Search

Your search keyword '"Lavado, Giovanna J"' showing total 33 results
Start Over Author "Lavado, Giovanna J"
33 results on '"Lavado, Giovanna J"'

1. Weakly and Strongly Irreversible Regular Languages

Author

Lavado, Giovanna J., Pighizzini, Giovanni, and Prigioniero, Luca

Subjects
Computer Science - Formal Languages and Automata Theory, F.4.3
Abstract

Finite automata whose computations can be reversed, at any point, by knowing the last k symbols read from the input, for a fixed k, are considered. These devices and their accepted languages are called k-reversible automata and k-reversible languages, respectively. The existence of k-reversible languages which are not (k-1)-reversible is known, for each k>1. This gives an infinite hierarchy of weakly irreversible languages, i.e., languages which are k-reversible for some k. Conditions characterizing the class of k-reversible languages, for each fixed k, and the class of weakly irreversible languages are obtained. From these conditions, a procedure that given a finite automaton decides if the accepted language is weakly or strongly (i.e., not weakly) irreversible is described. Furthermore, a construction which allows to transform any finite automaton which is not k-reversible, but which accepts a k-reversible language, into an equivalent k-reversible finite automaton, is presented., Comment: In Proceedings AFL 2017, arXiv:1708.06226

Published
2017
Full Text
View/download PDF

3. Minimal and Reduced Reversible Automata

Author

Lavado, Giovanna J., Pighizzini, Giovanni, and Prigioniero, Luca

Subjects
Computer Science - Formal Languages and Automata Theory, 68Q45, F.1.1, F.4.3
Abstract

A condition characterizing the class of regular languages which have several nonisomorphic minimal reversible automata is presented. The condition concerns the structure of the minimum automaton accepting the language under consideration. It is also observed that there exist reduced reversible automata which are not minimal, in the sense that all the automata obtained by merging some of their equivalent states are irreversible. Furthermore, a sufficient condition for the existence of infinitely many reduced reversible automata accepting a same language is given. It is also proved that, when the language is accepted by a unique minimal reversible automaton (that does not necessarily coincide with the minimum deterministic automaton), then no other reduced reversible automata accepting it can exist., Comment: Preliminary version presented at DCFS 2016 --- Descriptional Complexity of Formal Systems, Bucharest, Romania, Jul 5-8, 2016

Published
2016
Full Text
View/download PDF

7. Converting Nondeterministic Automata and Context-Free Grammars into Parikh Equivalent One-Way and Two-Way Deterministic Automata

Author

Lavado, Giovanna J., Pighizzini, Giovanni, and Seki, Shinnosuke

Subjects
Computer Science - Formal Languages and Automata Theory, F.1.1, F.4.2, F.4.3
Abstract

We investigate the conversion of one-way nondeterministic finite automata and context-free grammars into Parikh equivalent one-way and two-way deterministic finite automata, from a descriptional complexity point of view. We prove that for each one-way nondeterministic automaton with $n$ states there exist Parikh equivalent one-way and two-way deterministic automata with $e^{O(\sqrt{n \ln n})}$ and $p(n)$ states, respectively, where $p(n)$ is a polynomial. Furthermore, these costs are tight. In contrast, if all the words accepted by the given automaton contain at least two different letters, then a Parikh equivalent one-way deterministic automaton with a polynomial number of states can be found. Concerning context-free grammars, we prove that for each grammar in Chomsky normal form with h variables there exist Parikh equivalent one-way and two-way deterministic automata with $2^{O(h^2)}$ and $2^{O(h)}$ states, respectively. Even these bounds are tight., Comment: 30 pages, 2 figure. A preliminary version has been presented at DLT 2012, LNCS 7410, pp. 284-295. Version 2: an example has been added in Section 3

Published
2012

8. Concise Representations of Reversible Automata

Author

Lavado, Giovanna J., Prigioniero, Luca, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Pighizzini, Giovanni, editor, and Câmpeanu, Cezar, editor

