Use of a Bioinformatics-Based Toxicity Scoring System to Assess Serotonin Burden and Predict Population-Level Adverse Drug Events from Concomitant Serotonergic Drug Therapy.

STUDY OBJECTIVE: Numerous medications interact at serotonin (5-hydroxytryptamine [5-HT]) receptors directly or through off-target interactions, causing mild to severe serotonergic adverse drug events (ADEs), particularly among older adults. Our objective was to develop a novel molecular-based toxicity scoring system to assess serotonergic burden resulting from concurrently administered drugs. Quantitative methods to assess serotonergic burden may provide a useful clinical tool for improving pharmacotherapy. DESIGN: Retrospective cohort study. DATA SOURCES: PharMetrics Legacy health claims database (January 2001-December 2013) and ChEMBL bioactivity database. PATIENTS: A 2-serotonergic drug exposure cohort (78,172 patients) and a 3-serotonergic drug exposure cohort (19,900 patients) were generated, and population-level statistics were collected. Nonexposure cohorts were created for each drug exposure cohort and matched in a 4:1 ratio for age, sex, and length of enrollment. MEASUREMENTS AND MAIN RESULTS: Eight 5-HT medications were screened against multiple bioactivity databases to identify their off-target interactions at 5-HT receptors and serotonin reuptake transporter protein. A computational serotonin burden score (SBS) was derived from the receptor-specific interaction propensities reported from the comprehensive bioactivity screen. Linear regression was used to characterize associations between SBSs and combined total ADE incidence rate detected by International Classification of Diseases, Ninth Revision, Clinical Modification, diagnosis codes. A significantly greater incidence of 17 potential 5-HT-related ADEs was seen in exposed serotonergic drug cohorts (p<0.05). A positive correlation between SBS and overall ADE incidence rate in the 2-serotonergic drug exposure cohort (R2  = 0.69, p<0.34) and 3-drug cohort (R2  = 0.85, p<0.01) was observed. When both drug cohorts were combined, total drug SBSs strongly correlated with the composite 5-HT adverse event rate (R2  = 0.92, p<0.0001). Despite an increasing burden of illness, these data suggest that drug combinations with higher SBSs are associated with a higher rate of potential serotonergic ADEs. CONCLUSION: In this test of concept, positive associations between SBSs and serotonin-related ADEs suggest that it may offer a pharmacologic-based foundation for developing risk assessment tools to assist in optimizing pharmacotherapy.

ezCADD: A Rapid 2D/3D Visualization-Enabled Web Modeling Environment for Democratizing Computer-Aided Drug Design.

As abundant and user-friendly as computer-aided drug design (CADD) software may seem, there is still a large underserved population of biomedical researchers around the world, particularly those with no computational training and limited research funding. To address this important need and help scientists overcome barriers that impede them from leveraging CADD in their drug discovery work, we have developed ezCADD, a web-based CADD modeling environment that manifests four simple design concepts: easy, quick, user-friendly, and 2D/3D visualization-enabled. In this paper, we describe the features of three fundamental applications that have been implemented in ezCADD: small-molecule docking, protein-protein docking, and binding pocket detection, and their applications in drug design against a pathogenic microbial enzyme as an example. To assess user experience and the effectiveness of our implementation, we introduced ezCADD to first-year pharmacy students as an active learning exercise in the Principles of Drug Action course. The web service robustly handled 95 simultaneous molecular docking jobs. Our survey data showed that among the 95 participating students, 97% completed the molecular docking experiment on their own at least partially without extensive training; 88% considered ezCADD easy and user-friendly; 99-100% agreed that ezCADD enhanced the understanding of drug-receptor structures and recognition; and the student experience in molecular modeling and visualization was significantly improved from zero to a higher level. The student feedback represents the baseline data of user experience from noncomputational researchers. It is demonstrated that in addition to supporting drug discovery research, ezCADD is also an effective tool for promoting science, technology, engineering, and mathematics (STEM) education. More advanced CADD applications are being developed and added to ezCADD, available at http://dxulab.org/software .

Implications of Off-Target Serotoninergic Drug Activity: An Analysis of Serotonin Syndrome Reports Using a Systematic Bioinformatics Approach.

