Assessing Scientific Soundness and Translational Value of Animal Studies on DPP4 Inhibitors for Treating Type 2 Diabetes Mellitus

• Autor(a) principal: Franco, Nuno Henrique
• Data de Publicação: 2021
• Outros Autores: Miranda, Sonia Batista, Kovács, Nóra, Nagy, Attila, Thiện, Bùi Quốc, Reis, Flávio, Varga, Orsolya
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/103755; https://doi.org/10.3390/biology10020155
• topic: animal models; predictive validity; research bias; translational medicine; type 2 diabetes mellitus

Although there is a wide range of animal models of type 2 diabetes mellitus (T2DM) used in research; we have limited evidence on their translation value. This paper provides a) a comparison of preclinical animal and clinical results on the effect of five dipeptidyl peptidase-4 (DPP4) inhibitors by comparing the pharmaceutical caused glucose changes, and b) an evaluation of methodological and reporting standards in T2DM preclinical animal studies. DPP4 inhibitors play an important role in the clinical management of T2DM: if metformin alone is not sufficient enough to control the blood sugar levels, DPP4 inhibitors are often used as second-line therapy; additionally, DPP-4 inhibitors are also used in triple therapies with metformin and sodium-glucose co-transporter-2 (SGLT-2) inhibitors or with metformin and insulin. In our analysis of 124 preclinical studies and 47 clinical trials, (1) we found no evidence of species differences in glucose change response to DPP4 inhibitors, which may suggest that, for this drug class, studies in mice and rats may be equally predictive of how well a drug will work in humans; and (2) there is good reporting of group size, sex, age, euthanasia method and self-reported compliance with animal welfare regulations in animal studies but poor reporting of justification of group size, along with a strong bias towards the use of male animals and young animals. Instead of the common non-transparent model selection, we call for a reflective and evidenced-based assessment of predictive validity of the animal models currently available.