High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation
NIBIB - National Institute of Biomedical Imaging and Bioengineering
About This Grant
PROJECT SUMMARY This project aims to develop cryopreservation methods to extend organ viability from hours to years. This would improve transplantation by allowing more effective organ allocation, lower discard rates, improved immunological matching, reduced chronic rejection, and more flexible surgery scheduling. Current efforts to cryopreserve organs use cryoprotective agents (CPAs) to prevent ice formation, but these efforts are limited by the toxicity of existing CPAs. Previous research has mainly focused on <10 molecules out of the ~40 that have been tested as CPAs. However, the number of potential molecules is in the millions. This project leverages novel high throughput screening methods and machine learning to explore this vast chemical space to identify new CPA mixtures with reduced toxicity. The central hypothesis is that high throughput screening of CPAs for cell membrane permeability, toxicity, and promotion of glass formation can be combined with machine learning and decision-making algorithms to enable discovery of novel low toxicity compositions for organ cryopreservation. The project has four specific aims: Aim 1: Screen for chemicals with high membrane permeability and low toxicity. High throughput experiments will be combined with virtual screening using machine learning models to discover new molecules with promising properties for cryopreservation. Aim 2: Identify CPA interactions that reduce toxicity. To uncover synergistic CPA interactions, high throughput experiments will be performed to compare the toxicity of binary CPA mixtures to single-CPA solutions. The resulting data will be used to train a model for predicting the toxicity of CPA mixtures. Aim 3: Quantify the glass forming abilities of CPA mixtures. A high throughput approach will be used to determine the concentration required to form a glass upon cooling for each CPA mixture. Aim 4: Optimize CPA mixtures for organ cryopreservation. An iterative approach combining mathematical optimization, high throughput testing, and model retraining will be used to identify CPA compositions that can prevent ice with minimal toxicity. Overall, this work will establish high throughput screening and machine learning as a platform for discovery of novel CPA mixtures with reduced toxicity, laying the groundwork for future efforts to cryopreserve human organs.
Grant Summary
High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation is a NIBIB - National Institute of Biomedical Imaging and Bioengineering grant providing up to $560K for university, nonprofit, healthcare org. Applications are due 2029-05-31 (open). Check eligibility and apply with FindGrants.
Focus Areas
Eligibility
How to Apply
Up to $560K
2029-05-31
- 1Confirm your organization is eligible for High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation from NIBIB - National Institute of Biomedical Imaging and Bioengineering, checking organization type, location, and any population or project requirements.
- 2Gather the required documents and information, including your organization details, project plan, and budget figures.
- 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
- 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NIBIB - National Institute of Biomedical Imaging and Bioengineering before the deadline.
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High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation: Frequently Asked Questions
Who is eligible for the High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation?
High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation is offered by NIBIB - National Institute of Biomedical Imaging and Bioengineering and is generally open to university, nonprofit, healthcare org. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.
How much funding does the High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation provide?
High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation provides up to $560K per award from NIBIB - National Institute of Biomedical Imaging and Bioengineering. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.
When is the High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation deadline?
Applications for High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation are due 2029-05-31 (open). Because deadlines can change, verify the date with the funder, NIBIB - National Institute of Biomedical Imaging and Bioengineering, and give yourself enough time to prepare a complete, competitive application before the close date.
How do you apply for the High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation?
To apply for High throughput screening and machine learning for discovery of next generation chemicals for cryopreservation, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NIBIB - National Institute of Biomedical Imaging and Bioengineering.