Artificially intelligent tools are revolutionizing nearly every stage of the drug discovery process, offering substantial potential to reshape the speed and economics of the industry. As the drug discovery and preclinical stages speed up and potentially produce more drugs to test in the clinical trial phase, how do clinical researchers prepare and respond to these challenging opportunities?   In this episode, Toban Zolman, Chief Executive Officer at Kivo will share his thoughts on how AI-enabled successes in drug discovery will affect clinical operations and regulatory operations. We will discuss how advancements in technology and data analysis are reshaping the way we conduct clinical research.   --------------------------------------------------------   Episode Transcript:

http://traffic.libsyn.com/thelatestdose/The_Latest_Dose-S01_E43.mp3

 

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Hi, everyone, and welcome to the Latest Dose, the podcast that explores the depth of innovation and human compassion in clinical research. I'm your host, Katherine Vandebelt, global vice president of Clinical Innovation at Oracle Health Sciences. Traditionally drug discovery is a notoriously time consuming and expensive process. A host of artificial intelligence tools, AI, are said to be revolutionizing nearly every stage of the drug discovery process, offering substantial potential to reshape the speed and economics of the industry.

 

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According to the Boston Consulting Group, as of March 2022, “ biotech companies are using an AI first approach had more than 150 small molecule drugs in discovery and more than 15 already in clinical trials”. Once the drug discovery and preclinical stages speed up and potentially produce more drugs to test in the clinical trial phase, how do we prepare and respond to this exciting new and challenging opportunity?

 

00;01;11;15 - 00;01;42;17

Today, our guest will share his thoughts on how AI enabled successes in drug discovery will affect clinical operations and regulatory operations. We will discuss how advancements in technology and data analysis are reshaping the way we conduct clinical research. Joining me today is Toban Zolman, Chief Executive Officer of Kivo. Toban has 20 years of experience in regulatory and clinical operations, drafting some of the first guidelines for electronic submission at Image Solutions.

 

00;01;42;19 - 00;02;09;06

Toban has consulted with 45 of the top 50 pharma companies in the world. After working in regulatory, Toban ran product teams for several tech companies. Toban has been at the forefront of multiple tech revolutions, such as cloud computing and the Internet of Things. Toban thinks the time has come for clinical trial management to level up. Toban, it is great to speak with you today.

 

00;02;09;06 - 00;02;45;17

Welcome to the Latest Dose. Yeah, thank you. Great to speak with you as well. In the intro I mentioned that you believe the time has come for clinical trial management to level up. What do you mean by that? Well, let me give you some context maybe on where that comment is coming from. So, I spent a chunk of my career helping tier one pharma transition to electronic submissions and kind of the promise of electronic submissions was improved process, improved visibility, faster review times by regulatory agencies.

 

00;02;45;19 - 00;03;22;16

And the way that we went about that as an industry, you know, 15 to 20 years ago, was really to take this new challenge, process challenge, of managing a ten X increase in the amount of documents going back and forth to a regulatory agency and controlling that incredibly tightly. And so literally, you know, I spent years and in windowless conference rooms with committees trying to figure out how to manage every aspect of increasingly complex process.

 

00;03;22;18 - 00;04;03;13

And honestly, it was soul crushing. So, I left the industry and spent over a decade working in other industries that were kind of on the edge of major transformations. E-commerce, social, cloud, IoT, and eventually circled back to life sciences. And I think the thing that struck me the most as I came back into life sciences and started to talk to clinical and regulatory leaders who were dealing with all of these advancements in how clinical trials operate, as this was kind of the same song, new verse.

 

00;04;03;16 - 00;04;32;03

The pace of clinical trials was accelerating. The complexity of tools was increasing. And the number of assets that they were having to manage that resulted from those advancements was also increasing.  And the approach to managing all of that was to just have leaders in life sciences, you know, these pharma companies just literally tighten their grip on the process even more.

 

00;04;32;05 - 00;05;14;08

And that's just not a model that works, and it's not a model that any other industry has embraced. And so, really, I think what we've really focused on, at Kivo, is helping companies loosen their control a little bit, not control of process, but really trying to manage everything in a monolithic top down approach and instead move to more nimble, more decentralized, more collaborative processes to manage this massive increase in the amount of activity that's happening in the clinical pipeline.

 

00;05;14;10 - 00;05;41;16

Well, welcome back to the life sciences. So, you mentioned how these individuals are sort of holding on to the existing process. So, in preparation for this episode, I read a number of articles and they continued to talk about how pharmaceutical industry resists adopting digital tools, the need for them to change their strategic priorities, and also evolving the work place culture, perhaps in some of the ways you just mentioned.

 

00;05;41;18 - 00;06;08;08

What are your thoughts about these statements now that you're back? This is true? Are you seeing something else? What do you mean by that? Yeah, great question. So, yeah, I think you're correct in kind of meta level trends.  Life sciences and especially folks that work in operations, whether that's clin ops, reg ops, etc., that are a very risk averse group of people and for good reason.

 

00;06;08;12 - 00;06;37;11

I'm not throwing shade on anyone. The nature of those jobs and their remit within the drug development process is fundamentally to be risk adverse, and that's what helps create safety in drugs. With that said, you know, Kivo is focused pretty much exclusively on working with emerging life science companies. And so, the vast majority of our customers do not have a drug in market yet.

 

00;06;37;11 - 00;07;14;04

They have active clinical pipelines, but they are new companies, new in life science terms.  Many are 15 years old. But I think they are hitting growth inflection points really in a post pandemic world. And that's been super fascinating to be involved in because I think these smaller companies that are growing rapidly and hitting inflection points post-pandemic are really leaning into decentralized teams and maybe not even by choice.

 

00;07;14;04 - 00;07;48;02

It's just the nature of how you scale a company now. But they're leaning into that workplace culture of small, decentralized teams, relying heavily on partners; whether that's CROs, contract medical writers, reg affairs shops, whatever it is. And they are figuring out how to scale organizationally, to scale technologically, and scale as well, their clinical trial process in that landscape.

 

00;07;48;04 - 00;08;21;19

And so, the conversations we have with leaders in those companies who are really building the organization from the ground up, differ significantly from the conversations we have with companies that reached a scale point, you know, a decade ago or even pre pre-pandemic. Where the workplace culture was centered around in-person, everyone working in the same office sort of a culture.

 

00;08;21;21 - 00;08;51;24

And so, the industry is risk adverse. Ops folks are risk adverse. The customers we work with that are most successful are the ones that are baking into their corporate culture from the ground up, a more nimble, decentralized approach to managing this influx of data. So that makes sense to me about companies that are coming into the market a lot around the post pandemic and getting more decentralized.

 

00;08;51;27 - 00;09;14;12

But there, I still think there's a disparity that I'd love to get your thoughts on. So, we talk about AI, the promise, the culture, but we also see that we've had cloud around for more than 20 years. But there are some people that say in some articles that say that 50% of clinical trials are still utilizing paper processes somewhere in it.

 

00;09;14;15 - 00;09;41;23

So how do we deal with this disparity? How do these large companies deal with this? What are your thoughts on what they need to do? I think our experience aligns to that as well. Even with smaller companies, you know, half of our customers have some sort of paper element that they are navigating. I would frame the conversation about AI and cloud, this way.

 

00;09;41;26 - 00;10;22;18

Cloud and life sciences is very different than cloud in other industries. The majority of the incumbents, software vendors, especially that are offering part 11 compliant solutions software, that's used deep in the regulated process are they may be cloud based, but this is technology that was created before the iPhone was invented. And so, the paradigm in which a lot of these platforms use is not fundamentally changed from software and processes that were developed in the nineties and early 2000.

 

00;10;22;20 - 00;11;08;15

AI as a layer on top of that, creates so much acceleration, increase data process challenges, that those two are never going to play well together. So, I think what you are starting to see in the industry is kind of, it's almost like, you know, looking at geology where you've got three strata of incompatible technology.  You've got paper on top of that, you have SAS based cloud centric solutions that fundamentally aren't very cloud like and then you have AI swirling on top of all of that.

 

00;11;08;18 - 00;11;45;16

And those three things are very difficult to stitch together, especially if a company is attempting to take, you know, for lack of a better framing, a monolithic view of how to control that process. And so, I think if you look at organizations outside of life sciences that have adapted and grown quickly, there are some commonalities in how they approach new technology and apply that technology in the organization.

 

00;11;45;18 - 00;12;24;05

Amazon is a great company that comes to mind and in terms of how they approach this. So, Amazon, obviously massive in scale, but I think what the way that they run that company is, you know, following the two pizza rule where there's no team that can't be fed with two pizzas at lunch. And that team manages all aspects of a project or product and has effectively total autonomy to drive features, process, etc...

 

00;12;24;07 - 00;13;20;07

And within life sciences, it's possible to take a two pizza mentality, especially as AI help accelerate the pace at which net new assets are spun out that may be completely discrete from other products in the company's pipeline may be different. Really, I think what we've seen that's been successful with companies that have grown rapidly at Kivo, is not to try and scale the organization in proportion to the pipeline or in proportion to the amount of assets being created. But rather to create fairly tightly constrained in terms of remit teams that have high degrees of autonomy and authority.

 

00;13;20;10 - 00;13;51;07

And those roll up into, you know, ultimate decision makers on clinical and regulatory, but have the ability operationally to adapt and dictate their own workflows. And that may sound scary to some folks, especially coming from a paper world where it was possible to have a pharma company with 50,000 people and everyone does the same process.

 

00;13;51;09 - 00;14;23;12

That's just not super practical these days. And so, picking tools and defining process that enables teams to be autonomous and nimble is really the only way to proportionately scale an organization to keep up with the tsunami of advancements being driven by cloud and AI. In our last episode, prior to this one, we actually did… had a conversation about creating drugs at the speed of AI.

 

00;14;23;12 - 00;14;48;06

And so, you talked about the increased input that's coming into these organizations. You talked about the two pizza teams; you talked about utilizing technology. So, this is a big change for pharms. I've been in pharma for many years, is a big change. So not having people do the work but really doing the work differently. So, what the implications of this and what advice do you give folks to scale?

