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Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. We developed DeepBiomarker2 by leveraging deep learning and natural language processing to analyze lab tests, medication use, diagnosis, social determinants of health (SDoH) parameters, and psychotherapy for outcome prediction. To increase the model's interpretability, we further refined our contribution analysis to identify key features by scaling with a factor from a reference feature. We applied DeepBiomarker2 to analyze the EMR data of 38,807 patients from the University of Pittsburgh Medical Center diagnosed with post-traumatic stress disorder (PTSD) to determine their risk of developing alcohol and substance use disorder (ASUD). DeepBiomarker2 predicted whether a PTSD patient would have a diagnosis of ASUD within the following 3 months with an average c-statistic (receiver operating characteristic AUC) of 0.93 and average F1 score, precision, and recall of 0.880, 0.895, and 0.866 in the test sets, respectively. Our study found that the medications clindamycin, enalapril, penicillin, valacyclovir, Xarelto/rivaroxaban, moxifloxacin, and atropine and the SDoH parameters access to psychotherapy, living in zip codes with a high normalized vegetative index, Gini index, and low-income segregation may have potential to reduce the risk of ASUDs in PTSD. In conclusion, the integration of SDoH information, coupled with the refined feature contribution analysis, empowers DeepBiomarker2 to accurately predict ASUD risk. Moreover, the model can further identify potential indicators of increased risk along with medications with beneficial effects. [ABSTRACT FROM AUTHOR]

Psychotic symptoms are reported as one of the most common complications of Alzheimer's disease (AD), in whom they are associated with more rapid deterioration and increased mortality. Empiric treatments, namely first and second‐generation antipsychotics, confer modest efficacy in patients with AD and with psychosis (AD+P) and themselves increase mortality. Recent studies suggested the use and beneficial effects of antidepressants among patients with AD+P. This motivates our rationale for exploring their potential as a novel combination therapy option among these patients. We included electronic medical records of 10,260 patients with AD in our study. Survival analysis was performed to assess the effects of the combination of antipsychotics and antidepressants on the mortality of these patients. A protein–protein interaction network representing AD+P was built, and network analysis methods were used to quantify the efficacy of these drugs on AD+P. A combined score was developed to measure the potential synergetic effect against AD+P. Our survival analyses showed that the co‐administration of antidepressants with antipsychotics have a significant beneficial effect in reducing mortality. Our network analysis showed that the targets of antipsychotics and antidepressants are well‐separated, and antipsychotics and antidepressants have similar Signed Jaccard Index (SJI) scores to AD+P. Eight drug pairs, including some popular recommendations like aripiprazole/sertraline, showed higher than average scores which suggest their potential in treating AD+P via strong synergetic effects. Our proposed combinations of antipsychotic and antidepressant therapy showed a strong superiority over current antipsychotics treatment for AD+P. The observed beneficial effects can be further strengthened by optimizing drug‐pair selection based on our systems pharmacology analysis. [ABSTRACT FROM AUTHOR]

Around 50% of patients with Alzheimer's disease (AD) may experience psychotic symptoms after onset, resulting in a subtype of AD known as psychosis in AD (AD + P). This subtype is characterized by more rapid cognitive decline compared to AD patients without psychosis. Therefore, there is a great need to identify risk factors for the development of AD + P and explore potential treatment options. In this study, we enhanced our deep learning model, DeepBiomarker, to predict the onset of psychosis in AD utilizing data from electronic medical records (EMRs). The model demonstrated superior predictive capacity with an AUC (area under curve) of 0.907, significantly surpassing conventional risk prediction models. Utilizing a perturbation-based method, we identified key features from multiple medications, comorbidities, and abnormal laboratory tests, which notably influenced the prediction outcomes. Our findings demonstrated substantial agreement with existing studies, underscoring the vital role of metabolic syndrome, inflammation, and liver function pathways in AD + P. Importantly, the DeepBiomarker model not only offers a precise prediction of AD + P onset but also provides mechanistic understanding, potentially informing the development of innovative treatments. With additional validation, this approach could significantly contribute to early detection and prevention strategies for AD + P, thereby improving patient outcomes and quality of life. [ABSTRACT FROM AUTHOR]