Published
2017
Full Text
View/download PDF

10. Operational State Complexity under Parikh Equivalence

Author

Lavado, Giovanna J., Pighizzini, Giovanni, Seki, Shinnosuke, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Jürgensen, Helmut, editor, Karhumäki, Juhani, editor, and Okhotin, Alexander, editor

Published
2014
Full Text
View/download PDF

11. Converting Nondeterministic Automata and Context-Free Grammars into Parikh Equivalent Deterministic Automata

Author

Lavado, Giovanna J., Pighizzini, Giovanni, Seki, Shinnosuke, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yen, Hsu-Chun, editor, and Ibarra, Oscar H., editor

Published
2012
Full Text
View/download PDF

12. Parikh’s Theorem and Descriptional Complexity

Author

Lavado, Giovanna J., Pighizzini, Giovanni, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bieliková, Mária, editor, Friedrich, Gerhard, editor, Gottlob, Georg, editor, Katzenbeisser, Stefan, editor, and Turán, György, editor

Published
2012
Full Text
View/download PDF

19. Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite

Author

Mansouri, Kamel, primary, Karmaus, Agnes L., additional, Fitzpatrick, Jeremy, additional, Patlewicz, Grace, additional, Pradeep, Prachi, additional, Alberga, Domenico, additional, Alepee, Nathalie, additional, Allen, Timothy E.H., additional, Allen, Dave, additional, Alves, Vinicius M., additional, Andrade, Carolina H., additional, Auernhammer, Tyler R., additional, Ballabio, Davide, additional, Bell, Shannon, additional, Benfenati, Emilio, additional, Bhattacharya, Sudin, additional, Bastos, Joyce V., additional, Boyd, Stephen, additional, Brown, J. B., additional, Capuzzi, Stephen J., additional, Chushak, Yaroslav, additional, Ciallella, Heather, additional, Clark, Alex M., additional, Consonni, Viviana, additional, Daga, Pankaj R., additional, Ekins, Sean, additional, Farag, Sherif, additional, Fedorov, Maxim, additional, Fourches, Denis, additional, Gadaleta, Domenico, additional, Gao, Feng, additional, Gearhart, Jeffery M., additional, Goh, Garett, additional, Goodman, Jonathan M., additional, Grisoni, Francesca, additional, Grulke, Christopher M., additional, Hartung, Thomas, additional, Hirn, Matthew, additional, Karpov, Pavel, additional, Korotcov, Alexandru, additional, Lavado, Giovanna J., additional, Lawless, Michael, additional, Li, Xinhao, additional, Luechtefeld, Thomas, additional, Lunghini, Filippo, additional, Mangiatordi, Giuseppe F., additional, Marcou, Gilles, additional, Marsh, Dan, additional, Martin, Todd, additional, Mauri, Andrea, additional, Muratov, Eugene N., additional, Myatt, Glenn J., additional, Nguyen, Dac-Trung, additional, Nicolotti, Orazio, additional, Note, Reine, additional, Pande, Paritosh, additional, Parks, Amanda K., additional, Peryea, Tyler, additional, Polash, Ahsan H., additional, Rallo, Robert, additional, Roncaglioni, Alessandra, additional, Rowlands, Craig, additional, Ruiz, Patricia, additional, Russo, Daniel P., additional, Sayed, Ahmed, additional, Sayre, Risa, additional, Sheils, Timothy, additional, Siegel, Charles, additional, Silva, Arthur C., additional, Simeonov, Anton, additional, Sosnin, Sergey, additional, Southall, Noel, additional, Strickland, Judy, additional, Tang, Yun, additional, Teppen, Brian, additional, Tetko, Igor V., additional, Thomas, Dennis, additional, Tkachenko, Valery, additional, Todeschini, Roberto, additional, Toma, Cosimo, additional, Tripodi, Ignacio, additional, Trisciuzzi, Daniela, additional, Tropsha, Alexander, additional, Varnek, Alexandre, additional, Vukovic, Kristijan, additional, Wang, Zhongyu, additional, Wang, Liguo, additional, Waters, Katrina M., additional, Wedlake, Andrew J., additional, Wijeyesakere, Sanjeeva J., additional, Wilson, Dan, additional, Xiao, Zijun, additional, Yang, Hongbin, additional, Zahoranszky-Kohalmi, Gergely, additional, Zakharov, Alexey V., additional, Zhang, Fagen F., additional, Zhang, Zhen, additional, Zhao, Tongan, additional, Zhu, Hao, additional, Zorn, Kimberley M., additional, Casey, Warren, additional, and Kleinstreuer, Nicole C., additional

