CD value because they are making a call based on multiple CDs, multiple gene targets. So then they will have to show you what targets they use and it becomes a proprietary issue. I get that. So that's what I'm saying, but at some point we have to narrow this down. This is like a fundamental change that is required, where we have to have one gene target or two gene targets, which should be public. It shouldn't be too proprietary and should help. At least the clinicians have access to some kind of CD values that relates to where I load. Pia Abola: So in an earlier podcast, we had a speaker who said, "Oh, the way the assays are set up, now they are not quantitative." But what I'm hearing from you is that they are kind of quantitative or they're absolutely quantitative. I think maybe what is your response to that? And does it involve more work, creating standard curves or things like that? Priya Banada: Most of the well-known RT-PCR tests in market, give you the quantitative data but there are tests that are based on isothermal amplification. So isothermal amplification starts amplifying at cycle one. So you start getting the signal. So that is not directly related to the viral load. So it's like an amplification. So the isothermal is used in the I bought a test today, the 15 minute test that vitals uses. So that one is isothermal based on base test. That's why it's rapid, it can give you a good rate, but it is not very quantitative. Whereas, expert express is very quantitative and they are... Like, Roche is very quantitative. Most of the RT-PCR base tests are... They give you CD values. They don't give you CD values as an end result, that is what I am talking about, that physicians don't have access to. Pia Abola: What is the most important thing you think clinicians need to know about the state of SARS-CoV-2 testing? Priya Banada: Yeah. So the same thing that I have been saying now, I think they need to know the relationship between the wider load and the mortality. They need to pay more attention to the increased case fatality rates among the immunocompromised population. And they should demand CD values for the tests that they get so that they can manage patients because as I said, "No two patients are the same." They are very different and the patient care is individual care. You cannot generalize patient care. So for that reason, I think testing, we are really good with testing now. We did not have limitations of sensitivity, specificity with RT-PCR based tests, specifically. So we have good tests in the market. Most of the tests are very reliable and depending on their affordability and the convenience and the facilities. They can pick whatever test they want, but the patient care ultimately comes into play. So that would be my basic. Pia Abola: What about you, James? What do you think is the most important thing for clinicians to know? James Grayson: I think it's just being aware of what you have before you, how do you manage it? How do you manage supply? I guess my side of it is a bit more from, how do you keep the machine running? And it's just knowing what you have available to know what you need to support your research or testing institution and I'm going to speak to Software Tools to help to support that. But there are a lot of limitations that many of these testing institutions have to get around, whether it's resources, whether it's availability materials, but it's also do you have enough staff to cover what you're doing and how can you compensate for that? And I think from the perspective of a software company, that's where software and artificial intelligence can really help keep the machine running, keep the lab moving forward, and make the data reliable and reportable and viable for the patient in the end. Speaker 4: Okay. I think we're out of time, but thank you everybody. Pia Abola: I thank you for listening to this episode of Countering COVID. You can find additional resources on this topic in the show notes, or by visiting biosearchtech.com/covid-19. We hope you can join us for the next episode where we talk about three different ways artificial intelligence is helping us counter COVID.