Simple question, hard problem

On the opportunity for pharma to change its approach before the wheels roll...

It's easy to confuse questions and problems.

I was musing on the simple question: what would be the fastest I could travel between Lands End and John O'Groats, in an electric car (famously, in the UK, the two furthest apart points)?

There are, of course, a lot of answers, but all of them have a lot of variables: is the start time fixed?, for example.

I remember an illuminating chat with someone from Google X, who described how much different a route search on Google Maps is if you don't just want to know the fastest route, but add in an extra question: find me a good restaurant along the way, for example. That increases the demand on its algorithms dramatically - taking it from an easy question to a hard one. What does 'along the way' mean? What is a 'good restaurant'?...

So, let's come back to our electric car question. Because it's an area that attracts evangelists and nerds in equal quantities, of course there are lots of really smart people online keen to point out that long journeys in an electric car are possible (like this hero). So, let's think about the variables: which car, which charging points and what kinds of charger are available, what is the best recharging strategy, which route, even before you get to 'is cost a consideration?' or 'what speed is best to travel at?' (Never mind 'how much do you suffer from range anxiety?' or 'what if none of the chargers are working?', a constant problem in the UK at the moment.)

Then, consider that some of your variables are co-dependent: choose a Tesla and you have access to chargers that others don't (faster and more reliable). It also matters if you're allowed to recalculate en route, or have to fix your route ahead of time (as with the cycling records).

Of course, people will find it easier to fix one of the variables: you start with a new Tesla Model 3. (Tesla's (terribly-named) Autopilot will make a lot of choices for you, such as route that goes via its chargers.)

That is essentially what we have in pharma: our first prediction begets the TPP, which narrows the range of choices we have to make. It is more comfortable if the question is 'what's the best way to get this Tesla Model 3 to John O'Groats, given I am starting now...?'

What you know, if you answer the question this way, is that you'll have an answer that satisfies many. What you will never know if how else you might have answered the question. What if we'd splurged and started with a better/ different car (molecule)? In pharma, a portfolio review won't allow time to look at what else you might have considered, to its detriment.

Even with a fixed A and B, thinking time, and full consideration/ calculation of all the options before setting out, is the only way to arrive at a best answer. The problem may be immensely harder when its deeper structure is considered, but it is not unknowable/ unanswerable. It certainly forces more questions, but isn't that how we want to spend our time? Any good strategy should be asking: 'What is fixed and what is variable?' or 'how much do I trust the evidence at hand?' (If you rely on only published range figures, before choosing your car, do you trust the manufacturer or magazine reports, for example? The Model 3 'Long Range' in Car and Driver's hands achieved only 230 miles vs Tesla's claim of 353 miles.)

The opportunity in pharma is to understand how best to use our thinking time before phase I starts and the wheels roll. If we don't, we may find that we turn up at John O'Groats to see our competitor enjoying the view. The difference, the asymmetry, will be in how they chose to find out, not just in how they drove.