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Watch again: The future of data and AI in Australian healthcare

UOW researchers Stacy Carter, Lisa Smithers, Alberto Nettel-Aguirre and Yves Saint James Aquino discuss how the datafication of our health, and artificial intelligence systems, could change health services in Australia.

David Currow: Good afternoon. I'm David Currow, Deputy Vice Chancellor and Vice President of Research and Sustainable Futures at the University of Wollongong. It's a pleasure to welcome each and every one of you here today to our Luminaries webinar series. Luminaries brings together leading University of Wollongong researchers, industry experts and thought leaders for a one hour conversation every fortnight. We will discover how research and collaboration at the University of Wollongong is tackling global challenges.

Today we're hearing from a group of exceptional researchers as they discuss the role of data and artificial intelligence in Australian healthcare. But before we start, I would like to acknowledge Country.

On behalf of the university,  I would like to acknowledge that Country for Aboriginal peoples is an interconnected set of ancient and sophisticated relationships. The University of Wollongong spreads across many interrelated Aboriginal countries that are bound by the sacred landscape and intimate relationship with that landscape. Since creation from Sydney to the Southern Highlands to the South Coast, from fresh water to bitter water to salt, from city to urban to rural, the University of Wollongong acknowledges the custodianship of the Aboriginal peoples of this place and space that has kept alive the relationships between all living things. The university acknowledges the devastating impact of colonisation on our campuses, footprint and commit ourselves to truth telling, healing and education.

Data are now at the center of human lives. Artificial intelligence built on healthcare data promises a new and transformative kind of health technology. However, this raises big questions. The promised benefits of big data and artificial intelligence be reached and then delivered. And if they are, what are the ethical and social implications of sharing and using big data and employing artificial intelligence based technologies in health decision making? It's my pleasure to introduce you to our researchers today.

Professor Lisa Smithers is an epidemiologist whose research mostly encompasses perinatal and pediatric epidemiology. Much of Lisa's research involves the use of population based datasets and data from large cohort studies. However, she also conducts clinical trials in hospital and community settings. Lisa has a special interest in the application of methods to improve causal inference from observational studies.

Professor Alberto Nettel- Aguirre has developed his career working collaboratively in health and medical research and has worked extensively as a biostatistician in a range of projects, namely around pediatric nephrology, neonatology and injury prevention. His expertise and interests cover the correct application of biostatistics and the implementation of statistical learning methods in health and social research. Alberto is one of the University of Wollongong's representatives at the Australian Data Science Network and leads the Center for Health and Social Analytics within the National Institute for Applied Statistics Research Australia at the University of Wollongong.

Professor Stacy Carter is the founding director of the Australian Center for Health Engagement, Evidence and Values in the School of Health and Society at the University of Wollongong. Her training is also in public health and her expertise is in applied ethics and social research methods. Stacy's research program addresses the ethical and social dimensions of four key challenges for health systems using artificial intelligence, detecting disease in populations and individuals, reducing harm and waste, and encouraging vaccination.

Dr. Yves Saint James Aquino is a philosopher and physician with expertise in philosophy and ethics in health care. He is a post-doctoral researcher, research fellow at the Australian Center for Health Engagement, Evidence and Values. His research projects include the ethics of artificial intelligence, the ethics of cosmetic surgery, body image research and social justice in public health.

Before we begin, I encourage everyone online today to submit their own questions using the Q&A function. We will try to get through as many questions as possible.

However, to kick things off, I'd like to ask the panel about data and artificial intelligence in Australian healthcare. Where are we today and where could we be in the next ten years? I'd like to kick off with some thoughts.

Alberto Nettel-Aguirre: Well, everybody knows I never keep my mouth shut, so I'll just start. Why not? You know, I think it is very important. There's several things that we need to be thinking of. What we understand by what people think is a AI in health care. You know, things like dictation or voice recognition on medical note is already AI, CAT scans all of these things that we use, the tools, are they AI when it comes to using it as a tool for diagnosis or for actual treatment? I think that's the big difference on what the expectations of people are. So where are we? I think we're still at a baby steps in a way. Where could we be in ten years? It will depend a lot on what people decide to do regarding data, data quality gaps, collection, etc.. That's my first go at it.

David: Fantastic. Thank you Alberto.

Stacy Carter: It's interesting you say that. I love it when when I think about this, I actually think back to five or ten years ago. And for those of us who've been thinking about artificial intelligence and healthcare for a while, there was a lot of hubris around five or ten years ago.

You know, there were there were big tech companies saying that they were going to build learning health systems that could absorb all of the data from a hospital and could do almost everything. There were leading developers who were saying radiologists would be extinct in five years and and we are training them now. But there are these huge claims being made at that time. I feel like that's become a lot more measured now, and I feel like that's really shifted just in the last couple of years, actually. But I think there's still quite a lot of excitement about the promise of artificial intelligence built on data. You know, that you can't have artificial intelligence without data that's so interconnected. And I'm sure we'll come to that more as we go.

But there's a pattern, I guess, that's connected to the difference between the hubristic claims a decade ago and what's happening now that, you know, you see those headlines all the time 'AI beats panel of seven radiologists', you know, similar kind of shape of headline. And that's often based on research that's done by the manufacturers of the systems, research that's based on really quite artificial datasets, you know, synthetic datasets that have been essentially changed to make it easier for the AI to learn to do the thing that it's meant to be doing. And then when you get those AI out into the wild, right out into the real world, and when you have really good quality health and medical research done on their performance, they often don't perform as well as they did in those quite artificial situations. And I think what's changed recently is that the clinical and public health community has really come to terms with that problem. And they've really started to talk about, okay, what do the standards need to be for AI research in health? And maybe they need to be higher in health than they are in some other places. How can we evaluate real world performance? How can we make sure that these algorithms actually are fair, not just that they work, but that they don't treat some people unfairly relative to other people? How can we make sure that patients and clinicians and the public are involved in setting the agenda and in development?

So I feel like we're at quite an exciting point. But like Alberto said, we actually really have to make some commitments at this point to make sure that this technology goes the way that that we want it to. And Yves has been been talking with quite a lot of experts about this kind of issue, actually. How do we make this transition to the right kind of AI?

Yves Saint James Aquino:  I agree. And just to echo on that, especially what we've learned, what we have experienced during the pandemic about a lot of parts of the Australian healthcare system. They're really interested in the advantages and potential benefits of artificial intelligence and how it can automate things as well as support workforce, because at the moment Australian healthcare workforce would really need a lot of help, especially when it comes to screening programs. But at the moment what is happening is that Australia tends to import a lot of these systems instead of developing our own. And there are benefits. There are trade offs when it comes to that.

But one of the key challenges when doing that is that these AI systems developed elsewhere are built on data based on the local population and people assume that it's like any other technology where you can copy/paste the technology from one area to the next and have no problems. But as we are finding out, we need to understand how AI systems work in local populations. So I think that's what we have to look out for in the next few years, not just the excitement about the advantages of AI, but how can it work best within the Australian context.

Lisa Smithers: Can I pick up potentially, Yves, on your some of the things you're saying there. AI is built on data most of my work has been on big data really more administrative sources of data. And those systems can't work unless we get data quality right in the very first place. So based on my experience, I think over the last 15 years of using administrative data, I would actually propose that the quality of that data has improved along the way. But I think that it still depends a lot on how data was set up, who set it up, for what purpose and how it's used by people entering that data. So that sort of fits a little bit with what you were saying Yves.

My area is perinatal health, and so with administrative data, that perinatal data is collected extremely well across the whole of Australia because we have standardised definitions and fields, we have really good reporting systems for feeding up the quality of that data. And yet, although administrative data is great for these purposes, it's collected with a health service in mind.

Stacy: So I think that's something we really need to think about. If we think about the potential of AI sitting on routinely collected data. And perhaps just one more thing I wanted to mention here is what I perceive as being a current and potentially a future problem is having data analysts, fully qualified people who can actually do the work of analysing the data.

So most organisations are collecting data of some nature. Every organisation needs people to analyse that data. So who's going to do this work? I think we still don't have enough well qualified data analysts to be able to do this kind of work. So I think there's a gap in the market here, and although UOW is trying to do it, it's really hard to find people who know and understand these systems to be able to just design them.

Alberto: And just very quickly add to that not only who's going to do it, but they have to be well rounded, right? Not only number crunchers, then that's something that we need people and everybody needs to understand. Because a number cruncher without a well-rounded idea of how it's going to impact is not going to be the kind of people we want creating the systems.

David:  Yeah, it's so true. And, you know, as we think about that, these are not biostatisticians. This is not what we've been training for the last century. This is a new workforce that can deal with very large numbers with new tools.

Stacy, I'm really pleased that that people are still training radiologists. I think there's a new axiom in the 21st century, which goes 'artificial intelligence will not replace doctors, but doctors that don't use artificial intelligence will be replaced'. And and so as we we think about the the challenges of the future, how do we prepare a workforce not only, as Lisa says, for the the analytic programs, but as you've pointed out, Yves, how do we prepare a workforce to actually understand and interpret the outputs of these programs? Are we changing curricula fast enough in order to do this?

