Speakers:

• Bin Pan, TFS HealthScience
• Michael Mendoza, TFS HealthScience
• Sonya Hunt, Moderator, Xtalks

Speaker 1: Sonya Hunt: Sonya Hunt (00:09):

Good day to everyone joining us and welcome to today’s X Talks webinar. Today’s talk is entitled Enhancing Efficiency in Early Phase Oncology Trials Strategies for Accelerating Data Flow. My name is Sonya Hunt and it’s my pleasure to be your X Talks moderator for today’s webinar will run for approximately 60 minutes. This presentation includes a q and a session with our speakers. This webinar is designed to be interactive and webinars work best when you’re involved. So please feel free to submit questions and comments for our speakers throughout this presentation using the questions chat box, and we’ll try to attend to your questions during the q and a session. Now this chat box is located in the control panel which is found on the right hand side of your screen. If you require any assistance, please contact me at any time by sending a message using this chat panel.

(01:10)
At this time, all participants are in listen only mode. Please note that this event will be recorded and made available for streaming on x talks.com. At this point, I’d like to thank TFS HealthScience who developed the content for this presentation. TFS HealthScience is a full service global contract research organization that supports biotechnology and pharmaceutical companies throughout their entire clinical development journey. In partnership with customers, they build solution driven teams working towards a healthier future, bringing together nearly 800 plus professionals. TFS delivers tailored clinical research services in more than 40 countries with flexible clinical development and strategic resourcing solutions across key therapeutic areas including oncology, hematology, ophthalmology, dermatology, immunology and inflammatory diseases, internal medicine and neuroscience. Now it is my pleasure to introduce our speakers for today’s webinar. And first I’d like to introduce you to Dr. Bin Pan, executive director head of hematology and oncology at TFS HealthScience.

(02:25)
Bin has 23 years. Hi there, Bin. Bin has 23 years of experience in clinical research with a strong focus on early phase oncology trial strategy and execution. She has a scientific background in biochemistry and molecular biology with postdoctoral research in cellular signaling pathways. She maintains a broad knowledge of current oncology therapeutics and the competitive clinical landscape. She provides strategic guidance and operational leadership to internal cross-functional teams as well as a consultative approach to novel solutions. And next, it’s my pleasure to introduce you to Michael Mendoza. He’s the executive director eClinical Technology and Biometrics at TFS HealthScience. Mike is a highly experienced executive director of clinical data systems with expert global experience within the CRO and technology environments. He holds certifications in Metadata Rave and multiple EDC platforms. He has extensive experience that spans trials in phase one through four and encompasses a full spectrum of company profiles including large pharmaceutical companies, small biotech companies and device companies. Now it’s my pleasure to pass the mic over to Bin. So Bin, when you’re ready, you may begin and you can unmute yourself.

Speaker 2: Bin Pan (03:55):

Thank you Sonia, for that reminder to always hit the unmute button when you speak and thank you for that introduction and it’s a pleasure for me today to be presenting this webinar with my colleague Mike Mendoza. And so Mike, would you Thank you. Next slide. So first as a brief introduction or just a brief refresher, we will talk about some of the characteristics of early phase oncology trials. We’ll examine how those particular characteristics drive the inherent data flow patterns in early phase oncology trials. And then Mike and I will share our experiences in implementing strategies to enhance data flow in early phase oncology trials, including some of the tried and true operational strategies and tactics and as well as some of the technology advances that can really help to drive the faster data flow and drive a faster decision making. And finally, we will summarize the key takeaways from this presentation and we hope there will be some time left in the end for a few question and answers.

(05:34)
All right. When it comes to clinical development, as we all know, the early part of the development in terms of number of trials typically vastly outnumber the late phase trials. That’s just because of the nature of drug development. You need a wide funnel at the top to reach your end goal. So in that sense, oncology trial development is no exception as well as evidenced by the bar graph on the left hand side of this screen. As you can see, the number of oncology clinical trials started since the beginning of this year. There are a lot more early phase, i.e. phase one or phase one combined phase one two studies than the phase three studies. However, oncology early phase trials do have its distinct characters and its flavors. So I’m using the example on the right hand side of the screen to give you a visual of the complexity of early phase oncology trials.

