Systems model identifies baseline cytokine concentrations as potential predictors of rheumatoid arthritis inflammatory response to biologics

Mathematical modeling in autoimmune diseases: from theory to clinical application

The research & development (R&D) of novel therapeutic agents for the treatment of autoimmune diseases is challenged by highly complex pathogenesis and multiple etiologies of these conditions. The number of targeted therapies available on the market is limited, whereas the prevalence of autoimmune conditions in the global population continues to rise. Mathematical modeling of biological systems is an essential tool which may be applied in support of decision-making across R&D drug programs to improve the probability of success in the development of novel medicines. Over the past decades, multiple models of autoimmune diseases have been developed. Models differ in the spectra of quantitative data used in their development and mathematical methods, as well as in the level of "mechanistic granularity" chosen to describe the underlying biology. Yet, all models strive towards the same goal: to quantitatively describe various aspects of the immune response. The aim of this review was to conduct a systematic review and analysis of mathematical models of autoimmune diseases focused on the mechanistic description of the immune system, to consolidate existing quantitative knowledge on autoimmune processes, and to outline potential directions of interest for future model-based analyses. Following a systematic literature review, 38 models describing the onset, progression, and/or the effect of treatment in 13 systemic and organ-specific autoimmune conditions were identified, most models developed for inflammatory bowel disease, multiple sclerosis, and lupus (5 models each). ≥70% of the models were developed as nonlinear systems of ordinary differential equations, others - as partial differential equations, integro-differential equations, Boolean networks, or probabilistic models. Despite covering a relatively wide range of diseases, most models described the same components of the immune system, such as T-cell response, cytokine influence, or the involvement of macrophages in autoimmune processes. All models were thoroughly analyzed with an emphasis on assumptions, limitations, and their potential applications in the development of novel medicines.

Naringenin ameliorates collagen-induced arthritis through activating AMPK-mediated autophagy in macrophages

BACKGROUND

Naringenin is widely recognized for its notable attributes, including anti-inflammatory, anti-cancer, and immunomodulatory activities. However, its specific implications for rheumatoid arthritis (RA) and the underlying mechanisms remain to be explored. This study aimed to investigate the therapeutic efficacy and pharmacological mechanism of Naringenin in the treatment of collagen-induced arthritis (CIA).

METHODS

A CIA model was established in DBA/1 mice, and various doses of Naringenin were administered orally to assess its impact on RA. The study also involved lipopolysaccharides (LPS)-induced RAW264.7 cells to further evaluate the effects of Naringenin. Mechanistic studies were conducted to elucidate the signaling pathways involved in Naringenin's actions.

RESULTS

Naringenin significantly alleviated foot inflammation in DBA/1 CIA mice and attenuated the levels of pro-inflammatory cytokines in serum. It also enhanced antioxidant capacity in the CIA model. In vitro studies with LPS-induced RAW264.7 cells demonstrated that Naringenin attenuated pro-inflammatory cytokines and reactive oxygen species (ROS) levels. Mechanistic studies confirmed that Naringenin activated autophagy and increased autophagic flux. Blocking autophagy, either by silencing Atg5 or inhibiting autophagolysosome using chloroquine, effectively counteracted the impact of Naringenin on pro-inflammatory cytokines. Further exploration revealed that Naringenin activated the AMPK/ULK1 signaling pathway, and inhibition of AMPK reversed the initiation of autophagy and reduced pro-inflammatory cytokine secretion induced by Naringenin.

CONCLUSIONS

This study unveils a novel mechanism by which Naringenin may be used to treat RA. It demonstrates the therapeutic efficacy of Naringenin in a CIA model by reducing inflammation, modulating cytokine levels, and enhancing antioxidant capacity. Moreover, the activation of autophagy through the AMPK/ULK1 signaling pathway appears to play a critical role in Naringenin's anti-inflammatory effects. These findings suggest potential strategies for the development of anti-rheumatic medications based on Naringenin.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

Immune-Mediated Diseases from the Point of View of Psychoneuroimmunoendocrinology

Simple Summary

Immune-mediated inflammatory diseases (IMIDs) are growing in prevalence and relevance in our society, entailing notable consequences for the individual and healthcare systems. These medical conditions are associated with a systemic inflammatory milieu and an aberrant functioning of the immune system, establishing a bidirectional interplay with other organs and systems of the body. Psychoneuroimmunoendocrinology (PNIE) is an area of great interest relating the immune system with the individual’s psyche, nervous, and endocrine system. As compelling evidence supports the pivotal role of PNIE in the understanding and clinical management of IMIDs, the aim of the present review is to deepen the current basic and clinical knowledge in this field.

Abstract

Immune-mediated inflammatory diseases (IMIDs) represent a large group of diseases (Crohn’s, ulcerative colitis, psoriasis, lupus, and rheumatoid arthritis) evidenced by systemic inflammation and multiorgan involvement. IMIDs result in a reduced quality of life and an economic burden for individuals, health care systems, and countries. In this brief descriptive review, we will focus on some of the common biological pathways of these diseases from the point of view of psychoneuroimmunoendocrinology (PNIE). PNIE consists of four medical disciplines (psychology, nervous system, immune system, and endocrine system), which are key drivers behind the health–disease concept that a human being functions as a unit. We examine these drivers and emphasize the need for integrative treatments that addresses the disease from a psychosomatic point of view.