Application of Benchmark Concentration (BMC) Analysis on Zebrafish Data: A New Perspective for Quantifying Toxicity in Alternative Animal Models

Abstract

Over the past decade, the zebrafish is increasingly being used as a model to screen for chemical-mediated toxicities including developmental toxicity (DT) and neurotoxicity (NT). One of the major challenges is lack of harmonization in data analysis approaches, thereby posing difficulty in comparing findings across laboratories. To address this, we sought to establish a unified data analysis strategy for both DT and NT data, by adopting the benchmark concentration (BMC) analysis. There are two critical aspects in the BMC analysis: having a toxicity endpoint amenable for BMC and selecting a proper benchmark response (BMR) for the endpoint. For the former, in addition to the typical endpoints in NT assay (eg, hyper/hypo-response quantified by distance moved), we also used endpoints that assess the differences in movement patterns between chemicaltreated embryos and control embryos. For the latter, we standardized the selection of BMR, which is analogous to minimum activity threshold, based on intrinsic response variations in the endpoint. When comparing our BMC results with a traditionally used LOAEL method (lowest-observed-adverse-effect level), we found high active compound concordance (100% for DT vs 74% for NT); generally, the BMC was more sensitive than LOAEL (no. of BMC more sensitive/no. of concordant active compounds, 43/50 for DT vs 16/26 for NT). Using the BMC with standardized toxicity endpoints and an appropriate BMR, we may now have a unified data-analysis approach to comparing results across different zebrafish datasets, for a better understanding of strengths and challenges when using the zebrafish as a screening tool.

Detection and Prioritization of Developmentally Neurotoxic and/or Neurotoxic Compounds Using Zebrafish

“…Based on our criterium, we would not consider it active. In this regard, there is a publication focused on providing a new strategy for processing and analyzing data obtained from zebrafish developmental toxicity and neurotoxicity assessments to avoid inconsistency in data analysis strategies to classify chemical activity outcomes (Hsieh et al, 2019). They have used our dataset to construct this new strategy and the concordance in classification of active compounds between their approach and ours was 100% in case of embryo-toxicity and 74% in the case of neurotoxicity assay.…”

“…At least 2 of them already had robust data supporting their neuroactivity in zebrafish (Dukes et al, 2016;Farrell et al, 2011;Lee et al, 2018;Zhang and Zhao, 2018). On the contrary, Hsieh et al (2019) detected 4 additional active chemicals, all of them detected only in one of the rounds of the tracking. Besides zebrafish, Hagstrom et al (2019) also described a dataset obtained using freshwater planaria as a new alternative animal model for toxicity screening (Hagstrom et al 2019;Zhang et al, 2019).…”

Screening for Developmental Neurotoxicity at the National Toxicology Program: The Future Is Here

“…Although BMC modeling provides a unified data analysis approach for comparing across datasets, there are still many details to be addressed including guidance for the selection of best-fitting model and/or model averaging, model parameterization, etc. We found that by normalizing the chemical responses using vehicle control responses (per plate) and linearly shifting baseline response to 0, it facilitated the streamline BMC modeling processes (see Hsieh et al, 2018 for further details). Normalization of chemical responses using vehicle control responses per plate is a common practice in in vitro mediumand high-throughput assays.…”

“…It also addresses lessons learned with regards to data handling and design considerations, as well as knowledge gaps. Companion papers in this special issue highlight details on NTP's data analysis pipeline for alternative animal models (Hsieh et al,2018), findings from collaborators on primary studies in in vitro alternative animal models (Dach et al, 2018;Quevedo et al, 2018;Zhang et al, 2018), how actives compare across some of these models (Hagstrom et al, 2018) and insight on how this approach may be used in regulatory decision making (Sachana et al, 2018).…”

Determination of benchmark concentrations and their statistical uncertainty for cytotoxicity test data and functional in vitro assays

“…As the BMC method uses the information of the whole dataset, including the data uncertainties (i.e., variability produced by experimental errors and fluctuations of experimental parameters), it is less influenced by concentration spacing than a NOAEL, and it provides an estimate of statistical uncertainty of a point of departure (PoD). It is therefore a preferred method for modern toxicological approaches (Hartung and Leist, 2008;Leist et al, 2010Leist et al, , 2012Leist et al, , 2014, and the concept has been used for a number of algorithms to define non-cytotoxic concentrations (Stadnicka-Michalak et al, 2018;Hsieh et al, 2019;Calderazzo et al, 2019;Behl et al, 2015). Several software tools have been developed to calculate the BMC (Filer et al, 2017;Slob and Setzer, 2014;Ritz and Streibig, 2005) 1,2 , but applying the BMC concept still requires considerably more mathematical/ statistical and programming skills than the NOAEL/LOAEL approach and can be challenging for experimental biologists.…”