Effect of a Practice-wide Anti-TNF Proactive Therapeutic Drug Monitoring Program on Outcomes in Pediatric Patients with Inflammatory Bowel Disease

BACKGROUND

Reports on the feasibility and effectiveness of translating proactive, antitumor necrosis factor (TNF) therapeutic drug monitoring (TDM) for inflammatory bowel disease into practice-wide quality improvement (QI) are lacking. We aimed to determine whether a TDM QI program improved outcomes at a large academic pediatric gastroenterology practice.

METHODS

We instituted local anti-TNF TDM practice guidelines to proactively monitor and optimize drug levels (goal >5 μg/mL). We conducted a retrospective single-center cohort analysis of patient outcomes before (pre-TDM) and after (post-TDM) guideline institution and assessed the independent effect by multivariable regression. Primary outcome was sustained clinical remission (SCR22-52), defined as physician global assessment (PGA) of inactive from 22 to 52 weeks and off corticosteroids at 52 weeks.

RESULTS

We identified 108 pre-TDM and 206 post-TDM patients. The SCR22-52 was achieved in 42% of pre-TDM and 59% of post-TDM patients (risk difference, 17.6%; 95% CI, 5.4-29%; P = 0.004). The post-TDM group had an increased adjusted odds of achieving SCR22-52 (odds ratio, 2.03; 95% CI, 1.27-3.26; P = 0.003). The adjusted risk of developing high titer antidrug antibodies (ADAs) was lower in the post-TDM group (hazard ratio, 0.18; 95% CI, 0.09-0.35; P < 0.001). Although the risk of anti-TNF cessation for any reason was not significantly different, there was a lower adjusted risk of cessation related to any detectable ADA in the post-TDM group (hazard ratio, 0.45; 95% CI, 0.26-0.77; P = 0.003).

CONCLUSIONS

A practice-wide proactive anti-TNF TDM QI program improved key clinical outcomes at our institution, including sustained clinical remission, incidence of high titer ADA, and anti-TNF cessation related to ADA.

Improved Population Pharmacokinetic Model for Predicting Optimized Infliximab Exposure in Pediatric Inflammatory Bowel Disease

BACKGROUND

Many pediatric patients with inflammatory bowel disease (IBD) lose response to infliximab (IFX) within the first year, and achieving a minimal target IFX trough concentration is associated with higher remission rates and longer durability. Population pharmacokinetic (PK) modeling can predict trough concentrations for individualized dosing. The object of this study was to refine a population PK model that accurately predicts individual IFX exposure during maintenance therapy using longitudinal real-practice data.

METHODS

We exported data from the electronic health records of pediatric patients with IBD treated with originator IFX at a single center between January 2011 and March 2017. Subjects were divided into discovery and validation cohorts. A population PK model was built and then validated.

RESULTS

We identified 228 pediatric patients with IBD who received IFX and had at least 1 drug concentration measured, including 135 and 93 patients in the discovery and validation cohorts, respectively. Weight, albumin, antibodies to IFX (ATI) detected by a drug-tolerant assay, and erythrocyte sedimentation rate (ESR) were identified as covariates significantly associated with IFX clearance and incorporated into the model. The model exhibited high accuracy for predicting target IFX trough concentrations with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% confidence interval [CI], 0.81-0.91) for population-based predictions without prior drug-level input. Accuracy increased further for individual-based predictions when prior drug levels were known, with an AUROC of 0.93 (95% CI, 0.90-0.97).

CONCLUSIONS

A population PK model utilizing weight, albumin, ordinal drug-tolerant ATI, and ESR accurately predicts IFX trough concentrations during maintenance therapy in real-practice pediatric patients with IBD. This model, which incorporates dynamic clinical information, could be used for individualized dosing decisions to increase response durability.

Fishing for Pleiotropic QTLs in a Polygenic Sea

The application of factor analysis to human genetics has the potential to discover the coordinated control of multiple traits by common environment, common polygenes, or a single major gene. Classical factor analysis explains the covariation among the components of a random vector by approximating the vector by a linear transformation of a small number of uncorrelated factors. In the current paper we show how factor analysis dovetails with the classical variance decompositions of biometrical genetics. To explore the relationships between related quantitative variables, and avoid complicated positive definiteness constraints, we employ Cholesky and factor analytic decompositions. We derive an ECM algorithm and a competing quasi-Newton algorithm for estimating parameters by maximum likelihood and propose tactics for selecting initial parameter values. We also show how parameter asymptotic standard errors under these parameterizations propagate to asymptotic standard errors of the underlying variance components. Our genetic analysis program Mendel, which now incorporates the program Fisher, has performed well on a variety of data sets. We illustrate our methods, algorithms, and models on two data sets: a bivariate quantitative genetic example using total finger ridge count data and a multivariate linkage example using insulin resistance data.