APECx
- Deadline Dec 15 abstract
- Full prob Feb 16
IDEA: Future Proof Countermeasures
abstract length
total length should not exceed four (4) pages in length. The maximum page count excludes the cover page and the Rough Order of Magnitude. The Government will not review pages beyond 4; and any abstract submitted that exceeds four (4) pages will only be reviewed at ARPA-H’s discretion.
Abstract format
- A. Cover Page The cover page should follow the same format as the full proposal described in Section 4.2.2.A. The cover page does not count towards the page limit.
- B. Concept Summary Describe the proposed concept with minimal jargon and explain how it addresses the topic area(s) of the R&D Solicitation.
- C. Innovation and Impact Clearly identify the health outcome(s) sought and/or the problem(s) to be solved with the proposed technology concept. Describe how the proposed effort represents an innovative and potentially revolutionary solution to the technical challenges posed by the R&D Solicitation. Explain the concept’s potential to be disruptive compared to existing or emerging technologies. Describe how the concept will have a positive impact on at least one of ARPA-H's mission areas. To the extent possible, provide quantitative metrics in a table that compares the proposed technology concept to current and emerging technologies and includes: • State of the art / emerging technology “baseline” • Target for proposed technology in its final, commercializable form • Target for proposed technology at the end of the proposed ARPA-H project
- D. Proposed Work Describe the final deliverable(s) for the project, one (1) or two (2) key interim milestones, and the overall technical approach used to achieve project objectives. Discuss alternative approaches considered, if any, and why the proposed approach is most appropriate for the project objectives. Describe the background, theory, simulation, modeling, experimental data, or other sound engineering and scientific practices or principles that support the proposed approach. Provide specific examples of supporting data and/or appropriate citations to the scientific and technical literature. The list of citations does not count towards the page limit. Identify commercialization challenges to be overcome for the proposed technology to be successful in the health market. Describe why the proposed effort is a significant technical challenge and the key technical risks to the project. At a minimum, the abstract should address: Does the approach require one or more entirely new technical developments to succeed? How will technical risk be mitigated?
- E. Team Organization and Capabilities Indicate the roles and responsibilities of the organizations and key personnel that comprise the Project Team. Provide the name, position, and institution of each key team member and describe in 1-2 sentences the skills and experience they bring to the team.
- F. Rough Order of Magnitude (ROM) Please include a ROM estimate of timeline and federal funds requested, as well as the total project cost including cost sharing, if applicable. The ROM should also include a breakdown of the work by direct labor, labor rates, subcontracts, materials, equipment, other direct costs (e.g., travel), indirect costs, profit, cost sharing, and any other relevant costs. Cost sharing is neither required nor forbidden and is not considered a factor in evaluation.
Description
Current approaches to vaccine development are costly, time-consuming, and have not yielded broadly-efficacious vaccines for viral disease. Due to the technical complexities, most developers target a single virus species as the indication for a given vaccine, as the cost and time associated with evaluating the clinical efficacy of a vaccine leads to a risk-averse “one-virus, one-vaccine” development strategy. Novel protein structure prediction algorithms, such as AlphaFold2 (AF2) and RoseTTAFold (RF), have revolutionized protein structure prediction for various applications and have the potential – when combined with high-throughput functional experimentation discoveries – to unlock new possibilities within vaccine development approaches. However, the effectiveness of these algorithms correlates with the amount of experimentally-resolved structure data found in the open-source Protein Data Bank (PDB). The PDB’s structural repertoire is significantly biased toward soluble eukaryotic and bacterial proteins, with viral proteins constituting less than 6% of the total. Additionally, the existing prediction algorithms face challenges in accurately predicting the impact of mutations, post- translational modifications, multi-domain structures, and protein-protein interactions. These core capabilities are essential for in silico approaches to design vaccine Ag that create immune responses that are protective at the relevant mucosal surfaces and provide durable protection beyond a single virus species. These tools have not been paired with orthogonal data generation from functional assessments that would generate data beneficial to vaccine Ag design.
APECx will address these limitations by:
- Discovering and optimizing new methodologies to generate the necessary structural and functional data needed for modeling viral Ag and incorporating these data into vaccine design tool development – and sharing these data openly
- Building Artificial intelligence (AI)/Machine learning (ML)-enabled vaccine design tools for translational vaccine and therapeutic development – and sharing these tools openly
- Demonstrating the predictive and learning abilities of these tools through proof-of- concept studies that evaluate their applicability to broad-spectrum vaccine development
- Challenging the developers to demonstrate genus/family-level efficacy of these vaccine candidates with independent and validated assays and models
- Down-selecting the most promising candidates for evaluation in Phase I human clinical studies to demonstrate the capabilities built into the antigen development pipeline
TA1 TA2 TA3
- Technical Area 1 (TA1)- High-throughput (HT) Biochemical Analysis and Protein Engineering: Accelerated throughput of viral Ag discovery with high accuracy, utilizing techniques including but not limited to HT experimental structure determination, HT functional analysis, and model system development for screening of lead Ag candidates.
- Technical Area 2 (TA2)- Protein Modeling Toolkit for Antigen Design: Leverage 3- dimensional (3D) structural and HT functional data information from TA1 and existing viral protein structure data to confidently model challenging targets and predict and design consensus chimeric Ag suitable for genus-level vaccines against existing and emerging viral diseases. (TA2)- Discovery Pipeline Development: APECx will also prioritize integrated model development by soliciting TA2 Only performers to develop, train, and test team-developed toolkits across the performers (inclusive of training data and models developed).
