Pharma needs a science lesson
...and pretty damn quick!
[Posting an extract from Find It, File It, Flog It on this sunny Saturday]
Pharma needs a science lesson
Science is at the core of the industry—the lion’s roar. On that front is a two-part lesson. First, in the commercial world, science is impotent if not partnered with the translational methods in the world of engineering. Together, they travel from a good idea to a product in the hands of end users.
Second, far too little science is applied in the early stage of FILE IT.
I will explain more because I suspect industry stalwarts will be up in arms at this point.
Observations, Views, and Experiences of the Author
Important though science is in any endeavor, we don’t dwell on the physics or thermodynamics involved in flight, nor the science of the internal combustion engine, nor any other product that ends up on the market. We know it must have been there, but that’s it.
“Ah, but the human body is a million times more complex than an aircraft or a car.” they declare, “Medicine is different.”
Yes, to the first point, no to the second. Making medicine is no different.
There is so much we don’t understand about the sky, yet we make aircraft, rockets, and satellites by accepting the unknowns and working around them. Whether the sky is less complex than the human body, who knows? That is academic. Humans will probably never understand either.
Honestly, developing medicines for humans is no different. Yes, there is science in deciding what molecular structures could target particular disease mechanisms and receptors, but the dreadful attrition rates tell us this is something of a blunt instrument.
Remember, 249 out of 250 scientific theories were wrong, and it took years to discover that. Ironically, where science could really provide a return on investment, it is not applied.
There is a tremendous need to use the latest scientific advances in predictive technologies at the point where development candidates are chosen to enter a development programme.
Much can be achieved these days in predicting issues, using computer simulation and testing in animal and human tissue, for example (in silico and ex vivo techniques).
Sadly, the prospect of getting into clinical trials attracts far more attention, as the valley of death is about to claim another victim.
This is why I argue that Big Pharma needs a lesson in science.
Systems Thinking and Engineering
Systems thinking expert Peter Checkland makes this point nicely with his soft systems methodology. Science is a vitally important discipline in which, as Checkland puts it, “the highest value attaches to the advancement of knowledge.”
Scientists are trained to use reductionist thinking: running experiments and drawing conclusions from them. Checkland compares this way of working with the mind-set of engineers and technologists, who “prize most highly the efficient accomplishment of some defined purpose.”
He provides an example of his work in the science-based Imperial Chemical Industries. His team was charged with developing synthetic leather to seize a market opportunity. A research scientist gave the project a negative response. Checkland reports his comment:
“The three dimensional matrix of natural leather is so complex that it cannot at present be accurately described; therefore we cannot hope to simulate it.”
The research scientist assumed that the question was about furthering scientific knowledge. Checkland’s observation was:
“Had [the scientist] assumed the question to be a technological one, he would have asked not ‘can we copy leather?’ but ‘can we imagine a material which will perform satisfactorily in end users’ hands in which natural leather is now used?’” The search is then totally different.
It is about finding an alternative solution to an end user’s problem. This is a vitally important distinction when developing products. How many companies are studying their molecules and materials rather than developing solutions for patients?
We know from the US GAO figures that on average, they are studying 10,000 molecules for every one that gets to market.
Here then, we argue that Big Pharma companies are selecting compounds (development candidates) to register for sale when they have no idea about their chances of success. We are not saying they should be certain, because nothing in life is, but they should make a sufficient attempt to thoroughly check the robustness of the candidate for the rigors ahead, the journey through the valley of death.
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