Posted on October 16th, 2018 by Matthew Clark in Pharma R&D
Researchers are increasingly using their domain expertise to tackle many of the world’s problems such as hunger, pandemics and other societal issues. When I worked for a pharma company I was continually surprised experienced pharmacologists could tell me which toxicology events in animals were predictive of human responses and which events could be safely ignored. However, unless this knowledge is available to all and can be documented as part of the drug discovery process, it will not be possible to eliminate unnecessary animal testing as we cannot be sure of the impact on human safety or the efficacy of drugs.
I decided to use my professional experience to see if there was a way to reduce animal testing, particularly as it has surged in the last year, despite pressure from government bodies and environmentalist groups. In undertaking this analysis I had the opportunity to work with Thomas Steger-Hartmann from Bayer AG. For our research to be impactful, we needed proof that testing on a particular species provides results which are applicable to humans. And by selecting the best animal model, one can reduce testing by eliminating tests on species that are less predictive of human toxicity for specific areas.
Thomas Steger-Hartmann and I analyzed more than 1.6m FDA and EMA regulatory documents, to determine how predictive animal tests are of human responses, depending on the species and symptom being reported. The results confirmed applying data science to publicly available data will enable pharmaceutical and life science companies to reduce animal testing, while improving patient safety.
Our study required a combination of analytics technology, domain expertise and the ability to translate and analyze the data sets. The analysis relied on digitizing regulatory documents, mining the facts from the documents, and normalizing the terms used in the documents so that they could be compared with each other. Without these capabilities, researchers are unable to navigate the data or draw meaningful conclusions. With the right expertise in both analytics and science, scientists can use publicly available data to decide whether a cardiac or liver issue in an animal should halt development of a drug as exactly the same result will occur in humans or whether it should be flagged for monitoring during a clinical trial.
Elsevier will continue to develop the analysis it has created as part of this study by working on projects with customers and their proprietary datasets; the team also plans to add additional datasets on dosing to further improve accuracy. We hope our work will encourage other researchers to examine public data to see what other social issues can be addressed through information analytics.
Our study to reduce animal testing was featured in The Times’ most recent supplement on Responsible Business. You can read the full article here.
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