Published
2021
Full Text
View/download PDF

20. CATMoS: Collaborative Acute Toxicity Modeling Suite

Author

Mansouri, K, Karmaus, A, Fitzpatrick, J, Patlewicz, G, Pradeep, P, Alberga, D, Alepee, N, Allen, T, Allen, D, Alves, V, Andrade, C, Auernhammer, T, Ballabio, D, Bell, S, Benfenati, E, Bhattacharya, S, Bastos, J, Boyd, S, Brown, J, Capuzzi, S, Chushak, Y, Ciallella, H, Clark, A, Consonni, V, Daga, P, Ekins, S, Farag, S, Fedorov, M, Fourches, D, Gadaleta, D, Gao, F, Gearhart, J, Goh, G, Goodman, J, Grisoni, F, Grulke, C, Hartung, T, Hirn, M, Karpov, P, Korotcov, A, Lavado, G, Lawless, M, Li, X, Luechtefeld, T, Lunghini, F, Mangiatordi, G, Marcou, G, Marsh, D, Martin, T, Mauri, A, Muratov, E, Myatt, G, Nguyen, D, Nicolotti, O, Note, R, Pande, P, Parks, A, Peryea, T, Polash, A, Rallo, R, Roncaglioni, A, Rowlands, C, Ruiz, P, Russo, D, Sayed, A, Sayre, R, Sheils, T, Siegel, C, Silva, A, Simeonov, A, Sosnin, S, Southall, N, Strickland, J, Tang, Y, Teppen, B, Tetko, I, Thomas, D, Tkachenko, V, Todeschini, R, Toma, C, Tripodi, I, Trisciuzzi, D, Tropsha, A, Varnek, A, Vukovic, K, Wang, Z, Wang, L, Waters, K, Wedlake, A, Wijeyesakere, S, Wilson, D, Xiao, Z, Yang, H, Zahoranszky-Kohalmi, G, Zakharov, A, Zhang, F, Zhang, Z, Zhao, T, Zhu, H, Zorn, K, Casey, W, Kleinstreuer, N, Mansouri, Kamel, Karmaus, Agnes L, Fitzpatrick, Jeremy, Patlewicz, Grace, Pradeep, Prachi, Alberga, Domenico, Alepee, Nathalie, Allen, Timothy E H, Allen, Dave, Alves, Vinicius M, Andrade, Carolina H, Auernhammer, Tyler R, Ballabio, Davide, Bell, Shannon, Benfenati, Emilio, Bhattacharya, Sudin, Bastos, Joyce V, Boyd, Stephen, Brown, J B, Capuzzi, Stephen J, Chushak, Yaroslav, Ciallella, Heather, Clark, Alex M, Consonni, Viviana, Daga, Pankaj R, Ekins, Sean, Farag, Sherif, Fedorov, Maxim, Fourches, Denis, Gadaleta, Domenico, Gao, Feng, Gearhart, Jeffery M, Goh, Garett, Goodman, Jonathan M, Grisoni, Francesca, Grulke, Christopher M, Hartung, Thomas, Hirn, Matthew, Karpov, Pavel, Korotcov, Alexandru, Lavado, Giovanna J, Lawless, Michael, Li, Xinhao, Luechtefeld, Thomas, Lunghini, Filippo, Mangiatordi, Giuseppe F, Marcou, Gilles, Marsh, Dan, Martin, Todd, Mauri, Andrea, Muratov, Eugene N, Myatt, Glenn J, Nguyen, Dac-Trung, Nicolotti, Orazio, Note, Reine, Pande, Paritosh, Parks, Amanda K, Peryea, Tyler, Polash, Ahsan H, Rallo, Robert, Roncaglioni, Alessandra, Rowlands, Craig, Ruiz, Patricia, Russo, Daniel P, Sayed, Ahmed, Sayre, Risa, Sheils, Timothy, Siegel, Charles, Silva, Arthur C, Simeonov, Anton, Sosnin, Sergey, Southall, Noel, Strickland, Judy, Tang, Yun, Teppen, Brian, Tetko, Igor V, Thomas, Dennis, Tkachenko, Valery, Todeschini, Roberto, Toma, Cosimo, Tripodi, Ignacio, Trisciuzzi, Daniela, Tropsha, Alexander, Varnek, Alexandre, Vukovic, Kristijan, Wang, Zhongyu, Wang, Liguo, Waters, Katrina M, Wedlake, Andrew J, Wijeyesakere, Sanjeeva J, Wilson, Dan, Xiao, Zijun, Yang, Hongbin, Zahoranszky-Kohalmi, Gergely, Zakharov, Alexey V, Zhang, Fagen F, Zhang, Zhen, Zhao, Tongan, Zhu, Hao, Zorn, Kimberley