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.

Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity

BACKGROUND

Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity.

METHODS

Patients on vancomycin at the University of Vermont Medical Center from November 1, 2021, to February 14, 2023, were entered into the MIPD software. The inclusion criteria were body mass index (BMI) of at least 40 kg/m2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using metrics. Then, a priori and a posteriori predictions were evaluated using the normalized root mean squared error (nRMSE) for precision and the mean percentage error (MPE) for bias. The parametric model was evaluated in a simulated MIPD context using an external validation dataset.

RESULTS

In total, 83 patients were included in the model development, with a median age of 56.6 years (range: 24-89 years), and a median BMI of 46.3 kg/m2 (range: 40-70.3 kg/m2). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to c clearance and volume parameters, respectively. The a priori MPE and nRMSE for the parametric versus nonparametric models were -6.3% versus 2.69% and 27.2% versus 30.7%, respectively. The a posteriori MPE and RMSE were 0.16% and 0.84%, and 13.8% and 13.1%. The parametric model matched or outperformed previously published models on an external validation dataset (n = 576 patients).

CONCLUSIONS

Minimal differences were found in the model structure and predictive error between the parametric and nonparametric approaches for modeling vancomycin class 3 obesity. However, the parametric model outperformed several other models, suggesting that institution-specific models may improve pharmacokinetics management.

Personalized Antifungal Therapy Through Model-Informed Precision Dosing of Posaconazole

BACKGROUND AND OBJECTIVE

Posaconazole is a pharmacotherapeutic pillar for prophylaxis and treatment of invasive fungal diseases. Dose individualization is of utmost importance as achieving adequate antifungal exposure is associated with improved outcome. This study aimed to select and evaluate a model-informed precision dosing strategy for posaconazole.

METHODS

Available population pharmacokinetic models for posaconazole administered as a solid oral tablet were extracted from the literature and evaluated using data from a previously published prospective study combined with data collected during routine clinical practice. External evaluation and selection of the most accurate and precise model was based on graphical goodness-of-fit and predictive performance. Measures for bias and imprecision included mean percentage error (MPE) and normalized relative root mean squared error (NRMSE), respectively. Subsequently, the best-performing model was evaluated for its a posteriori fit-for-purpose and its suitability in a limited sampling strategy.

RESULTS

Seven posaconazole models were evaluated using 764 posaconazole plasma concentrations from 143 patients. Multiple models showed adequate predictive performance illustrated by acceptable goodness-of-fit and MPE and NRMSE below ± 10% and ± 25%, respectively. In the fit-for-purpose analysis, the selected model showed adequate a posteriori predictive performance. Bias and imprecision were lowest in the presence of two prior measurements. Additionally, this model showed to be useful in a limited sampling strategy as it adequately predicted total posaconazole exposure from one (non-)trough concentration.

CONCLUSION

We validated an MIPD strategy for posaconazole for its fit-for-purpose. Thereby, this study is an important first step towards MIPD-supported posaconazole dosage optimization with the goal to improve antifungal treatment in clinical practice.

Performance of Eight Infliximab Population Pharmacokinetic Models in a Cohort of Dutch Children with Inflammatory Bowel Disease

BACKGROUND AND OBJECTIVE

Efficacy of infliximab in children with inflammatory bowel disease can be enhanced when serum concentrations are measured and further dosing is adjusted to achieve and maintain a target concentration. Use of a population pharmacokinetic model may help to predict an individual's infliximab dose requirement. The aim of this study was to evaluate the predictive performance of available infliximab population pharmacokinetic models in an independent cohort of Dutch children with inflammatory bowel disease.

METHODS

In this retrospective study, we used data of 70 children with inflammatory bowel disease (443 infliximab concentrations) to evaluate eight models that focused on infliximab pharmacokinetic models in individuals with inflammatory bowel disease, preferably aged ≤ 18 years. Predictive performance was evaluated with prior predictions (based solely on patient-specific covariates) and posterior predictions (based on covariates and infliximab trough concentrations). Model accuracy and precision were calculated with relative bias and relative root mean square error and we determined the classification accuracy at the trough concentration target of ≥ 5 mg/L.

RESULTS

The population pharmacokinetic model by Fasanmade was identified to be most appropriate for the total dataset (relative bias before/after therapeutic drug monitoring: -20.7%/11.2% and relative root mean square error before/after therapeutic drug monitoring: 84.1%/51.6%), although differences between models were small and several were deemed suitable for clinical use. For the Fasanmade model, sensitivity and specificity for maximum posterior predictions for the next infliximab trough concentration to be ≥ 5 mg/L were respectively 83.5% and 80% with an area under the receiver operating characteristic curve of 0.870.