Yves: Mm hmm. Well, this is a very complex question, David, but for me in an ideal  world, there shouldn't be hard walls or borders between disciplines. And they think there. I mean, there is still a value for general education in general subjects and more technical courses still having some element of social sciences, because as we know, and this is the case for courses like medicine as well, if you don't have any understanding of the social implications of medical or clinical practice, you might not have the tools for empathy to be a more empathetic practitioner.

And I think that's true for data science and more technical courses. We need to be able to explain to them, or at least teach them a lot of the social issues that they probably will not be exposed to from studying and then practicing. So at the moment, that's one of the struggles or challenges that we have discovered in our study, is that there is difficulty in communications between experts. So data scientists have different language versus social scientists versus regulators versus public health experts. And they think we need to empower students to be able to discuss things from experts from another discipline. But I understand that will take a lot of work, but that would be my ideal scenario.

Alberto: And, you know, the other part that we need to be doing and at least we're trying to do here in our Bachelor of Data Science is that it's a full data science, right? Not just data, but science needs to be applied. There needs to be context. There needs to be a yearning for understanding the context to then be able to apply any technique.

It's again, not just techniques for the sake of applying techniques, but what is the context? Do they make sense? We're just getting the data to to be within the context of the problem. And that's how you start realising, you know, that you can get people, you know, to really work in industry or in health. David touched on, you know, the biostatistician tells us of a rare species who kind of tries to straddle the worlds. Right. And I think that's the type of not fully curriculum per se, but formative education that we need to be giving the people that we are creating now so that they can straddle the worlds and be useful.

Stacy: And it really needs to be at all levels of responsibility, doesn't it? I mean, one of the things that we've talked about a lot in the empirical work that we've done with all kinds of stakeholders that I've mentioned before is who should ultimately have responsibility around these systems. And I think that the final answer usually is everyone's got some responsibility, that the right question is actually which responsibility does this person have? So clinicians need to have the skills to be able to evaluate should I be using this tool with my patients because they have a duty to their patient, know they actually have an ethical responsibility to their patient, to know that the tools that they're using are doing what they think they're doing and they're not going to cause harm.

Hospital administrators who procure systems, they need to actually understand what they're buying and not just be sucked in by the hype of the developer, if that is an issue in a particular case. Regulators often really struggling with these systems because they present challenges that weren't really a problem previously when regulators were trying to do with medical devices.

So we've got an artificial intelligence system that can adapt constantly, can change itself all of the time based on new data that's presented to it. That's a much more difficult problem for a regulator to know how to manage, then back in the day when it was really just a physical object that if something went in, then same thing would come out. You know, it was it was a much more predictable beast to try to regulate, whereas now regulators are having to deal with the complexity of potentially changing systems.

And then there's the community like we have all discovered ChatGPT in the last year has become part of the public imagination. And all of us, I think, need to become aware of how AI's working because it's not always obvious that AI's in play, right? AI can be making quiet decisions in the background, so it's really important that everyone is aware of the, the fact that AI is everywhere. And in fact healthcare has been pretty good at being really careful about allowing AI in because healthcare has certain standards. It's helped to hold AI back and to ask, 'Is this good enough yet?' AI is much more prevalent in a lot of other areas of everyday life.

David: So, Stacy, both you and Yves touched on firstly the the inherent biases that can be in artificial intelligence if we don't build it properly and if we don't use the right populations to teach it as as Yves has pointed out. I think that leads very logically to a question around the ethical implications of collecting and using data, because if the data aren't being applied to the population where artificial intelligence is itself being applied, then you know, how do we move forward? So is is there a social license in Australia today for the use of of health data to improve health services and to teach and refine artificial intelligence?

Stacy: It's such a great question, David. So at the Australian Center for Health Engagement, evidence of values, we kind of specialise in a in a process that takes information to the community and asks them to work together to make recommendations about what should happen. And these are called community juries. So we we ran a series of community juries that were led by Annette Braunack-Mayer, who's a Professor in our center.

A year or so ago about sharing data from the health system with private companies for research and development. So it's not always the case that these AI systems are going to be developed by private companies. Sometimes they might be developed inside of health services, and there's often very entrepreneurial and innovative clinicians who notice a problem in their health system that they think that AI could help with. And they have the skills and they start to develop an application or there can be small spin offs inside universities. So things are things aren't necessarily purely kind of big tech or private, but we talk to people about sharing health data for R&D and private companies.

And interestingly, part of this process is people learning. And the more people learn about sharing data and what good can come of sharing data, the more willing people are to support data sharing. So that's a general kind of pattern that has been seen in a number of these similar processes around the world. So when people understand the benefits, they are supportive, but always with caveats. There's always quite strong conditions around that sharing. It's not just, I'm sure, go for it, share my data.

So generally the Australian process people said it has to be for public benefit. You have to be able to show me that this will actually do good in the community. It has to be done responsibly. There has to be a clear accountability framework. The data have to be secure that would really matter to people. We have to manage these data responsibly. There has to be proper penalties for misuse. You really have to make it hurt if people use these data in a way that they shouldn't so that there's a real reason to prompt them to be careful with these data. So there's a clear recognition of the value of the public benefit that can come from this use, but also a real knowingness about the value and the importance and the significance of these data for us and the need to be really careful about who uses them.

Alberto: Yeah. I mean, just a very quickly on data and you did touch on it, but is that need for people to feel comfortable about it, right? What are the processes in place for that data not being hacked, the data not getting access from other places, not being used for things that, you know, I may not really be happy. And we're again, with an ad, we're doing some other project. We're still on the analysis session part of it, but that's one of the things about, okay, what do you believe it should be used for? What do you already think it's being used for? Right. That's the other part.

A lot of of people give license when they think, oh, it's already being done, so what am I going to do? And a lot of people, you know, at least in some of the focus groups, apart from the surveys, you could see that tension about it's not necessarily consent per say, but what is the safety? Why do you do security? What is the confidentiality? What is the privacy? And those issues have to go hand-in-hand, I would say, with the benefit. Right? Because some people are like unless you guarantee the safety of it and security and the privacy, I don't know if I really care so much about the benefit. 

David: Yves, your thoughts? Because this is an area in which you're you're working all of the time.

Yves: Yeah, thank you so much for that. And in support of what Stacy mentioned as well, based on my conversations with different experts, I think they are aware that a lot of members of the society tend to mistrust the government and some private companies because of the recent examples of data breaches which are quite high profile. And if they don't understand the mechanisms that protect their data and the benefits on sharing their health data, they might not support it. But I think at the moment it's still unclear who really owns what anyway.

And I think for some experts, they believe that patients don't care. They always share very private information on social media anyway. But that's a view that we need to challenge as well, because some comments like some thoughts are not equivalent to health data, right? So some people might share what they eat for lunch, but they're not going to share their, you know, medical conditions or their age. So I think there is that misconception that just because people are freely discussing stuff online on social media, that they're just going to share whatever is in their personal health records.

David: And Lisa, I want to come to you for a moment because you deal with pediatrics, with neonates, newly born bubs right through. And they don't have a lot of say in how their data are used as far as I can tell. How do we as a community seek a social license in that space to ensure that we're actually using data as as people would want?

Lisa: Wow. That's a very tricky for me to address. But I do agree. Part of the the baby's data comes from the mother. Right. So in some way, the baby must have some right to the data from the mother as well. Which is it? Which is an interesting thing that's come up with an ethics committee that I've dealt with in the past, that if we're dealing with pediatric outcomes, we do need to know something about where the baby has come from in the circumstances in which they were delivered. But I think this is more a question for the Ethicist!

David: Well done. Great. So who wants to take that? Because it really is incredibly important as we think about social license, we can talk about people who can gift their data to the greater good. But what about those who don't have a voice?

Stacy: I'll give Yves a chance as well. So generally for people younger than a certain point in adolescence and depending on what kind of decision you're talking about, that point in adolescence can shift choices about all kinds of things, including what happens to your day to rest with the Guardian, who is usually the parent. So generally it's left to the adult to make the decision about what happens with data.

Then at a point in adolescence, depending on what decisions are being made, at least part of the control begins to go to the person themselves. So and it really depends on the the kind of data that are being shared and the kind of situation that you're in. So, for example, if the purpose is clinical care, then the young person might have control of their information a little earlier than if, for example, the purpose was for research.

So things can be different depending on the situation. But often I think this is partly about individuals and it's really important that individuals have control over their data and have an ability to have a say about how the data are used. It's also about the community. You know, all all of this really, we can call on big ethical concepts like the importance of confidentiality, the importance of respect for autonomy, so that people have the ability to make things go the way they want them to go in their life. And that and they're very important concepts and very widely shared. But really in terms of the way that we practice, a lot of it comes down also to a public conversation and what people are willing to sign up to as the social norms and they don't drop out of nowhere.

You know, they're actually part of a form of engagement really with the community. And as we saw in the community juries, you know, when people understand what's going on, their position will shift. You know, they'll make a different kind of judgment about what's the reasonable thing to do. So I think it's helpful to think of all of these things as a conversation, as needing a conversation, and us needing to engage communities and bring them into these considerations. But Yves, what about you? You've you've done a lot of medical ethics training over the years, what do you think about young people and their data?