(06:54)
And remember, this is just one of the examples and there are trials that are a lot more complicated than this. So keep in mind that when we develop trials for cancer patient develop treatment for cancer patient, these drugs are typically not suited to be tested in normal healthy volunteers because of their toxicity. And as a result, typically the first in human trials in oncology are tested in a relatively heterogeneous group of cancer patients. And with that in mind, we really have to ensure patient safety in this setting. And as a result, the trial design can be quite complex. Usually early phase oncology trials can be either a standalone dose escalation type of trial where you see the step ladders where you increase the dose sequentially a limited number of patients or more. It’s becoming more and more popular these days with a seamless adaptive design trial which include both dose escalation and dose expansion part.

(08:23)
And the goal of the dose escalation part of the early phase oncology trial is really to get a sense of the toxicity profiles of the drug as well as to ultimately determine what is the maximum tolerated dose or what is the biological dose in some cases. And ultimately to decide where we go from there. For the phase two testing of drug and the dose expansion part of the trial, the goal is to try to optimize the doses that are selected from the part one portion and hopefully we can hone in a few specific indications, cancer indications that have shown some promising results. So on top of this these days a lot of oncology trials are being developed in combination with other trials. So we’re testing not just one single drug but a combination of different drugs. So you could have cases where in your part one, you are not only escalating one drug dose of one drug, but you might be in parallel escalating the doses of a combination regimen.

(10:01)
And then in part two portion the escalation portion of the trial. Under FDA’s project Optimus, we really have to justify the selected dose for future testing. So now we could be introducing a second dose to test in different indications. So as you can see, it gets quite complicated as a result, we’re going to get a lot of data in this early phase oncology trials. So why is it important to talk about data flow in early phase oncology? Well, I can tell you as clinical operations professionals, we know there are a lot of things we need to look at in the day-to-day operations of the trial. This is a highly regulated industry, so we’re trained to look at every minutia, every detail of each trial. For example, we look at when did the patient last take their drug, when did the symptoms start, et cetera, et cetera.

(11:25)
So sometimes in our day-to-day, it’s really hard for us to think about the big pictures of a trial, right? What is the ultimate goal of our trial? Well, one of the ultimate goal is to collect data because it is data that is going to guide us ensuring patient safety first and foremost. And it is a data that will tell us or guide us where to go next, what to test next in order for this drug to reach a market to help the patients. And sometimes it is the data that we need to show our investors to convince them to continue to invest.

(12:19)
So when you think of it in that context, it is no doubt that we want to make sure that there is a consistent data flow at the optimal speed and ways the quality that’s needed to help us to make all the decisions down the road. So Mike, if you can move to the next slide please. Alright, so there has been data showing that since 2023 there has been an annual increase of 6% in the average number of trial endpoint year over year. So that’s a lot of increase over the course of 20 years. Our trial has undoubtedly become more and more complicated. We’re collecting more and more data. So let’s first examine what are some of the inherent characteristics in early phase oncology trial that drives the data flow of these type of trials? So we talked about the study design, the distinct characteristics of phase one oncology study design in which the enrollment is by cohort and the enrollment is gated by how the study is designed, obviously for safety reasons to ensure that patients are not receiving overly toxic dose of study drug. And on the other hand, we’re also trying to minimize the number of patients who might receive suboptimal dose of the study drug. So there are various very typical three plus three design in early phase oncology trial and there are various permutations of different Biogen designs as well. Those designs upfront determine how fast the patient can enroll in the trial.

(14:37)
And also some studies will have slot assignment rules that can further restrict enrollment pace. For example, if you have a drug that is first in class in terms of mechanism of action or first in class in its modality that we do not have any clinical data to reference to. So then you want be more cautious when you start giving these drugs to patient. And the sum protocol will require an observation period of the first patient being treated at a certain dose and it’s only after that observation period that the patient, subsequent patient can be enrolled. So that further restricted enrollment pace of the trial.

(15:33)
We know early phase oncology trials tend to collect lots of intensive PK data and there is also a wide array array of extraordinary biomarker datas that are being collected these days with the development of modern precision driven medicine. So that’s not surprising. And for some drugs it’s very important to have frequent ECG monitoring as well. Typically they’re done through central monitoring in multiple reads, multiple time points as well. So that as to a data volume as well. So taken together, we know there is a lot of data to collect in early phase oncology trials and this data don’t come all in a smooth stream. They come in stepwise based on the study design and the data flow is definitely not nonlinear. It’s almost like you are turning on in off so all the time. And on top of that, there is a time pressure, there is time crunch at the end of each cohort to rapidly clean the data in order for the safety review to be more effective. So how do we manage orders to ensure that we have a steady data flow throughout the course of the trial to minimize the impact on impact the resourcing pigs and valleys and to maximize the data cleaning efforts. So the data safety review committee has a clean package to review so they can make faster decision.

(17:44)
That’s something that’s very important in early phase on cardio trials. And Mike and I are going to share some of our strategies tactics to intensity flow. So at this point, yes, Sonia, I believe you are going to pull up a poll question.

Speaker 1: Sonya Hunt (18:08):

That’s right Bin, I’m going to launch it right now so our audience members can see it. We have a poll question for our members of our audience to interact with in real time. And if you are attending via GoToWebinar, the poll is appearing on your screen right now and you can vote by clicking on any of the answers. And those of you who are watching via LinkedIn live events, please feel free to share your thoughts and answers in the comment section. Okay. So the question that we have for you is what challenges have you encountered in collecting data during early phase oncology trials? So as I mentioned before, select all that apply and your options are slow enrollment, lagging data entry, or is it high query rate? Or the other option you have is time crunch to clean data for safety review or other. So please go ahead everyone and make your selections now. I’ll leave it open for a few more seconds to encourage everyone to participate in this live poll question that looks like it’s the majority of our audience. So I’m going to leave it open for a few more seconds to encourage everyone to participate. Oh, perfect. Thank you so much. Okay, I’m now going to close the polls and let’s take a look at the results.

(19:19)
Okay, so here are the results. 60% of our audience said slow enrollment while 68% chose lagging data entry, 32% of our audience said high query rate while 52% of our audience also selected time crunch to clean data for safety review and then 16% for other. So thank you very much everyone for participating in our first poll question. And Bin, before you get back into your great discussion there, any thoughts on the results of that poll question? Anything surprising there?

Speaker 2: Bin Pan (19:49):

Sure. Thanks Sonia for showing that poll and gather the answers. Yeah, it looks like the distribution is quite even amount of different answers. I think patient enrollment has been becoming a bigger and bigger topic these days with the amount of clinical trials being conducted. Definitely this would be another topic that we would very much like to discuss in the future webinar. And the other answer, lagging data entry, high numbers in that and the rapid need for data cleaning as well as hyper. So those are the things that I am going to touch upon in my next few slides to discuss some of the tactics that we can minimize those delays.

Speaker 1: Sonya Hunt (20:56):

Okay, thank you.

Speaker 2: Bin Pan (20:59):

All right, so Mike, let’s get back to our slide. There we go. All right. So really the meat of this presentation is to discuss how to implement some of the strategies to enhance state of flow in early phase oncology. So as an operations professional, I will say, I’ll share some from operational perspective what we can implement to help minimize the side burden and increase the data flow in trials. And then Mike will share with us how do we optimize this database design and testing before a database goes live and as well as some of the technology newer technologies that can power faster data cleaning and decision making from operational perspective. One of the things that to me that’s been very, very instrumental in managing early phase oncology trials is the cohort management plan. You’ve got to have a very robust cohort management plan with templates, tools, all the bells whistles that you can think of to roll into that plan.

(22:25)
So to increase the efficiencies when you are actually executing the trials. So I’ll spend a little bit more time in the next slides on the cohort management plan itself. We know that since the covid pandemics remote EDC data review, remote monitoring has becoming quite popular, especially in the oncology clinical research world. Some sites will only do remote monitoring. So that’s something I think that has really, really enhanced the flow of clinical trial data from the site to the sponsor and eventually to the review committee. And it’s enabled by the technology advancement. Now we can have access to remote access to electronic medical record, we can have remote access to some of the other clinical trial systems that the site is using to their source document. We can even have a video chat like what we’re doing right now to take a peek at what the site equipment, how they are storing samples, et cetera, and how they’re managing their investigational drug.

