# Improving precision use of antipsychotic medication in people with autism

> **NIH NIH P50** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2020 · $252,353

## Abstract

Autism spectrum disorder (ASD) is the most common neurodevelopmental condition, occurring in 1 in 59
children and commonly associated with behavioral problems that include aggression, irritability, and self-injury
that are highly disabling to children with ASD and their families. While behavioral approaches are sometimes
effective for these problems, they are may not be readily accessible to all families, are usually not covered in
older individuals, and may not provide complete benefit to some people with ASD. These issues leads to the
use of pharmacological intervention, often with atypical antipsychotics (ATAP) such as risperidone or
aripiprazole. These two ATAPs are FDA approved to treat severe behavior disturbances such as aggression
and irritability in ASD, and while ATAPs can be effective, these drugs are associated with increased weight
gain, with a high risk of developing obesity. Understanding the clinical and genetic predictors of weight gain,
and the differential effects of the most commonly used ATAPs on weight gain, is critical to improving the health
of individuals with ASD. The objective of the Research Project is to address the need for precision use of
ATAPs in ASD. Our Specific Aims will: (1) develop an electronic health record (EHR) based predictive model of
atypical antipsychotic (ATAP)-induced weight gain in ASD, using a large and unique de-identified institutional
database; (2) identify pharmacogenetic risk factors associated with ATAP-induced weight gain in ASD
harnessing existing genetic information linked to the EHR; and (3) compare rates of ATAP-induced weight gain
in children with ASD randomized to one of two FDA-approved ATAPs via a pragmatic trial that will take place in
an outpatient clinic setting. Other innovative aspects of the pragmatic trial include the use of a modified
electronic consent to decrease participant/caregiver burden, the incorporation of EHR embedded health
measures to increase trial efficiency, and inclusion of a caregiver-reported outcome, the Aberrant Behavior
Checklist – Irritability scale, embedded in the EHR. To accomplish these Aims, we will (1) Use machine
learning methods to develop predictive modeling of ATAP-induced weight gain; (2) Estimate the contribution of
genetic data to ATAP-induced weight gain, and (3) carry out a pragmatic clinical trial in children with ASD
requiring ATAP treatment. The Research Project is a key element of our IDDRC renewal through its interaction
with the IDDRC Cores, particularly the Clinical Translational Core which will manage the pragmatic trial, the
Data Science Core, which will analyze resulting data, and the Administrative Core, which will promote
dissemination efforts as well as stakeholder involvement in the design and conduct of the pragmatic trial. It
addresses three focus areas within the parent RFA: (1) Interventions and Management of Co-morbid Mental
Health Conditions; (2) Innovative Technologies to Improve Assessments, Interventions, and Outcom...

## Key facts

- **NIH application ID:** 10085553
- **Project number:** 1P50HD103537-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Lea K Davis
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $252,353
- **Award type:** 1
- **Project period:** 2020-08-06 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10085553

## Citation

> US National Institutes of Health, RePORTER application 10085553, Improving precision use of antipsychotic medication in people with autism (1P50HD103537-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10085553. Licensed CC0.

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