Presentation on theme: "Exploring Unique Learning in Bio- Pharmaceutical Innovation Deborah Dougherty Danielle Dunne Rutgers U Grounded theory building: core processes, activities."— Presentation transcript:
Exploring Unique Learning in Bio- Pharmaceutical Innovation Deborah Dougherty Danielle Dunne Rutgers U Grounded theory building: core processes, activities in this kind of innovation, and how to organize, manage Focus on drug discovery, early development (first 6 yrs of yr process) Trouble in bio-pharma city! Existing models little help (feel huge voids, not just gaps)!
Limits of what we know Technology: presumes linear, decomposable, scalable, path dependent, Newtonian (laws of physics), concrete, objective –Stage-gate, platforms, architectures, modularity, CE –Engineering based Life sciences (pharma): non-linear, non- decomposable, non-scalable, pathless, obey laws of life and social sciences; knowledge is descriptive, limited, requires multiple levels Others that do not fit: “bio” plus, “nano” plus, health care, ecologies and climate changes, terrorist and intelligence systems
Techno-hype Our business is very different from making cars. You make a car, you look at all the pieces and you know how to optimize the tires and how to put together the gears… You know in much more detail what you need to do. Here, it is very (uncertain); given a negative or a positive experiment it takes still a lot of intuition to make the next one… we value technologies that vastly accelerate things but we are still dealing with a very complex system. So imagine that you are in a race where you have this fancy suit where you can run 10X faster, but if you don’t know where you are going it does not make a difference how fast you got there The classical project management comes form engineers and people who are more attuned to set processes… and processes that are not so much in the state of flux as drug development. Every project is so unique even within the same therapeutic area… there are so many caveats and so many nuances to any… drug program A biological system is the ultimate in engineering. It has had billions of years of tweaking to get here…. The tools are different, the methods are different (from engineering), but still one thing has to work in concert with another thing to get a function. We just have many more regulatory levels, most things do multiple things at once… in five hundred thousand years, cars will probably be very biological in the sense of massive redundancies and systems overlap…
Past 30 years, all about sticking in technologies Microbiology: look directly at cell mechanisms Biotechnology: cloning, create huge quantities of proteins; gene replacement (nada); protein replacement (a few: insulin, factor VIII) Combinatory chemistry Rational drug design… Genomics: know all the genes… High throughput screening: test million at a time… Robotics Structural bio; bio-markers; analytical electroscopy Assays for everything… OUTPUT dropped in half nonetheless – NOT just technology!
Non-decomposable Hallmark of industry! Massive redundancies, cannot separate parts or assume known links Molecule, protein, cell pathways, mechanisms disease process, organ system, body… So, what to optimize? How to manage whole blob over 16 years???
Conceptually differentiate distinct knowledge systems –Core: sciences (biochem, phys, micro etc., chemistry, physiology, pharmacology…) –Recognize difference in goals from academic sciences –Technologies (instrumentation to collect and process data for science, industrialization, search, techniques (delivery etc.), representations of life system, lines for science –Strategy: new role? Risk mgt, long term view –Process mgt: how to enable each plus combos, oversee processes, progress, choices… –Therapy areas? Think is boundary “space”
Then figure out each one’s unique contributions, mechanisms of knowing, how enable others Academic Sciences: understandings, new insights (fragmented, limited integration, lab and person based?) Industrial Sciences: what we don’t know: questions to ask, how, where to search, what to make of answers? (searching for clues, iterating into partial wholes, based on entire DDD process) Technologies: what we do know: search engines, high throughput science; generates answers, bundles old stuff new ways, see wholes, “verify” certain options Strategy??? See what do not know better? Select, mge categories of risk? Absorb LT insights? Shape exploration (not exploitation)
Science is about knowing when to stay the course and when to leave. (how do you know). You know the data to access and the experiments that have to be done and whether or not they will cover enough (of the info or situation?) to answer the questions and test the hypotheses. And your experience with how long it takes to break through a technical barrier, and whether or not it is worth your while to stay with it or go over to something else. I have been in places when I was outlining the constraints a project faced, getting ready to drop it formally, and then we have a breakthrough… (referred to picture on the wall of a cover of Science with her and team…) We wanted to try and find other molecules like that but they must be smaller… The natural ligand is much larger… We had some clues that we thought would be important. There were other receptors in the same family, and other clues about what could make a connection. Molecules are bumpier than a flat surface… We visited another pharmaceutical company and different biotech companies, and even within our own company. We proved we should do it. We have a collaboration with a biotech start-up… looking through libraries with millions of possible ways of how to arrange peptides… I remember very vividly thinking it was a waste of time. Also we were trolling in a couple of other areas like natural products, anti- bodies. Once we got the peptide and they combined, we did our first crystal structure. We worked with XX at Scripts laboratory, we did that with an academic collaboration.
Some Noodles Somehow, if we know the connections among the systems, can “fire” them off, like lightening strikes, and see the drug possibility in the whole life system Will always take enormous judgements, but better than separate points Knowledge accumulates within the knowledge systems, and also somehow in the connectings This is purely social Everyone must do own integration All knowledge systems must translate to others proactively
Non-Scalable Due to complexities, each drug is unique (and they focus in on each, not on system) No standards, core operating principles or frames to manage Knowledge accumulation not automatic, but inherently limited Projects morph anyway (like starting to build a car, end up with a roller skate) Cannot just manage “outside”, must manage internal or emergent dynamics instead
A few noodles Manage the questions: good ones to ask when, why Manage total answers, not single inputs Develop platforms for connecting Cannot surface problems? So surface problem setting and ongoing iteration (problems have multiple causes) Fractals? But compounds vary? Cycles: over 6 years, circles go counter clockwise from here back to target, clockwise forward to drug
Back to beginning… Enabling searching for clues, iteration, sense of the scientist in concert with other modalities of knowing: what are key properties? (Earnst Mayr, 2000, age 90; AIBS distinguished service award, bioscience v. 50, 10:896) –The basic philosophy of biology has become quite different from the classical philosophy of science (rejecting vitalistic theories and physicalist concepts…) and their replacement by an acceptance of the importance of historical narratives, multiple causations, population thinking, and the greater importance of concepts than of laws in theory formations… Biology, he said, is far advanced with basic phenomena, lag in understanding complex systems. KLIC too!