Threshold of Toxicological Concern - an update for non-genotoxic carcinogens

The Threshold of Toxicological Concern (TTC) concept can be applied to organic compounds with known chemical structure to derive a threshold for exposure below which a toxic effect on human health by the compound is not expected. The TTC concept distinguishes between carcinogens that may act as genotoxic and non-genotoxic compounds. A positive prediction of a genotoxic mode of action, either by structural alerts or experimental data, leads to the application of the threshold value for genotoxic compounds. Non-genotoxic substances are assigned to the TTC value of their respective Cramer class even though it is recognized that they could test positive in a rodent cancer bioassay. This study investigated the applicability of the Cramer classes specifically to provide adequate protection for non-genotoxic carcinogens. For this purpose, benchmark dose levels based on tumour incidence were compared with no observed effect levels (NOEL) derived from non-, pre- or neoplastic lesions. One key aspect was the categorization of compounds as non-genotoxic carcinogens. The recently finished CEFIC LRI project B18 classified the carcinogens of the CPDB as either non- or genotoxic compounds based on experimental or in silico data. A detailed consistency check resulted in a data set of 137 non-genotoxic organic compounds. For these 137 compounds, NOEL values were derived from high quality animal studies with oral exposure and chronic duration using well known repositories including RepDose, ToxRef and COSMOS DB. Further, an effective tumour dose (ETD10) was calculated and compared to the lower confidence limit on benchmark dose levels (BMDL10) derived by model averaging. Comparative analysis of NOEL/EDT10/BMDL10 values showed that potentially bioaccumulative compounds in humans, as well as steroids, which both belong to the exclusion categories, occur predominantly in region of the 5th percentiles of the distributions. Excluding these 25 compounds resulted in significantly higher, but comparable 5th percentile chronic NOEL and BMDL10 values, while the 5th percentile EDT10 value was slightly higher, but not statistically significant. The comparison of the obtained distributions of NOELs with the existing Cramer classes and their derived TTC values supports the application of Cramer class thresholds to all non genotoxic compounds, including non_genotoxic carcinogens

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across

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Bayesian Estimation by Sequential Monte Carlo Sampling for Nonlinear Dynamic Systems

Precise estimation of state variables and model parameters is essential for efficient process operation, including model predictive control, abnormal situation management, and decision making under uncertainty. Bayesian formulation of the estimation problem suggests a general solution for all types of systems. Even though the theory of Bayesian estimation of nonlinear dynamic systems has been available for decades, practical implementation has not been feasible due to computational and methodological challenges. Consequently, most existing methods rely on simplifying assumptions to obtain a tractable but approximate solution. For example, extended Kalman filtering (EKF) linearizes the process model and assumes Gaussian prior and noise. Moving horizon based least-squares estimation (MHE) also assumes Gaussian or other fixed-shape prior and noise to obtain a least-squares optimization problem. MHE can impose constraints, but is non-recursive and requires computationally expensive nonlinear or quadratic programming. This dissertation introduces sequential Monte Carlo sampling (SMC) for Bayesian estimation of chemical process systems. This recent approach approximates computationally expensive integration by recursive Monte Carlo sampling, and can obtain accurate estimates of posterior distributions efficiently with minimum assumptions. This approach has not been systematically compared with estimation methods popular for chemical processes, including EKF and MHE. In addition to comparing various estimation methods, this dissertation also develops a practical framework of SMC for handling process constraints based on an acceptance/rejection algorithm. Furthermore, a novel empirical Bayes approach is developed to deal with practical challenges due to degeneracy and a poor in..