STUDY OBJECTIVE: Serotonergic adverse drug events (ADEs) are caused by enhanced intrasynaptic concentrations of 5-hydroxytryptamine (5-HT). No systematic process currently exists for evaluating cumulative 5-HT and off-target toxicity of serotonergic drugs. The primary study aim was to create a Serotonergic Expanded Bioactivity Matrix (SEBM) by using a molecular bioinformatics, polypharmacologic approach for assessment of the participation of individual 5-HT drugs in serotonin syndrome (SS) reports. DATA SOURCES: Publicly available databases including the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), ChEMBL, DrugBank, PubChem, and Kyoto Encyclopedia of Genes and Genomes (KEGG) were queried for computational and pharmacologic data. DESIGN: An in-house bioinformatics TargetSearch program ( http://dxulab.org/software) was used to characterize 71 serotonergic drugs interacting at 13 serotonin receptor subtypes and serotonin reuptake transporter protein (SERT). In addition, off-target interactions at norepinephrine transporter (NET), monoamine oxidase (MAO), and muscarinic receptors were included to define seven polypharmacological drug cohorts. Serotonin syndrome reports for each serotonergic drug were extracted from FAERS by using the Sternbach and Hunter criteria. MEASUREMENTS AND MAIN RESULTS: A proportional reporting adverse drug reaction (ADR) ratio (PRR) was calculated from each drug's total ADEs and SS case reports and aggregated by drug bioactivity cohorts. Triple-receptor interactions had a disproportionately higher number of SS cases using both the Hunter criteria (mean PRR 1.72, 95% CI 1.05-2.39) and Sternbach (mean PRR 1.54, 95% CI 1.29-1.79). 5-Hydroxytryptamine agonists were associated with a significantly lower proportion of SS cases using the Hunter and Sternbach criteria, respectively (mean PRR 0.49, 95% CI 0.17-0.81 and mean PRR 0.49, 95% CI 0.15-0.83). Drugs with disproportionately higher participation in SS vary considerably between the two diagnostic criteria. CONCLUSION: The SEBM model suggests a possible polypharmacological role in SS. Although further research is needed, off-target receptor activity may help explain differences in severity of toxicity and clinical presentation.

Molecular insights into the improved clinical performance of PEGylated interferon therapeutics: a molecular dynamics perspective.

PEGylation is a widely adopted process to covalently attach a polyethylene glycol (PEG) polymer to a protein drug for the purpose of optimizing drug clinical performance. While the outcomes of PEGylation in imparting pharmacological advantages have been examined through experimental studies, the underlying molecular mechanisms remain poorly understood. Using interferon (IFN) as a representative model system, we carried out comparative molecular dynamics (MD) simulations of free PEGx, apo-IFN, and PEGx-IFN (x = 50, 100, 200, 300) to characterize the molecular-level changes in IFN introduced by PEGylation. The simulations yielded molecular evidence directly linked to the improved protein stability, bioavailability, retention time, as well as the decrease in protein bioactivity with PEG conjugates. Our results indicate that there is a tradeoff between the benefits and costs of PEGylation. The optimal PEG chain length used in PEGylation needs to strike a good balance among the competing factors and maximizes the overall therapeutic efficacy of the protein drug. We anticipate the study will have a broad implication for protein drug design and development, and provide a unique computational approach in the context of optimizing PEGylated protein drug conjugates.

MSBIS: A Multi-Step Biomedical Informatics Screening Approach for Identifying Medications that Mitigate the Risks of Metoclopramide-Induced Tardive Dyskinesia.

In 2009 the U.S. Food and Drug Administration (FDA) placed a black box warning on metoclopramide (MCP) due to the increased risks and prevalence of tardive dyskinesia (TD). In this study, we developed a multi-step biomedical informatics screening (MSBIS) approach leveraging publicly available bioactivity and drug safety data to identify concomitant drugs that mitigate the risks of MCP-induced TD. MSBIS includes (1) TargetSearch (http://dxulab.org/software) bioinformatics scoring for drug anticholinergic activity using CHEMBL bioactivity data; (2) unadjusted odds ratio (UOR) scoring for indications of TD-mitigating effects using the FDA Adverse Event Reporting System (FAERS); (3) adjusted odds ratio (AOR) re-scoring by removing the effect of cofounding factors (age, gender, reporting year); (4) logistic regression (LR) coefficient scoring for confirming the best TD-mitigating drug candidates. Drugs with increasing TD protective potential and statistical significance were obtained at each screening step. Fentanyl is identified as the most promising drug against MCP-induced TD (coefficient: -2.68; p-value<0.01). The discovery is supported by clinical reports that patients fully recovered from MCP-induced TD after fentanyl-induced general anesthesia. Loperamide is identified as a potent mitigating drug against a broader range of drug-induced movement disorders through pharmacokinetic modifications. Using drug-induced TD as an example, we demonstrated that MSBIS is an efficient in silico tool for unknown drug-drug interaction detection, drug repurposing, and combination therapy design.