Improving precision use of antipsychotic medication in people with autism

自闭症谱系障碍(Asd)是最常见的神经发育疾病，每59例中就有1例。 儿童，通常与包括攻击性、易怒和自我伤害在内的行为问题有关 这对患有自闭症的儿童和他们的家人来说是高度残疾的。虽然行为方法有时是 对于这些问题是有效的，它们可能不是所有家庭都能轻易获得的，通常不在 老年人，可能不会为一些ASD患者提供完全的好处。这些问题导致了 使用药物干预，通常与非典型抗精神病药物(ATAP)，如利培酮或 阿立哌唑。这两种ATAP被FDA批准用于治疗严重的行为障碍，如攻击性 和ASD的过敏性，虽然ATAP可以有效，但这些药物与体重增加有关 体重增加，患肥胖症的风险很高。了解体重增加的临床和遗传预测因素， 而最常用的ATAP对体重增加的不同影响，对改善健康至关重要 患有自闭症的人。研究项目的目标是解决精确使用的需要 房间隔缺损中的ATAP。我们的具体目标是：(1)开发一个基于电子健康记录(EHR)的预测模型 非典型抗精神病药物(ATAP)诱导的ASD体重增加，使用大型和独特的非识别机构 数据库；(2)确定与ATAP诱导的ASD体重增加相关的药物遗传危险因素 利用与EHR相关的现有遗传信息；以及(3)比较ATAP诱导的体重增重率 在ASD儿童中，通过将于 一间门诊部。务实试验的其他创新方面包括使用修改后的 电子同意减轻参与者/照顾者负担，将电子健康记录纳入医疗保健 提高试验效率的措施，并纳入照顾者报告的结果，异常行为 清单-易怒程度，嵌入在EHR中。为了实现这些目标，我们将(1)使用机器 学习方法以建立ATAP诱导的体重增加的预测模型；(2)估计以下因素的贡献 基因数据与ATAP诱导的体重增加有关，以及(3)对ASD儿童进行务实的临床试验 需要ATAP治疗。研究项目是我们通过互动更新IDDRC的关键要素 有了IDDRC核心，特别是管理务实试验的临床翻译核心， 数据科学核心和管理核心，前者将分析产生的数据，后者将促进 传播努力以及利益攸关方参与设计和进行务实试验。它 解决父母RFA中的三个重点领域：(1)共病心理的干预和管理 卫生条件；(2)改进评估、干预措施和成果的创新技术 那些患有IDD的人；以及(3)干预或治疗的结果指标或生物标记物。

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 Outcomes for Those with IDD; and (3) Outcome Measures or Biomarkers for Interventions or Treatments.