(24:08)
So that has really advanced in our field quite a bit since the pandemic. The other thing that I think that we put a lot of emphasize on in early on in these types of trials is to really define what is the data package that is required for the safety committee review. So if you have that predetermined data package in the predetermined format that can be pre-programmed. That way when you end each cohort, your data package can be run through the program fairly quickly and to produce a presentable package for the Safety Data Safety Review committee. And then as in everything else in clinical research or generally in life, proactive communication is always important to read, minimize, misunderstanding and the intense reduce the time needed to get to your endpoint. And that’s part of the cohort management plan as well. How do you communicate with the site what’s needed for each cohort, which I’ll talk about a little bit more on the next slide.

(25:44)
And finally from operations perspective is the site support. I know Mike is going to talk about how we support the site in collecting data from technology perspective. For me in operations, there are also a lot of work that we can do to support the site from giving very relevant and meaningful training, refresher training to demonstration all the way to maybe staff augmentation if they are short on staff. As the poll has shown, lots of people are experiencing a delay in data entry that could be an indication of the resource constraint at the site. So working with a resourcing company could help provide solution on that front as well. Alright, so here I’m going to just go into a little bit of depth in terms of cohort management plan. And like I said to me, this is one of the key documents in early phase oncology trials.

(27:00)
How do we develop a robot cohort management plan to ensure the efficiency in operation and therefore the efficiency in data collection and data review? So your data management plan should cover things like what is the roles, what are some of the rules in slot assignment and slot replacement? What are the processes for requesting assigning and confirming slot and what are their timelines? You need to give the site time to request a slot to reserve the slot. And if their patient becomes a screen fill, they need to inform the study team in a timely manner so that another site can pick up that slot. And developing a patient wait list and cohort tracker template will help you get organized throughout. We would also include eligibility review form, DLT, notification form and et cetera. And then when it comes to safety review committee meeting, we would include the agenda template, minute template as well as a form to document review committee’s decision making.

(28:30)
And as I mentioned earlier, we want to have that defined data package to for safety review committee and the format, whether it’s listing or whether they’re more in a table or presentation. And lastly, don’t forget to define the meeting frequency. We have found it very, very helpful for us to actually set up a biweekly PI and site calls with all the sites involved. That way you have the meetings, all the meetings in their calendar and you give a chance for all the sites to come together to discuss their challenges, their success stories, their patients. And when it comes times for that SARC review, this meeting can be easily converted to the SRC meeting. That way you don’t have to spend time try to schedule a different meeting with busy PIs for that a review. Okay, and then so the right hand side of the screen, I’m sharing some sample of the SRC review meeting agenda just as a reference that we typically include in the cohort management plan. And on the bottom left hand side, I am sharing an example of the data flow from patient all the way to RRC review. And as you can see, this is an endeavor that a lot of include sites include sponsors and their CRO partners, many, many different functions, all in a concerted effort to help push the data to be generated from the patient all the way to the review data review.

(30:38)
So those are some of the operational tactics and strategies that I would like to share in this session. And I know Mike has a lot of exciting technologies also to share with us. So Michael, pass on to you.

Speaker 3: Michael Mendoza (30:58):

Yeah, thank you Bin. Hopefully everyone can hear me. Okay. So throughout the past, I would say the past five years there’s been significant advancement in technology when it comes to the different things that are available to support the clinical sites. And with that is being able to enable connections with electronic health records. This is particularly prevalent in the United States, it’s growing in Europe because of the new technology capabilities that allow adaption to the various amounts of electronic health record systems that are out there. So one of the things and strategies that can really help, particularly in phase one oncology trials because of the data volume and in more of the portability of the need for localized assessments such as labs and sometimes those EKGs and different MRI components that are surrounding imaging and that’s the connection to the electronic health record. So currently I would say in past five years, there’s a lot of data entry that occurs.

(32:09)
And as Bin pointed out earlier, you do tend to get the delays in because of the lack of manpower or just the sheer volume of data that’s being captured for oncology-based trials. It’s taxing, it’s daunting, it’s error prone. So what’s available today? So there are integrated technology tools which allow balancing between the electronic health records and to the EDCs and different portals that different vendors offer. So there’s two prevalent ones in the industry today. One are screen scrape methods. So screen scrape methods allow someone to basically select highlight text windows and it captures it and allows you to drag and drop that particular information into the ADC, which gets rid of the typing and transcription type issues. This is particularly important when you’re working in global trials and you have significant amount of sites that could be necessary in lower cost centers, but just areas of countries that have connectivity issues, infrastructures that are just very variant, this is particularly prevalent in Eastern Europe and different parts of Asia.

