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Best practices > Drug discovery & Development
Biopharmaceutical Best practices, lessons learned & ongoing
challenges DRAFT
Evolving Terminology for Emerging Technologies
Comments? Questions?
Revisions?
Mary Chitty MSLS mchitty@healthtech.com
Last revised
January 07, 2020
Challenges: Spending more and more on R&D and getting fewer and fewer new
drugs is not a sustainable business model. Does pharmacogenomics hold the
potential to make every drug an orphan drug and every disease a rare disease?
Will there be enough patients in the world to fill Randomized Controlled
Clinical Trials?
Bridging
the silos between biologists, biophysicists, chemists, IT, toxicologists, patent
attorneys, CFOs, CEOs and clinicians [and patients] present some of the biggest
challenges of all. My own undergraduate anthropology major is more relevant to
me now than it was when I graduated.
I'm realizing that the variety of skills and domain expertise needed for
breakthroughs in the life sciences is more than anyone discipline and/'or
lifetime(s) can muster. While new technologies might be helpful, cultural
shifts, incentives for collaborating, and pre-competitive cooperation will also
be important. Incremental changes can be helpful more frequent than true
paradigm shifts. Industrialization, scalability and automating and ramping up
processes to move from the R&D lab into the clinic are under-appreciated
challenges, as are marketing dilemmas that come about with disruptive
technologies.
Best
practices
Biology : Nature is very clever. Leveraging
biomimetics [high-tech pharmacognosy] can help the odds.
Chemistry: Fail fast
Killing compounds early has been said to be like “drowning puppies in front of
small children”. How many companies reward this vital task? But even failed
drugs may be repurposed and less interesting compounds licensed out for others
to investigate. Repositioning existing drugs can leverage existing toxicity
profiles. Who would have thought thalidomide would be back on the market? But
if compounds are going to fail, killing them quickly saves time and money
Company size is not an indicator of success in terms of R&D productivity … the
strongest single correlator with success (odds ratio 3.9) was having a high
termination rate in preclinical/Phase I stages. This indicates that companies
have an early idea of which assets are likely to succeed, and that the companies
most willing to face the hard decisions about which assets to terminate do
better than companies that let assets linger. Does size matter in R&D
productivity? If not, what does? Michael Ringel, Peter Tollman, Greg Hersch and
Ulrik Schulze Nature Reviews Drug Discovery 12:901-902, Dec 2013
Clinical
trials and personalized medicine
Selecting patients who will respond to compounds, without toxic effects
[pharmacogenomics/ personalized medicine] will enable better, faster, cheaper,
more definitive clinical trials, and help to meet unmet clinical needs. But
biomarkers for patient stratification to identify non-responders and toxic
responders are still nascent.
Innovation & productivity I had hoped drug companies which encouraged open sharing of
scientific information would prosper in the long run, without finding much
evidence (even anecdotal) until I read this report, which quantified the
positive correlation between companies encouraging (or requiring) peer reviewed
scientific publication and productivity (patents issued to company scientists
and articles published in peer- reviewed journals by company scientists). Diffusion
of Science Driven Drug Discovery Organizational Change in Pharmaceutical
Research, Iain M. Cockburn, Rebecca Henderson and Scott Stern, NBER, Sept.
1999 I asked a few years ago if this paper
had been updated – but it hasn't been. http://www.nber.org/papers/w7359
Are there other best practices we can identify?
Lessons learned
Biology Genes and
proteins, genomics and proteomics are just molecular biological starting points.
If DNA is 2-dimensional, and protein structures are 3D, then post-translational
modifications and alternative splicing are 4D temporal-spatial relationships
constituting profoundly dynamic systems. DNA makes RNA makes protein[s] is still
true -- but with infinitely more variations than originally envisioned.
in most contexts, talk about being "post- genomic" seems premature. "Post
Mendelian" seems more accurate as we move from an era in which genetics has been
rooted in monogenic diseases with high penetrance to a greater awareness (but
limited understanding) of polygenic diseases (and traits) often with relatively
low penetrance.
Biomarkers & molecular diagnostics Diagnostics
almost always precede therapeutics, and “companion diagnostics” or theranostics
are increasingly important and closely aligned.