M, Casey, Warren, Kleinstreuer, Nicole C, Mansouri, K, Karmaus, A, Fitzpatrick, J, Patlewicz, G, Pradeep, P, Alberga, D, Alepee, N, Allen, T, Allen, D, Alves, V, Andrade, C, Auernhammer, T, Ballabio, D, Bell, S, Benfenati, E, Bhattacharya, S, Bastos, J, Boyd, S, Brown, J, Capuzzi, S, Chushak, Y, Ciallella, H, Clark, A, Consonni, V, Daga, P, Ekins, S, Farag, S, Fedorov, M, Fourches, D, Gadaleta, D, Gao, F, Gearhart, J, Goh, G, Goodman, J, Grisoni, F, Grulke, C, Hartung, T, Hirn, M, Karpov, P, Korotcov, A, Lavado, G, Lawless, M, Li, X, Luechtefeld, T, Lunghini, F, Mangiatordi, G, Marcou, G, Marsh, D, Martin, T, Mauri, A, Muratov, E, Myatt, G, Nguyen, D, Nicolotti, O, Note, R, Pande, P, Parks, A, Peryea, T, Polash, A, Rallo, R, Roncaglioni, A, Rowlands, C, Ruiz, P, Russo, D, Sayed, A, Sayre, R, Sheils, T, Siegel, C, Silva, A, Simeonov, A, Sosnin, S, Southall, N, Strickland, J, Tang, Y, Teppen, B, Tetko, I, Thomas, D, Tkachenko, V, Todeschini, R, Toma, C, Tripodi, I, Trisciuzzi, D, Tropsha, A, Varnek, A, Vukovic, K, Wang, Z, Wang, L, Waters, K, Wedlake, A, Wijeyesakere, S, Wilson, D, Xiao, Z, Yang, H, Zahoranszky-Kohalmi, G, Zakharov, A, Zhang, F, Zhang, Z, Zhao, T, Zhu, H, Zorn, K, Casey, W, Kleinstreuer, N, Mansouri, Kamel, Karmaus, Agnes L, Fitzpatrick, Jeremy, Patlewicz, Grace, Pradeep, Prachi, Alberga, Domenico, Alepee, Nathalie, Allen, Timothy E H, Allen, Dave, Alves, Vinicius M, Andrade, Carolina H, Auernhammer, Tyler R, Ballabio, Davide, Bell, Shannon, Benfenati, Emilio, Bhattacharya, Sudin, Bastos, Joyce V, Boyd, Stephen, Brown, J B, Capuzzi, Stephen J, Chushak, Yaroslav, Ciallella, Heather, Clark, Alex M, Consonni, Viviana, Daga, Pankaj R, Ekins, Sean, Farag, Sherif, Fedorov, Maxim, Fourches, Denis, Gadaleta, Domenico, Gao, Feng, Gearhart, Jeffery M, Goh, Garett, Goodman, Jonathan M, Grisoni, Francesca, Grulke, Christopher M, Hartung, Thomas, Hirn, Matthew, Karpov, Pavel, Korotcov, Alexandru, Lavado, Giovanna J, Lawless, Michael, Li, Xinhao, Luechtefeld, Thomas, Lunghini, Filippo, Mangiatordi, Giuseppe F, Marcou, Gilles, Marsh, Dan, Martin, Todd, Mauri, Andrea, Muratov, Eugene N, Myatt, Glenn J, Nguyen, Dac-Trung, Nicolotti, Orazio, Note, Reine, Pande, Paritosh, Parks, Amanda K, Peryea, Tyler, Polash, Ahsan H, Rallo, Robert, Roncaglioni, Alessandra, Rowlands, Craig, Ruiz, Patricia, Russo, Daniel P, Sayed, Ahmed, Sayre, Risa, Sheils, Timothy, Siegel, Charles, Silva, Arthur C, Simeonov, Anton, Sosnin, Sergey, Southall, Noel, Strickland, Judy, Tang, Yun, Teppen, Brian, Tetko, Igor V, Thomas, Dennis, Tkachenko, Valery, Todeschini, Roberto, Toma, Cosimo, Tripodi, Ignacio, Trisciuzzi, Daniela, Tropsha, Alexander, Varnek, Alexandre, Vukovic, Kristijan, Wang, Zhongyu, Wang, Liguo, Waters, Katrina M, Wedlake, Andrew J, Wijeyesakere, Sanjeeva J, Wilson, Dan, Xiao, Zijun, Yang, Hongbin, Zahoranszky-Kohalmi, Gergely, Zakharov, Alexey V, Zhang, Fagen F, Zhang, Zhen, Zhao, Tongan, Zhu, Hao, Zorn, Kimberley M, Casey, Warren, and Kleinstreuer, Nicole C

