okay let's go ahead and get started good afternoon everyone uh my name is John cervas and I'm the director of commercialization education at fast forward medical Innovation also known as ffmi and I'd like to welcome you to today's webinar which is number three of our six-part series with the Department of internal medicine before we get started I do have a couple technical items that I would like to cover if you'd like to ask a question today please type it into the Q a feature our staff will be using both the Q a and the chat function to answer questions uh during the presentation as well as share some additional links to resources that may be discussed during the two presentations that we're going to hear today I'll note that the presentation is being recorded and we are going to share a version of the recording and the slides with you along with a post program evaluation after the program by via email so watch your inbox for that today's webinar is titled diagnostic Technologies uh where we are going to discuss some of the unique considerations of biomarkers and you'll hear Brad Martin the director of ffmi talk a little bit more about that here in a second as I mentioned the series is provided by ffmi a unit of the medical school's office of research and today in addition to the Department of internal medicine we are providing this in partnership with the University of Michigan's Innovation Partnerships Group which serves as the tech transfer office here on campus today's webinar as I mentioned is the second the third of sixth that we have planned as a part of this series uh all offer some basic instruction on commercialization of certain Technologies and in a case study example by a faculty member who's working in that particular space you'll see the February 1st date there is seminar number three which is today seminar four will discuss digital Health Technologies five will be around biomedical devices and then we'll wrap up the series on February 22nd with some next steps and some commercialization resources so our two speakers today uh you will hear first from Dr nihar Parikh who is the assistant who is an assistant professor of internal medicine uh also serves as the medical director of the multidisciplinary liver tumor clinic and the liver donor transplantation program uh and then after he we hear from Dr Parikh we'll hear from Jim Arthurs who serves as a mentor in Residence with the Innovation Partnerships group uh here on campus and has a long career in small biotech in the biomarker and diagnostic space so we are looking forward to hearing from both of them perhaps before we turn things over to nihar though um Brad would you like to say a few things around the biomarker discussion sure just yeah sure just a quick comment um first of all John thanks to you and Catherine reporting this uh the seminar series together and to our speakers today Jim and nihar for their expertise in lending their valuable time um but just as a quick reflection onto how we got to this biomarker discussion typically when we have run these courses previously uh for various departments or or schools across the campus we very often have a seminar based on Therapeutics devices Diagnostics and when we were talking about this program with your associate chair for research Viva um when we got to the topic of a diagnostic discussion it became very apparent that in Internal Medicine um when we talk about Diagnostics or when she thinks about Diagnostics she very much wanted to focus on biomarkers specifically if you're working in a disease area and your research leads you to a potential what you think might be a biomarker how do I know if this biomarker is truly uh specific to the disease State how do I know um when this biomarker may have value clinically and how do I get approvals to use this biomarker clinically that really became sort of the topic of discussion for Diagnostics I.E biomarkers and so we wanted to grab a couple people here who had some expertise and how do you develop a biomarker um project for a disease State how do you validate that biomarker in an assay how do you maybe measure that biomarker in a rapid clinically relevant fashion those are the topics for today and so I'll turn it over to our experts who can speak to this topic much more eloquently than I can and again I want to thank them both for being here so John I don't recall if Jim Ranier is going first here knee harder plans to go first and I've stopped sharing my screen so you can go ahead and launch burial all right thank you all um everything looking okay here yep looks great okay great thanks John and Brad for inviting us and Catherine for setting this up thank you all for attending um and uh uh you know it's a privilege to be here today talk to you all um again happy to take any questions after our presentations are over I'm going to talk a little bit about the clinical perspective of biomarker validation and some of the road map from Discovery to the clinic um and some kind of personal experience that we've had I'll tell you a little story about how um we've moved some biomarker work forward so as I mentioned as mentioned I'm a hepatologist I'm a liver physician I'm primarily interested in liver cancer and we'll talk a little bit about diagnostics from that standpoint these are my disclosures um so we'll talk about biomarker validation um why have a biomarker some examples in liver cancer and how do we get from bench to clinical use um we'll talk a little bit about liver cancer present in future biomarkers I won't talk too much about this um we'll talk a little bit about you know kind of the future and risk stratification biomarkers um but let's talk about the problem first you know I think anytime you think about having a new technology or a biomarker I think it's important to understand you know what is the problem what are you trying to improve on so you know right now there is there are National guidelines for liver cancer screening now liver cancer is a you know the sixth leading cause of uh cancer-related death in the U.S it's increasing in um you know in frequency uh in incidents in the U.S and it occurs primarily in two states and patients who have liver cirrhosis which is really just scar tissue in the liver and