Lean launch pad for Life Sciences and Health Care

of 14 Life Science Blog Post Series
Lean LaunchPad for Life Sciences

Reinventing Life Science Startups–Therapeutics and Diagnostics

Posted on August 19, 2013 by steveblank
It was the best of times, it was the worst of times, it was the age of wisdom, it was the
age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the
season of Light, it was the season of Darkness, it was the spring of hope, it was the
winter of despair, we had everything before us, we had nothing before us, we were all
going direct to Heaven, we were all going direct the other way..
Charles Dickens
Life Science (therapeutics- drugs to cure or manage diseases, diagnostics- tests and
devices to find diseases, devices to cure and monitor diseases; and digital health –
health care hardware, software and mobile devices and applications streamline and
democratize the healthcare delivery system) is in the midst of a perfect storm of
decreasing productivity, increasing regulation and the flight of venture capital.
But what if we could increase productivity and stave the capital flight by helping Life
Sciences startups build their companies more efficiently?
We’re going to test this hypothesis by teaching a Lean LaunchPad class for Life
Sciences and Healthcare (therapeutics, diagnostics, devices and digital health) this
October at UCSF with a team of veteran venture capitalists and angels.
It was the best of times and the worst of times
The last 60 years has seen remarkable breakthroughs in what we know about biology
underlying disease and the science and engineering of developing commercial drug
development and medical devices that improve and save lives. Turning basic science
discoveries into drugs and devices seemed to be occurring at an ever increasing rate.
Yet during those same 60 years, rather than decreasing, the cost of getting a new drug
approved by the FDA has increased 80 fold. Yep, it cost 80 times more to get a
successful drug developed and approved today than it did 60 years ago.

75% or more of all the funds needed by a Life Science startup will be spent on clinical
trials and regulatory approval. Pharma companies are staggering under the costs. And
medical device innovation in the U.S. has gone offshore primarily due to the toughened
regulatory environment.
At the same time, Venture Capital, which had viewed therapeutics, diagnostics and
medical devices as hot places to invest, is fleeing the field. In the last six years half the
VC’s in the space have disappeared, unable to raise new funds, and the number of
biotech and device startups getting first round financing has dropped by half. For exits,
acquisitions are the rule and IPOs the exception.
While the time, expense and difficulty to exit has soared in Life Sciences, all three
critical factors have been cut by orders of magnitude in other investment sectors such
as internet or social-local-mobile. And while the vast majority of Life Science exits
remain below $125M, other sectors have seen exit valuations soar. It has gotten so bad
that pension funds and other institutional investors in venture capital funds have told
these funds to stay away from Life Science – or at the least, early stage Life Science..
WTF is going on? And how can we change those numbers and reverse those trends?
We believe we have a small part of the answer. And we are going to run an experiment
to test it this fall at UCSF.
In this three post series, the first two posts are a short summary of the complex

challenges Life Science companies face; in Therapeutics and Diagnostics in this post
and in Medical Devices and Digital Health in Part 2. Part 3 explains our hypothesis
about how to change the dynamics of the Life Sciences industry with a different
approach to commercialization of research and innovation. And why you ought to take
this class.
——-
Life Sciences I—Therapeutics and Diagnostics
It was the Age of Wisdom – Drug Discovery
There are two types of drugs. The first, called small molecules (also referred to as New
Molecular Entities or NMEs), are the bases for classic drugs such as aspirin, statins or
high blood pressure medicines. Small molecules are made by reactions between
different organic and/or inorganic chemicals. In the last decade computers and
synthesis methods in research laboratories enable chemists to test a series of reaction
mixtures in parallel (with wet lab analyses still the gold standard.) Using high-throughput
screening to search for small molecules, which can be a starting point (or lead
compound) for a new drug, scientists can test thousands of candidate molecules
against a database of millions in their libraries.
Ultimately the FDA Center for Drug Evaluation and Research (CDER) is responsible for
the approval of small molecules drugs.
The second class of drugs created by biotechnology is called biologics (also referred to
as New Biological Entities or NBEs.) In contrast to small molecule drugs that are
chemically synthesized, most biologics are proteins, nucleic acids or cells and tissues.
Biologics can be made from human, animal, or microorganisms – or produced by
recombinant DNA technology. Examples of biologics include: vaccines, cell or gene
therapies, therapeutic protein hormones, cytokines, tissue growth factors, and
monoclonal antibodies.
The FDA Center for Biologics Evaluation and Research (CBER) is responsible for the
approval of biologicals.
It was the Season of Light
The drug development pipeline for both small molecules and biologics can take 10-15
years and cost a billion dollars. The current process starts with testing thousands of
compounds which will in the end, produce a single drug.
Target ID &
Validation
Hit
Generation
Lead Gen &
Optimization
Pre Clinical
Animal
Studies
Phase 1:
Safety
Phase 2:
Efficacy
Safety
Phase 3:
Efficacy
Safety
Drug R&D Clinical Trials
FDA Review
& Approval
Potency Studies
Selectivity Studies
PK/ADME-Tox properties
SAR pharmacophore modeling
Pharmacological profile
Administration route
Drug interactions
20-100 people
1-2 years
100-300 people
1-2 years
1,000-3,000 people
2-3 years 1-2 years
Link disease and target
Biomarkers
High-throughput screening
Rational Design
In silicon screening
Hits Confirmation
Potency & cytotoxicity
Prelim animal efficacy
Initial SAR
1-2 years 1-2 years 1-2 years 1 year

