Characterization of schizophrenia adverse drug interactions through a network approach and drug classification

Polypharmacy and Mental Health Issues in the Senior Hemodialysis Patient

Hemodialysis (HD) is the most common method of chronic kidney failure (CKF) treatment, with 65% of European patients with CKF receiving HD in 2018. Regular two to three HD sessions weekly severely lower their quality of life, resulting in a higher incidence of depression and anxiety, which is present in one third to one half of these patients. Additionally, the age of patients receiving HD is increasing with better treatment and care, resulting in more cognitive impairment being uncovered. Lastly, patients with other mental health issues can also develop CKF during their life with need for kidney replacement therapy (KRT). All these conditions need to receive adequate care, which often means prescribing psychotropic medications. Importantly, many of these drugs are eliminated through the kidneys, which results in altered pharmacokinetics when patients receive KRT. This narrative review will focus on common issues and medications of CKF patients, their comorbidities, mental health issues, use of psychotropic medications and their altered pharmacokinetics when used in HD, polypharmacy, and drug interactions, as well as deprescribing algorithms developed for these patients.

Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization

Drug-target interactions provide useful information for biomedical drug discovery as well as drug development. However, it is costly and time consuming to find drug-target interactions by experimental methods. As a result, developing computational approaches for this task is necessary and has practical significance. In this study, we establish a novel dual Laplacian graph regularized logistic matrix factorization model for drug-target interaction prediction, referred to as DLGrLMF briefly. Specifically, DLGrLMF regards the task of drug-target interaction prediction as a weighted logistic matrix factorization problem, in which the experimentally validated interactions are allocated with larger weights. Meanwhile, by considering that drugs with similar chemical structure should have interactions with similar targets and targets with similar genomic sequence similarity should in turn have interactions with similar drugs, the drug pairwise chemical structure similarities as well as the target pairwise genomic sequence similarities are fully exploited to serve the matrix factorization problem by using a dual Laplacian graph regularization term. In addition, we design a gradient descent algorithm to solve the resultant optimization problem. Finally, the efficacy of DLGrLMF is validated on various benchmark datasets and the experimental results demonstrate that DLGrLMF performs better than other state-of-the-art methods. Case studies are also conducted to validate that DLGrLMF can successfully predict most of the experimental validated drug-target interactions.

Dr. Query

Drugs can help us to treat disease, but sometimes medication can cause severe side effects. With a little knowledge, one can have drugs that are intended to prevent or avoid adverse outcome. Recognizing potential drugs enhances the quality of the healthcare system and reduces the risk associated with drug intake. Several factors like drug-drug interactions and side effects should be known to us before we intake drugs. So, the authors' motive is to develop a predictive mobile-based healthcare tool that would help drug consumers to find drugs which suit them best. As an outcome, the tool will provide the names of the top 10 medicines that will be best for specified indications and do not cause specified side effects and do not or least interact with mentioned drugs. Proposed mobile-based drug query tool will provide exact query matching drugs as well as close matches by leveraging machine learning in the tool.

Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Matrix Completion

Drug-target interactions play an important role for biomedical drug discovery and development. However, it is expensive and time-consuming to accomplish this task by experimental determination. Therefore, developing computational techniques for drug-target interaction prediction is urgent and has practical significance. In this work, we propose an effective computational model of dual Laplacian graph regularized matrix completion, referred to as DLGRMC briefly, to infer the unknown drug-target interactions. Specifically, DLGRMC transforms the task of drug-target interaction prediction into a matrix completion problem, in which the potential interactions between drugs and targets can be obtained based on the prediction scores after the matrix completion procedure. In DLGRMC, the drug pairwise chemical structure similarities and the target pairwise genomic sequence similarities are fully exploited to serve the matrix completion by using a dual Laplacian graph regularization term; i.e., drugs with similar chemical structure are more likely to have interactions with similar targets and targets with similar genomic sequence similarity are more likely to have interactions with similar drugs. In addition, during the matrix completion process, an indicator matrix with binary values which indicates the indices of the observed drug-target interactions is deployed to preserve the experimental confirmed interactions. Furthermore, we develop an alternative iterative strategy to solve the constrained matrix completion problem based on Augmented Lagrange Multiplier algorithm. We evaluate DLGRMC on five benchmark datasets and the results show that DLGRMC outperforms several state-of-the-art approaches in terms of 10-fold cross validation based AUPR values and PR curves. In addition, case studies also demonstrate that DLGRMC can successfully predict most of the experimental validated drug-target interactions.

A computational network analysis based on targets of antipsychotic agents

Currently, numerous antipsychotic agents have been developed in the area of pharmacological treatment of schizophrenia. However, the molecular mechanism underlying multi targets of antipsychotics were yet to be explored. In this study we performed a computational network analysis based on targets of antipsychotic agents. We retrieved a total of 96 targets from 56 antipsychotic agents. By expression enrichment analysis, we identified that the expressions of antipsychotic target genes were significantly enriched in liver, brain, blood and corpus striatum. By protein-protein interaction (PPI) network analysis, a PPI network with 77 significantly interconnected target genes was generated. By historeceptomics analysis, significant brain region specific target-drug interactions were identified in targets of dopamine receptors (DRD1-Olanzapine in caudate nucleus and pons (P-value<0.005), DRD2-Bifeprunox in caudate nucleus and pituitary (P-value<0.0005), DRD4-Loxapine in Pineal (P-value<0.00001)) and 5-hydroxytryptamine receptor (HTR2A-Risperidone in occipital lobe, prefrontal cortex and subthalamic nucleus (P-value<0.0001)). By pathway grouped network analysis, 34 significant pathways were identified and significantly grouped into 6 sub networks related with drug metabolism, Calcium signaling, GABA receptors, dopamine receptors, Bile secretion and Gap junction. Our results may provide biological explanation for antipsychotic targets and insights for molecular mechanism of antipsychotic agents.

