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Environmental health risk assessment of ambient lead levels in Lisbon, Portugal: A full chain study approach
Abstracts / Toxicology Letters 205S (2011) S60–S179
AcRif and MeORif seem to be promising compounds to induce P-gp activity in RBE4 cells. Acknowledgement: FCT grant (Project PTDC/SAU-OSM/101437/ 2008). doi:10.1016/j.toxlet.2011.05.345 Computational toxicology P1112 Environmental health risk assessment of ambient lead levels in Lisbon, Portugal: A full chain study approach E. Casimiro 1,∗ , P. Philippe Ciffroy 2 , P. Serpa 3 , E. Johansson 4 , C. Legind 5 , C. Brochot 6 1
INFOTOX, Lisbon, Portugal, 2 EDF, Paris, France, 3 CCIAM - Uni. Lisbon, Lisbon, Portugal, 4 Facilia AB, Bromma, Sweden, 5 DTU, Technical University of Denmark, Denmark, Denmark, 6 INERIS, Paris, France The multi-causality interactions between environment and health are complex and call for an integrated multidisciplinary study approach. Emerging computational toxicology tools that link toxicology, chemistry, environmental sciences, biostatistics, and computer sciences are proving to be very useful for integrated full-chain human health risk assessments. In this study we use a newly developed computational tool – the 2FUN player to conduct a full-chain assessment combining measured ambient air lead concentrations with multi-media modelling and PBPK simulations to estimate the health risks from ambient air levels of lead in air-borne particulates (PM10) in Lisbon, Portugal. Ambient air Pb concentrations were used together with local climate variables in the 2FUN atmospheric model to calculate the amount of Pb deposited (wet and dry) onto soil. The 2FUN environmental and PBPK models were then used to calculate the Pb concentration in various biota (leafy vegetables, root vegetables, grain, potatoes, and fruits) produced in the area as well as the amount of Pb a typical adult would inhale and ingest during this ten-year assessment period. The PBPK model of the 2FUN player was used to calculate the Pb levels in the various body systems. Our results showed a low health risk from Pb exposures. It also identified that ingestion of leafy vegetables (i.e. lettuce, cabbage, and spinach) and fruits contribute the most to total Pb blood levels. This full chain assessment approach of the 2FUN player is likely to be very useful for local health risk assessment studies (i.e. EIA and SEA studies). doi:10.1016/j.toxlet.2011.05.346
P1113 Use of in vivo and mechanistic evidence for the development of structural alerts for the prediction of non-genotoxic carcinogenicity L. Coquin ∗ , L. Gibson, M.L. Patel, S.A. Stalford Lhasa Limited, Leeds, UK Structural alerts represent a method of predicting the toxicity of a compound according to the presence of certain toxicophores within the molecule. The technique can be a valuable tool in the in silico prediction of complex endpoints such as carcinogenicity. Alerts may be derived based on available experimental data even when it is inconsistent, if knowledge of the chemical and biological
S95
mechanisms of toxicity can be elucidated. We explored whether this methodology could be used to develop alerts for the prediction of non-genotoxic carcinogenicity. The 1547 chemicals in the carcinogenic potency database (CPDB) were analysed to identify common structural features that could be linked to carcinogenicity. An existing knowledge base of structural alerts for carcinogenicity correctly predicted activity for 66% of the carcinogens in the data set. A visual analysis of the 267 false negative predictions identified 11 new chemical classes with the potential to be developed into full structural alerts. Amongst them, a set of 17 steroids was identified as a class for further development. Investigation revealed that 9 compounds from this set belonging to the structural classes represented by androstane and pregnane all induced liver tumours in the rat. Further research was conducted using the published literature to find additional supporting toxicity data and identify possible mechanistic rationales for these two classes. The present work has demonstrated that new structural alerts to predict the non-genotoxic carcinogenicity of two steroidal classes can be written using available data in the literature, together with an understanding of the mechanisms of toxicity. doi:10.1016/j.toxlet.2011.05.347
P1114 Withdrawn
doi:10.1016/j.toxlet.2011.05.348
P1115 A comprehensive approach for in silico risk assessment of impurities and degradants in drug products P. Japertas ∗ , K. Lanevskij, L. Juska, J. Dapkunas, A. Sazonovas, R. Didziapetris Vilnius Development Office, ACD/Labs, Inc., Vilnius, Lithuania Purpose: According to FDA Guidance for Industry, assessment of genotoxicity/carcinogenicity by computational methods is sufficient for impurities in drug products that are present at levels below the ICH qualification thresholds. The aim of this study was to develop a comprehensive in silico approach to aid this assessment. Methods: The overall evaluation of genotoxic and/or carcinogenic potential is based on four predictive models reflecting different mechanisms of hazardous activity. These include two probabilistic models and a knowledge-based expert system that identifies potentially hazardous structural fragments that could be responsible for carcinogenic activity of the test molecule. The probabilistic models estimate the compounds’ mutagenic potential in the Ames test, and the likelihood of causing endocrine system disruption due to interactions with estrogen receptor alpha (ER-␣). Results: The list of alerting structural fragments was compiled from various literature sources and refined by analyzing their performance on data from different assays (Ames test, chromosomal aberrations, micronucleus test, mouse lymphoma assay). Sensitivity of the expert system was further improved using carcinogenicity data obtained from FDA. The final list contained 67 alerting groups, 53 of which accounted for point mutational and/or clastogenic mechanisms of DNA damage, while the remaining 14 substructures ensured detection of carcinogens acting by non-genotoxic mechanisms. Together with Ames test and ER-␣ binding predictors the expert system was able to recognize >90% of compounds marked