Yves: I totally agree it does depend on the age. So the younger the patient or the health consumer is usually it is the guardian who has the control over what to do with the data. But at the moment there are also conceptual and philosophical conversations about what data are we talking about? Because there is a difference between personal data and sensitive data. I won't get into the definition, but so those kinds of classification of sensitivity, that will also impact on whether the responsibility is on the patient or the guardian. But just letting you know that this is an ongoing conversation.

It's not just about social license and people agreeing about sharing their data, but what happens once they share that data? Where does it go to this? It go to a private company? Does it go to the government? Will it be commercialised or monetised, or will it just be used for research? So I think these are massive conversations that you can't really explain quickly.

In an ideal scenario, if you're encountering a patient for the first time and you're collecting information, that's usually at least in the clinical context, that's where you explain where the data might go. But not everyone has the, you know, luxury of time to explain. Here I am collecting raw data now and this is where it might go and this might be the secondary use. It's not just for your clinician, but eventually your data might be used for other types of research and then expanding the benefits. So these are really giant conversations that can't be encapsulated in a very short clinical encounter, unfortunately.

David: So we've got a couple of questions from from people who are watching today. And I really do like the first of these, 'How do we stop artificial intelligence, exaggerating pre-existing biases in data collection?' There's a good, solid ethical question for you.

Alberto: Well before the ethicist says something. Well, it impacts a lot and Yves and I are working on how do we actually try to get the word algorithmic bias to be thrown out of the vocabulary. Right. Because, A, the algorithm itself is not biased. B, it is the generative process of the data that could have the social or racial or whatever bias in it, right? So one thing is how do we create collection methods that can do away with what we know are usually biased variables, variable settings.

Second is how do we train our data science tools in a way that we could again do away with some of these and still get the correct signal? Right? Because so far and that's the part where, as I said in the beginning, I don't really think we're at the point of intelligence. We are at the point of pattern recognition, so all the tools we have right now, the faster pattern recognition, and if the data is loaded with a signal that it has a social bias. The pattern is going to be recognised. So that's about all we need to work out. The generation of data, the process that generates the data, the process that collects it, and then the process that looks at the patterns to see can we do away with some of these things that are way to be seen telling us something that's wrong?

Stacy: I'm so glad, Alberto, that we need to stop calling it artificial intelligence. It's such a shame that that name really caught on, isn't it? Yeah, and it is actually very misleading. But I'm going to throw straight to Yves because here's a lot of work on both socio legal and data science approaches that mitigate bias, so over to you Yves.

Yves: So thank you so much, Stacy and Alberto. And one of the issues that I've discussed with the experts say I've spoken to was the problem of algorithmic bias. They mentioned data science mechanisms, so the things or approaches that you can do to de-bias, for example, an existing model or a model that is under development that I think is more of Alberto's expertise. But when people talk about social legal approaches, these are approaches that you can do outside data science, because as we know, when you talk about bias, biases in outputs of models, it's really based on the data that is already biased. And the bias data is really based on the healthcare system. So whether it's Australia, United States or United Kingdom, we know that the healthcare system has a lot of inequities, whether it's under servicing already marginalised groups or over servicing some marginalised groups.

So there is already data asymmetry that exist in the healthcare system. The other issue is in terms of what consists of experts that exist in data science community, but also in areas of decision making, whether it's in policy making or regulation. A lot of efforts coming from, for example, black in AI or women in AI have criticized these bodies as being exclusively a certain type of person and often the certain type of person are not aware of the impact of technologies on marginalised groups or are not aware of the existing injustices that contribute to the biases. And they are calling for more diversity in the workplace across the board, in any kind of workplace that is involved in the development, research, deployment or regulation of artificial intelligence in healthcare.

David: The other question we have here, and I'm going to paraphrase a little, but at the moment a clinical diagnosis is almost a black box from where the patient sits. You know, how do people arrive at that diagnosis? Is there an opportunity and indeed a right for patients through AI, artificial intelligence assisted diagnosis, to look into the black box, to actually see some of the workings that are helping clinicians to to make those diagnoses?

Alberto: You you know, this happens if no one goes for it, I just open my mouth. I may be a little bit devil's advocate here, the black box has existed before AI, but most of the time on a 5 to 10 minute meeting with your GP, there's so much and I think you kind of alluded a little bit to this in another sense Yves, you don't have enough time to go through the full discussion of, well, basically all of this is that I can put your diagnosis and I can tell you it's definitely not this because of that. I mean there would have to be a half hour appointment, right? So in that sense, it still kind of exists.

Now, I'm absolutely saying it ought to be that everybody has the right to know what's going in, even if it's, again, just with your GP, whether AI-aided or not, I the potential problem we may encounter is different AI or machine learning tools have different levels of explainability of interpretability, so even if there's a desire to, it might be really hard, just as it may be really hard for a GP to actually explain the overall interpretation when you have a pleural effusion or something like that.

David: Great. I think that's a wonderfully fulsome answer. We've touched a couple of times in the conversation so far on the quality of the data. What goes in dictates in many ways what comes out at the other end. Are they gaps in the way that health data are collected? And if so, how should we address these as we rely more and more heavily on artificial intelligence moving forward in health?

Lisa: Maybe I'll tackle this one first. I guess in terms of gaps in data,  I've got a couple of things to mention about that. We've seen a massive move in health services towards electronic management, electronic medical records, so the collection of data electronically. If health services aren't already doing that, they're certainly aiming towards that. And I think one of the difficulties as a researcher who sits outside the health system isn't knowing what data is being collected. And I sort of alluded to this in the beginning as well when I mentioned that the collection systems that we have are based on what the health service needs, not necessarily what we might want as researchers or even what the patient might want. So getting a seat at that table to decide what data is going to be collected I found, is extremely difficult. So if there's a solution to this, I'd like someone to tell me. I haven't figured out how to do it yet.

And the other thing that I wanted to touch on in terms of gaps in data collection is that I don't think, at least in my view so far we've done very well at all or got very far in collecting patient reported outcomes. I think if we are intending to have patient centered care, then we need to know and have patient reported outcomes. And if they were part of the system like the the electronic medical record, that kind of information might be collected more frequently, more systematically using some of the systematic tools that we have for patient reported outcomes that would benefit patients because the health services could then potentially use that to improve their care in areas where it's potentially not as good and they can do it evaluation so that just two little snippets of my opinion on where this could go.

David: Fantastic. Lisa. I mean, patient reported measures, including outcomes and experience, are critical and health systems around the world are starting at last to invest in that space and to respond to the feedback that people are providing. And I think it's that latter bit that will not only improve the quality of those data, but also encourage people to engage in that process.

Lisa: And I couldn't agree more, David, because I think it is about you don't just collect data for the just collection purpose itself. You have to want to do something with it. You have to be able to do something with it. And that really, I think is the most important part.

David: Categorically, yes. Stacy, thoughts on this?

Stacy: So I just wanted to build on what Lisa said about data and collecting data for a purpose. So I think that needs to pull through to artificial intelligence as well, right? Because it's really easy for artificial intelligence to be built because there are data that are easy to build it on. And because it's an easy application to build with data and we've seen quite a lot of AI developed for that reason I think because data are readily available in that space and developers want to develop AI, that's their job.

You know, it's exciting to develop a new app. It's exciting to think that you might be doing some good by focusing in the health space if you've been working in other spaces before, you know, so it actually often is very altruistically motivated, I think. But just like Lisa said, the data have to be collected for a purpose. I think that AI has to be developed for a purpose, and that purpose needs to be driven by clinicians noticing gaps in health systems, patients saying this isn't working for us, communities saying this is a really important goal for our community. So it needs to serve that purpose needs to be pulled all the way through I think.

Yves: And they think some of the frustrations of overseas developers trying to get into Australia. They mentioned that there is lack of integration of data amongst states, not even within the same state, but different in a hospital or health service institution. There is lack of integration and it's sort of it is a roadblock. It is an obstacle to really take advantage of the potential of health data if every institution has a different form or is not connected.

It's a frustration for researchers, but it's also frustration for patients because they feel that if they move to another institution or to another state, they have to say the same thing, when they were promised that once we have digital copies or electronic medical records, that will lessen this burden. But that hasn't really happened yet. So there is frustration for different stakeholders.

The other issue that we are trying to look into, and this is something that Alberto mentioned because a lot of the data sets are collected for purposes of clinical service, there might be some missing information. So we mentioned about bias and one way that people have suggested to combat bias is that we need to increase diversity of datasets, right? We make sure that marginalised groups are represented in the datasets. But the question remains what do we mean by diversity? How do we represent social groups or marginalised groups when that information is not yet collected? So think about the information about race, these are sensitive information, and at the moment, even about gender and sexual identity, these are not collected. So we are trying to develop a project where we examine should we or should we not collect sensitive information to improve the diversity of datasets, hoping that that response can also minimise bias. So there is that ongoing conversation, not only what we're collecting, but what we are not collecting from our citizens, from our from members of the community.

Alberto: I think it lands back to what Lisa and Stacy were saying, the purpose. So just as we need to get purpose for the data we're getting, we should have a reason for not getting certain data rather than then just blindly ignoring it because people are going 'Oh, we can always link, right? But linkages have already, there's a huge area and linkage error and not, as you were mentioning, Yves, within the state you may not be able to link within different health places. Think about judiciary, education, right? If you really want to be thinking of holistic help on on what you're going to do for your healthcare, that's the other part where we're falling apart.

