Friday, April 19, 2024 4/19/2024

Identifying treatment responders in medication trials for AUD using machine learning approaches

Award Number: R03AA029244
ORGANIZATION: NATIONAL INSTITUTE ON ALCOHOL ABUSE AND ALCOHOLISM
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

Group Awards By Issue Date FY or Funding FY:

View Award Abstract

ABSTRACT Alcohol use disorder (AUD), as defined in DSM-5, represents a highly prevalent, costly, and often untreated condition in the United States. Pharmacotherapy offers a promising avenue for treating AUD and for improving clinical outcomes for this debilitating disorder. While developing novel medications to treat AUD remains a high priority research area, there remain major opportunities to further elucidate clinical response in completed medication trials. To that end, a key question in randomized clinical trials (RCTs) is which patients respond to a given pharmacotherapy. Identifying treatment responders provides major opportunities to advance clinical care for AUD by personalizing medication practices on the bases of variables/predictors of good clinical response. For example, while the effect size for medications such as naltrexone is deemed small-to-moderate, a host of studies over the past decade have shown that its effect size may be considerably larger for certain subgroups of patients. Towards advancing precision medicine for AUD and leveraging data from a host of carefully conducted RCTs for AUD, this R03 application seeks to conduct secondary data analysis. Specifically, we propose to analyze data from four RCTs conducted by the NIAAA Clinical Investigations Group (NCIG). These state-of-the-art RCTs for AUD have tested the following pharmacotherapies: (a) quetiapine, (b) Levetiracetam XR (Keppra XR®), (c) Varenicline (Chantix®), and (d) HORIZANT® (Gabapentin Enacarbil) Extended-Release. In this R03 application, we propose to use a machine learning approach to identify treatment responders in the NCIG RCTs. Machine learning represents a highly promising and underutilized data analytic strategy in the field of AUD treatment response. Machine learning models prioritize the ability to predict future outcomes over creating perfectly fitting models for the data at hand. This results in models which are more generalizable to future observations, which fits well with our goal of identifying responders in RCTs. Leveraging data from these pivotal RCTs through secondary data analysis and using novel analytic methods, namely machine learning, provides a cost-effective approach to identifying AUD pharmacotherapy responders.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2023 ( Subtotal = -$2,530 )
2023	2022	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Alcohol Research Programs	000	2	9/11/2023	NON-COMPETING CONTINUATION	-$2,530
														Subtotal = -$2,530

Issue Date FY: 2022 ( Subtotal = $74,776 )
2022	2022	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Alcohol Research Programs	000	2	3/29/2022	NON-COMPETING CONTINUATION	$74,776
														Subtotal = $74,776

Issue Date FY: 2021 ( Subtotal = $74,776 )
2021	2021	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90095	LOS ANGELES	USA	Alcohol Research Programs	000	1	4/8/2021	NEW	$74,776
														Subtotal = $74,776

Grand Total All Awards = $147,022

Top

All Categories

About

Search

Reports

Data Submission

Award Information

Identifying treatment responders in medication trials for AUD using machine learning approaches

Award Number: R03AA029244

ORGANIZATION: NATIONAL INSTITUTE ON ALCOHOL ABUSE AND ALCOHOLISM

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer