Key Dates

Related Announcements

None

Issued by

National Center for Advancing Translational Sciences (NCATS)

Purpose

This Notice is a Request for Information (RFI) soliciting input on the need for better tools to predict toxicities resulting from oligonucleotide therapeutics.  This RFI will inform and frame the National Institutes of Health’s (NIH) potential future funding opportunities on this topic. 

Background

Oligonucleotides (oligos) are an emerging class of therapeutics most commonly used to the treatment of rare genetic diseases.  As used here, the term “oligos” refers to short (typically 15-30 nucleotides in length) nucleic acids intended to be used as therapeutics to treat human diseases by modulating gene expression.  Typically, oligo therapeutics contain chemical modifications in the phosphodiester backbone or sugar moiety to limit degradation by cellular nucleases Examples of therapeutic oligos include anti-sense oligos (ASOs), siRNAs, and microRNA mimics.  To date, there are 17 FDA approved oligo therapeutics, and many more in clinical development, to treat rare genetic diseases, infectious diseases, and cancer. 

As with any new molecular entity to be used as a therapeutic, the consideration of toxicity is a critical requirement for assessing safety.  Toxicities of oligo therapeutics may be sequence-dependent (e.g. hybridization to nucleic acids other than the intended target) or sequence-independent (e.g. binding to cellular proteins).  At the present time, toxicity is assessed in animal studies conducted under GLP conditions, which are expensive and time consuming . Notably, recent FDA guidance has indicated a plan to phase out animal testing FDA Announces Plan to Phase Out Animal Testing Requirement for Monoclonal Antibodies and Other Drugs | FDA.  One possible strategy for assessing the toxicity of oligo therapeutics would be the use of in vitro human cell systems, including micro physiological systems (i.e. tissue chips, organs-on-a-chip) and 3-dimensional human organoids.  NIH has already invested in both of these areas, and research is ongoing. 

In recent years, we have witnessed dramatic increases in the use of advanced computational methods to address complex biological problems, such as AlphaFold for the accurate prediction of protein structures.   Quantum computing represents another powerful computational approach.  In parallel, artificial intelligence methods, including machine learning, are also becoming widely applied to challenging problems in many areas of science and society. 

The NIH seeks input from interested individuals about the potential value of and strategy for applying advanced computing methods and/or AI to develop an approach to accurately predict toxicity of oligo therapeutics, with the long-term goal of reducing and ultimately eliminating the use of animals for toxicology studies, decreasing development time and cost for oligos.  It also seeks to determine the extent to which those developing or possessing relevant data on the toxicology of oligo therapeutics can share it, and how NIH can better support such data sharing to facilitate the rapid development of predictive models for accurately predicting to toxicity of oligo therapeutics. 

Information Requested

NIH seeks comments from interested individuals on the topic of predictive models for accurately predicting to toxicity of oligo therapeutics.

NIH seeks responses from interested individuals to the following questions:

For which tissues/organs/cell types is toxicity of most concern for oligo therapeutics development ?

What publicly available datasets could be used to predict the toxicity of oligo therapeutics? What additional, currently unavailable datasets would be most useful?  What datasets that do not exist currently would have the most value to predict the toxicity of oligo therapeutics?

What would be the most effective way to assess the toxicity of chemical modifications to oligos in the context of different sequences (chemical modification X sequence interaction)?

What approaches are currently being used to predict the toxicity of oligo therapeutics other than animal testing or human cell-based systems (micro physiological systems/organoids)?

What would be the most effective way to assess the validity, accuracy, and robustness of predictive models for the toxicity of oligo therapeutics?

What types of collaborative, team-science based approaches would be most effective for developing predictive models for the toxicity of oligo therapeutics?

Comment on any other topic which may be relevant for NIH to consider on the topic of predictive models for accurately predicting toxicity of oligo therapeutics.

How to Submit a Response

All comments should be submitted via email at   [email protected].   Responses to this RFI will be accepted through July 30, 2025.

Page limit: 5 pages.

Responses to this RFI are voluntary and are meant for information and planning purposes only. Do not include any proprietary, classified, confidential, trade secret or sensitive information in your response. Respondents are advised that the U.S. Government is under no obligation to acknowledge receipt of the information provided or to provide feedback to respondents. This RFI is for information and planning purposes only and should not be considered as a solicitation, grant, or cooperative agreement, or as an obligation on the part of the Federal Government, the NIH, or NCATS to provide support for any ideas identified in response to it. The Government will use the information submitted in response to this RFI at its discretion. The Government reserves the right to use any submitted information on public websites, in reports, in summaries of the state of the science, in any possible resultant solicitation(s), grant(s), or cooperative agreement(s), or in the development of future funding opportunity announcements. Please note that the Government will not pay for the preparation of any information submitted or for use of that information.

Inquiries

Please direct all inquiries to:

Name: P.J. Brooks 

IC Name: National Center for Advancing Translational Sciences 

Telephone: 301-443-0513

Email: [email protected]