David: And as we think about that, you know, I'm reminded that there are some European countries where it is illegal to collect race.

Yves: That's right.

David: At one level, you say fantastic. At another level you say, how do you build artificial intelligence where you can guarantee that that algorithm is going to meet the needs of the entire community and know with it leads to some interesting challenges in areas with which I've been associated in cancer surveillance, in screening, participation and outcomes. You know, in Australia we do not collect any data on someone's cultural or linguistic background with the cervical screening program. And as you've pointed out, Alberto, linkages is not going to to fill that gap perfectly. So it has some very big implications for people.

Another question from one of the people online, and again, to paraphrase, we've talked about artificial intelligence, the need for good data. This question relates even further upstream. 'What about the the investment in the hardware and the actual process of collection? How do we get health systems and indeed other systems, as you've just alluded to, justice, education, community services to invest in in sufficiently robust systems for collecting it in the first place?'

Alberto: That question in itself had a lot of pieces, right? And so I'm really right now thinking about. I know where to start. Any help is welcome because...I'm a little blacked out right now. Could you just repeat that?

David: How do we get the the investment in the hardware, the processes, all of those things. I'll start off if you like...I think there needs to be a value proposition again, that that the end product is so valuable to to health, to patients, the community and to health systems that it becomes a no brainer almost.

Alberto: But the part where I think there's a lot more public than what the wording of the question says is that it's not just an investment on making the tool easier to implement, you know, getting a better stethoscope or better imaging. It is we are in an economy where you need to take away from one source to put in the other source guide, which then it's thinking what is the value that it's really going to bring? Because maybe we go back to the hubris of five years ago where, okay, it's going to give me no error and it's going to give me the perfect diagnosis and the perfect treatment. Okay. If you're going there, sure. Mobile where you're funding in together, getting better hardware. Just just making a farm for your predictions and for your treatment ideas. Right. But that's the problem, right? At which point are we going to saturate? And again, if we're still depending on that data. And so I guess I'd rather than hardware per say, we need to invest in intelligent data collection, quality, safety and transmission processes.

Stacy: And kind of integrated and organized systems that that don't overlap, that don't conflict with one another. There was some great work done in the U.S. about ten years ago by Wachter on the effect of digitization on doctors and the way that digitization actually came between doctors and their patients, because it creates what we always talk about autonomous systems.

We talk about AI is making our lives easier, you know, it's kind of like The Jetsons. We're going to be able to delegate all the bits we don't like to the AI, and then we'll just get to kick back and do the fun stuff, whether it's in a professional context or in our personal lives. But actually, the research shows, the socio technical research shows that that really what it tends to do is just delegate different kinds of work to the humans. And that's the work that it takes for the humans to create the inputs that the AI system needs to do what it does.

So there's a tendency actually just to move the work around or create different kinds of work. So I think in thinking about investment, I I don't know, I think you're right, David. It just has to be a really good business proposition to get the investment. But in in strategising that investment there also really has to be an eye to that kind of streamlining that can make it so that the humans aren't just there to serve the system, but the system is actually serving the humans. The kind of system is really important.

David: I love that phrase that the system is there to serve the humans and we mustn't lose sight of that as we struggle with a world that is going to evolve very quickly, not as quickly as we perhaps thought five years ago, but it will still evolve quickly.

Which really leads us to the the ultimate question for the afternoon, which is how can we work with the community, with policymakers, professionals, healthcare workers, researchers to actually realise the potential of big data turned into artificial intelligence? How are we going to, as a community, take those important steps forward if we're going to, to really see the benefits that we hope for?

Stacy: I can start if you like, and then I'll pass to the others because I've done a lot of talking. So we're running a community jury really soon. This is in the AI domain. We're asking a randomly selected group of Australians. We're going to engage with them all over the country. We're going to give them information about artificial intelligence and how it can be used to detect or diagnose diseases. And we're going to ask them, under what conditions should we use AI for disease detection and diagnosis in Australia? After they've learned they're going to come together in Sydney, they're all going to fly from all over the country and they're going to spend a long weekend together deliberating on what should happen. And to my mind, that's the kind of engagement that we need, that we really actually need to bring the community into this conversation to help to make recommendations to guide policy making. And in fact, we have very generously we have support from the Royal Australian College of Radiologists, the Royal Australian New Zealand College of Radiologists .

David: Because we're still training them.

Stacy: That's right. And as we are, it turns out there are still radiologists and they probably always will be and they're really keen to support and they want to hear what Australians have to say. We've got a number of R-Techs which are big groups that connect the health system to the research system in Australia, funded by the National Health and Medical Research Council. We have three of those organisations from all over the country coming and those policymaking bodies. They want to know what Australians have to say about these questions. So for me that's the way forward, that kind of partnership.

Alberto: So just building on that because obviously, you know, the real patient point of view on integration is crucial. But the other thing that I think we still live in a society where there's providers and customers in a way, and we need to move away from that. You know, we don't need to think that policymakers are going to be the customers of the product that researchers and data scientists have to produce to give to them.

We need to be thinking of just as we integrate patients to get their ideas and the population. We need to be working really, really together from the get go off is this research that's also going to have already a vision of impact on policy and then have policymakers in the group right, rather than saying, okay, well now we created this has know suppliers, we created the data, we created the output, we're going to the policymakers. Here you go, it's in your court to play with it and see what we can do. Right. And that is one of those things that we'll start getting the circle to actually feedback onto itself and I think make a better a better sense of developing the AI with a purpose and with potential policy implications going forward.

Lisa: Yeah, I guess my my favorite way of working is to work with clinicians or practitioners, health practitioners. And so I really enjoy that because that brings questions to me that are definitely relevant to their situations in their day to day practice and where I feel like the translational impact is most powerful because they have the potential to change those systems from the inside. So that sort of flows along the way with what we're talking about here that is just for me is the most rewarding as a researcher, and I just thoroughly enjoy the challenge of getting to know their fields and how they operate and what the issues are so that I can do my little piece towards making that bigger impact.

David: Yves?

Yves: Thank you so much. I think Stacy, Lisa and Alberto have said a lot on strategies and I think co-design is very important, but also just making sure that our research doesn't remain just within the university. I think University of Wollongong is good at really sharing what we are finding out into the world and I think the public, we should empower the public to understand the issues and not just as part of our projects but just the public in general. Share your research. What are your findings? How is it impacting or how would it potentially impact the lives of Australians?

I think we have to be more active in sharing our research and not just being siloed in our office. And I think that that's what really inspires me as well, is if there's something that we find in our studies that we can share that with the public. We're not just working with the public, but we're also making sure that they have the information. They can also access the information that we gather through our empirical studies.

David: It's imperative that this is a whole of community conversation and that we're sharing openly and fully what we find and and how we have found it. It's a bit like, you know, Year 8 maths.  You might get the right answer, but you've got to show the working as well. And I think that's where we have not done well as as researchers in communicating and and working alongside the community. We've only got a couple of minutes left, so it's up to me to ask the fun and very obvious question and to come full circle. Will GPT have any impact on health in Australia? 

Lisa: I'm enjoying it so far. A little anecdote, as many of you here on the panel know, maybe the audience don't we have a new MPH that we're currently starting now and we have a big data specialisation within the MPH, so check it out. I'm just doing a quiet plug there. But with the new MPH, I'm developing a new subject and so there's all of this ChatGPT. So I'm thinking, all right, what is this thing? So I signed up and I thought, I know I have to create a tutorial, so I'm going to ask ChatGPT what is selection bias? And ChatGPT comes back with this nice stream on selection bias, which is exactly what some of the panelists were saying before. It repeats essentially regurgitates what's out there on the internet. But some of it was actually wrong and I thought, admittedly my students are doing this, so don't listen to ChatGPT, "you're wrong. This is incorrect". And so it comes back to me in apologise. Yes, I was wrong. This is the correct answer. So here is going to be a lesson for the students in my class. I hope there's none in the audience about the use of ChatGPT. And so I'm trying to get wiser in what it does, how it works, and how I can use it and teach people how to use it. So that's one little anecdote.

David: That's beautiful. I'm totally with you. If we think about the first time any search engine came out or even nowadays. Three, five different people are looking for the same thing. Three of them get to it first, Right? So knowing what to ask, whether you have a faster because that's really what ChatGPT is, it's a way, way, way, way faster collation. Right? It's not intelligent yet because the semantics are wrong. Then our exercise is going to be on being intelligent when we ask people to be intelligent, about to ask GPT to produce. 

Lisa: Yeah, I think maybe this will improve over time. I'm hoping it does, because I know that my use of it will drop away if I don't see it doing things that are correct. Yep. So that's just my personal view. And I've had a couple of a look at a couple of others as well. Some of them I haven't been particularly impressed by, but yeah, let's see how.

Yves:  And I think there is an ongoing conversation how different it is from, say, Google search. So we'll find out their ongoing research about how a patient can use it or how a clinician or a public health expert can use it. And there is ongoing research on that.

David: I look forward to to seeing that. Please join me in thanking Lisa, Alberto, Stacy and Yves for joining us today and giving us a great insight into the brilliant research from across the University of Wollongong.

