Certara Talks: The role of MIDD in Increasing Pharmaceutical R&D Efficiency

0時05分
Hello and welcome to another episode of Certara Talks.

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I’m Doctor Suzanne Minton and I’m a director of Content Strategy, and I’m here with my colleagues from Certara, Dr Hannah Jones, who’s a Senior Vice President of PBPK Consultancy Services, and Doctor Piet Van de Graaff, who is a senior Vice president of Applied Bio Simulation.

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Welcome to Certara Talks.

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Piet and Hannah, thanks for having me.

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Thank you always, always a pleasure to talk with both of you.

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So I thought we could talk about a recent paper that came out of Pfizer about the adoption of model informed drug development within the Pharmaceutical industry.

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So Hannah, could you tell us a little bit about what that paper discussed?

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The study looked to assess and actually quantify which is a little bit different to others in the past, the impact of model informed drug Development or MIDD on the development timelines and clinical trial costs across the entire Pfizer portfolio for a single year.

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They essentially reviewed each programme and the MIDD related activities at various development stage gates right up to regulatory submission.

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And overall the findings demonstrate really that systematic use of MIDD significantly improves efficiency and reduces costs in in drug development programmes, which of course we all know.

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But more specifically, they cite average annual savings of around 10 months in in development cycle time and $5,000,000 per programme.

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And this reduction in timeline really reflects efficiencies gain through kind of more optimised trial design, faster dose selection, better prediction of clinical outcomes and obviously by using MIDD approaches, avoiding unnecessary studies and the cost savings really referred to like like clinical trial expenditures and also like the associated development costs.

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In my mind, these results really support a much broader application of MIDD as a strategic tool.

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We’re now enhancing decision making and obviously accelerating timelines and reducing costs across portfolios.

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And this is obviously a publication that Pfizer have made, but many other kind of large pharma are doing these kind of approaches as well.

2時42分
Fantastic.

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Well, we’ve always known that MIDD was a valuable worthwhile investment.

2時47分
So it’s nice to hear that Pfizer went and did this work to quantify the value.

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Piet, could you elaborate on what you think the importance of this paper was for the field?

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I think as you said, you know, quantifying the value and the return on the investment of MIDD is, is really important to demonstrate the value of what we do to our sales and our stakeholders.

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The Pfizer papers, which kind of just very nicely summarises focused on the impact of MIDD on clinical trial cost and timelines or to phrase it differently, on the efficiency of actual drug developments.

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Although this is very important, this is of course only part of the story.

3時33分
And to get a full picture of the impact of mulling a simulation on the overall R&D return on investments.

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You also also need to look at the effectiveness by which I mean, you know, the probability that an idea ultimately results in a successful new medicine.

3時49分
Now for this you would also need to take into account for example, the increasing role of mechanistic bio simulation such as PVPK and QSP in the preclinical stage.

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Now a very recent paper from AstraZeneca and Drug Discovery Today shed some very interesting light on this.

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They did a retrospective analysis of compounds across the portfolio taken forward as candidates from for clinical development between 2007 and 2019.

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And they found that application of mechanism based translational bio simulation increase the probability to achieve a positive proof of mechanism in the clinic by a staggering 2 1/2 fault.

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Now in the paper, they did not convert this finding into a dollar figure, but I would argue that it is an order of magnitude higher than the 5 million quotes in the Pfizer paper.

4時46分
That’s really interesting.

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So now that we’ve these papers have come out in your mind, what do you think the next logical step would be?

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I guess at least from the Pfizer paper, the authors primarily focused on MIDD examples in the clinical space and this is much easier to quantify given the impact of MIDD on clinical trial efficiency.

5時10分
But I think the impact for me, at least in the discovery space, although harder to quantify, warrants more investment and assessment.

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Especially when you think about how tools like QSP then that can be used early to assess target feasibility, how they kind of enable project teams to really focus the resource on on projects and targets that have the best chance of success and deprioritizing or stopping early, those programmes are more likely to fail.

5時45分
And also thinking about like using AI informal modelling tools in lieu of measured data early in the discovery space for small molecule programmes, we need to drive sort of faster chemistry, designate test cycles.

5時59分
So there’s a huge opportunity and modelling is used quite extensively in in the discovery space and it’s just kind of quantifying that impact I think would could be nicely assessed as well.

6時14分
Absolutely.

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Piet, do you have anything you want to add?

6時16分
Yeah, well, I first of all agree with Hannah that that mechanistic modelling such as PBPK and QSP integrated with AI will play an increasingly critical role in early and also virtual drug discovery.

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Now at the same time, I also think it will branch out at the other end of the spectrum, namely in clinical practise.

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Now the term MIPD has now been coined for modal informants, you know, dosing and the P stands for, you know, precision dosing to, to guide personalised medicine in the clinic.

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And we are seeing an increased interest from, you know, clinicians and regulators to use mechanistic bio simulation in inpatient care.

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For example, we’re working, you know, with Cincinnati Children’s Hospital on the development of a paediatric QSP digital twin model for inflammatory bowel disease with the goal to individualise the therapy and dosing regimen for each patients.

7時13分
And lastly, also see a great potential for, you know, really combining our digital twin platforms with organomic chip technology to create virtual triplets, which will take MIPD and precision medicine to the next level.

7時28分
That’s so exciting.

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I, I really look forward to seeing this technology mature.

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And, and like you said, Piet, the next frontier is taking this technology from R&D and, and bringing it into the clinic so we can really help patients get the most benefit from their medicines.

7時41分
Yeah, absolutely.

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With that, Piet and Hannah, do you have any final thoughts?

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I think one more thing just to say is that I think what’s important here is border industry adoption of of these kind of techniques.

7時55分
So I know large pharm like Pfizer and obviously AstraZeneca are using modelling and simulation techniques routinely to impact the development programmes.

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But I think we need to move this forward to the small and and mid sized pharma.

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And this is a challenge really for these smaller companies that maybe don’t necessarily have the resources they need to, to really implement these approaches in the same kind of manner as large pharmas like Pfizer.

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But the key here is enabling access to the expertise they need and the training they need to be able to do this.

8時35分
Hannah, I couldn’t agree more.

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I’ve heard it called the democratisation of MIDD and I’m grateful that for some of the smaller companies out there, they have access to resources like you and Piet to help them get on this journey and get the most out of these approaches.

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So thank you so much Piet and Hannah.

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It was, it was fantastic talking and talking with you and learning from you.

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Thank you.

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Great.

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Thank you.

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You’ve been watching Sartara Talks.

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If you want to learn more about the work that Piet and Hannah are doing in model informed drug development, you can visit us on our website.

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I’m Suzanne Minton.

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You’ve been watching Sartara Talks.

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We’ll see you next time.