Abstract

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silica models built using existing data facilitate rapid acute tox- icity predictions without using animals. Objkctivks: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organ- ized an international collaboration to develop in silica models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50 ≤ 50 mg/kg)], and nontoxic chemicals (LD50 > 2,000 mg/kg). Mkthods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in viva results. Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in viva rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemica

Published
2021

22. CATMoS: Collaborative Acute Toxicity Modeling Suite

Author

Mansouri, Kamel, primary, Karmaus, Agnes L., additional, Fitzpatrick, Jeremy, additional, Patlewicz, Grace, additional, Pradeep, Prachi, additional, Alberga, Domenico, additional, Alepee, Nathalie, additional, Allen, Timothy E.H., additional, Allen, Dave, additional, Alves, Vinicius M., additional, Andrade, Carolina H., additional, Auernhammer, Tyler R., additional, Ballabio, Davide, additional, Bell, Shannon, additional, Benfenati, Emilio, additional, Bhattacharya, Sudin, additional, Bastos, Joyce V., additional, Boyd, Stephen, additional, Brown, J.B., additional, Capuzzi, Stephen J., additional, Chushak, Yaroslav, additional, Ciallella, Heather, additional, Clark, Alex M., additional, Consonni, Viviana, additional, Daga, Pankaj R., additional, Ekins, Sean, additional, Farag, Sherif, additional, Fedorov, Maxim, additional, Fourches, Denis, additional, Gadaleta, Domenico, additional, Gao, Feng, additional, Gearhart, Jeffery M., additional, Goh, Garett, additional, Goodman, Jonathan M., additional, Grisoni, Francesca, additional, Grulke, Christopher M., additional, Hartung, Thomas, additional, Hirn, Matthew, additional, Karpov, Pavel, additional, Korotcov, Alexandru, additional, Lavado, Giovanna J., additional, Lawless, Michael, additional, Li, Xinhao, additional, Luechtefeld, Thomas, additional, Lunghini, Filippo, additional, Mangiatordi, Giuseppe F., additional, Marcou, Gilles, additional, Marsh, Dan, additional, Martin, Todd, additional, Mauri, Andrea, additional, Muratov, Eugene N., additional, Myatt, Glenn J., additional, Nguyen, Dac-Trung, additional, Nicolotti, Orazio, additional, Note, Reine, additional, Pande, Paritosh, additional, Parks, Amanda K., additional, Peryea, Tyler, additional, Polash, Ahsan H., additional, Rallo, Robert, additional, Roncaglioni, Alessandra, additional, Rowlands, Craig, additional, Ruiz, Patricia, additional, Russo, Daniel P., additional, Sayed, Ahmed, additional, Sayre, Risa, additional, Sheils, Timothy, additional, Siegel, Charles, additional, Silva, Arthur C., additional, Simeonov, Anton, additional, Sosnin, Sergey, additional, Southall, Noel, additional, Strickland, Judy, additional, Tang, Yun, additional, Teppen, Brian, additional, Tetko, Igor