Thank you also to you, our audience, we hope you enjoyed the discussion. The event was recorded so everyone who registered will receive a link to the recording through email. I'd finally like to thank Jill McGarn and her team for bringing this together and the excellent work they're doing behind the scenes to bring the Luminaries program together. Look forward to seeing you in two weeks time. Have a great evening. Thank you so much.


Watch again: How UOW researchers are fighting pancreatic cancer

David Currow: Good afternoon. My name is David Currow and I'm the Deputy Vice-Chancellor for Research and Sustainable Futures at the University of Wollongong. I'd like to start by acknowledging Country.

On behalf of the university, I would like to acknowledge the country for Aboriginal peoples as an interconnected set of ancient and sophisticated relationships. The University of Wollongong spreads across many inter-related Aboriginal countries that are bound by this sacred landscape and have an intimate relationship with that landscape since creation. From Sydney to the Southern Highlands to the south coast, from fresh water to bitter water to salt, from city to urban to rural. The University of Wollongong acknowledges the custodianship of the Aboriginal peoples of this place and space that has kept alive the relationships between all living things. The University acknowledges the devastating impact of colonisation on our campuses, footprint and commit ourselves to truth telling, healing and education.

I'd like to welcome you all today to the Luminaries series. It's wonderful to have you here. It brings together leading University of Wollongong researchers, industry experts and thought leaders for a one hour conversation every fortnight. We'll discover how research and collaboration at the University of Wollongong is tackling global challenges. As we think about the challenges that we face, the Luminaries series is an opportunity across our network of Australian and international campuses to come together to learn what everyone is doing and where we might build bridges in our own teaching and learning and research.

To showcase the work of people from our junior to our most senior research colleagues, to engage more with the communities in which we live and work. With health in every one of our faculties, we have a wonderfully wide array of people to share passionately the work that they are doing and how it is making a difference to our understanding and knowledge, to practice, to policy and ultimately to outcomes.

Today we are hearing from a group of exceptional colleagues as they discuss new therapies for the treatment of pancreatic cancer. Our challenge is quite simple. Pancreatic cancer is the eighth most common cancer in Australia and one with one of the lowest survival rates. Although in New South Wales we deliver some of the best outcomes in the world, so much more needs to be done.

So today, as we join researchers, as they discuss development for novel treatment approaches for pancreatic cancer, it's really exciting that we may be turning the corner, that we may be moving beyond the therapies that have been the mainstay for the last several decades. Every day, University of Wollongong researchers learn more about how new treatments could treat pancreatic cancer more effectively. Today, we welcome leading researchers and early career researchers to discuss the development and include with that a very active clinician who's day to day work involves treating people with pancreatic cancer. It's my pleasure this afternoon to welcome Associate Professor Kara Vine-Perrow, who will introduce the panel today.

Kara established the targeted Cancer Therapeutics Research Laboratory based in the Illawarra Health and Medical Research Institute. Kara's research program is centered on understanding the mechanisms that drive chemotherapeutic drug resistance in cancer and the development of novel nano medicines and polymer scaffolds for drug delivery. Kara, over to you. 

Kara Vine-Perrow: Thanks, David. And good afternoon everyone, and thank you all for joining us today. I'd like to start by introducing you to our amazing panelists.

With us today, we have Senior Professor Marie Ranson, who has a longstanding research interest in molecular biomarkers of cancer invasion and metastasis. In particular, she has made substantial contributions to the Plasminogen activation system field in cancer as well as in inflammatory diseases. And we'll hear a little bit more about that system later on when Ashna give the presentation. We have with us today

Dr. Gary Ticknell, who's a medical oncologist specialising in upper gastrointestinal cancers, including pancreatic cancers. He works at the Illawarra Shoalhaven Cancer Care Centers and in his spare time, in addition to his clinical work, he's currently completing a PhD here at the University of Wollongong.

We have Elahe Minaei a second year PhD student working in the targeted Cancer Therapeutics lab with me. She completed her masters degree in immunology in 2014 and before commencing her PhD., she actually worked as a full time research assistant for many years at the Illawarra Health and Medical Research Institute at UOW across a number of cancer related projects. So she brings a wealth of knowledge.

Finally, we have Ashna Kumar, who's a third year PhD candidate at UOW, and her research involves better understanding the role of the urokinase plasminogen activation system in cancer.

Now, today, we're going to hear from both Ella and Ashna about how they're thinking outside the box and approaching treatment for advanced and inoperable pancreatic cancer.

You might hear Asna and Ella talking about pancreatic ductal adenocarcinoma or PDAC today. So PDAC is the most common type of pancreatic cancer that accounts for more than 90% of all pancreatic cancer cases. And this cancer occurs in the lining of the dux of the pancreas. Now, as David already mentioned, pancreatic cancer is quite a lethal disease. It's the fifth leading cause of cancer death in Australia, and it's projected to be the second leading cause of worldwide cancer deaths by 2030. So you can see it's a significant and growing health problem.

Pancreatic cancer patients have few treatment pathways with surgical removal of the tumor remaining the only viable option. However, less than 20% of patients are candidates for surgery because of the advanced stage of the disease at the time of diagnosis. Because of this, we need to explore new and more efficacious treatment approaches for this population of patients in order to improve their survival outcomes. So with that, I'm going to hand over to Elahe, and I encourage members of the audience to submit questions using the Q&A function. Elahe is going to talk to us about her innovative, innovative approach to treating inoperable pancreatic cancer using a combination of immunotherapy and chemotherapy. Thanks, Elahe. 

Elahe: Hello, everyone. I hope everyone is seeing my screen at the moment. I just want to start my presentation by saying a huge thank you for the opportunity to present some of the exciting research that's happening in our lab. And today I'm going to talk to you about a combination of localised approach in treating pancreatic cancer. And I'm going to explain the rationale behind this approach in this webinar.

Now, the conventional treatment for pancreatic cancer has been largely ineffective. 80% of the patients are ineligible for the life saving surgery and radiotherapy and chemotherapy has been ineffective in treating this cancer. Even immunotherapy that has revolutionised the cancer treatment horizons in many cancers such as melanoma and renal carcinoma has been an ineffective in treating pancreatic cancer.

Now, before I explain why immunotherapy has been unsuccessful in treating pancreatic cancer, I'm going to first explain what immunotherapy is. So if we think of immune system as a military force, the immune system is often unable to distinguish between the body's true citizens and cancer cells because cancer cells arise from the body's normal cells. And even if they do recognise them, they are often not trained enough to kind of eliminate those or they can't, and they often get even outnumbered by cancer cells. So what immunotherapy does is act as a secret agent that can distinguish those tricky tumor cells and it can inform the immune system of their presence, and it can empower the immune system to kind of attack them and eliminate them.

Now, with that in mind, I tell you how immunotherapy works in general. So as you can see here in a hot tumor microenvironment, we have infiltrating immune cells in the microenvironment, so these are the fighting cancer cells. They often start expressing these inhibitory receptors on themselves. And one of these inhibitory receptors are PD one. And if they attach to their ligand, it sends inhibitory signals that tell them to stop fighting cancer and there's no point and give up. However, what immunotherapy does, it can block these inhibitory signals and tell the immune cells to keep fighting. So this is perfectly fine in a hot tumor microenvironment, as you can see in the right side of the picture. Now in a cold tumor microenvironment such as PDAC, we don't have any fighting immune cells to help with these anti-PD-1 immunotherapy and all we have are these suppressor immune cells.

So immunotherapy should aim at building an army first. How do we do that? We use combination therapies. Now when chemotherapy kills cancer cells, it releases danger signals that can attack immune cells to the crime scene. And that's great. However, these signals are not strong enough to elicit a strong immune response, so scientists use these to actually empower that response, and that's when the immunotherapy can be useful.

So we are lucky to have collaborators and clinicians such as Gary who is present here because they help us to evaluate the effects of chemotherapy in the context of pancreatic cancer. So we did this experiment that we received samples from pancreatic cancer patients who received chemotherapy prior to pancreatic resection and patients who did not receive any treatment and were treatment naive. Then we extracted genetic materials from these patients and we did immune gene expression analysis on these patients to explore the effect of chemotherapy.

And what we found was very interesting, we found that chemotherapy was actually very effective and it could improve and increase the number of fighting immune cells such as T cells and all the molecules that are involved in activating immune system activation and cancer treatments. So if that's the case, what is the reason behind failure in using these combination approaches in human clinical trials? So there are two main reasons for that. First of all, the preclinical models of PDAC or pancreatic cancer do not fully reflect the human immune system to address these problems, we are aiming to establish a humanised mouse model, which I explain in more details later. And the second reason is that so the effective dose to elicit a good immune response against cancer are usually very high, and this high dose usually leads to very severe side effects and immune-related adverse events such as cytokine release syndrome. so to tackle this problem, our group has developed this amazing implantable device that is biocompatible, I mean that it's been evaluated in different non-human animal models such as mouse and pig, and it's been perfectly safe and non-toxic.

