Background: Systemic sclerosis (SSc) is a rare connective tissue disease that can affect different organs and has extremely heterogenous presentations. This complexity makes it difficult to perform an early diagnosis and a subsequent subclassification of the disease. This hinders a personalized approach in clinical practice. In this context, machine learning (ML), a branch of artificial intelligence (AI), is able to recognize relationships in data and predict outcomes. Methods: Here, we performed a narrative review concerning the application of ML in SSc to define the state of art and evaluate its role in a precision medicine context. Results: Currently, ML has been used to stratify SSc patients and identify those at high risk of severe complications. Additionally, ML may be useful in the early detection of organ involvement. Furthermore, ML might have a role in target therapy approach and in predicting drug response. Conclusion: Available evidence about the utility of ML in SSc is sparse but promising. Future improvements in this field could result in a big step toward precision medicine. Further research is needed to define ML application in clinical practice.

Background: Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is one of the most relevant complications of SSc and the major cause of death. The pathogenesis of SSc-ILD involves a complex interplay of multiple cell types and different molecular pathways, with both inflammation and fibrosis as pathological hallmarks. To date, there are no treatments able to target both components of the disease. Janus kinase inhibitors (JAKinibs) represent an interesting therapeutic option because they exert both anti-inflammatory and anti-fibrotic properties. Methods: Here, we performed a narrative review concerning the potential role of JAKinibs in SSc-ILD to define the state of art and to evaluate the pathogenetic rationale behind this type of treatment. Results: Currently, few studies investigated SSc-ILD response to JAKinibs treatment. Data were analyzed from three clinical studies and four case reports and progression of SSc-ILD was not evident in 93.5% of patients treated with JAKinibs. Conclusions: Available evidence of efficacy of JAKinibs in SSc-ILD is sparse but promising. JAKinibs could be an interesting treatment in SSc-ILD because of their potential inhibition of the fibrotic processes combined with their anti-inflammatory action. Moreover, JAKinibs were also shown in some studies to have a potential effect on pulmonary arterial hypertension (PAH), another threatening complication in SSc. More data are necessary to define JAKinibs role in SSc-ILD treatment.

(1) Background: Gut microbiota (GM) is the set of microorganisms inhabiting the gastroenteric tract that seems to have a role in the pathogenesis of rheumatic diseases. Recently, many authors proved that GM may influence pharmacodynamics and pharmacokinetics of several drugs with complex interactions that are studied by the growing field of pharmacomicrobiomics. The aim of this review is to highlight current evidence on pharmacomicrobiomics applied to the main treatments of Rheumatoid Arthritis and Spondyloarthritis in order to maximize therapeutic success, in the framework of Personalized Medicine. (2) Methods: We performed a narrative review concerning pharmacomicrobiomics in inflammatory arthritides. We evaluated the influence of gut microbiota on treatment response of conventional Disease Modifying Anti-Rheumatic drugs (cDMARDs) (Methotrexate and Leflunomide) and biological Disease Modifying Anti-Rheumatic drugs (bDMARDs) (Tumor necrosis factor inhibitors, Interleukin-17 inhibitors, Interleukin 12/23 inhibitors, Abatacept, Janus Kinase inhibitors and Rituximab). (3) Results: We found a great amount of studies concerning Methotrexate and Tumor Necrosis Inhibitors (TNFi). Conversely, fewer data were available about Interleukin-17 inhibitors (IL-17i) and Interleukin 12/23 inhibitors (IL-12/23i), while none was identified for Janus Kinase Inhibitors (JAKi), Tocilizumab, Abatacept and Rituximab. We observed that microbiota and drugs are influenced in a mutual and reciprocal way. Indeed, microbiota seems to influence therapeutic response and efficacy, whereas in the other hand, drugs may restore healthy microbiota. (4) Conclusions: Future improvement in pharmacomicrobiomics could help to detect an effective biomarker able to guide treatment choice and optimize management of inflammatory arthritides.