REIMAGINING DRUG DEVELOPMENT:

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Madlyn Lesley Watson
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1 Biology Reconstructed REIMAGINING DRUG DEVELOPMENT: Accurate Disease Modeling To Drive Successful Therapies Julia Kirshner, CEO 1

2 SUCCESS RATES OF DRUG DEVELOPMENT ARE LOW, " PARTICULARLY IN ONCOLOGY Success rates from first-in-man to registration 19 out 20 oncology drugs fail Clinical Development Success Rates David W. Thomas, et. al. 2

THE COST OF DRUG DEVELOPMENT $1,098 million phase I safety phase II effectiveness $1,460 million phase III safety/effectiveness risk-to-benefit ratio $2.

3 THE COST OF DRUG DEVELOPMENT $1,098 million phase I safety phase II effectiveness $1,460 million phase III safety/effectiveness risk-to-benefit ratio $2.6 billion 3-6 years 6-7 years Innovation in the pharmaceutical industry: New estimates of R&D costs. DiMasia, et.al. Journal. of Health Economics. v. 47: (2016) 3

4 COST REDUCTION STRATEGIES:" Reduce costs of discovery/preclinical testing $1,098 million $1,460 million $2.6 billion * 3-6 years 6-7 years *Accounting for failures Innovation in the pharmaceutical industry: New estimates of R&D costs. DiMasia, et.al. Journal. of Health Economics. v. 47: (2016) 4

5 COST REDUCTION STRATEGIES:" Reduce costs of clinical trials $1,098 million $1,460 million $2.6 billion * 3-6 years 6-7 years *Accounting for failures Innovation in the pharmaceutical industry: New estimates of R&D costs. DiMasia, et.al. Journal. of Health Economics. v. 47: (2016) 5

COST REDUCTION STRATEGIES:" Improve accuracy of preclinical testing & patient population selection DISCOVERY PRE-CLINICAL 10,000+ 250 Poor

6 COST REDUCTION STRATEGIES:" Improve accuracy of preclinical testing & patient population selection DISCOVERY PRE-CLINICAL 10, Poor clinical relevance of conventional in vitro & in vivo models CLINICAL 20 95% failure rate of drug candidates due to poor clinical efficacy 1 6

7 PRECLINICAL MODELS IN ONCOLOGY " DO NOT ACCURATELY REPRESENT HUMAN DISEASE Clinical Development Success Rates David W. Thomas, et. al. 7

clinical outcomes CLINICAL Select candidates with high probability to show efficacy in

8 COST REDUCTION STRATEGIES:" Select drug candidates based on probability of clinical response DISCOVERY PRE-CLINICAL Human-specific disease models with capacity to predict clinical outcomes CLINICAL Select candidates with high probability to show efficacy in patients: increased success rate of clinical trials reduced cost multiple timely cures reaching patients 8

zpredicta APPROACH:" Physiologically relevant disease models HUMAN data signal Disease models: Data set: Bio-simulation: 1-to-1 biological reconstruction of human diseases (cancer, infection, etc.

9 zpredicta APPROACH:" Physiologically relevant disease models HUMAN data signal Disease models: Data set: Bio-simulation: 1-to-1 biological reconstruction of human diseases (cancer, infection, etc.) mapping cellular behavior in response to therapeutic agents large, multi-parameter data obtained from testing therapeutic agents in zpredicta disease models computational platform powered by AI for prediction of clinical outcomes based on data from disease models 9

10 zpredicta DISEASE MODELS DEMONSTRATE " HIGH CORRELATION WITH CLINICAL RESPONSE 1-to-1 reconstruction of human tissue In Vivo Bone Marrow Reconstructed Bone (r-bone) Cell cluster Cell cluster 10 out of 12 multiple myeloma cases were predicted correctly Cryo-electron microscopy (cryo-em) of bone marrow reconstructed in r-bone model compared to in vivo bone marrow. Reconstructed Bone 78% 100% 22% 10

CORRECT BIOLOGY + AUTOMATION + COMPUTATION =" ACCURATE DRUG CANDIDATE SELECTION CORRECT

drugs in <2 weeks COMPUTATION Bio-simulation to predict clinical outcomes disease models

real-time multi-parameter drug response profiling map cellular behavior in response to

11 CORRECT BIOLOGY + AUTOMATION + COMPUTATION =" ACCURATE DRUG CANDIDATE SELECTION CORRECT BIOLOGY 1:1 reconstruction of human tissues AUTOMATION High throughput screening 10,000+ drugs in <2 weeks COMPUTATION Bio-simulation to predict clinical outcomes disease models representing multiple organ sites product lines across multiple disease indications real-time multi-parameter drug response profiling map cellular behavior in response to drugs end-to-end drug development discovery preclinical companion diagnostics personalized medicine 11

12 ACCURATE DISEASE MODELING = " SUCCESSFUL THERAPIES Drug candidate screening Biomarker discovery Identification of new drug combinations Target discovery Evaluation of off-target toxicity Rescue failed drug candidates 12

13 MISSION: To enable successful treatments for every patient Biology Reconstructed Julia Kirshner, Ph.D., Founder & CEO Optical Court San Jose, CA,

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