So we load our drugs into these implants and inserted right inside to the tumor through endoscopy procedures where it can locally release the drug instead and pass all those systemic side effects. With that background in mind, I'm going to talk about my project that I load my immunotherapy drugs like stimulatory molecules and anti-PD-1 immunotherapy antibodies that I talked to you about and chemo into these implants. And I put these implants right adjacent to the tumors where they can locally release their content and recreate immune cells and eliminate cancer cells. To do that, I actually load my drugs into this implant formulation and using a 3D bio-printer to make these implants. We order mice, we inject the mouse tumor cells, which are attached to a bioluminescence enzyme that can release these bioluminescence signals and it can be captured by device and bioluminescence imaging, and it can show the growth of tumor over time.

Now, once the tumor is developed, we then put the implant inside and treat these mice either locally with those implants or we deliver those treatments, the equivalent treatments in a fraction of those through a systemic root and systemic administration. So I'm going to walk you through these exciting results.

You see in this slide on the top, you see our localised treatment group. In the middle, you see the systemic treatment group. And at the bottom you see the control group, which only had the empty implant with no treatment. So as you can see here, by time, we captured the bioluminescence signals on day zero, day three, day ten, day 18 and day 22. And over time, you can see an increase in those signals and tumor growth to quite a large extent in the systemic treatment and control.

However, we can see how localised treatment does contain the growth of tumor in a great deal. And not only that, the other exciting thing we found out was that our treatments could actually prevent metastasis. So metastasis is the outbreak of the tumor cells into the other parts of the body. So we saw in the systemic treatment group and the control group, some mice developed metastases in their abdominal area, which we can also see in actual advanced human metastatic pancreatic cancer. But in our localised treatment group, none of the mice developed the metastasis. Now, Ashna is going to talk to you more about metastasis, pancreatic cancer metastasis and how to prevent that, but we were also excited to see that our drug could not only prevent and fight the primary tumor, but it can also contain the metastasis, which was very interesting.

We actually quantified our data as well. Here, on the y axis, you can see the radiance and the bioluminescence intensity and on the x axis you can see different time points in days. At the middle there is demise in the localised treatment group in blue. And on the left you see the systemic treatment group and on the right, you see mice in the control group. And as you can see here from day 10 on, you can see a significant difference between different cohorts and you can see how the localised treatment reduced the growth of the tumor significantly compared to the other treatment groups, which was very interesting.

Now, all I presented to you so far we've done in the mouse models, so it's great, it's fantastic, but for us to be able to translate our findings to human models, we should be able to make sure that these can be equivalently good and efficient in a human model. So what we're aiming to do next is to bring these genetically modified mice who don't have an established immune system in them, and we inject either human immune cells or stem cells to them and let the human immune system to get established in them.

Once established, we're going to use actual human tumor cells and human tumor tissues to establish the pancreatic cancer in them and try new human immunotherapy agents in them. So that helps us to evaluate the effect of our drug in a more effective way. And that makes us understand if our treatment might be equally as good in a humanised preclinical model. And hopefully we can go to phase one human clinical trial from here. Now, I want to end my presentation by thanking my amazing team, very brilliant, brilliant, talented team and all the funding partners who, without the help and the funds, we couldn't be where we are today. Thank you very much for your attention. 

Marie Ranson: Thanks Elahe. That's really exciting results you presented today. I might start with a question and ask some questions from the audience, but I want to start with one first. You're method is about shrinking an inoperable tumor so that it can be surgically removed because that's a problem with PDAC patients present quite late and they're not operable. So in your model, would you be able to test that surgical resection in your mouse models? Would you be able to do that? You think? 

Elahe: So mouse models. The problem with mouse models is that their pancreas is very small and it doesn't mirror the human situation very much. That's why we're thinking about moving on to rat models after the mouse model, because they have a bigger tumor and it can be more relevant to this. But I'm not sure if you're able to do that with the mice model to see if we can surgically resect, because we're not doing that with them. We just can either measure how they shrink and if they shrink and see if they metastasise or not. 

Kara:  Yeah, that's right. I think given the small size of the rodent models we use, it is quite difficult to recapitulate what would happen in a clinical context. We are looking to use larger models so that we can do that in the first instance. It's proof of concept that we can shrink the primary tumor across both murine and human models with a fully functioning immune system. Okay. Thank you.

Okay. We have a question here. From MK, for people with PDAC in the tail of the pancreas., METs are usually prolific and hence wouldn't it be difficult to use the implant method? Yeah. So I guess 80% of pancreatic cancer cases arise in the head of the pancreas. And so they're the cases that we're particularly interested in targeting with this treatment approach. So the idea would be that we could easily access the head of the pancreas via this endoscopic ultrasound method that Elahe has introduced to bypassing sort of the needle a short distance through the stomach wall or through the duodenum so that we could implant it there.

The idea is, and we have designed a first in human phase one clinical trial, we've designed it. We don't have funding to run it yet for just chemotherapy loaded implants. And in that particular study, what we would actually do would be to try and control the the tumor at the primary site, but also give systemic chemotherapy if there is disseminated disease as well. I'm not sure if Gary would like to comment any further on that. 

Gary Tincknell: So my experience and I'm not a gastroenterologist, I don't perform the procedure. Typically, we can get to any part of the pancreas using the endoscopic ultrasound method. They just have to access it from a different part of the stomach. So opposed to going down into the bowel and accessing it at the head of the pancreas, going through the stomach wall into a more distal part of the pancreas.

I think this treatment would be, in my understanding, aiming to treat patients who have got localised disease opposed to systemic treatment. So we are not necessarily looking at the effect of shrinking down the tumor in that space to improve the resectability of the cancer if you've already developed metastatic disease because ultimately you need systemic treatment, chemotherapy via a drip or a cannula to maximise patient exposure to the drug and general exposure. 

Kara: One more thing I might add to that point that Gary has made there, so while we have pitched this as a localised delivery strategy, we do see evidence that we do see systemic effects. And so this is the beauty of working with immunotherapy, is that we can actually activate the immune system. And the idea about using this localised delivery approach is really we're doing that in a more controlled way. So we're not seeing the hepatotoxicity, the cytokine storm syndrome. If we were to deliver these agents as bolus injection or infusion or systemically. So we do see evidence of systemic immune activation which could potentially control systemic disease, as Gary mentioned. Am I correct in asking that the chemo that you're co-administering, is that delivered systemically or is it in the implant? 

Elahe: We have tried both ways. In one of the experiments we tried to deliver the chemo and immunotherapy, both in one implant locally and in another experiment, we just delivered chemo systemically and immunotherapy through using the implant and they both had kind of similar effects. And does something I forgot to add is that one of the things we love about immunotherapy and we see with these treatments and experiments we've done and we're hoping to test this in future studies by re-challenging the tumor in the mice and see if it provides memory.

Because one of the things about immunotherapy is that it makes these memory cells that can remember these tumor cells. So if the tumor recur and come back, they can immediately recognise and kill it. So what we want to do is that once we do treatment in these mice and after it's done, we want to put those tumors in again and see like tumor in them again and see if they can immediately recognise that tumor and eliminate it. So yeah, that that would be very great. [00:26:35][83.6]

Marie: That's an exciting prospect. I have a question from the floor from Philip Cantrell. That's an interesting question. Are you alkalising the pancreas in chemotherapy? The Japanese found they could quadruple the lives of pancreatic cancer patients with such a treatment. Are you aware of that? 

Elahe: I never heard of it. 

Gary:  We don't routinely do it in clinical practice, I guess I would say definitely, I think with the upper GI cancers as well, that the Japanese seem to have different life expectancy in respondents to certain treatments, certainly with gastric and esophageal cancers, and I guess we see different treatments based on background for patients as well. So there may be a location in the world, it may have an effect on these treatments as well.

Kara: Gary, I might ask you a question. So as a medical oncologist, a very busy one who came racing in from the clinic just now. Thank you. Who's also completing a PhD, how important is it for research to focus on new treatment options beyond conventional treatments? 

Gary: And so, I mean, if we just take our pancreas that we're dealing with here today, our options are very limited at the moment. We have chemotherapy. And that's it. We do have a few sort of experimental things of combining systemic treatments with immunotherapy and chemotherapy, certainly in the what we call neoadjuvant before surgery space. However, we don't know the outcomes of these yet. And I think especially with pancreas cancer, where prognosis is so atrocious that we need new ways of treating this to improve the outcomes for our patients, not just from a survival outcome, but also from toxicity outcome.

You know, can we deliver treatment locally, which is what we're doing here, to improve that patient's outcomes without giving them all the side effects of chemotherapy that come with it, nausea, vomiting, hair loss, risk of infections, liver failure, things like that, that can happen with chemotherapy. And I also think, you know, any sort of novel idea that can get us better survival outcome for patients is vitally important for everyone. You know, it's a bit different for treatment, say, with melanoma, where we're seeing five year survivals in cancers where we've got limited options, chemotherapy that's not ready moved on in the last ten, 15 years. I think it's vitally important that we explore new options.

Marie: But having said that, chemotherapy is too often used in combination with new treatments, right? 

Gary: Yeah, exactly. I think even with immunotherapy across the board, we're seeing that immunotherapy alone isn't doing what we need it to do. The response rates are too slow and you need it in combination with chemotherapy to sort of ramp up the immune system to work in combination. So we're seeing patients in lung cancer, for instance, where a combination chemotherapy and immunotherapy improves the response rates to patients and therefore you can then just maintain them with immunotherapy, but we aren't there yet with pancreas cancer, and I think, anything we can do to improve those response rates and as you said at the start, Elahe, you pointed out, you know, we've got these cold tumors that we need to activate and we need to see what other agents we can use and combinations really heat them up and get them activated so our immune systems can clear and deal with them.

