Q&A: A conversation with Lovern, Mark

Model Informed Drug Development is not new, but it is evolving fast – at Fortrea, it is evolving into Model Integrated Evidence. Prompted by strong indications from regulators as to its acceptance, MIE has the potential to support development efficiencies and inform decision-making by supplementing traditional patient-based evidence with modeling input that is increasingly being considered an evidentiary framework.

We caught up with Mark and asked him some key questions about both MIDD and MIE.

Q: We hear a lot about Model Informed Drug Development. Why is now the right time to talk about going “beyond” MIDD?

Mark Lovern: MIDD has been incredibly successful at improving how we interpret data and make development decisions. But in most cases, models are still used to support decisions rather than to generate evidence. What we’re seeing now is that the science, the tools, and the regulatory dialogue have matured to a point where model derived results can be treated as evidence in their own right. That’s what opens the door to addressing areas of development that have historically been very difficult to study efficiently.

Q: How do you define Model Integrated Evidence, and how is it different from traditional MIDD?

Mark Lovern: Model Integrated Evidence, or MIE, is best thought of as an extension of MIDD. With MIDD, models help interpret clinical data and guide decisions. With MIE, validated models actually generate data that can be integrated with, or in some cases, supplement clinical data. The key distinction is that model derived results are treated as evidence, not just analysis.

Q: Where do you see MIE having the biggest impact today?

Mark Lovern: The impact is greatest in situations where traditional clinical trials are inefficient, impractical, or limited in the data they can realistically generate. That includes early phase development, dose optimization, rare diseases, pediatric populations, and scenarios where extrapolation across populations or dosing regimens is needed. These are precisely the areas where white space tends to persist.

Q: Some sponsors worry that regulators may not be ready to accept model generated evidence. How do you respond to that?

Mark Lovern: It’s understandable, but the regulatory conversation has evolved significantly. We’re seeing increasing openness to advanced modeling approaches, in regulatory discussion and guidance. Regulators are focused on scientific validity, transparency, and governance, not on whether the evidence originated from a traditional trial or a well validated model.

Q: What roles do technologies like AI and machine learning play in the future of MIE?

Mark Lovern: AI and machine learning have strong potential to support model development, explore complex datasets, and identify patterns that would be difficult to detect otherwise. At the same time, they reinforce the importance of validation, explainability, and governance. These technologies don’t replace pharmacological understanding, they augment it.

Q: What needs to happen next for MIE to become more widely adopted?

Mark Lovern: Progress depends on collaboration. Sponsors, regulators, technology providers, and academia all have a role to play in standardizing approaches, sharing best practices, and continuing open scientific dialogue. As confidence grows, we’ll see model integrated evidence move from the exception to a routine part of development strategy.

Q: If there’s one takeaway you’d like sponsors to remember, what would it be?

Mark Lovern: MIE isn’t about replacing clinical science; it’s about strengthening it. By integrating validated models into the evidentiary framework, we can help address areas of white space, make better decisions sooner, and ultimately deliver therapies to patients more efficiently.