V., additional, Thomas, Dennis, additional, Tkachenko, Valery, additional, Todeschini, Roberto, additional, Toma, Cosimo, additional, Tripodi, Ignacio, additional, Trisciuzzi, Daniela, additional, Tropsha, Alexander, additional, Varnek, Alexandre, additional, Vukovic, Kristijan, additional, Wang, Zhongyu, additional, Wang, Liguo, additional, Waters, Katrina M., additional, Wedlake, Andrew J., additional, Wijeyesakere, Sanjeeva J., additional, Wilson, Dan, additional, Xiao, Zijun, additional, Yang, Hongbin, additional, Zahoranszky-Kohalmi, Gergely, additional, Zakharov, Alexey V., additional, Zhang, Fagen F., additional, Zhang, Zhen, additional, Zhao, Tongan, additional, Zhu, Hao, additional, Zorn, Kimberley M., additional, Casey, Warren, additional, and Kleinstreuer, Nicole C., additional

Published
2021
Full Text
View/download PDF

28. Automated Integration of Structural, Biological and Metabolic Similarities to Improve Read-Across.

Author

Gadaleta, Domenico, Bakhtyari, Azadi Golbamaki, Lavado, Giovanna J., Roncaglioni, Alessandra, and Benfenati, Emilio

Abstract

Read-across (RAX) is a popular data gap-filling technique that uses category and analogue approaches to predict toxicological endpoints for a target. Despite its increasing relevance, RAX relies on human expert judgement and lacks a reproducible and automated protocol. It also relies only on structural similarity for identifying the analogues, while other aspects are often neglected. In this paper, we propose an automated procedure for the selection of analogues for data gap-filling. Analogues were identified by a decision algorithm that integrates three similarity metrics, each considering different toxicologically relevant aspects (i.e., structural, biological and metabolic similarity). Structural filters based on the presence of maximum common substructures (MCS) and common functional groups (FGs) were applied to narrow the chemical space for the analogue search. The procedure has been implemented as a workflow in KNIME and is freely available. The workflow provides informative tabular and graphical outputs to support toxicologists and risk assessors in drawing conclusions based on the RAX approach. The procedure has been validated for its predictive power on two datasets related to high-tier in vivo toxicological endpoints, i.e., human hepatotoxicity and druginduced liver injury (DILI). The validation results gave good accuracy values (i.e., up to 0.79 for the binary hepatotoxicity classification and up to 0.67 for the three-class DILI classification) that were higher than those returned by RAX based on the sole use of structural similarity. Results confirmed the suitability of the procedure as a source of data to support regulatory decision-making. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