Marie:  So that's what I think Elahe and Kara's work is so exciting. Yeah, well, I think looking at the schedule, it's time for us to move on to Ashna's presentation. You can share your screen and unmute yourself, I'm going to hand over to Ashna. 

Ashna: Thank you. And also, I just firstly, just wanted to thank the organisers and my supervisors for including me in this initiative and allowing me this opportunity to share my research, which is focused on targeting metastasis in pancreatic cancer. And as we've already had earlier, PDAC is an intensely aggressive cancer for which its five year survival rate of less than 9% is actually the lowest of all solid organ cancers, and unfortunately, this has shown no improvement in the last 40 years so the treatment has lagged behind quite significantly in comparison to other cancer types that have shown more improvement in the therapeutics and in the management.

This really reflects that the currently available treatments have been showing limited effect, urging the need for new treatments. One of the major hallmarks of pancreatic cancer that Elahe has gone over as well is this really early invasion and migration of cancerous tumor cells that migrate and spread to distant sites in the body in a process known as metastasis, which forms a major challenge in the therapy of PDAC.

So what we're trying to do is target this rapid metastatic progression that's characteristics of PDAC by targeting the urokinase Plasminogen activator or uPA, and higher expression levels of uPA is significantly correlated with poorer survival in patients with PDAC. And you can see that from the survival probability graph over here where patients that show low uPA expression levels have a higher survival probability compared to those that have high uPA. I might just turn on my pointer to make it easier to follow.

And we also have extensive evidence, clinical evidence for the pro metastatic role of uPA and it being implicated in PDAC. And this is something that we've comprehensively reviewed in the last year and published. So PDAC metastasis and uPA, they all go hand in hand. And we've shown that uPA is abundantly overexpressed on human pancreatic cancer cells and this at the surface of the cells, which makes it an easily accessible location for targeting with anti-cancer agents and therefore making uPA an excellent druggable target.

So for those who might be unfamiliar with how the uPA system works, or from a non science background, uPA is a protease meaning that it degrades proteins and it attaches to its specific receptor, which is tethered to the tumor cell surface and activates the conversion of plasminogen into plasmon. And now plasmon is another protease. So it is responsible for really driving the breakdown of tissue barriers or what we know as extracellular matrix. And it does this both directly and by activating a whole bunch of other proteolytic pathways that drive this breakdown and this breakdown of these tissue barriers creates this really leaky environment that allows tumor cells to escape from their local environment and go into blood vessels of vasculature to migrate into distant sites.

So what we're trying to do is effectively block the active site of uPA to hopefully block this step in the process here to limit the breakdown of these tissue barriers and to effectively limit metastasis. And we're doing this by repurposing amiloride as a lead uPA inhibitor. So some of you guys might be familiar with Amiloride, right? It is a clinically available drug, and it is used in the clinic and indicated for patients with hypertensive disorders. It works as a diuretic, but in the 1980s, it was found to exert antitumor effects in in mice at high doses, and this arised as a result of an uPA inhibition. These doses are much too high to be administered into humans because it would risk an overdose and hyperkalemia and cardiac arrhythmias. But this knowing that the antitumor effects of amiloride uPA inhibition makes amiloride really popular scaffold to work from and repurpose into anti-cancer agents or uPA targeting anti-cancer agents, rather.

So this is what the labs at UOW have done. And I'm focusing on these two positions. They have created a new drug that not only is non cytotoxic, is highly selective for uPA, and they've eliminated the traditional diuretic effects that amiloride carries. And so our new drug, the BB2 30F, again, as I said, is nontoxic. And you can see that in these graphs over here that show the effect of the drug on the percentage of cell viability at increasing doses. And you can see that even at the highest dose, it isn't it isn't toxic to any cells. And the drug also selectively and potently targets uPA activity on the surface of the pancreatic cancer cells. And you can see that it inhibits uPA in a dose dependent manner.

And a similar dose dependent effect is also seen when it comes to inhibiting plasmon activity at the surface of these pancreatic tumor cells. And so the fact that we now know that the drug can inhibit plasmon as a result of inhibiting uPA, what we really want to know and confirm is can this have an effect on downstream metastasis? And this was investigated in a mouse model of early stage pancreatic cancer, where pancreatic cancer cells were implanted into the tail of the pancreas in immunocompromised mice. And the tumor was allowed to grow for seven days before treatment was commenced. And these mice were treated with either our drug alone or in combination with one of the standard of care first line treatments gemcitabine and then they were compared to gemcitabine on its own and the mice that received no treatment.

And as you can see over here, we can see equivalence to the standard of care drug of our drug alone. However, when combined with gemcitabine, we have an improved reduction in the tumor volume. But what was most exciting was this result over here that we can see where all of the mice that were treated with either our drug alone or in combination with gemcitabine showed a complete knockdown of metastasis. So we saw no liver metastasis. And these control in these groups compared to the control and in the mice that were treated with first line treatment gemcitabine. This was then carried out in a late stage disease model where the treatment commenced four weeks post implantation. And in a very similar process, the pancreatic cancer cells were implanted, however, ultrasound was used at four weeks to confirm the tumor size and before the treatment was commenced.

And we saw very similar results where the combination of our drug and gemcitabine inhibited primary tumor growth and completely eliminated liver metastasis once again. So these were really exciting results. And and the next step we wanted to carry out was to use this technique called MALDI- MSI, which is abbreviated because it's full form, is a bit of a mouthful and we wanted to look at this in tumor tissue sections. And this is basically just to confirm whether the drug is reaching the the tumor and where is the drug being distributed in the tissue as well as is the drug being localized to the drug target or uPA?

And it's a bit of a lengthy process, but we receive the frozen tumor tissue from our collaborators, which we then embed and we slice and we apply a matrix which allows for the detection of the molecule, and we can correlate this to the mass to charge ratio of the molecule. So as you can see over here, this is the signal that is consistent with our drug. And in this overlayed image over here, you can see the control tumor tissues from a mouse that has not received any treatment. And those and two sections from mice having received treatment.

And I want you to focus on the green areas over here, which is showing where the drug is localised on these tumor tissue sections, ignoring the brand on the outside as that's just an excess margin. And what we find is the signal intensity of the drug, which is represented by these green dots, is higher in specific regions across the tissue and particularly across the tumor margin, which is quite interesting. And this could correlate to regions of uPA rich areas of the tissue. And we're yet to confirm this using histological techniques to coregister where we're seeing the drug and where the target is. And so now just to wrap up with a timeline of where we are, we have identified our target uPA and a molecule that effectively inhibits its targets.

we're currently in the pre-clinical phase where we are really rigorously evaluating the drug mechanism of action and where an assessment of the pharmacokinetic properties has been done, both here at UOW and in collaboration with Monash University and we're currently working through in vitro trials, so really understanding what's going on and how the drug is inhibiting. The processes involved in metastasis at the cellular level and the in vivo trials that were done in collaboration with UNSW and the Ingham Institute. And lastly, I just wanted to acknowledge everyone, all of our collaborators that have helped us to get this far in this process. 

Kara: Thanks, Ashna, for a fantastic talk and some really great insights into developing these anti metastasis drugs based on targeting uPA. Really exciting stuff. And I think also what you've done is provided a fantastic example of collaboration that David was talking about earlier.

You know, it's really important for everyone to to realise that science doesn't happen in isolation. And we often work quite closely and collaboratively with others, you know, nationally and internationally to achieve a common goal. So it's a fantastic example of that. So we'll start the panel discussion now. And I guess, you know, you gave a really kind of nice overview of where you're at in terms of the pre-clinical testing. The results do look really exciting, so what comes next? How are you going to develop this into a drug that might one day be translated into the clinic? What are the next steps for you? 

Ashna: And so at this stage, I mean, we've got some really exciting in vitro data. So at the cellular level, as well as in vivo data here. Unfortunately, our pharmacokinetic data or bioavailability data that we've seen hasn't been something that we can use to progress into a clinical trial stage. But the drug is showing it's potent. It potently inhibits its target. It's selective as well. And so it would be really interesting to see what other methods or formulations we can use with the drug to really advance it to the next stage. There's a huge focus on nano delivery methods at this stage in cancer therapeutics, so it would be really interesting to kind of combine any nano and a development nano delivery methods along with this drug to encapsulate it perhaps, and to really drive that forward. But we're in an exciting place and at this stage, like we really want to nail down on what the mechanism of action is and to confirm that. 

Marie: How about using implants here? I just wanted to add to that to the slightly lost my train of thought, just then. Oh, yes. So, we need to optimise it for human use. This is a proof of concept molecule that works really well, but it's not got the best properties for human use. And one of the things we'd like to do is make it into a pill form, which means tweaking the molecule so it can be used that way. So it needs development for before we can get to clinical trials. And Gary might be able to talk to, I guess, what that means for treatment in terms of changing or formulating a drug so that it's orally bioavailable versus giving bolus doses or infusions in the clinic. Can you comment on that, Gary? 