 

00;14;48;09 - 00;15;20;14

First off, it's been fascinating to kind of be on both sides of this, right? Helping companies really codify a process in the early 2000 around how to manage, how to transform the entire organization from paper to electronic. Obviously, there's still some holdouts in the process there, but really, that was a transformational change and now seen another transformation of industry, which is, you know, cloud and AI.

 

00;15;20;14 - 00;16;09;26

Really driving further up the pipeline changes in how assets are developed and more rapidly finding promising new drugs. So, I think customers that we are working with that are managing this transition effectively are really doing two things. The first thing is they're taking what I kind of call a or they are running guardrail management. Which means for their organization from the top down, they are defining the guardrails in terms of process and technology that they want individual groups to follow.

 

00;16;09;28 - 00;16;44;27

They are not dictating every step, every workflow that has to happen with every team. But rather creating a North Star that everyone is working towards, defining policies, and operating procedures that define the parameters in which individuals and departments have authority and autonomy to work within. But generally, giving those teams the discretion to identify the most effective way to work.

 

00;16;44;29 - 00;17;25;09

Because let's be real about that. The speed at which things happen in clinical pipelines today, is faster than what a typical company could author, approve, and train a SOP. So, by the time you get your, you know, massive global process defined and implemented, enough tech has changed, enough insights have been drawn out of the data, that it no longer makes sense.

 

00;17;25;12 - 00;18;18;22

And so, defining guardrails, defining guardrails for groups, and then letting them operate within those ...while still staying compliant, still meeting the goals of the company is kind of the key. A chief medical officer or a VP doesn't necessarily have the operational insights to be that prescriptive anymore. So, I think that guardrail based management is super effective. We have a customer that in the past, maybe sixteen months, has gone from something like 2 to 15 assets that they're managing with a very small number of employees, and they have not scaled their organization.

 

00;18;18;24 - 00;18;51;13

You know, they have not doubled, and then doubled, and then doubled again in terms of headcount. They've maybe grown 30%. But that growth has been really centered and focused around those asset classes where individual groups have the ability to kind of figure that out on their own, within budget and some general guardrails. And they're one of the fastest moving life sciences organizations I've worked with as a result of that.

 

00;18;51;16 - 00;19;42;05

So, I think, you know, those are kind of key lessons that we are seeing is changing that top down mentality. The second trend that I would point to, is really taking a similar view of technology. And, you know, at Kivo, we see this from a document management or a process management perspective because that's the software we build. But this really is true throughout the entire stack and especially on the tools that are used on the AI side, the machine learning side. Either, you know, workbench tools to try and find insights into pharmaceuticals or tools to speed up and better analyze data on the clinical side.

 

00;19;42;05 - 00;20;20;09

Throughout that stack, I think teams that have the ability to select, implement, and iterate on those tools in a rapid fashion, probably goes without saying, but those are the ones that seem to be adapting and increasing their pipelines the fastest.  And with modern cloud tools, with APIs, you know, less reliance on centralized IT, It's possible for a very small company to go very quickly and do all of that in a really pretty controlled way.

 

00;20;20;14 - 00;20;53;00

But it takes really thinking through the tools and thinking through the process in a way that is not nearly as top down and prescriptive as it may have been a decade ago. So, thinking through those two suggestions you have around the guardrails and also how to handle technology advancement with clinical operations and regulatory operations, are they prepared for this big change?

 

00;20;53;03 - 00;21;20;16

I get the examples you've given with companies that are starting and growing and so forth, but what sort of investments or what improvements or what have they experienced was going from paper to digital in the early 2000s enough to prepare them? What else have you seen prepare them for the change? So, I think what I would say is it is a mixed bag and that's not a way to dodge the question.

 

00;21;20;16 - 00;22;00;25

But the amount of deviation that we see across organizations is significant. There are, I think there are, individuals in the industry who get it and really are embracing these trends as a way to accelerate development. And see that there is a path to do that, while preserving the safety of the drug development process.

 

00;22;00;28 - 00;22;51;27

And I think that there are others, some of whom have, you know, legitimate perspectives but that are very much underprepared for the sea change that is happening with these tools. And are continuing to frame everything, not just on a, you know, all the way back to paper. Much of what I think happened in the trends transition from paper to electronic is that electronic processes were still fundamentally rooted in how you operated in paper.

 

00;22;51;29 - 00;23;56;03

They were more efficient. But literally the constructs in software UI, the steps in the process, all of that still kind of came back to underlying philosophies around where document sat in what file cabinet and what that file cabinet represented; whether that was draft documents or approved documents or, you know, things of that sort. And so, the entire paradigm of managing electronic data is still fundamentally anchored in a paper view of the world. And organizations and software that I think have gone beyond that, have been able to create much more nimble processes and are probably better prepared for the AI tsunami. Organizations and individuals that are still managing electronic data in a paper paradigm are in for a world of hurt.

 

00;23;56;03 - 00;24;31;03

And I think, that's probably the most common sort of trigger insight. I'm not sure what tell; this probably the best way to frame it; when we're talking to a life science company for the first time and they're asking questions about, you know, how they solve specific problems - - if it's anchored in references to file cabinets. You know, we have one set of responses.

 

00;24;31;05 - 00;25;03;00

If it's anchored in in terms of decentralized teams and collaboration and process management, it's a different set of responses. And so, I think what you're really seeing in the industry right now is, a… hate to use the term paradigm… but a paradigm shift and really a transformation in how work gets done and how companies think about what that is.

 

00;25;03;00 - 00;25;36;00

We have a close partner at Kiva who talks about how the product of pharma companies is documents, not pharmaceuticals. And I think for the majority of individuals within a pharma company, that's totally true. That is what they do day in and day out, is prepare documents that represent some portion of the narrative of their clinical trial and ensure that those get teed up to a regulatory body for approval.

 

00;25;36;02 - 00;26;28;19

I think in a new world view, while that narrative element and communicating everything to regulatory agencies through documents is true, organizations that can shift that thinking and really understand that they're developing pharmaceuticals, biologics, whatever they are working on. And that exists in the context of an ongoing, evolving process are the ones that are being are the ones that are ultimately going to be able to adapt to this and be prepared for the transition most effectively.

 

00;26;28;21 - 00;26;55;09

That's great. So, I agree with you that I still come across individuals that are working with technology based on a paper originated, a paper fundamental process. So, my question to those individuals that you're familiar with or collaborate with or discussed with…. through the COVID-19 pandemic, we had to change the way we were working.

 

00;26;55;09 - 00;27;22;26

And in many cases, it pushed us to do things very differently than we had to do in the past. And to be very creative, yet safe and produce quality. So, do you feel that these trends actually helped people to move towards where they should be going, or do you think it's more of the same? I actually do think that that COVID was a game changer.

 

00;27;22;26 - 00;28;07;18

And I mean, I think that's true in a lot of industries, but certainly in life sciences, I think it affected two things in a fundamental way. One was business process had to move to a decentralized approach. And the second is so many clinical trials were affected by COVID, in terms of, you know, if you had a clinical trial in progress when COVID hit, well, doctors can't be in the same room as their patients for every single visit, or there has to be social distancing.

 

00;28;07;18 - 00;28;57;20

So all of the sorts of data collection methods changed and that forced many, most clinical trials, to switch up protocols, update documentation and frankly created a highly dynamic environment on the clinical side where changes to protocols for clinical trials changed, you know, on a weekly, daily basis and change from one country to another in a way that was way more aggressive and dynamic than I think most individuals had had ever dealt with before.

 

00;28;57;23 - 00;30;04;17

And that frankly was a forcing function to be more adaptive, to leverage a different class of tools, a different class of partner, and I think forever changed the operational aspects of how clinical trials are run. And so, I think the adoption of AI and the willingness to transition to more decentralized models, in terms of process and technology, was accelerated by a decade or more due to COVID because it just it changed the baseline of what is acceptable in terms of how dynamic a process can or should be… to move drug development forward while still maintaining safety.

 

00;30;04;20 - 00;30;45;23

So, I think more than AI, more than cloud, COVID was probably the biggest accelerant in the drug development process in the last two decades at least. So now that we have your advice around guardrails, decentralize teams, leveraging the cloud, leveraging AI,  take the learnings from COVID-19, right, like embrace them, take the learning, and keep going. Is there any other improvement or change that the regulatory operations and clinical operations teams need to take in account that we haven't touched on yet?

 

00;30;45;25 - 00;31;23;04

There's really kind of three aspects, from a meta level to kind of any sort of transition, really in industry, but specifically in life sciences, that you can anchor against. So, people, process, and technology.  On the people side, I think redefining who makes up a team is a critical part of this. And life sciences, this is a pendulum in life sciences where, you know, business process outsourcing becomes supercritical.

 

00;31;23;07 - 00;32;00;15

Organizations move to that to restructure cost, etc. The pendulum swings back the other way. They bring those roles in-house. When I talk about people, there is a different trend happening now, and it's not just business process outsourcing and small companies work with CROs. It is really a fundamental rethink of how organizations scale and how teams are built. And it is not where you have employees of the sponsor and employees of a partner and there is a wall where documents are lobbied over from one organization to another.

 

00;32;00;17 - 00;32;40;25

But really what we are seeing is a much more fluid arrangement between sponsors and partners. Where partners; and they could be sizable organizations, they could be independent contractors; but they are bringing specific domain expertise into the team and are a core part of that team and process for whatever duration makes sense.

 

00;32;40;27 - 00;33;25;16

Could be six months, could be six years. But companies that seem to be doing exceptionally well are ones that really are looking for the most efficient way to bring the right domain expertise into a team. And it doesn't matter, necessarily, at least in the short run, where that person sits, if they're a sponsor employee, or a partner.  And we can almost see at Kivo, you know, companies that are moving very quickly and, you know, our internal stories about them are, man, did you see Acme Pharma, they are cruising!

 

00;33;25;16 - 00;33;52;25

 

If you look at their users in our system, you see a high intermix of email addresses that belong to the sponsor and email addresses that belong to partners. Because they are shoulder to shoulder, elbow to elbow, working through the challenges of bringing a drug to market as a team, everyone bringing kind of their best expertise to the table.

