A new computational model would help determine the safety of existing pesticides and aid in the design of safer, next-generation pesticides that meet increasing global agricultural demand

The majority of commercial chemicals that enter the market in the United States every year have insufficient health and safety data. For pesticides, the U.S. Environmental Protection Agency uses a variety of techniques to fill data gaps to evaluate chemical hazards, exposure, and risk. Nonetheless, public concern over the potential threat that these chemicals pose has grown in recent years, along with the realization that traditional animal-testing methods are not pragmatic in using speed, economics, or ethics. Now, researchers at George Washington University have developed a new computational approach to rapidly screen pesticides for safety, performance, and how long they will endure in the environment. Moreover, and most importantly, the new approach will aid in the design of next-generation molecules to develop safer pesticides.

“In many ways, our tool mimics computational drug discovery, in which vast libraries of chemical compounds are screened for their efficacy and then tweaked to make them even more potent against specific therapeutic targets,” Jakub Kostal, an assistant professor of chemistry at GW and principal investigator on the project, said. “Similarly, we use our systems-based approach to modify pesticides to make them less toxic and more degradable, while, at the same time, making sure they retain good performance. It’s a powerful tool for both industry and regulatory agencies that can help design new, safer analogs of existing commercial agrochemicals, and so protect human life, the environment, and industry’s bottom line.”

Using their model, the team analyzed 700 pesticides from the EPA’s pesticide registry. The model considered a pesticide’s likely persistence or degradation in the environment over time, its safety, and how well it performed at killing, repelling, or controlling the target problem. 

They found that only 52, or 7%, of the chemical compounds, analyzed fulfilled the criteria for a safe chemical. According to the researchers, while the results from the analysis suggest most pesticides are likely not safe, many could be made safer by modifying their molecular structure in ways that would reduce their toxicity without sacrificing performance.

“Our analysis reveals there is definitely room for improvement when it comes to developing safer pesticides,” Jessica Lewer, a graduate student at GW and lead author on the paper, said. “Moreover, the computational approach we’ve developed to better screen and design safe pesticides can be used as a blueprint and applied to other industries that rely on commercial chemicals, for example, cosmetics and cleaning products.”

Going forward, the team hopes to augment their model with pesticide design from biobased, renewable chemical building blocks to advance sustainability goals in chemical design.

The study, “Structure-to-Process Design Framework for Developing Safer Pesticides,” was published in the journal Science Advances on March 30, 2022. The National Science Foundation (NSF1943127) provided funding for this research.

Mayo Clinic researchers have proposed a new model for mapping the symptoms of Alzheimer's disease to brain anatomy. This model was developed by applying machine learning to patient brain imaging data. It uses the entire function of the brain rather than specific brain regions or networks to explain the relationship between brain anatomy and mental processing. 

"This new model can advance our understanding of how the brain works and breaks down during aging and Alzheimer's disease, providing new ways to monitor, prevent and treat disorders of the mind," says David T. Jones, M.D., a Mayo Clinic neurologist and lead author of the study.

Alzheimer's disease typically has been described as a protein-processing problem. The toxic proteins amyloid and tau deposit in areas of the brain, causing neuron failure that results in clinical symptoms such as memory loss, difficulty communicating, and confusion.

However, the relationship between clinical symptoms, patterns of brain damage, and brain anatomy are not clear. People also can have more than one neurodegenerative disease, making diagnosis difficult. Mapping brain behavior with this computational model may give a new perspective to clinicians.

The new model was developed using brain glucose measurements from fluorodeoxyglucose positron emission tomography (FDG-PET) performed on 423 study participants who are cognitively impaired and involved with the Mayo Clinic Study of Aging and the Mayo Clinic Alzheimer's Disease Research Center. FDG-PET is an imaging test that shows how glucose is fueling parts of the brain. Neurodegenerative diseases, such as Alzheimer's disease, Lewy body dementia, and frontotemporal dementia, for example, have different patterns of glucose use.

The model compresses complex brain anatomy relevant to dementia symptoms into a conceptual, color-coded framework that shows areas of the brain associated with neurodegenerative disorders and mental functions. Imaging patterns shown in the model relate to the symptoms patients experience.

The predictive ability of the model for changes associated with Alzheimer's physiology was validated in 410 people. Additional validation was obtained by projecting a large amount of data from normal aging and dementia syndromes targeting memory, executive functions, language, behavior, movement, perception, semantic knowledge, and visuospatial abilities.

The researchers found that 51% of the variances in glucose use patterns across the brains of patients with dementia could be explained by only 10 patterns. Each patient has a unique combination of these 10 brain glucose patterns that relate to the type of symptoms they experience. In follow-up work, Mayo Clinic's Department of Neurology Artificial Intelligence (AI) Program, which is directed by Dr. Jones, is using these 10 patterns to work on AI systems that help interpret brain scans from patients who are being evaluated for Alzheimer's disease and related syndromes.

"This new computational model, with more validation and support, has the potential to redirect scientific efforts to focus on dynamics in complex systems biology in the study of the mind and dementia rather than primarily focusing on misfolded proteins," Dr. Jones says.

"If the mental functions relevant for Alzheimer's disease are performed in a distributed manner across the entire brain, a new disease model like what we are proposing is needed. We think this model can potentially impact diagnostics, treatments, and the fundamental understanding of neurodegeneration and mental functions in general."