Article
Environmental Sciences
M. Sigurnjak Bures, S. Ukic, M. Cvetnic, V. Prevaric, M. Markic, M. Rogosic, H. Kusic, T. Bolanca
Summary: The study focuses on developing QSAR models to predict the toxicity of binary mixtures towards bioluminescent bacteria Vibrio fischeri. The models successfully predict toxicity and identify factors influencing toxicity levels. The analysis of descriptors in the models provides insight into toxic mechanisms of binary systems.
ENVIRONMENTAL POLLUTION
(2021)
Article
Toxicology
Sanjeeva J. Wijeyesakere, Tyler Auernhammer, Amanda Parks, Dan Wilson
Summary: We developed a mechanistic machine-learning model to predict mammalian acute oral toxicity. The model was trained using a rat toxicity database and accurately predicted the oral LD50 of compounds. It showed high sensitivity and has potential applications in assessing highly toxic substances.
TOXICOLOGICAL SCIENCES
(2023)
Article
Management
Gianluca Biggi, Elisa Giuliani, Arianna Martinelli, Emilio Benfenati
Summary: Researchers propose a method that combines patent analysis with computational toxicology to measure the chemical toxicity components within patents. By analyzing the toxicity of ten hazardous chemicals and comparing them with other groups of chemical patents, they suggest that this method could have significant policy implications for future research.
Article
Environmental Sciences
Giovanna J. Lavado, Diego Baderna, Domenico Gadaleta, Marta Ultre, Kunal Roy, Emilio Benfenati
Summary: Research interest in environmental toxicity assessment using T. platyurus has increased, but there are currently no computational models to predict acute toxicity in this organism. This study developed QSAR models for predicting acute toxicity in T. platyurus, following OECD principles and using advanced machine learning techniques to achieve promising statistical quality in the dataset.
Article
Toxicology
Joshua A. Harrill, Logan J. Everett, Derik E. Haggard, Thomas Sheffield, Joseph L. Bundy, Clinton M. Willis, Russell S. Thomas, Imran Shah, Richard S. Judson
Summary: This study evaluated the TempO-Seq assay for HTTr concentration-response screening of chemicals in the human-derived MCF7 cell model. A robust bioinformatics pipeline was developed using open-source tools, along with a novel gene expression signature-based concentration-response modeling approach. Analysis showed highly reproducible differential gene expression signatures, along with in vitro transcriptional biological pathway altering concentrations closely aligned with previous high-throughput screening assays.
TOXICOLOGICAL SCIENCES
(2021)
Article
Environmental Sciences
Warangkana Arpornchayanon, Subhawat Subhawa, Kanjana Jaijoy, Nirush Lertprasertsuk, Noppamas Soonthornchareonnon, Seewaboon Sireeratawong
Summary: This study investigated the acute and chronic toxicities of the Triphala formula in a rat model. The results showed that long-term use of Triphala in rats is safe, with no toxic effects observed.
Review
Biochemistry & Molecular Biology
Tao Huang, Guohui Sun, Lijiao Zhao, Na Zhang, Rugang Zhong, Yongzhen Peng
Summary: Nitroaromatic compounds are widely present in the environment due to industrial use, posing potential threats to human health and the environment. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to predict their toxicity and reduce animal testing. However, systematic reviews on the QSAR modeling of NACs toxicity are less reported.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Toxicology
Gul Karaduman, Feyza Kelleci Celik
Summary: By creating a QSAR model, the risk level of drugs during pregnancy can be predicted, providing guidance on the safe use of pharmaceuticals without the need for animal tests or clinical trials on pregnant women.
JOURNAL OF APPLIED TOXICOLOGY
(2023)
Article
Environmental Sciences
Justin Scott, Ryan Grewe, Matteo Minghetti
Summary: This study compared the sensitivity of the Fathead minnow fish embryo acute toxicity (FET) test, the fish gill cells (RTgill-W1) in vitro assay, and the fish larvae acute toxicity test for evaluating whole-effluent toxicity (WET) testing. The results showed a significant correlation between FET and fish larvae tests, while no correlation was found between RTgill-W1 cells and FET. The in vitro alternative models demonstrated good predictability of toxicity in fish for WET chemicals.
ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY
(2022)
Article
Biochemistry & Molecular Biology
Fjodor Melnikov, Lennart T. T. Anger, Catrin Hasselgren
Summary: Due to challenges with historical data and assay formats, in silico models for safety endpoints often rely on discretized data. However, these models have limitations that affect compound design. In this study, a consistent data inference approach was used to estimate IC50 for hERG inhibition. The resulting models showed high accuracy and can be valuable in pharmaceutical projects for compound ranking and evaluation against specific inhibition thresholds.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Review
Toxicology
Niels Hadrup, Marie Frederiksen, Eva B. Wedebye, Nikolai G. Nikolov, Tanja K. Caroe, Jorid B. Sorli, Karen B. Frydendall, Biase Liguori, Camilla S. Sejbaek, Peder Wolkoff, Esben M. Flachs, Vivi Schlunssen, Harald W. Meyer, Per A. Clausen, Karin S. Hougaard
Summary: The study revealed that some substances in spray cleaning products may induce asthma, while also identifying significant knowledge gaps for most substances. More data are needed to prevent safety issues in future spray cleaning products, and prioritizing substances for further testing is recommended.
JOURNAL OF APPLIED TOXICOLOGY
(2022)
Article
Medicine, Legal
Jessica C. Graham, Maryann Rodas, Jedd Hillegass, Gene Schulze
Summary: Acute oral toxicity (AOT) information is used for compound classification and hazard evaluation, with in silico models being developed to predict AOT and reduce reliance on animal testing. Using historical data, the study found that these in silico models can effectively identify compounds with low acute oral toxicity and assist in determining starting doses for in vivo AOT studies.
REGULATORY TOXICOLOGY AND PHARMACOLOGY
(2021)
Article
Chemistry, Medicinal
Tarapong Srisongkram
Summary: A stacked ensemble quantitative read-across structure-activity relationship algorithm was developed for predicting skin irritation toxicity, and its reliability and accuracy were validated using validation and test datasets.
CHEMICAL RESEARCH IN TOXICOLOGY
(2023)
Article
Environmental Sciences
Simon Hansul, Andreas Fettweis, Erik Smolders, Karel De Schamphelaere
Summary: The study tested the prediction of Cu-Ni-Zn mixture toxicity to Daphnia magna populations using the DEB-IBM model, finding that mixture effects were concentration and endpoint dependent. The DEB-IBM accurately predicted effects on 6-week density, including antagonistic effects at high concentrations, while initial population growth rate effects were more challenging to predict.
ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY
(2021)
Article
Environmental Sciences
Guohui Sun, Yifan Zhang, Luyu Pei, Yuqing Lou, Yao Mu, Jiayi Yun, Feifan Li, Yachen Wang, Zhaoqi Hao, Sha Xi, Chen Li, Chuhan Chen, Lijiao Zhao, Na Zhang, Rugang Zhong, Yongzhen Peng
Summary: The study developed computational methods for evaluating acute oral toxicity of PAHs in rats, identified main influencing factors, compared with existing predictions, and demonstrated the prediction reliability.
ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY
(2021)
Letter
Environmental Sciences
Skylar W. Marvel, John S. House, Matthew Wheeler, Kuncheng Song, Yi-Hui Zhou, Fred A. Wright, Weihsueh A. Chiu, Ivan Rusyn, Alison Motsinger-Reif, David M. Reif
ENVIRONMENTAL HEALTH PERSPECTIVES
(2021)
Article
Environmental Sciences
Alina T. Roman-Hubers, Thomas J. McDonald, Erin S. Baker, Weihsueh A. Chiu, Ivan Rusyn
Summary: This study examined the potential of IMS-MS as a high-throughput method for the chemical characterization of crude oils, finding it to be either equal or better than GC-MS in classifying the origins of crude oils and greatly increasing sample analysis throughput. The study demonstrated the utility of IMS-MS for rapid fingerprinting of complex samples and showed its advantages over traditional GC-MS-based analyses in emergency decision-making situations.
ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY
(2021)
Article
Pharmacology & Pharmacy
Courtney Sakolish, Celeste E. Reese, Yu-Syuan Luo, Alan Valdiviezo, Mark E. Schurdak, Albert Gough, D. Lansing Taylor, Weihsueh A. Chiu, Lawrence A. Vernetti, Ivan Rusyn
Summary: The human microfluidic liver acinus microphysiology system (LAMPS) was evaluated for drug pharmacokinetics and toxicology, showing robustness and reproducibility when seeded with primary human hepatocytes or iPSC-derived hepatocytes, and demonstrating more physiologically and clinically relevant effects compared to 2D cultures.
Review
Biotechnology & Applied Microbiology
Samantha Goodman, Grace Chappell, Kathryn Z. Guyton, Igor P. Pogribny, Ivan Rusyn
Summary: This study systematically reviewed the epigenetic alterations induced by occupational and environmental human carcinogens. The evidence of epigenetic effects varied across different agents, with DNA methylation being the most studied area and histone modifications/chromatin state alterations being less investigated. Future studies should consider comprehensive study designs and investigate the persistence of effects following cessation of exposure.
MUTATION RESEARCH-REVIEWS IN MUTATION RESEARCH
(2022)
Article
Engineering, Environmental
Heather L. Ciallella, Daniel P. Russo, Swati Sharma, Yafan Li, Eddie Sloter, Len Sweet, Heng Huang, Hao Zhu
Summary: This study developed a predictive and explainable computational model for evaluating the developmental toxicity potential of chemicals. The model was constructed by combining data from public repositories and literature sources, and successfully predicted developmental toxicity in external validation.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2022)
Article
Chemistry, Physical
Ali Abou Taka, Shao-Yu Lu, Duncan Gowland, Tim J. Zuehlsdorff, Hector H. Corzo, Aurora Pribram-Jones, Liang Shi, Hrant P. Hratchian, Christine M. Isborn
Summary: The simulation of optical spectra is essential but traditional methods may lead to state mixings and inaccurate description. In this study, an alternative method using self-consistent field and maximum overlap model is proposed, which produces spectra more aligned with vertical gradient and molecular dynamics. The study warns against using excited-state adiabatic Hessian in simulation and showcases three alternatives.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Environmental Sciences
Alan Valdiviezo, Noor A. Aly, Yu-Syuan Luo, Alexandra Cordova, Gaston Casillas, MaKayla Foster, Erin S. Baker, Ivan Rusyn
Summary: PFAS are widely present in water and require analytical methods for rapid comprehensive assessment and fingerprinting. After a fire incident, PFAS-containing firefighting foams were released into water, raising concerns about the contamination level. Untargeted LC-IMS-MS analysis revealed additional PFAS in the water samples, improving our understanding of PFAS presence in complex environmental samples.
JOURNAL OF ENVIRONMENTAL SCIENCES
(2022)
Article
Pharmacology & Pharmacy
Emily Marques, Marisa Pfohl, Wei Wei, Giuseppe Tarantola, Lucie Ford, Ogochukwu Amaeze, Jessica Alesio, Sangwoo Ryu, Xuelian Jia, Hao Zhu, Geoffrey D. Bothun, Angela Slitt
Summary: It has been found that certain per- and polyfluoroalkyl substances (PFAS) are associated with hepatic steatosis. Shorter chain PFAS and alternative PFAS are more potent gene inducers and should be evaluated for their potential health effects in humans.
