So this is a short video around our approach
to portfolio decision-making. So early phase
to late phase and why our new approach actually
has been built over many years to be better
at making decisions in portfolio decisions,
product development decisions, new product
planning decisions. Where the folks that build
the pharmaceutical innovation index and, if
you remember why it was because 10 years ago
we asked ourselves the question when we wanted
to understand what tends to lead to innovation
to successful commercialization. Are there
processes or are there are systems that tend
to lead to drugs launching better than either
than a direct competitor or, than not launching
at all. So the fundamental question was if
you gave the same drug to two different companies
in early phase, ideally phase one, would they
be equally successful? And this you kind of
go through trying to understand that.
Well, you know, we wanted to understand the
behaviors and the processes that tend to lead
to innovation. And by successful innovation
we mean asymmetrically better performance
than somebody else. So outperforming someone
who's been running in parallel with you. Clearly
sitting at the core of that, our definitions
of invention and innovation imported from
everywhere else, which is that they're not
the same. So the things that tend to lead
to good pipelines aren't necessarily the same
behaviors that tend to lead to successful
commercialization of those pipelines. So,
you know, clearly we need to think about the
role of, of surprise and agility within the,
within the process. So we started to play
with this idea of, you know, if you wanted
to plan serendipity, could you do it? Could
you be serendipitous by design? And of course
we think that you can. So we want to play
with that and store this idea on the shelf
for a little while in this video of plan serendipity.
And then of course a lot of drugs fail because
of the question that they're asked in the
studies.
So, you know, you've got good drugs asked
the wrong question in the study and they go
back on the pipe on the, on the pile of drugs
that probably will never see daylight. Let's
think about all of the recent case studies
where drugs have outperformed somebody else.
They haven't come from better molecules. They've
come from better decisions taken by teams.
You look at, I mean the one of the best case
studies in my general recently we've seen
two companies heading straight to market after
a bunch of interesting, you know, problems
with these drugs were in early development,
Opdivo, Keytruda is playing out in front of
us. But this isn't about the drugs actually
themselves being different. This is about
the decisions that were taken by the humans.
So how do we think about human decision making
in a situation where the molecules themselves
are unpredictable in terms of their success?
So how do we begin to think about what tends
to lead to better decisions by humans within
a development process?
Rather than the kind of, we're going to suggest
failed decision making model that we currently
have. So our big observation or big leap,
if you like, is to think about the problem
of what we have today is that it is a prediction
paradigm. So most of you are working with,
and what we would call a prediction paradigm
and we can show that the prediction paradigm
has failed. You know, the creation of TPP
is a creation of eNPVs and the forecast and
everything that goes with it, our failures
as templates and systems as processes. And
you can talk about how your version is slightly
better than somebody else's, but it's still
part of the prediction paradigm. If you decide
to differentiate rather than just fit one
of the current market landscape, you're going
to need to do something different. So let's
think about what's important. What's important
isn't getting two decimal places in terms
of your forecast or in terms of your probability
of technical success. It's being directionally
right. It's getting you into the right place,
not into the right sort of, you know, two
square centimeters of the market.
So your job, the task of the teams in this
early phase is to be iterative, not to be
right, but to be iterative, to get the direction
of the opportunity right from having examined
lots of different opportunities. So it's liberating.
It means you can come up with ideas as a team
rather than just trying to predict the ones
that you, that you currently have tends to
lead to the this basic idea, which is, which
decisions are you taking? Well, in early phase,
the decisions that you take are what we would
call contingent. It's an 'if then' decision.
So we're going to do this and if this happens,
then we're going to pursue this. But also
we might look at this so that if this happens,
we can go down this. Well that's a very different
kind of decision making process than the one
that we're employing based on, you know, the
old gray suited consultancies that have told
you that a template needs to standardize everything
because we're not making a final decision
in early phase. We're making a contingent
one. And it involves a lot of what if on the
way through.
