Donna L. Mendrick

Molecular Docking to Identify Associations Between Drugs and Class I Human Leukocyte Antigens for Predicting Idiosyncratic Drug Reactions

Idiosyncratic drug reactions (IDRs) are rare, somewhat dose-independent, patient-specific and hard to predict. Human leukocyte antigens (HLAs) are the major histocompatibility complex (MHC) in humans, are highly polymorphic and are associated with specific IDRs. Therefore, it is important to identify potential drug-HLA associations so that individuals who would develop IDRs can be identified before drug exposure. We harvested the associations between drugs and class I HLAs from the literature. The results revealed that there are many drug-HLA pairs without clinical data. For better potential interactions of the drug-HLA pairs, molecular docking was used to explore the potential of associations between the drugs and HLAs. From the analysis of docking scores between the 17 drugs and 74 class I HLAs, it was observed that the known significantly associated drug-HLA pairs had statistically lower docking scores than those not reported to be significantly associated (t-test p < 0.05). This indicates that molecular docking could be utilized for screening drug-HLA interactions and predicting potential IDRs. Examining the binding modes of drugs in the docked HLAs suggested several distinct binding sites inside class I HLAs, expanding our knowledge of the underlying interaction mechanisms between drugs and HLAs.

The Next Generation of Risk Assessment Multi-Year Study—Highlights of Findings, Applications to Risk Assessment, and Future Directions

Background: The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions. Objective: Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures. Methods: New data and methods were applied and evaluated for use in hazard identification and dose–response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling. Discussion: NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure–response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams. Conclusions: While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study—highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671–1682;u2002http://dx.doi.org/10.1289/EHP233

Adverse outcome pathways: From research to regulation scientific workshop report.

An adverse outcome pathway (AOP) helps to organize existing knowledge on chemical mode of action, starting with a molecular initiating event such as receptor binding, continuing through key events, and ending with an adverse outcome such as reproductive impairment. AOPs can help identify knowledge gaps where more research is needed to understand the underlying mechanisms, aid in chemical hazard characterization, and guide the development of new testing approaches that use fewer or no animals. A September 2014 workshop in Bethesda, Maryland considered how the AOP concept could improve regulatory assessments of chemical toxicity. Scientists from 21 countries, representing industry, academia, regulatory agencies, and special interest groups, attended the workshop, titled Adverse Outcome Pathways: From Research to Regulation. Workshop plenary presentations were followed by breakout sessions that considered regulatory acceptance of AOPs and AOP-based tools, criteria for building confidence in an AOP for regulatory use, and requirements to build quantitative AOPs and AOP networks. Discussions during the closing session emphasized a need to increase transparent and inclusive collaboration, especially with disciplines outside of toxicology. Additionally, to increase impact, working groups should be established to systematically prioritize and develop AOPs. Multiple collaborative projects and follow-up activities resulted from the workshop.

Machine Learning Methods for Predicting HLA–Peptide Binding Activity

As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA-peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA-peptide binding prediction. We also summarize the descriptors based on which the HLA-peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA-peptide binding prediction method based on network analysis.

Evaluating variations of genotype calling: a potential source of spurious associations in genome-wide association studies

Genome-wide association studies (GWAS) examine the entire human genome with the goal of identifying genetic variants (usually single nucleotide polymorphisms (SNPs)) that are associated with phenotypic traits such as disease status and drug response. The discordance of significantly associated SNPs for the same disease identified from different GWAS indicates that false associations exist in such results. In addition to the possible sources of spurious associations that have been investigated and discussed intensively, such as sample size and population stratification, an accurate and reproducible genotype calling algorithm is required for concordant GWAS results from different studies. However, variations of genotype calling of an algorithm and their effects on significantly associated SNPs identified in downstream association analyses have not been systematically investigated. In this paper, the variations of genotype calling using the Bayesian Robust Linear Model with Mahalanobis distance classifier (BRLMM) algorithm and the resulting influence on the lists of significantly associated SNPs were evaluated using the raw data of 270 HapMap samples analysed with the Affymetrix Human Mapping 500K Array Set (Affy500K) by changing algorithmic parameters. Modified were the Dynamic Model (DM) call confidence threshold (threshold) and the number of randomly selected SNPs (size). Comparative analysis of the calling results and the corresponding lists of significantly associated SNPs identified through association analysis revealed that algorithmic parameters used in BRLMM affected the genotype calls and the significantly associated SNPs. Both the threshold and the size affected the called genotypes and the lists of significantly associated SNPs in association analysis. The effect of the threshold was much larger than the effect of the size. Moreover, the heterozygous calls had lower consistency compared to the homozygous calls.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis

BackgroundAs the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding.MethodsQualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network.ResultsNine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature.ConclusionsNetwork analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.

Proteomic analysis of acetaminophen-induced hepatotoxicity and identification of heme oxygenase 1 as a potential plasma biomarker of liver injury

Overdose of acetaminophen (APAP) is a major cause of acute liver failure. This study was aimed to identify pathways related to hepatotoxicity and potential biomarkers of liver injury.

FutureTox III: Bridges for Translation

Future Tox III, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in November 2015. Building upon Future Tox I and II, Future Tox III was focused on developing the high throughput risk assessment paradigm and taking the science of in vitro data and in silico models forward to explore the question-what progress is being made to address challenges in implementing the emerging big-data toolbox for risk assessment and regulatory decision-making. This article reports on the outcome of the workshop including 2 examples of where advancements in predictive toxicology approaches are being applied within Federal agencies, where opportunities remain within the exposome and AOP domains, and how collectively the toxicology community across multiple sectors can continue to bridge the translation from historical approaches to Tox21 implementation relative to risk assessment and regulatory decision-making.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.

Correlating in vitro data to in vivo findings for risk assessment

A special session at the Toxicology and Risk Assessment Conference in Cincinnati, OH, USA in May, 2012 presented approaches expanding upon current uses of in vitro toxicity data for risk assessment. Evaluation of xenobiotics through use of in vitro study methods is increasing exponentially and these methodologies offer a relatively fast and considerably cheaper way to determine toxicities in comparison to traditional approaches. One of the challenges with in vitro data is to effectively use this information for risk assessment purposes. Currently, in vitro studies are used as supportive for hazard characterization and identifying mechanisms associated with toxicity. Being able to effectively correlate in vitro effects to in vivo observations represents a major challenge for risk assessors. The presentations in this special session provided innovative approaches toward effectively using in vitro data for the human health risk assessment process.