The Tox21 robotic platform for the assessment of environmental chemicals – from vision to reality

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Toxicity Testing in the 21st Century: A Vision and a Strategy

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Toxicity testing in the 21st century: A vision and a strategy

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Quantitative high-throughput screening: A titration-based approach that efficiently identifies biological activities in large chemical libraries

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Transforming Environmental Health Protection

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Improving the Human Hazard Characterization of Chemicals: A Tox21 Update

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In Vitro Screening of Environmental Chemicals for Targeted Testing Prioritization: The ToxCast Project

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A Quantitative High-Throughput Screening Data Analysis Pipeline for Activity Profiling

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Acetylcholinesterase Inhibition Assays for High-Throughput Screening

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Gene-environment interactions in birth defect etiology: Challenges and opportunities

Lipinski, R. J., & Krauss, R. S. (2022) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/bs.ctdb.2022.10.001 · Cited by 27

Predicting the future: opportunities and challenges for the chemical industry to apply 21st-century toxicity testing.

Settivari, R. S., Ball, N. S., Murphy, L. A., Rasoulpour, R. J., Boverhof, D. R., & Carney, E. W. (2015) · PubMed Central · Cited by 24

Elucidating Mechanisms of Toxicity Using Phenotypic Data from Primary Human Cell Systems—A Chemical Biology Approach for Thrombosis-Related Side Effects

Berg, E., Polokoff, M., O'Mahony, A., Nguyen, D., & Li, X.-T. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms16011008 · Cited by 19

Arbitrarily Accessible 3D Microfluidic Device for Combinatorial High-Throughput Drug Screening

Chen, Z., Li, W., Choi, G., Yang, X., Miao, J., Cui, L., & Guan, W. (2016) · Sensors · doi:10.3390/s16101616 · Cited by 23

Cellular Metabolism in High-Throughput In Vitro Reporter Gene Assays and Implications for the Quantitative In Vitro–In Vivo Extrapolation

Fischer, F. C., Abele, C., Henneberger, L., Klüver, N., König, M., Mühlenbrink, M., Schlichting, R., & Escher, B. I. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00037 · Cited by 24

High-Throughput Screening for Drug Combinations

Shinn, P., Chen, L., Ferrer, M., Itkin, Z., Klumpp-Thomas, C. A., McKnight, C. G., Michael, S., Mierzwa, T., Thomas, C. J., Wilson, K. M., et al. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9089-4_2 · Cited by 22

Integrative Modeling Strategies for Predicting Drug Toxicities at the eTOX Project

Sanz, F., Carrió, P., López-Massaguer, O., Capoferri, L., Kooi, D. P., Vermeulen, N. P. E., Geerke, D. P., Montanari, F., Ecker, G. F., Schwab, C. H., et al. (2015) · Molecular Informatics · doi:10.1002/minf.201400193 · Cited by 21

On the Utility of ToxCast-Based Predictive Models to Evaluate Potential Metabolic Disruption by Environmental Chemicals

Filer, D. L., Hoffman, K., Sargis, R. M., Trasande, L., & Kassotis, C. D. (2022) · Environmental Health Perspectives · doi:10.1289/ehp6779 · Cited by 21

A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols

Pradeep, P., Mansouri, K., Patlewicz, G. Y., & Judson, R. (2017) · Computational Toxicology · doi:10.1016/j.comtox.2017.09.001 · Cited by 21

Workflow for Defining Reference Chemicals for Assessing Performance of In Vitro Assays

Judson, R. (2018) · ALTEX · doi:10.14573/altex.1809281 · Cited by 21

Repurposing drugs as COVID-19 therapies: A toxicity evaluation

Ngan, D. K., Xu, T., Xia, M., Zheng, W., & Huang, R. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.04.001 · Cited by 21

Predictive ADMET study of rhodanine-3-acetic acid chalcone derivatives

da Rocha, M. N., Marinho, M. M., Teixeira, A. M. R., Marinho, E. S., & dos Santos, H. S. (2022) · Journal of the Indian Chemical Society · doi:10.1016/j.jics.2022.100535 · Cited by 19