can be caused by lots of different things or patients with chronic Hepatitis B and those are emotionally Asian board patients that have had it all their lives and are at risk of liver cancer right now there are recommendations from our liver society called the American Association for the study of liver disease and the guidelines is um ultrasound which is an Imaging based test every six months so when you talk about cancer screening this is a very intense type of cancer screening you know think about colonoscopy or you know breast cancer screening a lot of these are either you know every five to ten years every couple years but uh and sometimes even annually but liver cancer screenings every six months and we do use a biomarker in liver cancer screening the updated guidelines are coming out there's a blood test called Alpha feta protein that we use with the ultrasound to increase the sensitivity of ultrasound based screening we still need the ultrasound we don't have purely biomarker approaches and so it's intense screening and ultrasound is a separate appointment requires patients to come in to get it done so you can have these limitations there's issues with false positives people see things they're not sure what they are they end up getting MRIs biopsies Etc so lots of lots of uh issues with ultrasound based screening and you know in this meta-analysis that was conducted we the you know we looked at what's the heterogeneity and Ultra Sound Performance you can imagine it's provider dependent right so somebody comes in does an ultrasound of somebody's liver that may be different than it is from you know Ann Arbor and Muskegon the ultrasound tech may be a different person who reads it may be a different person so what we see is heterogeneity and sensitivity which is a problem when you're trying to detect cancer early you want your test to be reliable across patients across Health Systems um so this is one of the issues that we've seen with ultrasound and really one of the the um uh problems we wanted to address so we know that getting an ultrasound as I mentioned is hard it's you know it requires a separate order and what we see you know nationalist compliance is really low compliance rates is around 10 for ultrasound-based screening invasions with liver disease which is you know very poor um even if we look at Specialists people who are getting um uh seeing by like people like myself gastroenterologists in the community it only gets as high as 25 on average in this meta-analysis so compliance is an issue and you know there are attractive you know so this is one of the issues with ultrasound and if you could have a point of care test maybe you could improve improve compliance and compliance really it matters adherence matters so this looks at test sensitivity and test adherence so even if you have an inferior test say if you have a 50 sensitive test but if you can get adherence at least 50 you could have a much more effective cancer screening strategy that if you have a very sensitive test that nobody uses um and so I think the the one of the attractive parts of using biomarkers in a clinical space as opposed to Imaging based techniques is that this Paradigm is you know the real world is what we see it's much easier for patients to get a point of care test their blood draw at any lab then come in and get an Imaging like a mammogram or come in and get a colonoscopy Etc so this is one of the attractive pieces of biomarker-based screening so I think I've touched on some of these advantages before it's easy ease for patients and providers maybe you can improve on sensitivity and specificity um if you have the right biomarker can be more cost effective than Imaging uh techniques like other Imaging techniques some people say well why don't people everybody just get MRIs for their liver cancer screening because the sensitivity isn't that great but MRIs are expensive we have capacity issues and the one of the key things that I haven't mentioned is that you know liver cancer screening is limited because we don't have level one evidence there's not randomized controlled data and patients with cirrhosis and so biomicrobase approaches you know if you can if you can go forward with this maybe we can generate higher levels of evidence behind its use and increase uptake but there are disadvantages as well you know how do you deal with a false positive and biomarker based work we'll get into that when we talk about validation like what if you get a positive test but then you get an MRI for Diagnostic purposes uh what does negative imaging mean does it mean that they're higher risk and they're to develop it they may develop it they or may already have and you just can't detect it or is it really a false positive and the other part is some of the pressures of biomarker bringing biomarkers to to Market a lot of these are driven by biotech companies and obviously there are there is this balance between academic uh validation and the interests of commercial entities in uh biomarker use and so um you know there's there's the potential for things coming to Market before it's ready to use um and there's plenty of examples of this um but um it's just something that we uh talk about uh frequently so let's step back a minute what is a biomarker really a biomarker is any substance you can measure in the body it could be blood urine saliva Imaging um is a biomarker you know a group here does this morphomics work with Grace Sue and Stuart Wang's lab but this is uh you know this is these are examples of biomarkers anything that's really measurable I'll Focus mostly on blood-based biomarkers but then the question is you know how do you get from the the lab into a human patient here somebody with a liver just which is everyone but uh you know how do you get there um and there's there's actually several discrete steps for biomarker validation um prior to clinical use and these have been laid out by the NCI the National Cancer Institute as a seminal paper uh by Pepe at all and they uh really lay out the steps for adequate biomarker validation I'll talk a little bit about them right now and give you some examples so it's a five-phase thing and it's really akin to you know the the we think about drug development um