IQVIA Holdings Inc. (IQV) Earnings Call Transcript & Summary

Hello, everyone Jeanne Linke Northrop , Managing Editor, and I'd like to welcome you to today's broadcast: Using Integrated Scenario Planning to Drive Success in Clinical Development sponsored today by IQVIA. In today's program, we'll discuss why integrating multiple perspectives is crucial for effective clinical development planning. Our presenters will also showcase how sophisticated analytical software tools powered by clinical benchmarks can optimize critical development planning. IQVIA is a leading global provider of advanced analytics, technology solutions and clinical research services to the life sciences industry. IQVIA creates intelligent connections to deliver powerful insights with speed and agility, enabling their customers to accelerate the clinical development and commercialization of innovative medical treatments that improve health care outcomes for patients. With approximately 82,000 employees, IQVIA conducts operations in more than 100 countries. Learn more at www.iqvia.com. Before we begin with today's broadcast, I just have a few important quick announcements to make. First and foremost, this webcast is designed to be interactive. We have some great experts joining us today from IQVIA, and we encourage you to ask questions to them throughout the event. You can submit your questions by typing them in the Q&A box, and that can be found at the bottom of your video player. You can enlarge or slide window by clicking on the small icon in the bottom right corner of your media player. Please note, however, that all slides will abate automatically throughout the event. If you do happen to experience any technical problems viewing or hearing this presentation, please click on the question mark help widget, in the top right of your presentation window. I would now like to introduce today's experts and guest presenter, David Wolter; and Rob Narayana, both of IQVIA's Commercial Solutions Group. David, Vice President of the group, has more than 18 years of experience in management, consulting and modeling system development. He's also the co-lead of the U.S. Corporate and Portfolio Strategy Center of Excellence. Rob joins us with more than 7 years of experience building software, used to solve big data visualization and advanced analytics problems in pharma. His passport spans across clinical development planning, forecasting, market access as well as commercial launch strategy. So gentlemen, thank you so much for joining us today. And David, at this time, I'm going to welcome you to kick off today's discussion. So please feel free to get us started.

