Big data analysis reveals ability of animal studies to predict human safety

The ability of animal studies to predict human safety has been evaluated by global information analytics business, Elsevier, through big data analysis of adverse events reported to the US Food and Drug Administration (FDA) and European Medicines Agency (EMA).

More than 1.6 million adverse events were looked at that had been reported for both humans and the five most commonly used animals in regulatory documents. These reports related to documentation for over 3,000 approved drugs and formulations.

Based on the statistical study, it was found that some animal tests are far more predictive of human responses than others, depending on the species and symptom being reported. This finding can be used to help pharmaceutical companies decide on specific tests to use and those to rule out, ultimately reducing the amount of unnecessary testing on animals.

“All life science companies have a desire to decrease animal testing, and with continued pressure from governments, societies, and animal welfare groups, pharmaceutical organisations are exploring ways to do that,” said Dr Matthew Clark, director of Scientific Services at Elsevier. “Though generally accepted that animals predict human responses, the concordance has never been investigated on this scale before. Our big data study shows that through improved analysis of data, researchers can select tests based on the species that have the most predictive relationship with a human depending on the drug in question, and therefore rule out needless testing. This is important because it enables pharmaceutical firms to continue safely and humanely innovating, while searching for the life-changing therapies that will save many patients’ lives.”

The study, which was published in the Journal of Regulatory Toxicology and Pharmacology, revealed that there was a high degree of concordance between animal and human responses in cardiac events, however, there were also incidences where events identified in animal models were never reported in humans and vice-versa.

In light of their findings, 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 for the design of safer and more robust clinical trials.

“Ensuring patient safety is a crucial concern for all pharmaceutical firms, and along with a lack of efficacy, safety issues are one of the main reasons drugs fail clinical trials and never make it to market,” continued Clark. “Being able to anticipate and respond to the likely human reaction helps researchers build more complete patient safety plans and improve patient recruitment for trials. Today, we have access to more data than ever before, and more technology to help us gain this understanding. We have demonstrated through this study that applying a big data approach to very large data sets has potential for huge benefits in reducing animal testing and improving patient safety.”

The study was performed in conjunction with the Bayer AG Pharmaceuticals Investigational Toxicology department.