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 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 improve the predictive power of preclinical research. Applied to human cell-based models, qualification reduces animal testing, since only effective drug candidates are subjected to final animal testing and subsequently to clinical trials.

Faster, sharper, more precise: Automated Cluster-FLIM in preclinical testing directly identifies the intracellular fate of theranostics in live cells and tissue.

Fluorescence microscopy is widely used for high content screening in 2D cell cultures and 3D models. In particular, 3D tissue models are gaining major relevance in modern drug development. Enabling direct multiparametric evaluation of complex samples, fluorescence lifetime imaging (FLIM) adds a further level to intensity imaging by the sensitivity of the fluorescence lifetime to the microenvironment. However, the use of FLIM is limited amongst others by the acquisition of sufficient photon numbers without phototoxic effects in live cells. Herein, we developed a new cluster-based analysis method to enhance insight, and significantly speed up analysis and measurement time for the accurate translation of fluorescence lifetime information into pharmacological pathways. Methods: We applied a fluorescently-labeled dendritic core-multishell nanocarrier and its cargo Bodipy as molecules of interest (MOI) to human cells and reconstructed human tissue. Following the sensitivity and specificity assessment of the fitting-free Cluster-FLIM analysis of data in silico and in vitro, we evaluated the dynamics of cellular molecule uptake and intracellular interactions. For 3D live tissue investigations, we applied multiphoton (mp) FLIM. Owing to Cluster-FLIM's statistics-based fitting-free analysis, we utilized this approach for automatization. Results: To discriminate the fluorescence lifetime signatures of 5 different fluorescence species in a single color channel, the Cluster-FLIM method requires only 170, respectively, 90 counts per pixel to obtain 95% sensitivity (hit rate) and 95% specificity (correct rejection rate). Cluster-FLIM revealed cellular interactions of MOIs, representing their spatiotemporal intracellular fate. In a setting of an automated workflow, the assessment of lysosomal trapping of the MOI revealed relevant differences between normal and tumor cells, as well as between 2D and 3D models. Conclusion: The automated Cluster-FLIM tool is fitting-free, providing images with enhanced information, contrast, and spatial resolution at short exposure times and low fluorophore concentrations. Thereby, Cluster-FLIM increases the applicability of FLIM in high content analysis of target molecules in drug development and beyond.