AcRif and MeORif seem to be promising compounds to induce P-gp activity in RBE4 cells. Acknowledgement: FCT grant (Project PTDC/SAU-OSM/101437/ 2008). doi:10.1016/j.toxlet.2011.05.345 Computational toxicology P1112 Environmental health risk assessment of ambient lead levels in Lisbon, Portugal: A full chain study approach E. Casimiro 1,∗ , P. Philippe Ciffroy 2 , P. Serpa 3 , E. Johansson 4 , C. Legind 5 , C. Brochot 6 1
INFOTOX, Lisbon, Portugal, 2 EDF, Paris, France, 3 CCIAM - Uni. Lisbon, Lisbon, Portugal, 4 Facilia AB, Bromma, Sweden, 5 DTU, Technical University of Denmark, Denmark, Denmark, 6 INERIS, Paris, France The multi-causality interactions between environment and health are complex and call for an integrated multidisciplinary study approach. Emerging computational toxicology tools that link toxicology, chemistry, environmental sciences, biostatistics, and computer sciences are proving to be very useful for integrated full-chain human health risk assessments. In this study we use a newly developed computational tool – the 2FUN player to conduct a full-chain assessment combining measured ambient air lead concentrations with multi-media modelling and PBPK simulations to estimate the health risks from ambient air levels of lead in air-borne particulates (PM10) in Lisbon, Portugal. Ambient air Pb concentrations were used together with local climate variables in the 2FUN atmospheric model to calculate the amount of Pb deposited (wet and dry) onto soil. The 2FUN environmental and PBPK models were then used to calculate the Pb concentration in various biota (leafy vegetables, root vegetables, grain, potatoes, and fruits) produced in the area as well as the amount of Pb a typical adult would inhale and ingest during this ten-year assessment period. The PBPK model of the 2FUN player was used to calculate the Pb levels in the various body systems. Our results showed a low health risk from Pb exposures. It also identified that ingestion of leafy vegetables (i.e. lettuce, cabbage, and spinach) and fruits contribute the most to total Pb blood levels. This full chain assessment approach of the 2FUN player is likely to be very useful for local health risk assessment studies (i.e. EIA and SEA studies). doi:10.1016/j.toxlet.2011.05.346
P1113 Use of in vivo and mechanistic evidence for the development of structural alerts for the prediction of non-genotoxic carcinogenicity L. Coquin ∗ , L. Gibson, M.L. Patel, S.A. Stalford Lhasa Limited, Leeds, UK Structural alerts represent a method of predicting the toxicity of a compound according to the presence of certain toxicophores within the molecule. The technique can be a valuable tool in the in silico prediction of complex endpoints such as carcinogenicity. Alerts may be derived based on available experimental data even when it is inconsistent, if knowledge of the chemical and biological
S95
mechanisms of toxicity can be elucidated. We explored whether this methodology could be used to develop alerts for the prediction of non-genotoxic carcinogenicity. The 1547 chemicals in the carcinogenic potency database (CPDB) were analysed to identify common structural features that could be linked to carcinogenicity. An existing knowledge base of structural alerts for carcinogenicity correctly predicted activity for 66% of the carcinogens in the data set. A visual analysis of the 267 false negative predictions identified 11 new chemical classes with the potential to be developed into full structural alerts. Amongst them, a set of 17 steroids was identified as a class for further development. Investigation revealed that 9 compounds from this set belonging to the structural classes represented by androstane and pregnane all induced liver tumours in the rat. Further research was conducted using the published literature to find additional supporting toxicity data and identify possible mechanistic rationales for these two classes. The present work has demonstrated that new structural alerts to predict the non-genotoxic carcinogenicity of two steroidal classes can be written using available data in the literature, together with an understanding of the mechanisms of toxicity. doi:10.1016/j.toxlet.2011.05.347
P1114 Withdrawn
doi:10.1016/j.toxlet.2011.05.348
P1115 A comprehensive approach for in silico risk assessment of impurities and degradants in drug products P. Japertas ∗ , K. Lanevskij, L. Juska, J. Dapkunas, A. Sazonovas, R. Didziapetris Vilnius Development Office, ACD/Labs, Inc., Vilnius, Lithuania Purpose: According to FDA Guidance for Industry, assessment of genotoxicity/carcinogenicity by computational methods is sufficient for impurities in drug products that are present at levels below the ICH qualification thresholds. The aim of this study was to develop a comprehensive in silico approach to aid this assessment. Methods: The overall evaluation of genotoxic and/or carcinogenic potential is based on four predictive models reflecting different mechanisms of hazardous activity. These include two probabilistic models and a knowledge-based expert system that identifies potentially hazardous structural fragments that could be responsible for carcinogenic activity of the test molecule. The probabilistic models estimate the compounds’ mutagenic potential in the Ames test, and the likelihood of causing endocrine system disruption due to interactions with estrogen receptor alpha (ER-␣). Results: The list of alerting structural fragments was compiled from various literature sources and refined by analyzing their performance on data from different assays (Ames test, chromosomal aberrations, micronucleus test, mouse lymphoma assay). Sensitivity of the expert system was further improved using carcinogenicity data obtained from FDA. The final list contained 67 alerting groups, 53 of which accounted for point mutational and/or clastogenic mechanisms of DNA damage, while the remaining 14 substructures ensured detection of carcinogens acting by non-genotoxic mechanisms. Together with Ames test and ER-␣ binding predictors the expert system was able to recognize >90% of compounds marked