Therapeutic drug monitoring (TDM) with measurement of serum drug and antidrug antibodies (ADAb) is used widely to confirm therapeutic exposure, rule out immunogenicity, and optimize treatment of biologics in patients with inflammatory bowel diseases.¹ A recent genome-wide association study found the variant HLA-DQA1∗05 to increase the risk of development of antibodies against infliximab (IFX) and adalimumab (ADM) 2-fold, regardless of concomitant immunomodulator use.^2,3 However, there is currently limited evidence showing whether patients who develop antibodies to 1 anti-tumor necrosis factor (TNF) are prone to develop antibodies to the subsequent anti-TNF. Our aim was to investigate the risk of subsequent antibody development in cases (with ADAb to prior anti-TNF) versus control subjects (without ADAb to prior anti-TNF) using a large cohort of patients with inflammatory bowel diseases who underwent TDM with a drug-tolerant assay.

Background & aims

We analyzed markers of fibrosis in serum samples from patients with nonalcoholic fatty liver disease (NAFLD), assessed by liver biopsy. We used serum levels of markers to develop an algorithm to discriminate patients with advanced fibrosis from those with mild or moderate fibrosis and validated its performance in 2 independent cohorts of patients with NAFLD.

Methods

We performed a retrospective analysis of serum samples from 396 patients with NAFLD and different stages of fibrosis (F0-F4), collected from 2007 through 2017 on the day of liver biopsy (training cohort 1). We measured serum concentrations of alpha-2 macroglobulin (A2M), hyaluronic acid (HA), and TIMP metallopeptidase inhibitor 1 (TIMP1), and used measurements to develop an algorithm that could discriminate patients with NAFLD with advanced fibrosis (F3-F4; 24.1% of cohort) from those with mild or moderate fibrosis (F0-F2; 79.5% of cohort). We validated the algorithm using serum samples collected from a separate 396 patients from the same time period and location (validation cohort 1), as well as 244 patients with NAFLD evaluated at a separate location, from 2011 through 2017, within a median of 11 days of liver biopsy (cohort 2).

Results

The algorithm identified patients with advanced fibrosis vs mild or moderate fibrosis in training cohort 1 with an area under the receiver operating characteristic (AUROC) curve of 0.867 (95% CI, 0.827-0.907), 84.8% sensitivity (95% CI, 75.5%-91.0%), and 72.3% specificity (95% CI, 66.9%-77.3%), at a cutoff score of 17. The AUROC for the combined validation cohorts 1 and 2 (n=640) was 0.856 (95% CI, 0.820-0.892), identifying patients with 79.7% sensitivity (95% CI, 71.9%-86.2%) and 75.7% specificity (95% CI, 71.8%-79.4%) at the predetermined cutoff score of 17. The algorithm had negative predictive values that ranged from 92.5% to 94.7% in the validation cohorts; it correctly classified 90.0% of F0 samples, 75.0% of F1 samples, 77.4% of F3 samples, and 94.4% of F4 samples.

Conclusion

We developed an algorithm that identifies patients with advanced fibrosis from those with mild to moderate fibrosis in patients with NAFLD with an AUROC value of approximately 0.86, based on levels of serum biomarkers. We validated the findings in 2 separate sets of patients with biopsy-proven NAFLD. The algorithm can be used non-invasively to determine risk of advanced fibrosis in patients with NAFLD.