In the last few decades scientists searching for new drugs have had the benefit of new
tools — DNA sequencing, 3D protein database for structure data,, high throughput
screening for “hits”, computational drug design, etc. — which have sped up their search
dramatically.
The problem is that the probability that a small molecule drug gets through clinical trials
is unchanged after 50 years. In spite of the substantial scientific advances and
increased investment, over the last 20 years the FDA has approved an average of 23
new drugs a year. (To be fair, this is indication-dependent. For example, in oncology,
things have gotten significantly better. In most other areas, particularly cns and
metabolism, they have not.)
It was the Season of Despair
With the exception of targeted therapies, the science and tools haven’t made the drug
discovery pipeline more efficient. Oops.
There are lots of reasons why this has happened.
Regulatory and Reimbursement Issues
• Drug safety is a high priority for the FDA. To avoid problems like Vioxx, Bexxar
etc., the regulatory barriers (i.e. proof of safety) are huge, expensive, and take
250
Compounds
Clinical Trials FDA Approval
10,000
Compounds
Drug Discovery
250
Compounds 5 Compounds 1 Drug
PreClinical

lots of time. That means the FDA has gotten tougher, requiring more clinical
trials, and the stack of regulatory paperwork has gotten higher.
• Additional trials to demonstrate both clinical efficacy (if not superiority) and cost
outcomes effectiveness are further driving up the cost, time and complexity of
clinical trials.
Drug Discovery Pipeline Issues
• The belief was that basic research + mass screening would make the drug
discovery pipeline more efficient. That’s hasn’t happened. There’s still a debate
about why high throughput computer automated screening - going from “hits to
leads” – isn’t producing better than the manual animal-based screening it
replaced.
• Biologics have a 2 ½ times higher success rate in clinical trials than small
molecules.
Drug target Issues
• In a perfect world the goal is to develop a drug that will go after a single target(a
protein, enzyme, DNA/RNA, etc. that will undergo a specific interaction with
chemicals or biological drugs) that is linked to a disease.
• Unfortunately most diseases don’t work that simply. There are a few diseases
that do, (i.e. insulin and diabetes, Gleevec -Philadelphia Chromosome and
chronic myeloid leukemia), but most small molecule drugs rarely act on a single
target (target-based therapy in oncology being the bright spot.)
• To get FDA approval new drugs have to be proven better than existing ones.
Most of the low-hanging fruit of easy drugs to develop are already on the market.
Venture Capital Issues
• For the last two decades, biotech venture capital and corporate R&D threw
dollars into interesting science (find a new target, publish a paper
in Science,Nature or Cell, get funded.) The belief was that once a new target was
found, finding a drug was a technology execution problem. And all the new tools
would accelerate the process. It often didn’t turn out that way,
although
there
are

important
exceptions.
Target ID &
Validation
Hit
Generation
Lead Gen &
Optimization
Pre Clinical
Animal
Studies
Phase 1:
Safety
Phase 2:
Efficacy
Safety
Phase 3:
Efficacy
Safety
Drug R&D Clinical Trials
FDA Review
& Approval
Potency Studies
Selectivity Studies
PK/ADME-Tox properties
SAR pharmacophore modeling
Pharmacological profile
Administration route
Drug interactions
20-100 people
1-2 years
100-300 people
1-2 years
1,000-3,000 people
2-3 years 1-2 years
Series A Series B Out Licensing
or Series C
Seed Round
Link disease and target
Biomarkers
High-throughput screening
Rational Design
In silicon screening
Hits Confirmation
Potency & cytotoxicity
Prelim animal efficacy
Initial SAR
1-2 years 1-2 years 1-2 years 1 year