30. Integrated In SilicoModels for the Prediction of No-Observed-(Adverse)-Effect Levels and Lowest-Observed-(Adverse)-Effect Levels in Rats for Sub-chronic Repeated-Dose Toxicity

Author

Gadaleta, Domenico, Marzo, Marco, Toropov, Andrey, Toropova, Alla, Lavado, Giovanna J., Escher, Sylvia E., Dorne, Jean Lou C. M., and Benfenati, Emilio

Abstract

Repeated-dose toxicity (RDT) is a critical endpoint for hazard characterization of chemicals and is assessed to derive safe levels of exposure for human health. Here we present the first attempt to model simultaneously no-observed-(adverse)-effect level (NO(A)EL) and lowest-observed-(adverse)-effect level (LO(A)EL). Classification and regression models were derived based on rat sub-chronic repeated dose toxicity data for 327 compounds from the Fraunhofer RepDose database. Multi-category classification models were built for both NO(A)EL and LO(A)EL though a consensus of statistics- and fragment-based algorithms, while regression models were based on quantitative relationships between the endpoints and SMILES-based attributes. NO(A)EL and LO(A)EL models were integrated, and predictions were compared to exclude inconsistent values. This strategy improved the performance of single models, leading to R2greater than 0.70, root-mean-square error (RMSE) lower than 0.60 (for regression models), and accuracy of 0.61–0.73 (for classification models) on the validation set, based on the endpoint and the threshold applied for selecting predictions. This study confirms the effectiveness of the modeling strategy presented here for assessing RDT of chemicals using in silicomodels.

Published
2021
Full Text
View/download PDF

31. SAR and QSAR modeling of a large collection of LD50 rat acute oral toxicity data.

Author

Gadaleta, Domenico, Vuković, Kristijan, Toma, Cosimo, Lavado, Giovanna J., Karmaus, Agnes L., Mansouri, Kamel, Kleinstreuer, Nicole C., Benfenati, Emilio, and Roncaglioni, Alessandra

Subjects
STATISTICAL models, STRUCTURE-activity relationships, RATS, CHEMICAL potential, SCIENTIFIC community, IN vivo studies, ENVIRONMENTAL protection
Abstract

The median lethal dose for rodent oral acute toxicity (LD50) is a standard piece of information required to categorize chemicals in terms of the potential hazard posed to human health after acute exposure. The exclusive use of in vivo testing is limited by the time and costs required for performing experiments and by the need to sacrifice a number of animals. (Quantitative) structure–activity relationships [(Q)SAR] proved a valid alternative to reduce and assist in vivo assays for assessing acute toxicological hazard. In the framework of a new international collaborative project, the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods and the U.S. Environmental Protection Agency's National Center for Computational Toxicology compiled a large database of rat acute oral LD50 data, with the aim of supporting the development of new computational models for predicting five regulatory relevant acute toxicity endpoints. In this article, a series of regression and classification computational models were developed by employing different statistical and knowledge-based methodologies. External validation was performed to demonstrate the real-life predictability of models. Integrated modeling was then applied to improve performance of single models. Statistical results confirmed the relevance of developed models in regulatory frameworks, and confirmed the effectiveness of integrated modeling. The best integrated strategies reached RMSEs lower than 0.50 and the best classification models reached balanced accuracies over 0.70 for multi-class and over 0.80 for binary endpoints. Computed predictions will be hosted on the EPA's Chemistry Dashboard and made freely available to the scientific community. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

32. CATMoS: Collaborative Acute Toxicity Modeling Suite

Author

Mansouri, Kamel, Karmaus, Agnes L, Fitzpatrick, Jeremy, Patlewicz, Grace, Pradeep, Prachi, Alberga, Domenico, Alepee, Nathalie, Allen, Timothy EH, Allen, Dave, Alves, Vinicius M, Andrade, Carolina H, Auernhammer, Tyler R, Ballabio, Davide, Bell, Shannon, Benfenati, Emilio, Bhattacharya, Sudin, Bastos, Joyce V, Boyd, Stephen, Brown, JB, Capuzzi, Stephen J, Chushak, Yaroslav, Ciallella, Heather, Clark, Alex M, Consonni, Viviana, Daga, Pankaj R, Ekins, Sean, Farag, Sherif, Fedorov, Maxim, Fourches, Denis, Gadaleta, Domenico, Gao, Feng, Gearhart, Jeffery M, Goh, Garett, Goodman, Jonathan M, Grisoni, Francesca, Grulke, Christopher M, Hartung, Thomas, Hirn, Matthew, Karpov, Pavel, Korotcov, Alexandru, Lavado, Giovanna J, Lawless, Michael, Li, Xinhao, Luechtefeld, Thomas, Lunghini, Filippo, Mangiatordi, Giuseppe F, Marcou, Gilles, Marsh, Dan, Martin, Todd, Mauri, Andrea, Muratov, Eugene N, Myatt, Glenn J, Nguyen, Dac-Trung, Nicolotti, Orazio, Note, Reine, Pande, Paritosh, Parks, Amanda K, Peryea, Tyler, Polash, Ahsan H, Rallo, Robert, Roncaglioni, Alessandra, Rowlands, Craig, Ruiz, Patricia, Russo, Daniel P, Sayed, Ahmed, Sayre, Risa, Sheils, Timothy, Siegel, Charles, Silva, Arthur C, Simeonov, Anton, Sosnin, Sergey, Southall, Noel, Strickland, Judy, Tang, Yun, Teppen, Brian, Tetko, Igor V, Thomas, Dennis, Tkachenko, Valery, Todeschini, Roberto, Toma, Cosimo, Tripodi, Ignacio, Trisciuzzi, Daniela, Tropsha, Alexander, Varnek, Alexandre, Vukovic, Kristijan, Wang, Zhongyu, Wang, Liguo, Waters, Katrina M, Wedlake, Andrew J, Wijeyesakere, Sanjeeva J, Wilson, Dan, Xiao, Zijun, Yang, Hongbin, Zahoranszky-Kohalmi, Gergely, Zakharov, Alexey V, Zhang, Fagen F, Zhang, Zhen, Zhao, Tongan, Zhu, Hao, Zorn, Kimberley M, Casey, Warren, and Kleinstreuer, Nicole C

Subjects
Government Agencies, 13. Climate action, Toxicity Tests, Acute, Animals, Computer Simulation, United States Environmental Protection Agency, United States, 3. Good health, Rats
Abstract

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

33. In Silico Methods for Chromosome Damage.

Author

Baderna D, Van Overmeire I, Lavado GJ, Gadaleta D, and Mertens B

Subjects
Chromosome Aberrations, DNA Damage, Humans, Mutagenicity Tests, Mutation, Chromosomes, Mutagens chemistry, Mutagens toxicity
Abstract

Due to the link with serious adverse health effects, genotoxicity is an important toxicological endpoint in each regulatory setting with respect to human health, including for pharmaceuticals. To this extent, a compound potential to induce gene mutations as well as chromosome damage needs to be addressed. For chromosome damage, i.e., the induction of structural or numerical chromosome aberrations, several in vitro and in vivo test methods are available. In order to rapidly collect toxicological data without the need for test material, several in silico tools for chromosome damage have been developed over the last years. In this chapter, a battery of freely available in silico chromosome damage prediction tools for chromosome damage is applied on a dataset of pharmaceuticals. Examples of the different outcomes obtained with the in silico battery are provided and briefly discussed. Furthermore, results for coumarin are presented in more detail as a case study. Overall, it can be concluded that although they are in general less developed than those for mutagenicity, in silico tools for chromosome damage can provide valuable information, especially when combined in a battery., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results