How to support new technologies

A version of this post was first published on LinkedIn

Drug discovery using new technologies, including AI, needs a new form of programme and portfolio management and a wider skill set than normally assumed for these roles. Why do I say this?

Project or programme management roles in drug discovery used to focus on tracking progress, only occasionally monitoring bottlenecks and capacity constraints. But there is now a huge opportunity - and need! - to contribute to the implementation and validation of entirely new technology and processes.

This means asking, systematically:

• Where does the new technology have an impact on the programme?

• What is the nature of the impact? Can it speed some things up? Slow others down? What new things to look out for (risks, in PM speak) come with the technology?

• Where are the proof points and how can they be monitored?

• Are portfolio-level considerations influenced by the new technology? For example, there may be a choice to run programmes with a higher commercial risk because they may be valuable for technology implementation or development.

To answer these questions, tech- or AI-enabled drug discovery programmes need to be data-smart: what type of data is generated where along the way, what decisions does it influence, where could it help identify issues early so they can be addressed, how can it be used for further technology validation, or highlight where new processes are needed?

In addition, small companies looking to prove that a new technology really makes a difference for drug discovery - often aiming to build capacity for the latter up from scratch - will also gain a real advantage if supporting roles, including programme management and portfolio ops, are aware of the many different disciplines involved in drug discovery.

For example, you may discover compounds that look like a perfect match, but can you assess developability, toxicity, or other specific potential risks early as well so they can be built into your programme plan and portfolio-level decisions? Many of these questions are obvious if you know that they need to be asked, but they can trip up companies that are new to this area.

I’ve always found it essential to spend time understanding what scientist and tech colleagues are trying to do - quite similar to the approach used in UX Design or product management. Not on a detailed science level (although I am always curious, and learning new things makes me happy), but in a “what could this mean for programme implementation or the portfolio” sense. I also find operations awareness and understanding capacity modelling helpful: what does it take to make sure things run smoothly? Maybe even more importantly, how do we assess impact? Again, making sure the right data is gathered at the right time is essential.

This isn’t “cookie-cutter” drug discovery programme management and portfolio management: using the valuable opportunities and drivers new technology can offer needs a new, data-smart and data-driven approach.

Whether you find this helpful or have feedback, questions or additional thoughts, I would love to hear from you: consult@steffisuhr.com