that's what this is really based upon it's it's a it's a way to validate things from a discovery phase into a clinical utilization phase similar to drugs and the timelines and costs can be on the same scale uh to be honest so there are several challenges with this but we'll go through the phases briefly so phase one is this pre-clinical exploratory thing so this is how you discover a biomarker um it's exploratory studies usually have cancer tissue um or Imaging to identify potential biomarkers and it must be reproducible this early quality control is really important um in order to say Can you can you develop an assay to measure this can you do it repeatedly and can it be reliable um that's really critical to get any any biomarker out of that initial phase um and so in this scenario you're looking at patients with the cancer can you find you know proteomic signatures genetic signatures Imaging based signatures that you can measure reliably and reproduce in this early phase pre-clinical phase um and you maybe must be able to distinguish between patients with cancer and without cancer you know these don't have to be huge studies but there at least has to be a a signal here and again I mentioned some of these microarrays proteomics to identify unique genetic and protein signatures this is a very nice example and I'll walk you through this is a very complicated slide but this is a patient a paper that was from the UCLA group that does a lot of work in liver biomarker development this is extracellular vest Vehicles okay so extracellular vesicles are little you know compartmentalized um uh you know uh things that are you know basically vesicles that are able to be isolated in the blood um and there's a lot of interest in cancer because there's a belief that cancer cells excrete these and you can uh basically um you know distinguish them so what they did was they looked they had a chip technology and they were able to purify these extracellular vesicles from patients with um you know cancer and they were able to you know have a technique where they could uh incubate these with antibody cocktails um these antibody cocktails were able to bind up these uh extracellular vesicles um and they were able to do a you know basically a chemistry mediated capture using these antibodies that bound up and so they had different targets again some of these um for you and the cancer for those of you in the cancer world like epcam um are would be familiar and they'd be able to bind up these extracellular vesicles um they were able to cleave them and then eventually they were able to do these kind of uh rtbcr analysis of these extracellular vesicles and they were able to very well using their bioinformatics core distinguished between HCC and non-hcc hc's liver cancer pedicellar carcinoma and so this is their Discovery work they had some early validation data in here kind of a phase one phase two type of study but this is a really really nice example of how you get from a patient with cancer and try to discover a potential biomarker and show that you could reliably distinguish cases and controls these two is the next step after you've done that phase one I've kind of alluded to this and one of these two is is patients with cancer versus patients without cancer and this is really clinical assay development again you know making sure that you basically can reliably tell distinguish between cases and controls you can do consist there's consistency of the assay across Laboratories and you can really start the characterization if the biomarker is useful in early stage patients um so you want to know what the true positive rate is and the false positive rate so this can really give you signals is this useful um you're also able to tell if there are other covariates that matter depending how big your your uh validation set is so age sex ideology liver disease degree of liver dysfunction or examples of liver cancer but for whatever cancer whatever disease State what are the covariates that matter and how do you stratify your populations this is a this is the stage where you want to be in try to think through those and and make sure that this is going to be a biomarker that's applicable across the population of patients that are important to you and your disease State again this is case control design but the pitfalls is really finding appropriate controls right um you know for example in patients with liver liver cancer a control patient is not a normal healthy patient because those patients don't really develop liver cancer that's not a screening population in patients with liver cancers patients who are at risk of the development of liver cancer you also want to make sure that those patients don't actually have cancer so oftentimes we'll do like a make sure they have a one year follow-up they haven't been diagnosed with cancer in that stage we also want to make sure that there are early stage cases you know I I can very easily identify somebody with late stage cancer um that may not be a great outcome for that patient what we really want to do is able to identify these patients in an early stages so you really want to have a mix and uh and I mentioned that ensuring controls are not actually cases this is a very nice example of something called gallet score which is a three different biomarkers age and sex put into a formula this is the initial validation study for that and this is the type of things that you'll see this is a big multinational validation of patients with HCC which is liver cancer chronic liver disease from different countries across the world um they have even in here they have some other cancers like chalangeal carcinoma which is a bile duct cancer pancreatic cancers um I would argue the healthy controls is probably not relevant here but this is what you would see it's kind of cases versus controls now the cat the the caveat with this phase two studies is you can overestimate your performance of your biomarker because in a you know one of the things that dictates performance is prevalence of the cancer prevalence of whatever disease and case control studies it's usually one to one you have a 50 prevalence um or incidence um but in the real world you know in liver cancer patients have a one to four