Speaker 1: All of us in this room are involved with clinical trials, writing, delivering,
Speaker 1: participating in clinical trials, and we know that often in
Speaker 1: those trials we need to make changes. So today I'm
Speaker 1: going to talk to you about to amend or not
Speaker 1: to amend. That truly is the question. So what's the
Speaker 1: problem right in our world? We need to be flexible,
Speaker 1: We need to be innovative. We need to be able
Speaker 1: to move and change our studies as the information comes
Speaker 1: in that we require it. But what we've seen is
Speaker 1: that certainly within ROSH, we traditionally have had a high
Speaker 1: level of protocol amendments, even compare to our peers. Now,
Speaker 1: some of my colleagues would say, well, because we're on
Speaker 1: the forefront of innovation, a science demands that we are flexible.
Speaker 1: And that's true, But is there a case for some
Speaker 1: protocol amendments that are not needed, They don't need that process.
Speaker 1: Can we group some of those protocol amendments so you
Speaker 1: only do it once? Can we learn as we're designing
Speaker 1: studies from the protocol amendments we've done in the past,
Speaker 1: so you don't make that mistake again. So the first
Speaker 1: thing we did was we started to think about what's
Speaker 1: the problem why should we change? When we looked at
Speaker 1: actually what is the cost and the burden of doing
Speaker 1: a protocol amendment, we really found some surprising data. Yes,
Speaker 1: there's the direct costs. You may need to pay fees
Speaker 1: to submit a protocol amendment. You know that you need
Speaker 1: to put it through some approval process, and that takes time.
Speaker 1: But when we added up the amount of resource it
Speaker 1: took for the people in house, but importantly the burden
Speaker 1: it pays it placed on the sites that we're trying
Speaker 1: to implement our protocols, we found that the amount of
Speaker 1: both direct and indirect costs through the time, delays and
Speaker 1: the resources we used, we're in the region of up
Speaker 1: to half a million dollars per amendment. We showed that.
Speaker 1: One of the first things we did in our change
Speaker 1: management process was we just formed a simple infographic that
Speaker 1: explained how many protocol amendments we were doing per study,
Speaker 1: what the cost of those amendments were, and how possibly
Speaker 1: we could redirect that cost, that time, and that effort
Speaker 1: into things that were going to add more value to patients.
Speaker 1: So then the question became, so what is driving these
Speaker 1: protocol amendments? And I would often hear the conversation. Well,
Speaker 1: I would never do a protocol amendment that wasn't absolutely needed.
Speaker 1: My regulatory colleague might make me do it because this
Speaker 1: is a very regimented industry. Well, I would never do
Speaker 1: a protocol amendment that wasn't needed. It's a lot of
Speaker 1: works and a lot of process. But my safety colleague
Speaker 1: might think that it's necessary. I would never do a
Speaker 1: protocol amendment that's not needed. It's only for the safety
Speaker 1: of the patient. But for some of these smaller issues,
Speaker 1: the quality department will say we need to do a
Speaker 1: protocol a member. So it really was the time to
Speaker 1: address this as an issue in how we work as
Speaker 1: an organization. So what did we do. The first thing
Speaker 1: we did was we got together a team of real
Speaker 1: experts cross functionally so that we could figure out what
Speaker 1: the key questions you need to ask of the change
Speaker 1: that you want to make in your protocol that will
Speaker 1: help you decide whether an amendment is needed or not.
Speaker 1: It comes out into a pretty simple decision framework. We
Speaker 1: could implement that in a system that is able to
Speaker 1: pick up the metadata about that study easily, so the
Speaker 1: teams don't have a lot of burden in the process
Speaker 1: that they're following, it guides them through these questions. If
Speaker 1: they've got a particularly complex case, then it directs them
Speaker 1: to this team of experts who can all get round
Speaker 1: a table. So you've got your regulatory input, your safety input,
Speaker 1: your quality input, legal if necessary, all round the table
Speaker 1: and can give that team advice In the moment. The
Speaker 1: decision whether they make a protocol amendment or not is
Speaker 1: still down to that study team, and so they can
Speaker 1: pull this through. By standardizing this amendment approach, it helped
Speaker 1: us to reduce unnecessary amendments and collect key data and
Speaker 1: insights so that we can learn from that in the future.
