External Evaluation of Population Pharmacokinetic Models of Infliximab in Patients With Inflammatory Bowel Disease

Does Sample Size, Sampling Strategy, or Handling of Concentrations Below the Lower Limit of Quantification Matter When Externally Evaluating Population Pharmacokinetic Models?

BACKGROUND AND OBJECTIVES

Precision dosing requires selecting the appropriate population pharmacokinetic model, which can be assessed through external evaluations (EEs). The lack of understanding of how different study design factors influence EE study outcomes makes it challenging to select the most suitable model for clinical use. This study aimed to evaluate the impact of sample size, sampling strategy, and handling of concentrations below the lower limit of quantification (BLQ) on the outcomes of EE for four population pharmacokinetic models using vancomycin and tobramycin as examples.

METHODS

Three virtual patient populations undergoing vancomycin or tobramycin therapy were simulated with varying sample size and sampling scenarios. The three approaches used to handle BLQ data were to (1) discard them, (2) impute them as LLOQ/2, or (3) use a likelihood-based approach. EEs were performed with NONMEM and R.

RESULTS

Sample size did not have an important impact on the EE results for a given scenario. Increasing the number of samples per patient did not improve predictive performance for two out of the three evaluated models. Evaluating a model developed with rich sampling did not result in better performance than those developed with regular therapeutic drug monitoring. A likelihood-based method to handle BLQ samples impacted the outcomes of the EE with lower bias for predicted troughs.

CONCLUSIONS

This study suggests that a large sample size may not be necessary for an EE study, and models selected based on TDM may be more generalizable. The study highlights the need for guidelines for EE of population pharmacokinetic models for clinical use.

Best Practice for Therapeutic Drug Monitoring of Infliximab: Position Statement from the International Association of Therapeutic Drug Monitoring and Clinical Toxicology

BACKGROUND

Infliximab, an anti-tumor necrosis factor monoclonal antibody, has revolutionized the pharmacological management of immune-mediated inflammatory diseases (IMIDs). This position statement critically reviews and examines existing data on therapeutic drug monitoring (TDM) of infliximab in patients with IMIDs. It provides a practical guide on implementing TDM in current clinical practices and outlines priority areas for future research.

METHODS

The endorsing TDM of Biologics and Pharmacometrics Committees of the International Association of TDM and Clinical Toxicology collaborated to create this position statement.

RESULTS

Accumulating data support the evidence for TDM of infliximab in the treatment of inflammatory bowel diseases, with limited investigation in other IMIDs. A universal approach to TDM may not fully realize the benefits of improving therapeutic outcomes. Patients at risk for increased infliximab clearance, particularly with a proactive strategy, stand to gain the most from TDM. Personalized exposure targets based on therapeutic goals, patient phenotype, and infliximab administration route are recommended. Rapid assays and home sampling strategies offer flexibility for point-of-care TDM. Ongoing studies on model-informed precision dosing in inflammatory bowel disease will help assess the additional value of precision dosing software tools. Patient education and empowerment, and electronic health record-integrated TDM solutions will facilitate routine TDM implementation. Although optimization of therapeutic effectiveness is a primary focus, the cost-reducing potential of TDM also merits consideration.

CONCLUSIONS

Successful implementation of TDM for infliximab necessitates interdisciplinary collaboration among clinicians, hospital pharmacists, and (quantitative) clinical pharmacologists to ensure an efficient research trajectory.

The Effect of Polymorphisms and Other Biomarkers on Infliximab Exposure in Paediatric Inflammatory Bowel Disease: Development of a Population Pharmacokinetic Model

BACKGROUND

Therapeutic drug monitoring (TDM) of infliximab has been shown to be a effective strategy for inflammatory bowel disease (IBD). Population pharmacokinetic (PopPK) modeling can predict trough concentrations for individualized dosing.

OBJECTIVE

The aim of this study was to develop a PopPK model of infliximab in a paediatric population with IBD, assessing the effect of single nucleotide polymorphisms (SNPs) and other biomarkers on infliximab clearance.

METHODS

This observational and ambispective single-centre study was conducted in paediatric patients with IBD treated with infliximab between July 2016 and July 2022 in the Paediatric Gastroenterology Service of the Hospital Universitari Vall d'Hebron (HUVH) (Spain). Demographic, clinical, and analytical variables were collected. Twenty SNPs potentially associated with variations in the response to infliximab plasma concentrations were analysed. infliximab serum concentrations and antibodies to infliximab (ATI) were determined by ELISA. PopPK modelling was performed using nonlinear mixed-effects analysis (NONMEM).