percent annual instance so um once that incidence goes down your performance of your biomarker may go down with it so that's important to understand when you evaluate case control and that's part of the reason that case control in itself is not adequate to getting into clinical uh studies and honestly this is where most biomarkers stall and the reason is is that they're not great phase three cohorts out there for many disease States so what is a phase three cohort a phase three cohort is a longitudinal cohort of patients who are at risk of the disease um and you collect their uh blood samples serially and before their clinical diagnosis of whatever you're interested in in this case liver cancer and then you're able to really get kind of real world incidents of the cancer you're able to do serial testing of your marker over time and get a real sense of how your marker performs things like false positives and all that will be very uh we'll uh the performance of that marker in that setting will be you know much more apparent um usually what happens uh with these collections is again it's it's a Serial collection that's prospective and then there's a retrospective blinded evaluation of your biomarkers so you take all the samples that you collected um you know over six months and then you test your biomarker and you don't know who has who has cancer and who doesn't um you know when you do the test it's blinded um and then you say evaluate how is how well does your biomarker work and that's called probe design okay it's really to determine the ability of biomarker as a function of time to detect preclinical disease um and then you really you can also Define you can set you set your thresholds for what a positive screening test would be based upon you know false positives true positive those sort of performance metrics so again there's not a lot of these cohorts out there but um there's a lot of value in potentially collecting these cohorts so that you can test biomarkers and see which ones you want to move on to the next phase and the next phase is a very uh resource intensive phase these are prospective cohort studies clinical utility trials is what we call them in the space and what they are is a randomized study um just like you would randomize for other studies these are prospective cohort which is where a biomarker is tested in real time with a diagnostic workup for a positive test um and you would test this against the gold standard so in our case we would test a biomarker versus ultrasound and you would randomize patients into those and you can stratify your randomization however it matters the endpoints can vary the endpoints could be stage migration could you can you can you detect patients at an earlier stage a bigger endpoint would be overall survival that's a harder endpoint and a bigger powered study but it's important both for phase three and for phase four that you power your studies appropriately to what your incidence is okay what your incidence your your incidence of the outcome is going to be doing a well-powered study is really important in this case otherwise your signal may not be um you know detectable um the the uh again this can determine the detection rate and the false positive rate in a defined population of patients there's an alternate approach that many companies have gone which is a um more rapid okay it's it's a basically they they do a similar population they do a gold standard test and a biomarker test um and they test each other at the same time um and the main uh outcome is performance of the biomarker for cancer detection versus the the test so in in liver cancer some accompanies are what they're doing is they're doing a ultrasound a biomarker and then they also pay for an MRI and so they get all three tasks and they can determine how well the biomarker performs against the ultrasound with the MRIs being the gold standards that's an example um what are the issues with this the issues with this are false positive management the longitudinal of the nature of this biomarker is lost um but you get rapid cohort maturation so this is attractive from a company perspective phase four studies can take years four to eight years on average um there are several disadvantages there's false positives as I mentioned the longitudinal performance of the biomarker but an example is the colaguard test which some of you may be familiar with it's a multi-targeted stool DNA test this is the New England Journal paper this is essentially the design that exact Sciences which is the company that developed that test has used um for this and they're doing a study in liver cancer and it's similar in the phase four phase five is mostly cancer control studies these are generally kind of post-talk analyzes and these are really addresses whether screening reduces the burden of cancer in the population ultimately that's what we want we want your biomarker to decrease the burden of cancer um and uh you know these can be pragmatic studies where they're randomized biomarker or the standard of care or there can be kind of evaluation of the biomarker as in clinical practice and it's decreasing cancer related to all cause mortality um and again there are several issues with real world studies like this like compliance utilization um and underutilization of Curative therapies again just because you diagnosed the cancer early doesn't mean it leads to a better outcome you have to have a whole Continuum of Cancer Care same thing with any the other issue is also over diagnosis if you can't if you and we've seen this in the prostate world where PSA was over diagnosing patients with indolent cancers leading to lots of issues so you have to you know there's kind of a yin and a Yang to early diagnosis um so the gallot score I'm gonna just talk a minute about this this is something an example of something that we've worked with extensively I've mentioned it earlier I showed the case control study this is a composite score these these um proteins have been around for 15 20 years and they just have never been validated in a way that allows them allows us to move on from a phase two study to a phase three study to a phase four study but that has recently changed and again this is this is a few