Big data study by Elsevier probes how well animal studies predict human safety

- May 15, 2018 - A big data analysis conducted by global information analytics business, Elsevier, has evaluated the ability of animal studies to predict human safety. The statistical study examined the consistency between preclinical animal testing and observations made in human clinical trials. The study analysed 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 also has considerable implications for improving patient safety, can help pharmaceutical firms decide which tests are appropriate and which might be ruled out to reduce 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.”

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 are 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, and critical in supporting the industry’s wider to shift to adopt evidence-based medicine.

“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 Dr 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 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; 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’) will be published in the Journal of Regulatory Toxicology and Pharmacology; the article has also been made available through open access, via ScienceDirect.