Approximately 50% of Alzheimer's disease (AD) patients will develop psychotic symptoms and these patients will experience severe rapid cognitive decline compared with those without psychosis (AD-P). Currently, no medication has been approved by the Food and Drug Administration for AD with psychosis (AD+P) specifically, although atypical antipsychotics are widely used in clinical practice. These drugs have demonstrated modest efficacy in managing psychosis in individuals with AD, with an increased frequency of adverse events, including excess mortality. We compared the differences between the genetic variations/genes associated with AD+P and schizophrenia from existing Genome-Wide Association Study and differentially expressed genes (DEGs). We also constructed disease-specific protein–protein interaction networks for AD+P and schizophrenia. Network efficiency was then calculated to characterize the topological structures of these two networks. The efficiency of antipsychotics in these two networks was calculated. A weight adjustment based on binding affinity to drug targets was later applied to refine our results, and 2013 and 2123 genes were identified as related to AD+P and schizophrenia, respectively, with only 115 genes shared. Antipsychotics showed a significantly lower efficiency in the AD+P network than in the schizophrenia network (P  < 0.001) indicating that antipsychotics may have less impact in AD+P than in schizophrenia. AD+P may be caused by mechanisms distinct from those in schizophrenia which result in a decreased efficacy of antipsychotics in AD+P. In addition, the network analysis methods provided quantitative explanations of the lower efficacy of antipsychotics in AD+P. [ABSTRACT FROM AUTHOR]

Identifying patients with high risk of suicide is critical for suicide prevention. We examined lab tests together with medication use and diagnosis from electronic medical records (EMR) data for prediction of suicide-related events (SREs; suicidal ideations, attempts and deaths) in post-traumatic stress disorder (PTSD) patients, a population with a high risk of suicide. We developed DeepBiomarker, a deep-learning model through augmenting the data, including lab tests, and integrating contribution analysis for key factor identification. We applied DeepBiomarker to analyze EMR data of 38,807 PTSD patients from the University of Pittsburgh Medical Center. Our model predicted whether a patient would have an SRE within the following 3 months with an area under curve score of 0.930. Through contribution analysis, we identified important lab tests for suicide prediction. These identified factors imply that the regulation of the immune system, respiratory system, cardiovascular system, and gut microbiome were involved in shaping the pathophysiological pathways promoting depression and suicidal risks in PTSD patients. Our results showed that abnormal lab tests combined with medication use and diagnosis could facilitate predicting SRE risk. Moreover, this may imply beneficial effects for suicide prevention by treating comorbidities associated with these biomarkers. [ABSTRACT FROM AUTHOR]

Opioids are widely used for treating different types of pains, but overuse and abuse of prescription opioids have led to opioid epidemic in the United States. Besides analgesic effects, chronic use of opioid can also cause tolerance, dependence, and even addiction. Effective treatment of opioid addiction remains a big challenge today. Studies on addictive effects of opioids focus on striatum, a main component in the brain responsible for drug dependence and addiction. Some transcription regulators have been associated with opioid addiction, but relationship between analgesic effects of opioids and dependence behaviors mediated by them at the molecular level has not been thoroughly investigated. In this paper, we developed a new computational strategy that identifies novel targets and potential therapeutic molecular compounds for opioid dependence and addiction. We employed several statistical and machine learning techniques and identified differentially expressed genes over time which were associated with dependence-related behaviors after exposure to either morphine or heroin, as well as potential transcription regulators that regulate these genes, using time course gene expression data from mouse striatum. Moreover, our findings revealed that some of these dependence-associated genes and transcription regulators are known to play key roles in opioid-mediated analgesia and tolerance, suggesting that an intricate relationship between opioid-induce pain-related pathways and dependence may develop at an early stage during opioid exposure. Finally, we determined small compounds that can potentially target the dependence-associated genes and transcription regulators. These compounds may facilitate development of effective therapy for opioid dependence and addiction. We also built a database () for all opioid-induced dependence-associated genes and transcription regulators that we discovered, as well as the small compounds that target those genes and transcription regulators. [ABSTRACT FROM AUTHOR]