• Moreover, the prospect of the FDA also evaluating drugs for their cost-
effectiveness is adding another dimension of uncertainty as the market
opportunity at the end of the funnel needs to be large enough to justify venture
investment
------
In Part 2 of this series, we describe the challenges new Medical Device and Digital
Health companies face. Part 3 will offer our hypothesis how to change the dynamics of
the Life Sciences industry with a different approach to commercialization of research
and innovation. And why you ought to take this class.

Reinventing Life Science Startups – Medical Devices and
Digital Health
What if we could increase productivity and stave the capital flight by helping Life
Sciences and Health Care (therapeutics, diagnostics, devices and digital health) this
October at UCSF with a team of veteran venture capitalists.
In this three post series, Part 1 described the challenges Life Science companies face in
Therapeutics and Diagnostics. This post describes the issues in Medical Devices and
Digital Health. Part 3 will offer our hypothesis about how to change the dynamics of the
Life Sciences industry with a different approach to commercialization of research and
innovation. And why you ought to take this class.
——–
Medical devices prevent, treat, mitigate, or cure disease by physical, mechanical, or
thermal means (in contrast to drugs, which act on the body through pharmacological,
metabolic or immunological means). They span they gamut from tongue depressors and
bedpans to complex programmable pacemakers and laser surgical devices. They also
diagnostic products, test kits, ultrasound products, x-ray machines and medical lasers.
Incremental advances are driven by the existing medical device companies, while truly
innovative devices often come from doctors and academia. One would think that
designing a medical device would be a simple engineering problem, and startups would
be emerging right and left. The truth is that today it’s tough to get a medical device
startup funded.
Life Sciences II – Medical Devices
Regulatory Issues
In the U.S. the FDA Center for Devices and Radiologic Health (CDRH) regulates
medical devices and puts them into three “classes” based on their risks.
Class I devices are low risk and have the least regulatory controls. For example, dental
floss, tongue depressors, arm slings, and hand-held surgical instruments are classified
as Class I devices. Most Class I devices are exempt Premarket Notification 510(k) (see
below.)
Class II devices are higher risk devices and have more regulations to prove the device’s
safety and effectiveness. For example, condoms, x-ray systems, gas analyzers, pumps,
and surgical drapes are classified as Class II devices.

Manufacturers introducing Class II medical devices must submit what’s called a 510(k)
to the FDA. The 510(k) identifies your medical device and compares it to an existing
medical device (which the FDA calls a “predicate” device) to demonstrate that your
device is substantially equivalent and at least as safe and effective.
Class III devices are generally the highest risk devices and must be approved by the
FDA before they are marketed. For example, implantable devices (devices made to
replace/support or enhance part of your body) such as defibrillators, pacemakers,
artificial hips, knees, and replacement heart valves are classified as Class III devices.
Class III medical devices that are high risk or novel devices for which no “predicate
device” exist require clinical trials of the medical device a PMA (Pre-Market Approval).
• The FDA is tougher about approving innovative new medical devices. The
number of 510(k)s being required to supply additional information has doubled in
the last decade.
• The number of PMA’s that have received a major deficiency letter has also
doubled.
Percent of PMAs With Major Deficiency Letter (MAJR) on 1st FDA
Review Cycle*
*Includes&all&ﬁled&original&PMAs&(1st&cycle&completed&for&all&cohorts)&
24&
Percent of 510(k)s With Additional Information (AI) Request on 1st
FDA Review Cycle
9&