New approach methods for testing chemicals for endocrine disruption potential

Judson, R., Friedman, K. P., Houck, K. A., Mansouri, K., Browne, P., & Kleinstreuer, N. (2018) · Current Opinion in Toxicology · doi:10.1016/j.cotox.2018.10.002 · Cited by 19

A Deep Learning-Based Quantitative Structure–Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance

Matsuzaka, Y., & Uesawa, Y. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23042141 · Cited by 18

Disclosing Environmental Ligands of L-FABP and PPARγ: Should We Re-evaluate the Chemical Safety of Hydrocarbon Surfactants?

Gong, Y., Yang, D., Liu, J., Barrett, H., Sun, J., & Peng, H. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c02898 · Cited by 19

admetSAR—A valuable tool for assisting safety evaluation

Gu, Y., Lou, C., & Tang, Y. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15339-6.00004-7 · Cited by 19

Effects of Chemicals in Reporter Gene Bioassays with Different Metabolic Activities Compared to Baseline Toxicity

Huchthausen, J., Braasch, J., Escher, B. I., König, M., & Henneberger, L. (2024) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.4c00017 · Cited by 18

Generic and Automated Data Evaluation in Analytical Measurement

Adam, M., Fleischer, H., & Thurow, K. (2016) · SLAS TECHNOLOGY · doi:10.1177/2211068216672613 · Cited by 18

Flexible Automation System for Determination of Elemental Composition of Incrustations in Clogged Biliary Endoprostheses Using ICP-MS

Fleischer, H., Ramani, K., Blitti, K., Roddelkopf, T., Warkentin, M., Behrend, D., & Thurow, K. (2017) · SLAS TECHNOLOGY · doi:10.1177/2472630317727451 · Cited by 18

Embryonic vascular disruption adverse outcomes: Linking high throughput signaling signatures with functional consequences

Ellis‐Hutchings, R. G., Settivari, R. S., McCoy, A. T., Kleinstreuer, N., Franzosa, J. A., Knudsen, T. B., & Carney, E. W. (2017) · Reproductive Toxicology · doi:10.1016/j.reprotox.2017.05.005 · Cited by 18

Exploring Potential Carcinogenic Activity of Per- and Polyfluorinated Alkyl Substances Utilizing High-Throughput Toxicity Screening Data

Singh, N., & Hsieh, C. (2021) · International Journal of Toxicology · doi:10.1177/10915818211010490 · Cited by 18

High-Throughput Assessment of the Abiotic Stability of Test Chemicals in In Vitro Bioassays

Huchthausen, J., Henneberger, L., Mälzer, S., Nicol, B., Sparham, C., & Escher, B. I. (2022) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00030 · Cited by 17

A mechanistic framework for integrating chemical structure and high-throughput screening results to improve toxicity predictions

Nelms, M., Mellor, C. L., Enoch, S. J., Judson, R., Patlewicz, G. Y., Richard, A. M., Madden, J. M., Cronin, M., & Edwards, S. W. (2018) · Computational Toxicology · doi:10.1016/j.comtox.2018.08.003 · Cited by 17

Advances in high-throughput screening technology for toxicology

Hsu, C. W., Huang, R., Attene‐Ramos, M. S., Austin, C. P., Simeonov, A. M., & Xia, M. (2017) · International Journal of Risk Assessment and Management · doi:10.1504/ijram.2017.082562 · Cited by 17

In-vitro antiviral activity and in-silico targeted study of quinoline-3-carboxylate derivatives against SARS-Cov-2 isolate

Mittal, R. K., Purohit, P., Murugesan, S., Muzaffar-Ur-Rehman, M., Taramelli, D., Signorini, L., Dolci, M., & Basilico, N. (2023) · Molecular Diversity · doi:10.1007/s11030-023-10703-w · Cited by 16