RESULTS

Thirty patients (21 males) were included. The median age (range) at the start of infliximab treatment was 13 years (16 months to 16 years). A total of 190 samples were obtained for model development (49 [25.8%] during the induction phase). The pharmacokinetics (PK) of infliximab were described using a two-compartment model. Weight, erythrocyte sedimentation rate (ESR), faecal calprotectin (FC), and the SNP rs1048610 (ADAM17) showed statistical significance for clearance (CL), and albumin for inter-compartmental clearance (Q). Estimates of CL1 (genotype 1-AA), CL2 (genotype 2-AG), CL3 (genotype 3-GG), Q, Vc, and Vp (central and peripheral distribution volumes) were 0.0066 L/h/46.4 kg, 0.0055 L/h/46.4 kg, 0.0081 L/h/46.4 kg, 0.0029 L/h/46.4 kg, 0.6750 L/46.4 kg, and 1.19 L/46.4 kg, respectively. The interindividual variability (IIV) estimates for clearance, Vc, and Vp were 19.33, 16.42, and 36.02%, respectively.

CONCLUSIONS

A popPK model utilising weight, albumin, FC, ESR, and the SNP rs1048610 accurately predicted infliximab trough concentrations in children with IBD.

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).

External Model Performance Evaluation of Twelve Infliximab Population Pharmacokinetic Models in Patients with Inflammatory Bowel Disease

Infliximab is approved for treatment of various chronic inflammatory diseases including inflammatory bowel disease (IBD). However, high variability in infliximab trough levels has been associated with diverse response rates. Model-informed precision dosing (MIPD) with population pharmacokinetic models could help to individualize infliximab dosing regimens and improve therapy. The aim of this study was to evaluate the predictive performance of published infliximab population pharmacokinetic models for IBD patients with an external data set. The data set consisted of 105 IBD patients with 336 infliximab concentrations. Literature review identified 12 published models eligible for external evaluation. Model performance was evaluated with goodness-of-fit plots, prediction- and variability-corrected visual predictive checks (pvcVPCs) and quantitative measures. For anti-drug antibody (ADA)-negative patients, model accuracy decreased for predictions > 6 months, while bias did not increase. In general, predictions for patients developing ADA were less accurate for all models investigated. Two models with the highest classification accuracy identified necessary dose escalations (for trough concentrations < 5 µg/mL) in 88% of cases. In summary, population pharmacokinetic modeling can be used to individualize infliximab dosing and thereby help to prevent infliximab trough concentrations dropping below the target trough concentration. However, predictions of infliximab concentrations for patients developing ADA remain challenging.

External Evaluation of Population Pharmacokinetic Models and Bayes-Based Dosing of Infliximab

Despite the well-demonstrated efficacy of infliximab in inflammatory diseases, treatment failure remains frequent. Dose adjustment using Bayesian methods has shown in silico its interest in achieving target plasma concentrations. However, most of the published models have not been fully validated in accordance with the recommendations. This study aimed to submit these models to an external evaluation and verify their predictive capabilities. Eight models were selected for external evaluation, carried out on an independent database (409 concentrations from 157 patients). Each model was evaluated based on the following parameters: goodness-of-fit (comparison of predictions to observations), residual error model (population weighted residuals (PWRES), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE)), and predictive performances (prediction-corrected visual predictive checks (pcVPC) and Bayesian simulations). The performances observed during this external evaluation varied greatly from one model to another. The eight evaluated models showed a significant bias in population predictions (from -7.19 to 7.38 mg/L). Individual predictions showed acceptable bias and precision for six of the eight models (mean error of -0.74 to -0.29 mg/L and mean percent error of -16.6 to -0.4%). Analysis of NPDE and pcVPC confirmed these results and revealed a problem with the inclusion of several covariates (weight, concomitant immunomodulatory treatment, presence of anti-drug antibodies). This external evaluation showed satisfactory results for some models, notably models A and B, and highlighted several prospects for improving the pharmacokinetic models of infliximab for clinical-biological application.

Predictors of Infliximab Trough Concentrations in Inflammatory Bowel Disease Patients Using a Repeated-Measures Design

BACKGROUND AND AIMS

Treating patients based on a treat-to-trough approach has been shown to be a cost-effective strategy for inflammatory bowel disease (IBD) patients who have become unresponsive to infliximab (IFX). However, the documented evidence for this is limited, and some controversy remains regarding the use of routine proactive therapeutic drug monitoring (TDM). To support routine TDM of IFX and regimen optimization in IBD patients, more in-depth knowledge of the covariates that affect the pharmacokinetic (PK) variability of IFX is needed. The aim of this study was to identify the characteristics of the patient, disease, and treatments that influence IFX PK and exposure in our cohort of IBD patients using a repeated-measures design.