(33:30)
So this particular technology enables those sites to be able to utilize the ability to capture from their electronic health records and placing it in EDC for other organizations that have EMRs and again prevalently in North America and portions of Western Europe is the direct integration. So this is done by a common file structure called fire, FHIR. And this actually brings the EHR data directly into EDC. So anything that’s considered standards, health care and things that are particularly common in things such as C disc and just general health data that is common to most hospitals and standard practices immediately comes in the EDC. These technologies work that it imports the information directly, the site just confirms the data that’s there and it’s basically source data that’s signed on the spot and it really reduces the need of the monitoring burden because it is direct sourced, it’s confirmed the source from the EHR. So it is source data which then ends up removing the volume and need for source document verification. It has been pointed out before that relieves it to what sites are driving towards anyways. And that’s just source data review and the ability to remote monitoring to lessen the burden of crash on site, but then having the site staff being available to accommodate those onsite visits.

(35:20)
And just to display an example, this is real technology that’s out there today and it’s in use in various trials that are being executed to date. So this is an example where it ties directly into the EHR, it pulls the data referenced on the visit that’s occurring and then it places that information directly into the EDC and then eliminates that burden of having to retype and transcribe all that information from one point to the other. So this is pretty critical to impacting the site burden. I myself came from a product development background and did over a thousand interviews with various sites across the globe and many of them were skeptical about the type of technology making their life easier because it always adds new training, new things. But this is actually one that was open armed received from the sites that I was interviewing because it does allow that direct capture, it helps them eliminate certain resources that are needed in order to do all that data transcription.

(36:33)
So now that leads me into the next phase because whenever you’re dealing with technology and leveraging new forms of efficiencies in delivering technology for a clinical trial, it’s allowing the importance of robust ECRF design testing, the qualification of that technology. So one of the things I do want to share to date is the things to consider when you’re going into deploying technology as such, especially for high profile type studies that oncology offers and the importance because of the safety to our patients. First and foremost, patients always come first. So the one things to majorly consider is the software validated that you’re using? If so, what’s it compliant to? So some of the things that when you’re utilizing technology, there’s a lot of vendors out there, but it’s ensuring one from the FDA standpoint, is it 21 CFR part 11 compliant in Europe? Does it meet both 21 CFR and annex 11 and all the various other regulations that are outstanding?

(37:44)
And then to what extent that’s the biggest component because anything can be considered 21 7 R part 11 compliant if it basically essentially goes through the checklist and then takes and documents responsibility of the vendor versus what the vendor defers. That is a critical element because for anyone executing these trials, it then outlines the level of burden to complete the validation lifecycle for that product, for the use in your clinical trial. The next, and as I go through this, this all builds to the level of testing required for this study design and that’s going to actually be used in the course of the study execution, but it’s then evaluating is the software scalable and robust enough? So a key element that Bin had pointed out in the designs of those trials is the scalability of going from dose escalation to then a part one, part two type design and complexity because the one thing that’s basically known without within our industry is the complexity of oncology trials.

(38:56)
So having systems that can adapt to the complexity of regimen and cycles is extremely important because they can evolve, especially as the protocol can evolve, but also the way the patient maneuvers through the actual protocol there is evolution to what cycles might be required and design and based on those tumor assessments. The next is that reporting and analytics. What is canned, meaning what is available directly from the vendor and then versus what can be generated, what can be added into the study that gives the visibility, as Bin had mentioned before, and then really scalable for repeat. Because what everyone hopes for is the success of their phase one and having a system that is cost-effective at the phase one level but then can adapt and evolve into the phase three program. So you get the reusability, the consistency of that data throughout the course of your program, all of which is very, very critical.

