2.1 Innovating in large life sciences companies
Lessons from 60 years of pharmaceutical innovation - Munos (2009)
New molecular entities (NME) = medication with active ingredient that has not been previously
approved for marketing in any form in USA → small-molecule drugs → in this article the term
includes biologics (all therapeutic proteins) → NME output differs widely with different firms.
Rate of production of NMEs by companies responsible for output has been constant.
→ (a) raises questions about sustainability R&D, (b) challenges rationale for major mergers and
acquisitions (M&A), (c) suggests drug companies need to be bolder in redesigning research.
→ M&A is not an effective way to promote an innovation culture/remedy a deficit of innovation.
Prescription Drug User Fee Act (PDUFA) = US law (1992) that allows the FDA to collect fees from
drug manufacturers to fund the new-drug approval process.
Blockbuster = NME with peak sales that exceed $1 billion, expressed in year-2000 dollars.
Large pharmaceutical companies: the top 15 drug companies, or their predecessors and joint
ventures → need to produce 2–3 NMEs per year to meet growth objectives (none have) → all
other companies, including biotech companies, are small pharma companies.
Sustainable innovation possible by: focussing on particular disease area or therapeutic strategy.
- Some sell products and services in addition to drugs → some are anchored in their home
country market → some are conglomerates → some concentrate on generics.
Larger number of companies accelerates the
acquisition of knowledge, creating a spillover =
industry-wide benefit that enables all
companies to be more productive.
Cost of NME: average cost per NME was
$802 million in 2000 for small molecules
and $1,318 million in 2005 for biologics (do
not include post-approval costs, phase 4) → NME
costs: dividing company’s annual R&D spending by its rate of NME production.
Countries with demanding regulatory apparatus (UK, US) promote more innovative, competitive
pharma industry → force companies to be more selective in choosing compounds for marketing.
Increase in NME output of small companies driven by 2 factors: (1) rise in number of small
companies producing NMEs, (2) mean annual NME output of small companies has increased.
→ decline in output large companies driven by decreasing number of large pharma companies.
Orphan drugs = drugs specifically developed for diseases affecting fewer than 200.000 patients.
What’s next? ( 1) scaling patent cliffs: discovery NMEs is elusive (ongrijpbaar) and sales prospects
are nearly zero → reduces odds of obtaining a return on investment in R&D → solution; combine
knowledge of drug innovation and new-product sales with patent expirations to model how
firms survive large revenue losses due to patent expiration of blockbuster drugs (patent cliffs).
(2) choosing a course: industry must embrace more radical change and seize the opportunity to
redesign the model → 4 points of improvement/redesign for pharma industry;
1. Change its innovation dynamics to move beyond constant NME output → R&D
productivity is the number one issue → it is not fixed.
2. Ursing radical and successful experiments as building blocks → FEX. public– private
partnerships (PPPs), innovation networks and open-source R&D = virtual network of
volunteers that uses online tools to address a problem of shared interest → advantages
open architecture for R&D; heightened competition, reduced costs, increase in ability to
initiate and terminate projects and makes it easier to manage disruptive innovation =
, turn cutting-edge science into novel products with superior features to create new
markets, which unsettles established products and tech.
3. Short-term priorities to encourage marginal innovation → more reliable returns on
investment, at the expense of major changes→ a separate, protected area to generate
disruptive innovation for companies relying on breakthrough discoveries.
4. Rethink industry’s process culture → success depends on random occurrence of black
swan products = rare events of key importance, reshaping markets, industries, societies.
Jean-Pierre Garnier: R&D assumed as scalable, could be industrialized and driven by detailed
statistics and automation → result; loss of personal accountability, transparency and passion of
scientists in discovery and development.
Diagnosing the decline in pharmaceutical R&D efficiency - Scannell et al. (2012)
Advances in R&D (high-throughput screening (HTS), X-ray, identification drug targets), new
inventions (biotech, transgenic mice) and advances in scientific knowledge (biomarkers).
R&D efficiency = measured by number of new drugs brought to
market by global biotech and pharmaceutical industries per billion
US dollars of R&D spending → declined steadily.
Moore’s law = techs that improve exponentially over time → Eroom’s
law = powerful forces have outweighed scientific, technical and
managerial improvements → unpleasant consequences; there will be
fewer new drugs and/or drugs will become expensive.
→ explaining Eroom’s Law should address 2 things: (1) progressive
nature of R&D decline in the number of new drugs per billion US dollars of R&D spending, and (2)
scale of decline. Primary causes Eroom’s law (innovation struggles):
1. “Better than the Beatles” problem: hard to achieve commercial success with new pop
songs if it has to be better than the Beatles → yesterday’s blockbuster is today’s generic
→ increases complexity and hurdles for approval, adoption and reimbursement.
