Model-informed precision dosing (MIPD) software tools are used to optimize dosage regimens in individual patients, aiming to achieve drug exposure targets associated with desirable clinical outcomes. Over the last few decades, numerous MIPD software tools have been developed. However, they have still not been widely integrated into clinical practice. This study focuses on identifying the requirements for and evaluating the performance of the currently available MIPD software tools. First, a total of 22 experts in the field of precision dosing completed a web survey to assess the importance (from 0; do not agree at all, to 10; completely agree) of 103 pre-established software tool criteria organized in eight categories: user-friendliness and utilization, user support, computational aspects, population models, quality and validation, output generation, privacy and data security, and cost. Category mean ± pooled standard deviation importance scores ranged from 7.2 ± 2.1 (user-friendliness and utilization) to 8.5 ± 1.8 (privacy and data security). The relative importance score of each criterion within a category was used as a weighting factor in the subsequent evaluation of the software tools. Ten software tools were identified through literature and internet searches: four software tools were provided by companies (DoseMeRx, InsightRX Nova, MwPharm++, and PrecisePK) and six were provided by non-company owners (AutoKinetics, BestDose, ID-ODS, NextDose, TDMx, and Tucuxi). All software tools performed well in all categories, although there were differences in terms of in-built software features, user interface design, the number of drug modules and populations, user support, quality control, and cost. Therefore, the choice for a certain software tool should be made based on these differences and personal preferences. However, there are still improvements to be made in terms of electronic health record integration, standardization of software and model validation strategies, and prospective evidence for the software tools' clinical and cost benefits.

Background Unlike other anti–tumor necrosis factor alpha antibodies, golimumab does not deliver on its promise of effectiveness for treating patients with ulcerative colitis. We investigated the value of therapeutic drug monitoring for optimizing golimumab therapy. Methods We analyzed the golimumab pharmacokinetics data of 56 patients with moderate to severe ulcerative colitis. Induction and maintenance golimumab concentrations (296 venipuncture, 414 serum) were used to develop a population pharmacokinetic model. Exposure–response relationships were analyzed using the data of 40/56 patients with available endoscopy data. Receiver operating characteristic curve analysis was performed, and an exposure–response Markov model was developed, linking golimumab exposure to probabilities of transitioning between Mayo endoscopic subscore (MES) states from baseline to week (w)14. Results Golimumab pharmacokinetics was best described by a 2-compartment model with linear absorption and elimination. Antibodies to golimumab and previous biological therapy reduced golimumab exposure. Still, interindividual pharmacokinetic variability (IIVPK) remained largely unexplained. Endoscopic remission (ER; MESw14 ≤ 1) was achieved in 14/40 (35%) patients. Golimumab serum trough concentration thresholds of 7.4 mg/L (w6) and 3.2 mg/L (w14) predicted ER at w14 (positive predictive values [pv+] 83% and 91%, pv- 82% and 67%, respectively). The 3.2-mg/L target predicted 38% and 44% chances of achieving ER in patients with MESbaseline of 3 and 2, respectively. Conclusions Personalized, model-based induction dosing aiming at here-established target concentrations may account for IIVPK and thus provide patients with more equal chances of achieving ER. As <50% of patients attained the exposure targets, higher golimumab induction dosing requires investigation to secure its future in clinical practice.

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 multimodel 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).

Aims Controversies regarding infliximab treatment in elderly patients with inflammatory bowel diseases remain. We evaluated the effect of patient's age on infliximab exposure, efficacy and safety. Methods Retrospective case‐control data of patients receiving infliximab induction treatment were analysed. A population pharmacokinetic model was developed to estimate individual pharmacokinetic parameters. A logistic regression model was used to investigate the effect of exposure on endoscopic remission. Repeated time‐to‐event models were developed to describe the hazard of safety events over time. Results A total of 104 patients (46 elderly, ≥65 years) were included. A two‐compartment population pharmacokinetic model with linear elimination adequately described the data. Infliximab clearance decreased with older age, higher serum albumin, lower fat‐free mass, lower C‐reactive protein and absence of immunogenicity. Yet, infliximab exposure was not significantly different between elderly and nonelderly. Regardless of age, an infliximab trough concentration at week (w)14 of 15.6 mg/L was associated with a 50% probability of attaining endoscopic remission between w6 and w22. Infliximab exposure during induction treatment was not a risk factor of (severe) adverse events. The hazard of severe adverse events and malignancy increased by 2% and 7%, respectively, with increasing year of age. Concomitant immunomodulator use increased the hazard of infection by 958%, regardless of age. Conclusions Elderly patients attained infliximab exposure and endoscopic remission similarly to nonelderly patients. Therefore, the same infliximab trough concentration target can be used in therapeutic drug monitoring. The hazards of severe adverse events and malignancy increased with age, but not with infliximab exposure.

Background and aims Adequate infliximab concentrations during induction treatment are predictive for deep remission (corticosteroid-free clinical and endoscopic remission) at six months in children with inflammatory bowel diseases (IBD). Under standard infliximab induction dosing, children often have low infliximab trough concentrations. Model-informed precision dosing (MIPD) (i.e., model-based therapeutic drug monitoring) is advocated as a promising infliximab dosing strategy. We aimed to develop and validate an MIPD framework for guiding paediatric infliximab induction treatment. Methods Data from 31 children with IBD (4-18years) receiving standard infliximab induction dosing (5mg/kg at week [w]0, w2, and w6) were repurposed. Eight paediatric population pharmacokinetic models were evaluated. Modelling and simulation were used to identify exposure targets, an optimal sampling strategy, and develop a multi-model prediction algorithm for implementation into an MIPD software tool. A role for infliximab clearance monitoring was evaluated. Results A 7.5mg/L infliximab concentration target at w12 was associated with 64% probability of deep remission at six months. With standard dosing, less than 80% of simulated children &lt;40kg attained this target. The w12 target was most accurately and precisely achieved by implementing MIPD at w6 using the w6 infliximab concentration (rapid assay required). The multi-model algorithm outperformed single models when optimising the w6 dose based on both w2 and w4 concentrations. MIPD using only the w2 concentration resulted in biased and imprecise predictions. Infliximab clearances at w6 and w12 were predictive for deep remission. Conclusions A freely available, multi-model MIPD tool facilitates infliximab induction dosing and improves deep remission rates in children with IBD.