Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization

Abstract

Use of high-throughput, in vitro bioactivity data in setting a point-of-departure (POD) has the potential to accelerate the pace of human health safety evaluation by informing screening-level assessments. The primary objective of this work was to compare PODs based on high-throughput predictions of bioactivity, exposure predictions, and traditional hazard information for 448 chemicals. PODs derived from new approach methodologies (NAMs) were obtained for this comparison using the 50th (PODNAM, 50) and the 95th (PODNAM, 95) percentile credible interval estimates for the steady-state plasma concentration used in in vitro to in vivo extrapolation of administered equivalent doses. Of the 448 substances, 89% had a PODNAM, 95 that was less than the traditional POD (PODtraditional) value. For the 48 substances for which PODtraditional < PODNAM, 95, the PODNAM and PODtraditional were typically within a factor of 10 of each other, and there was an enrichment of chemical structural features associated with organophosphate and carbamate insecticides. When PODtraditional < PODNAM, 95, it did not appear to result from an enrichment of PODtraditional based on a particular study type (eg, developmental, reproductive, and chronic studies). Bioactivity:exposure ratios, useful for identification of substances with potential priority, demonstrated that high-throughput exposure predictions were greater than the PODNAM, 95 for 11 substances. When compared with threshold of toxicological concern (TTC) values, the PODNAM, 95 was greater than the corresponding TTC value 90% of the time. This work demonstrates the feasibility, and continuing challenges, of using in vitro bioactivity as a protective estimate of POD in screening-level assessments via a case study.

Keywords: high-throughput screening; high-throughput toxicokinetics; new approach methodologies; point-of-departure (POD); threshold of toxicological concern (TTC).

Figure 1.. Overall workflow of the case study.

This case study includes 448 substances with exposure predictions, in vitro assay data, HTTK information, and in vivo hazard information. The 50^th and 95^th percentile from the Monte Carlo simulation of inter-individual toxicokinetic variability were used to estimate AEDs, and the minimum of either the ToxCast or HIPPTox-based AEDs were selected as the POD_{NAM, 50} or POD_{NAM, 95}. The POD_NAM estimates were compared to the 5^th percentile from the distribution of the POD_traditional values obtained from multiple sources to obtain the log₁₀POD ratio. The log₁₀BER was obtained by comparing the POD_NAM estimates to exposure predictions. All values used for computation were in log₁₀-mg/kg-bw/day units

Figure 2.. Substance diversity.

Generic functional use categories from ACToR for the 448 case study substances are illustrated. One substance, represented as a row in the heatmap, may be associated with multiple use categories

Figure 3.. Comparison of the Exposure, POD_NAM, and POD_traditional.

Comparison of ExpoCast (gray circles), POD_NAM (green circles), maximum AED (black triangles), and POD_traditional values (blue boxes) for 448 substances. The green line segment indicates the POD_NAM,95 to POD_NAM,50. Inset images A, B, and C correspond to the red boxes overlaid on the main plot. Image 3A provides a magnification on the substances with the largest log₁₀POD ratio values. Image 3B displays a sample of substances that approach the median log₁₀POD ratio. Image 3C includes all 48 substances for which the POD_{NAM, 95 >} POD_traditional.

Figure 4:. Illustration of the log10-bioactivity-exposure-ratio (BER).

A) The cumulative frequency distributions for BER estimates are plotted. The BER₉₅ values used the 95^th percentile from the credible interval to predict the median total US population exposure from ExpoCast, whereas the BER₅₀ values used the median exposure estimate. BER₉₅ and BER₅₀ values were calculated as the “95^th%-ile” and “50^th%-ile,” using the POD_NAM,95 and POD_NAM,50, respectively. Orange line = BER₉₅ using POD_NAM,50; black line = BER₉₅ using POD_NAM,95; blue line = BER₅₀ using POD_NAM,50; gold line = BER₅₀ using POD_NAM,95. B) Eleven chemicals had a BER₉₅, 95^th%-ile < 0, indicating overlap between the POD_NAM,95 and the 95th percentile exposure prediction. Dashed red lines indicate where BER₉₅, 95th%-ile = 0.

Figure 5:. Exposure and in vitro bioactivity that defined chemicals with log₁₀BER < 0.

In (A), a scatterplot of log₁₀ ExpoCast SEEM2 95^th percentile value versus the POD_NAM,95, with dotted red lines for the respective median values. The names of the 11 substances with log₁₀BER₉₅ < 0 are labeled. In (B) and (C), distributions of the exposure and the ToxCast AC50 data for all 448 substances are shown in the middle panels (gray histograms). Below these histograms in (B) and (C), side by side boxplots (showing the 1st quartile, median, and 3^rd quartile) of the log₁₀ ExpoCast SEEM2 95^th percentile values and the ToxCast AC50 values are illustrated for the 11 substances with log₁₀BER₉₅ < 0. In (C), gold triangles indicate the 5th percentile of the AC₅₀ distribution.

Figure 6:. Comparison of Exposure Predictions from ExpoCast and Health Canada Evaluations.

The total maximum values (in log₁₀-mg/kg/day units) curated from Health Canada exposure assessments for 18 substances in this case study were compared to the ExpoCast (A) median and (B) 95^th percentile predictions (in log10-mg/kg/day units), respectively. CASRN for these substances are labeled. The gray line shows a linear relationship. All CASRN and substance identifiers, including substance name, can be found in Supplemental File 2.

Figure 7.. Further understanding of the POD ratio distribution.

(A) The log₁₀POD ratio is illustrated for the POD_NAM,95 and the POD_{NAM, 50}. The solid black line indicates where the log10-POD ratio₉₅ is 0. Using the more conservative (i.e., lower) POD_NAM,95, 48 of the 448 substances (10.7%) demonstrated a log₁₀POD ratio < 0 (to the left of the dashed vertical line), whereas 92 of the 448 substances (20.5%) demonstrated a log10-POD ratio < 0 using the POD_NAM,50. The medians of the log10-POD ratio distributions are indicated by dashed lines for POD_{NAM, 95} and POD_{NAM, 50} as 2 and 1.2, respectively. (B) Maximum AED (max AED) was less than the POD_traditional (5th-%ile POD) in 60% of the cases where the log₁₀POD ratio₉₅ > 0 (using POD_{NAM, 95}). For the 48 chemicals with log₁₀POD ratio₉₅ < 0, the max AED was within the range of POD_traditional.

Figure 8.. Study types enriched in the log₁₀POD ratio₉₅ < 0 set.

The matrices used to evaluate study type enrichment are shown. Neither developmental/reproductive (grouped together) (p = 0.88) nor chronic (p = 0.45) study types appeared to be enriched in the log₁₀POD ratio₉₅ < 0 subset.

Figure 9.. POD_NAM,95 compared to the TTC.

The log₁₀TTC: POD_NAM,95 ratio is illustrated for the 448 case study chemicals in (A). In (B), the log₁₀ TTC value bin is compared to the log₁₀POD_NAM,95, in units of log₁₀-mg/kg/day; dots represent all points and violin plots capture the shape of the distribution.