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A computational system for Bayesian benchmark dose estimation of genomic data in BBMD
Graphical abstract Display Omitted
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.
A computational system for Bayesian benchmark dose estimation of genomic data in BBMD
Graphical abstract Display Omitted
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.
A computational system for Bayesian benchmark dose estimation of genomic data in BBMD
Ji, Chao (author) / Weissmann, Andrew (author) / Shao, Kan (author)
2022-02-03
Article (Journal)
Electronic Resource
English
Benchmark dose , Genomic data , Bayesian , 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
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