 

00;33;52;27 - 00;34;26;15

So, I think, you know, rethinking where that domain expertise comes from and how you scale a team internally versus through partners is a key piece of that. On the process side, I won't repeat what I've said previously, but I think, you know, a guardrail based approach to process development versus a prescriptive approach seems to be what is really helping successful companies drive innovation faster.

 

00;34;26;17 - 00;34;54;19

And finally, on the technology side, this is drug development is no longer a monolithic process. Even within a single company. Individual teams are going to be using individual tools. You'll use tools for short periods of the process and then wind those down, especially with AI.

 

00;34;54;25 - 00;35;28;07

And so, I think the tools that you do pick to be anchor points in your process, need to be nimble, need to be highly configurable so that you can adapt them over time. And frankly you need to be able to have a change management process that can go incredibly fast so that you can adapt those systems to your people and process as they change because they're going to be highly dynamic. So, all three of those areas have to be able to scale and change quickly and all adapt around each other.

 

00;35;28;07 - 00;36;03;07

The days of mapping out a process in a conference room, paying a vendor to implement that process in software and then revisiting that in five years is no longer a model that makes sense. So, people, process, and technology, that's the key. They all got to work together in a dynamic fashion. Thank you for that great advice. So, as we come to the close about this episode and you've shared some excellent, great content for our listeners to think about, I really want to ask you this question.

 

00;36;03;07 - 00;36;52;15

What do I; what do the listeners do; what do we need to start doing today in order to embrace this new way of working; this new reality to really capitalize on this opportunity of a faster drug development coming at us? Share your thoughts with us…what we could do tomorrow after we've listened to you. So, I think there's a couple ,a couple levels to think about this, from a kind of a top level, taking one step back from the day to day and not necessarily focusing on what do I need to do next, but instead thinking about like what is the ultimate objective in my role, in the trial, whatever, and what actions actually move the needle the most to achieve that, I think is important.

 

00;36;52;15 - 00;37;19;13

As someone who talks to customers every single day and gets asked for, you know, for features; told a process is changing, you know, whatever it is. What I have found to be really effective in understanding that is  asking why, you know, why …. why do you want that feature?

 

00;37;19;18 - 00;38;06;06

Why are you making that process change? And so, the five why’s, you know asking why five times, to get to the root reason why something is; it is turns out to be really effective. Because a lot of …. a lot of decisions, a lot of process, it turns out the original reason why you are doing that as has been so mutated by process, by committee, by interpersonal challenges, political challenges within the company, that if you really stripped down what is the end goal and why are we making changes to get to that?

 

00;38;06;08 - 00;38;45;13

It turns out you can often short circuit a whole lot of added complexity and instead strip away complexity to really get to the, you know, the core challenge. So, you know, that's kind of a big idea. But in terms of what people can do today or tomorrow, I really think it is less about focusing on explicit deliverables, even though that's how everybody gets measured for their job. And instead, really thinking about overall outcomes.

 

00;38;45;15 - 00;39;27;02

And I don't mean to sound like a process consultant when I say that. But fundamentally that's what drug development is about, is producing positive outcomes for patients that are safe and effective and reframing process based on the outcomes of a process; not based on individual deliverables, not based on a description and composition document was produced, but instead, you know, the ultimate goal is not the document, but it's getting engagement with the regulatory authority faster or moving a clinical trial forward faster.

 

00;39;27;02 - 00;40;24;04

Those sorts of reframing day to day tasks in a way that is measurable and visible across an organization turn out to really be key. And then the conversations seem to be less about horse trading on deadlines and trying to figure out, you know, who's responsible for a certain deliverable and really more about how do you align … how do you align what you're working on with other contributors, employees, partners, etc. to really drive the ultimate outcome.  Which for clinical it could be getting into a clinical trial, completing a clinical trial. On the regulatory side could be getting a submission to a regulatory agency or approved by a regulatory agency.

 

00;40;24;06 - 00;40;53;11

So, I think understanding what the ultimate outcome is, less than the deliverable and then you just figure out how to get the deliverables marshaled through; turns out to be a positive way to reframe things and think about things at an organizational level. Well, Toban, as we prepare and respond to this exciting new and challenging opportunity, I am very grateful and I'm sure my audience is grateful for you sharing your experience and advice on how to embrace this change, prepare for the bottleneck.

 

00;40;53;11 - 00;41;18;08

And I really appreciate your time with us today. Thank you so much. Thank you. I enjoyed it. Thank you for listening to The Latest Dose, the podcast that explores the depths of innovation and human compassion in clinical  research.  After learning from more than 60 guests over 40 episodes, the Latest Dose will be taking a break. I encourage listeners, both new and old, to go back and enjoy the fabulous content shared by our thought leaders.  Thank you for all you do to bring new medical inventions to the market so family, friends, colleagues, and I can live longer and healthier lives.  Thank you for listening and subscribing to the Latest Dose! Till we connect with you again, stay well!  Good bye.

Artificial intelligence (AI) is one of the most discussed technologies across all industries. Life science professionals working in the pharmaceutical industry strive to improve people’s lives tackling incredibly complex diseases. Drug development is often perceived as slow. As the pharma industry looks to improve the drug development process AI promises nothing less than a revolution.   Can AI help speed up the drug development process? Identify new drug molecules that have so far eluded scientists?   Will AI–designed medicines be safe for people? Have the desired effect on the disease? Meet the rigorous regulatory standards to actually be approved for human use?   In this episode, Andreas Busch, Ph.D., Chief Innovation Officer at Absci will answer these questions and shares the value generative-AI is providing drug development today.   --------------------------------------------------------   Episode Transcript:

00;00;00;00 - 00;00;31;24

Hi, everyone, and welcome to the Latest Dose, the podcast that explores the depth of innovation and human compassion in clinical research. I'm your host, Katherine Vandebelt, global vice president of Clinical Innovation at Oracle Health Sciences. Artificial Intelligence, AI, is one of the most popular technologies on the planet, and I find it referenced in most, if not all, industries.

 

00;00;31;26 - 00;00;59;16

Those of us working in the pharmaceutical industry strive to improve people's lives. Can AI help scientists develop better medicines faster? Human bodies are incredibly complex. Drug development is slow. Since I've been engaged in drug development, many people, teams, organizations, and companies have been working tirelessly to improve the drug development process, the promise, is nothing more than a revolution for the pharmaceutical industry.

 

00;00;59;19 - 00;01;26;21

The March 8th, 2023 Politico article states “nearly 270 companies are working in AI driven drug discovery”.  Let's start learning more about AI driven drug discovery and discuss if or when the promise of AI will be realized.  Can AI help speed up the drug development process? Identify new drug molecules that have so far eluded scientists?

 

00;01;26;23 - 00;02;02;02

Can AI-designed medicines, be safe for people? Have the desire effect on the disease?  Meet the rigorous regulatory standards to actually be approved for human use? You know, many of these questions can be answered today with my guest, Andreas Busch, Ph.D. Chief Information Officer at Absci.  Andreas brings substantial R&D expertise to Absci’s leadership, a world renowned leader in drug discovery and has led R&D efforts for some of the globe's top pharma companies, including Sanofi, Bayer, and Shire.

 

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Andreas’ leadership has resulted in over ten commercial drugs starting from bench to FDA approval, with several more in late stage clinical development. Andreas holds the title of Extraordinary Professor of Pharmacology at the Johann Wolfgang Goethe University in Frankfurt, Germany, where he also received his Ph.D. in pharmacology. Andreas loves, real football a.k.a soccer, enjoys riding his motorcycle through Alps and playing with his beloved dogs Zorro.

 

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Welcome, Andreas. Thank you for making the time to speak with me today. Hey, it's a pleasure talking to you Katherine. So, Andreas I have been taught that artificial intelligence, referred to as AI, are computer intelligence programs that can handle real-time problems and help organizations and everyday people achieve their goal. And AI is obviously a topic of discussion these days and getting way more attention with the release of the articles around ChatGPT.

 

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Today I'd like to focus our discussion on generative AI, but I thought it would be helpful if you could share with me what's important for me to actually know about this type of AI. I'm glad to talk about it. I guess ChatGPT was certainly a breakthrough in AI and the use of AI for a general population and everybody knows now what AI can do through a GPT.

 

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And if you look at generative AI, what we're trying to accomplish simply is to have artificial intelligence supporting us, creating drugs. And as you know, with ChatGPT, you have to give ChatGPT the right prompt in order to get ChatGPT to do the job for you. And this is similar with our generative AI. We need to give the prompt, which is we need to give our models the target, the mechanism we want to work on.

 

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And then the model produces for us, in our case for Absci, a de novo designed antibody. So that's fascinating. How long have you been developing this approach with these prompts and these programs and actually been using this at your organization? I mean, Absci is actually a company which started as a cell line development company and realized then that for AI to be very productive, you need a ton of data and you need a ton of very consistent, high quality data.

 

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So, these two things have to come together, you know, improvement of AI models, but feeding the AI models with plenty of data. So, the models can get better and better. And we've started really implementing AI for our E.coli expression systems for antibody a bit more than two years ago. And the progress we saw in our generative AI approaches were really very significant, very fast.

 

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Already a year ago we were at a stage that we could optimize existing antibodies, so we basically gave the model the information of … look here is a known antibody, …. can you optimize it for affinity, … can you optimize it for immunogenicity and so forth.  And we managed to do that. And just half a year ago, for the first time,  give the model the information of the structure of a protein  that we wanted to address, to produce for us a binding sequence completely de novo or without any idea of an antibody structure before.  I think there was …. really for us …. the breakthrough.

 

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And that is something which we have meanwhile even further progressed in the last half year. We extended this approach to more than one binding regions and we are ready now in a situation to address three of the binding regions of an antibody. And we are very, very optimistic that this progress is going to be extremely meaningful and helpful and what we believe disruptive in biologics research in the future.

