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Bio Fund
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
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)
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.
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
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 $5,000 and is
expected to drop even
lower.
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
10,000
1,000
100
10
1
Cost(thousandsUS$)
Putting these trends together
This is disrupting traditional biotech
= SOFTWARE IS EATING BIO
Compute Sensors Biology
+ +
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
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
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
Three emerging areas
Cloud Biology Computational MedicineDigital
Therapeutics
Slow and expensive to develop
Toxic side effects
FDA regulated
Traditional therapeutics
IMAGE: Champlax
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
Large capital outlay
Poor reproducibility
Empirically driven
doesn’t scale
The four horsemen of Eroom’s law
Traditional biology
Title Text
Subtitle text
Biology that works like programming:
Cloud experiments run through software
Cloud biology
IMAGE: Emerald Therapeutics
Tsunami of data
Flood of new drugs
Traditional medicine
Title Text
Subtitle text
Personalized cancer treatments based on
patient tumor genetic screenings
Computational biomedicine
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
Title Text
Subtitle text
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
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
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
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)

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Software is Eating Bio

  • 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
  • 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)

Editor's Notes

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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