Bioprocessing & manufacturing Just
getting enough of compounds can be the first challenge. Biotech started as a
means for bioproduction, but now biotechnologies, genomic, proteomic and
metabolomic tools and insights permeate every/many? stage of pharmaceutical R&D.
Scaling up is a profoundly underestimated challenge.
Business: Some
technologies are improved in a linear fashion or incrementally. Others
truly change the paradigm. Clayton Christensen writes about these
in The Innovator's Dilemma. What is interesting about Christensen's
analysis (based on data from the disk drive industry) is that he found disruptive
technologies tended to be much cheaper than existing technologies.
Existing companies were quite capable of developing the technologies (and had).
What they couldn't do was figure out how to market them and whether it
made sense to devote sufficient resources to them (which in many cases would not
have been the responsible thing to do.) The pharmaceutical industry is
mentioned only in passing, but the success of larger established companies
either partnering with smaller less established ones (clearly happening in the
pharmaceutical and biotechnology sectors) or spin- off of promising developments
as separate companies makes a lot of sense.
Chemistry We
conclude that a substantial sector of the pharmaceutical industry has not
modified its drug design practices and is still producing compounds with
suboptimal physicochemical profiles. Paul D. Leeson and Stephen A St-Gallay The
influence of the "organizational factor" on compound quality in drug discovery,
Nature Reviews Drug Discovery, 10:749-765, Oct 2011 http://www.nature.com/nrd/journal/v10/n10/full/nrd3552.html
Ongoing challenges
Chemistry There isn’t
enough matter in the universe to make all possible compounds. High throughput
screening needs careful compound prioritization to avoid being the chemical
parallel of trying to get monkeys with typewriters reproducing Shakespeare,
Drug development Proofs of concept enable better experiments and
fewer [expensive] unpleasant surprises.
ADME -- We know a lot about
absorption and distribution, not so much about metabolism and excretion.
Drug safety & pharmacovigilance From
preclinical through Phase IV and post marketing surveillance, drug safety just
never ends. Idiosyncratic toxicities don’t emerge until drugs reach larger
populations.
Drug targets Greatly
increased numbers of targets mean even more need to be validated as hits and
to optimize leads. Papers per compound have dropped from > 100 to < 10 according
to Lehman Brothers 2001 report The Fruits of
Genomics.
Informatics isn’t
as much about old knowledge becoming obsolete, but becoming more and more
granular, with elements of chaos theory thrown in. .
"A month in the lab can save
you an hour in the library." said bioinformatician Phoebe Roberts.
"The biomedical literature is even noisier than microarray data." says John
Quackenbush, of the Dana Farber Cancer Center
Moravec’s Paradox: the discovery by artificial intelligence and robotics researchers
that, contrary to traditional assumptions, high-level reasoning requires
very little computation, but low-level sensorimotor skills require enormous computational
resources. The principle was articulated by Hans
Moravec, Rodney
Brooks, Marvin
Minsky and
others in the 1980s. As Moravec writes, "it is comparatively easy to make
computers exhibit adult level performance on intelligence tests or playing
checkers, and difficult or impossible to give them the skills of a one-year-old
when it comes to perception and mobility."[1]
http://en.wikipedia.org/wiki/Moravec%27s_paradox
Artificial intelligence and machine learning have been developing for decades,
but recent advances in computing power and scale seem to hold promising
developments for pharmaceuticals and healthcare.
Question from Nature column Lifelines put to Michel Brunet, palaeontologist
"What is the one thing about science you wish the public understood better?"
Answer "That the 'truth' is always an asymptotic ideal." Dreams of the past,
Nature 423 (6939): 121, 8 May 2003
Innovation, productivity and diversity
Novelty is an essential
feature of creative ideas, yet the building blocks of new ideas are often
embodied in existing knowledge. From this perspective,
balancing atypical knowledge with conventional knowledge may be critical to the
link between innovativeness and impact.
Our analysis of 17.9 million papers spanning all scientific fields suggests that
science follows a nearly universal pattern: The highest-impact science is
primarily grounded in exceptionally
conventional combinations of prior work yet simultaneously features an intrusion
of unusual combinations. Papers of this type
were twice as likely to be highly cited works. ... the
production and consumption of boundary-spanning ideas can also raise well-known
challenges. If, as Einstein believed, individual scientists inevitably become
narrower in their expertise as the body of scientific knowledge expands, then
reaching effectively across boundaries may be increasingly challenging,
especially given the difficulty of searching unfamiliar domains. Moreover, novel
ideas can be difficult to absorb and communicate, leading scientists to
intentionally display conventionality.
