PREventing EMerging Pathogenic Threats (PREEMPT) Proposers Day

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1 PREventing EMerging Pathogenic Threats (PREEMPT) Proposers Day Dr. Jim Gimlett, Program Manager DARPA Biological Technologies Office (BTO) January 30, 2018 Arlington, VA

2 PREEMPT Agenda Proposers Day Objectives PREEMPT Program Motivation and Overview PREEMPT Program Objectives PREEMPT Technical Areas Timeline and Milestones Proposal Guidance Disclaimer: In the event of a disagreement between the contents of the BAA and the information in this briefing, please follow the BAA. No exceptions. 2

3 PREEMPT Proposers Day Objectives Introduce the science and technology community (industry, academia, and government) to the PREventing EMerging Pathogenic Threats program vision and goals Explain the general mechanics of a DARPA program and the specific objectives and milestones of this program Encourage and promote teaming arrangements among organizations with the expertise, research facilities, and capabilities needed to execute research and development responsive to PREEMPT program goals 3

PREEMPT Vision: Far-forward biosurveillance and preemptive countermeasure pipeline DOD

SOF deployments Low risk High risk Overlay of 2016 US SOF troops global deployment and

, 2016) Fact: Most infectious diseases came from animals or are transmitted by insect vectors

4 PREEMPT Vision: Far-forward biosurveillance and preemptive countermeasure pipeline DOD Problem: Military personnel deployed to areas at high risk of endemic and emerging diseases SOF deployments Low risk High risk Overlay of 2016 US SOF troops global deployment and bathuman virus risk map (Brierley et al., 2016) Fact: Most infectious diseases came from animals or are transmitted by insect vectors DARPA Goal: Eliminate viral threat surprise Preemptively block new outbreaks by targeting animal viruses before they emerge in humans Reservoirs Vector X X X Warfighter 4

Emerging infectious diseases: A growing threat to the DOD and to national security US Army hospital admissions during war The problem is not getting better: Higher rate of pathogen emergence due to

5 Emerging infectious diseases: A growing threat to the DOD and to national security US Army hospital admissions during war The problem is not getting better: Higher rate of pathogen emergence due to increasing human-animal contact, population density, globalization Limited or no countermeasures for many emerging pathogens Potential for major US impact pathogens can spread rapidly, cause significant morbidity and mortality, be weaponized Source: US Army, 1999 Historically, more hospital admissions from infectious diseases than from combat injuries in deployed US forces Especially at risk: deployed active and reserve military, humanitarian operations, field troops Recent examples: Ebola, SARS, MERS, Nipah, Influenza HxNy, Chikungunya, Zika New proactive, preemptive approaches are needed 5

Background the path to viral species jump and emerging threats to

bottleneck Stochastic bottleneck Viral Quasispecies (QS) Jump

mutation adapts to new host and expands 75% of human viruses

bottlenecks) Viral diversity via development of quasispecies (QS)

6 Background the path to viral species jump and emerging threats to humans Reservoir Intermediary reservoir or vector Human Stochastic bottleneck Stochastic bottleneck Viral Quasispecies (QS) Jump enabler mutation adapts to new host and expands Jump enabler mutation adapts to new host and expands 75% of human viruses started in animals To successfully jump into humans, a virus must clear several hurdles (e.g., cell entry, different host immune response, transmission bottlenecks) Viral diversity via development of quasispecies (QS) is key jump enabler Replicating virus will mutate to further adapt to new host Target viral evolution and bottlenecks to preempt species jump 6

PREEMPT Technical Areas A path to preempting new biological threats TA1: Develop models that quantify the likelihood of virus jump Use far-forward field surveillance data and lab data to quantify

7 PREEMPT Technical Areas A path to preempting new biological threats TA1: Develop models that quantify the likelihood of virus jump Use far-forward field surveillance data and lab data to quantify viral dynamics, evolutionary trajectories and mutations that enable species jump Identify bottleneck targets for intervention and prevention TA2: Develop and validate method(s) for species jump preemption Develop and test new approaches to suppress species jump in reservoirs Develop and test new approaches to suppress viral transmission via mosquitoes 7

