DIA Global 2023

CluePoints: 'there is true recognition, risk-based quality management is not just monitoring'

By Liza Laws

- Last updated on GMT

© Getty Images
© Getty Images

Related tags RBQM risk assessment Research Risk-based monitoring Data management Artificial intelligence Clinical trial

Updated guidelines for risk-based quality management (RBQM) are much more than just for monitoring purposes and can show quality by design concepts, save immeasurable time on manpower and shows there has been a complete mindset shift within the last 15 years, CluePoints says.

This comes on the back of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) releasing a new draft guideline, ICH E6(R3), on May 19 this year (2023). It is a revision of the previous ICH E6(R2).

The R3 guideline was designed to provide a unified standard for the European Union, the United States, Canada, Japan, and Switzerland to implement the mutual acceptance of clinical data by the regulatory authorities in these jurisdictions.

At DIA Global, OSP had the pleasure of speaking to CluePoints’ Francois Torche, chief product and technology officer and co-founder along with Steve Young, chief scientific officer about what the speed the industry is adopting guidelines and what the latest draft means for drug development. The aim of ICH E6 (R3) is to provide a framework for planning, designing, conducting, recording, and reporting trials that involve the participation of human subjects. These guidelines are a furtherance of R2.

OSP: Can you give us a brief outline of CluePoints’ work and platform?

SY:​ Clue points is a primarily risk-based quality management (RBQM) solution provider. We have an enabling platform, and we are driven at the core by advanced analytics that the industry is increasingly using.

Our clients are increasingly using it to drive and enable a much more efficient and effective oversight of their clinical research specifically around quality. With our platform, they can manage quality very efficiently and very effectively compared to what was used in the past, that is really at the core of what we do.

Of course, our platform is getting more mature over time, and providing end-to-end, risk-based quality management support. So, it is not just an analytics engine, it is all the workflow and all the methodologies and documentation that is being supported as well.

OSP: How is the industry responding to RBQM?

SY:​ I would say in terms of clinical trial operations there is still a fair amount of resistance in the industry to moving into this new RBM paradigm. For decades, the industry has been taking a very manually intensive, resource-intensive approach to overseeing clinical research - sending site monitors out to clinics every four weeks, spending a day or two at remarkably excessive cost and high resource time. It takes a long time for people to get convinced there is a different way. However, I would say we are in a much better place than we were five or seven years ago, the industry is adopting at a much, much faster rate but there is still a fair amount of resistance out there.

OSP: Pharma is one of the slower industries to get to grips with technology, AI and making changes, it is still quite traditional in some ways, isn’t it?

SY:​ It has always been that way; I have been in the industry for a long time, and I remember the days of going from paper case report forms to EDC - to electronic case report forms. It is a journey that started in the mid-1990s and even 10-15 years later, in 2005, it was still only a minority of clinical trials that were using this, even though the technology had been out there for 10 years already.

It has taken a while to socialise the new paradigm, get people on board or get enough people on board with it and now we are seeing finally throughout the last few years, a real acceleration.

OSP: So, do you think it is out of fear or cynicism, this resistance?

SY:​ It is a combination of both from my perspective. There is just a natural conservatism because people are trying to get their new molecule through seven to ten years of clinical research later – at a huge investment cost with the promise of finally getting it to market and getting the return on their investment.

If you then tell people to do something differently that they have not been thinking about and in their minds, it is not tried and tested, there is going to be an automatic hands-up resistance towards trying new things. They think of their drug as their baby and do not want to experiment with anything different – even if it makes perfect sense.

There's just that natural backdrop I think in the industry, and then with the people who have been running clinical trials and doing things their way for so many years, this seems interrupting, disrupting what they instinctively have always known as the right way.

FT:​ However, we have been obliged to do so, as we have the regulatory bodies we work with in a very highly regulated environment where processes are so well documented. In the past, a long time ago, we would be writing a standard operating procedure (SOP), that is one thing we have been doing forever, but now that is one thing you don’t want to do - update your SOP every six months. The nature of what we do is patient first, safety first and it is highly regulated, that is normal.

