Historeceptomic Fingerprints for Drug-Like Compounds

Investigating drug–target interactions in frontotemporal dementia using a network pharmacology approach

Background

Frontotemporal dementia (FTD) is the second most common type of dementia in individuals aged below 65 years with no current cure. Current treatment plan is the administration of multiple medications. This has the issue of causing adverse effects due to unintentional drug–drug interactions. Therefore, there exists an urgent need to propose a novel targeted therapy that can maximize the benefits of FTD-specific drugs while minimizing its associated adverse side effects.

In this study, we implemented the concept of network pharmacology to understand the mechanism underlying FTD and highlight specific drug–gene and drug–drug interactions that can provide an interesting perspective in proposing a targeted therapy against FTD.

Results

We constructed protein–protein, drug–gene and drug–drug interaction networks to identify highly connected nodes and analysed their importance in associated enriched pathways. We also performed a historeceptomics analysis to determine tissue-specific drug interactions.

Through this study, we were able to shed light on the APP gene involved in FTD. The APP gene which was previously known to cause FTD cases in a small percentage is now being extensively studied owing to new reports claiming its participation in neurodegeneration. Our findings strengthen this hypothesis as the APP gene was found to have the highest node degree and betweenness centrality in our protein–protein interaction network and formed an essential hub node between disease susceptibility genes and neuroactive ligand–receptors.

Our findings also support the study of FTD being presented as a case of substance abuse. Our protein–protein interaction network highlights the target genes common to substance abuse (nicotine, morphine and cocaine addiction) and neuroactive ligand–receptor interaction pathways, therefore validating the cognitive impairment caused by substance abuse as a symptom of FTD.

Conclusions

Our study abandons the one-target one-drug approach and uses networks to define the disease mechanism underlying FTD. We were able to highlight important genes and pathways involved in FTD and analyse their relation with existing drugs that can provide an insight into effective medication management.

How polypharmacologic is each chemogenomics library?

Aim:

High-throughput phenotypic screens have emerged as a promising avenue for small-molecule drug discovery. The challenge faced in high-throughput phenotypic screens is target deconvolution once a small molecule hit is identified. Chemogenomics libraries have emerged as an important tool for meeting this challenge. Here, we investigate their target-specificity by deriving a ‘polypharmacology index’ for broad chemogenomics screening libraries.

Methods:

All known targets of all the compounds in each library were plotted as a histogram and fitted to a Boltzmann distribution, whose linearized slope is indicative of the overall polypharmacology of the library.

Results & conclusion:

Comparison of libraries clearly distinguished the most target-specific library, which might be assumed to be more useful for target deconvolution in a phenotypic screen.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

BACKGROUND

Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR).

METHODS

The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes.

RESULTS

Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)).

CONCLUSIONS

Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness.

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.

Molecular basis of atypicality of bupropion inferred from its receptor engagement in nervous system tissues

Despite decades of clinical use and research, the mechanism of action (MOA) of antidepressant medications remains poorly understood. Selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) are the most commonly prescribed antidepressants-atypical antidepressants such as bupropion have also proven effective, while exhibiting a divergent clinical phenotype. The difference in phenotypic profiles presumably lies in the differences among the MOAs of SSRIs/SNRIs and bupropion. We integrated the ensemble of bupropion's affinities for all its receptors with the expression levels of those targets in nervous system tissues. This "combined target tissue" profile of bupropion was compared to those of duloxetine, fluoxetine, and venlafaxine to isolate the unique target tissue effects of bupropion. Our results suggest that the three monoamines-serotonin, norepinephrine, and dopamine-all contribute to the common antidepressant effects of SSRIs, SNRIs, and bupropion. At the same time, bupropion is unique in its action on 5-HT3AR in the dorsal root ganglion and nicotinic acetylcholine receptors in the pineal gland. These unique tissue-specific activities may explain unique therapeutic effects of bupropion, such as pain management and smoking cessation, and, given melatonin's association with nicotinic acetylcholine receptors and depression, highlight the underappreciated role of the melatonergic system in bupropion's MOA.

Chemistry-based molecular signature underlying the atypia of clozapine

The central nervous system is functionally organized as a dynamic network of interacting neural circuits that underlies observable behaviors. At higher resolution, these behaviors, or phenotypes, are defined by the activity of a specific set of biomolecules within those circuits. Identification of molecules that govern psychiatric phenotypes is a major challenge. The only organic molecular entities objectively associated with psychiatric phenotypes in humans are drugs that induce psychiatric phenotypes and drugs used for treatment of specific psychiatric conditions. Here, we identified candidate biomolecules contributing to the organic basis for psychosis by deriving an in vivo biomolecule-tissue signature for the atypical pharmacologic action of the antipsychotic drug clozapine. Our novel in silico approach identifies the ensemble of potential drug targets based on the drug's chemical structure and the region-specific gene expression profile of each target in the central nervous system. We subtracted the signature of the action of clozapine from that of a typical antipsychotic, chlorpromazine. Our results implicate dopamine D4 receptors in the pineal gland and muscarinic acetylcholine M1 (CHRM1) and M3 (CHRM3) receptors in the prefrontal cortex (PFC) as significant and unique to clozapine, whereas serotonin receptors 5-HT_2A in the PFC and 5-HT_2C in the caudate nucleus were common significant sites of action for both drugs. Our results suggest that D4 and CHRM1 receptor activity in specific tissues may represent underappreciated drug targets to advance the pharmacologic treatment of schizophrenia. These findings may enhance our understanding of the organic basis of psychiatric disorders and help developing effective therapies.

Data sources for in vivo molecular profiling of human phenotypes

Molecular profiling of human diseases has been approached at the genetic (DNA), expression (RNA), and proteomic (protein) levels. An important goal of these efforts is to map observed molecular patterns to specific, mechanistic organic entities, such as loci in the genome, individual RNA molecules or defined proteins or protein assemblies. Importantly, such maps have been historically approached in the more intuitive context of a theoretical individual cell, but diseases are better described in reality using an in vivo framework, namely a library of several tissue-specific maps. In this article, we review the existing data atlases that can be used for this purpose and identify critical gaps that could move the field forward from cellular to in vivo dimensions. WIREs Syst Biol Med 2016, 8:472-484. doi: 10.1002/wsbm.1354 For further resources related to this article, please visit the WIREs website.

Delineation of Polypharmacology across the Human Structural Kinome Using a Functional Site Interaction Fingerprint Approach

Targeted polypharmacology of kinases has emerged as a promising strategy to design efficient and safe therapeutics. Here, we perform a systematic study of kinase-ligand binding modes for the human structural kinome at scale (208 kinases, 1777 unique ligands, and their complexes) by integrating chemical genomics and structural genomics data and by introducing a functional site interaction fingerprint (Fs-IFP) method. New insights into kinase-ligand binding modes were obtained. We establish relationships between the features of binding modes, the ligands, and the binding pockets, respectively. We also drive the intrinsic binding specificity and which correlation with amino acid conservation. Third, we explore the landscape of the binding modes and highlight the regions of "selectivity pocket" and "selectivity entrance". Finally, we demonstrate that Fs-IFP similarity is directly correlated to the experimentally determined profile. These improve our understanding of kinase-ligand interactions and contribute to the design of novel polypharmacological therapies targeting kinases.