METHODS

We performed a prospective observational study of adult IBD patients who received IFX between July 2013 and March 2017. We obtained repeated IFX trough concentration (Cmin) measurements and implemented a previously described population pharmacokinetic model to estimate individual clearance (CL). From the individual primary parameters, the area under the curve (AUC), half-life (t1/2), and central elimination rate constant (K10) were estimated. We performed a repeated-measures analysis to evaluate whether patient characteristics, disease status, concomitant immunosuppressive therapy, and immunogenicity are associated with IFX Cmin and PK parameters.

RESULTS

We collected 429 Cmin measurements from 112 patients. The median of the Cmin values was 3.62 mg/L (1.47-6.02). Antibodies to IFX (ATI) were detected in 14 patients. The predicted median AUC was 28,421 mg/h/L (22,336-36,903). The median individual predicted CL, K10, and t1/2 values were 4.77 mL/kg/day (3.88-5.90), 0.09 days (0.08-0.12), and 12.22 days (9.49-14.87), respectively. IFX Cmin, AUC, CL, and K10 were significantly influenced by ATI and serum albumin concentrations. Moreover, body weight was significantly associated with AUC, CL, and K10. Patients receiving concurrent immunosuppressive therapy had higher Cmin and AUC values and lower CL and K10 values than those treated with IFX monotherapy. We also observed high intrapatient variability in Cmin values during the study period.

CONCLUSIONS

In this repeated-measures study in a population of IBD patients, we observed significant associations between ATI, serum albumin concentration, concomitant immunosuppressive therapy, body weight and gender, and IFX Cmin, and CL. The high PK variability observed in this study supports the need for proactive TDM to optimize the use of IFX as early as possible in IBD patients.

Estimation of drug exposure by machine learning based on simulations from published pharmacokinetic models: The example of tacrolimus

We previously demonstrated that Machine learning (ML) algorithms can accurately estimate drug area under the curve (AUC) of tacrolimus or mycophenolate mofetil (MMF) based on limited information, as well as or even better than maximum a posteriori Bayesian estimation (MAP-BE). However, the major limitation in the development of such ML algorithms is the limited availability of large databases of concentration vs. time profiles for such drugs. The objectives of this study were: (i) to develop a Xgboost model to estimate tacrolimus inter-dose AUC based on concentration-time profiles obtained from a literature population pharmacokinetic (POPPK) model using Monte Carlo simulation; and (ii) to compare its performance with that of MAP-BE in external datasets of rich concentration-time profiles. The population parameters of a previously published PK model were used in the mrgsolve R package to simulate 9000 rich interdose tacrolimus profiles (one concentration simulated every 30 min) at steady-state. Data splitting was performed to obtain a training set (75%) and a test set (25%). Xgboost algorithms able to estimate tacrolimus AUC based on 2 or 3 concentrations were developed in the training set and the model with the lowest RMSE in a ten-fold cross-validation experiment was evaluated in the test set, as well as in 4 independent, rich PK datasets from transplant patients. ML algorithms based on 2 or 3 concentrations and a few covariates yielded excellent AUC estimation in the external validation datasets (relative bias < 5% and relative RMSE < 10%), comparable to those obtained with MAP-BE. In conclusion, Xgboost machine learning models trained on concentration-time profiles simulated using literature POPPK models allow accurate tacrolimus AUC estimation based on sparse concentration data. This study paves the way to the development of artificial intelligence at the service of precision therapeutic drug monitoring in different therapeutic areas.

Therapeutic drug monitoring of anti-tumour necrosis factor-α agents in inflammatory bowel disease: Limits and improvements

AIMS

Since the publication of the American Gastroenterological Association's recommendations in 2017, there have been no significant changes in the biological monitoring recommendations in inflammatory bowel disease. Possible limitations are the lack of evidence to recommend proactive therapeutic drug monitoring (pTDM) over reactive TDM (rTDM), and the limited information about individualized dosing methods. This article aims to review the TDM strategy updates and the use of individualized dosing methods.

METHODS

For the analysis of the TDM strategies and individualized dosing method, a search was carried out in PubMed and Cochrane Central. In the TDM case, since August 2017.

RESULTS

A total of 263 publications were found, but only 7 related to proactive TDM. Five of these publications directly compared pTDM vs rTDM and 2 were randomized clinical trials. Six studies found benefits of pTDM and 1 found no differences. Regarding the individualized dosing method, 229 distinct results were found. Population pharmacokinetics was the most widely used method to develop individual dosage models and to analyse the influence of factors on drug concentrations (albumin concentration, weight, presence of anti-drug antibodies etc).

CONCLUSION

We have found no major changes in TDM strategies. There is a growing trend towards the use of pTDM because it has shown a longer duration of treatment response, lower rates of discontinuation and relapses. However, the available evidence is limited and of low quality. Despite the common use of population pharmacokinetic methods to analyse pharmacokinetic factors, they are not commonly used for personalized dosing.