Analytical Quality Evaluation of the Tox21 Compound Library

Richard, A. M., Tao, D., LeClair, C. A., Leister, W., Tretyakov, K. V., White, E., Lewis, K. C., Sefler, A. M., Shinn, P., Collins, B. J., et al. (2024) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.4c00330 · Cited by 18

Harnessing In Silico, In Vitro, and In Vivo Data to Understand the Toxicity Landscape of Polycyclic Aromatic Compounds (PACs)

Hsieh, J., Sedykh, A., Mutlu, E., Germolec, D. R., Auerbach, S. S., & Rider, C. V. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00213 · Cited by 17

Toxicity prediction: An application of alternative testing and computational toxicology in contaminated groundwater sites in Taiwan

Arcega, R. D., Chen, R.-J., Chih, P., Huang, Y.-H., Chang, W., Kong, T.-K., Lee, C.-C., Mahmudiono, T., Tsui, C.-C., Hou, W.-C., et al. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.116982 · Cited by 16

A cross-platform modular software solution for automated data evaluation applied in elemental and structural mass spectrometry

Fleischer, H., Adam, M., & Thurow, K. (2015) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2015.7294172 · Cited by 15

Prioritization of contaminants and biological process targets in the North Sea using toxicity data from ToxCast

Barbosa, J., Schamphelaere, K. D., Janssen, C., & Asselman, J. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.144157 · Cited by 15

Occurrence, risk assessment, and priority screening of 102 emerging contaminants in the Yangtze River estuary and Hangzhou Bay, China

Liu, S., Liu, K., Zheng, N., Ma, X., Yao, W., Liu, X., Yao, Z., & Zhang, H. (2025) · Environmental Research · doi:10.1016/j.envres.2025.122336 · Cited by 14

Emerging contaminants and nanoplastics in the water environment: a matter of rising concern

Ginebreda, A. (2021) · doi:10.20517/wecn.2021.02 · Cited by 14

Identification of nonmonotonic concentration-responses in Tox21 high-throughput screening estrogen receptor assays

Shi, Z., Xia, M., Xiao, S., & Zhang, Q. (2022) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2022.116206 · Cited by 15

Systematic transcriptome-wide meta-analysis across endocrine disrupting chemicals reveals shared and unique liver pathways, gene networks, and disease associations

Zamora, Z., Wang, S. S.-M., Chen, Y.-W., Diamante, G., & Yang, X. (2023) · Environment International · doi:10.1016/j.envint.2023.108339 · Cited by 17

Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals

Maertens, A., Luechtefeld, T. H., Knight, J., & Härtung, T. (2024) · ALTEX · doi:10.14573/altex.2312291 · Cited by 25

Identification of Environmental Compounds That May Trigger Early Female Puberty by Activating Human GnRHR and KISS1R

Yang, S., Zhang, L., Khan, K., Travers, J., Huang, R., Jovanovic, V. M., Veeramachaneni, R., Sakamuru, S., Tristan, C. A., Davis, E. E., et al. (2024) · Endocrinology · doi:10.1210/endocr/bqae103 · Cited by 14

Confirmation of high-throughput screening data and novel mechanistic insights into VDR-xenobiotic interactions by orthogonal assays

Mahapatra, D., Franzosa, J. A., Roell, K. R., Kuenemann, M. A., Houck, K. A., Reif, D. M., Fourches, D., & Kullman, S. W. (2018) · Scientific Reports · doi:10.1038/s41598-018-27055-3 · Cited by 14

Using adverse outcome pathways to contextualise (Q)SAR predictions for reproductive toxicity – A case study with aromatase inhibition

Myden, A., Hill, E., & Fowkes, A. (2022) · Reproductive Toxicology · doi:10.1016/j.reprotox.2022.01.004 · Cited by 14

Flexible software solution for rapid manual and automated data evaluation in ICP-MS

Fleischer, H., Adam, M., & Thurow, K. (2015) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2015.7151518 · Cited by 14