Atypical combinations and scientific impact. Uzzi B, Mukherjee S., Stringer M.
Jones B. Science 2013 Oct 25;342(6157):468-72. doi: 10.1126/science.1240474. http://www.ncbi.nlm.nih.gov/pubmed/24159044
http://www.kellogg.northwestern.edu/faculty/uzzi/htm/papers/Science-2013-Uzzi-468-72.pdf
This
article talks about how Newton presented his laws of gravitation in Principia
using accepted geometry instead of his newly developed calculus, and Darwin used well
accepted knowledge of selective breeding of animals in the first part of Origin
of the Species.
Expertise
does matter: Teams publishing in high-impact journals have a high fraction of
incumbents. But diversity matters too: Teams with many former collaborative
links offer inferior performance. …When forming a “dream team” make an effort to
include the most experienced people, whether or not you have worked with them
before. The temptation to work mainly with friends will eventually hurt
performance … A mathematical theory of human dynamics may not be the solitary
achievement of a genius scientist but will likely emerge from the combined
efforts of an expert team with just the right combination of expertise,
collaborative experience, and fresh ideas. Network Theory--the Emergence of the
Creative Enterprise Albert-László Barabási* Science 29
April 2005: Vol. 308 no.5722 p. 639-641
DOI: 10.1126/science.1112554 http://science.sciencemag.org/content/308/5722/639
[abstract
only]
Here, we investigate how the mechanisms by which creative teams self-assemble
determine the structure of these collaboration networks. We propose a model for
the self-assembly of creative teams that has its basis in three parameters: team
size, the fraction of newcomers in new productions, and the tendency of
incumbents to repeat previous collaborations. ...research
shows that the right balance of diversity on a team is elusive. Although
diversity may potentially spur creativity, it typically promotes conflict and
miscommunication … We analyzed data from both
artistic and scientific fields where collaboration needs have experienced
pressures such as differentiation and specialization, internationalization, and
commercialization (4, 10, 11):
(i) the Broadway musical industry (BMI) and (ii) the scientific disciplines of
social psychology, economics, ecology, and astronomy (Table
1).
For the BMI, we considered all 2258 productions in the period from 1877 to 1990
(12, 13).
…. For each of the scientific disciplines, we considered all
collaborations that resulted in publications in recognized journals within the
fields studied (14):
seven social psychology journals, nine economics journals, 10 ecology journals,
and six astronomy journals
Guimerà R, Uzzi B, Spiro J, Amaral LA. Team assembly mechanisms determine
collaboration network structure and team performance. Science. 2005 Apr
29;308(5722):697-702. doi: 10.1126/science.1106340. PMID: 15860629; PMCID:
PMC2128751.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2128751/
[free full text]
The most productive teams were composed of experienced members and novices.
These outperformed teams composed only of experienced people – a great
argument for diversity.
Women and
Biotech: Defining and analyzing the problem; Solutions and Strategies,
Radcliffe Symposium
https://www.radcliffe.harvard.edu/event/2015-women-in-biotech-symposium
Molecular Medicine Medicine is being reorganized at the molecular and biochemical
levels. Traditional pharmaceutical franchises and clinical medicine are being supplemented by molecular
networks, pathways and insights.
Regulatory: GxP
-- from GLP Good Laboratory Practice to GCP Good Clinical Practice and GMP Good
Manufacturing Practice to INDs Investigational New Drugs and NDAs New Drug
Approvals -- and truly NCEs New Chemical Entities and NBEs New Biological
Entities -- is a long, expensive, and often unpredictable journey. Regulatory
authorities are struggling with trying to figure out how to regulate new and
emerging technologies.
Research: Progress
from basic to applied research and commercial R&D is distinctly non-linear.
Human lifetimes aren't long enough to learn everything needed to get drugs to
market safety -- and keep them there. Collaboration and partnerships seem
essential
Learning to live with uncertainty and tradeoffs is
also essential and unavoidable. Uncertainty became a PubMed MeSH heading in
2003.
Managing change and expectations are also crucial. Homeostasis is a very
powerful barrier to change. More research is pre-competitive than anyone likes
to admit.
