Using existing knowledge for the risk evaluation of crop protection products in order to guide exposure driven data generation strategies and minimise unnecessary animal testing

Traditionally, human health risk assessment focuses on defining the hazard through mammalian toxicity studies followed by exposure estimation. We have explored ways of predicting exposure based primarily on the use scenario and comparing the exposure to reference dose values derived by various regulatory agencies (US EPA, JMPR, and EU Commission) in order to identify mammalian toxicity studies that are relevant to human health risk assessment. Human dietary exposure was based on existing residue data for substances with comparable use on the same or similar crops. Human occupational exposures were based on the use scenarios and application methods. To provide a point of comparison for the exposure predictions, data were collated for acute, chronic and occupational reference dose values derived by various regulatory agencies (US EPA, JMPR, and EU Commission). The exposure predictions and range of hazard endpoints were compared using the ILSI HESI Risk21 risk matrix plots in order to visualise and contextualise the level of potential concern for the exposure prediction. In addition, an approach is proposed to categorise the likelihood of acceptability of risk based on where the exposure sits relative to the distribution of reference dose values. The approaches proposed in this study allow for exposure prediction based on the Good Agricultural Practice (GAP) in conjunction with the use of existing hazard data for crop protection products in order to make an initial determination on acceptability of risk and to identify key studies that are required for human health risk assessment and also opportunities for study waivers.

The development of response surface pathway design to reduce animal numbers in toxicity studies

BACKGROUND

This study describes the development of Response Surface Pathway (RSP) design, assesses its performance and effectiveness in estimating LD50, and compares RSP with Up and Down Procedures (UDPs) and Random Walk (RW) design.

METHODS

A basic 4-level RSP design was used on 36 male ICR mice given intraperitoneal doses of Yessotoxin. Simulations were performed to optimise the design. A k-adjustment factor was introduced to ensure coverage of the dose window and calculate the dose steps. Instead of using equal numbers of mice on all levels, the number of mice was increased at each design level. Additionally, the binomial outcome variable was changed to multinomial. The performance of the RSP designs and a comparison of UDPs and RW were assessed by simulations. The optimised 4-level RSP design was used on 24 female NMRI mice given Azaspiracid-1 intraperitoneally.

RESULTS

The in vivo experiment with basic 4-level RSP design estimated the LD50 of Yessotoxin to be 463 μg/kgBW (95% CI: 383-535). By inclusion of the k-adjustment factor with equal or increasing numbers of mice on increasing dose levels, the estimate changed to 481 μg/kgBW (95% CI: 362-566) and 447 μg/kgBW (95% CI: 378-504 μg/kgBW), respectively. The optimised 4-level RSP estimated the LD50 to be 473 μg/kgBW (95% CI: 442-517). A similar increase in power was demonstrated using the optimised RSP design on real Azaspiracid-1 data. The simulations showed that the inclusion of the k-adjustment factor, reduction in sample size by increasing the number of mice on higher design levels and incorporation of a multinomial outcome gave estimates of the LD50 that were as good as those with the basic RSP design. Furthermore, optimised RSP design performed on just three levels reduced the number of animals from 36 to 15 without loss of information, when compared with the 4-level designs. Simulated comparison of the RSP design with UDPs and RW design demonstrated the superiority of RSP.

CONCLUSION

Optimised RSP design reduces the number of animals needed. The design converges rapidly on the area of interest and is at least as efficient as both the UDPs and RW design.