Quantitative relationship between infliximab exposure and inhibition of C-reactive protein synthesis to support inflammatory bowel disease management

The Influence of Subclinical Active Inflammation on IFX Pharmacokinetic Modeling and Disease Progression Assessment: Findings from a Prospective Real-World Study in Inflammatory Bowel Disease Patients

BACKGROUND AND AIMS

Effective management of inflammatory bowel disease (IBD) relies on a comprehensive understanding of infliximab (IFX) pharmacokinetics (PK). This study's primary goal was to develop a robust PK model, identifying key covariates influencing IFX clearance (CL), while concurrently evaluating the risk of disease progression during the maintenance phase of IBD treatment.

METHODS

The multicenter, prospective, real-world DIRECT study was conducted in several care centers, which included 369 IBD patients in the maintenance phase of IFX therapy. A two-compartment population PK model was used to determine IFX CL and covariates. Logistic and Cox regressions were applied to elucidate the associations between disease progression and covariates embedded in the PK model.

RESULTS

The PK model included the contributions of weight, albumin, antidrug antibody (ADA), and fecal calprotectin (FC). On average, higher ADA, FC concentration and weight, and lower albumin concentration resulted in higher IFX CL. In the multivariate regression analyses, FC levels influenced the odds of disease progression in the majority of its definitions, when adjusted for several confounding factors. Additionally, alongside FC, both IFX and CL demonstrated a significant impact on the temporal aspect of disease progression.

CONCLUSION

In this 2-year real-world study, readily available clinical covariates, notably FC, significantly impacted IFX availability in IBD patients. We demonstrated that subclinical active inflammation, as mirrored by FC or CRP, substantially influenced IFX clearance. Importantly, FC emerged as a pivotal determinant, not only of IFX pharmacokinetics but also of disease progression. These findings underscore the need to integrate FC into forthcoming IFX pharmacokinetic models, amplifying its clinical significance.

Drug Clearance in Patients with Inflammatory Bowel Disease Treated with Biologics

Biological therapy is very effective for treating patients with moderate to severe inflammatory bowel disease (IBD). However, up to 40% can have primary non-response, and up to 50% of the patients can experience a loss of response to anti-tumor necrosis factor therapy. These undesirable outcomes can be attributed to either a mechanistic failure or pharmacokinetic (PK) issues characterized by an inadequate drug exposure and a high drug clearance. There are several factors associated with accelerated clearance of biologics including increased body weight, low serum albumin and immunogenicity. Drug clearance has gained a lot of attention recently as cumulative data suggest that there is an association between drug clearance and therapeutic outcomes in patients with IBD. Moreover, clearance is used by model informed precision dosing (MIDP) tools, or PK dashboards, to adjust the dosing for reaching a target drug concentration threshold towards a more personalized application of TDM. However, the role of drug clearance in clinical practice is yet to be determined. This comprehensive review aims to present data regarding the variables affecting the clearance of specific biologics, the association of clearance with therapeutic outcomes and the role of clearance monitoring and MIPD in patients with IBD.

A prognostic baseline blood biomarker and tumor growth kinetics integrated model in paclitaxel/platinum treated advanced non-small cell lung cancer patients