• An FDA delay or clinical challenge is increasingly fatal to Life Science startups,
where investors now choose to walk away rather than escalate the effort required
to reach approval.
Business Model Issues
• Cost pressures are unrelenting in every sector, with pressure on prices and
margins continuing to increase.
• Devices are a five-sided market: patient, physician, provider, payer and regulator.
Startups need to understand all sides of the market long before they ever
consider selling a product.
• In the last decade, most device startups took their devices overseas for clinical
trials and first getting EU versus FDA approval
• Recently, the financing of innovation in medical devices has collapsed even
further with most Class III devices simply unfundable.
• Companies must pay a medical device excise tax of 2.3% on medical device
revenues, regardless of profitability delays or cash-flow breakeven.
• The U.S. government is the leading payer for most of health care, and under
ObamaCare the government’s role in reimbursing for medical technology will
increase. Yet two-thirds of all requests for reimbursement are denied today,
and what gets reimbursed, for how much, and in what timeframe, are big
unknowns for new device companies.
Venture Capital Issues
• Early stage Venture Capital for medical device startups has dried up. The
amount of capital being invested in new device companies is at an 11 year low.
• Because device IPOs are rare, and M&A is much tougher, liquidity for investors
is hard to find.
• Exits have remained within about the same, while the cost and time to exit have
doubled.
Life Sciences III – The Rise of Digital Health
Over the last five years a series of applications that fall under the category of “Digital
Health” has emerged. Examples of these applications include: remote patient
monitoring, analytics/big data (aggregation and analysis of clinical, administrative or
economic data), hospital administration (software tools to run a hospital), electronic
health records (clinical data capture), and wellness (improve/monitor health of
individuals). A good number of these applications are using Smartphones as their
platform.
510(k)
Approval
PMA
Approval
Reimburse-
ment
Assignment
Concept /
Design
1-3 years
1-3 years
~1 year
Pre-clinical
engineering
development
Clinical Trials
Class 1
510(K) exempt
Class 2
Class 3
1-9 months
9-36 months

Business Model Issues
• A good percentage of these startups are founded by teams with strong technical
experience but without healthcare experience. Yet healthcare has its own unique
regulatory and reimbursement issues and business model issues that must be
understood
• Most of these startups are in a multisided market, and a few have the same five-
sided complexity as medical devices: patient, physician, provider, payer and
regulator.
• Reimbursement for digital health interventions is still a work in progress
• Some startups in this field are actually beginning with Customer Development
while others struggle with the classic execution versus search problem
Regulatory Issues
• Digital Health covers a broad spectrum of products, unless the founders have
domain experience startups in this area usually discover the FDA and the 510(k)
process later than they should.
Venture Capital
• Seed funding is still scarce for Digital Health, but a number of startups
(particularly those making physical personal heath tracking devices) are turning
to crowdfunding.
• Moreover, the absence of recent IPOs and public companies benchmarks
creates uncertainty for VCs evaluating later investments too
Try Something New
The fact that the status quo for Life Sciences is not working is not a new revelation. Lots
of smart people are running experiments in search of ways to commercialize basic
research more efficiently.
Universities have set up translational R&D centers; (basically university/company
partnerships to commercialize research). The National Institute of Health (NIH) is also
setting up translational centers through its NCATS program. Drug companies have tried
to take research directly out of university labs by licensing patents, but once inside
Pharma’s research labs, these projects get lost in the bureaucracy. Realizing that this
is not optimal, drug companies are trying to incubate projects directly with universities
and the researchers who invented the technology, such as the recentJanssen
Labs program.
510(k)
Regulatory
Approval
1-3 months
1-24 months
Class 1 No
Approval
Needed
0 months
Reimburse-
ment
Assignment
Customer
Development
3-12 months 3-12 months3-12 months
Pilot Design
Trials and
Iteration

But while these are all great programs, they are likely to fail to deliver on their promise.
The assumption that the pursuit of drugs, diagnostics, devices and digital health is all
about the execution of the science is a mistake.
The gap between the development of intriguing but unproven innovations, and the
investment to commercialize those innovations is characterized as “the Valley of Death.”
We believe we need a new model to attract private investment capital to fuel the
commercialization of clinical solutions to todays major healthcare problems that is in
many ways technology agnostic. We need a “Needs Driven/Business Model Driven”
approach to solving the problems facing all the stakeholders in the vast healthcare
system.
We believe we can reduce the technological, regulatory and market risks for early-stage
life science and healthcare ventures, and we can do it by teaching founding teams how
to build new ventures with Evidence-Based Entrepreneurship.
------
Part 3 in the next post will offer our hypothesis how to change the dynamics of the Life
Sciences industry with a different approach to commercialization of research and
innovation in this sector. And why you ought to take this class.