(40:04)
But then it goes into the ECRF building and testing. So based on the systems being used, particularly for myself working in CRO for many, many years over 24, but through basically any assessment throughout air industry, are there enough knowledgeable resources. So when utilizing and building studies is having a very knowledgeable team not only of the product that’s being used in the EDC but the experience level in study design against the protocol, the things of considerations and then mechanisms that need to be able to be set up to be able to make those fast decision processes in working with those external data components and vendors to make sure all that data flow is coming through the system. And then based on the resources that are doing that work, have they been tested, have they been accredited in the system being used? All of these things are important because within the world that we live in today with risk-based approaches, it helps then go into what is the right level of testing for deploying the systems being used and particularly the EDC that’s being designed, it’s most important to ensure that three factors are met.

(41:26)
One is making sure the system is designed with the patient in mind and then second the site and then third, the study team. So as the design lifecycle goes for the EDC build, it’s really important to ensure that when you approach UAT that the three different personas I just mentioned are considered. So ensuring that when you’re going through the build cycle into UAT, make sure the sponsors involved. If you’re a CRO from a CRO, make sure your team from medical monitoring, the clinical operations, the sponsor data managers, all the way through statistics, ensuring that critical endpoints are maneuvered, well, capturing the information as expected and then ensuring that all of that is documented through and again, tested from each persona. Is the data aligning to what is being captured particularly at the patient level. Things that come in such as E pros, e COAs, are all user-friendly and capable of capturing the endpoints to be the most compliant according to your protocol. Sonia, I see we’ve come to a poll question, so I’ll hand that off to you.

Speaker 1: Sonya Hunt (42:49):

Okay, thank you very much Mike. And I’m going to launch our final poll question. And so as a reminder to our audience, you remember this is done in real time and you can go ahead by clicking on any of the answers. And this one is just select one and a reminder for those of you watching via LinkedIn live events, please feel free to share your thoughts and answers in the comments section. Okay, so I’ll leave it open for a few more seconds here. And the question we have for you is how do you currently utilize data surveillance tools in your clinical trials? And here are the options below extensively integrated into our processes or is it used occasionally for specific needs not used but considered implementation or is it not used and no plans to implement? And then the final one, unsure. So thank you very much everyone who have voted thank you for that. For those of you who have not, I’ll leave it open for a few more seconds as I see you’re trying to make your selection now.

(43:46)
Okay, perfect. Thank you very much everyone for participating in our final poll. I’m now going to close the polls. Okay, Mike, let’s take a look at the results here. So in our audience, Mike, 27% said extensively integrated into our processes. While 32% of our audience said used occasionally for specific needs, then 18% said not used. But considering implementation, zero of our audience, 0% said not used and no plans to implement while 23% of our audience is still unsure. So thank you very much everyone for participating in our final poll question. And Mike, before you continue, any thoughts on the results of that poll question?

Speaker 3: Michael Mendoza (44:26):

No, it’s actually quite in line with what we’re seeing in the industry to date. So there’s quite a bit of behavioral modification that’s occurring within the industry with the use of kind of the data surveillance. We’re kind of blended with the poll responses that I’ve seen of kind of the old school methodologies of getting to end of cohorts and getting to end of different dose levels and then doing data cuts with review plans and listings and things of that and then moving on, but also tied with new technology available because again, that technology’s been really getting a firm grip in our industry in the past five years. So behavioral modification and changing how we do things is in line with what you expect with just change within our industry. It’s taking the pioneers that are willing to take the risk and use data in that manner tied with a comfort level of how he’s always done things and flowing through the decision-making process.

(45:33)
So it’s about what I expect. So we’re here to hopefully share with our presentation changing some of those numbers as we move forward to the paradigm in our industry and be able to leverage very powerful tools that we have to make those decisions faster. So with that, I’ll go ahead and continue because this is stepping into where we can leverage technology today. So really what we’re looking at is there are technologies out there now that offer AI that have the ability to aggregate information quickly and be able to interrogate it quickly without heavy burden programming and then really having the ability to create patient profiles that are both agile and traceability to be able to make these decisions. And that’s really what we’re leveraging in having this technology in our hands is changing behaviors as Bin had mentioned before, with drug monitoring committees, drug safety boards, and having the ability to be more fluid with the decision-making process because the data is able to be input into ADCs and aggregated more efficiently.

