Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine

The Extracellular Matrix-Derived Biomarkers for Diagnosis, Prognosis, and Personalized Therapy of Malignant Tumors

The tumor biomarkers already have proven clinical value and have become an integral part in cancer management and modern translational oncology. The tumor tissue microenvironment (TME), which includes extracellular matrix (ECM), signaling molecules, immune and stromal cells, and adjacent non-tumorous tissue, contributes to cancer pathogenesis. Thus, TME-derived biomarkers have many clinical applications. This review is predominately based on the most recent publications (manuscripts published in a last 5 years, or seminal publications published earlier) and fills a gap in the current literature on the cancer biomarkers derived from the TME, with particular attention given to the ECM and products of its processing and degradation, ECM-associated extracellular vesicles (EVs), biomechanical characteristics of ECM, and ECM-derived biomarkers predicting response to the immunotherapy. We discuss the clinical utility of the TME-incorporating three-dimensional in vitro and ex vivo cell culture models for personalized therapy. We conclude that ECM is a critical driver of malignancies and ECM-derived biomarkers should be included in diagnostics and prognostics panels of markers in the clinic.

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.