Speaker 1: So you may ask, yeah, that's all very nice and
Speaker 1: pretty logical, but how do you change the mindset of
Speaker 1: people to actually make a difference. Well, it's important that
Speaker 1: this works for them. The features of this tool is
Speaker 1: that is integrated into their daily work pattern. They would
Speaker 1: normally have written up a brief of what their amendment
Speaker 1: would be and ask for some medical writing support and
Speaker 1: other support to put the work through the system. This
Speaker 1: feeds into that and the automation enables the process to
Speaker 1: follow three freely. It creates a dashboard so that we
Speaker 1: can see the overview of all of the submissions the
Speaker 1: status and we can get insights from them, and we
Speaker 1: have this deddicated support and continuous improvement through our in
Speaker 1: house team. So did it work. We've established a huge
Speaker 1: amount of uptake across the organization, in particularly in our
Speaker 1: late stage trials where this is focused. And if you
Speaker 1: look at the graphic in the middle, when we started
Speaker 1: this process in twenty twenty three, the blue blocks show
Speaker 1: you that there was a really high proportion of our
Speaker 1: studies would submit an amendment in any one year and
Speaker 1: all of them were processed. When we started this initiative
Speaker 1: in twenty twenty four, we still saw a high proportion
Speaker 1: of our studies submitting an amendment, but we were able
Speaker 1: to say more than twenty five percent of them were
Speaker 1: not necessary and we avoided the teams going through that process.
Speaker 1: As we've got into twenty twenty five, we've actually seen
Speaker 1: a mindset shift. The teams are not coming to us
Speaker 1: requesting to do a protocol amendment in the first place.
Speaker 1: They've learned from this behavior, they've learned what the guidance is,
Speaker 1: and we're not seen as many come through, and even
Speaker 1: of those that do come through, we're now seeing that
Speaker 1: we're avoiding about a third of those protocol amendments. So
Speaker 1: what does that mean, Well, if you want to put
Speaker 1: it in the sort of crudest terms, I suppose in
Speaker 1: terms of money, in terms of the avoided direct costs,
Speaker 1: indirect costs, and the time we've saved in getting our
Speaker 1: trials to patients more quickly. We have saved money in
Speaker 1: the region of tens of millions of dollars that can
Speaker 1: get reinvested into places where it will really make a difference.
Speaker 1: And for our teams, they've found this process easy to use, intuitive,
Speaker 1: it goes with their normal workflow, and they can see
Speaker 1: the impact that it's having directly. So what's next. We've
Speaker 1: got lot of rich data. Now we are using AI
Speaker 1: to take all of these amendments and categorize them into
Speaker 1: a standard categorization framework. We can then take all of
Speaker 1: those amendments from early and late stage development and learn
Speaker 1: from these amendments. We intend to pull that right back
Speaker 1: into the study design phase and so that really building
Speaker 1: literally quality by design. We can avoid these amendments in
Speaker 1: the first place by giving visualizations and data to the
Speaker 1: teams as they put pen to paper. So I thank
Speaker 1: you for your attention, and I don't know if there
Speaker 1: are any questions. We've got it maybe a couple of minutes, Mary,
Speaker 1: But other than that, thank you.
Speaker 2: Take one question, how are you handling amendments? How are
Speaker 2: you avoiding amendments under the EU CTR and the CTIS process.
Speaker 1: So it's interesting some of the amendments that we do.
Speaker 1: We know that you can't avoid all amendments. There are
Speaker 1: some amendments that are required. What we're trying to do
Speaker 1: is be more thoughtful about it, and is there a
Speaker 1: way that we can group those the way that the
Speaker 1: time procesing it's taking for those amendments to go through.
Speaker 1: You don't want to be doing an amendment more than
Speaker 1: once a year anyway. So what we've tried to do
Speaker 1: is figure out which the ones that are absolutely required
Speaker 1: versus those that can be handled in a different way,
Speaker 1: for for instance, via clarification letter. But that wouldn't apply.
Speaker 2: To that Based on this the last part, when you
Speaker 2: were researching what kind of amendments were happening, do you
Speaker 2: have any examples that you saw the specific areas that
Speaker 2: came up a lot in your analysis.
Speaker 1: Yeah, I think there's a couple of areas. One area
Speaker 1: was inconsistencies across a protocol document. So where we have
Speaker 1: the same topic discussed in various parts of the protocol document,
Speaker 1: if you've got inconsistencies, then we were needing to correct them.
Speaker 1: We've approached that in a couple of different ways. We've
Speaker 1: simplified our protocol template using the transcelerate as a model,
Speaker 1: and created a lot more standard content that we're autogenerating
Speaker 1: so that we're removing those inconsistencies as we go. We're
Speaker 1: also using an AI check to provide consistency checks across
Speaker 1: the protocol as well.
Speaker 2: M mm hmm.