"We really need some planning support..."

You're an early stage drug discovery and development start up and your scientists keep saying that they could sure use some support with planning and managing programmes. Or you are worried that you may lack the visibility of what is really going on across your portfolio (you may even sit in on as many programme meetings as you can, but that's really getting unsustainable and is way too much in the detail for what you need right now, anyway - fun though!).

Having worked in a variety of related roles in different start ups and watching closely what can make a real impact vs. where things (often quickly) fall through the cracks, here's what you might want to consider when you really start thinking about planning - even if you don't have the headcount yet to open a position.

Developing strategy

• Any work to define (and redefine) overarching strategy has to look across all relevant areas - e.g. both tech and drug discovery. This particular case is really quite hard as both worlds tend to pull away from each other if left alone, due to different ways of communicating, thinking, and prioritising work!

• The overarching strategy must be complemented by domain-specific strategies, respectively: the practical things that need to happen to bring each closer to the other.

• To develop the overarching strategy, relatively in depth knowledge of all relevant areas is needed - what are the things to look out for? How can they be addressed? This involves a large variety of things, from how to think about data as a core resource, to understanding different planning cultures (or lack of), the different pace of work or even mentality and approach to problems for example in tech vs. drug discovery, the major risks, typical bottlenecks and practical and operational aspects (including CRO management), and more. Many of these can be resolved by solutions even completely outside of tech, such as better processes or communication across functions or teams.

Identifying and monitoring impact

• For example, if the goal is to apply AI at different points along the drug discovery pipeline: this could quickly become a distractingly broad problem. To succeed, and to be able to demonstrate successes, there is a real need to identify and, at least initially, focus on specific problems along the drug discovery pipeline: where can AI really impact choices that need to be made, or speed, or produce higher quality output?

• AI may be used to model certain things that would then be followed up more selectively than is normally the case, resulting in gains in speed and accuracy, and potentially leading to cost savings (NB modelling and selective follow-up is of course already possible and routine for various things, e.g. certain toxicity risks at early stages of drug discovery).

• Once strategy is defined, ongoing monitoring of its implementation is key, as well as the ability to adjust strategy to respond to data, technical challenges, the wider landscape the organisation is operating in, etc.

• Programme and portfolio management - embedded across different functions at working level - is a key tool to support the implementation of overarching strategy.

Data-smart programme management

• Results from experiments need to feed back into tech development for ongoing groundtruthing and improvements or adjustments.

• It is really important to examine how and where data for this groundtruthing is generated - at CROs vs. in house? At which stages along the drug discovery pipeline? In terms of planning, when will this data be available (these are key points to connect drug discovery and tech development cycles!).

• Is the necessary data infrastructure in place, together with the necessary tools and a FAIR data culture, where *all* stakeholders routinely think about data as a valuable resource? Suitable and knowledgeable programme management can help connect the dots (and keep them connected).

• Clear, transparent and proactive communication and knowledge sharing across tech and drug discovery is absolutely critical; all functions must talk to each other and be able to talk to each other and learn from each other: developing a “shared language” across the two quite different “cultures” is hard work and needs dedicated, ongoing effort and bandwidth/resource.

When you're ready to develop those planning positions further, do get in touch! consult@steffisuhr.com