Thank you, Jeanne. Thank you all for joining. We are very excited to be here with you to discuss scenario planning in the area of clinical development decision-making. We're also pleased to have 2 leaders from our Drug Development Solutions group joining, and those are Eric Groves and Chris Hart. So you'll hear from them during the presentation, and we encourage you to be actively involved. We will have a poll during part of the presentation today, where we'll share the -- and we'll share the results of that. And we also have Q&A available during the presentation as well as at the end of the presentation. Well, let's dive in. We have 3 topics today. We'll do an introduction to the overall topic of clinical development decision-making. And then we'll zero in on 2 aspects, we'll zero in on cross-functional -- the importance of cross-functional input of having multiple groups from your company involved in these decisions. And then we'll do a technical demonstration of a software tool that can be very helpful to companies in gathering and processing that cross-functional input. Clinical development decisions are not only some of the most important decisions that pharmaceutical companies and biotech companies make, but they're also very challenging decisions and increasingly so. Part of that challenge is that the landscape continues to evolve. For example, we've all witnessed new endpoints coming on stronger over the last few years, new endpoints like patient-reported output, patient-reported outcomes. There's also increase in competition. And so it's important for teams doing this planning to think about not just what the environment is now, but what the environment will be when they launch. So teams often have to think of what will be the dominant endpoints when they launch, what will be the standards that they need to meet, what will be the clinical competitors that they need to be proving their product against. And then there are new types of trials. So there are decentralized trials, adaptive trials, there are different ways to do the placebo testing and trials and other items, and so teams need to be able to generate scenarios that take advantage of these new options in clinical trials. And then finally, this whole area of clinical development planning remains organizationally complex. The very nature -- the uncertainty of the outcomes and the very nature of the questions demands expertise from various parts of companies: regulatory, commercial, biostatistics, developments, operations, and so that cross-functional nature makes decision-making complex. So when we work with companies in this area, our proposed framework for value maximizing decision includes generating scenarios. So the first point is we recommend that teams not just align immediately on 1 development path, but rather compare scenarios, compare scenarios that may have different risk profiles, different timing profiles, different cost profiles. And then to choose amongst those scenarios, a framework we like to use is one that includes 4 different measures of the results of a clinical development plan. First of all, the risk, the probability of reaching the market, the probability of, we call it, technical and regulatory success. Secondly, the cost. What are the costs the company incurs to get there, both internally and with any outside spend. Thirdly, the time how long it will take to get to market. And last, the impact. How many patients will be accessed, and what will be the resulting commercial value. Often, we bring those 4 measures together in an overall measure called expected net present value. This is a financial measure. But in the end, all 4 measure -- it's important to look at the 4 measures independently in conjunction with the company's situation, the situation for the company's portfolio, the risk tolerance of the company and the competitive situation. So that is a framework, and we've done other webinars where we've really dug into that framework. But today, we're going to talk a lot about the -- about 2 things. As you -- if you've been in to our other webinars, you know, we like to talk about both the why and the how. And in the why area today, we're going to talk about why it's important to have multiple representatives in your company involved in this early-stage planning. The -- we'll talk about the cross-functional nature, and we'll get into some very specific examples that show the need for having the groups of different functions come together and have a voice in these decisions. And then secondly, we'll move to the how. We'll talk -- we'll actually show in action a next-generation software tool that helps companies do this efficiently and effectively. Okay. Let's dive into our first major topic, which is cross-functional scenario planning and the illustrations of why it's important to have cross-functional input. We'll do 3 illustrations today. First, we'll talk about maximizing patient reach. So we'll talk about why it's important to have cross-functional input and to eventually reach as many patients as possible. Secondly, we'll talk about the regulatory environment and changes in the regulatory environment. We'll use the Inflation Reduction Act as an example, of, again, proving why cross-functional input is crucial to this early-stage planning, and then third, we'll look at the benefits of cross-functional planning on optimizing a product's label as well as planning for the rest of the life cycle. So the follow-on indications and new formulations, et cetera. So 3 topics. Let's dive into Topic 1. The development decision-making drives patient access in many ways. So basically, these decisions that are made on things like the endpoints for a trial, the comparator we compare against, the patient population we go after, whether it's a broad population or a more narrow population, the inclusion, exclusion criteria. These decisions have a large impact on patient access at the end of the day. The choice is on endpoints, and trial comparators impact how health technology assessment bodies and how payers will assess a product -- assess a product, and therefore, how much access patients will have. The decisions on these early -- early elements of the trial also drive how physicians will view products and to what extent they'll prescribe the products. And then, of course, they drive -- decisions at this early stage also drive how patients will receive a product and how their compliance and persistence, et cetera. Let's look at a few examples of this. We -- do we -- if we have Eric Groves connected, we can jump into one or we can skip to a second one.

Can you hear me?

Yes. Over to you, Eric.

Yes. So actually, if you can put your -- I'm suffering from an absence, I think. So one of the interesting things is, whether or not, to include a PRO in the development plan that the company conducts. PROs are annoying, expensive and difficult to interpret. So clinical development groups frequently feel that they are actually not an advantage in the development process. Nonetheless, the data that are accumulated are incredible importance to the commercial aspects of the development process. So there has to be a balance here as to whether or not this should be included. Our usual recommendation is to include this kind of information in the development process and to make sure that, during the registration study, a proper PRO is used. So back to you, David.

Great. Thank you, Eric. The -- we'll share a case study here. This is a case where we worked with the client on a development decision and pulled together a cross-functional group. In this case, they were considering various trial structures, and what we did is bring together a group to generate alternative scenarios. They had certain biomarker strategy versus more of an all-comer strategy. It was more 2 of the scenarios we looked at, and these scenarios were evaluated using the tool, we'll show later today called pipeline architect, and they were evaluated across various key performance indicators, like time to market, net present value, development costs, et cetera. Let's take a poll from the audience, and I'll hand it over to you, Jeanne.

