Infliximab Maintenance Dosing in Inflammatory Bowel Disease: an Example for In Silico Assessment of Adaptive Dosing Strategies

Abstract

Infliximab is an anti-tumour necrosis factor alpha monoclonal antibody used to treat inflammatory diseases. Many patients fail during induction and others respond initially but relapse during maintenance therapy. Although anti-drug antibodies (ADA) are associated with some clinical failures, there is evidence that some failures may be due to subtherapeutic exposure. Adapting doses based on clinical outcomes and trough concentrations can improve response and reduce the proportion that develop ADA, but identification of appropriate doses in the presence of time-varying patient factors is complicated. Several adaptive dosing strategies (label recommendations versus therapeutic drug monitoring with an established stepwise algorithm or proportional dose adjustments or Bayesian population pharmacokinetic model-based dosing) were simulated on a virtual population (constructed with time-varying covariates and random effects on individual pharmacokinetic parameters) using R to assess their relative performance. Strategies were evaluated on their ability to maintain trough infliximab concentrations above an established target, 3 mg/L, during maintenance phase. Model-based dosing was superior in maintaining target trough concentrations, showing individuals in maintenance achieving concentrations above the target faster and a lower proportion of individuals who developed ADA. Model-based dosing results were consistent across a range of baseline covariate groups. This in silico assessment of adaptive dosing strategies demonstrated that, when challenged with dynamic covariate and random effect changes occurring in individual pharmacokinetic parameters, model-based approaches were superior to other strategies. Model-based dosing has not been tested clinically; however, the potential benefits of model-based dosing for infliximab suggest that it should be investigated to reduce subtherapeutic exposure.

Prediction of Individual Serum Infliximab Concentrations in Inflammatory Bowel Disease by a Bayesian Dashboard System

“…Several dashboard systems were developed to improve dosing in pediatric patients, but are not fully Bayesian systems (eg, only population mean parameters and patient factors are used). This current system has been described in the literature previously …”

Clinical Pharmacokinetic and Pharmacodynamic Considerations in the Treatment of Ulcerative Colitis

“…Dashboard systems for clinical decision support use Bayesian approaches in which a priori information is combined with a posteriori information to predict future concentrations [196]. A recent paper by Wojciechowski et al showed the potential benefits of model-based dosing for IFX [197]. They compared several dosing strategies by simulating a virtual population with time-varying covariates.…”

Precision Dosing: Public Health Need, Proposed Framework, and Anticipated Impact

“…The clinician could also be reminded about the value of collecting additional information (e.g., biomarker data) to make more informed decisions. Several groups have pioneered the use of model‐based clinical decision support tools to facilitate individualization of drug dosing …”

“…Subtherapeutic aminoglycoside concentrations can result in infection progression, treatment failure, and may contribute to the emergence of resistant bacteria, while supratherapeutic concentrations can cause nephrotoxicity . As another example, the benefit of precision dosing has not been established clinically for most monoclonal antibodies; however, precision dosing could potentially help maximize drug efficacy and minimize toxicities, as well as reduce the treatment costs of these drugs …”

“…Several groups have pioneered the use of model-based clinical decision support tools to facilitate individualization of drug dosing. 21,27,[34][35][36][37][38][39] The main goal of the HITECH Act, which was implemented in a staged manner, was to develop an electronic healthcare environment that improves individual healthcare quality and efficiency. 32,40 This Act is intended to increase patient engagement in their own health care, improve coordination between healthcare providers and the patient, and control costs.…”