(46:51)
So with these examples, I’ll give you the visibility of being able to approve the data visibility and decision-making process. So data access is extremely important in the oncology world for the exact points that Bin had aforementioned and that’s the ability to be able to see the data, be able to make decisions often. So with that, again, the way you use patient surveillance is to be able to tie in different visual analytics to be able to, if you look at what I have on my screen now, the middle screen where you do particular graph plots or data plots and to be able to aggregate data to look for outliers specifically to know that if data’s aligning within the parameters that you set for your trial and everything’s falling within the containment, you’re safe to move to the next steps. Or you have the ability to be able to investigate any of those outliers to see if there are concerns that would make you want to change the path for your patient for both the safety and or dosing decisions.

(48:07)
And then to be able to have the ability, looking at the third screen here in the back, be able to tie in treatments and adverse event occurrences and frequencies to understand the impact that your drug’s having on patients and actually be able to mine if there’s the deference of patient disease progression and seeing improvements that way based on events that are naturally occurring by the indication under study. All these things tie together really give the ability to make strong safety detection assessments and evolve into okay, the technology can deliver it. So now it becomes the behavior of those charters and meeting components to be able to say you don’t need to wait for the end of a cohort now you don’t need to wait for an end of a dose escalation. You can be having biannual meetings as Bin had mentioned before, or not biannual, bimonthly, sorry, to be able to have the visibility in that data to make that decision faster, to know from patient-to-patient meeting to meeting that your project’s on the right track.

(49:25)
And so with that, the importance of what we’re trying to address here today is the availability of information and utilizing technology to make that data available for you, not only alleviating site burden, but be able to create that aggregation and change the behaviors to make decisions faster. And in the end that gives an improved chance for any organization to have success behind their trial and then in those that are seeking funding to have more updates to keep their investors enticed and engaged in their product in the potential successful outcomes. And with that, send it back to you Sonia for any questions or things that may come up.

Speaker 1: Sonya Hunt (50:21):

Okay. Well I want to say firstly, thank you very much Mike and Bin for that great presentation. I hope everyone enjoyed it. And now we’re going to begin the, actually I’d like to invite the audience now to continue sending their questions or comments for the q and a portion of the webinar as we’re going to start that now. And if you are attending bio GoToWebinar, please use the questions window For those of you watching on LinkedIn live events, please feel free to post your comments in the comment section. And we are going to begin now. So while you were speaking Mike and Bin, I did receive a bunch of questions. So let me start with this first one here. And it looks like this question is for you Bin, this audience member is asking, what are your strategies to minimize delay in SRC review? Bin, what are your thoughts on that?

Speaker 2: Bin Pan (51:05):

Yeah, thank you Sonia. Right, so that’s a good question and it’s actually tied back to what my has just presented about having that data readily available in the system in a visualized way for the SRC or safety review committee members to review. So I mentioned earlier that one of the tactics that we have been employing is to pre-schedule the bi biweekly site and PI calls throughout the course of the trial and that prescheduled calls can be leveraged to be used as a SRC review meeting. And so that’s one thing. And the second thing is to having that SRC review data package readily available for them to review. And you want to provide that information ahead of the SRC meeting so that they don’t come into the meeting code, they have seen the data in the package, they have a good sense, and then come to the meeting for a discussion with the members. And so that will enable the committee to be able to make a fairly quick decision.

Speaker 1: Sonya Hunt (52:32):

Okay, thank you so much for that answer Bin. Okay. This audience members curious about this. Can you share any case studies or examples where data surveillance significantly improved trial outcomes? So this sounds like it might be for you, Mike.

Speaker 3: Michael Mendoza (52:47):

Yeah, thank you Sonia. Yeah, actually we’ve launched a data surveillance tool that allowed us to get insight on a patient reported outcome and allowed us to detect any type of convergence of the ePRO assessment that was being done where we saw compliance issues that were being unmet by the patient. So in the particular trial we had, there was a minimum compliance that we want to see 85%. And based on predictive behaviors and patients week-to-week assessments, we’re able to see where compliance was going to become an issue. So it allowed us to reach out to particular sites and get their patient reengaged about, make sure they’re completing their ePRO often and making sure we were adhering to that compliance measure, which really helped protect an endpoint. So that in combination with some of the diary information also helped us with the AI tool detect some unreported adverse events. So those two things in itself were of high value to what we saw in the execution of the patient surveillance.

