Miranda Oshin, Qi Xiguang, Brannock M Daniel, Whitworth Ryan, Kosten Thomas R, Ryan Neal David, Haas Gretchen L, Kirisci Levent, Wang Lirong
Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA.
RTI International, Durham, NC 27709, USA.
Biomedicines. 2024 Dec 5;12(12):2772. doi: 10.3390/biomedicines12122772.
Comorbid post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD) patients are at a significantly higher risk of adverse outcomes, including opioid use disorder, depression, suicidal behaviors, and death, yet limited treatment options exist for this population. This study aimed to build on previous research by incorporating drug target information into a novel deep learning model, T-DeepBiomarker, to predict adverse outcomes and identify potential therapeutic medications. We utilized electronic medical record (EMR) data from the University of Pittsburgh Medical Center (UPMC), analyzing 5565 PTSD + AUD patients. T-DeepBiomarker was developed by integrating multimodal data, including lab results, drug target information, comorbidities, neighborhood-level social determinants of health (SDoH), and individual-level SDoH (e.g., psychotherapy and veteran status). The model was trained to predict adverse events, including opioid use disorder, suicidal behaviors, depression, and death, within three months following any clinical encounter. Candidate medications targeting significant proteins were identified through literature reviews. T-DeepBiomarker achieved high predictive performance with an AUROC of 0.94 for adverse outcomes in PTSD + AUD patients. Several medications, including OnabotulinumtoxinA, Dronabinol, Acamprosate, Celecoxib, Exenatide, Melatonin, and Semaglutide, were identified as potentially reducing the risk of adverse events by targeting significant proteins. T-DeepBiomarker demonstrates high accuracy in predicting adverse outcomes in PTSD + AUD patients and highlights candidate drugs with potential therapeutic effects. These findings advance pharmacotherapy for this high-risk population and identify medications that warrant further investigation.
创伤后应激障碍（PTSD）和酒精使用障碍（AUD）共病的患者出现不良后果的风险显著更高，这些不良后果包括阿片类药物使用障碍、抑郁症、自杀行为和死亡，但针对这一人群的治疗选择有限。本研究旨在在先前研究的基础上，将药物靶点信息纳入一种新型深度学习模型T-DeepBiomarker，以预测不良后果并识别潜在的治疗药物。我们利用了匹兹堡大学医学中心（UPMC）的电子病历（EMR）数据，分析了5565例PTSD+AUD患者。T-DeepBiomarker通过整合多模态数据而开发，这些数据包括实验室检查结果、药物靶点信息、合并症、社区层面的健康社会决定因素（SDoH）以及个体层面的SDoH（如心理治疗和退伍军人身份）。该模型经过训练，以预测在任何临床接触后三个月内发生的不良事件，包括阿片类药物使用障碍、自杀行为、抑郁症和死亡。通过文献综述确定了针对重要蛋白质的候选药物。T-DeepBiomarker在预测PTSD+AUD患者的不良后果方面具有较高的预测性能，AUC为0.94。包括A型肉毒毒素、屈大麻酚、阿坎酸、塞来昔布、艾塞那肽、褪黑素和司美格鲁肽在内的几种药物被确定为通过靶向重要蛋白质可能降低不良事件风险的药物。T-DeepBiomarker在预测PTSD+AUD患者的不良后果方面表现出较高的准确性，并突出了具有潜在治疗效果的候选药物。这些发现推进了针对这一高风险人群的药物治疗，并确定了值得进一步研究的药物。
