Presentation on theme: "Next Generation BioMarkers and Business Models J. Reynders EMP72, Kellogg School of Management, Northwestern University."— Presentation transcript:
Next Generation BioMarkers and Business Models J. Reynders EMP72, Kellogg School of Management, Northwestern University
Business Opportunity We have the opportunity to create a pre-competitive alliance to accelerate the discovery and application of Biomarkers to our pipeline
Overview BioMarkers Introduction The economics of Biomarkers Composite Biomarkers Business Model Step 1: A captive joint venture between multiple pharma to leverage biomarker platforms Business Model Step 2: An adjunct open innovation center to tap into and accelerate biomarker efforts
Definition of Biomarkers A characteristic that is objectively measured and evaluated as an indicator of normal biologic or pathogenic processes or pharmacological responses to a therapeutic intervention - FDA
Types of Biomarkers Stratification Markers – Predict the likelihood of drug response Efficacy Markers – Predict a clinical outcome Toxicity Markers – Predict the likelihood of toxic effects Screening Markers – Predict the early detection of diseases Prognostic Markers – Predict likely cause of disease
Examples of Biomarkers Insulin level – biomarker for assessing diabetic condition Genetic variations – biomarkers which could be used to determine Huntington’s Disease Imaging Biomarkers – measure amaloid beta plaque load for Alzheimer’s
Integrated Biomarker Development: Therapeutic Area and Platform matrix Proteomics Gene Expression Imaging Pharmacogenomics Informatics InflammationEndocrine CVPainPsychNeuro Platforms Therapeutic Areas Open Innovation Cross-Platform Asia Footprint Academic Collab Design a knowledge-focused organization out-source commodity platforms globally – leveraging Asia talent (China/India) Open innovation models: Government investment (Singapore), and open innovation (Academia/NIH). Cross-platform integration of biomarkers into highly predictive composite “meta-markers”. We can make this a focus and leap-frog the current single-platform model.
The Ante…. Guess the ballpark cost of the following An MRI imaging platform An FTMS Proteomics platforms Sequencing the genomes of 200 patients with latest high-throughput sequencing
The Ante…. Guess the ballpark cost of the following An MRI imaging platform An FTMS Proteomics platforms Sequencing the genomes of 200 patients with latest high-throughput sequencing About $1M each – and these are just a few of the platforms that are needed.
Guohao Dai, et. al. Brigham & Women's, Harvard Medical School, MIT PNAS, October 12, 2004, vol. 101 Next Generation: Composite Biomarkers! Combining different classes of information to better understand outcomes
The Bet: Many, Many platforms Cell/Assay Technologies CapEx 20 types * 10 platforms * $1M = $200M OpEx 20 types * (5 FTEs + reagents) ~$50M 20% Overhead ~$50M Total ~$300M in first year 10% of midsize pharma R&D budget – too much!!!
Risks Sub-critical investment May not align platform capability with therapeutic areas Platform limitations constrain ability to discover composite biomarkers which can truly stratify populations Technologies Many of the platforms are cutting edge and unproven – large investment w/ big risks
Sharing Risk: Learning from Others Semiconductor Industry Joint development of next-gen fabrication IBM/Samsung 32 nm process – take on Intel Oil & Gas Share in oil field exploration BP & Arco Prudhoe Bay operations Media Collaboration on high risk online-media Hulu.com – FOX & NBC take on YouTube
Company ACompany B Pre-competitive BioMarker Joint Venture: Company A and B combine/leverage platforms Proteomics Gene Expression Imaging Pharmacogenomics Informatics InflammationEndocrineCVPainPsychNeuroInflammationEndocrineCVPainPsychNeuro - Share in the high- risk development of capabilities - Level capacity across multiple organizations - Retain NME IP
Integrate Translate Tailor Corporate Boundary JV - BioMarker Research Projects References: H. Chesbrough, Open Innovation, HBS Press E. Raymond, Cathedral and the Bazaar, O’Reilly Joint Dev. Joint Dev. Leverage Talent Xco Yco Non- Profits & Academia Non- Profits & Academia Government Dollars $$$.edu $ $ Venture Capital Venture Capital Markets New Markets $ Diagnostic New Products Tech Transfer Tech Transfer Corporate Investment OtherCo $$$ Sharing More Risk: Open Innovation
Adjunct Open Innovation Center Benefits of a Non-Profit Open Innovation center Compete and partner directly for government grants Bring together a unique blend of funding streams to enable Biomarker platforms/capabilities Co-location/rotation with Company Scientists enables rapid uptake of new “free” capabilities into Company programs Alternative access to talent Physical footprint in a location of interest to Consortia Companies Note: knowledge-work only, no capital-intensive infrastructure Avoid IP issues common in University/Government channels ( Open vs. Closed Innovation ) Company A Company B JV OI Center
Converge and TargetIntegrate and Analyze End Game: Personalized Medicine Biomarker platform JV and OI center enables the partners to share risk and cost-effectively increase pipeline value through improved attrition. More importantly, partners transform from Blockbuster to Personalized Medicine model, delivering patients the right drug at the right dose.
The NPV of a single new drug on the market Assumptions 4 year linear ramp to steady state sales $1B annual sales at steady state 20 year patent window This launch value is discounted in time by the WACC and yield This NPV depends upon the pipeline’s actual cycle time
Model Structure/Assumptions Constant flow of 1 drug/year Discount factor assigned a Weighted Average Cost of Capital (WACC) of 10% Pre-Launch Work-in-Process (WIP) value discounted by yield and time Phase Cost is assumed to be in perpetuity to sustain 1 drug/year Cost is similar to that required to discharge all assets currently in pipeline WIP is balanced to capacity at each phase In reality there are capacity bottlenecks which constrain flow in phases This bottleneck in turn impact cycle time for all assets in the pipeline KMR benchmark and other estimates utilized
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