A new analysis of safety reporting data by analytics group Elsevier and Bayer has found that some animal tests are far more predictive of human response than others.
The large-scale study – which looked at more than 1.6 million adverse events reported to the regulators in the EU and US – found 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 researchers better determine which tests would likely be most appropriate and thus could potentially cut 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.
“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, published in the Journal of Regulatory Toxicology and Pharmacology, showed a high level of concordance between animal and human responses when it comes to cardiac events such as arrhythmia, but at the other end of the scale some events were uniquely reported in animals and some in humans.
However, at the other end of the spectrum, some events identified in animals have never been reported in a human, and some events observed in humans have never been reported in an animal study.
Elsevier has created a dataset that it says 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”.
The 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”.