Post-traumatic stress disorder (PTSD) is a prevalent mental disorder marked by psychological and behavioral changes. Currently, there is no consensus of preferred antipsychotics to be used for the treatment of PTSD. We aim to discover whether certain antipsychotics have decreased suicide risk in the PTSD population, as these patients may be at higher risk. A total of 38,807 patients were identified with a diagnosis of PTSD through the ICD9 or ICD10 codes from January 2004 to October 2019. An emulation of randomized clinical trials was conducted to compare the outcomes of suicide-related events (SREs) among PTSD patients who ever used one of eight individual antipsychotics after the diagnosis of PTSD. Exclusion criteria included patients with a history of SREs and a previous history of antipsychotic use within one year before enrollment. Eligible individuals were assigned to a treatment group according to the antipsychotic initiated and followed until stopping current treatment, switching to another same class of drugs, death, or loss to follow up. The primary outcome was to identify the frequency of SREs associated with each antipsychotic. SREs were defined as ideation, attempts, and death by suicide. Pooled logistic regression methods with the Firth option were conducted to compare two drugs for their outcomes using SAS version 9.4 (SAS Institute, Cary, NC, USA). The results were adjusted for baseline characteristics and post-baseline, time-varying confounders. A total of 5294 patients were eligible for enrollment with an average follow up of 7.86 months. A total of 157 SREs were recorded throughout this study. Lurasidone showed a statistically significant decrease in SREs when compared head to head to almost all the other antipsychotics: aripiprazole, haloperidol, olanzapine, quetiapine, risperidone, and ziprasidone (p < 0.0001 and false discovery rate-adjusted p value < 0.0004). In addition, olanzapine was associated with higher SREs than quetiapine and risperidone, and ziprasidone was associated with higher SREs than risperidone. The results of this study suggest that certain antipsychotics may put individuals within the PTSD population at an increased risk of SREs, and that careful consideration may need to be taken when prescribed. [ABSTRACT FROM AUTHOR]

Around 800,000 people worldwide die from suicide every year and it's the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Aripiprazole, Levomilnacipran, Sertraline, Tramadol, Fentanyl, or Fluoxetine, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, were strong indicators for no SREs within one year. The use of Trazodone and Citalopram at baseline predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making. [ABSTRACT FROM AUTHOR]

Benzodiazepines is a class of medications frequently prescribed to patients with post-traumatic stress disorder. Patients with PTSD have a notable increased risk of suicide compared to the general population. These medications have been theorized to increase suicidality and pose a risk when used in this patient population. Previous research has found little utility of using benzodiazepines in the PTSD population. However, benzodiazepines are still commonly prescribed by some clinicians for their symptomatic benefit. This study aims to identify the comparative efficacy of commonly prescribed benzodiazepines including midazolam, lorazepam, alprazolam, clonazepam, diazepam and temazepam in relation to suicide-related behaviors (SRBs). A total of 38,807 patients who had an ICD9 or ICD10 diagnosis of PTSD from January 2004 to October 2019 were identified through an electronic medical record database. Inclusion criteria include patients that initiated one of the above benzodiazepines after PTSD diagnosis. Exclusion criteria include previous history of benzodiazepine usage or history of SRBs within the last year prior to enrollment. For patients enrolled in this study, other concomitant drugs were not limited. The primary outcome was onset of SRBs with each respective benzodiazepine. SRBs were identified as ideation, attempt, or death from suicide. We emulated clinical trials of head-to-head comparison between two drugs by pooled logistic regression methods with the Firth option adjusting for baseline characteristics and post-baseline confounders. A total of 5753 patients were eligible for this study, with an average follow up of 5.82 months. The overall incidence for SRB was 1.51% (87/5753). Head-to-head comparisons identified that patients who received alprazolam had fewer SRBs compared to clonazepam (p = 0.0351) and lorazepam (p = 0.0373), and patients taking midazolam experienced fewer relative incidences of SRBs when compared to lorazepam (p = 0.0021) and clonazepam (p = 0.0297). After adjusting for the false discovery rate (FDR), midazolam still had fewer SRBs compared to lorazepam (FDR-adjusted p value = 0.0315). Certain benzodiazepines may provide a reduced risk of development of SRBs, suggesting careful consideration when prescribing benzodiazepines to the PTSD population. [ABSTRACT FROM AUTHOR]

