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Software is Eating Bio

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Software is Eating Bio

  1. Bio Fund
  2. Bio startups that operate like software startups The emerging macro trend: Bio 2.0 Bio in 2015 is like software in 2005 Software is eating Bio Cloud biology emergence of low CapEx startups Software at the center Machine learning, cloud computing Minimize FDA Risk DTC, digital health, consumer genomics, etc
  3. Storage cost-performance and computing cost-performance Moore’s law: cost of storage, compute ⇒ zero 0.01 0.1 1. 10. 100. 1000. 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 Dollars($) Compute cost ($ per 1 million transistors) Storage cost ($ per gigabyte)
  4. Silicon content: $20-22 NAND Flash $18-20 Display $15-17 Applications Processor $8-10 DRAM $10-13 Baseband $4-5 RF and FEM $3-4 Power Amplifier $3-4 PMIC $3-4 Combo-chip (WIFI/BT/FM) $3-4 Touch Controller $3-4 GPS $3-4 Image Sensors Silicon content: $9-10 Camera Module $7-11 Touch Panel $5-6 Battery (Li Polymer) $3-5 HDI PCB $1-2 Camera Lens $2-3 Gyroscope/Accelerometer $0.70-0.80 Audio Codecs $0.50-0.60 Speaker IC Substrate $1-2 LCD Drive IC $0.70-0.80 MEMs Microphone $1.00-1.50 LCD Drive IC Moore’s law: cost of storage, compute ⇒ zero Source: Nomura Securities, Gartner 2013 report.
  5. Side benefits of Moore’s law: cost of sensors ⇒ zero Source: Qualcomm IntegratedSensors,UserExperiences Ambient Light Accelerometer Magnetometer Ambient Light Accelerometer Magnetometer Ambient Light Accelerometer Magnetometer Ambient Light Accelerometer Magnetometer Ambient Light Accelerometer Magnetometer Gyroscope Proximity Gyroscope Proximity Gyroscope Proximity Pressure RGB Pressure RGB Pressure RGB Gyroscope Proximity Temperature Humidity Hall Effect Temperature Humidity Hall Effect Heart Rate Fingerprint 2010 2011 2012 2013 2014 2015+ GALAXY 1 GALAXY S2 GALAXY S3 GALAXY S4 GALAXY S5
  6. Beyond Moore’s law: cost of sequencing ⇒ zero Source: Nature, 2014 Cost of genome sequencing. Next generation sequencers enter the market. Moore’s law for computing costs. The price of sequencing a whole human genome hovers around $5,000 and is expected to drop even lower. 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 10,000 1,000 100 10 1 Cost(thousandsUS$)
  7. Putting these trends together This is disrupting traditional biotech = SOFTWARE IS EATING BIO Compute Sensors Biology + +
  8. Bio 2.0 is not Biotech BIOTECH STARTUPS SOFTWARE STARTUPS Subject Uncontrollable organisms Perfectly determinate code Environment Poorly understood, natural Well understood, artificial Approach Indefinite, random Definite, engineering Regulation Heavily regulated Basically unregulated Cost Expensive ( > $1B per drug) Cheap (a little seed money) Team High-salaried, unaligned lab drones Committed entrepreneurial hackers & BIO 2.0 From Peter Thiel’s Zero to One
  9. Eroom’s law: $/drug exponentially increasing Source: Nature Biotechnology $10M $100M $1,000M $10,000M 1949.97 1970.05 1990.11 2010.28 Development$perdrug
  10. Bio in 2015 is like software in 2005 SOFTWARE IS EATING BIO EROOM’S = NO MOORE’S = YES The emerging macro trend: Bio 2.0
  11. Three emerging areas Cloud Biology Computational MedicineDigital Therapeutics
  12. Slow and expensive to develop Toxic side effects FDA regulated Traditional therapeutics IMAGE: Champlax
  13. Title Text Subtitle text Digital therapeutics for chronic disease Mobile enables traction, patient success, and a new model for insurance companies Digital health & digital therapeutics IMAGE: Omada Health
  14. Large capital outlay Poor reproducibility Empirically driven doesn’t scale The four horsemen of Eroom’s law Traditional biology
  15. Title Text Subtitle text Biology that works like programming: Cloud experiments run through software Cloud biology IMAGE: Emerald Therapeutics
  16. Tsunami of data Flood of new drugs Traditional medicine
  17. Title Text Subtitle text Personalized cancer treatments based on patient tumor genetic screenings Computational biomedicine
  18. New a16z fund for Bio 2.0 Vijay Pande new GP: uniquely suited for Bio 2.0 a16z approach applied to Bio Bio 2.0 software + cloud bio, w/o traditional FDA risk
  19. Title Text Subtitle text
  20. Vijay Pande: Bio2.0 background Chemistry Camille and Henry Dreyfus Professor, Stanford Univ. Thomas Kuhn Paradigm Shift Award, American Chemical Society Teacher-Scholar Award, Dreyfus Foundation Physics AB Princeton University, 1992, PhD MIT, 1995 | Michael and Kate Bárány Award for Young Investigators, Biophysical Society Fellow, American Physical Society Computer Science: Founder, Folding@home Distributed Computing | MIT TR100 Guinness World Record, Folding@home First to a Petaflop Netxplorateur of the Year Biology Chair, Biophysics, Stanford University Medical School Delano Award for Computational Biosciences, American Soc. for Biochem. and Molecular Biology | Irving Sigal Young Investigator Award, Protein Society
  21. Title Text Subtitle text Crowd sourced computational biology for biomedicine Anticipated the future Cloud before Loudcloud GPU computing before CUDA Crowdsourcing before wikipedia Founding Director, Folding@home Approximately 2,000,000 people have donated computer time. More processing power than Amazon Web Services, Guinness World Record FOLDING@HOME
  22. Title Text Subtitle text $900M Next generation approaches to therapeutics for infectious disease Computation at its heart Regulatory Innovations: drug repurposing Co-founder, Globavir BioSciences
  23. Vijay Pande – Bio & IT entrepreneur ADVISOR TO STARTUPS IN BIO Acumen Pharmaceuticals (Alzheimer’s Disease) Counsyl (Carrier testing) Globavir (Infectious Disease) EMPLOYEE #1 AT NAUGHTY DOG Started at 15 years old writing computer games Naughty Dog later sold to Sony ADVISOR TO STARTUPS IN IT Clearspeed (high speed compute hardware) Discovery Engine (Semantic search) Numerate (Computational Drug Design) Omnipod (Software for pharma) OpenEye (Software for pharma) Pharmix (Computational Drug Design) Protein Mechanics (molecular simulation) Schrodinger (Software for pharma) Stack IQ (Cloud software stack)