Identification of acetylcholinesterase inhibitors using homogenous cell‐based assays in quantitative high‐throughput screening platforms

Li, S., Huang, R., Solomon, S., Liu, Y., Zhao, B., Santillo, M. F., & Xia, M. (2017) · Biotechnology Journal · doi:10.1002/biot.201600715 · Cited by 14

The Toxmatrix: Chemo-Genomic Profiling Identifies Interactions That Reveal Mechanisms of Toxicity

Tong, Z., Huang, R., Wang, Y., Klumpp-Thomas, C. A., Braisted, J. C., Itkin, Z., Shinn, P., Xia, M., Simeonov, A. M., & Gerhold, D. L. (2017) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.7b00290 · Cited by 13

Quality Control of Quantitative High Throughput Screening Data

Shockley, K. R., Gupta, S., Harris, S. F., Lahiri, S. N., & Peddada, S. D. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00387 · Cited by 13

Exploring non-linear distance metrics in the structure–activity space: QSAR models for human estrogen receptor

Balabin, I. A., & Judson, R. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0300-0 · Cited by 15

Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure

Luo, X., Xu, T., Ngan, D. K., Xia, M., Zhao, J., Sakamuru, S., Simeonov, A. M., & Huang, R. (2024) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2024.117098 · Cited by 12

One science-driven approach for the regulatory implementation of alternative methods: A multi-sector perspective

Negro, G. D., Eskes, C., Belz, S., Bertein, C., Chlebus, M., Corvaro, M., Corvi, R., Dhalluin, S., Halder, M., Harvey, J., et al. (2018) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2018.08.002 · Cited by 12

Integrative Data Mining Approach: Case Study with Adverse Outcome Pathway Network Leading to Pulmonary Fibrosis

Jeong, J., Kim, D., & Choi, J. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00325 · Cited by 12

Efficiency of pharmaceutical toxicity prediction in computational toxicology

Uesawa, Y. (2024) · Toxicological Research · doi:10.1007/s43188-023-00215-y · Cited by 12

Identification of compounds that modulate retinol signaling using a cell-based qHTS assay

Chen, Y., Sakamuru, S., Huang, R., Reese, D. H., & Xia, M. (2016) · Toxicology in Vitro · doi:10.1016/j.tiv.2016.01.011 · Cited by 12

Probabilistic diagram for designing chemicals with reduced potency to incur cytotoxicity

Shen, L. Q., Judson, R., Melnikov, F., Roethle, J., Gudibanda, A., Zimmerman, J. B., & Anastas, P. T. (2016) · Green Chemistry · doi:10.1039/c6gc01058j · Cited by 12

Use of Tox21 Screening Data to Evaluate the COVID-19 Drug Candidates for Their Potential Toxic Effects and Related Pathways

Sakamuru, S., Huang, R., & Xia, M. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.935399 · Cited by 11

Synthesizing the impact of sea-dumped munition and related chemicals on humans and the environment

Barbosa, J., Asselman, J., & Janssen, C. (2023) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2023.114601 · Cited by 11

Revisiting the approaches to DNA damage detection in genetic toxicology: insights and regulatory implications

Alnasser, S. M. (2025) · BioData Mining · doi:10.1186/s13040-025-00447-8 · Cited by 15

Editor’s Highlight: Development of anIn vitroAssay Measuring Uterine-Specific Estrogenic Responses for Use in Chemical Safety Assessment

Miller, M. M., Alyea, R. A., Sommer, C., Doheny, D. L., Rowley, S. M., Childs, K. M., Balbuena, P., Ross, S. M., Dong, J., Sun, B., et al. (2016) · Toxicological Sciences · doi:10.1093/toxsci/kfw152 · Cited by 11

Automation for Life Science Laboratories

Thurow, K. (2021) · Advances in biochemical engineering, biotechnology · doi:10.1007/10_2021_170 · Cited by 10