Gary: I can. So I would say in our public system, which is where most of our patients will fall and most of the patients who we should be focusing on, let's be honest, we shouldn't be focusing on patients who have just got private health insurance or anything like that. Our wait list is incredibly long. A lot of treatments have been developed, such as immunotherapies and maintenance treatments that have gone on for several years now. So you go from five, ten years ago when immunotherapy say wasn't around, we would have a fair amount of space, in our chemotherapy suites. Patients would have a limited course, six cycles of chemo because toxicities would be building up. But now we're going on to treatments that last two years, and these patients have to come in and out.

That's the toxicity burden on finances. It's the toxicity burden on our bed space. We have a set number of beds. We've not been able to expand. We haven't got space to expand. We haven't got the nursing staff to necessarily administer such drugs. So anything that can be done, which is localised, can keep people away from hospitals, tablets, injections, depos which are slow release, reduce that amount of toxicity to both patients and to our our health care system. 

Kara: Thanks, Gary. So a significant benefit all around. We've got a question here from Vineet Keer and maybe Ashna you can answer this one. Does BB2 30F or can it be used for other cancer types or is it just limited to pancreatic cancer? 

Ashna: Sure, thanks for your question. BB2 30F is a uPA inhibitor and its target UPA is overexpressed not just in pancreatic cancer but in a number of different cancer types. And there's a lot of clinical evidence that supports this in multiple cancers. In fact, it is one of the strongest biomarkers or prognostic markers in breast cancer as well. So I would say as a uPA inhibitor, it would be really interesting to see and apply it in other other cancer types. And we've actually done some preliminary analysis on this as well. Looking at the inhibition of uPA activity on the surface of breast cancer cells as well. And it does inhibit effectively inhibit that uPA activity there as well. It does have broad applicability and we're hoping that it can be used across a range of cancers and not just be limited to pancreatic cancer.

Marie: So I guess while we're on the topic of drugs that inhibit uptake, are there any currently approved drugs for its downstream effectors? And how effective have these drugs been in clinical trials, do you know? 

Ashna: And so there hasn't been any uPA inhibitors that have been approved in the clinic for human use. But there has been one drug, maziopron, which is a uPA inhibitor that has successfully completed phase one and phase two clinical trials. So it is effective. It has gone a long way. And although it hasn't gone further than the phase two clinical trials because it has broad selectivity against other proteases involved in the same family of uPA. That's something that our drug has advantages over. Our drug is a lot more selective for uPA compared to this drug that has gone on to that stage. But we're hoping this really restarts that conversation of looking into the uPA targeting inhibitors. 

Kara: Thanks, Ashna. So maybe one for Marie, as someone who's sort of worked on the urokinese Plasminogen activation system for some time, I was hoping that you might be able to comment on some of the challenges that have, I guess, are involved in developing these uPA targeted drugs, including issues related to drug delivery and toxicity, maybe Ashna's already hit on those. But we've known about the uPA system for a long time. There's so much clinical evidence to support it as a good target. Why aren't we seeing drugs translated?

Marie: Yeah, that's a great question Kara, part of the problem has been to ensure that the drug is totally selective to uPA. As Ashna mentioned, it belongs to a family. One of them that's really important is TPA, the blood clot busting enzyme, because you don't want to muck around with that particular target. So there's not too many drugs that have been developed that are really selected only for uPA, and that's part of the problem. So that that enhance toxicity issues.

And there's also been problems with the drug themselves and having good drug like properties, like I was talking about before being able to progress them to patients that they people - they are optimised for human use or made into pills that make it easier to administer. We don't have to inject every day and it has cleared the system really quickly and you have to give large doses. So there's been problems around the drug composition themselves and the fact that they haven't been that selective to date. And also, I think it's with the models that people have used in the past, people have just traditionally used the primary tumor model. You know, the growth of the primary tumor.

But what we're targeting is the metastatic process. And that's hard to model in an animal system. And that's why our colleagues have really pioneered this metastasis model from the pancreas where we can measure that process really well and inhibit it, and say we're definitely inhibiting metastasis, not just attacking the primary tumor. So a whole range of things, I think, and as Ashna really nicely said, I think our data, our findings should really reignite the urokinase inhibitor world. And I know, I can say it has been has been increasing the interest back to this. Yeah, I think this project is a really nice example of when you bring in people with different expertise, so you have this multidisciplinary team, you can tackle all the aspects quite nicely.

So you were talking before selectivity and some of the medicinal chemistry aspects were potentially lacking in the past. You bring together experts in the system, medicinal chemists, your clinicians, your cell biologists and you can quite quickly start to overcome those obstacles because you have all of the expertise there at the ready. And I think that's a really nice example that. There is a plethora of uPA inhibitors out there, but all designed by chemists, and then they just do very, very simple assays and not really take it any further. It is that interaction between different different disciplines that really is important to make things move forward. So we might sort of open the discussion up a little bit now, both to Elahe and Gary as well. I was thinking, Gary, I wonder if you can comment on some of the clinical trials that you've been seeing in the pancreatic cancer space. And and what's exciting you there? What's coming through that's looking promising. 

Gary: So I've already mentioned our accommodation, chemotherapy, immunotherapy. This is in the localised space still early clinical phase trials. Fortunate enough to actually be led by some of our local clinicians, Professor Lorraine Chantrill from both the university and the hospital here, who's the principal investigator on that one. So I think that's really exciting. The few patients we've seen that we've been able to downstage the cancer from what we see on imaging to what we're removing at time of surgery.

However, most of the other things that we're seeing are chemotherapies combined with some sort of novel molecule to try and work on a various different stream to limit their spread or activity downstream. And most of these, unfortunately, are still in the early phase trials. So, you know, phase one, phase two trials where we're really looking at patient safety, toxicity profile, opposed to their efficacy, and they're not making it far enough to be in the big clinical trials, phase three trials that we're seeing. And so this really is trying to translate between the lab and real life use, which is the big thing. And hence what you've already talked about, getting all the colleagues on boards and specialists in these areas can hopefully improve things for us. 

Kara: So I guess you know, research in the pancreatic cancer space is a little bit behind other cancers like breast cancer, for example. We know that in breast cancer there's sort of ten different molecular subtypes and we're actually starting to learn through some really nice genomic sequencing studies on patient samples that there may be subtypes that exist within pancreas cancer cases as well. How important do you think it is to, I guess, tailor treatment to those molecular subtypes? 

Gary: I think it's vitally important if you want to make sure the patient's getting the right treatment, you need to know what's actually going down on a molecular or genomic level as otherwise you're just throwing everything in the same basket and hoping for the same outcome. Opposed to really targeting what that patient wants or needs. Sorry. You know, we do try our best, if you get for treatment, we will send you up for genomic profiling at St Vincent's Hospital and they'll try and match you with open clinical trials, which you might be able to target your cancer. But knowing upfront what you've got, well, we could easily say, well, you'd benefit with chemo, with this agent or immunotherapy in that agent before you have metastatic spread or even tried to prevent metastases occurring, if you've been fortunate enough to catch it early. 

Marie: On, I guess too, it allows you potentially to repurpose drugs, right? So drugs that have been found to target the same receptor or the same pathway in other cancer types could potentially be used here? I think Ashna, I think you mentioned earlier that you you might have a slide about repurposing drugs and I guess the the opportunity it provides to fast track translation of those repurposed drugs into the clinic a lot faster than if we're dealing with a new drug and having to go through, you know, the the regulated steps to commercialisation. Can you maybe comment on that?

Ashna: I might share that slide, actually. Yeah. So this you can definitely fast track drug discovery or starting from scratch and designing a molecule. If you already are aware or you have a drug that is indicated for a target and you know that it has an excellent safety profile, it's been used for decades, it's more likely that that drug will move forward to the market all through clinical trials because you're not so worried about all of the adverse effects, or clinical safety in comparison to drugs that you're starting or synthesizing from from scratch, because you have a whole lot of other experiments and other investigations that you need to do in terms of their pharmacokinetic, like absorption, distribution, metabolism, excretion, all of the pharmacokinetic parameters that come into play. So definitely repurposing drugs is a huge advantage. And there's there's a whole field of science that's that's focused on looking at drugs, finding already existing drugs, and seeing whether you can just take that and apply it to other diseases or to see what other targets you can inhibit.

Kara: Thanks. Do we have any more questions? Any more questions from the audience at all? If not, we're close to time. I might take this opportunity to give a big thank you to Elahe, Ashna, Gary and Marie today for joining us. And thank you to our audience. We really hope you enjoyed the discussion and the presentations today and that it gave you just a little bit of an insight into the types of research that we are doing here at the University of Wollongong. This event was recorded so everyone who registered will receive a link to the recording via email. And if you have any follow up questions about the work that was presented or something that you didn't get to ask today, then please feel free to reach out to us. You can find that contact details on the university website.

Marie: Thank you Kara for hosting.

Kara: Thanks again, everyone, and good evening. 

“I want to encourage interdisciplinary cooperation and coordination across research sectors, and across UOWs global and domestic campuses. Through this series we’ll see some of the brightest minds from across the globe sharing ideas and planting the seeds of further conversations.” Professor David Currow Deputy Vice-Chancellor and Vice-President (Research and Sustainable Futures)

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