SPECIAL NOTICE
A -- Notice of Intent to Sole Source
- Notice Date
- 9/28/2023 11:52:18 AM
- Notice Type
- Special Notice
- NAICS
- 541714
— Research and Development in Biotechnology (except Nanobiotechnology)
- Contracting Office
- DEPT OF DEFENSE
- ZIP Code
- 00000
- Solicitation Number
- HT9425-23-NOI-NA01
- Response Due
- 11/14/2023 6:00:00 AM
- Archive Date
- 11/29/2023
- Point of Contact
- Nathan Anderson
- E-Mail Address
-
nathan.p.anderson19.civ@health.mil
(nathan.p.anderson19.civ@health.mil)
- Description
- This Research and Development support for BHSAI is to develop predictive computational tools and platforms using deep neural networks to assess the mechanisms of adverse effects associated with chemical exposures, including industrial and commercial chemicals, chemical agents, toxins, and drugs. This is in response to the increasing risk of adverse health effects for military personnel who are exposed to chemicals during training exercises, overseas deployments, and national defense operations. Additionally, the potential for chemical exposures is expected to increase in the future as conflicts occur in urban, industrialized megacities. The proposed platform will utilize in vitro and in vivo experimental screening data to predict the mechanisms of adverse outcomes. Currently, the military lacks the tools to rapidly screen chemicals and evaluate their molecular mechanisms. Traditional toxicity testing approaches that utilize animal models are low throughput, time-consuming, resource-intensive, and raise ethical considerations. There are also more than 80,000 chemicals produced worldwide, making it logistically impractical to conduct a full battery of toxicology assessment tests to understand the mechanisms associated with these chemicals. To address these concerns, there is a growing need and emphasis on developing new AI-based approach methodologies that rely on non-animal alternative testing approaches. In order to quantify the biomechanical responses and injury risks, subject-specific, three-dimensional FE models of the tibia, metatarsal, and lumbar spine for each subject by using the collected CT images will be developed. This entails using the 3-Matic software (Materialise, Leuven, Belgium) to create tetrahedral meshes for each bone based on the bone geometry extracted from the CT images. Optimization schemes available in 3-Matic needs to be used to ensure the generation of high-quality meshes required for FE simulations. If the element-quality check performed within the Abaqus/Explicit software (Dassault Syst�mes, Velizy-Villacoublay, France) fails, HyperMesh software (Altair Inc., Troy, MI) needs to be deployed to identify the faulty mesh elements and change their size and shape by rotating, extending, and morphing them. Next, subject-specific material properties (i.e., the elastic modulus) will be calculated from the CT images and mapped to the subject-specific bone meshes. By coupling the bone meshes with the developed musculoskeletal models, the muscle/ligament insertion points for providing the boundary conditions for the FE model can be identified. Furthermore, the body forces and moments obtained from the musculoskeletal model will be used as subject-specific loading conditions for the FE model. All FE simulations will be performed using Abaqus/Explicit software, using high-end computing resources at the DoD Supercomputing Resource Center at the U.S. Army Research Laboratory. Analysis of exposure study data using metabolic network modeling that integrate computational systems toxicology approaches to dynamically analyze existing chemical and biological assay data at the National Institute of Environmental Health Sciences (NIEHS) in order to establish and enable AI-based predictive toxicology. Specifically, systems-based predictions of toxic response mechanisms using transcriptional data from animal and in vitro studies of different toxicants at multiple exposure levels and time to map out mechanisms of toxicity and predict organ injury, including comparison of different species and examination of gender difference in Sprague Dawley rats.
- Web Link
-
SAM.gov Permalink
(https://sam.gov/opp/1198180e401b4f5cabdc986a164bb137/view)
- Place of Performance
- Address: Frederick, MD 21702, USA
- Zip Code: 21702
- Country: USA
- Zip Code: 21702
- Record
- SN06848152-F 20230930/230928230048 (samdaily.us)
- Source
-
SAM.gov Link to This Notice
(may not be valid after Archive Date)
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