Revolutionizing Pharmaceutical Industry: The Radical Impact of Artificial Intelligence and Machine Learning

Chhina, A., Trehan, K., Saini, M., Thakur, S., Kaur, M., Shahtaghi, N. R., Shivgotra, R., Soni, B., Modi, A., Bakrey, H., et al. (2023) · Current Pharmaceutical Design · doi:10.2174/1381612829666230807161421 · Cited by 12

Profiling 58 compounds including cosmetic-relevant chemicals using ToxRefDB and ToxCast

Pham, L. L., Truong, L., Ouédraogo, G., Loisel-Joubert, S., Martin, M. T., & Friedman, K. P. (2019) · Food and Chemical Toxicology · doi:10.1016/j.fct.2019.110718 · Cited by 10

Whole Animal Feeding FLat (WAFFL): a complete and comprehensive validation of a novel, high-throughput fly experimentation system

Jaime, M., Salem, G. H., Martinez, D. J., Karott, S., Flores, A., Palmer, C. D., Mahadevaraju, S., Krynitsky, J., Garmendia‐Cedillos, M., Anderson, S., et al. (2023) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkad012 · Cited by 10

High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures

Asako, Y., & Uesawa, Y. (2017) · Molecules · doi:10.3390/molecules22040675 · Cited by 10

Challenges for Integrating Immunotoxicology into the Twenty-First-Century Toxicology Testing Paradigm

Karmaus, P. W. F., & Karmaus, A. L. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8549-4_23 · Cited by 9

Efficient application of dual-arm robots in analytical measurements using motion frames

Chu, X., Klös, M., Thurow, K., & Fleischer, H. (2018) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2018.8409532 · Cited by 9

Bioactivity Signatures of Drugs vs. Environmental Chemicals Revealed by Tox21 High-Throughput Screening Assays

Ngan, D. K., Ye, L., Wu, L., Xia, M., Rossoshek, A., Simeonov, A. M., & Huang, R. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00050 · Cited by 9

Identifying Chemical Groups for Biomonitoring

Krowech, G., Hoover, S., Plummer, L. E., Sandy, M. S., Zeise, L. A., & Solomon, G. M. (2016) · Environmental Health Perspectives · doi:10.1289/ehp537 · Cited by 9

Analysis of SIKVAV’s receptor affinity, pharmacokinetics, and pharmacological characteristics: a matrikine with potent biological function

Filgueiras, L. A., de Andrade, F. D. C. P., Horita, S. I. M., Shirsat, S. D., Achal, V., Rai, M., Henriques‐Pons, A., & Mendes, A. N. (2024) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2024.2313709 · Cited by 8

Identification and Profiling of Environmental Chemicals That Inhibit the TGFβ/SMAD Signaling Pathway

Wei, Z., Sakamuru, S., Zhang, L., Zhao, J., Huang, R., Kleinstreuer, N., Chen, Y., Shu, Y., Knudsen, T. B., & Xia, M. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00228 · Cited by 8

The digital evolution in toxicology: pioneering computational education for emerging challenges

Wang, J., & Li, J. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06163-x · Cited by 7

Emerging advances in intestinal models for in vitro preclinical research

Adesina, P. A., Ooka, M., TeKrony, C., & Xia, M. (2025) · American Journal of Physiology-Gastrointestinal and Liver Physiology · doi:10.1152/ajpgi.00087.2025 · Cited by 7

Gaining confidence in in vitro toxicity data with measured exposure concentrations

Henneberger, L., Bardehly, S., König, M., & Escher, B. I. (2025) · NAM Journal · doi:10.1016/j.namjnl.2025.100048 · Cited by 7

Development and validation of CYP26A1 inhibition assay for high‐throughput screening

Sakamuru, S., Ma, D., Pierro, J. D., Baker, N., Kleinstreuer, N., Cali, J. J., Knudsen, T. B., & Xia, M. (2024) · Biotechnology Journal · doi:10.1002/biot.202300659 · Cited by 7