biomarkers age and sex this is a nice example of a face to study with uh 670 patients 331 with liver cancer and the AUC is for early detection was were very good very promising um you know and 86 early stage sensitivity and 75 percent um you know uh early stage by treatment intent so really excellent but again this is going to overestimate the performance um there was another validation that occurred that also showed this but again case control um and the sensitivity is the early stage was we have to use a criteria called the Milan criteria um in these phase two trials was around 80 if you look at across studies so really uh very uh you know excellent um and so you know how are we going to figure these things out again these longitudinal cohorts these phase three studies it really is difficult to set these up though you need infrastructure for serial collection with reliable follow-up you have to make sure that your collection methodology is generalizable and it takes time we've been involved in this study from the NCI that has been serial collection in 1500 patients with cirrhosis called the head study it's from the early detection research Network and it's taken you know over seven years eight years for this cohort to mature it takes forever for these phase three studies and you know we estimated an incidence of liver cancer in this population but it was much lower because hepatitis C has been cured and so that's one of the things that happens when you take a long time for a cohort to mature things happen so I'm going to give you an example of a cohort study that was done here um briefly from 2004 to 2006. sample collection from patients with cirrhosis so it's really like an early phase three study one of the first ones that was done it was they were um serially collected for six months serum and plasma they're stored at negative 80 never defrosted or never thawed and patients were followed with semi-annual surveillance until incident liver cancer liver transplant death or loss to follow-up and you know just last year we used these samples that were sitting in the fridge for 15 years and did a phase three study looking at gallant and you can see here Gala longitudinal gallid um all that now this is an underpowered study but this gives you a signal of the AUC now these aucs are lower again these are phase three then in the phase two studies and that's what you would expect and sensitivity is around 50 to 60 depending on the cohort now this has been since replicated in a larger phase three study that head study that I mentioned from the edrn and similar performance measures so you know not not you know 90 sensitivity but pretty good for these uh for biomarker uh performance and again if you can get adherence that's great now these are examples of national studies uh in liver disease that are currently ongoing this head study that I mentioned 1800 patients the Texas HCC Consortium gonna have 5 000 patients that's ongoing so these are examples of phase three big efforts lots of money lots of funding that are necessary to move forward this we just got word of last week is going to go forward this is a national liver cancer screening trial this is going to be funded by the NCI the edrn using core funds it's going to be a 15 side trial um we're going to be one of the lead sites and it's patients with cirrhosis and this is based upon all that work we did in the phase three space this is going to move on to a phase four study and this will start later this year um uh and so we've been hard at work putting it together but it's patients with cirrhosis uh you know well compensated no other masses it's going to be standard of care ultrasound plus AFP versus a biomarker what biomarker we're using gallant and the primary endpoint will be stage migration this will be if it meets its primary endpoint it'll be moved on to a phase five study which will look on look at HCC related mortality and harms um and so this is a combined hybrid design and we worked with the folks at Dana farbery um but this is actually happening um and we're super excited about it and it's um uh you know there are 15 sites um in the US that are going to be involved so what are some logistical considerations when you're thinking about biomarker from a clinical perspective you have to really understand your test performance uh for early stage detection you know your hazard here is that you could be have an inferior test to Imaging based screening um some solutions are non-inferiority design for acceptable margins and a pragmatic trial design to incorporate the impact of adherence as I mentioned you can improve your adherence doesn't matter if your sensitivity might be as marginally lower than those patients with uh standard of care screening how do you manage false positives you have to really you know if you have a false positive how you're going to manage that down the road that's something you really have to think about it's you know your hazards are lack of care Pathways for false positive results and you really don't know what the implications of false positives are on the future cancer risk longitudinal trial design to understand and delineate the Optimal Care Pathways really are important so I think that's this is where these longitudinal studies are critical costs you know lock lack of pair coverage Jim will talk a little bit about this um low liver cancer instance we'll need a number of high numbers to screen so there really has to be rational price setting from a company perspective I will say that having a court have a having a company as a partner is really critical for any development of these you need more funding than things like the NIH can give you um to really run these trials well and so these Partnerships are really critical and a lot of these biomarkers that come from companies the uh and you know we're we're tasked with the designing validation studies and then you can also calibrate the intensity of screening based on patient risk um how do you do uh blood processing do you do Central Blood processing is there a lack in your lab run these um you know how do you do results across centers and how do you make sure they're standardized so there has to be quality control for shipping if you're going to do centralized