TA1: Quantify likelihood of virus jump Field Initial viral quasispecies

knowledge: known jump events; ecology, season, geospatial Validate Bat

Train Model (Data-driven) Virus QS 0 Pig sample Mosquito sample Human cell

Jump risk Low Predict: QS 0 jump risk to humans High Terraforma Animal

virus jump to humans Stochastic models quantifying bottlenecks

8 TA1: Quantify likelihood of virus jump Field Initial viral quasispecies (t=0; QS 0 ) Lab tests Time series data & Deep sequencing Analysis Prior knowledge: known jump events; ecology, season, geospatial Validate Bat sample QS 1 QS 2 QS 3 Pig cell lines Virus competition, evolution dynamics Train Model (Data-driven) Virus QS 0 Pig sample Mosquito sample Human cell lines Human cell lines * * * * Mutations Fitness landscapes Classifier: Jump risk Low Predict: QS 0 jump risk to humans High Terraforma Animal models TA1 deliverables: Integrated models that establish likelihood of virus jump to humans Stochastic models quantifying bottlenecks (transmission, cell entry, infection rate) and mutational fitness maps to guide preemptive intervention strategy 8

TA1 Metrics Data source Objectives Parameters to be quantified & metrics Case study examples: Ebola, SARS, Nipah Longitudinal sampling in bat cave t=0 QS 0 =? t=6 mo QS 6mo =? t=12 mo QS 12mo =?

9 TA1 Metrics Data source Objectives Parameters to be quantified & metrics Case study examples: Ebola, SARS, Nipah Longitudinal sampling in bat cave t=0 QS 0 =? t=6 mo QS 6mo =? t=12 mo QS 12mo =? Field surveillance Quantify viral QS in selected highrisk areas (e.g. bat cave) and/or from prior outbreak event Frequency QS reservoir =? Qs human =? Mutation Multi-species lab test data Integration of field & lab data Quantify QS for cell entry and adaptation across species in vitro Quantify transmission factors and QS adaptation between two species in vivo in contained, bio secure facilities Develop model to quantify the probability of viral species jump Develop model to identify enabler mutations and targets to preempt species jump Bat sample Case study examples (reservoir): Ebola, SARS, Nipah QS 1 QS 2 QS 3 Pig / human cell lines QS infected host =? QS bat =? QS pig/human cells =? Case study examples (vector): Dengue, Zika, Chikungunya Rate of infection vs. viremia Amplification rates Incubation time Longitudinal QS diversity QS mosquito (gut/head) =? QS transmision =? N virions (transmitted)=? Validate model accuracy to within +/- 50% in terraforma multispecies environment in contained, bio secure facilities Predict top 3 targets to reduce probability of transmission between two species by >5X; targets to be validated in TA2 9

10 TA2: Develop block-before-jump methods 1. Reduce viral entrance into new host species Virus QS harvested from reservoir/vector Single mutation identified at high risk to jump to human cells * CRISPR/Cas * Targeted gene degradation Human cell CRISPR/Cas treatment of reservoir/vector specifically removes mutant from pool 2. Reduce amount of virus in carrier or reservoir Virus QS harvested from reservoir/vector Multiple mutations identified as high-risk for jump to human cells Human cell Reduce virus in reservoir/vector using anti-virals, vaccines, interfering particles TA2 deliverables: Proof of concept preemptive intervention platform that reduces the probability of virus species jump 10

TA2 Metrics Data source Class of species jump Potential block-beforejump approach Parameters to be quantified within vector or reservoir Metrics of success Crossspecies lab models Single enabler