 

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So, this is exciting and extremely fascinating. So, I'm going to go to a statement you made about the data. So, can we talk a little bit about that? So where do these sources of data come from? What types of volume are you talking about? And I guess more importantly, as somebody who has worked with data for many, many years,

 

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and one of the things that people will often ask about is ….should you use that data? Is that data appropriate? Is it reliable? Some people use the word quality. So, in order to achieve these impressive results, can you tell us a little bit about, more about, the data that's being used? Where does it come from and all those things?

 

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Sure. To make it clear, what we're doing is, once we know the structure of a mechanism we want to address, let's assume whatever a membrane protein like a G protein coupled receptor, whatever you name it, we identify the region to which we want our antibody to bind and we give this information in the structure of this region to the model.

 

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The model then delivers to us a number of model hits.  Artificial intelligence generated hits.  Information about what the model thinks the binder should look like.  And what we do then, and that's the very straightforward answer to your question of the quality, is we generate those hits in the laboratory, we express the genes relevant for those binding regions in our expression system.

 

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That's a microbial expression system, E coli. And then we simply have a test available called the Ace assay, in which we then validate what is indeed the binding affinity of those calculated binder. So that gives us then immediately an experimental validation of the AI suggestions and of the AI results. And therefore, we feel very, very comfortable that of course the quality of our predictions is very high as we validate them right afterwards.

 

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Not only that, we validate them, but we can then again also use the information of those data to further improve the model. You ask, how many data do we generate? Well, the nice thing about E coli is that it replicates very, very fast and we can express huge libraries. The libraries again are the genes suggested by the model, and we can express easily your libraries of 500,000 or 1 million binding regions and as a consequence can measure 2-3 million of individual binders in a week or two.

 

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And we can of course, also then see how well those binders are expressed in the cells and can measure up to a billion data points and protein interactions per week. Okay. So, I have to ask,  if you didn't have the generative AI and the capabilities that you've just talked about, how long would it take for a human to do this without these additional tools and capabilities?

 

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I think the really exciting piece about what I'm describing to you is that the model not only spits out a binder of a certain quality, but it spits out, already something which we can in a multidimensional way, optimize. So, if you go back to a traditional way of how to generate an antibody, which would be through mouse immunization or rapid immunization or what is called a phage display, you also can get a binder.

 

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However, that binder comes without any potential optimization you would want to see. For example, you know, you get a binder.   But you cannot influence in this traditional way the affinity, you cannot influence the solubility, the immunogenicity, and so forth. All of those parameters are very, very important for an antibody. Our model can spit that out, and I think that is a breakthrough, especially if you consider this is indeed a rounded up, optimized candidate.

 

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This is not just, you know, a first antibody, which then can take over years, years really to get finely optimized. So again, going back to revolutionizing this and actually making it very different. So, but this is so different than what some people are familiar with or what they've been educated. Absolutely. They've done in the past.  Are you familiar,

 

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you probably are, but I'll just check;  are you familiar with the technology adoption curve where they use the terms innovator, early adopter, early majority, late majority and lagger.  Sure. Yeah, that's what's kind of coming to my mind. This is so different than what scientists have been doing in the past. I guess how broadly is this currently being used or where do you see the industry right now with this way of working?

 

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Are we in still the innovator stage?  early adopter?  or am I a bit behind and we're actually in the majority stage?  so can you talk us through that, please? That would be great. Yeah, I think we certainly consider our approach at the forefront of biologics research right now. And our focus is, of course, entirely on generation of antibodies.

 

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That is our focus and I think it really needs this focus to make the progress which we are having right now. But how in the context of in general, R&D of biopharmaceuticals, there are many, many more aspects which AI can address what we are doing with large molecules, with antibodies other companies are doing with small molecules, with the chemicals.

 

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Then you can of course, beside the generation of drugs, discuss options of AI to identify the right mechanisms. Because of course you always need to start in a disease with the right mechanism to address. Otherwise wonderful antibodies or wonderful small molecules are not really worth a lot if you're working on the wrong mechanism or target.

 

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So, I think when it comes to generation of antibodies, we are at the forefront. We certainly want to extend our knowledge in the future to other biologics beyond antibodies. But there are other approaches of AI which of course are also very productive and they all really did grow over the last couple of years based on the existence and availability of vast amount of data.

 

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So how much,  how expensive is this? So, we've talked about how it works. We talked about how it's going to save significant time, what it needs to run. Totally appreciate your focus area in antibodies and so forth and other companies are doing other things but how expensive it this is?  Is it really cheap? Is it moderate? And I'm not necessarily asking you to tell us the price, but what sort of investment, I guess, or what sort of expense should companies think about as they get engaged in this type of work?

 

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Yeah, I think that we should try probably to look at the end game. What is the end game, really.  I mean, our goal clearly is , once we know a target, at the click of a button we will have the information of how the optimized antibody looks like. The consequence, and of course, the click of a button does not cost a lot of money.

 

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As you can imagine, you're doing that every day yourself. But as you can imagine, the traditional path is a very, very different one. The path it takes from a target to a traditional antibody really means tons of lab work… it means tons of iterative processes… it involves many, many people, consumables and so forth. Until you indeed have an antibody in hands which you then start producing first in vitro and in vivo data later on, those data will still be needed.

 

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So, you will need of course, once you have the antibody spit out of the model to characterize the antibody in the relevant disease models. But until then, of course, I would say the cost saving and the time saving are enormous. My assumption is right now, if you look at benchmark and the industry, the cost to come from a target to a candidate antibody is somewhere in the range of $5 - $10 million.

 

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And you can imagine that a click of a button is certainly going to be faster and cheaper. I think McKinsey actually coined this phrase, pilot purgatory, which means that organizations are hesitant to take on new ways of working. They see better ways, they see exciting ways, but because they don't necessarily understand them or they're not that familiar,  they require a lot of change in their organization, they’re hesitant.

 

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And so often, we pilot things or I have piloted things or my company has piloted things in my past. And then what I notice across the industry, this slow adoption can kill very valuable innovation because we're constantly piloting them. Do you see those concerns with what you're talking about, or how do you recommend that we prevent that or escape it in this particular situation?

 

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Because it looks so, so promising. I think like every breakthrough technology, there will be the winners and fast adopters and there will be the slow adopters. Listen, I've been R&D head in pharmaceutical industry for over 20 years. I was R&D at Bayer and I was R&D at Shire and I've certainly dealt with a lot of associates, you know, which were skeptic of new technologies and like you heard from the McKinsey reports, not readily available always in a situation to adopt technological breakthroughs.

 

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Having said that, once the breakthrough is obvious, that's the latest moment. Then you can get on board and everybody knows that at the end there is going to be, if really the promise comes through, which I just described to you, that there is no way that you could say, okay, let's wait. And I think this is going to go much, much faster than a number of other breakthroughs in the past, I think, not just the entire world's population got prepared to apply AI to ChatGPT, but the industry is really eager to apply AI along the entire value chain of R&D and even beyond that, onto marketing aspects of drugs.

 

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So I have to say, yes, there always is a chance of resistance, of adoption of technologies in R&D organizations, but I am completely convinced that once our approach has been validated on a couple of targets,  that will be the case in my assumption is within the next year, it is going to be a must without very little alternatives for industries to adopt it because it brings them into the situation to be faster, to come up with molecules which have a higher probability of success based on a multi parameter optimized profile.

 

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And the two things together; being faster & being better optimized, gives you a competitive advantage, which you cannot, cannot give up. Do AI designed medicine, meet the rigorous regulatory standards that are being used to get drugs approved to humans?  So, it sounds like this might be changing the data package, it might be changing how we actually might need to talk to regulators.

 

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Am I understanding this correctly or what is it I need to understand with regards to regulatory requirements? Actually, we should distinguish between what I expect over the next 5 to 10 years versus in the more distant future. What we will deliver will undergo exactly the same regulatory processes as all drugs, no matter how they are delivered, no matter whether they come from traditional small molecule approaches or traditional biologic approaches, the regulatory process will be exactly the same.

 

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The regulatory process will be… you need to show in phase one, phase two and phase three clinical trials that the compounds are safe and efficacious in patients. You will go through before you go to the clinic through extensive pre IND activities to get to that stage. Those regulatory aspects will not be different from generative AI generated drugs versus the drugs coming from traditional pathways.

 

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The only difference I can see immediately versus the future , I can see that based on the chance that we should be able to predict with AI a much better profile. And already also if we go into systems biology, get more information about potential side effects, mechanism based and so forth, the probability of success to get through those regulatory processes is going to be increased.

 

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That is the one aspect in the long term off course, I do see that regulators want to understand what really is the productivity also of AI methods in clinical development. They want to see how valid my predictions were of, you know, development aspects based on AI information and I can see that AI will have a significant impact in the future also on regulatory processes.

 

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Again, I know this sounds repetitive, but that's so exciting to me. Being working in this industry so long to see these types of changes is it's just very, very inspirational. As well as I'm getting older, hopefully some of the targets we're looking at, well, hopefully bring some solutions for things that have eluded us for many, many, many years.

 

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So, I guess as a place right now, I thought it might be helpful to our listeners for me to sort of go back to our introduction, where I asked a number of questions.  You shared such great information and I wanted to just sort of make it a little more simpler. So, if you're able to sort of answer these questions as yes, no or to be determined or that type of thing, I will pose the questions. Can AI help speed up drug development process?

 

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Yes, it was a simple one. Yeah, that was a simple one. Can AI identify new drug molecules that have eluded us?  Yes, very clearly!  We can get into a space where traditional methods may not be able to get to. There are a number of,  if I look at antibody research, a number of targets, which right now with the traditional methods are not really addressable, particularly in membrane proteins, ion channels, and so forth.

 

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We believe that we will be available immediately, more, or less, to address all of those difficult drug targets and make them amenable for treatment.  Fantastic!  And then you just address this with the regulatory question, but can AI design medicines, be safe for people? Again, you know, my assumption is and that's not a yes or no, it's a yes.

 

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My assumption is we can deliver multidimensional optimized compounds which will be tested. My assumption always is we can predict higher safety or the safety in general. However, we will test it. There's at this point no way around, in the regulatory process, to avoid testing for safety. And then can AI design medicines, have the desired effect on the disease?