Speaker 1: Sonya Hunt (54:15):

Okay, thank you so much for that Mike. Okay, so this audience member here is curious about what are the key components of a comprehensive site training program for early phase oncology trials? Who would like to answer this question,

Speaker 2: Bin Pan (54:29):

Sonia? I think that would be me.

Speaker 3: Michael Mendoza (54:32):

Okay,

Speaker 2: Bin Pan (54:32):

Thanks. I’ll take it. Yes. I’ve also touched upon site training in my discussion. I think it is one of the key components to ensure that we have a consistent and high quality data flow. So we know site training typically starts from the INITIAT visit or the investigator meeting. What I want to emphasize here is the quality of the site training. So you want your training to be meaningful to the audience that you are giving training to, which is the site staff and investigators and different team staff members at the investigator side will need different trainings. So you have to really tailor your training to role of the person that you are training, which means sometimes you have to schedule multiple training sessions depending on the topic. So that’s what we usually do. You break up a big training into smaller training sessions to make it more meaningful and impactful for the people that you are training. Constant refresher training is also important because if you don’t use it, you can forget it really quickly. So site staff need reminders. One of the things that we do a lot of refresher training on is the actually with the lab personnel at the site where they have to take samples and process the samples, ship the samples, all those little details matter and have impact on the quality of data that we receive. So those are the things that we do a lot in terms of refresher training.

Speaker 1: Sonya Hunt (56:37):

Okay, thank you very much for that Bin. We’ll jump to another question here. I’ll see if you can squeeze two more in this audience member is asking, how are some of the challenges in the EMREDC direct integration would like to take that question?

Speaker 3: Michael Mendoza (56:51):

Yeah, I definitely think that one’s probably for me. So there’s, there’s not a ton, but there are a few meaningful ones that are important to address. With direct EMR integration, one of the biggest challenges is the coordination just with the site and access to that EMR. Various EMRs have pretty high security requirements behind them. And just coordinating with the site’s it and getting access to those for the connections is a challenge that’s definitely there. It’s very achievable to overcome. It really comes down just the timing in getting into the connection with the site and maneuvering that. The second is kind of mapping. One of the other challenges both in the US and globally is the standardization of care. It varies. So understanding what it looks like to be implementing direct capture versus the screencap link for example, comes down to having technology that can service both because there is variability in that standardization of care. So not one, two centers or alike. So it’s ability to flex into work with the mappings at various organizations and then how you’re going to handle the items that don’t map. And again, it’s just making sure that you have tools that can maximize that particular end goal in mind and then being able to again, have something that can facilitate easing the site burden and capturing that data directly.

Speaker 1: Sonya Hunt (58:37):

Okay, thank you very much Mike and Bin for all those answers. We have reached the end of the Q&A portion of this webinar and if we couldn’t attend to your questions and there’s a number of them out there, the team at TFS HealthScience will follow up with you after this presentation. If you have any further questions here we go right here, please direct them to the email address that you see there on your screen. And just wanted to make a mention here that if you are in the Boston area, TFS HealthScience will be at the 13th Annual Clinical Trials in Oncology East Coast 2024. And I’m going to put the link in your chat box there to make it easy for you to check it out. And there you go. Okay, I’ll go right here. So thank you very much everyone for participating in today’s webinar, you will be receiving a follow-up email from X talks with access to the recorded archive of this event and momentarily I will be sharing a link to view the recording of today’s event in the chat box, which you can also share with your colleagues once they register for the recording here as well.

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And for those of you watching via LinkedIn live events, we encourage you to visit xtalks.com to register for this event and gain access to features such as saving, as well as viewing the recording and more and a survey window will be popping up on your screen. Your participation is appreciated as it will help us to improve on our further webinars. So encourage you to do that now. Please join us in thanking our speakers for their time here today. So thank you very much, Mike and Bin.

Speaker 2: Bin Pan (01:00:05):

Thank you Sonia. And thank you everyone.

Speaker 1: Sonya Hunt (01:00:08):

We hope you found this webinar informative. It has been my pleasure to be your webinar moderator. On behalf of the team here at X Talks, we thank you for joining us. Until next time, please take care and bye for now. I’m Sonya Hunt. Bye everyone. Bye. Bye Bin. Bye Mike.