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Translational drug discovery and development with the use of tissue‐relevant biomarkers: Towards more physiological relevance and better prediction of clinical efficacy
Author(s) -
Florian Peter,
Flechsenhar Klaus R.,
Bartnik Eckart,
DingPfennigdorff Danping,
Herrmann Matthias,
Bryce Paul J.,
Nestle Frank O.
Publication year - 2020
Publication title -
experimental dermatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 96
eISSN - 1600-0625
pISSN - 0906-6705
DOI - 10.1111/exd.13942
Subject(s) - secukinumab , medicine , atopic dermatitis , drug development , ustekinumab , psoriasis , clinical trial , dupilumab , drug , translational research , personalized medicine , drug discovery , clinical significance , precision medicine , biologic agents , disease , bioinformatics , dermatology , pharmacology , pathology , biology , psoriatic arthritis , adalimumab
Abstract Due to the clinical development of drugs such as secukinumab, ustekinumab and dupilumab, major changes have been achieved in the treatment of patients diagnosed with psoriasis and atopic dermatitis. In academia and the pharmaceutical industry, research is increasingly moving towards the development of bispecific antibodies and multi‐specific nanobodies, as there is a compelling need for new treatment modalities for patients suffering from autoimmune or malignant disease. The purpose of this review is to discuss aspects of translational drug development with a particular emphasis on indications such as psoriasis and atopic dermatitis. The identification of biomarkers, the assessment of target organ pharmacokinetic and pharmacodynamics interactions and a wide range of in vitro, ex vivo and in vivo models should contribute to an appropriate prediction of a biological effect in the clinical setting. As human biology may not be perfectly reflected by approaches such as skin equivalents or animal models, novel approaches such as the use of human skin and dermal microperfusion assays in healthy volunteers and patients appear both reasonable and mandatory. These models may indeed generate highly translationally relevant data that have the potential to reduce the failure rate of drugs currently undergoing clinical development.