So, you want to build a biotech company? I. Elaine Allen, PhD Visiting Professor of Epidemiology & Biostatistics, UCSF [email protected] 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:       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       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?