A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships between drugs, their targets and diseases with quite a few successful cases reported. Especially in the study of GES-guided drugs–disease associations, researchers believe that if a GES induced by a drug is opposite to a GES induced by a disease, the drug may have potential as a treatment of that disease. In this study, we data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarity between GES profiles from drugs and their indicated diseases. Our study aims to explore the application domains of GES-guided drug–disease associations through the analysis of the similarity of GES profiles on known pairs of drug–disease associations, thereby identifying subgroups of drugs/diseases that are suitable for GES-guided drug repositioning approaches. Our results supported our hypothesis that the GES-guided drug–disease association method is better suited for some subgroups or pathways such as drugs and diseases associated with the immune system, diseases of the nervous system, non-chemotherapy drugs or the mTOR signaling pathway. [ABSTRACT FROM AUTHOR]

Background: This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics.Design: Boys (N = 494) and girls (N = 206) were recruited using a high-risk paradigm at 10-12 years of age and followed up at 12-14, 16, 19, 22, 25 and 30 years of age.Measurements: At each visit, the subjects were administered a comprehensive battery to measure psychological makeup, health status, substance use and psychiatric disorder, and their overall harmfulness of substance consumption was quantified according to the multidimensional criteria (physical, dependence, and social) developed by Nutt et al. (2007). Next, high- and low- substance use severity trajectories were derived differentially associated with probability of segueing to substance use disorder (SUD). ML methodology was employed to predict trajectory membership.Findings: The high-severity trajectory group had a higher probability of leading to SUD than the low-severity trajectory (89.0% vs 32.4%; odds ratio = 16.88, p < 0.0001). Thirty psychological and health status items at each of the six visits predict membership in the high- or low-severity trajectory, with 71% accuracy at 10-12 years of age, increasing to 93% at 22 years of age.Conclusion: These findings demonstrate the applicability of the machine learning methodology for detecting membership in a substance use trajectory with high probability of culminating in SUD, potentially informing primary and secondary prevention. [ABSTRACT FROM AUTHOR]

Background: Autophagy and apoptosis are the basic physiological processes in cells that clean up aged and mutant cellular components or even the entire cells. Both autophagy and apoptosis are disrupted in most major diseases such as cancer and neurological disorders. Recently, increasing attention has been paid to understand the crosstalk between autophagy and apoptosis due to their tightly synergetic or opposite functions in several pathological processes., Objective: This study aims to assist autophagy and apoptosis-related drug research, clarify the intense and complicated connections between two processes, and provide a guide for novel drug development., Methods: We established two chemical-genomic databases which are specifically designed for autophagy and apoptosis, including autophagy- and apoptosis-related proteins, pathways and compounds. We then performed network analysis on the apoptosis- and autophagy-related proteins and investigated the full protein-protein interaction (PPI) network of these two closely connected processes for the first time., Results: The overlapping targets we discovered show a more intense connection with each other than other targets in the full network, indicating a better efficacy potential for drug modulation. We also found that Death-associated protein kinase 1 (DAPK1) is a critical point linking autophagy- and apoptosis-related pathways beyond the overlapping part, and this finding may reveal some delicate signaling mechanism of the process. Finally, we demonstrated how to utilize our integrated computational chemogenomics tools on in silico target identification for small molecules capable of modulating autophagy- and apoptosis-related pathways., Conclusion: The knowledge-bases for apoptosis and autophagy and the integrated tools will accelerate our work in autophagy and apoptosis-related research and can be useful sources for information searching, target prediction, and new chemical discovery., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)