Applying new approach methodologies to assess next-generation tobacco and nicotine products

Thorne, D., McHugh, D., Simms, L., Lee, K. M., Fujimoto, H., Moses, S., & Gaça, M. (2024) · Frontiers in Toxicology · doi:10.3389/ftox.2024.1376118 · Cited by 7

In Situ Quantitative Monitoring of Adsorption from Aqueous Phase by UV–vis Spectroscopy: Implication for Understanding of Heterogeneous Processes

Yang, X., Gong, B., Chen, W. Q., Chen, J., Qian, C., Lu, R. M., Min, Y., Jiang, T., Li, L., & Yu, H. (2024) · Advanced Science · doi:10.1002/advs.202402732 · Cited by 7

Evaluation of QSAR models for tissue-specific predictive toxicology and risk assessment of military-relevant chemical exposures: A systematic review

Fisher, J. L., Williams, K. T., Schneider, L. J., Keebaugh, A. J., German, C. L., Hott, A. M., Singh, N., & Clewell, R. A. (2024) · Computational Toxicology · doi:10.1016/j.comtox.2024.100329 · Cited by 7

Recent developments in in vitro toxicology: perspectives of European research and Tox21

Marchan, R., van Thriel, C., & Bolt, H. M. (2013) · Archives of Toxicology · doi:10.1007/s00204-013-1164-8 · Cited by 7

Coupled molecular design diagrams to guide safer chemical design with reduced likelihood of perturbing the NRF2-ARE antioxidant pathway and inducing cytotoxicity

Shen, L. Q., Melnikov, F., Roethle, J., Gudibanda, A., Judson, R., Zimmerman, J. B., & Anastas, P. T. (2016) · Green Chemistry · doi:10.1039/c6gc02073a · Cited by 7

Incorporating Epigenetics Into a Risk Assessment Framework

Cote, I., Vandenberg, J. J., Druwe, I. L., & Angrish, M. M. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812433-8.00013-7 · Cited by 7

Embryonic vascular disruption adverse outcomes: Linking high throughput signaling signatures with functional consequences

Ellis‐Hutchings, R. G., Settivari, R. S., McCoy, A. T., Kleinstreuer, N., Franzosa, J. A., Knudsen, T. B., & Carney, E. W. (2017) · Reproductive Toxicology · doi:10.1016/j.reprotox.2017.04.003 · Cited by 7

Predictive Modeling of Tox21 Data

Huang, R. (2019) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-030-16443-0_14 · Cited by 6

Integrating NHANES and toxicity forecaster data to compare pesticide exposure and bioactivity by farmwork history and US citizenship

Forté, C. A., Millar, J. A., & Colacino, J. A. (2023) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/s41370-023-00583-5 · Cited by 6

The Application of Omics Data to the Development of AOPs

McBride, M. T. (2018) · Springer eBooks · doi:10.1007/978-3-319-66084-4_9 · Cited by 6

Conditionally Reprogrammed Cells and Robotic High-Throughput Screening for Precision Cancer Therapy

Alkhilaiwi, F. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.761986 · Cited by 6

Identifying candidate reference chemicals for in vitro testing of the retinoid pathway for predictive developmental toxicity

Baker, N. (2022) · ALTEX · doi:10.14573/altex.2202231 · Cited by 6

Future platforms for toxicity testing

McBride, M. T. (2017) · International Journal of Risk Assessment and Management · doi:10.1504/ijram.2017.082556 · Cited by 6

Poor recognition of O6-isopropyl dG by MGMT triggers double strand break-mediated cell death and micronucleus induction inFANC-deficient cells

Hashimoto, K., Sharma, V., Sasanuma, H., Tian, X., Takata, M., Takeda, S., Swenberg, J. A., & Nakamura, J. (2016) · Oncotarget · doi:10.18632/oncotarget.10928 · Cited by 5

The Changing Paradigm in Preclinical Toxicology: in vitro and in silico Methods in Liver Toxicity Evaluations

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