Drug knowledge bases and their applications in biomedical informatics research

Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases.

SZGR 2.0: a one-stop shop of schizophrenia candidate genes

SZGR 2.0 is a comprehensive resource of candidate variants and genes for schizophrenia, covering genetic, epigenetic, transcriptomic, translational and many other types of evidence. By systematic review and curation of multiple lines of evidence, we included almost all variants and genes that have ever been reported to be associated with schizophrenia. In particular, we collected ∼4200 common variants reported in genome-wide association studies, ∼1000 de novo mutations discovered by large-scale sequencing of family samples, 215 genes spanning rare and replication copy number variations, 99 genes overlapping with linkage regions, 240 differentially expressed genes, 4651 differentially methylated genes and 49 genes as antipsychotic drug targets. To facilitate interpretation, we included various functional annotation data, especially brain eQTL, methylation QTL, brain expression featured in deep categorization of brain areas and developmental stages and brain-specific promoter and enhancer annotations. Furthermore, we conducted cross-study, cross-data type and integrative analyses of the multidimensional data deposited in SZGR 2.0, and made the data and results available through a user-friendly interface. In summary, SZGR 2.0 provides a one-stop shop of schizophrenia variants and genes and their function and regulation, providing an important resource in the schizophrenia and other mental disease community. SZGR 2.0 is available at https://bioinfo.uth.edu/SZGR/.

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.

Disruptions of sensorimotor gating, cytokines, glycemia, monoamines, and genes in both sexes of rats reared in social isolation can be ameliorated by oral chronic quetiapine administration

The pathogenesis of schizophrenia in patients with metabolic abnormalities remains unclear. Our previous study demonstrated that isolation rearing (IR) induced longitudinal concomitant changes of pro-inflammatory cytokine (pro-CK) levels and metabolic abnormalities with a developmental origin. However, the general consensus, believes that these abnormalities are caused by antipsychotic treatment in schizophrenic patients. The IR paradigm presents with face, construct, and predictive validity for schizophrenia. Therefore, we employed IR rats of both sexes to examine whether chronic quetiapine (QTP, a second-generation antipsychotic medication) treatment induces disruptions of metabolism (body weight, blood pressure, and the glycemic and lipid profiles) or cytokines [interleukin (IL)-1 beta, IL-6, IL-10, interferon-gamma, and tumor necrosis factor (TNF)-alpha], and further, whether it reverses deficits of behaviors [locomotor activity and prepulse inhibition (PPI)] and the expression of monoamines (dopamine and serotonin) and related genes (Htr1a, Htr2a, Htr3a, Drd1a, and Gabbr2). IR induced higher levels of pro-CK, dysglycemia, blood pressure, locomotor activity, and impaired PPI, simultaneously destabilizing cortico-striatal monoamines and relevant genes in both sexes, while QTP demonstrated dose-dependent reversal of these changes, suggesting that QTP might reduce the pro-CKs to regulate these abnormalities. Our data implied that antipsychotics may not be the solitary factor causing metabolic problems in schizophrenia and suggested that inflammatory changes may play a vital role in the developmental pathophysiology of schizophrenia and related metabolic abnormalities.

Neuropharmacology beyond reductionism - A likely prospect

Neuropharmacology had several major past successes, but the last few decades did not witness any leap forward in the drug treatment of brain disorders. Moreover, current drugs used in neurology and psychiatry alleviate the symptoms, while hardly curing any cause of disease, basically because the etiology of most neuro-psychic syndromes is but poorly known. This review argues that this largely derives from the unbalanced prevalence in neuroscience of the analytic reductionist approach, focused on the cellular and molecular level, while the understanding of integrated brain activities remains flimsier. The decline of drug discovery output in the last decades, quite obvious in neuropharmacology, coincided with the advent of the single target-focused search of potent ligands selective for a well-defined protein, deemed critical in a given pathology. However, all the widespread neuro-psychic troubles are multi-mechanistic and polygenic, their complex etiology making unsuited the single-target drug discovery. An evolving approach, based on systems biology considers that a disease expresses a disturbance of the network of interactions underlying organismic functions, rather than alteration of single molecular components. Accordingly, systems pharmacology seeks to restore a disturbed network via multi-targeted drugs. This review notices that neuropharmacology in fact relies on drugs which are multi-target, this feature having occurred just because those drugs were selected by phenotypic screening in vivo, or emerged from serendipitous clinical observations. The novel systems pharmacology aims, however, to devise ab initio multi-target drugs that will appropriately act on multiple molecular entities. Though this is a task much more complex than the single-target strategy, major informatics resources and computational tools for the systemic approach of drug discovery are already set forth and their rapid progress forecasts promising outcomes for neuropharmacology.

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.

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

Antipsychotic drugs are medications commonly for schizophrenia (SCZ) treatment, which include two groups: typical and atypical. SCZ patients have multiple comorbidities, and the coadministration of drugs is quite common. This may result in adverse drug-drug interactions, which are events that occur when the effect of a drug is altered by the coadministration of another drug. Therefore, it is important to provide a comprehensive view of these interactions for further coadministration improvement. Here, we extracted SCZ drugs and their adverse drug interactions from the DrugBank and compiled a SCZ-specific adverse drug interaction network. This network included 28 SCZ drugs, 241 non-SCZs, and 991 interactions. By integrating the Anatomical Therapeutic Chemical (ATC) classification with the network analysis, we characterized those interactions. Our results indicated that SCZ drugs tended to have more adverse drug interactions than other drugs. Furthermore, SCZ typical drugs had significant interactions with drugs of the "alimentary tract and metabolism" category while SCZ atypical drugs had significant interactions with drugs of the categories "nervous system" and "antiinfectives for systemic uses." This study is the first to characterize the adverse drug interactions in the course of SCZ treatment and might provide useful information for the future SCZ treatment.