Notas del editor

  • Powerful new methods put software at the center
    Machine Learning, Large-scale data, Cloud computing
    Emergence of a “Cloud Bio” infrastructure
    Analogs to cloud computing: low CapEx startups in Bio
    Approaches that seek to avoid FDA risk
    DTC, consumer genomics, etc
  • Powerful new methods put software at the center
    Machine Learning, Large-scale data, Cloud computing
    Emergence of a “Cloud Bio” infrastructure
    Analogs to cloud computing: low CapEx startups in Bio
    Approaches that seek to avoid FDA risk
    DTC, consumer genomics, etc
  • Cloud biology
    Biology is following in IT’s footsteps with its own cloud experimental infrastructure
    Ecosystem gives ability to startup with minimal investment

    Computational medicine
    Genomic advances enabling personalized medicine
    Machine learning enabling smart drug discovery

    Digital Health
    Mobile phone and software enabling broad population management tools
  • Cloud biology
    Biology is following in IT’s footsteps with its own cloud experimental infrastructure
    Ecosystem gives ability to startup with minimal investment

    Computational medicine
    Genomic advances enabling personalized medicine
    Machine learning enabling smart drug discovery

    Digital Health
    Mobile phone and software enabling broad population management tools
  • Software can positively impact our health, wellness, and lives
    Mobile leads to more natural human connections, sensors, reminders
    Quantified self + Cloud brings everything together

    Smartphones enable better population management tools
    Smartphones and notifications allow engagement with healthcare apps
    Smartphone hardware can be used for diagnostic purposes or to power medical add-ons
    Teledermatology use phone camera to send pictures of skin conditions

    Decentralization as a general theme
    Bringing the patient to the center of the decision making process
  • Cloud biology
    Biology is following in IT’s footsteps with its own cloud experimental infrastructure
    Ecosystem gives ability to startup with minimal investment

    Computational medicine
    Genomic advances enabling personalized medicine
    Machine learning enabling smart drug discovery

    Digital Health
    Mobile phone and software enabling broad population management tools
  • Hardware: Cloud Experiments
    equivalents to data centers for experiments: low startup CapEx
    Cloud labs can use commodity hardware from mobile supply chain
    Software
    Machine Learning/Statistics powerfully complement biology
    Open source tools create an established tool chain
    People
    New generation of students who know biology and can code
    Coding is more than programming — it’s the way to break down challenging problems, applicable to biology broadly
  • Cloud biology
    Biology is following in IT’s footsteps with its own cloud experimental infrastructure
    Ecosystem gives ability to startup with minimal investment

    Computational medicine
    Genomic advances enabling personalized medicine
    Machine learning enabling smart drug discovery

    Digital Health
    Mobile phone and software enabling broad population management tools
  • Consumer: consumer genomics: eg, actionable choices
    Clinical
    personalized medicine: eg, use genomics to predict the best drugs for cancer patients
    advanced computational imaging: eg coupling machine learning with advances in imaging hardware
    clinical genomics & testing: eg disrupting existing clinical tests with genomic approaches that are cheaper and open new doors
    B2B: new tools to accelerate others, building out the infrastructure
  • Powerful new methods put software at the center
    Machine Learning, Large-scale data, Cloud computing
    Emergence of a “Cloud Bio” infrastructure
    Analogs to cloud computing: low CapEx startups in Bio
    Approaches that seek to avoid FDA risk
    DTC, consumer genomics, etc
  • Software can positively impact our health, wellness, and lives
    Mobile leads to more natural human connections, sensors, reminders
    Quantified self + Cloud brings everything together

    Smartphones enable better population management tools
    Smartphones and notifications allow engagement with healthcare apps
    Smartphone hardware can be used for diagnostic purposes or to power medical add-ons
    Teledermatology use phone camera to send pictures of skin conditions

    Decentralization as a general theme
    Bringing the patient to the center of the decision making process

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