Bayes-based dosing of infliximab in inflammatory bowel diseases: Short-term efficacy

AIMS

Therapeutic drug monitoring of infliximab can guide clinical decisions in patients with loss of response and in those who can benefit from a de-intensification. The aim of this study was to determine the impact of therapeutic drug monitoring combined with Bayesian forecasting methodology on clinical response in a real-world dataset of patients suffering from inflammatory bowel disease.

METHODS

We performed a single-centre prospective study with one-group pre-test/post-test design in 108 adult inflammatory bowel disease patients treated with model-based dosing of infliximab maintenance treatment. We recorded clinical activity scores (Harvey-Bradshaw index and partial Mayo) and inflammatory biomarkers per patient.

RESULTS

The initial infliximab regimen was maintained in 49 (45.4%) patients and was adjusted in 59 (54.6%) patients (34 treatment intensifications, 9 de-intensifications and 16 treatment discontinuations or therapy replacements). The median time from intervention to index measurement was 126 (103-160) days. The overall proportion of patients in clinical remission increased from 65.7% to 80.4% (P < .0001) and the median infliximab trough concentrations increased from 3.21 (0.99-5.45) to 5.13 mg/L (3.57-6.53) (P < .0001). In the intensified group, the remission rate increased from 35.3% to 61.8% (P = .001) and the percentage of patients in clinical remission or with mild symptoms increased from 76.5% to 94.1%. In the de-intensification cohort, no patients experienced an increase in the Harvey-Bradshaw index or partial Mayo scores, and all patients maintained an infliximab trough concentration of >5 mg/L.

CONCLUSION

In our cohort of inflammatory bowel disease patients, Bayes-based optimized dosing improved the short-term efficacy of infliximab treatment.

Usefulness of therapeutic drug monitoring of infliximab during the induction period in patients with inflammatory bowel disease

INTRODUCTION

previous studies have shown that higher infliximab trough levels are associated with favorable short-term and long-term therapeutic outcomes in inflammatory bowel disease. There is a need to determine which patients could benefit from proactive therapeutic drug monitoring in the induction phase. The aim of this study was to evaluate the pharmacokinetic variability of infliximab, determine the factors associated with achieving target infliximab trough levels in the induction phase and analyze the clinical and biochemical response at week 26 of treatment.

PATIENTS AND METHODS

a retrospective observational study was performed of patients with inflammatory bowel disease and data available on serum levels of infliximab during the induction period. The percentage of patients that achieved target infliximab trough levels at week 6 was determined. Clinical remission and response and biochemical remission were evaluated at week 26.

RESULTS

thirty patients were included and only 13 (43.3 %) had infliximab trough levels > 15 µg/mL at week 6. A clinical response was observed during the maintenance period in 71.4 % of patients, their infliximab levels were significantly higher than in non-responders (6.3 µg/mL [IQR: 6.7] vs 1.0 µg/mL [IQR: 5.0], respectively; p = 0.016). Likewise, 53.6 % of patients achieved biochemical remission (responders 6.2 µg/mL [IQR: 5.2] vs non-responders 3.2 µg/mL [IQR: 5.0]; p = 0.031).

CONCLUSION

less than half of patients had target infliximab levels during the induction period. Therapeutic drug monitoring during this period is related to the achievement of therapeutic levels of infliximab and may lead to a better clinical response in these patients.

Association between infliximab concentrations and clinical response in psoriasis: a prospective cohort study

BACKGROUND

Infliximab (IFX) trough concentrations (C_min) have been linked to treatment efficacy in psoriatic patients. Inter-individual IFX C_min variability and factors influencing IFX pharmacokinetics could explain differences in treatment response.

OBJECTIVE

To evaluate the association between IFX C_min and clinical outcomes in psoriatic patients.

METHODS

Prospective study of 33 patients with moderate to severe psoriasis receiving IFX at Bellvitge University Hospital, between October 2013 and November 2016. IFX C_min and antibodies toward infliximab (ATI) were measured.

RESULTS

We collected 155 IFX C_min and ATI values (mean age, 46 (14) years; 11 (33.3%) women). Mean IFX C_min was 2.5 (2.4) mg/L and ATIs were detected in six patients, resulting in undetectable IFX C_min. IFX C_min was significantly associated with ATI and body mass index (BMI) (β -2.51, 95% CI -3.56 to -1.4 and β -0.05, 95% CI -0.09 to -0.01). PASI score and PASI 90/100 response were significantly associated with IFX C_min (IRR 0.80, 95% CI 0.70 to 0.92; OR 1.79, 95% CI 1.18 to 2.71 and OR 1.79, 95% CI 1.14 to 2.81).

CONCLUSION

IFX C_min significantly influences PASI 90/100 response rates. IFX C_min wa significantly associated with ATI and BMI. The observed inter-individual variability in IFX C_min supports the need for IFX drug monitoring.