Making Dependencies Count: Smarter Portfolio Decisions

At the recent Why Summit in Vegas, I shared insights about key modeling features that improve how we evaluate projects and portfolios in drug development. One of the features I discussed was dependencies—a feature that has always been central to how we think about portfolio risks at Captario.

Dependencies exist whether we model them or not, and they can add substantial risk to our portfolios. Addressing these risks systematically is critical for better forecasting and decision-making.

From the beginning, Captario SUM has allowed users to model dependencies between projects seamlessly. In the original implementation, all assumptions and outputs for every project were accessible to other projects in the same portfolio. Setting up a dependency was straightforward: for instance, if Project B’s probability of success (POS) depends on Project A’s Phase 3 success, we could write an expression like this:

If ( [Project A].Phase3_Success = False ) Then

Bernoulli (0.2)

Else

Bernoulli (0.5)

End

This configuration ensured that if Project A failed, Project B’s POS dropped from 50% to 20%. This change, in turn, affected Project B’s evaluation and increased the portfolio’s overall risk. The ability to make such connections has allowed our clients to model complex interdependencies, leading to more accurate evaluations of projects and portfolios.

🔍 However, feedback from our clients highlighted two areas for improvement:

1️⃣need for a central overview to manage dependencies.

2️⃣ The ability to toggle dependencies on and off.

In response, the latest release of Captario SUM elevates dependency modeling to the portfolio level. Now, dependency-related assumptions are published by the independent project, and dependencies are managed centrally through the new portfolio orchestration. This update provides:

✅ A clear overview of all dependencies, making them easier to manage at scale.

✅ The ability to toggle dependencies on or off, allowing analysts to evaluate projects independently and assess the portfolio with dependencies included.

By understanding and modeling dependency risks, we can better navigate the complexities of drug development portfolios.

Dependencies are always there, but making them explicit allows us to measure their impact, optimize decisions, and reduce surprises down the road.