CONCLUSIONS

In our paediatric cohort, various models provided acceptable predictive performance, with the Fasanmade model deemed most suitable for clinical use. Model-informed precision dosing can therefore be expected to help to maintain infliximab trough concentrations in the target range.

Maximum a posteriori Bayesian methods out-perform non-compartmental analysis for busulfan precision dosing

Dose personalization improves patient outcomes for many drugs with a narrow therapeutic index and high inter-individuality variability, including busulfan. Non-compartmental analysis (NCA) and model-based methods like maximum a posteriori Bayesian (MAP) approaches are two methods routinely used for dose optimization. These approaches vary in how they estimate patient-specific pharmacokinetic parameters to inform a dose and the impact of these differences is not well-understood. Using busulfan as an example application and area under the concentration-time curve (AUC) as a target exposure metric, these estimation methods were compared using retrospective patient data (N = 246) and simulated precision dosing treatment courses. NCA was performed with or without peak extension, and MAP Bayesian estimation was performed using either the one-compartment Shukla model or the two-compartment McCune model. All methods showed good agreement on real-world data (correlation coefficients of 0.945-0.998) as assessed by Bland-Altman plots, although agreement between NCA and MAP methods was higher during the first dosing interval (0.982-0.994) compared to subsequent dosing intervals (0.918-0.938). In dose adjustment simulations, both NCA and MAP estimated high target attainment (> 98%) although true simulated target attainment was lower for NCA (63-66%) versus MAP (91-93%). The largest differences in AUC estimation were due to different assumptions for the shape of the concentration curve during the infusion phase, followed by how the methods considered time-dependent clearance and concentration-time points collected in earlier intervals. In conclusion, although AUC estimates between the two methods showed good correlation, in a simulated study, MAP lead to higher target attainment. When changing from one method to another, or changing infusion duration and other factors, optimum estimated exposure targets may require adjusting to maintain a consistent exposure.

Clinical decision support for chemotherapy-induced neutropenia using a hybrid pharmacodynamic/machine learning model

Consensus guidelines recommend use of granulocyte colony stimulating factor in patients deemed at risk of chemotherapy-induced neutropenia, however, these risk models are limited in the factors they consider and miss some cases of neutropenia. Clinical decision making could be supported using models that better tailor their predictions to the individual patient using the wealth of data available in electronic health records (EHRs). Here, we present a hybrid pharmacokinetic/pharmacodynamic (PKPD)/machine learning (ML) approach that uses predictions and individual Bayesian parameter estimates from a PKPD model to enrich an ML model built on her data. We demonstrate this approach using models developed on a large real-world data set of 9121 patients treated for lymphoma, breast, or thoracic cancer. We also investigate the benefits of augmenting the training data using synthetic data simulated with the PKPD model. We find that PKPD-enrichment of ML models improves prediction of grade 3-4 neutropenia, as measured by higher precision (61%) and recall (39%) compared to PKPD model predictions (47%, 33%) or base ML model predictions (51%, 31%). PKPD augmentation of ML models showed minor improvements in recall (44%) but not precision (56%), and data augmentation required careful tuning to control overfitting its predictions to the PKPD model. PKPD enrichment of ML shows promise for leveraging both the physiology-informed predictions of PKPD and the ability of ML to learn predictor-outcome relationships from large data sets to predict patient response to drugs in a clinical precision dosing context.

Early At-Home Measurement of Adalimumab Concentrations to Guide Anti-TNF Precision Dosing: A Pilot Study

BACKGROUND AND OBJECTIVE

Underdosing of adalimumab can result in non-response and poor disease control in patients with rheumatic disease or inflammatory bowel disease. In this pilot study we aimed to predict adalimumab concentrations with population pharmacokinetic model-based Bayesian forecasting early in therapy.

METHODS

Adalimumab pharmacokinetic models were identified with a literature search. A fit-for-purpose evaluation of the model was performed for rheumatologic and inflammatory bowel disease (IBD) patients with adalimumab peak (first dose) and trough samples (first and seventh dose) obtained by a volumetric absorptive microsampling technique. Steady state adalimumab concentrations were predicted after the first adalimumab administration. Predictive performance was calculated with mean prediction error (MPE) and normalised root mean square error (RMSE).

RESULTS

Thirty-six patients (22 rheumatologic and 14 IBD) were analysed in our study. After stratification for absence of anti-adalimumab antibodies, the calculated MPE was -2.6% and normalised RMSE 24.0%. Concordance between predicted and measured adalimumab serum concentrations falling within or outside the therapeutic window was 75%. Three patients (8.3%) developed detectable concentrations of anti-adalimumab antibodies.