A useful metaphor is sailing and tacking” When I started to sail I wanted to go
straight ahead – which gets you “in irons” and unable to steer. Only by tacking
from side to side do you get anywhere – but that also required ongoing
adjustments. Another goal – as hockey great Wayne Gretzky is known for saying
“I just skate to where the puck is going to be.:
I've taken heart from Steven Weinberg's
Four Golden Lessons, Nature 426: 389, 27 Nov 2003 "How
could I do anything without knowing everything that had already been done? ...
[graduate school] was sink or swim.... I did learn one big thing: that no one
knows everything, and you don't have to. Another lesson to be learned ... is
that while you are swimming and not sinking you should aim for rough waters.
My advice is to go for the messes -- that's where the action is... My
third piece of advice is probably the hardest to take. IT is to forgive yourself
for wasting time... If you want to be creative, then you will have to get used
to spending most of your time not being creative, to being becalmed on the ocean
of scientific knowledge. Finally, learn something about the history of
science, or at a minimum the history of your own branch of science. The least
important reason for this is that the history may actually be of some use to
your ... More importantly, the history of science can make your work seem more
worthwhile to you.
Robert Weinberg's Racing
to the Beginning of the Road : The Search for the Origin of Cancer
is a very readable account of top rate biomedical research, a good reminder that
these "races" are marathons and not 100 yard dashes. The title is one of my
favorite metaphors for the complexity of biology. His explanation of how nonlinear progress
from lab to clinic can be is highly recommended.
failure, learning from: Dr. [Stephen] Friend criticized the current scientific process as one that catalyzes failure because there is no way to learn from others. “Our understanding of disease is based on narrow or generalized knowledge… He added that there needs to be a cultural change in science where scientific results are completely shared. “Good research shouldn’t have to always be a positive result. Negative results should also be reported.” FasterCures, Learning to Love Failure panel video http://archive.milkeninstitute.org/videos/view/panel-learning-to-love-failure
Technologies.
The biotechnological
innovations of the 1970’s took until the 1990’s to integrate. "The
Pharmaceutical Industry and the Revolution in Molecular Biology: Exploring the
Interactions between Scientific, Institutional and Organizational Change, Iain
M. Cockburn, Rebecca Henderson, Scott Stern, 1999. No longer on the web?
Integration of genomics (and
proteomics and transcriptomics) into drug discovery and development seems likely
to be an ongoing process as well.
New
technologies such as microarrays, next generation sequencing and RNAinterference
coexist with improvements in century old mass spectrometry and Nuclear Magnetic
Resonance. DNA, genes, RNA and proteins are intimately related, but the
technologies for learning about nucleic acids and proteins can constitute almost
unbridgeable chasms
Amara’s law: "We tend to overestimate the effect of a technology in the short
run and underestimate the effect in the long run.” http://en.wikipedia.org/wiki/Roy_Amara
Roger
Brent compared microarrays to the microscope and telescope because they "enable
observation of the previous unobservable" [transcripts expressed under different
conditions in cells, tissues, and organisms. But “Making new technology work may
be easier than using it to discover truth” Roger Brent, "Functional genomics:
learning to think about gene expression data" Current Biology 9: R338- R341,
1999
Industrialization, scalability and automating and ramping up processes
to move from the R&D lab into the clinic are under-appreciated challenges. too
What are your
biggest hurdles and challenges? I welcome your comments. Mary Chitty
mchitty@healthtech.com
Heath, Chip and Dan, Switch: How to change things when change is Hard, Broadway
Books, 2010
https://heathbrothers.com/books/switch/
Sutton, Robert L. and Huggy Rao, Scaling Up Excellence, Crown Business, 2014
https://www.gsb.stanford.edu/faculty-research/books/scaling-excellence-getting-more-without-settling-less
Scaling up Excellence video
https://www.youtube.com/watch?v=l1ATdH-TvJw
Tett, Gillian, The Silo Effect:
The Peril of Expertise and the Promise of Breaking Down Barriers, Gillian Tett http://www.simonandschuster.com/books/The-Silo-Effect/Gillian-Tett/9781451644746
CHI’s
Drug Discovery and Development Map
text
http://www.healthtech.com/drugdiscoverymap.asp We're trying to update
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