Do animal studies predict human safety?

Study of FDA and EMA reports analyzes predictivity of animal-human relationship in relation to patient safety

Mel J. Yeates
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NEW YORK—A big data analysis conducted by Elsevier has evaluated the ability of animal studies to predict human safety. The study examined the consistency between preclinical animal testing and observations made in human clinical trials. The study analyzed 1,637,449 adverse events reported for both humans and the five most commonly used animals in FDA and EMA regulatory documents for 3,290 approved drugs and formulations.
The results revealed that some animal tests are far more predictive of human response than others, depending on the species and symptom being reported. This finding, which has considerable implications for improving patient safety, could help pharmaceutical firms decide which tests are appropriate and which might be ruled out to reduce unnecessary animal testing.
“All life-science companies have a desire to decrease animal testing, and with continued pressure from governments, societies and animal welfare groups, pharmaceutical organizations are exploring ways to do that,” says Dr. Matthew Clark, director of scientific services at Elsevier.
“Through this study, Elsevier has demonstrated that applying a big data approach to very large data sets has potential for huge benefits—in this case, in reducing animal testing and improving patient safety. The findings from the study have considerable implications for improving patient safety and can help pharmaceutical teams decide which animal tests are appropriate throughout drug development and testing, and which might be ruled out as unnecessarily testing on animals,” continues Clark. “Elsevier is pleased to be playing a role in directly addressing this important issue, by not just publishing research but normalizing data from different sources and performing analytics.”
One of the main conclusions of the study, published in the Journal of Regulatory Toxicology and Pharmacology, is that when it comes to cardiac events such as arrhythmia, there is a high degree of concordance between animal and human responses. However, at the other end of the spectrum, some events identified have never been reported in a human, and some events observed in humans have never been reported in an animal study.
As a result of the analysis, Elsevier has created a dataset that will offer researchers a way to more accurately predict human risk, based on parameters such as species, adverse event and drug formulation, allowing them to design safer and more robust clinical trials. This knowledge of which species are most predictive for each adverse event is key to avoiding safety issues.
“This kind of analysis has important implications for safety of patients, as it allows researchers to more accurately estimate the implied risk to humans from a given observed effect in an animal. With access to this kind of data and understanding, future researchers can design safer clinical trials,” Clark notes.
“Just because there are no findings in animals, this often does not mean that no adverse events will occur when the trial is undertaken on humans,” he adds. “The study showed that, for many adverse events, the negative predictive value is low. There is an entire class of events that require verbal descriptions of sensation—headache and nausea are among the most common—which are not well represented in animal studies. One of the most important issues seen in humans but not animals is cholestatic hepatitis. There is also a cluster of other liver issues not represented well in animal models, which have not been reported in animal studies in drug approval documents. This points out that human biology has some unique points, especially around the liver, and research is continuing to better understand and predict drug-induced liver injury.”
Clark tells DDNews that the data analysis was performed over the span of a year. “The discussions about what should be done and how the results should be interpreted took the largest period of time. Our team loaded the data, and the preparation of the actual queries and data analytics was refined during that time. Once the queries and analytics had been defined, re-running the analysis to generate the updates and looking at the selected subsets was done in under an hour.”
The statistical study was carried out in conjunction with the Bayer AG Pharmaceuticals Investigational Toxicology department and is the broadest published to date using publicly available data.
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, and the team also plans to add additional datasets on dosing to further improve accuracy. The full study, “A Big Data Approach to the Concordance of the Toxicity of Pharmaceuticals in Animals and Humans,” has also been made available through open access via ScienceDirect.
“Elsevier works very closely with pharmaceutical researchers worldwide, discussing issues faced and how we can help address them,” Clark points out. “As a result, this study was launched after several discussions with Bayer identified this topic as a significant issue—and one which Elsevier had the data to address and Bayer’s toxicology expertise could support. Bayer also has an ongoing interest in assessing how predictive animal studies are of human outcomes, as one of the best ways to reduce the amount of animal testing is by using studies that only have a high predictivity of outcomes.”
“Already, the industry is committed to reducing the number of animal tests that take place, yet standards regarding patient safety are paramount. Our study demonstrates how big data can help navigate that dilemma by building trials that account for potential risks and can better refine the stratification of patients for the trial,” he remarks. “It is possible to reduce animal testing by focusing on those species that best predict human risks for the adverse events in question. Moreover, these data are key to supporting the shift to adopt evidence-based medicine. My hope is that the life-science industry notes this kind of big data analysis that shows how the continued application of technology will help us to make even more safe and humane breakthroughs in the future.”

Mel J. Yeates

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