SY:​ It is normal but frustrating. I became really excited about RBM when I first heard about it in 2008. By 2010, I was convinced that within three to five years the whole industry would transform but of course I should have known better, and here we are 14 years later…

CluePoints FT and SY (1)

OSP: But there has been some movement hasn’t there?

FT:​ Things have been evolving, if you look back to 2010/2011, we were talking about risk-based monitoring. There was FDA guidance was about it - now we are talking about risk-based quality management because we are realising as an industry that risk base is not just for monitoring activities. It is not just for operations; you can think of risk-based approach for data management for many other areas. So, we started with risk-based monitoring - and it was a quick win - a low hanging fruit because reducing your monitoring activities had a direct and immediate effect on your cost. We can see that we can apply the same concepts of ‘think before you act,’ perform risk assessment to identify what the potential risks are, then we try to mitigate those risks.

SY:​ The final draft (R3) of the new ICH E6 GCP (the international ethical, scientific, and quality standard for the conduct of clinical trials for the development of new drugs and biologics - involving human participants that are intended to support regulatory applications) is out for public consultation and feedback right now.

We are looking forward to it being published next year. I think, bringing that whole concept together in regulatory GCP expectations, there is a true recognition that risk-based is not just for monitoring, it is showing quality by design concepts - the way you design your protocol and your trial from the beginning is a risk-based approach or should be a risk-based approach.

You will still be using a lot of the same processes or exercises of assessing risk from the very beginning of clinical research all the way up to the beginning of your trial - and all the way through your trial. So, it is great, and we are finally seeing the regulations fully representing that concept for the industry, which I think is going to itself be helpful moving forward. It is a complete mindset shift from where we were 15 years ago, and it makes perfect sense for the industry to be doing it.

OSP: You've also been very instrumental with the FDA and that regulation, so it is quite interesting what is happening now, and how the FDA is shifting to looking at remote devices. You are very forward thinking on this.

SY:​ I think the FDA and the ICH committee in terms of really trying to look forward and anticipating how quickly technology is evolving in our space. In these different evolving paradigms, like decentralized trials and, and adaptive trials and wearables, - it is moving very quickly now. I think they have done a good job with the new ICH guidelines, to encompass all that to allow for it.

OSP: How much resistance is there to precisely what you are doing?

FT:​ There has been resistance for 10 years, and the resistance will always exist. Guidelines will always be open to interpretation. The changes are not dramatic and nothing new is being introduced.

SY:​ It is really a furtherance of the RBQM concepts that were already in place. It is not like the revision is going to create this additional resistance. It is going to help mute some of that resistance. It is all spelled out and reinforced better.

OSP: Can you tell me about quality tolerance limits (QTLs)?

SY:​ These were introduced in R2 a lot of people in the industry have been trying to interpret that, implement it, and figure out exactly what it means, there is all sorts of workgroups. I have been involved with several of them and now in R3, there is similar language, but mysteriously the term ‘quality tolerance limit’ has been removed. So now everyone in the in this public consultation period is wanting to get clarity on it.

We still must do this; they have the same the same concept spelled out, but they do not mention quality tolerance limits anymore. That was laid out in R2 and is something people paid a lot of attention to. They were being told they must measure and have parameters that are metrics - measuring the overall quality of their trial and if they deviate from an expected range on these metrics, that must be reported in the formal submission to the to the FDA and in the study report, to the FDA. This showed it had to be taken seriously, which is both a good thing and a scary thing. They are not using the term quality tolerance limits anymore and everyone is wondering whether the FDA are backing off from this.

FT: ​it is a notable example, but the fundamental question behind that is, when you define the questions limits, when you define thresholds and when you define what is acceptable and what is not acceptable, you need data. You can make assumptions you can say ‘I think,’ but the more data you have to support your limits, there's less risk you're going to do something wrong.

Our opportunity as a company we now have increased data - we have more than 1,000 trials on the platform. We can support that work of defining thresholds, we can apply more deep learning techniques and go back to previous risk assessment to suggest for future studies. We need to be asking questions like what are the risks that should be mitigated? What are the risks that potentially may happen? So, we are sitting on a lot of data, and we are investing quite a lot of resources in trying to exploit those data for the benefit of the industry and our clients.

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