Reinventing Life Science Startups – Evidence-based
Entrepreneurship
What if we could increase productivity and stave the capital flight by helping Life
Sciences (therapeutics, diagnostics, devices and digital health) this October at UCSF
with a team of veteran venture capitalists.
Part 1 of this post described the issues in the drug discovery. Part 2 covered medical
devices and digital health. This post describes what we’re going to do about it.
——
When I wrote Four Steps to the Epiphany and the Startup Owners Manual, I believed
that Life Sciences startups didn’t need Customer Discovery. Heck how hard could it be?
You invent a cure for cancer and then figure out where to put the bags of money. (In
fact, for oncology, with a successful clinical trial, this is the case.)
Pivots in life sciences companies
But I’ve learned that’s not how it really works. For the last two and a half years, we’ve
taught hundreds of teams how to commercialize their science with a version of the Lean
LaunchPad class called the National Science Foundation Innovation Corps. Quite a few
of the teams were building biotech, devices or digital health products. What we found is
that during the class almost all of them pivoted - making substantive changes to one or
more of their business model canvas components.
In the real world a big pivot in life sciences far down the road of development is a very
bad sign due to huge sunk costs. But pivoting early, before you raise and spend
millions or tens of millions means potential disaster avoided.
Some of these pivots included changing their product/service once the team had a
better of understanding of customer needs or changing their position in the value chain
(became an OEM supplier to hospital suppliers rather than selling to doctors directly)
Other pivots involved moving from a platform technology to become a product supplier,
moving from a therapeutic drug to a diagnostic or moving from a device that required a
PMA to one that just needed a 510(k).
Some of these teams made even more radical changes. For example when one team
found the right customer, they changed the core technology (the basis of their original
idea!) used to serve those customers. Another team reordered their device’s feature set
based on customer needs.

These findings convinced me that the class could transform how we thought about
building life science startups. But there was one more piece of data that blew me away.
Control versus Experiment – 18% versus 60%
For the last two and a half years, the teams that were part of the National Science
Foundation Innovation Corps were those wanted to learn how to commercialize their
science, applied to join the program, fought to get in and went through a grueling three
month program. Other scientists attempting to commercialize their science were free to
pursue their startups without having to take the class.
Both of these groups, those who took the Innovation Corps class and those who didn’t,
applied for government peer-reviewed funding through the SBIR program. The teams
that skipped the class and pursued traditional methods of starting a company had an
18% success rate in receiving SBIR Phase I funding.
The teams that took the Lean Launchpad class – get ready for this – had a 60%
success rate. And yes, while funding does not equal a successful company, it does
mean these teams knew something about building a business the other teams did not.
The 3-person teams consisted of Principal Investigators (PI’s), mostly tenured
professors (average age of 45,) whose NSF research the project was based on. The
PI’s in turn selected one of their graduate students (average age of 30,) as the
entrepreneurial lead. The PI and Entrepreneurial Lead were supported by a mentor
(average age of 50,) with industry/startup experience.
This was most definitely not the hoodie and flip-flop crowd.
Obviously there’s lots of bias built into the data – those who volunteered might be the
better teams, the peer reviewers might be selecting for what we taught, funding is no
metric for successful science let alone successful companies, etc. – but the difference
in funding success is over 300%.
The funding criteria for these new ventures wasn't solely whether they had a innovative
technology. It was whether the teams understood how to take that idea/invention/patent
and transform it into a company. It was whether after meeting with partners and
regulators, they had a plan to deal with the intensifying regulatory environment. It was
whether after talking to manufacturing partners and clinicians, they understood how they
were going to reduce technology risk. And It was after they talked to patients, providers
and payers whether they understood the customer segments to reduce market risk by
having found product/market fit.
So after the team at UCSF said they’d like to prototype a class for Life Sciences, I
agreed.
Here’s what we’re going to offer.

The Lean LaunchPad Life Sciences class
The goal of the Lean LaunchPad Life Sciences class at UCSF is to teach researchers
how to move their technology from an academic lab into the commercial world.
We're going to help teams:
• assess regulatory risk before they design and build
• gather data essential to customer purchases before doing the science
• define clinical utility now, before spending millions of dollars
• identify financing vehicles before you need them
We’ve segmented the class into four cohorts: therapeutics, diagnostics, devices and
digital health. And we recruited a team of world class Venture Capitalists and
entrepreneurs to teach and mentor the class including
Alan May, Karl
Handelsman, Abhas Gupta, and Todd Morrill.
The course is free to UCSF, Berkeley, and Stanford students; $100 for pre-revenue
startups; and $300 for industry. – See more here
The syllabus is here.
Class starts Oct 1st
and runs through Dec 10th
.

Lean launch pad for Life Sciences and Health Care

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