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01.12.2017 | Review | Ausgabe 1/2017 Open Access

Journal of Translational Medicine 1/2017

Translatability score revisited: differentiation for distinct disease areas

Zeitschrift:
Journal of Translational Medicine > Ausgabe 1/2017
Autoren:
Alexandra Wendler, Martin Wehling
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12967-017-1329-y) contains supplementary material, which is available to authorized users.

Abstract

Background

Translational science supports successful transition of early biomedical research into human applications. In 2009 a translatability score to assess risk and identify strengths and weaknesses of a given project has been designed and successfully tested in case studies. The score elements, in particular the contributing weight factors, are heterogeneous for different disease areas; therefore, the score was individualized for six areas (cardiovascular, oncology, psychiatric, anti-viral, anti-bacterial/fungal and monogenetic diseases).

Results

FDA reviews and related literature were used for modifications of the score with emphasis on biomarkers, personalized medicine and animal models. 113 new medical entities approved by FDA from 2012 through 2016 were evaluated and metrics obtained for companion diagnostics and animal models as starting points for author-based individualization of the score. Most drugs approved in this period were related to oncology (46%), while the approvals were lowest for psychiatrics (4%). The evaluation of the FDA package inserts revealed that companion diagnostics play an important role in every field except psychiatrics. Further the analysis of the FDA reviews showed the weakness of animal models in psychiatrics and anti-virals, while useful animal models were present for all other fields. Consequently the scoring system was adapted to the different fields, resulting in increased weights for animal models, biomarker and personalized medicine in oncology. For psychiatrics the weights for animal models, biomarker and personalized medicine were decreased, while the weight for model compounds, clinical trials and surrogate or endpoint strategy were increased. For anti-viral drugs weights for in vitro data and personalized medicine were increased, while the weight for animal models was decreased. Further, for anti-bacterial/fungal drugs weights for animal models and personalized medicine were increased. Weights were increased for genetics and personalized medicine and decreased for model compounds for monogenetic orphans.

Conclusions

Adaptation of the score to different disease areas should help to support a structured and diverse approach to translation and encourage researchers in the private or public sectors to further customize the score.
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