A computational system for Bayesian benchmark dose estimation of genomic data in BBMD. (March 2022)

• Record Type: Journal Article
• Authors: Ji, Chao; Weissmann, Andrew; Shao, Kan
• Is Part Of: Environment international. Volume 161(2022)
• Publication Date: 2022-03
• Subjects: Benchmark dose -- Genomic data -- Bayesian -- BBMD; BBMD Bayesian Benchmark Dose Modeling System -- BMA Bayesian model average -- BEPOD Biological Effect Point of Departure -- BMD Benchmark Dose -- BMDL Statistical Lower Bound of BMD -- BMDS Benchmark Dose Software -- BMDU Statistical Upper Bound of BMD -- BMR Benchmark Response -- IVIVE In-vitro to In-vivo Extrapolation -- MCMC Markov Chain Monte Carlo -- NTP National Toxicology Program -- POD Point of Departure; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2022.107135
• Languages: English
• ISSNs: 0160-4120

Abstract

Graphical abstract: Abstract: Background: Existing studies have revealed that the benchmark dose (BMD) estimates from short-term in vivo transcriptomics studies can approximate those from long-term guideline toxicity assessments. Existing software applications follow this trend by analyzing omics data through the maximum likelihood estimation and choosing the "best" model for BMD estimates. However, this practice ignores the model uncertainty and may result in over-confident inferences and predictions, leading to an inadequate decision. Objective: By generally following the National Toxicology Program Approach to Genomic Dose-Response Modeling, we developed a web-based dose–response modeling and BMD estimation system, Bayesian BMD (BBMD), for genomic data to quantitatively address uncertainty from various sources. The performances of BBMD are compared with BMDExpress. Methods: The system is primarily based on the previously developed BBMD system and further developed in a genomic perspective. Bayesian model averaging method is applied to BMD estimation and pathways analyses. Generally, the system is unique regarding the flexibility in preparing/storing data and in characterizing uncertainties. Results: This system was tested and validated versus 24 previously published in-vivo microarray dose–response datasets (GSE45892) and 64 molecules data from the Open TG-Gates database. Short term transcriptional BMD values for the median pathway in BBMD are highly correlated with the long-term apical BMD values (R = 0.78–0.91). The BMD estimates obtained by BBMD were compared to those by BMDExpress. The results indicate that BBMD provides more adequate results in terms of less extreme values and no failure in BMD and BMDL calculations. Also, the pathway analysis in BBMD provides a conservative estimate because a broader confidence interval is established. Discussion: Overall, this study demonstrates that dose–response modeling using genomic data can play a substantial role in support of chemical risk assessment. BBMD represents a robust and user-friendly alternative for genomic dose–response data analysis with outstanding functionalities to quantify uncertainty from various sources.