An elevated polyclonal free light chain level reflects a strong interferon signature in patients with systemic autoimmune diseases

High amount of polyclonal free light chains (FLC) are reported in systemic autoimmune diseases (SAD) and we took advantage of the PRECISESADS study to better characterize them. Serum FLC levels were explored in 1979 patients with SAD (RA, SLE, SjS, Scl, APS, UCTD, MCTD) and 614 healthy controls. Information regarding clinical parameters, disease activity, medications, autoantibodies (Ab) and the interferon α and/or γ scores were recorded. Among SAD patients, 28.4% had raised total FLC (from 12% in RA to 30% in SLE and APS) with a normal kappa/lambda ratio. Total FLC levels were significantly higher in SAD with inflammation, active disease in SLE and SjS, and an impaired pulmonary functional capacity in SSc, while independent from kidney impairment, infection, cancer and treatment. Total FLC concentrations were positively correlated among the 10/17 (58.8%) autoantibodies (Ab) tested with anti-RNA binding protein Ab (SSB, SSA-52/60 kDa, Sm, U1-RNP), anti-dsDNA/nucleosome Ab, rheumatoid factor and negatively correlated with complement fractions C3/C4. Finally, examination of interferon (IFN) expression as a potential driver of FLC overexpression was tested showing an elevated level of total FLC among patients with a high IFNα and IFNγ Kirou's score, a strong IFN modular score, and the detection in the sera of B-cell IFN dependent factors, such as TNF-R1/TNFRSF1A and CXCL10/IP10. In conclusion, an elevated level of FLC, in association with a strong IFN signature, defines a subgroup of SAD patients, including those without renal affectation, characterized by increased disease activity, autoreactivity, and complement reduction.

Clinical Characteristics of Pruritus in Systemic Sclerosis Vary According to the Autoimmune Subtype

Pruritus is a frequent symptom in systemic sclerosis (SSc), with a prevalence of 40-65%, but its pathophysiology is poorly understood. This study investigated the immunological component of pruritus. Fifty-six patients with SSc responded to a standardized questionnaire regarding both SSc disease and pruritus characteristics. Among patients with SSc, those with pruritus did not display a particular immunological profile (inflammatory, humoral, and/or cellular factors), but pruritus was, in most cases, concomitant with the development of SSc. Thus, pruritus characteristics were evaluated further, according to the detection of anti-centromere autoantibodies (ACA), into ACA+ (n = 17) and ACA- (n = 19). The ACA+ subgroup was characterized by a longer evolution of SSc and pruritus, pruritus present outside the sclerotic area, and a shorter daily duration of pruritus. In conclusion, the concomitant appearance of the 2 processes and the differences observed between ACA+ and ACA- subgroups support the presence of an immunological component in pruritus.

[An example of simulation for a better understanding of PK/PD relationship of antibiotics]

The interpretation of PK/PD indices is specific to each class of antibiotics. In order to illustrate this, we developed a multidisciplinary tutorial program based on simulation of clinical cases. Three drugs were included in this software: tobramycin, vancomycin and azithromycin. From the dosage regimen proposed by the user, the model simulates a plotting of antibiotic plasma concentrations vs. time (tobramycin, vancomycin and azithromycin) and tissue concentrations (azithromycin). Peak and trough concentrations are calculated at steady-state. A commentary is provided to evaluate the efficacy of treatment and to assist the user in improving his prescription of tobramycin or vancomycin. T(> MIC) (time the concentration remains above the MIC) and AUC(24) (area under the concentration-time curve) are calculated in plasma and tissues for azithromycin. In order to create a link between theoretical pharmacokinetics and clinical practice, we propose this model as a simulation of antibiotic monitoring. We put the emphasis on interactivity and simulation, leading to applied reasoning and decision making. It illustrates (i) the influence of pharmacokinetic parameters, location of infection and bactericidal kinetics on the use of three different classes of antibiotics, (ii) the role of route of administration, dosing and intervals between administrations on therapeutic response and (iii) the influence of erratic administrations on clinical efficacy.