So, you want to build a biotech
company?
I. Elaine Allen, PhD
Visiting Professor of Epidemiology &
Biostatistics, UCSF
Isabel.allen@ucsf.edu
When is science ready to become a business?
Is Translational Science: From
Bench to Bedside or From Bench
to Boardroom?
“There are too many science projects masquerading as companies…”
Karen Bernstein, BioCentury 2010
You can spend a considerable amount learning PoC PoC
http://www.healthtech.com/Proof-Of-Concept/
Overview
 Let me hear a little bit about your start-up ideas,
plans…
 A little more background on my companies
 A short Proof of Concept (PoC) story to get
started
 Some Caveats & Basics for Start-ups
 Different types of PoC & when you might use
each
Let’s start with a short proof of concept story
SciFluor
L i f e
S c i e n c e s
Enhancing Drug Properties…
…through Late-Stage Fluorination
an allied minds company

Founded in 2011 by Tobias Ritter, PhD, MBA & Assoc. Prof @ Harvard in
Chemistry, Takeru Furuya, PhD, & Don Ciappenelli, PhD

The Proof of Concept (for the next round of funding & for Big Pharma partners)
was to show the improved profile of 20 marketed pharmaceutical compounds
chosen from a database that shows compounds:



we can fluorinate with high probability;
we can own (i.e. the fluorinated version is a NCE we can patent); and
once fluorinated, have the potential to improve drug properties

increased half-life / reduced dosing frequency

blood-brain-barrier / cell membrane penetration

increased bioavailability
How would you do this?
Create an integrated database of compounds
 Takeru’s initial matrix
 FDA approved drugs
 FDA designated orphan drugs
 Drugs in Phase 3
 Drugs that failed in Phase 1 or 2
 Include the chemical structure in the database
The final (proprietary) database was an integration of all of the
above and linked to papers & FDA & company documents and
was (ultimately) a great marketing tool as well as scientific index
of compounds.
Caveats for successful startups...

Select science with commercial potential – solve a
problem rather than just a brilliant idea

Secure intellectual property

Bet on the jockey, not on the horse

Establish frequent and candid dialogue among
investigators and stakeholders

What about a business plan?

What is your ideal (and not so ideal) exit strategy?
What NOT to do:



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Involve people who are problematic (ever)
Share too much with VCs/Angels without NDA
Screw up the IP
Limit the possible upside
Set a price for a small, early financing round
Start believing your own BS
8
A Mantra of Sorts:
$
DATA
Dollars & Data: One leads to the other
How to Think About Proof of Concept
 PoC is not just one experiment at one time
 PoC is harder in biotech start-ups than established
biotech/pharma companies
 Think of PoC as part of the exploratory science and
have a checklist of what you need to show
 Realize that therapeutic/diagnostic studies may be
valuable even if they have low power
 Don’t undertake POC studies unless you understand
how you might use it to cancel projects
Typical value inflection points in biotech development
 Early:
 Clearly articulated strategy (or maybe a business plan)
 Team (needs to show previous accomplishments & potential)
 License the key IP (may not be necessary in software based
company)
 Initial proof-of-concept (in vitro, small animal, device
prototype, validity & reproducibility)
 Later:
 IND-enabling studies (biodistribution, GLP tox)
 Clinical safety (animal models, human in Phase 1)
 Clinical proof-of-concept (small study)
 Phase 3 (large study with efficacy & effectiveness)
 Market validation (sales ramp-up)
11
Proof of Concept Objectives in Healthcare
 Validation of the relevance of your therapeutic or
diagnostic in pre-clinical & early clinical
 Defining your potential market
 Show early evidence of clinical efficacy
 Eliminate blind alleys/failures early on (ARIAD
example with the thromboerythrocyte)
 Provide an assessment of commercial potential
Potential GO/NO GO Decision Criteria
 Safety & tolerability
 Bioavailability/Pharmacokinetics (PK)
 Pharmacodynamics (PD)
 Duration of action
 Relationship of PD to dose
 Early efficacy (what about effectiveness?)
 Commercial viability
Proof of Concept should include an
Evidence Evaluation of Competitors
 Systematic review of all the papers/reports/data that
exist on
 Same outcome/disease
 Same therapeutic/diagnostic/type
 Search of all patents existing or filed for
Example 1 – An evidence-based analysis for
a proposed new asthma drug
 Question: Will a planned multicenter trial succeed in
proving that drug P is better than drug Z?
 Step 1: Evidence synthesis (meta-analysis) of all
sponsor’s trials of drug P vs. SBA drug Z.
!
Drug P or Z vs. Placebo: All Outcomes
Odds Ratio and 95% C.I.
#Study/
Outcome Treatment Arms #Pts
FEV 1
Z1
3
1042
FEV 1
P1
4
805
AM PEFR
AM PEFR
Z
P1
3
4
1042
805
PM PEFR
Z1
3
1042
PM PEFR
P1
4
2805
Z
P1
3
4
1042
805
agonist
agonist
-6
-4
-2
Favors Placebo
0
2
4
Favors Treatment
DerSimonian & Laird Random Effects Model
6
Drug P or Z vs. Placebo: All Outcomes, adults
Odds Ratio and 95% CI
Outcome
#Study/Arms
FEV1 - All
5
REV1 - Adults only
3
A.M. PEFR - All
A.M. PEFR - Adults only
5
3
P.M. PEFR - All
4
P.M. PEFR - Adults only
2
agonist - All
4
agonist - Adults only
2
-6
-4
-2
Favors Placebo
0
2
4
Favors Treatment
DerSimonian & Laird Random Effects Model
6
Overlapping Treatments for new Asthma Drug
 Step 2: To show a difference between treatments,
we can use the summary statistics from the metaanalyses to calculate the sample size needed
 Sample size needed = 27,000 patients per treatment
group!
 The development of this compound was scratched
Examples 2 & 3: Software/algorithm
based start-up
 StatSystems – algorithm for titrating the Protamine
dose in bypass surgery
 Algorithm developed in my MBA stat class by MD/MBA
student
 Initial validation by randomized open study – safety outcomes
 Prototype built for handheld device and single-blind study at
MedCollPA & HospUnivPA
 Plans for handheld bedside device for titrating asthma drugs
but company sold
Examples 2 & 3: Software/algorithm
based start-up
 MetaWorks, Inc – Evidence-based synthesis company
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Meta-analyses are time consuming & require special skills
Build up client-base running as a consulting company
Receive AHRQ grant
Develop proprietary database – create Breast CA product
Raise $2 million but difficult – not patentable product
Merge company with United Biosource (non-compete for 1
year)
Finally, what if data are your product or generated by
your product?
Example 4: Data are the product
SmartSports develops the SmartKage
Partners with Sportvision for on
the field data
How to leverage the remaining
data?
Financing - Critical Path
$
 Generate $$ thru side application
(Centocor diagnostics, Vertex
technologies)
 3 F’s (friends, family, fools)
 Grants (SBIR, DARPA)
 Angel investors
 Venture capital
 Partnering
$
 Public offering (institutions)
 Merger/acquisitions
What companies are getting financed?

Experienced management (remember the Jockey)

Companies with products - clinical stage or later
(should you license in something? Stromedix
example)

Companies already owned by investors

Companies with clear milestone-driven events

Companies with revenue that provide services
leading to products
What is the future of Biotech?
Questions?