gms | German Medical Science

There has been a recent rise in the use of combination therapies with the FDA reporting over 10000 clinical trials that contained combination therapies in 2017 alone. While this is a promising development, it poses some new challenges for pharmaceutical companies. One of the biggest challenges regarding a portfolio of combinations is selecting which studies should be run from the large set of possibilities.

In the academic setting public funders face a similar challenge. They are routinely asked to make funding decisions about trials using the same drug either in different combinations or different indications.

Existing methods for portfolio planning use optimisation techniques to find the optimal set of studies, and their associated schedule, based on inputs such as study success probabilities and expected revenues. While these methods are often able to capture the uncertainty that is inherent to drug development, they are not able to capture the relatedness of the decisions.

We outline a method that allows us to consider the fact that some of the studies within the portfolio may be related and their outcomes may be correlated. A key features of the method is that the study success probabilities are updated dynamically throughout the decision-making procedure each time a relevant outcome, such as the outcome of a related combination study, is observed. This allows our decision-making procedure to capture all available information relating to a study and use this to inform future decisions based on emerging information.

We will discuss the potential gains in applying portfolio decision methods on the projected revenue of a company in the future and draw parallels to using such an approach in the academic context when exploring funding decisions.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.