TOXICOLOGY AND APPLIED PHARMACOLOGY
(2022)
Article
Engineering, Environmental
Xuelian Jia, Xia Wen, Daniel P. Russo, Lauren M. Aleksunes, Hao Zhu
Summary: This study developed an adverse outcome pathway (AOP) to predict hepatotoxicity using computational modeling and in vitro assays. The mechanistic hepatotoxicity model showed good predictability for compounds tested, and the strategy can be expanded to develop predictive models for other complex toxicities.
JOURNAL OF HAZARDOUS MATERIALS
(2022)
Article
Chemistry, Physical
Tong Wang, Dimitrios Bitounis, Philip Demokritou, Xuelian Jia, Heng Huang, Hao Zhu
Summary: Modern nanotechnology provides efficient and cost-effective nanomaterials, but concerns about nanotoxicity in humans are rising. Traditional animal testing for nanotoxicity is expensive and time-consuming. Modeling studies using machine learning approaches offer a promising alternative, but the complex structures of nanomaterials make them difficult to annotate and quantify. This study addresses this issue by constructing a virtual graphene library using nanostructure annotation techniques and demonstrates good predictivity in toxicity-related endpoints.
Review
Chemistry, Multidisciplinary
Xiliang Yan, Tongtao Yue, David A. A. Winkler, Yongguang Yin, Hao Zhu, Guibin Jiang, Bing Yan
Summary: Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. This review discusses the role of artificial intelligence (AI) and molecular simulation in transforming nanotoxicity data into critical information, by constructing quantitative nanostructure-toxicity relationships and elucidating toxicity-related molecular mechanisms. Several obstacles must be overcome for AI and molecular simulation to have their full impact, including the lack of high-quality nanomaterials, standardized nanotoxicity data, model-friendly databases, specific and universal nanodescriptors, and realistic simulation of nanomaterials. The review also provides a comprehensive summary of current capability gaps and tools required to fill these gaps, as well as a perspective on future trends and challenges.
Article
Engineering, Environmental
Elena Chung, Daniel P. Russo, Heather L. Ciallella, Yu-Tang Wang, Min Wu, Lauren M. Aleksunes, Hao Zhu
Summary: Traditional methodologies for assessing chemical toxicity are expensive and time-consuming, but computational modeling approaches, particularly QSAR models, provide a low-cost alternative. However, conventional QSAR models have limited training data and poor predictivity for new compounds. This study developed data-driven models for carcinogenicity and successfully identified potential new human carcinogens.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2023)
Review
Engineering, Environmental
Xuelian Jia, Tong Wang, Hao Zhu
Summary: Chemical toxicity evaluations have critical impact on human health. Computational toxicology utilizing machine learning and deep learning techniques is a promising alternative approach. However, many toxicity models are difficult to interpret, which hampers their use in chemical risk assessments.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2023)
Article
Engineering, Environmental
Daniel P. Russo, Lauren M. Aleksunes, Katy Goyak, Hua Qian, Hao Zhu
Summary: Animal models are not effective in predicting hepatotoxicity in humans, leading to the development of biological pathway-based alternatives such as in vitro assays. Public screening programs have tested thousands of chemicals using high-throughput screening assays. Developing pathway-based models for complex toxicities like hepatotoxicity remains challenging. This study aimed to develop a computational strategy for developing pathway-based models for complex toxicities.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2023)
Article
Engineering, Environmental
Oladayo Oladeji, Mariana Saitas, Toriq Mustapha, Natalie M. M. Johnson, Weihsueh A. A. Chiu, Ivan Rusyn, Allen L. L. Robinson, Albert A. A. Presto
Summary: On February 3, 2023, a train carrying hazardous chemicals derailed in East Palestine, OH, leading to temporary evacuation and controlled burn of some of the hazardous cargo. Residents reported health symptoms, and initial data from air monitoring indicated potential concern for air toxics. Mobile air monitoring conducted later showed that the levels of some chemicals were below risk levels, but acrolein levels were high and additional unique compounds were found, suggesting the need for further monitoring to characterize long-term exposure and risk levels.
ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS
(2023)