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1. Scannell et al.Diagnosing the decline in pharmaceutical R&D efficiency
Moore’s Law is a term that was coined to describe the exponential increase in the number of
transistors that can be placed at a reasonable cost onto an integrated circuit.
Erooms law: R&D efficiency, measured simply in terms of the number of new drugs brought to
market by the global biotechnology and pharmaceutical industries per billion US dollars of R&D
spending, has declined fairly steadily.
Eroom’s Law indicates that powerful forces have outweighed scientific, technical and managerial
improvements over the past 60 years, and/or that some of the improvements have been less
‘improving’ than commonly thought.
R&D improvement productivity relatively not very high.
Reversing does not address core of productivity problem.
high-affinity binding of a single target by a lead compound is the best place from which to start the
R&D process
Shareholders ultimately appoint executives and control resource allocation, so their perceptions
matter.
We think that any serious attempt to explain Eroom’s Law should try to address at least two things:
the progressive nature of the decline in the number of new drugs per billion US dollars of R&D
spending, and the scale (~80-fold) of the decline
However, with the aim of prompting debate and analysis, here we discuss what we consider to be
the four primary causes of Eroom’s Law:
the ‘better than the Beatles’ problem; intellectual novelty: people get bored with last year’s
creations, which maintains demand for novelty. Unfortunately for the drug industry, doctors are not
likely to start prescribing branded diabetes drugs because they are bored with generic metformin.
Potential cause: the ‘low-hanging fruit’ problem, which results from the progressive exploitation of
drug targets that are more technically tractable
the ‘cautious regulator’ problem; Progressive lowering of risk tolerance of drug regulatory agencies
raises the bar. the availability of safe and effective drugs to treat a given disease raises the regulatory
bar for other drugs for the same indication.
the ‘throw money at it’ tendency; has generally led to a rise in R&D spending in major companies,
and for the industry overall. It is probably due to several factors, including good returns on
investment in R&D, as well as a poorly understood and stochastic innovation process that has long
pay-off periods. In addition, intense competition between marketed drugs (where being second or
third to launch is often worth less than being first) provides a rationale for investing additional
resources to be the first to launch. There may also be a bias in large companies to equate
professional success with the size of one’s budget.
the ‘basic research–brute force’ bias. the tendency to overestimate the ability of advances in basic
research (particularly in molecular biology) and brute force screening methods (embodied in the first
,few steps of the standard discovery and preclinical research process) to increase the probability that
a molecule will be safe and effective in clinical trials. Increase quality of filtering and selection ->
better ADMET. most of the costs of new drug development are related to the costs of failed projects
when expensive labour-intensive animal models — rather than cheap automated molecular assays —
formed the basis of initial drug screening
Critique on modern research:
First, much of the pharmaceutical industry’s R&D is now based on the idea that high-affinity binding
to a single biological target linked to a disease will lead to medical benefit in humans ->
The second potential problem follows from the nature of chemical space and a shift from iterative
medicinal chemistry coupled with parallel assays (pre-1990s) to serial filtering that begins with HTS of
a static compound library against a target, Directed iteration — even if each cycle is slow — may be a
much more efficient way of searching a large and high-dimensional chemical space than fast HTS of a
predefined collection of compounds
There may also be some contribution from a fifth factor, termed ‘the low-hanging fruit’ problem, but
we consider this to be less important.
several reasons why the ‘basic research–brute force’ bias has come to dominate drug research:
1. Cautious regulator: Better than beatles started to occur, low hanging fruit and ndustry had
started to run out of good animal models to screen drugs for still poorly treated diseases.
2. the ‘basic research–brute force’ bias matched the scientific zeitgeist
3. the ‘basic research–brute force’ bias matched the inclination of many commercial managers,
management consultants and investors.
Fortunately, the ‘basic research–brute force’ issue is tractable in several ways. First, in a handful of
therapeutic areas the research process does appear to be delivering better systems-level insights,
better targets (or sets of targets) and better candidate drugs. . Second, more emphasis could be put
on iterative approaches, on animal-based screening or even on early proof of clinical efficacy in
humans, and less on the predictive power of high-affinity binding to the target of a molecule from a
static library. Third, in some therapeutic areas people could just stop believing in the current
predictive ability of ‘basic research–brute force’ screening approaches, and resist the temptation to
put molecules into clinical trials without having more compelling evidence of the validity of the
underlying therapeutic hypothesis. Finally, we note that it would be easier to improve the signal-to-
noise ratio of drugs that enter clinical trials if: first, there was a detailed understanding of why drugs
fail in the clinic; second, this led to the discovery of a small number of common failure modes; and
third, this knowledge could be used to change the early stages of the R&D process.
Moore’s Law is a term that was coined to describe the exponential increase in the number of
transistors that can be placed at a reasonable cost onto an integrated circuit.