Column switching UHPLC–MS/MS with restricted access material for the determination of CNS drugs in plasma samples

Background: Polypharmacy is a common practice in schizophrenia. Consequently, therapeutic drug monitoring is usually adopted to maintain the concentrations of the drugs in the plasma within a targeted therapeutic range, to maximize therapeutic efficiency and to diminish adverse side effects. Methodology: This study reports on a column switching UHPLC–MS/MS method to determine psychotropic drugs in plasma samples simultaneously. Results: The method was linear from 0.025 to 1.25 ng ml-1 with R2 above 0.9950 and the lack of fit test (p > 0.05). The precision values presented coefficients of variation lower than 12%, and the relative standard error of the accuracy were lower than 14%. Conclusion: The column switching UHPLC–MS/MS method developed herein successfully determined drugs in schizophrenic patients’ plasma samples for therapeutic drug monitoring.

Predictive and Personalized Drug Query System

Several factors need to be carefully considered in using pharmaceutical drugs, such as drug interactions, side effects, and contraindications. To further complicate the matter, the presence of some drug properties, such as side effects, depends on patient characteristics, such as age, gender, and genetic profiles. Our goal is to provide a tool to assist medical professionals and drug consumers in choosing and finding drugs that suit their needs. We develop an approach that allows querying for drugs that satisfy a set of conditions. The approach can tailor the answers based on given patient profiles. Considering the noisiness and incompleteness of publicly available drug data, in contrast to traditional query systems, our approach considers both the answers that exactly match the query and those that closely match the query. We represent drug information as a heterogeneous graph and model answering a query as a subgraph matching problem. To rank answers, our approach leverages the structure and the heterogeneity of the drug graph to quantify the likelihood of edges and score the answers. Our evaluation shows that for quantifying the edge likelihood, our network-based approach can improve the area under receiver operating characteristic by up to 18%, comparing to a baseline approach. We develop a prototype of our system and demonstrate its benefits through several examples.

A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case

BACKGROUND

Computational pharmacology can uniquely address some issues in the process of drug development by providing a macroscopic view and a deeper understanding of drug action. Specifically, network-assisted approach is promising for the inference of drug repurposing. However, the drug-target associations coming from different sources and various assays have much noise, leading to an inflation of the inference errors. To reduce the inference errors, it is necessary and critical to create a comprehensive and weighted data set of drug-target associations.

RESULTS

In this study, we created a weighted and integrated drug-target interactome (WinDTome) to provide a comprehensive resource of drug-target associations for computational pharmacology. We first collected drug-target interactions from six commonly used drug-target centered data sources including DrugBank, KEGG, TTD, MATADOR, PDSP K(i) Database, and BindingDB. Then, we employed the record linkage method to normalize drugs and targets to the unique identifiers by utilizing the public data sources including PubChem, Entrez Gene, and UniProt. To assess the reliability of the drug-target associations, we assigned two scores (Score_S and Score_R) to each drug-target association based on their data sources and publication references. Consequently, the WinDTome contains 546,196 drug-target associations among 303,018 compounds and 4,113 genes. To assess the application of the WinDTome, we designed a network-based approach for drug repurposing using mental disorder schizophrenia (SCZ) as a case. Starting from 41 known SCZ drugs and their targets, we inferred a total of 264 potential SCZ drugs through the associations of drug-target with Score_S higher than two in WinDTome and human protein-protein interactions. Among the 264 SCZ-related drugs, 39 drugs have been investigated in clinical trials for SCZ treatment and 74 drugs for the treatment of other mental disorders, respectively. Compared with the results using other Score_S cutoff values, single data source, or the data from STITCH, the inference of 264 SCZ-related drugs had the highest performance.

CONCLUSIONS

The WinDTome generated in this study contains comprehensive drug-target associations with confidence scores. Its application to the SCZ drug repurposing demonstrated that the WinDTome is promising to serve as a useful resource for drug repurposing.

Network-assisted prediction of potential drugs for addiction

Drug addiction is a chronic and complex brain disease, adding much burden on the community. Though numerous efforts have been made to identify the effective treatment, it is necessary to find more novel therapeutics for this complex disease. As network pharmacology has become a promising approach for drug repurposing, we proposed to apply the approach to drug addiction, which might provide new clues for the development of effective addiction treatment drugs. We first extracted 44 addictive drugs from the NIDA and their targets from DrugBank. Then, we constructed two networks: an addictive drug-target network and an expanded addictive drug-target network by adding other drugs that have at least one common target with these addictive drugs. By performing network analyses, we found that those addictive drugs with similar actions tended to cluster together. Additionally, we predicted 94 nonaddictive drugs with potential pharmacological functions to the addictive drugs. By examining the PubMed data, 51 drugs significantly cooccurred with addictive keywords than expected. Thus, the network analyses provide a list of candidate drugs for further investigation of their potential in addiction treatment or risk.