- Technical Area 3 (TA3)- Translational Candidate Development and Clinical Evaluation: Utilize the dataset generated by TA1 and TA2 to discover novel MCMs, optimize them for relevant delivery platforms that are efficacious at a viral genus-level and easily accessible to the public, and iteratively validate approaches generated by TA1 and TA2.
Virus Targets
Virus Genera Table with Characteristics, Biosafety Levels, and Expected Mortality
| Virus Genus | Key Viruses | Vaccine | Fast Evolution | BSL | Combined Info (Mortality, Military Benefit, Global Relevance) |
|---|---|---|---|---|---|
| Orthomyxovirus | Influenza | Annual vaccines, frequent updates | Yes | 2 | Mortality: Low (<0.1%); Military: Crucial for readiness; Global: Yes |
| Alphacoronavirus | Human Coronavirus | No vaccines | Moderate | 2 | Mortality: Very low; Military: Important for health; Global: Yes |
| Flavivirus | Zika, Dengue | Limited efficacy | Yes | 2/3 | Mortality: Varies; Military: Important in endemic areas; Global: Yes |
| Enterovirus | Poliovirus, Coxsackie | Not for all strains | Yes | 2 | Mortality: Low; Military: Relevant in outbreaks; Global: Yes |
| Hantavirus | Sin Nombre, Andes | No vaccines | Moderate | 2/3 | Mortality: Varies; Military: Important in field settings; Global: Yes |
| Henipavirus | Nipah, Hendra | No vaccines | Moderate | 4 | Mortality: High; Military: Relevant in risk regions; Global: Yes |
| Togaviridae | Chikungunya | No widespread vaccine | Moderate | 3 | Mortality: Low; Military: Relevant in outbreaks; Global: Yes |
| Nairovirus | CCHF | No vaccines | Moderate | 4 | Mortality: High; Military: Important in endemic regions; Global: Yes |
| Arenavirus | Lassa Fever | No widespread vaccine | Moderate | 4 | Mortality: High (1-15%); Military: Important in West Africa; Global: Yes |
| Filovirus | Ebola, Marburg | Limited availability | Moderate | 4 | Mortality: Very High; Military: Crucial in outbreak regions; Global: Yes |
Certainly! Here's a more focused table excluding Influenza and Coronaviruses, but including Filoviruses, Hantavirus, Bunyaviridae (specifically Nairovirus for CCHF), Arenaviruses, Flaviviruses, and Togaviridae (for Chikungunya):
Focused Virus Genera Table
| Virus Genus | Key Viruses | Vaccine | Fast Evolution | BSL | Combined Info (Mortality, Military Benefit, Global Relevance) |
|---|---|---|---|---|---|
| Flavivirus | Zika, Dengue | Limited efficacy | Yes | 2/3 | Mortality: Varies; Military: Important in endemic areas; Global: Yes |
| Hantavirus | Sin Nombre, Andes | No vaccines | Moderate | 2/3 | Mortality: Varies; Military: Important in field settings; Global: Yes |
| Nairovirus | CCHF | No vaccines | Moderate | 4 | Mortality: High; Military: Important in endemic regions; Global: Yes |
| Arenavirus | Lassa Fever | No widespread vaccine | Moderate | 4 | Mortality: High (1-15%); Military: Important in West Africa; Global: Yes |
| Filovirus | Ebola, Marburg | Limited availability | Moderate | 4 | Mortality: Very High; Military: Crucial in outbreak regions; Global: Yes |
| Togaviridae | Chikungunya | No widespread vaccine | Moderate | 3 | Mortality: Low; Military: Relevant in outbreaks; Global: Yes |
This table provides a succinct overview of the specified virus genera, focusing on those with significant implications for global health and U.S. military operations, particularly in terms of vaccine availability, evolutionary rate, biosafety level, and combined information on mortality, military benefit, and global relevance.
Team
| No. | Name | Affiliation | Expertise | TA Association | |
|---|---|---|---|---|---|
| 1 | Ishanu Chattopadhyay | University of Chicago | ML/AI | TA2 | ishanu@uchicago.edu |
| 2 | Paul Bogdan | USC | AI/ML/Predictive Modeling | TA2 | pbogdan@usc.edu |
| 3 | Rick Stevens | Argonne National Lab | ML/AI | TA2 | stevens@cs.uchicago.edu |
| 4 | Arvind Ramanathan | Argonne | Computational Modeling of Protein | TA2 | ramanathana@anl.gov |
| 5 | Andrzej Joachimiak | Argonne | Protein Structural Experiments | TA1 | andrzejj@anl.gov |
| 6 | Balaji Manicassamy | University of Iowa | Virology | TA3 | balaji-manicassamy@uiowa.edu |
| 7 | Nicholas Wu | UIUC | Structural Chemistry | TA1 | nicwu@illinois.edu |
| 8 | Nicholas Kotov | University of Michigan | Nanochemistry | TA1/TA3 | kotov@umich.edu |
| 9 | Patrick Wilson | Weill Cornell | Immunology | TA3 | pcw4001@med.cornell.edu |
| 10 | Aaron Esser-Kahn | University of Chicago | Vaccine Science | TA3 | aesserkahn@uchicago.edu |
| 11 | Kevin Legge | University of Iowa | Immunology | TA3 | kevin-legge@uiowa.edu |