Microbiota Analysis to Predict Outcomes of Rheumatoid Arthritis Patients Treated With JAK-inhibitor
Microbiota Analysis to Predict Outcomes of Rheumatoid Arthritis Patients Treated With JAK-inhibitor
Personalized medicine in which each patient would receive the ideal personalized treatment and regimen, holds great promise to improve patient's care. However, previous studies failed to establish validated predictors of response to disease-modifying anti-rheumatic drugs (DMARDs) in patients with rheumatoid arthritis (RA). JAK inhibitors is a new class of DMARDs with great efficacy that might be even superior to anti-TNF drugs. As there are chemicals, their production cost is much cheaper than biological therapies and they will probably be central in patient's care in the coming years. Three are currently available: upadacitinib (UPA) tofacitinib and baricitinib. Our study will focus on UPA. Clinical outcomes mainly depend on i) factors influencing drug metabolism & concentrations and ii) adequacy between drug target and the inflammation pathways involved in the patient's disease. Humans carry in their gut trillions of germs, which are now known to be key players in health and disease. Those germs possess many enzymes and strongly modulate human enzymes expression. Gut-microbiota can, indeed, directly metabolize oral drugs and control the expression of the cytochrome P450 3A4 (CYP3A4), the main enzyme metabolizing TOFA. We showed, in a preliminary mouse experiment, that modifying gut-microbiota composition changes JAKi effects on signaling pathways. We thus believe that models including gut-microbiota composition together with markers of immune activation will predict clinical outcomes in RA patients treated with UPA.
Main and secondary objectives: To build predictive models for clinical outcomes (efficacy and safety) of RA patients treated with UPA based on microbiota analysis and markers f immune activation.
Methodolgy:
This multicentric longitudinal prospective study will include 60 patients with RA and inadequate response to methotrexate. The clinical outcomes studied will be EULAR non-response at 3 months as defined by the European league against rheumatism EULAR (primary outcome), achievement of low-disease activity at 6 months or incident adverse events (secondary outcomes). Gut microbiota will be assessed at baseline and M3 from thawed fecal samples. DNA will be purified using QIAamp DNA stool mini kit (Qiagen) and qualify using Qubit and TapeStation 4200 (Agilent). Library will be prepared by amplification of V1-V2 and V3-V4 regions from the bacterial 16S rRNA genes and will be qualified by q-PCR and amplicons will be sequenced by MiSeq (Illumina). Initial bioinformatic analysis and taxonomies will be carried out using the QIIME2 software. Immune activation will be assessed through JAK-STAT pathway activation by JAK STAT signaling pathway RT² profiler PCR Array (Qiagen) which profiles expression of 84 genes related to Jak and Stat-mediated signaling. UPA concentrations will be assessed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) at baseline and 3 months. Statistical classifiers (Neural network algorithm, Linear and Quadratic Discriminant Analysis, Support Vector Machine, Random forests, Shrinkage Methods, or Nearest Neighbors) incorporating microbiome, JAK STAT signaling pathway gene expression and clinical data, will be used to determine profiles associated with UPA clinical response and safety. Patients who will prematurely stop UPA (before 3 months) for adverse events or loss of follow-up will be considered as non-responders.
Gut microbiota is becoming an important predictor of response and tolerance with anti-cancer drugs. However, its potential of prediction in other fields has poorly been explored.
Drug metabolism and concentrations of tofacitinib depend on body mass index, liver function and cytochrome P450 activity (especially CYP3A4). Humans carry in their gut trillions of germs, which are now known to be key players in health and disease. Those germs can strongly impact drug metabolism and concentrations based on 3 mechanisms. First, gut bacteria possess a huge pool of enzymes which catalyzes drug metabolism reactions. Second, gut microbiota regulates bile acid metabolism which play critical role in drug metabolism. Third, gut microbiota modulates the expression of cytochrome P450, especially CYP3A4, the main enzyme catabolizing Tofacitinib (TOFA).
In addition to drug metabolism, gut microbiota is a key driver of immune activation. Clinical response to CTLA-4 or anti-PD-1 strongly depends on gut microbiota in different cancers. The experiments performed to decipher the mechanisms involved suggested that microbiota composition affects immune responses, which will facilitate or not anti-tumoral efficacy of checkpoints inhibitors.
RA is a heterogeneous disease with predominant inflammation pathways varying dependent on patients. Some RA seem to be more dependent on IL-6 whereas others rely more on TNF-alpha, B or T cells. Gut microbiota was shown to affect all those different targets. Assessing baseline levels of JAK STAT signaling pathway gene expression will help us to link immune activation, gut microbiota and clinical response to UPA.
We hypothesize that gut-microbiota composition impacts JAKi metabolism, immune activation, and thus clinical response and has a great potential to predict clinical outcomes in patients with RA treated with UPA.
Study objectives :
Main objective
To construct a model based on gut-microbiota composition, immune activation markers (JAK-STAT signalling pathway) and clinical data to predict UPA non-response at 3 months in RA patients with inadequate response to methotrexate.
Secondary objectives
To construct a model based on gut-microbiota composition, immune activation markers (JAK-STAT signalling pathway) and clinical data to predict low-disease activity at 6 months of UPA in RA patients with inadequate response to methotrexate.
To compare responders and non-responders and patients with or without adverse effects on UPA in terms of:
To correlate changes in disease activity score based on 28 joint evaluation (DAS28, see annex for calculation) between month-3 and baseline with:
Inclusion Criteria:
Exclusion Criteria:
c-daien@chu-montpellier.fr467338710 ext. +33
a-boissin@chu-montpellier.fr4 67 33 24 82 ext. +33