9 research outputs found
Inflammatory bowel disease addressed by Caco-2 and monocyte-derived macrophages : an opportunity for an in vitro drug screening assay
Infammatory bowel disease (IBD) is a widespread disease, afecting a growing demographic. The treatment of chronic
infammation located in the GI-tract is dependent on the severity; therefore, the IBD treatment pyramid is commonly applied.
Animal experimentation plays a key role for novel IBD drug development; nevertheless, it is ethically questionable and
limited in its throughput. Reliable and valid in vitro assays ofer the opportunity to overcome these limitations.
We combined Caco-2 with monocyte-derived macrophages and exposed them to known drugs, targeting an in vitro-in vivo correlation (IVIVC) with a focus on the severity level and its related drug candidate. This co-culture assay addresses namely the
intestinal barrier and the immune response in IBD. The drug efcacy was analyzed by an LPS-infammation of the co-culture
and drug exposure according to the IBD treatment pyramid. Efcacy was defned as the range between LPS control (0%) and
untreated co-culture (100%) independent of the investigated read-out (TEER, Papp, cytokine release: IL-6, IL-8, IL-10, TNF-α).
The release of IL-6, IL-8, and TNF-α was identifed as an appropriate readout for a fast drug screening (“yes–no response”).
TEER showed a remarkable IVIVC correlation to the human treatment pyramid (5-ASA, Prednisolone, 6-mercaptopurine,
and infiximab) with an R2
of 0.68. Similar to the description of an adverse outcome pathway (AOP) framework, we advocate
establishing an “Efcacy Outcome Pathways (EOPs)” framework for drug efcacy assays. The in vitro assay ofers an easy
and scalable method for IBD drug screening with a focus on human data, which requires further validation
Oligodendroglia in cortical multiple sclerosis lesions decrease with disease progression, but regenerate after repeated experimental demyelination
DNA methylation-based classification of central nervous system tumours
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challengingwith substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology
DNA methylation-based classification of central nervous system tumours
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging - with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology