Stefan Platz addresses the high attrition rates in drug development, particularly due to safety and efficacy issues, and proposes the integration of advanced cellular models to improve preclinical predictions. Traditional models, which rely heavily on in vitro and in vivo studies, often fall short in detecting chronic or delayed toxicities. Advanced cellular models, especially those using primary or iPS-derived cells in multi-cellular 3D environments, offer more physiologically relevant insights and enable more accurate translational research.
Platz highlights a cardiac microtissue model that mimics the human heart using multiple cell types. This ‘beating heart’ model effectively captures both functional and structural drug-induced cardiac changes. For example, it demonstrates cardiotoxic effects of doxorubicin and is used to explore electrophysiological and hemodynamic changes. The incorporation of high-resolution technologies like proteomics and transcriptomics further enhances mechanistic insights, although it introduces the challenge of managing complex data sets.
He also presents a gastrointestinal (GI) organoid model that replicates the crypt-villus structure of the intestine, ideal for studying the effects of drugs like 5-FU. By mapping cellular transitions and applying mathematical modeling, this system can predict drug-induced GI toxicity such as diarrhoea with high accuracy. It illustrates how preclinical data can be mechanistically linked to clinical outcomes.
A third example is the bone marrow model built on a ceramic scaffold with dynamic flow. This 3D system maintains stem cells and supports differentiation into various blood cell lineages. It allows longitudinal sampling and modeling of hematological toxicity, predicting the onset and severity of cytopenias during cancer treatment. Platz underscores that although this approach requires detailed data and modeling, it yields clinically relevant predictions.
In conclusion, Platz advocates for integrating these advanced models with systems pharmacology to improve prediction, reduce animal testing, and guide safer and more effective drug development.