or you have to calibrate local labs for consistency and then how do you report tests do you report tests back to the ordering patient do you do a level is it dichotomous positive or negative um you have to develop develop these reporting Pathways for results this is really important with commercial partners and then you have to providing continuous test results really gives you the power of longitudinal testing so this Chrono Bayesian uh way of looking at tests and if it's going up even if it's not a positive test that may be an early signal of cancer um and so these are logistical issues you really have to figure out as you develop these biomarkers so biomarker development and validation is process but there are several good reasons why a biomarker based approach could be Advanced advantageous for cancer screening or any disease screening there are several several promising biomarkers out there without sufficient validation this is across disease States um and then you know we're on the verge for liver cancer and other studies for large-scale clinical utility trials which may change change liver cancer screening um and other types of cancer and the NCI is a lot of interest in this approach um thank you for your attention these are my acknowledgments my coordinator team and some of the networks that we're part of um uh I'm happy to let Jim go for a premiere I actually are and thanks uh John Brad and Castle Catherine for coordinating this thanks to everybody for participating uh let me get my uh screen shared here all right thanks um we're not stopping for questions right now are we going to go ahead go ahead and go Jim yeah all right great so I'm going to take a little bit different tact I'm I'm going to um start from the point that says that you've done everything that nihar went through in terms of basic research um by the way brought up a lot of points they'll be overlapping some of the things that I might emphasize um but how do you get it from where he kind of left off in his talk to where uh somebody can actually use it in a in a clinic and Bill for it and actually um have it be paid for so there there are really four steps the the first one starts kind of with the validated biomarkers that that were just discussed however if you're going to go into a clinical lab um there may be another bio biomarker validation or passay validation uh step in there uh whether you go to FDA or whether you're doing your own you could you could face that then the lab itself clear FDA are involved in that the basic process of handling samples reporting them and then billing so what what I intend to do is um talk about some of the considerations in each of these four areas but before we go there what what do you want to do how do you commercialize first of all for U of M first thing you do is invention disclosure and then the question always is do we want this to go out to another company are we going to do a startup uh by the way those are not mutually exclusive you could form a startup and continue to build value into your asset um and then exit to a larger company or sub-licensed to a larger company so there are a lot of interesting things that you can do to um improve the value of your technology um and get it to Market at the same time and regardless of which way you intend to go or you're going you basically have the same things that you have to do to attract that licensee or investors you have to show that it works you have to show there's a market for it so where do you start with this so I I always like to say um what is it you're doing what's the overall picture so I think everybody's sort of intended you statements probably seen them with um FDA package inserts and things um so I always think to start at the beginning what are you going to tell the market and then what are you going to show them after you're done so draft those as early in the process as you can if you can um that talk about claims as well that you might want to talk about if you're if you're going to go through an FDA clearance um claims will come into play that might affect your intended use statement customer Discovery is primo in this area again it's the old question is there a market for this that's worth investing in and will it be accepted by the market um is this an assay or a test that is usable and useful by clinicians I'm going to throw a pitch in here for Quality Systems and I don't mean everybody run out and spend sixty eighty thousand dollars for a consultant to build you a quality system absolutely not but start thinking of things like uh one component is a design history file where you're tracking changes in your assay uh why you're making them what data supports the changes because if you go uh toward an FDA clearance uh this question will come up very early about show us your design history file so the early also it's not a bad way to manage yourself so as early as you can just start thinking about things like that maybe get yourself familiar with the quality system uh contents in the FDA requirements and in the clear requirements and pick the things that might help you as you're evolving even before you decide you're going to spin out of the com out of the University there are things you can be doing like the design history like tracking lot numbers like making sure all the equipment you're using and all the fridges and autoclaves and things you're using are qualified and and measured and you know monitored so things like that if you get record of that while you're developing or validating your essay it just adds credibility to your results in the end um and then really uh early on engage people who can help uh fast forward medical Innovation FDA themselves Innovation Partnerships as a great organization within U of M called mishar uh that has constant connection with FDA and they can give you advice on things that you might be able to do early on to prepare for that so why do I think Quality Systems are so important I don't mean to belabor this but you don't want you want to head yourself off from having any kind of out of control processes if you don't pay attention to some of this stuff early on it'll catch up to you when you talk to somebody about running your your test through a clear lab