Paclitaxel/platinum chemotherapy, the backbone of standard first-line treatment of advanced non-small cell lung cancer (NSCLC), exhibits high interpatient variability in treatment response and high toxicity burden. Baseline blood biomarker concentrations and tumor size (sum of diameters) at week 8 relative to baseline (RS8) are widely investigated prognostic factors. However, joint analysis of data on demographic/clinical characteristics, blood biomarker levels, and chemotherapy exposure-driven early tumor response for improved prediction of overall survival (OS) is clinically not established. We developed a Weibull time-to-event model to predict OS, leveraging data from 365 patients receiving paclitaxel/platinum combination chemotherapy once every three weeks for ≤six cycles. A developed tumor growth inhibition model, combining linear tumor growth and first-order paclitaxel area under the concentration-time curve-induced tumor decay, was used to derive individual RS8. The median model-derived RS8 in all patients was a 20.0% tumor size reduction (range from -78% to +15%). Whereas baseline carcinoembryonic antigen, cytokeratin fragments, and thyroid stimulating hormone levels were not significantly associated with OS in a subset of 221 patients, and lactate dehydrogenase, interleukin-6 and neutrophil-to-lymphocyte ratio levels were significant only in univariate analyses (p value < 0.05); C-reactive protein (CRP) in combination with RS8 most significantly affected OS (p value < 0.01). Compared to the median population OS of 11.3 months, OS was 128% longer at the 5th percentile levels of both covariates and 60% shorter at their 95th percentiles levels. The combined paclitaxel exposure-driven RS8 and baseline blood CRP concentrations enables early individual prognostic predictions for different paclitaxel dosing regimens, forming the basis for treatment decision and optimizing paclitaxel/platinum-based advanced NSCLC chemotherapy.

Ulcerative Colitis and Acute Severe Ulcerative Colitis Patients Are Overlooked in Infliximab Population Pharmacokinetic Models: Results from a Comprehensive Review

Ulcerative colitis (UC) is part of the inflammatory bowels diseases, and moderate to severe UC patients can be treated with anti-tumour necrosis α monoclonal antibodies, including infliximab (IFX). Even though treatment of UC patients by IFX has been in place for over a decade, many gaps in modelling of IFX PK in this population remain. This is even more true for acute severe UC (ASUC) patients for which early prediction of IFX pharmacokinetic (PK) could highly improve treatment outcome. Thus, this review aims to compile and analyse published population PK models of IFX in UC and ASUC patients, and to assess the current knowledge on disease activity impact on IFX PK. For this, a semi-systematic literature search was conducted, from which 26 publications including a population PK model analysis of UC patients receiving IFX therapy were selected. Amongst those, only four developed a model specifically for UC patients, and only three populations included severe UC patients. Investigations of disease activity impact on PK were reported in only 4 of the 14 models selected. In addition, the lack of reported model codes and assessment of predictive performance make the use of published models in a clinical setting challenging. Thus, more comprehensive investigation of PK in UC and ASUC is needed as well as more adequate reports on developed models and their evaluation in order to apply them in a clinical setting.

Multi‐model averaging improves the performance of model‐guided infliximab dosing in patients with inflammatory bowel diseases

Infliximab dosage de‐escalation without prior knowledge of drug concentrations may put patients at risk for underexposure and trigger the loss of response. A single‐model approach for model‐informed precision dosing during infliximab maintenance therapy has proven its clinical benefit in patients with inflammatory bowel diseases. We evaluated the predictive performances of two multi‐model approaches, a model selection algorithm and a model averaging algorithm, using 18 published population pharmacokinetic models of infliximab for guiding dosage de‐escalation. Data of 54 patients with Crohn’s disease and ulcerative colitis who underwent infliximab dosage de‐escalation after an earlier escalation were used. A priori prediction (based solely on covariate data) and maximum a posteriori prediction (based on covariate data and trough concentrations) were compared using accuracy and precision metrics and the classification accuracy at the trough concentration target of 5.0 mg/L. A priori prediction was inaccurate and imprecise, with the lowest classification accuracies irrespective of the approach (median 59%, interquartile range 59%–63%). Using the maximum a posteriori prediction, the model averaging algorithm had systematically better predictive performance than the model selection algorithm or the single‐model approach with any model, regardless of the number of concentration data. Only a single trough concentration (preferably at the point of care) sufficed for accurate and precise prediction. Predictive performance of both single‐ and multi‐model approaches was robust to the lack of covariate data. Model averaging using four models demonstrated similar predictive performance with a five‐fold shorter computation time. This model averaging algorithm was implemented in the TDMx software tool to guide infliximab dosage de‐escalation in the forthcoming prospective MODIFI study (NCT04982172).