Secundary Symptoms:
The narrow clinical search problem: the shift from an approach that looked broadly for therapeutic
potential in biologically active agents to one that seeks precise effects from molecules designed with
a single drug target in mind. new therapeutic uses of drugs have been discovered by motivated and
observant clinicians working with patients in the real world. If a drug has an effect but this is not the
precise effect that the trial designers anticipated, then the trial fails
,Big clinical trial problem: The number of principal investigators per drug in clinical trials has doubled
over the past decade73. The consequence of this is multicentre trials that add noise and
heterogeneity, and are therefore bigger and more expensive
Multiple clinical trial problem: More often several methods and drugs are used to treat disease. The
cautious regulator is less prepared to assume that the safety and efficacy of new drugs can be
generalized across such heterogeneous and fragmented patient populations. Cost-sensitive health-
care funders are also wary. This means narrower indications and more clinical trials per drug
Long cycle time problem: . In the 1950s and 1960s, cycle times were remarkably short by modern
standards
Chief Dead Drug Officer (CDDO): to compose a detailed report that aims to explain the causes of
Eroom’s Law. First, the CDDO has no incentive to be irrationally optimistic. Second, R&D costs are
dominated by the cost of failure. Most molecules fail. Most research scientists spend most of their
time on products that fail. It seems fitting that someone on the board should focus on the products
that consume most of the R&D organization’s time, energy and money. Third, an expertise in drug
failure should qualify the CDDO to produce a good explanation of Eroom’s Law
Erooms law prognosis: mechanical factor: the amount spent on R&D is not going to increase. The
‘throw money at it’ tendency is being tackled by most companies, with varying degrees of intensity.
The second mechanical factor is the cumbersome biosimilar approval pathway that is emerging in the
United States. Every aspect of the biosimilar production process can be scrutinized by the originator’s
lawyers, and this raises the prospect of endless blocking litigation. Consequently, developers of
biosimilar products anticipate to get at least some of these products approved via the standard new
biologics approval pathway (the FDA’s biologics license application (BLA) process). These products
will be approved as though they were novel agents, so they will inflate the number of novel
approvals at very low R&D costs. the interesting reason, we suspect that the signal-to-noise ratio
may be improving among the compounds being developed for oncology indications. One or two
other therapeutic areas may be similar in this respect.
Orphan drugs are less prone to many of the factors discussed above, including the ‘better than the
Beatles’ problem, the ‘cautious regulator’ problem and the big clinical trial problem. Flat to declining
R&D costs, as well as a bolus of oncology drugs, more orphan drugs and ‘biosimilars as BLAs’, might
put an end to Eroom’s Law at an industry level. Whether this improves things enough to provide
decent financial returns on the industry’s R&D investment is a different question. Financial markets
don’t think so. Industry executives do. It would be interesting to see what CDDOs think.
2. Munos 2009
Rate of amount of new drugs approved from 1960s remained the same.
A lot of companies in drug innovation (4300), but only 6% registered at least one NME since 1950.
Only 32 companies survived all years.
The stable rates of output that are apparent in FIGS 2a,b suggest that NME production at a
pharmaceutical company follows a Poisson distribution. Importantly, as Poisson distributions are
characterized by a constant but stochastically variable rate of occurrence, this implies that the
average annual NME output of drug companies is constant
, If the NME output of drug companies is constant, the only way to increase the overall industry
output is to increase the number of companies
One possible interpretation is that a larger number of companies accelerates the acquisition of
knowledge, creating what economists call a spillover — an industry-wide benefit that enables all
companies to be more productive.
Costs of NME have been increasing for decades
Although the overall output of NMEs has therefore stagnated, the industry is producing them more
efficiently as it has been able to meet the increase in the cost per NME with a less than
commensurate increase in R&D spending. In other words, the industry is better at what it does than
it was previously, much of which is to generate data to meet FDA requirements.
As inflation has been ~3.7% since 1950 and the annual growth in R&D spending has been 12.3%, one
can infer that regulatory and other costs have been growing at ~8.3% annually, which translates into
a doubling every 8.5 years. This increase has often been attributed to the increasing prudence of
regulatory bodies following the high-profile withdrawals of drugs such as rofecoxib.
It shows that countries with a more demanding regulatory apparatus, such as the United States and
the UK, have fostered a more innovative and competitive pharmaceutical industry. This is because
exacting regulatory requirements force companies to be more selective in the compounds that they
aim to bring to market.
Bigger is not always better:
The increase in the NME output from small companies has been driven by two factors. The first is a
rise in the number of small companies that have produced an NME, which nearly doubled from 78 to
145 during the 1980s and 1990s. This was facilitated by the growth of venture capital that has funded
much of the ‘biotech boom’.
Second, the mean annual NME output of small companies has increased from ~0.04 to ~0.12 since
1995, owing to the emergence of new, more productive companies (FIG. 4b). conversely, the decline
in the output of large companies has been driven by the dwindling number of large pharmaceutical
companies, which has decreased by 50% over the past 20 years.
the NME output of small companies has increased as they have become more enmeshed in
innovation networks; second, that large companies are making more detailed investigations into
fundamental science, which stretch research and regulatory timelines; and third, that the heightened
safety concerns of regulators affect large and small companies differently, perhaps because a
substantial number of small firms are developing orphan drugs and/or drugs that are likely to gain
priority review from the FDA owing to unmet medical needs.
small firms collectively can explore far more directions, and investigate areas that their larger, more
conservative competitors avoid. However, only a small fraction of these small companies will be
rewarded with an FDA approval.
Individual less productive, collectively way more productive even if larger companies have a bigger
collective share.
If large companies could organize innovation networks, they might be able to reverse forces that
undermine their research model -> lower costs and increased output.
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