Thank you so much Okay. Question is up on the screen. How do you rate the extent of cross-functional input in early clinical development planning at your company? And this should be on a scale of 1 to 5. So 5 being very likely, all key groups, medical, commercial, operational, are highly involved. Decision-making reflects the trade-offs across these dimensions. And number 1 would be very low, meaning not very actively involved in the decision-making, seems to be a bit more siloed. So we will give everyone a moment to respond to this question on your screen. Thank you so much. And then David, I'm going to -- it looks like we're starting to get some great responses in. I'm going to hand this back to you. And go ahead and interpret these results. Thank you.

Great. Thank you so much, Jeanne. We're seeing that -- we're seeing a mix. It looks like the highest answer is somewhere in the middle, around medium, with around 39% of respondents, and the second highest is at 5, very high, about 31%. So this isn't surprising. It fits with what we see across companies, that one of the challenges is it's difficult to get all the functions together, to have the time to make the discussion, to have good discussions, to make alternatives, to present those to a portfolio committee. So it's difficult to do, but it's also very, very value creating because as we're showing here today, the inputs are very -- the decisions are cross-functional in nature. For companies that are already at a very high level of integration, the -- one of the questions is often, how do we become more efficient at doing that? How do we do that faster, better in a more regular basis? And we'll show how pipeline architect can fit into that later in the presentation. Okay. Thank you for that poll. And I'll hand it over to my colleague, Rob, for our second topic.

Awesome. All right. Our second topic involves on more impactful legislation passed in recent history, which is the Inflation Reduction Act or the IRA. One moment. I'm going to go to the next slide. Be safe. All right. As many of you are aware, the IRA allows for the Center for Medicare and Medicaid services to negotiate the prices of top 20 drugs, with the highest budget impact on Medicare Parts B and D. Now, branded drugs will have some time on market before this IRA clock hits, so to speak, and manufacturers are forced to negotiate. And so as you can see on the slide, this is about 9 years for small molecules, and about 13 years for biologic. Now, you'll see some of the other key tenets of this -- on this slide, including the fact that if a drug is selected, the manufacturers will be forced to take a minimum negotiate discount between 25% to 60%, depending on the length of time that the product -- the branded product has been on market. Now, there's more to this log than the key tenets kind of state here. But the focus of our webinar is to think about the potential second and third order effects of this legislation, specifically on this clinical development, and how a cross-functional approach can help teams assess the impact of this legislation and make the most optimal value-maximizing decisions. So moving on aside from the immediate pricing impact, should [indiscernible] be selected, we've identified 3 possible implications of the IRA for early clinical development planning. The first is around indication expansion, specifically in oncology. So speaking in very broad general terms, the trend that we see in oncology is for manufacturers to go after small patient populations with high unmet need where it's easy to show meaningful clinical outcomes. Now, assuming success in this small indication, manufacturers generally tend to look to larger indications and larger addressable patient populations thereafter to continue maximizing the potential of the drug. However, with the recent passing of the IRA, the IRA clock starts immediately upon the first launch. So manufacturers want to maximize the commercial potential and the return on investment for their program. We could potentially see them pursue some of these larger patient populations for these first to second indication launches. Similarly, minimizing the time between expansions could be favored for the same reason, which would require parallel trials and increased spend, sometimes potentially before understand -- fully understanding the accuracy of the underlying asset. Second, it could very well be possible that, even if your product isn't selective for negotiation by CMS, you could still face downward pricing pressure if a competitor in your class is selected. So it will be especially interesting to see us if this happens with the recent rise in popularity of the GLP-1s. So semaglutide, marketed as Ozempic, was the first of these GLP-1s to be approved and launched in the U.S. in 2017. Conversely, the most recent of these was Mounjaro, which was approved and launched in 2022. So Ozempic is selected for the IRA after the appropriate time in market, what will be the class spillover impact on Mounjaro and the resulting impact. Now without going into too many possibilities, I think the key main takeaway here is that going forward, it will be very, very important for a cross-functional team to consider scenarios that account for the direct or indirect impact of the IRA, and make the most appropriate decision for their particular program. Similarly, it will be very important for manufacturers to do a risk assessment of their entire portfolio for the potential impact of IRA. So there may be pipeline products with no perceived IRA impact, especially if they're not targeting an old age population where CMS spend is very high. However, there could be others that are, again, affected directly by CMS selection or indirectly via this classical over concept we just outlined. And finally, products that are targeted towards multiple indications, it will be important for manufacturers to determine the appropriate launch sequencing and prioritization to maximize commercial potential. Following some of these key points that we've discussed, we wanted to showcase a case study where we helped the client think through alternative development decisions for an oncology asset. This established pharma company, was looking at development options for an asset, planned to launch in second line of therapy, followed by first line, and they wanted to assess the impact of the IRA on their development scenarios. To start, IQVIA helped them model their current development program in pipeline architect, and then collaborate with the client to outline alternative scenarios under consideration. We then aligned with the client on input assumptions to model those alternatives, specifically focusing on the impact of the IRA on post-launch revenue. And after doing so, all of these scenarios are evaluated across KPIs, such as NPV, time to market and development costs to determine the most optimal strategy accounting for IRA impact. Pipeline architect allowed for rapid, iterative and consistent evaluation is development options, allowing us to reach a decision and a determination on the most optimal strategy in about 6 to 8 weeks. So in short, the Inflation Reduction Act is going to be an important policy change that will indirectly and directly affect clinical decision making. It's important for manufacturers that have a cross-functional team to kind of evaluate the impact of this legislation in the term of the most optimal development plan going forward. I'm now going to pass off to my colleague, Chris, to talk about the next illustration we should discuss.

