Key Dates

Related Announcements

PA-20-183 - NIH Research Project Grant (Parent R01 Clinical Trial Required)

PA-20-185- NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed

PA-20-184 – NIH Research Project Grant (Parent R01 Basic Experimental Studies with Humans Required)

Issued by

National Institute on Drug Abuse (NIDA)

Purpose

The purpose of this Notice is to inform potential applicants to the National Institute on Drug Abuse (NIDA) of NIDA's interest ingrant applications that will develop or utilize advanced computational approaches to describe complex drug-disease relationships in ways that will rapidly advance the development of new treatments, allow for targeted funding of substance use disorder (SUD) drug discovery and improve health care.. NIDA has a particular interest in applications thatinclude approaches in one or more of the following categories: 1) artificial intelligence, including machine learning and deep learning, 2) supercomputing/parallel computing, and/or 3) quantum computing.

Background

Addiction is a complex disease that includes many neuropathological, psychiatric, and environmental influences, and biomedical research in this area is generating unprecedented amounts of data, much of which is distributed and exists in a wide variety of formats. Utilizing and/or combining artificial intelligence (including machine learning and deep learning), supercomputing/parallel computing, and quantum computing approaches can help us better understand the various underlying mechanisms (genetic, cellular, molecular) of addiction. These approaches allow for models that mimic the human brain and address issues of scale, complexity, and processing speed of the data, as well as the integration of disparate data elements (such as gene expression data, multi-omics capture data, cellular signaling, receptor-drug interactions, pharmacological and behavioral responses, brain region activity, electronic medical records, etc.). Developing or utilizing advanced computational approaches can also help us address unmet needs in clinical and outcome studies, identifying translational biomarkers, and discovering new therapeutics and combinations of therapeutics that can more effectively treat SUDs. Collectively, these approaches will enable predictive disease modeling, advance the development of new treatments, and improve healthcare. Additionally, combining a variety of advanced computational approaches will allow researchers from multiple disciplines and backgrounds (industry and academia) to come together to model and study disease pathology addiction in novel ways.

Research Objectives

NIDA is interested in combining the latest computational capabilities with biomedical data to enhance our understanding of biology of addiction and improve drug discovery and development. AI, including ML and DL, enables computers to mimic the human brain, while supercomputing/parallel computing addresses the scale and complexity of data, and quantum computing addresses processing speed. One or more of these approaches may be used to analyze gene expression data, multi-omics capture data, cellular signaling, receptor-drug interactions, pharmacological and behavioral responses, brain region activity, electronic medical records, or other data to address fundamental research questions associated with the prevention, diagnosis, and treatment of addiction.

Examples of approaches that are encouraged include, but are not limited to:

• • Developing disease ontologies
  • Identifying biomarkers
  • Identifying precision medicine approaches
  • Identifying already marketed drugs for repurposing
  • Decreasing time and cost of drug development
  • Automating clinical trial designs

Areas of programmatic interest to NIDA include:

• Combining a variety of advanced computational approaches to allow researchers from various disciplines and backgrounds to come together to pursue the most cutting-edge approaches to model and study addiction disease pathology
• Developing cutting-edge computational algorithms to revolutionize our understanding of neurobiological, genetic, epigenetic, social, and environmental factors that contribute to the vulnerability to drug use, abuse, and addiction
• Utilizing big data-based knowledgebases for (predictive) modeling of disease, the discovery of translational biomarkers, and new therapeutics with an application for clinical practice
• Utilizing and combining AI and other advanced computational approaches to detect the potential for addiction treatment amongst already marketed drugs for another indication

Application and Submission Information

This notice applies to due dates on or after October 5, 2021 and subsequent receipt dates through January 8, 2025.

Submit applications for this initiative using one of the following funding opportunity announcements (FOAs) or any reissues of these announcement through the expiration date of this notice.

• PA-20-183 - NIH Research Project Grant (Parent R01 Clinical Trial Required)
• PA-20-185- NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed)
• PA-20-184 – NIH Research Project Grant (Parent R01 Basic Experimental Studies with Humans Required)

All instructions in the SF424 (R&R) Application Guide and the funding opportunity announcement used for submission must be followed, with the following additions:

• For funding consideration, applicants must include “NOT-DA-21-004” (without quotation marks) in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered for this initiative.

Applications nonresponsive to terms of this NOSI will not be considered for the NOSI initiative.

Inquiries

Scientific/Research Contact(s))

Susan Wright, PhD
National Institute on Drug Abuse (NIDA)
Telephone: (301) 402-6683
Email:susan.wright@nih.gov

Financial/Grants Management Contact(s)

Ericka Wells, MHS
National Institute on Drug Abuse (NIDA)
Telephone: (301) 827-6705
Email:wellse2@nida.nih.gov