 

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Absolutely. But I think we should be very clear that in the first place you have to work on the right target. The antibody has to be, in our case, directed against a highly validated target. If the target is, if the mechanism, if the target is not the right one, then the very best antibodies with highest affinity with wonderful other parameters is not helpful, is not useful.

 

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So, you have to start off with the right mechanism, but then absolutely and generative AI generated antibody will be of gigantic use. Fantastic!  So, as we close, where do I go to learn more about the work you're doing in Generative AI? Where would you suggest I expand my knowledge? I think reading always helps and I think if you if you really look there, there is, especially in a number of nature magazines a very frequent report on updates of news around generative AI.

 

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We have published our papers and our news and bio archive, which is a place where we can publish the results without giving away all of the details of our technologies, which is of course important for us at our present stage of the company. But there are also, meanwhile, a number of meetings and platforms for AI which are worthwhile to attend.

 

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Well, thanks for that. And those that are listening and they wish to connect with you, would they go to Absci.com, is that a good location? That's a good one. Wonderful. Well, thank you so much, Andreas, for sharing this exciting, innovative way to really bring new antibodies and align them with targets and really help bring some new solutions to the community, really appreciate your efforts.

 

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Thank you for all that you do to making our lives better. And thank you for spending the time with me today. Thanks, Katherine, for having the possibility to talk to you. Thank you for listening to the Latest Dose, the podcast that explores the depths of innovation and human compassion in clinical research. Before you go, show us some love by subscribing and make sure to look for us next month.

 

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Goodbye. [Music]

Cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. President Biden has reignited the Cancer Moonshot initiative and set a new national goal: “if we work together, we can cut the death rate from cancer by at least 50% over the next 25 years and improve the experience of people and their families living with and surviving cancer”. “To achieve [the cancer moonshot goals], we must amplify digital innovation,” stated Dr. Catharine Young, Assistant Director of Cancer Moonshot Engagement and Policy, White House Office of Science and Technology.   CancerX, an initiative to rapidly accelerate the pace of cancer innovation in the U.S., will harness the power of innovation to reduce the burden of cancer for all people. Oracle is excited and honored to join Cancer Moonshot's new CancerX public-private partnership.   In this episode Jennifer Goldsack, Chief Executive Officer at Digital Medicine Society (DiMe), Santosh Mohan, Vice President, Digital at Moffitt Cancer Center with Moffitt Cancer Center, and Stephen Konya, Senior Advisor to the Deputy National Coordinator, and Innovation Portfolio Lead for the Office of the National Coordinator for Health IT (ONC) will share more about Cancer Moonshot, CancerX and the importance of digital innovation to achieve the goals.   --------------------------------------------------------   Episode Transcript:

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Hi, everyone, and welcome to the latest dose, the podcast that explores the depth of innovation and human compassion in clinical research. I'm your host, Katherine Vandebelt, global vice president of Clinical Innovation at Oracle Health Sciences. Cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020, President Biden has reignited the Cancer Moonshot and set a new national goal.

 

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If we work together, we can cut the death rate from cancer by at least 50% over the next 25 years and improve the experience of people and their families living with and surviving cancer. In response to the White House Cancer Moonshot, CancerX is formed, an initiative to rapidly accelerate the pace of cancer innovation in the United States.

 

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CancerX will harness the power of innovation to reduce the burden of cancer for all people. Oracle is excited and honored to join Cancer's Moonshot New CancerX Public Private Partnership. Here with me today to share more about these inspirational initiatives, our Jennifer Goldsack, Santosh Mohan, and Stephen Konya. Jennifer, Jen, Goldsack is the CEO of the Digital Medicine Society, also known as DIME.

 

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Jen's research focuses on applied approaches to the safe, effective, and equitable use of digital technologies to improve health, health care and health research. Jen is a member of the roundtable on Genetics and Precision Health at the National Academies of Science, Engineering and Medicine. Jen serves on the World Economic Forum Global Leadership Council on Mental Health. Previously, Jen spent several years developing and implementing projects with Clinical Trials Transformation Initiative, also known as CTTI.

 

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This is a public private partnership co-founded by Duke University and the FDA. Jen conducted research at the hospital of the University of Pennsylvania, helped launch the Value Institute, a pragmatic research and innovation center embedded in the large academic medical center in Delaware. Jen earned her master's degree in chemistry from the University of Oxford, England, her master's in history and sociology of medicine from the University of Pennsylvania and her MBA from George Washington University.

 

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Jen is a retired athlete, formerly a Pan American Games champion, Olympian, and world champion silver medalist. Santosh Mohan, vice president of digital at Moffitt Cancer Center, is also with us today. Santosh brings more than 15 years of digital health and health information technology experience to this role. Previously, he served as the managing director of the Innovation Hub at Brigham and Women's Hospital, where he led digital transformation through the use, development, evaluation and commercialization of digital health applications.

 

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Throughout his career, Santosh has worked to leverage data and analytics to create and design new programs and digital abilities, with a strong focus on emerging technology to advance care and improve the clinician and patient experience. Santosh holds a master’s degree in clinical informatics from Duke University’s Fuqua School of Business and a bachelor’s degree in bioinformatics from Vellore Institute of Technology in India.

 

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Santosh is a certified professional in healthcare information and Management Systems, a member of American Medical Informatics Association, a senior member and fellow of the Healthcare Information and Management Systems Society, known also as HIMMS. You will also hear from Stephen Konya, the senior advisor to the Deputy National Coordinator and the Innovation Portfolio Lead for the Office of the National Coordinator for Health I.T., also known as ONC, which is part of the U.S. Department of Health and Human Services, HHS.

 

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Stephen is shaping the agency's long term strategy. The primary liaison to the White House Office of Science and Technology Policy. The primary liaison to the external health care startup and investor community. Stephen leads the Digital Health Innovation Workgroup under the Federal Health I.T. Coordinating Council, an interagency collaboration community comprised of innovation representatives from 40 other federal agencies. Previously, Stephen has led several key ONC projects, including the HHS Pandemic X Innovation Accelerator, the National Health I.T. Playbook, the Agency Patient Engagement Playbook for Providers, the Smart App Gallery, the FHIR at Scale Task Force, also known as FAST, and is a founding co-chair of the Together.Health Collaborative Effort.  Prior to  his position with the federal government,

 

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Stephen served the state of Illinois in a variety of key positions and diverse responsibilities. Stephen holds a BBA in finance and international business from Loyola, University of Chicago, is fellow and mentor of the Mid-American Regional Public Health Leadership Institute Program at the University of Illinois-Chicago School of Public Health. Welcome, Jen, Santosh and Stephen to the Latest Dose and thank you so much for making time to speak with me today.

 

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When I hear the word cancer, it elicits fear and anxiety, at least in me. So, researching the cancer trends does not provide me with much comfort. According to the World Health Organization, cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. Or stated another way, nearly one in six deaths. It appears that the medical community's understanding of cancer is growing,

 

00;06;01;06 - 00;06;27;21

yet the death rate remains so high. What do we need to do differently? Thanks, Katherine. Cancer is out every day at Moffitt. We come face to face with this terrible, very difficult disease. Every single day. But we also see the courage of our patients fighting it. And that really inspires us to bring hope to every patient we serve and deliver to them some of the best outcomes.

 

00;06;27;22 - 00;06;52;26

Up to four times the national average. Now, cancer deaths in the US are actually falling, but they're not falling fast enough so the death rate needs to decline by a more rapid percentage to reach the Moonshot goal of reducing cancer deaths by 50% in the next 25 years.  It's very clear that we need a multifaceted approach to tackle this complex issue.

 

00;06;52;29 - 00;07;21;18

First and foremost, prevention and early detection must be at the forefront, emphasizing lifestyle and behavior changes, like adopting a healthy diet, regular exercise, smoking cessation. All of these can significantly reduce cancer risks. Equally important is promoting awareness about the importance of regular screenings and recognizing early signs and symptoms. And we know that screening rates have declined for all cancers since the pandemic started.

 

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So, we will likely soon start seeing cancers presenting at more advanced stages requiring longer and more complex treatment, as well as decreasing positive outcomes. This means that we need to move engagement upstream and increase those screening rates. And this is where digital channels can help. We've been at the forefront of prevention and screening for years now, and really the reignited

 

00;07;48;28 - 00;08;13;16

Moonshot has been an opportunity for us to accelerate these efforts around re-energizing the community to prioritize cancer screenings. Early interventions can make a world of difference, but prevention and early detection are just the beginning, and they require a lot of behavioral change within our society. And while we advance that, we should also recognize that cancer will continue to occur.

 

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So, we need to change the trajectory of cancer mortality, not just the incidence with therapeutic advancements, including immunotherapy and especially CAR-T. And therefore, we need to continue investing in cancer research and innovation. And collaboration is really key in this space. Collaboration among academia, with the industry, research institutions and entrepreneurs, it's really vital to expedite progress in this space.

 

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But progress also means nothing if it is not accessible to everyone. And so, ensuring affordable and accessible cancer care is a must. So again, this is another space where organizations must work together to bridge that gap and provide quality care to all individuals regardless of their socioeconomic background. I feel we also need to take a very patient centered approach, that is crucial.

 

00;09;11;13 - 00;09;36;20

Cancer care should encompass more than just medical treatment; which is supported emotionally, it should provide symptom management and certainly address financial toxicity. So, these are all very, very important things that we need to do, to decline, to help that that decline faster, at a faster pace. And we really do need a collaborative effort from everyone.

 

00;09;36;23 - 00;10;06;05

Beating cancer truly demands delivery and collaboration, but also bold innovation. And this is where I feel CancerX is creating a dynamic ecosystem where people can come together, organizations can come together, ideas can flourish, expertise and resources can be shared, and innovative solutions can rapidly be developed and equitably deployed to really prevent and cure cancer in this fight.