So you think about predictions and why they're
wrong. I mean, know we work with them all
of the time now and we're all tasked with
coming up with numbers. But you miss one thing
in a prediction paradigm and everything's
off. So we know that they're always wrong,
but that's why, because we're making predictions
on things which have to be wrong, just because
many of them are unknowable, not just unknowns
in the process. And that's if just one variable,
the, you know, the one missed side effect
is off. But, you know, within our development
process, we have hundreds of variables which
can affect, you know, how that drug tends
to perform. So then we get back to, well,
why does serendipity seem to be more important
than, than planning? Well, it's, that's the
reason which is there's too many variables
to fit in. So, and anything about how hard
it is to understand the market, well, it's
impossible for a TPP to accurately represent
a product to anybody. So what are predictions
for, if they're always wrong? Can we use any
parts of that process in a better process
and take out some of the problems that are,
that are there?
So one of our tenets was, if you can't be
right, be useful. So the goal of evaluation
for the teams that do this work is not to
be right. It's to be useful. To the heads
of R&D, to portfolio managers, to their colleagues.
It's to ideate this, to evaluate. It's to
say, well, what if we were to head to this
place and if we can move from prediction to
generating hypotheses, that kind of, if then
kind of hypotheses, then we're doing something
useful. Then let's think about what phase
two traditionally is forward. We tend to interchange
with the idea of proof of concept. Well, the
idea of proving concepts is that those concepts
of course are variables. We could have different
concepts in phase two or that same one molecule.
So if we can engage the R&D folks in a variety
of proofs rather than just the one that you
know, was laid down a little while back, then
we've got the possibility to generate differentiated
insight from anyone else.
And this is the key to this asymmetry that
we talked about before. We want to move from
the idea of directed predicted, development
to one which is more open, more agile to different
ideas on the way through. And we moved towards
what we would call asymmetric learning. So
the idea of taking time to make decisions,
taking time to explore is that you move towards
to asymmetric potential because you're going
to learn faster, better, harder, what the
drug does, what the market wants, what all
of those things are for. And this kind of
idea of a learning environment is a world
away from the prediction paradigm, which we're
all stuck in at the moment. If we look at
different options for a drug early and creative
options for a drug early, then really the
most useful thing is that we're showing, a
picture, not necessarily numbers, but a picture
of risk and opportunity. Where does the risk
come from? Where does the opportunity come
from?
Better loop that back in the iterative process
so that people can address those risks if
they're shown transparently. And we know the
problem with the prediction paradigm is that
people game the numbers. So, you know, probabilities
of success tend to be, you know, over 50%
and probably a lot over 50%. But we know that
that's not what happens. You know, the number
of drugs that had that number assigned to
them then go on to fail shows that it's a
useless piece of the journey. So understanding
that means that's thinking about the humans
involved or why they're doing that. They're
playing with the incentives. If their incentive
was to help the organization be useful, then
we come up with different options. So, you
know, one thing that we want to focus on is
if you give the same product to two different
companies, that's what's happening today.
Giving the same product to two different companies
because very few people have that window of
exclusivity on the way through now. So if
we focus on human decision making, contingent
decision making, our job is to harness asymmetric
learning, learning more, faster, more creatively
than the competition about the market, about
the molecule, about the regulatory process.
It's about asking questions, not about trying
to predict. So within a deliberate, the decision
making process, how do we establish those
known knowns and the areas of uncertainty.
But also let's think as a portfolio head is
a head of R&D, your teams are guessing your
priors. They're guessing whether you want
fast or big or easy or hard. What about if
they showed you different options which have
those things included but not pre-decided
before it gets to you? What happens then is
you engage the teams in a interdependency,
so properly multidisciplinary teams where
everyone is creative rather than just taking
this Baton pass of ideas as they go along
the engine. So we're going to talk to you
more about FIVE-X and how that addresses this
problem of getting to asymmetric learning
and away from the prediction paradigm within
development. So watch this space. And we'll
back to you with some more soon Thanks.
