Abstract:
As a complicated chronic autoimmune disease, Rheumatoid arthritis (RA) often causes
progressive cartilage damage and grave inflammatory comorbidities in the joint tissues,
substantially reducing the life quality and mortality in patients. The primary cornerstones of
this study are the cytokines IL-6 and TNF-α as they are elevated in patients with RA and play
a vital role in the pivotal administration of inflammatory responses in the disease process as
well as being linked with polymorphism in genes incorporated in monitoring inflammatory
passageways. Therefore, various reports suggest targeting both IL-6 and TNF signaling could
be a propitious strategy to treat RA because of crucial role of TNF-α and Il-6 in its
pathophysiology. The pathways to be targeted in this study are the (MAPK) signaling
pathway and (JAK/STAT) signaling pathway which together form the Toll-like receptor
pathway that suppresses activation by generating pro-inflammatory mediators as well as self-
regulating signals. Here, in this study, molecular modeling strategies along with supervised
machine learning models were implemented to predict important 2D and 3D anti-
inflammatory features of TNFα and IL-6, respectively. To achieve this purpose, molecular
docking protocol optimization was performed to probe the binding hypothesis of both the
targets and to validate the binding pattern and explore the stability of these inhibitors‟
complexes with the TNF-α, MD simulation was used. Moreover, machine learning
classification models were used to identify the most relevant 2D features for both targets.
Conjointly, this study elucidates the salient 2D features along with crucial interactions present
in the protein targets that might assist in the development of more potent drugs for the
treatment of this inflammatory disease.