 

00;10;06;07 - 00;10;30;02

Well, thank you for sharing all of that. That's extremely motivating. You mentioned the Moonshot and so I believe the White House Cancer Moonshot has been reignited. So, what I know, you mentioned a couple of things that the Moonshot focusing on, but is it possible that you could actually walk us through what the mission is currently or what it is globally around all of that?

 

00;10;30;02 - 00;11;02;14

So, Stephen, is that something you can talk us through? Yes, Katherine, thank you so much for the question because it is important to know how we got to where we are today. So, you know, it was actually in 2006 when the Cancer Moonshot was first launched. And like any Moonshot, you know, dating back to President Kennedy's Moonshot, is all about really how do we refocus everyone's attention, prioritize our resources, and like Santosh mentioned, collaborate together to try to tackle this massive challenge that we're still faced with today.

 

00;11;02;16 - 00;11;28;11

So, in 2016, when they launched the Cancer Moonshot, then Vice President Biden was put in charge of it. And it was a very personal story to him and something that he was very passionate about. Fast forward to now President Joseph Biden. The president has now reignited that Cancer Moonshot to build upon what was originally launched in 2015. The idea is really around two things at this point.

 

00;11;28;13 - 00;11;53;17

Number one, as Santosh just mentioned, we need to reduce the death rate for cancer by 50% in the next 25 years. That is the huge Herculean task that we have before us. And while, though it seems like it's a very difficult task, I think it is very realistic. As Santosh mentioned,  the current death rate is going in the right direction at a rate about 2.3%.

 

00;11;53;19 - 00;12;26;20

But we need to drop that to 2.7. Recently, the National Cancer Institute released a study in a report that said we could accelerate that death rate to dropping it at 2.7% in order to achieve that target of a 50% reduction in death rates by in 25 years. So that's the number one thing we're focused on. And that's going to take everybody working together, not just, you know, industry, who's already begun to answer the call of the Moonshot organizations like Moffitt and DIME working Together and many others throughout the country all collaborating.

 

00;12;26;23 - 00;12;48;17

And then on the government side, you know, we're not alone. We need or we need to also work together across federal agencies and do it in partnership with organizations like Moffitt and DIME and others. And that's really where CancerX is just becoming a vehicle for that collaboration. We will get into that a little bit. The amount of response that we've seen so far has been amazing.

 

00;12;48;19 - 00;13;21;20

But, but, again, it's really about reducing that death rate. The second key area of the Moonshot that we're also focused on, that the president has mentioned as part of this reignited Moonshot is all around how can we help patients and their families and their caregivers navigate the complexities and the challenges of a cancer diagnosis, and going through that treatment and essentially, you know, dealing with all the challenges that come along with that, including things like financial toxicity, what they believe Jen's going to cover in a little bit here.

 

00;13;21;23 - 00;13;46;07

But, but the idea that it's not just about developing new drugs or new diagnostics, but really, we also need digital tools and other solutions that can help manage the complexities of cancer care and helping those families navigate and go through that with at least disruption as possible. We know that often the concerns around the complexity of the care and the cost of the care can lead to people avoiding getting screened.

 

00;13;46;14 - 00;14;13;28

They don't want to know if they have something because they're afraid of getting that bad outcome of hearing that they do have cancer because now they've got to deal with it financially or emotionally or other things. So, if we can figure out a best way to make that more, more friendly, and easier to try to minimize the amount of adverse impact it has and to try to reduce some of that fear and anxiety around what it's like to go through a diagnosis of treatment of cancer.

 

00;14;14;00 - 00;14;47;11

I think that could also severely help us in getting, you know, encouraging more patients to do go get screened early and then to help them navigate that. Well, those are very lofty and inspiring goals to decrease the death rate and to also make it significantly easier for people to move through any diagnosis, whether it's cancer or others, it's very stressful, very daunting, and takes usually a community to deal with that.

 

00;14;47;11 - 00;15;12;10

So that is awesome! And thank you for your leadership and thank you for your commitment to bring these changes to the United States. So, when I was reading about the Moonshot and when I was reading about you mentioned CancerX Stephan, Dr. Katherine Young, I believe she's the assistant director of Cancer Moonshot Engagement and policy, and she works at the White House Office of Science and Technology Policy.

 

00;15;12;13 - 00;15;38;05

She shared in March this year, so 2023, “ To achieve the Cancer Moonshot goals, we must amplify digital innovation, which is the mission of our newly formed CancerX”. So, you mentioned that Jen can talk a bit about CancerX.  How did CancerX come about? And will you share more about the mission of CancerX?

 

00;15;38;08 - 00;16;02;26

Yeah. Happy to, Katherine. The CancerX was announced by the White House on the one year anniversary of the reignited Moonshot. So, February 2023, was that one year anniversary and it was announced as a public private partnership with the goal of harnessing the power of innovation to support and drive towards the achievement of the reignited Cancer Moonshot goals.

 

00;16;02;29 - 00;16;41;08

So, February was that announcement. We are very proud between the Digital Medicine Society and Moffitt Cancer Center to have a history of pre-competitive, multi-stakeholder set of research and implementation at the intersection of innovation and oncology. And so, we were privileged to have the opportunity to host CancerX alongside our colleagues at the Federal Government with Stephen Konya as lead from ONC, but also from the office of the Assistant Secretary for Health and the White House of course.

 

00;16;41;09 - 00;17;14;03

The mission is we have come together, to state it is, to unite to diverse and inclusive community of stakeholders, to rapidly develop and equitably deploy innovative solutions that can prevent and cure cancer. That mission is wholly in support of the Moonshot goals that Santosh and Stephen describe so well. And we are incredibly proud to have just announced over 90 members, including Oracle, Katherine, who has come to the table, raised their hand, and said they want to work with us.

 

00;17;14;03 - 00;17;52;15

They want to join the charge to harness the power of innovation, to reduce the burden of cancer for all people. So that's a little bit about the history and also our exceptional partners who share this vision. So, wow, 90 members already! That is fantastic!  That's I think it reflects the decision making from our colleagues at the White House to structure CancerX as a public private partnership, bringing the very best of government and expertise and capacity together with cutting edge research and sort of clinical knowledge from the private sector.

 

00;17;52;17 - 00;18;13;05

This is how we are going to achieve the goals of the Moonshot. This is how we are going to harness the power of innovation. And I think it speaks to the industry's commitment as well as government's commitment, that we have this kind of engagement right out of the gates. So, I read that CancerX is using a three pronged approach to generate this impact.

 

00;18;13;08 - 00;18;40;09

So, is there important scope of these three prongs that are important for people to understand? And what will this mean for physicians? And you've already talked very clearly about the importance of patients. So, what does this mean for physicians and patients? Maybe I'll give a quick overview and then ask Stephen to talk more about the vision for the accelerator and Santosh to pick up the implications for sort of clinicians as these are his partners every day over at Moffitt.

 

00;18;40;11 - 00;19;07;25

So, you're exactly right, Katherine. When we when we were thinking about as a team how we would structure CancerX, we wanted to do several things. One, we wanted to create a structure that provided a truly big tent environment for stakeholders from across industry, academia, and government to come together and contribute their knowledge and expertise in an optimized way.

 

00;19;07;28 - 00;19;39;20

We also wanted to make sure that we were structured to be as productive as possible.  Harnessing the power of innovation to improve the lives of people with cancer and the goals of the Moonshots mean that we have to make sure that all of our efforts are optimized. So essentially, we set up this CancerX to be run by the community with the 91 members that we were just discussing, really helping drive the strategic direction and the tangible activities almost operate as a flywheel.

 

00;19;39;21 - 00;20;03;00

So ,you can imagine this evidence generation engine where we are conducting pre-competitive research around topics that are defined by the CancerX community to make sure that we can actually articulate and demonstrate what good looks like as we innovate in cancer prevention and research. Then we think about this as we think about defining what good looks like.

 

00;20;03;00 - 00;20;25;19

We also have to make sure that there are the skills and capacity in the market to actually be able to deliver on that. That's the purpose of the rolling series of accelerator cohorts. And so, Stephen will talk more about that. And then finally, it's one thing to come up with, look here, the methodological best practices, here’s what good implementation looks like, here's an evaluation framework.

 

00;20;25;21 - 00;20;45;17

It's great to make sure the industry is prepared, but we know to actually drive definitive change, we also have to show not just tell, but innovation works. And that's the logic behind the demonstration projects that Santosh will cover. So, Stephen, do you want to jump in and talk a little bit more about the vision for the accelerators? Yes, thank you.

 

00;20;45;17 - 00;21;10;02

Jennifer. Absolutely. So once, you know, just as Jennifer mentioned, once we have a good idea of what good looks like and what we need to do as far as what are the biggest challenges that need to be tackled based on those pre-competitive evidence generation projects. We then need to provide some sort of a vehicle to help entrepreneurs and innovators work together and develop these solutions to meet that need.

 

00;21;10;05 - 00;21;28;02

And so, if you're familiar with the accelerator model, whether it's one that's led by non-for-profit or one that's associated with a corporate entity like a payer or provider or one that's led by the government; you know, this actually isn't our first time we've done a public private partnership in the past, in fact, HHS has led several of them.

 

00;21;28;04 - 00;21;54;23

So, we're really trying to build on what we've learned in the past with KidneyX, one of our first significant national public private partnership accelerators. And then after that, we launched LimeX, followed by PandemicX, which is a one year accelerator. The other two are still going. And then now, now we're on to CancerX. So, we've seen kind of what works well and what doesn't work well.

 

00;21;54;25 - 00;22;22;11

Generally speaking, when it comes to setting up a public private partnership and running an accelerator, we also know that there's a lot of lessons to be learned from external accelerators, ones that are led by some of those other organizations I mentioned, whether they be an independent non-for-profit arm or a for profit accelerator or even some that are associated with providers or payers or others, and we're taking the best ideas from all of those and putting together to make something that's truly unique for CancerX.

 

00;22;22;11 - 00;22;44;19

If you've seen one accelerator, you've seen one. No two are like, they all have some common methodologies as far as how they run it. There's some common core elements. You need to have a strong mentorship core. You need to have some strong curriculum that's built into it. You need to have a clear call to action and an identification of what challenges you're trying to tackle through that accelerator.