11 TA2 Metrics Data source Class of species jump Potential block-beforejump approach Parameters to be quantified within vector or reservoir Metrics of success Crossspecies lab models Single enabler mutation * * Specific targeting of enabler mutation to suppress species jump (e.g. CRISPR/CAS, RNAi, specific antibodies) Virus incubation extended >3X Virus amplification rate reduced >3X Probability of crossspecies jump reduced >5X mutation Viral genome Multiple enabler mutations ** * * ** * * Non-specific targeting of highrisk viruses within host or vector (e.g. TIP-like interference, antiviral peptide) Viremia reduced >5X Viral incubation time extended >3X Probability of crossspecies jump reduced >5X Reservoir Vector Species1 Species2 Species1 Species2 11

Vision of a deployable preemptive capability Leverage ongoing surveillance systems PACOM, SOUTHCOM, CENTCOM, AFRICOM Experimental testing Deep seq analysis of sample Input to model and classifier

Preemption targets Entry into human cells Propagation of enabler mutants Viremia Transmission to/from reservoirs or vectors Focus on areas at high risk for viral jump Samples obtained from

12 Vision of a deployable preemptive capability Leverage ongoing surveillance systems PACOM, SOUTHCOM, CENTCOM, AFRICOM Experimental testing Deep seq analysis of sample Input to model and classifier * * * * Model: Predict imminent risk of transmission to humans and best preemptive strategies Block-before-jump capability platform Generate pipeline for rapid rollout of preventive measures Preemption targets Entry into human cells Propagation of enabler mutants Viremia Transmission to/from reservoirs or vectors Focus on areas at high risk for viral jump Samples obtained from biosurveillance hotspots Predict whether virus QS has imminent potential to jump to human and identify reservoir/vector targets for preemption * * * * Suppress high-risk jump-enabling mutations within QS 10

13 Program Structure and Timeline Phase I: 24 months Phase II: 18 months TA1 Quantify probability of virus jump Deep sequence far-forward biosurveillance samples in selected high-risk regions Develop experimental and theoretical models for species jump Identify intervention opportunities, strategies Model of viral amplification Intra- and cross-species transmission models Integrated model for risk assessment and preemption TA2 Block-beforejump platform Develop platform to deter, redirect viral evolution from species jump Develop scalable reservoir inoculation technologies Develop scalable vector inoculation Validate in reservoir Validate in vector Milestones 12M Identify signatures of fitness and spillover potential of a pathogen between two species 24M Develop initial risk classifier; predict pathways of adaptation; test initial intervention approaches 42M Demonstrate capability to suppress viral jump to a new species in the lab 13

14 Proposal guidance Read the BAA carefully! For all Technical Areas: Justify virus candidate/s, animal reservoirs, and hotspots selected for study Summarize key innovations, how your approach advances beyond current practice Back up your idea and technical approach (e.g. by theoretical arguments, models, past results, new data) Provide quantitative metrics feasible within the proposed timeline Summarize key expected outputs and deliverables 14

15 Proposal guidance continued Proposers must present a plan to address both Technical Areas It is anticipated that teaming will be necessary to meet the goals of this program. Tips for teaming: Listen to presenters during today s attendee presentation sessions, where attendees will briefly present their expertise and capabilities Reach out to colleagues and collaborators 15

16 Some advice Read the BAA, carefully and respond accordingly Some instructions are specific required and must Most of the instructions are non-specific you decide on what is the best possible science to support the objectives of the program Be honest about risks and demonstrate thoughtful consideration for how to mitigate those risks Ask for clarification as needed. FAQs will be updated regularly Take advantage of today s opportunities to meet potential teammates and ask questions 16

17 PREEMPT 17

INTERfering and Co-Evolving Prevention and Therapy (INTERCEPT) Proposers Day

INTERfering and Co-Evolving Prevention and Therapy (INTERCEPT) Proposers Day Dr. Jim Gimlett, Program Manager DARPA Biological Technologies Office (BTO) April 28, 2016 Arlington, VA INTERCEPT Agenda INTERCEPT