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Predicting organ toxicity using in vitro bioactivity data and chemical structure

06 Aug

I get to work with some great scientists in my job. I am getting to work on projects that a couple of years ago were way out of my depth. Let’s be honest, I have no formal training as a toxicologist and my training is formally as an analytical scientist, then cheminformatician, then into publishing and informatics and now in the National Center for Computational Toxicology. I didn’t realize that the trial by fire would be so stimulating and fun but working at EPA is great. So many people make flippant comments about working for the government, leaving early, etc. We work HARD and are productive and, for me at least, I feel we are doing important work and making real contributions. The latest paper I am involved with is “Predicting organ toxicity using in vitro bioactivity data and chemical structure” (http://dx.doi.org/10.1021/acs.chemrestox.7b00084). The abstract is listed below…

“Animal testing alone cannot practically evaluate the health hazard posed by tens of thousands of environmental chemicals. Computational approaches making use of high-throughput experimental data may provide more efficient means to predict chemical toxicity. Here, we use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level. A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays. Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50 positive and 50 negative). Supervised machine learning was performed using Naïve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification approaches. Model performance was assessed based on F1 scores using five-fold cross-validation with balanced bootstrap replicates. Fixed effects modeling showed the variance in F1 scores was explained mostly by target organ outcome, followed by descriptor type, machine learning algorithm, and interactions between these three factors. A combination of bioactivity and chemical structure or chemotype descriptors were the most predictive. Model performance improved with more chemicals (up to a maximum of 24%) and these gains were correlated (ρ= 0.92) with the number of chemicals. Overall, the results demonstrate that a combination of bioactivity and chemical descriptors can accurately predict a range of target organ toxicity outcomes in repeat-dose studies, but specific experimental and methodologic improvements may increase predictivity.”

 

About tony

Antony (Tony) J. Williams received his BSc in 1985 from the University of Liverpool (UK) and PhD in 1988 from the University of London (UK). His PhD research interests were in studying the effects of high pressure on molecular motions within lubricant related systems using Nuclear Magnetic Resonance. He moved to Ottawa, Canada to work for the National Research Council performing fundamental research on the electron paramagnetic resonance of radicals trapped in single crystals. Following his postdoctoral position he became the NMR Facility Manager for Ottawa University. Tony joined the Eastman Kodak Company in Rochester, New York as their NMR Technology Leader. He led the laboratory to develop quality control across multiple spectroscopy labs and helped establish walk-up laboratories providing NMR, LC-MS and other forms of spectroscopy to hundreds of chemists across multiple sites. This included the delivery of spectroscopic data to the desktop, automated processing and his initial interests in computer-assisted structure elucidation (CASE) systems. He also worked with a team to develop the worlds’ first web-based LIMS system, WIMS, capable of allowing chemical structure searching and spectral display. With his developing cheminformatic skills and passion for data management he left corporate America to join a small start-up company working out of Toronto, Canada. He joined ACD/Labs as their NMR Product Manager and various roles, including Chief Science Officer, during his 10 years with the company. His responsibilities included managing over 50 products at one time prior to developing a product management team, managing sales, marketing, technical support and technical services. ACD/Labs was one of Canada’s Fast 50 Tech Companies, and Forbes Fast 500 companies in 2001. His primary passions during his tenure with ACD/Labs was the continued adoption of web-based technologies and developing automated structure verification and elucidation platforms. While at ACD/Labs he suggested the possibility of developing a public resource for chemists attempting to integrate internet available chemical data. He finally pursued this vision with some close friends as a hobby project in the evenings and the result was the ChemSpider database (www.chemspider.com). Even while running out of a basement on hand built servers the website developed a large community following that eventually culminated in the acquisition of the website by the Royal Society of Chemistry (RSC) based in Cambridge, United Kingdom. Tony joined the organization, together with some of the other ChemSpider team, and became their Vice President of Strategic Development. At RSC he continued to develop cheminformatics tools, specifically ChemSpider, and was the technical lead for the chemistry aspects of the Open PHACTS project (http://www.openphacts.org), a project focused on the delivery of open data, open source and open systems to support the pharmaceutical sciences. He was also the technical lead for the UK National Chemical Database Service (http://cds.rsc.org/) and the RSC lead for the PharmaSea project (http://www.pharma-sea.eu/) attempting to identify novel natural products from the ocean. He left RSC in 2015 to become a Computational Chemist in the National Center of Computational Toxicology at the Environmental Protection Agency where he is bringing his skills to bear working with a team on the delivery of a new software architecture for the management and delivery of data, algorithms and visualization tools. The “Chemistry Dashboard” was released on April 1st, no fooling, at https://comptox.epa.gov, and provides access to over 700,000 chemicals, experimental and predicted properties and a developing link network to support the environmental sciences. Tony remains passionate about computer-assisted structure elucidation and verification approaches and continues to publish in this area. He is also passionate about teaching scientists to benefit from the developing array of social networking tools for scientists and is known as the ChemConnector on the networks. Over the years he has had adjunct roles at a number of institutions and presently enjoys working with scientists at both UNC Chapel Hill and NC State University. He is widely published with over 200 papers and book chapters and was the recipient of the Jim Gray Award for eScience in 2012. In 2016 he was awarded the North Carolina ACS Distinguished Speaker Award.
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Posted by on August 6, 2017 in EPA Presentations

 

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