or if you're talking to the FDA and clearly the boards board and investors employees will lose a lot of confidence if they don't see some control um I'm not speaking the the the the subject of quality per se although that becomes part of it the quality system comes into play in terms of how you operate your company I use the term internalize it within a company or within your your operation within your lab um definitely phase it in work on function over form there may I mentioned the design history file there are examples of that um if you're keeping your design history notes in a notebook that that's the function over you don't need to go uh follow somebody else's form uh this is not um at the time when you should just copy what somebody else does you should Implement things that are practical for you where you are um and where your assay is so let's go to those four steps um some considerations there those are these fairly briefly because um any one of these subjects has a myriad of detail and a lot of interrelations so um I just want to highlight some of the considerations some things you got to think about obviously your platform and any equipment that that is involved will become important as you move through trying to raise money or trying to commercialize an assay um and anything you can do to to build a spec sheet for this kind of thing uh will help um talk about the kind of biomarkers a single marker compared to one that's running with an algorithm the algorithm itself in my opinion becomes a component of the assay and needs the same care and feeding as any other part of the assay there's a lot of work now being done to deal with softwares of medical advice you may fall into that if you have a multi-marker algorithm as part of your assay so it's something to to be concerned about if all you're doing is doing a multi-marketer panel and all you care about is the the value of each marker and that's all you're reporting that's a different thing that will help you decide how to how to phase or phrase your uh your results reports um your algorithm algorithm is very important if it helps Drive obviously the um uh the value in your test results so that's um actually I was happy to hear nihar mention um prevalence of disease one of the things a lot of developers forget to do is factor that in when they're evaluating their biostatistics in terms of number of samples they need and what the results really mean so all of that comes into play here as well um expect sheet should handle everything from the sample itself to collecting it skipping it handling and processing it so I think everybody knows this any medical test has to be run in a clear lab a CLIA registered certified lab um although that's a lie if you have a wave test that can be run outside and you can get a lot of wave tests over the counter um test complexity is either waived moderate or high the moderate and high tests are the ones that run in a lab a medical lab the complexity level is determined by seven criteria based on how how much um uh med tech involvements required how complex the test itself is whether it needs further evaluation and once they once you go through those seven steps um you get a score of it used to be if it's less than 12 it's a medium complexity if it's above 12 it's high uh virtually everything I've been involved in and these have been cancer Diagnostics are high complexity so anything that has any kind of detail or complexity in it in terms of running the assay or the content of the assay uh usually would end up in high so there are two options lab develop tests where only the lab who developed the tests can run it they can make no claims or if it's an FDA cleared test it can be sold in kits and run anywhere these are not mutually exclusive you can start if you want to build your own lab or work with the lab you can do a lab develop test while and and service that while you are preparing an FDA application for clearance manufacturing is a big deal especially if you're going to FDA that's one of the big things they look at is the manufacturing process I'll throw in the Quality Systems Thing by the time you get to this point you will have a quality system either um if you've started your own company or if you're having to talk with people um to to license your assay out um you'll be good you'll be coming very very familiar with quality system um again quality system comes in this is where all these these three letter terms four letter terms come in you might see these in different ways um ISO comes in real heavily when you're talking about FDA manufacturing um it's really important and nice to build a flow chart just like anything else pictures work pictures help um to go from the sample how do you what's the sample collection spec and then refer to all the Sops at every step in the flow chart so that there's a reference there for employees it's a good way to communicate it to somebody who's interested in investing this can be done even now while you're still in the university in research mode and probably should be if it isn't because it's it can kind of build the framework for moving it into a a lab or moving it to a cloud license so billing is always the is what you drive for um the cash only is becoming a lot more popular it turns out there's a lot of concierge labs uh forming up where they don't even make a claim to insurance and they only do with cash for for running tests um the normal thing though still is to to to submit a claim your insurance company which then would go to either Medicare or to that insurance company they all have their own um polishing manuals but basically everything is based on CM CMS fee schedules uh or yeah Medicare fee schedules um if you're going to do an ldt I I I I would never and we've thought about this we tried to do this but I would never recommend trying to do the billing yourself that's very complicated um not so much in finding this CPT code that you might use or the Z code that you might use but every state that turns out has their own insurance company um so if you're servicing multiple States uh you end up having to deal with different things in every state different um yeah now it's a it's a real profession in itself um there are codes people talk about