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As a Senior Consultant for Evaluate, Tiffany’s work has spanned a range of quantitative and qualitative projects, including indication and asset prioritization modeling, opportunity assessments, and competitive monitoring. Tiffany has particular expertise and interest in oncology, having previously worked as a Healthcare Research & Data Analyst in oncology.
In this series, the consulting teams at Citeline and Evaluate discuss some of the key challenges that face the companies they work with across the pharma space. Learn more about how Tiffany Chan, Senior Consultant, Evaluate Consulting & Analytics, conducts opportunity assessments and indication prioritization approaches.
We have clients that come to us for various reasons for indication prioritization, and what they’re really looking for is how to help them guide their portfolio strategy. What diseases should they go into? Do we think it’s going to be the most commercially viable for the asset they have?
We have clients that come to us with, let’s say, an early stage asset. And they have a lot of different indications they’re considering based on scientific evidence, but from a commercial point of view, what is actually going to make sense in terms of the market, competitive intensity, sales, et cetera? And then we’ve also had more BD&L (business development & licensing) type projects come in as well where clients are looking for opportunities to expand their portfolio through in-licensing in a particular therapy area, and they’re interested in knowing which indications they should target for those sorts of activities.
Just knowing where to go, a lot of the time. I think a lot of these companies will have done a more scientific review of these diseases or indications and they have a scientific rationale for why their drug will make sense. But in terms of a commercial perspective, we obviously have a lot of commercial data through our solutions that enables us to give them that perspective as well in terms of assessing that which is easiest to pursue. I guess that’s two quite different things, whether it makes sense scientifically and then, at the end of the day, is that going to make sense as a commercial opportunity?
We usually like to at least start with a data-driven approach using our databases. We will typically align with the client on quantitative-based metrics to use to feed into our prioritization. Things like, what are the current sales for the indication, are those indications forecasted to grow in sales? We can look at things like competitive intensity. How many drugs are in the pipeline at the moment? How many drugs are marketed? What do we think the unmet need is going to be?
Then once we have some quantitative metrics, we will usually build and extract the high-level indication information from our databases. And we will build a prioritization model that the client can use to weigh different aspects depending on how they want to test different scenarios, and it will rank the indications just based on the data.
Once we have got the top view from those models, we will then do further due diligence either through secondary or primary research. For instance, we can test the TPP (target product profile) with the KOLs (key opinion leaders) to see for your particular product, which indication do KOLs think is going to be best? The client provides information on their product, such as the expected efficacy and safety, and we will interview key opinion leaders and ask them, on a scale of 1 to 10, how would you rate the product? Where do you think it will be best situated? Would you prescribe it? And that information can feed into these prioritization exercises.
The typical stakeholders can vary quite a lot depending on the client. On these types of projects, I’ve worked with people on the business development side of things, but I’ve also had stakeholders from the clinical team as well. Usually it does mean having to present to a wide range of different stakeholders and trying to mesh everyone’s different opinions.
It depends what the client’s objective is. A lot of it is just to help them make a more informed decision in terms of their development strategy. For example, one client had an early stage asset that was a target that’s implicated in inflammation, which is linked to many different diseases. They were finding it difficult to know which disease they should pick. And if you’re going to make that investment and run a clinical trial, you want to be quite certain that that is the right disease.
So, I think it is really just helping to reinforce their thinking and perhaps suggest indications they hadn’t thought of because they were much more focused on the scientific rationale, which is just one part of the puzzle when it comes to drug development.
A lot of times [the client] will come with quite a broad scope. For example, they will come and say they want to look at oncology, but they don’t know which disease within oncology they want to look at. There are a lot of different indications that fall within that bucket. In terms of how our data help, we obviously have a wealth of data in our platforms and we are able to extract information for a lot of indications quite easily. It’s tough to build that comparison without having access to a lot of data. It’s almost impossible to know where to even start because you can’t just do a huge lit review on all of these different diseases. So, the fact that we even have the data and we’re able to analyze it and manipulate it for the client is already a big help in terms of helping guide their decisions.
I think it’s interesting to see how different the use cases can be. As I said, we’ve had some people come to us in terms of just informing their own strategy for their assets. And then we’ve also had people come to us and say, “We’re looking for new assets within this area to in-license, but we don’t know which disease we should go for.” So, it’s two quite separate things.
But then in terms of the actual prioritization, I think it can also be inherently quite subjective. Even if we are using a data-driven approach, people interpret things in different ways and have different ways of thinking about it. For example, one thing that we use quite often in these models is probability of success. We have had clients prefer indications where the probability of success has been high, and we’ve also had the opposite where clients say, “Actually, we want to target indications where historically the probability of success has been low because then maybe that’s an opportunity for us.” So you can have quite different thinking from different clients, which is interesting.
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