 

00;22;44;19 - 00;23;24;27

So, what types of solutions are you sourcing for in order to get into such a cohort? And just like Jennifer said, you know, this is going to be a truly inclusive, open, big tent type of opportunities. So, we're doing something not only in a way that's creating an opportunity for anybody to apply to be part of the accelerator, but also trying to figure out how can we make it accessible in some ways to those who don't get into the accelerator.  How can we help provide greater clarity around the entire ecosystem for investors, for entrepreneurs, for incubators, for the nations accelerators that want to help support this as well.

 

00;23;24;29 - 00;23;46;13

How can we create the platform for all these organizations to collaborate with each other through this effort and to have a good sense of who's doing what, where. How can we make it easier to identify what solutions in entrepreneurs are, in startups already out there building something just to be able to source them, even if they're not the focus of what we are currently sourcing for.

 

00;23;46;16 - 00;24;11;03

We still want to use this as a public utility to make it easier for, say, one cancer center to find a solution, even if that's not something every other cancer center is looking for. So again, it's really being driven by what everybody identifies as the greatest area of need as far as what we're going to source for and then having the most open, collaborative type of environment for actually running the accelerator itself.

 

00;24;11;06 - 00;24;41;16

And then what we get out of that accelerator hopefully is a more mature, well-tuned, you know, refined solution to meet the need with access and relationships to potential customers and investors to accelerate the pace of bringing that innovation to market. And that's something that's unique about CancerX when it comes to other initiatives underneath the Cancer Moonshot. You know, if you think about it, just to step back a sense, you know, the Cancer Moonshot is really a call to action, kind of the big picture vision.

 

00;24;41;18 - 00;25;06;13

The National Cancer Plan, which was just recently released as well by NCI, is really kind of the strategy of how to achieve that, that big call to action. And I encourage you to look at the eight categories and goals under that national cancer plan. I mean, CancerX is one of the just one of many initiatives that is underneath that that can serve as a vehicle for how do we actually take action and get things done.

 

00;25;06;16 - 00;25;44;12

And unique to CancerX is all about how do we provide a platform again for startups, entrepreneurs, innovators, accelerators, bootcamps, you know, investors, how do we provide a forum for them to come together and to collaborate? Not just the research community and others, but really help them come together to accelerate that pace of innovation getting to market.  Because it's, you know, we have a lot of great innovations that are being developed throughout the US and the world to deal with the oncology field on diagnostics, therapies, treatments, and there are solutions out there to help on the administrative side of managing care.

 

00;25;44;15 - 00;26;17;13

But what we've heard collectively from the cancer centers like Moffitt, and maybe it's interesting to expand upon this, is that in many cases there are ones that aren't tailored enough to their needs and often don't deal with the complexities of cancer care specifically, and they need more of those solutions. S, it's very powerful when you have all these cancer centers working together to say and other institutions and patient navigating organizations and others to community based organizations, all saying with one voice, this is what we need to source.

 

00;26;17;15 - 00;26;35;04

And then that sends a signal for entrepreneurs to either pivot or to help raise their hand and say, I've got a solution to that. And then the accelerator helps really vet that opportunity and see like, how real is it and what other support do they need in order to bring it to market at a faster pace than that.

 

00;26;35;04 - 00;26;54;26

Otherwise, it's not good enough to have those ideas hopefully make it to market in 5 to 10 years in widespread use. We need to try to accelerate that to being in the market in two years or three years. That way we can really start to make an impact and a dent on that cancer death rate at a much faster clip.

 

00;26;54;29 - 00;27;20;28

That was great Stephen. And I'll add that this flywheel from what Jen described with regard to the evidence generation, what Stephen described as the accelerator sort of feeds into them the demonstration projects. We strongly believe that the value that Moffitt and other providers add to this ecosystem, to this flywheel, is really our day to day clinical expertise.

 

00;27;21;04 - 00;28;06;07

And our connection to actual clinical care and operations, as well as our familiarity with the business side of care delivery. So CancerX will be opening up this silo of care delivery experts from us, from member sites to the rest of the ecosystem so that we can bring our expertise to the table and bring expertise our other esteemed members and figured out answers to important nitty gritty questions and workflow considerations, not only to encourage, but more importantly, to bring real solutions to life and demonstrate that they can actually work.

 

00;28;06;09 - 00;28;33;04

And that's really the beauty of the last piece of demonstration projects so that we show and not just tell or talk about how we are doing this. Well, that's fantastic! And thank you for sharing all that. It's very clear and I've heard everyone sort of mention the importance of digital innovation and leveraging new digital innovation, as I know, and I'm sure all of you know, is a challenge.

 

00;28;33;06 - 00;29;04;16

And throughout my pharmaceutical career I have experienced that the challenge and the benefits of embracing new ways of working exist. So, what are the current enablers and blockers to achieve the CancerX mission or what comes out of the accelerator cohorts or what comes out of the pre competitive work? So, Santosh, can you talk a little bit about that since you'd have to implement it in your company and your industry and with your colleagues?

 

00;29;04;18 - 00;29;56;20

So, at Moffitt we are always thinking about reimagine changing how we tackle cancer by pairing our expert care with innovative technology. And we think oncology sits squarely at the nexus of technology and humanities. And we see two realities that are world. Reality one is where oncology is leading innovation in research and in care. Because if you think about digital pathology, digital radiology, next gen sequencing, patient reported outcomes, precision treatment approaches, especially those driven by complex data and biomarkers, these are all hallmarks of oncology, but these advances are not the standard of care for everyone.

 

00;29;56;22 - 00;30;29;00

So, there's a lot of work to do in advancing the adoption of them. And then there's the other reality where oncology is still playing catch up with digital transformation. Digital innovation is lagging in oncology relative to other therapeutic areas and digital solutions that work very well in other parts of healthcare don't translate so easily to our space. And what would be one appointment in any other health setting is a dozen or a half a dozen appointments in our world on the day of treatment.

 

00;30;29;03 - 00;30;53;05

And this makes it really hard for someone to just flip open their phone and be able to book an appointment online. It's complex. It's complex. These are complex challenges. So now more than ever, oncology needs innovators that want to make a meaningful difference.

 

00;30;53;05 - 00;31;28;12

And there are so many opportunities for innovative action. Cancer diagnosis must be early and accurate, which means we need consistent improvement of new diagnostics technologies. Treatment is highly complex, highly variable, which means new technology innovations must also simultaneously simplify the process. They must break away the administrative work from the providers and aid the patients, because throughout this process, which is often traumatic for patients, we also must be able to help care staff better support the recovery.

 

00;31;28;15 - 00;32;02;11

So, there's so many ways in which digital innovation can actually make a difference. And I think CancerX is really creating that ecosystem and the power to bring together a diverse and inclusive community of pioneers, all shared, all driven by the shared commitment to advance the goals of the Moonshot. So, a lot of work ahead of us, especially to harness the power of digital innovation to push the boundaries of cancer care.

 

00;32;02;14 - 00;32;22;18

Just wanted to add to that from the government side. You know, when we think of historically, what are some of the major blockers for any, any innovation, you know, area in health care, it's typically been around the challenges of interoperability. You know, you have great digital solutions and tools that are being built, whether they're on the clinical side or whether they're on the administrative side.

 

00;32;22;18 - 00;32;57;19

And either they can't get access to the data they need to prove that their tools work, especially SOGI data and REL data and other things like that that help us be able to analyze its impact on different patient populations to make sure that it has an equitable outcome when it's deployed in different scenarios. A well as actually being able to have this, these tools get implemented and connect the systems to both, you know, receive data that it needs to continue operating as well as feeding that data back into the systems.

 

00;32;57;22 - 00;33;22;09

And that's been a challenge for years for any area of health care that you can think of. And oncology is no different in that. But on the enabler side, one of the things that we're excited about at HHS is the progress we made over the past several years on implementing the 21st Century Cures Act. And so again, this gets back to why is this now the right time to have the reignited Cancer Moonshot really take off with something like CancerX

 

00;33;22;09 - 00;33;39;14

Well, frankly, a part of it is because of the fact that it's going to be much easier for the data to flow in the directions that it needs to because of the 21st Century Cures Act. We've got one major provision at LONC  that we're responsible for implementing that will take place at the end of this calendar year.

 

00;33;39;16 - 00;34;10;12

But otherwise, we've done a lot of work to both standardize the data and better formats through USCDI, the US coordinator for interoperability to make that data, you know, at a base level available for all these different scenarios. And we're also looking at expanding into areas where what we call USCDI plus, it's been announced that we're also focused on doing that specifically for cancer to make sure that that we can, you know, have the right types of data standards in place to support these innovative solutions and so on.

 

00;34;10;14 - 00;34;40;26

So, we're really at this critical juncture now, this pivot point. And when it comes to interoperability, to the access to the data and the ability to share the data in the ways that these innovators need it in order to be effective and in order to ensure that they have equitable health care outcomes and experiences and access to care and cost of care and all those other things that are important to helping families navigate care.  We really believe we're providing that foundation as a nation when it comes to access to all of that data, because it's not just been ONC alone.

 

00;34;40;29 - 00;35;01;26

The C stands for Coordinator, and we've been working very closely with our colleagues at the FDA, at the CMS and many other agencies with the Federal Health Coordinating Council to ensure that we're advancing interoperability. And again, we're at a place in time in history where those challenges, those blockers in the past aren't going to be quite what they used to be and

 

00;35;01;26 - 00;35;28;17

I'm really excited about enabling a whole new era of innovation based on that advanced interoperability. Yhat is extremely motivating. And it's great to hear that things are coming together that allow us to be very successful in achieving the mission and the goals. One of the phrases that I hear from everybody and most recently from Santosh and yourself, Stephen, is equity and another phrase is all Americans

 

00;35;28;17 - 00;36;04;20

So when I think about, you know, we are focusing on these types of goals, a quote came to mind from the White House Cancer Moonshot coordinator Dr. Danielle Carnival and she said that “President Biden's vision for ending cancer as we know it is building on the progress we've made with an all hands on deck effort to develop new ways to prevent, detect and treat cancer and ensure that the tools we have and those we develop along the way reach all Americans.”