Thank you, Rob. Okay. So we've already heard mention of cross-functional planning, label optimization and consideration of multiple factors in determining the optimal route to develop a product. What we have found is that cross-functional planning really helps optimize decisions on the desired label and optimize the transition through life cycle management. So in considering the label, this really does require cross-functional input, and that can affect many product and portfolio level decisions, such as the potential indications in their order of launch, which populations to target and how to target them. And even things such as regimen selections. So this can be optimized for particular indications or for the product as a portfolio component. We also find that cross-functional decision-making and assessments lead to a number of trade-offs. This is inevitable, as different considerations can push a product in different ways, as we'll see with an example in a moment. And this choice of indication or population selection can maximize patient outcomes in some personalized medicine approaches or alternatively, arrange for a mass benefit to a broader population, depending on the product's particular characteristics. We also find that consideration of the dosing scenarios and formulation can be influenced by cross-functional input so that the appropriate trade-offs can be made between CMC and formulation development costs versus speed to clinical trial and ultimate launch formulation. All of this cross-functional input, particularly early and throughout a product's development program, can ensure effective and timely decision-making and enable optimized label planning. This, we believe, is cross-functional approach is the way to ensure an effective product development as well as effective balance of portfolio. Thinking of how those cross-functional perspectives can optimize the product's life cycle planning, we have an example based upon a biomarker selected product in it's mid-stage development. It was showing that it had a beneficial effect in a biomarker-selected population. But commercial assessment suggested this population may not be sufficient to justify continued high-value development and ultimate launch. However, a medical and scientific perspective on emerging data in the prevalence of this mutation and its presence in other diseases meant that a reconsideration could be made, and so commercial forecast was reassessed on the basis of additional scenarios, appropriate trade-offs made, and then development determined in a primary indication, followed by successive mutation positive indications, and the result of this was that an optimal product was developed. It was launched, and is set to achieve peak sales of approximately $10 billion, which is a reasonable outcome, and only possible through effective cross-functional working and effective consensus and trade-offs in the groups. So I'm going to hand back to Rob now, who's going to take us through a technical demonstration. And over to you, Rob.