→ “Low-hanging fruit” problem as fifth, less important cause of Eroom’s law → gradual
exploitation of more manageable drug targets → easy-to-pick fruit is gone, while “better than
the Beatles” problem argues fruit that has been picked reduces the value of the fruit that is left.
2. “Cautious regulator” problem: lowering the risk tolerance by regulatory agencies raises
the bar for the intro of new drugs and increases R&D costs → medicines have to
demonstrate efficacy, s afety hurdles are increased → rise in R&D efficiency in 90s due to
2 regulatory factors: (1) clearing the regulatory backlog (getting rid of the accumulation of
uncompleted work) and (2) rapid development and approval of HIV drugs.
→ follows the “better than the Beatles” problem → regulator is more risk-tolerant when few good
treatment options exist → availability safe, effective drugs raises regulatory bar for other drugs.
3. “Throw money at it” tendency: tendency to add resources to R&D, because it is believed
every dollar spent gives a return → due to; (a) good returns on investment in R&D, (b)
poorly understood and stochastic innovation process (sequence of random outcomes),
(c) long pay-off periods and intense competition between marketed drugs.
4. “Basic research–brute force” bias: tendency to overestimate advances in basic research
and brute force screening methods to increase probability of a safe, effective molecule in
clinical trials → drug discovery and development sound more prospective than really are.
→ (4) dominates drug research, because; (a) slowing pace of pharmaceutical innovation (new drugs
had only modest incremental benefit over generics). (b) older approaches for early stage drug
R&D seemed to yield less (molecular reductionism = genetics and molecular biology provide the
, best, most fundamental understandings of biological systems). (c) matches the opinion of many
commercial managers, management consultants and investors → (4) manageable in several ways:
A. Analysing the better systems-level insights, (sets of) targets and candidate drugs of
research from other therapeutic areas.
B. Putting more emphasis on iterative approaches, animal-based screening or early proof of
clinical efficacy in humans → less on predictive power of molecules from a static library.
C. Stop believing in predictive ability “basic research–brute force” screening approaches
and putting molecules into clinical trials without more validity of the hypothesis.
How can parts of R&D process improve, but overall
efficiency decline? → i ndustry industrialized and
optimized the wrong set of F&D activities.
2 potential problems: (1) much of R&D is now based
on idea that high-affinity binding to a single target
linked to a disease will lead to medical benefit in humans (no attention to off-target effects). (2)
shift from iterative processes to serial filtering (with HTS) of a static compound library against a
target (directed iteration may be more efficient).
Signal-to-noise ratio = chosen drug candidates should demonstrate effectiveness and safety in
clinical trials successfully → comparing level of desired signal to level of background noise.
→ improve signal-to-noise ratio by: (a) a detailed understanding of why drugs fail, (b) leading to
discovery of common failure modes and (c) using this to change early stages of the R&D process.
Secondary symptoms Eroom’s law:
- Narrow clinical search problem: shift from broad focus on therapeutic potential in
active agents to a focus on precise effects from molecules designed with a single target.
- Big clinical trial problem: expansion of patients in clinical trials results from; (1) “better
than the Beatles” problem increases trial size (if the effect size halves, 4 times as many
patients have to be recruited). (2) phase III trials are messy mixture of science,
regulation, public relations and marketing (trying to satisfy multiple constraints inflates
size + cost).
- Multiple clinical trial problem: increased complexity of medical practice and many
different treatment options means narrower indications and more clinical trials per drug.
- Long cycle time problem: increase in duration of clinical trials between 60s and 80s.
Solution: Chief Dead Drug Officer (CDDO) = someone who focuses on drug failure at all stages of
R&D → has fixed time to compose a detailed report with the explanation of the causes of Eroom’s
Law → this gets submitted to the board of the company and health institutes (FDA in the USA).
→ advantages: (1) CDDO has no incentive to be irrationally optimistic. (2) R&D costs dominated
by cost of failure (lot of time spent on failed molecules). (3) CDDO has expertise in drug failure.
Other solutions: reorganization of R&D (smaller/larger R&D units, outsourcing to lower-cost
countries) - quick-kill strategies (sudden and rapid victory) - mergers - connect with science
(universities at frontrow for new scientific discoveries) - radical experiments (PPPs).
Prognosis Eroom’s law: (A) amount spent on R&D will not increase (“throw money at it” tackled by
most companies). (B) cumbersome biosimilar approval pathway in US (causes endless conflicts).
(C) signal-to-noise ratio may improve among compounds developed for oncology.
→ declining R&D costs, oncology drugs, more orphan drugs and biosimilars can put an end to
Eroom’s Law at an industry level.
2.2 Project-based innovation