 

00;36;04;20 - 00;36;39;23

So, I'm going to focus on the word all. So, I'm trying to reconcile it with what I read and learn about health disparities, which you guys have already mentioned. So how me we understand, how are we going to ensure the tools reach all Americans? It's such a great question, and I'm delighted, Katherine, that you honed in on the necessity of making sure that all of these innovations are accessible to effective end and built for all Americans.

 

00;36;39;23 - 00;37;07;09

And I think, I think there are two interesting things to discuss here. Firstly, the digitization of health care, public health, clinical research provides a once in a lifetime opportunity for us to redefine what it means to care for people in the digital era and insist that every solution and every innovation that we advance works equally well for all individuals.

 

00;37;07;11 - 00;37;40;24

So it is in fact, you mentioned very correctly, the challenges we face with health equity and health disparities, a lack of inclusion in the health care and research in public health spaces, currently.   What we are laser focused on at CancerX and with our 90 plus partners is at every juncture are we advancing practices and science and approaches that work equally well for every American?

 

00;37;40;25 - 00;38;11;28

Are we thinking about insisting as table stakes that considerations related to equity and inclusion are baked into the development of these new solutions, new approaches, new incentive structures that will fundamentally change care? And that is a theme that will be woven throughout all of our work and all of our decision making in CancerX, as it should be for any innovator in the health care environment today.

 

00;38;12 – 00;39;08

And we can point at a very tangible example too. So, we discussed the flywheel, we discussed the structure of CancerX. We begin with evidence generation engine, then we have the accelerator, rolling series of accelerators, then we have our demonstration projects to show not just tell these new approaches work.  But it is not my accident that the first project we have on deck is focused on advancing digital innovation in cancer care and research, to improve equity and reduce financial toxicity. For us there was never a question that this will be our primary area of focus when we launched CancerX and the findings from this inaugural project will be translated, embraced, and adopted across the portfolio. Equity and inclusion must be the cornerstone of all the work that we do to innovate in the way we care for people with cancer.

 

00;38;09 – 00;39;38

Jennifer is 100% accurate and corrected in what stated there. And thank you for that Jennifer and  I am glad that we have partners like you and Moffitt to work with, it is so critical. Because as President Biden stated, we do need to have a greater focus on equity for all, especially in the area healthcare because we know if they look at the statistics, it is not equitable as far as the access to care, the cost care, and the outcomes of the care, often aren’t equitable across different populations.

 

00;39;39 - 00;40;03

And so although you hear this come down from the top of the administration into every agency that is focused on equity, one thing that my boss, here at ONC, the National Coordinator himself, Micky Tripathi, since day one of his appointment when he came to the agency, he made it very clear to everybody at ONC that he was going to focused on and once of his significant legacies at ONC was going to be having us focus on health equity by design.

 

00;40;04 – 00;40;40

And when I say by design, it is really about how do you factor in health equity and the considerations of equity into everything we do.  Whether it is designing a policy, a program, executing that program, thinking of the impact of the stakeholders of that program, how the money is spent, etc.. You are always thinking about is this going to have an equitable impact. And not only how this can have a positive impact on improving equity but also is there a risk at this having some adverse impact on equity some unintended consequences that we didn’t mean to have that can actually set up backwards when it comes to equity.

 

00;40;41 – 00;41;25

And so we been very intentional on that ever since Mickey has taken over the agency, in fact one of the initiatives I mentioned earlier when we launched the PandemicX initiative accelerator which again is the one year accelerator based on funding we had through the Cares Act during the pandemic We made sure we actually took what Micky said to heart and thought through while designing the accelerator, how can we factor in equity into this.  And we did it in a number of ways.  One example was we made sure that we had all the right people at the table and we are doing that now with CancerX as well. You see the representatives in the announcement of the inaugural membership of the 80 to 90 organizations.  

 

00;41;26 - 00;41;51

You are going to see every different type of organization you can think of, from biopharma to hospitals to investors to government, non-for-profit, to community-based care centers, patient navigators, patient advocacy organizations, patient themselves.  The idea is that we have a seat at the table for everybody and that ensures that we have an equal voice that can help us identify when we’re getting off course or off path or not taking certain groups into consideration.

 

00;41;52 - 00;42;19

That is number 1 and number 2 is we make that we actually factor in antibias training into the selection of what companies go into the accelerator and that is something we are planning to replicate through this accelerator as well. How can we make sure that those who are scoring and deciding which companies get in the accelerator itself are not letting bias creep in and making sure that again we’re giving all organization a fair shake at being part of this.

 

00;42;20 - 00;42;52

Another factor was during PandemicX, we asked companies that were applying to answer simple question in their application which was how are you designing your solution to reach communities that historically don’t have solutions built for them and to take their own cultural needs and challenges into consideration. So how are they designing them to be impactful in all communities not just one demographic in their backyard that may be easy to access to test it on. 

 

00;42;53 - 00;43;13

So, and are they being inclusive in the design of that solution.  So, these are some of the small ways we can nudge the industry into being more equitable.  But specifically, for CancerX and its accelerator there are intentional ways we can make sure that not only are we doing are part but also encouraging others who are participating in the effort to do their part in ensuring they are focused on equity.

 

00;43;14 - 00;44;17

Thanks Stephen, that was beautifully said and I will add, Katherine, that as we look ahead at the work that we have to accomplish with CancerX we’ll also be focusing on how to harness digital channels and digital innovation to help extend the best specialist care and clinical trial opportunities to all cancer patients, regardless of their place. Especially in ways that can reduce the cost of travel to specialist treatment centers and trial sites and really thinking about reducing the disparities in access to that best possible treatment no matter what their zip code is and what their income levels are.  So, these are important ways in which digital can be an enabler and so we are going to explore it through that lens even more critically as we make progress through CancerX.

 

00;44;18 - 00;44;46

But also making sure we do not create a digital divide.  This has been fantastic and our time has gone so quickly and I imagine minds thinking about how I can be involved, what can I do I am sure a lot, or I believe in a lot of our listeners it’s going to spark some thoughts and considerations, and so, I am going to end us with sort of three quick questions. And I will start with Stephen, so, how can people learn more about Cancer Moonshot where should they go?

 

00;44;47 - 00;45;43

For the Cancer Moonshot you can certainly go just google White House Cancer Moonshot if you like there is a primary landing page there that explains the background, everything we talked about earlier, it also has great examples of where the private sector and others outside of government have stepped up to start to answer the call of the Cancer Moonshot. In fact there is even a place where patient, caregivers, providers, anybody who is doing any work in the cancer space or dealing with a cancer diagnosis in their family or themselves can actually submit their stories of the challenges, testimonials of their experiences, of their hope, their progress, And so we certainly encourage because we know this is very personal to everybody and it is hard to be in any room and not find someone who has had their lives or families’ lives impacted by cancer. So, We want to hear more stories as that helps us again make sure that we take everybody’s needs into consideration

 

00;45;44 - 00;46;28

And when it comes to CancerX specifically, it is easy we have a website CancerX.health and all the information on CancerX including an option for how to submit your interest to join and be part of it and basically it asked you a few questions on is there any type of resource that you would like to contribute to be part of CancerX. Whether that is data, time, money, etc., there is an interest form you can fill out and I know the DIME team, Jennifer and her team have been amazing at following up nearly a 1000 plus organizations who has filled out this form already, it has really truly been an amazing response that we have seen.

 

00;46;29 - 00;46;48

CancerX.health is the main website for CancerX itself.  Thank you and I would love to hear from Santosh and Jennifer, can anyone participate so we heard that everyone having a seat at the table we heard about non-profits and organizations in health but can anyone participate.  So, I will start with you Jennifer and Santosh, you can finish us off. 

 

00;46;49 - 00;47;55

Anyone who share our passion for the goals of Cancer Moonshot and is working in either oncology  or in innovation is more than welcome to come to the table. Kathy it is important to note the way CancerX is structured  currently we are convening at the organizational level as opposed to the individual level that relates to both to project participating and CancerX membership.  however, as the program continues to evolve and we continue to introduce our accelerator cohort and that programming as we move toward demonstration projects there will be opportunities for individuals down the line.  However, currently we are keen to welcome any organization who shares as I said shares our passion, dedication, and commitment to the mission.  It is an extraordinary and exceptional group of leaders that have put their hands up as part of the founding members cohort pulled their chair to the table saying we want to contribute our knowledge, our expertise and collectively work towards the lofty goal of reducing the number of deaths of cancer by 50%.

 

00;47;56 - 00;48;13

Just as Stephen has said anyone who is interested can jump on the CancerX website at CancerX.health sign up for more information about membership and subscribe to the newsletter so that they get first-hand knowledge and updates about where we are in pursuit of our goals.

 

00;48;14 - 00;49;20

Very nicely said Jennifer and really our vision always, all along has been in that beating cancer requires collaboration as we shared earlier so we need everyone to join us as Jennifer said we are open all like-minded innovators.   All of us have been touched by some form of this disease in some way and it continues to take form us and we have to stop that and we can do that together. So CancerX is for doctors, developers, designers, entrepreneurs, for the scientific community, certainly for the industry innovators, investors. We are fortunate to have government support and encouragement for this initiative. So, it is all about coming together so that we can bring more limitless creativity more entrepreneurial spirit so that we have more opportunity to push the boundaries and really turn some of these aspirations into action and end cancer as we know it.

 

00;49;21 - O0;49;42

Thank you, Santosh, thank you, Stephen, thank you, Jennifer for joining us today. Thank you for sharing this great work that is underway associated with Cancer Moonshot, CancerX and providing our listeners with information about how to learn more, how to get involved.  I greatly appreciate you taking the time and I wish you a great day.

 

O0;49;43 – 00;50;11

Thank you.  Thank you it has been fun.  Thank you for listening to the Latest Dose, the podcast that explores the depths of innovation and human compassion in clinical research. Before you go, show us some love by subscribing and make sure to look for us next month. Goodbye. [Music]

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