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

ABSTRACT: 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).

SUBMITTER: Kantasiripitak W

PROVIDER: S-EPMC9381887 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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

Kantasiripitak Wannee W Outtier An A Wicha Sebastian G SG Kensert Alexander A Wang Zhigang Z Sabino João J Vermeire Séverine S Thomas Debby D Ferrante Marc M Dreesen Erwin E

CPT: pharmacometrics & systems pharmacology 20220615 8

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 pharm ...[more]

PMID: 35706358

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Project description:BackgroundInadequate infliximab (IFX) drug exposure remains a clinical challenge and leads to high loss of response rates and therapy failure in inflammatory bowel disease (IBD). We aimed to determine the feasibility and pilot effectiveness of a novel, web-based, mobile IFX dosing calculator (mIDC) for therapy optimization.MethodsWe developed an mIDC leveraging the known clinical variables of C-reative protein (CRP), albumin, patient's weight, disease activity indices, calprotectin, drug trough levels, and antibodies to IFX that significantly affect pharmacokinetics and/or outcomes. A prospective observational cohort study in pediatric and young adult IBD patients receiving maintenance IFX was performed. System-wide practice adoption of mIDC was achieved through a quality improvement (QI) initiative within a hospital-based infusion unit.ResultsForty-nine patients (median age: 16.0 years; 55% female; 65% Crohn's disease) were followed over 9 months. mIDC recommendations for dose optimization were followed by the treating physicians in 198 (89%) out of 222 infusions. Twenty-eight (13%) of 222 mIDC recommendations were to escalate IFX dosing; 15 (54%) of 28 escalation recommendations were declined, and these patients were more likely to already be receiving IFX dose intensification compared with those in whom escalation recommendations were followed (P < 0.05). From mIDC initiation to end of follow-up, mean albumin levels remained unchanged at 3.8 g/dL. Median CRP remained unchanged at 2 g/L. Median calprotectin levels showed a downward trend from 30 to 27 μg/g (n = 9, P < 0.05). The percentage of patients undergoing therapeutic drug monitoring in clinical care increased from 34% to 86% with the QI initiative. The target median IFX trough goal of >5 μg/mL was achieved with 81% probability throughout the QI initiative, an increase of 12% compared with pre-QI values.ConclusionsThe use of a novel mIDC is feasible and potentially effective, facilitating both standardization and individualization of therapy in clinical care. mIDC appears to be a practical IFX dosing tool for point-of-care use, leveraging individual pharmacokinetic considerations.

Project description:BackgroundMany 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.MethodsWe 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.ResultsWe 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).ConclusionsA 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.

Project description:BackgroundThe initial dose of tacrolimus after liver transplantation (LT) is critical for rapidly achieving the steady state of the drug concentration, minimizing the potential adverse reactions and warranting long-term patient prognosis. We aimed to develop and validate a genotype-guided model for determining personalized initial dose of tacrolimus.MethodsBy combining pharmacokinetic modeling, pharmacogenomic analysis and multiple statistical methods, we developed a genotype-guided model to predict individualized tacrolimus initial dose after LT in the discovery (n = 150) and validation cohorts (n = 97) respectively. This model was further validated in a prospective, randomized and single-blind clinical trial from August, 2021 to February, 2022 (n = 40, ChiCTR2100050288).FindingsOur model included donor's and recipient's genotypes, recipient's weight and total bilirubin, which achieved an area under the curve of receiver operating characteristic curve (AUC of ROC) of 0.88 and 0.79 in the discovery and validation cohorts, respectively. We found that patients who were given tacrolimus within the recommended concentration range (RCR) (4-10 ng/mL), the new-onset metabolic syndromes are lower, especially for new-onset diabetes (p = 0.043). In the clinical trial, compared to those in experience-based (EB) group, patients in the model-based (MB) group were more likely to achieving the RCR (75% vs 40%, p = 0.025) with a more variable individualized dose (0.023-0.096 mg/kg/day vs 0.045-0.057 mg/kg/day). Moreover, significantly fewer medication adjustments were required for the MB group than the EB group (2.75 ± 2.01 vs 6.05 ± 3.35, p = 0.001).InterpretationOur genotype-based model significantly improved the initial dosing accuracy of tacrolimus and reduced the number of medication adjustments, which are critical for improving the prognosis of LT patients.FundingNational Natural Science Foundation of China, Shanghai three-year action plan, National Science and Technology Major Project of China.

Dataset Information

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

Publications

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

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