How Qualification of 3D Disease Models Cuts the Gordian Knot in Preclinical Drug Development
Preclinical research struggles with its predictive power for drug effects in patients. The clinical success of preclinically approved drug candidates ranges between 3% and 33%. Regardless of the approach, novel disease models and test methods need to prov
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Contents 1 Current Efficiency in Preclinical Research 1.1 Phases of Preclinical Research 1.2 Models and Test Methods 2 Reasons for Poor Translational Success 3 From Validation to Qualification 3.1 Validation 3.2 Quality Function Deployment: Learning from Industry 3.3 Qualification 3.4 Qualification of 3D In Vitro Models 3.5 Qualification of Test Methods 3.6 Selection of Relevant Drug Doses 4 Current Strategies to Rethink Preclinical Drug Research 4.1 Strategy 1: Characterized Cell Lines 4.2 Strategy 2: Primary Cells to Recapitulate Human Heterogeneity 4.3 Strategy 3: Patient-Derived Cells 4.4 Strategy 4: New Technologies in Tissue Engineering 4.5 Strategy 5: Comparing New Test Methods to Current Standards 5 Phases of Innovative Preclinical Drug Research 5.1 Preclinical Phase I 5.2 Preclinical Phase II 5.3 Preclinical Phase III 6 The Price of Quality References
Monika Schäfer-Korting and Christian Zoschke contributed equally to this work. M. Schäfer-Korting (*) · C. Zoschke Freie Universität Berlin, Institute of Pharmacy (Pharmacology and Toxicology), Berlin, Germany e-mail: [email protected]; [email protected] # Springer Nature Switzerland AG 2020 Handbook of Experimental Pharmacology, https://doi.org/10.1007/164_2020_374
M. Schäfer-Korting and C. Zoschke
Abstract
Preclinical research struggles with its predictive power for drug effects in patients. The clinical success of preclinically approved drug candidates ranges between 3% and 33%. Regardless of the approach, novel disease models and test methods need to prove their relevance and reliability for predicting drug effects in patients, which is usually achieved by method validation. Nevertheless, validating all models appears unrealistic due to the variety of diseases. Thus, novel concepts are needed to increase the quality of preclinical research. Herein, we introduce qualification as a minimal standard to establish the relevance of preclinical models and test methods. Qualification starts with prioritizing and translating scientific requirements into technical parameters by quality function deployment. Qualified models use authenticated cells, which resemble the corresponding cells in humans in morphology and drug target expression. Moreover, disease models differ from normal models in the expression of relevant biomarkers. As a result, qualified test methods can discriminate effects of treatment standards and the effects of weakly effective or ineffective substances. Observer-blind readout, adequate data documentation, dropout inclusion, and a priori power studies are as crucial as realistic dosage regimens for qualified approaches. Here, we showcase the implementation of qualification. Adjusting the level of model complexity and qualification to three defined phases of preclinical research assures the optimal level of certainty at each step. In conclusion, qualification strengthens the researchers’ impact by defining basic requirements that novel approaches must fulfill while still allowing for scientific creativity. Qualification helps to im
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