Thank you, Chris. All right. So today, as Chris mentioned, we'll be showing a brief technical demonstration of the pipeline architect program, and how it can help your team kind of assess different scenario options. I'm now on our project screen. So each one of these rectangular boxes represents an asset indication combination. So if you have 1 asset that's being targeted towards 3 indications, you can create 3 separate projects to kind of assess the different scenarios. So today, we're going to be looking at a Phase II demo oncology asset that we've created, and it's a new molecular entity that's being targeted for MCRPC or Metastatic Prostate Cancer. So as you'll see, I'll be able to click these scenarios, and you'll see on this next screen, there are some several alternative considerations for this particular MCRPC asset, and then the KPIs for these scenarios are left on the left-hand side. So on the x axis, you'll see the total development cost of each one of these scenarios. On the y axis, you'll see the risk-adjusted net present value of each of those scenarios and the bubble size represents the relative risk or PTRS. So the pipeline architect tool, I'm actually going to go into what we call the baseline scenario. And look at what the inputs are that allowed us to kind of determine some of these KPIs. So in this baseline scenario, we assume that we're targeting an all-comers population, with progression-free survival as the primary endpoint, and what you're seeing here around on the screen is the activities page. So each column represents a phase of development. So as you can see, Phase II here will end in a decision point where you can choose to move to Phase III. And subsequently, you'll see 1 clinical trial in Phase III, so on and so forth, within our decision to move to the submission point. And you'll see that this effectively represents the clinical development program for this particular asset. So anything in green represents the clinical trials, anything in orange represents preclinical studies, anything in blue represents regulatory interactions and/or launch-related activities, and at the bottom, you'll see that there's 4 key KPIs: time to market, probability of technical and regulatory success, total development costs and expected NPV. So how does this program, the software tool, determine these estimates, right? So let's actually go into this pivotal proof-of-concept study and look at the level of detail. So you'll see for the study, there's a couple of key parameters that are being used to calculate cost and timeline estimates. So for this pivotal study, we're assuming that there's 720 subjects being enrolled, and that they'll primarily -- the location of the recruitment sites are in Eastern Europe, South America and China. And they will be using 300 sites to recruit these 720 patients. These parameters, using clinical benchmarks that the tool is determined, based on 70 trials, the tool is pointing to about a 14-month recruitment duration. And below that, we also have protocol information, specifically the duration of treatment. So you can see, it's about 25 months here, along with 30 days of follow-up period. And these assumptions combined together allow us to generate cost estimates, both for investigator grants and for all other study costs, which we can see point to about $42 million for this particular study. And it's pretty cool because, as you change some of these assumptions, the tool automatically recalculate some of these cost assumptions and timeline assumptions. So if I were to reduce the number of sites from 300 to, let's just say, 175, hypothetically, we would expect to see the recruitment duration go up and also the costs go down because we're using less sites, right? Similarly, I decided to increase the number of patients. You'll see the costs go up from $38.5 million to $47.5 million. So it's a great iterative way to kind of understand how different clinical development designs, trial designs, can impact key value measures such as time and also cost. So in -- sorry about that. So in a particular project setting, as we kind of mentioned 2 separate case studies, we will work with clients to kind of model their baseline clinical development program, consider alternatives and then collaborate to figure out what assumptions should be used to model those alternatives. So this could include everything, from the clinical trial design