getting their own CPT codes getting their own Z codes Z codes are a little bit um newer um they tend to be applicable more to the dna-based tests and they they're I'm not going to say there's well I'll say it's straightforward but not necessarily easy to do getting your own CPT code is a little harder uh it takes actually some some actual commercial experience before you can do that so what's the cost of taking a test from the you know the lab or from the academic lab through to a full commercial um usage um it can it could be as low as 10 million dollars but it's tens of millions if you think in terms of a drug getting a drug cleared and out it's hundreds of millions Diagnostics are generally tens of millions if you're doing your own ldt lab develop tests it could be a little less uh but uh you still have limits on not being able to make claims and the tests can only be run in your lab or the lab where it was developed in so how do you get help to do this uh there are a lot of great resources within the university and and within the Ann Arbor area some of which I've highlighted here this isn't all of them I apologize to anybody if I've left somebody off uh I won't go through through all these in real detail other than to highlight that uh that um the Amtrak Life Sciences um funding uh is a really great way to get money uh to help move things along internally um it's non-dilutive and uh is extremely valuable for um for technologies that are you know tending to eventually license or or out license uh ffmi has great programs for commercializing one of the better ones that I've been involved in is called fast pace which is kind of a an i-core shaped program which helps you with customer Discovery and helps you begin to build a pitch deck for your company uh Ann Arbor spark is great in terms of being actually providing state and federal funds for entity formation and some other um project type work they have a boot camp as well new Enterprise Forum was a local organization that does great work at helping groups do business planning and Pitch decks and yeah the rest here you can read I won't go into all the detail but of course Innovation Partnerships I have to put in a a pitch for that as well one of the one of the newest things there is accelerate blue fund which is a which is an investment arm that lets uh lets Innovation Partnerships actually help some of these uh Technologies Move Along who who aren't quite ready for um VC or Angel funding and of uh out run all of the non-dilutive funding and grant funding that might be available to them uh Innovation Partnerships also puts out a pipeline report to 600 or so investor in groups on a quarterly basis um and um have all kinds of other networking and other services available and of course this the the classic Angel and VC investors are out there um anybody in this panel in this group at ffmi or innovation Partnerships can help you connect with uh with those kind of people and uh and there are others like I said I I don't this is not an exhaustive list so that's kind of my um rambling kind of approach to how to go from there or from here to there um at without getting into too much detail in any of it as I said there's a lot of detail involved in any of them and they are all interrelated but um just remember to start with uh customer discovery and do your intended use statement and your results report and that'll help that'll help set the charter for your team and for communicating with investors and that's that's the end of my talk thank you I appreciate the time well thank you very much Jim um and I'll point out that in the chat feature I was able to link to several of the resources that you were mentioning on your your last slide thank you as a reminder we will send a copy of the slides and the recording to everybody uh via email after this call but we do have a few minutes if somebody has a question for either Jim or nihar please feel free to unmute yourself and ask or submit by the uh the Q a function no questions at this time well if you do have a post event question please feel free to reply to that oh we do have one question uh all right go ahead when'd you yes um my name is win jinji from University of Michigan uh renal division Department of internal medicine and thanks Jim and Nate Harvard excellent presentations I have a question I have a biomarker and and for kidney disease progression um we have patent on this biomarker however the essay has been you know it's a commercial essay would that be very different in developing this biomarker um differently than what you mentioned this in terms of commercializing it you mean yeah yeah so what it sounds to me like is if you if you've taken an existing marker and and the one thing you can't protect is you can't protect a protein and a gene and things like that I think we all know that but what you can protect from an IEP point of view is a method of use and so if you've got a method of use that is novel um then there's there's a possibility I think to do that is that does that get to your question sorry that those those proteins um that we're using in this in this clinical trial is a good example that those those assays have been available for 15 20 years okay but the FDA just gave Roche a you know uh a designation of combining those proteins for this specific indication for liver cancer so you know these are commercially available but the intended usage uh is what got the um the approval and the you know excellent thank you very much but it sounds like you may very well have to reach out to the current manufacturer of that assay just to understand their willingness to to partner with you in some degree or to Take This research research-based assay and turn it into a clinical assay fantastic thank you okay we are very near the end of our time so we'll go ahead and wrap things up uh again if you have a question that comes up feel free to reply to that email and we can work to get that answered for you I want to close by thanking uh nihar and Jim again for your time and expertise this afternoon uh we really do appreciate it it was a fantastic session um and I want to wish everybody else that uh attended the call today I want to thank you and then wish you a fantastic day
2023-02-09