Scientists of Biomedcode present in PLOS Computational Biology a new computational framework revealing key differences between four rheumatoid arthritis medications and their impact on biological pathways in mice.

Published in PLoS Computational Biology 2019 May 9;15(5):e1006933. doi: 10.1371/journal.pcbi.1006933

An integrative transcriptome analysis framework for drug efficacy and similarity reveals drug-specific signatures of anti-TNF treatment in a mouse model of inflammatory polyarthritis

Niki Karagianni, Ksanthi Kranidioti, Nikolaos Fikas, Maria Tsochatzidou, Panagiotis Chouvardas, Maria C. Denis, George Kollias, Christoforos Nikolaou

Anti-TNF agents have been in the first line of treatment of various inflammatory diseases such as Rheumatoid Arthritis and Crohn’s Disease, with a number of different biologics being currently in use. A detailed analysis of their effect at transcriptome level has nevertheless been lacking. We herein present a concise analysis of an extended transcriptomics profiling of four different anti-TNF biologics upon treatment of the established hTNFTg (Tg197) mouse model of spontaneous inflammatory polyarthritis. We implement a series of computational analyses that include clustering of differentially expressed genes, functional analysis and random forest classification. Taking advantage of our detailed sample structure, we devise metrics of treatment efficiency that take into account changes in gene expression compared to both the healthy and the diseased state. Our results suggest considerable variability in the capacity of different biologics to modulate gene expression that can be attributed to treatment-specific functional pathways and differential preferences to restore over- or under-expressed genes. Early intervention appears to manage inflammation in a more efficient way but is accompanied by increased effects on a number of genes that are seemingly unrelated to the disease. Administration at an early stage is also lacking in capacity to restore healthy expression levels of under-expressed genes. We record quantifiable differences among anti-TNF biologics in their efficiency to modulate over-expressed genes related to immune and inflammatory pathways. More importantly, we find a subset of the tested substances to have quantitative advantages in addressing deregulation of under-expressed genes involved in pathways related to known RA comorbidities. Our study shows the potential of transcriptomic analyses to identify comprehensive and distinct treatment-specific gene signatures combining disease-related and unrelated genes and proposes a generalized framework for the assessment of drug efficacy, the search of biosimilars and the evaluation of the efficacy of TNF small molecule inhibitors.


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