parameters, including a number of studies, number of patients, number of sites. We also have inputs for probability of technical and probability of technical and regulatory success. And then we also have the ability to kind of model commercial revenue assumptions. So you'll see here, since this is an early stage asset, we used a peak revenue approach where we're assuming about $2.1 billion in revenue for gross peak revenue. And then for the potential in Europe to be about, 70% of that or roughly around $1.5 billion, and assuming some uptake curves and LOE curves, you'll see a revenue projection. And all of this equates out to the ENPV calculation below. So similarly, if I were to increase this, let's just say, I think it's going to be -- I'm very optimistic, and it's going to be a $4.5 billion asset. You'll see the revenue assumptions kind of update and the ENPV goes up. So similar to kind of commercial revenue, we can model commercial costs, any partnership level details and so on and so forth. So now that we've kind of shown how modeling 1 scenario works, I'm actually going to go back to the scenario section, and talk about how -- what alternatives were considered in this illustrative demo project. So here, you'll see 3 scenarios that are labeled as green in baseline. So you have the baseline scenario, which considers an all-comers population with progression-free survival. There's also an option where based on early clinical trial results, maybe the data of this product would be more effective in a biomarker population or in a subpopulation, right? And you'll see progression-free survival as the primary endpoint in both of these scenarios as well. And so we can kind of see that, based on the assumptions used, the all-comer scenario would have a higher ENPV or higher risk-adjusted net present value, which would make sense compared to the smaller populations here. But we also see the biomarker population costs more, likely due to the inclusion of a companion diagnostic that patients must use in order to be in order to qualify for this particular trial, right? However, we see in these blue circles alternatives that were considered for each one of these scenarios. So we see an option here, where in the subpopulation PFS scenario, there's an option to consider filing with overall survival as the primary endpoint, which also assumed a higher price in EU. Now, this trial costs more and it results ultimately, unfortunately, in a lower risk-adjusted net present value. So that may not be the most optimal consideration for this manufacturer. However, if we assume the higher prevalence of this subpopulation or more optimistic commercial potential, you'll see that it could be potentially worth more than the subpopulation scenario, with PFS as the primary endpoint. Similarly, we see that for the biomarker scenario with PFS SD endpoint, there's another assumption using OS as the primary endpoint, with higher price and access in EU. And similarly, again, we see that, that strategy may not pay off. But ultimately, the key takeaway is that the tool is an analytical engine that allows you to basically use different assumptions and model different alternatives to understand the key value measures that result. And so we can also look at, instead of costs, we can look at time to market, or years until first launch, to understand which of these scenarios launched first versus launched later in line. That's a factor in our decision-making. Additionally, the tool allows us to basically compare some of these scenarios that we've created in what we call a compare board, and so I'm going to go back to that same oncology asset, and you'll see some of the scenarios that I mentioned, where you can see kind of time series KPIs for each of these assets. So for commercial potential, you can see that the baseline scenario, which considers an all-comers population as part of the label, has higher commercial revenue potential than some of the other scenarios. We can also see that scenarios that may have OS may take longer due to a longer follow-up period for the development timelines. And we also see some of these scenarios also cost more in terms of development cost spending. So I'll pause this demo right here, but we're happy to kind of answer more questions or kind of talk a little bit more about this in the Q&A session. I will now pass back to Jean, to kind of discuss -- to conclusion, and also for Q&A.

Let's go ahead and let's go ahead and jump into Q&A here. And I see a first question on, in what way does pipeline architects support cross-functional development? Or cross-functional collaboration rather, in what way does pipeline architects support cross-functional collaboration? Can we -- let's open that up to our experts.

This is Eric Groves. One of the core questions with the team operations is to get everybody on the same page at the same time and speaking definitively at the same moment, and pipeline architecture allows you to map out the development process and thereby recruit these people into speaking early and definitively at their point time. That really helps an enormous amount at getting to rapid and well-fined solutions.

Rob, do you want to talk a little bit about the efficiency we've seen? That -- and how pipeline architect gives efficiency to teams?

Yes, yes. So I think one of the common difficulties with this clinical development, right, is that there's different cross-functional groups, each of whose inputs must be considered, right? So you may have a clinical operations team that has the real expertise around recruitment timelines and/or number of recruitment sites, and whether a certain design is feasible. You may have biostatistics providing a view on risk, both from a probability of technical success for each phase and also a probability of technical and regulatory success for the overall program. You'll obviously have medical [indiscernible] in with the efficacy of the asset, right? And then also some aspects of early commercial, especially if we're looking at different indications or even for the commercial potential of a single indication, right? So all these different inputs need to be considered. And as you can imagine, in a matrix organization, as many established pharma companies operate in, it's very difficult to kind of take all these inputs, run the analysis and come up with like the key value measures to evaluate that scenario. And so our hope with kind of the development of this platform is to reduce some of those cycle times, right? So pipeline architect, as you change different assumptions, hopefully, you saw that the key value measures we're updating automatically, and it also allows for kind of a consistent medium or a single source of truth for all these different cross-functional groups to test their different assumptions, right? And so I would say, that's kind of how the efficiency aspect comes in because especially as we saw, a lot of you point to the fact that, there is actually valid collaboration and a lot of the key groups are involved in development, but the use of these analytical tools, driven by clinical benchmarks, can really help make those processes more efficient and allow you to take either more scenarios in consideration or kind of complete these analysis in a faster fashion.

Great. Thanks. And there are a couple of questions around data sources on the costs and how we keep the tool updated. I think that's over to you, Rob.

Yes. Thank you. So pipeline architect kind of -- it relies on both public and proprietary data sources for the key value measures. So it really depends on which estimate you're referring to that determines how -- which database source informs it, right? So first, the proprietary database is clinicalTrials.gov and Trialtrove, and it forms things like recruitment timelines, enrollment timelines and median sample sizes for certain studies. And then the proprietary databases involve kind of sense of IQVIA full-service contracts and proposals, and those point to certain value measures like activity costs and the variance and potential of the estimate, so on and so forth. So the data in the platform is updated once a year where applicable, right? So for instance, if there's no risk measures coming out every year, it would be improbable to see that, right? So that data source may not be updated annually. But we will kind of run through clinicalTrials.gov and Trialtrove to get latest estimates for those benchmarks as part of kind of a license-based SaaS offering.

Great. Thanks, Rob. Here's a question on, how does the tool and how decision-making in this area in general, account for approvals of competitor products that happened during development? So let me open that up to Chris and Eric. And I mean, how do you think about this issue of a changing landscape, if you will, during your product development?

I think Eric will have certainly some comments here, but from my perspective, one of the contributing factors, we talked about cross-functional contributions, is that idea of the competitive intelligence view and the anticipated standard of care. So we would incorporate that in terms of either adding in a marker for when we would anticipate a change and that would influence perhaps a push for faster trial scenarios or an accommodation to determine if an alternate strategy could be developed, following an anticipated change in, for example, standard of care.

So you can -- you can use pipeline architect not only to map your own development process, but you can use pipeline architect to map your competitors by development process as well. Depending a little bit on the degree to which they're going to influence the outcome of your product, you may choose to actually map their process out in some detail. But certainly, you can accommodate things like changes in prospective accrual rates, changes in distribution of sites so as to anticipate when your studies will be completed. And you can also even anticipate changing the number of sites and the response to potential successes than your competitor, or failures of your competitors, so as to save money. So these things can all be mapped out in pipeline architect. It's a degree of enthusiasm that you have for doing this kind of planning, but almost always, it turns out to be a very good decision to make to plan better.

Great. Thank you. Okay. With that, folks, we're going to wrap up. We very much appreciate your time for this webinar and for, in general, for joining this long series of webinars we've had. We look forward to future iterations, and thank you for joining today. We would be happy to talk with folks offline on additional questions or comments. I'll hand it back to Jeanne.

Thank you, David, and thank you, team, for all of the great insight today. As David mentioned, he and his team are happy to respond to questions offline, which is important because I've seen such an engaged audience in today's webcast. A lot of questions still in the queue. So a reminder not to be worried, they will respond to any questions you may have offline. I'd like to thank you again for your engagement in the webinar. I'd like to thank our presenters and our special guests for the great insight into today's presentation. And I'd like to thank IQVIA for making today's webcast possible. As we do conclude, if I could just ask our audience if they would kindly participate in a brief survey. The survey will auto populate on your screen just after today's presentation concludes. You will also receive an e-mail alerting you when this webcast is available for replay, and we invite you to forward that announcement to your colleagues who may have missed today's live event. So thank you again to all. We'll see you next time. Bye-bye.