CONCLUSION

This prospective study demonstrates that adalimumab concentrations at steady state can be predicted from early samples during the induction phase.

CLINICAL TRIAL REGISTRATION

The trial was registered in the Netherlands Trial Register with trial registry number NTR 7692 ( www.trialregister.nl ).

Clinical Decision Support for Precision Dosing: Opportunities for Enhanced Equity and Inclusion in Health Care

Precision dosing aims to tailor doses to individual patients with the goal of improving treatment efficacy and avoiding toxicity. Clinical decision support software (CDSS) plays a crucial role in mediating this process, translating knowledge derived from clinical trials and real-world data (RWD) into actionable insights for clinicians to use at the point of care. However, not all patient populations are proportionally represented in clinical trials and other data sources that inform CDSS tools, limiting the applicability of these tools for underrepresented populations. Here, we review some of the limitations of existing CDSS tools and discuss methods for overcoming these gaps. We discuss considerations for study design and modeling to create more inclusive CDSS, particularly with an eye toward better incorporation of biological indicators in place of race, ethnicity, or sex. We also review inclusive practices for collection of these demographic data, during both study design and in software user interface design. Because of the role CDSS plays in both recording routine clinical care data and disseminating knowledge derived from data, CDSS presents a promising opportunity to continuously improve precision dosing algorithms using RWD to better reflect the diversity of patient populations.

Evaluation of Neonatal and Paediatric Vancomycin Pharmacokinetic Models and the Impact of Maturation and Serum Creatinine Covariates in a Large Multicentre Data Set

BACKGROUND AND OBJECTIVE

Infants and neonates present a clinical challenge for dosing drugs with high interindividual variability due to these patients' rapid growth and the interplay between maturation and organ function. Model-informed precision dosing (MIPD), which can account for interindividual variability via patient characteristics and Bayesian forecasting, promises to improve individualized dosing strategies in this complex population. Here, we assess the predictive performance of published population pharmacokinetic models describing vancomycin in neonates and infants, and analyze the robustness of these models in the face of clinical uncertainty surrounding covariate values.

METHODS

The predictive precision and bias of nine pharmacokinetic models were compared in a large multi-site data set (N = 2061 patients, 5794 drug levels, 28 institutions) of patients aged 0-365 days. The robustness of model predictions to errors in serum creatinine measurements and gestational age was assessed by using recorded values or by replacing covariate values with 0.3, 0.5 or 0.8 mg/dL or with 40 weeks, respectively.

RESULTS

Of the nine models, two models (Dao and Jacqz-Aigrain) resulted in predicted concentrations within 2.5 mg/L or 15% of the measured values for at least 60% of population predictions. Within individual models, predictive performance often 2 differed in neonates (0-4 weeks) versus older infants (15-52 weeks). For preterm neonates, imputing gestational age as 40 weeks reduced the accuracy of model predictions. Measured values of serum creatinine improved model predictions compared to using imputed values even in neonates ≤1 week of age.

CONCLUSIONS

Several available pharmacokinetic models are suitable for MIPD in infants and neonates. Availability and accuracy of model covariates for patients will be important for guiding dose decision-making.

Prospective Validation and Refinement of a Population Pharmacokinetic Model of Fludarabine in Children and Young Adults Undergoing Hematopoietic Cell Transplantation

Fludarabine is a nucleoside analog with antileukemic and immunosuppressive activity commonly used in allogeneic hematopoietic cell transplantation (HCT). Several fludarabine population pharmacokinetic (popPK) and pharmacodynamic models have been published enabling the movement towards precision dosing of fludarabine in pediatric HCT; however, developed models have not been validated in a prospective cohort of patients. In this multicenter pharmacokinetic study, fludarabine plasma concentrations were collected via a sparse-sampling strategy. A fludarabine popPK model was evaluated and refined using standard nonlinear mixed effects modelling techniques. The previously described fludarabine popPK model well-predicted the prospective fludarabine plasma concentrations. Individuals who received model-based dosing (MBD) of fludarabine achieved significantly more precise overall exposure of fludarabine. The fludarabine popPK model was further improved by both the inclusion of fat-free mass instead of total body weight and a maturation function on fludarabine clearance. The refined popPK model is expected to improve dosing recommendations for children younger than 2 years and patients with higher body mass index. Given the consistency of fludarabine clearance and exposure across its multiple days of administration, therapeutic drug monitoring is not likely to improve targeted exposure attainment.

Model-Based Tacrolimus Follow-up Dosing in Adult Renal Transplant Recipients: A Simulation Trial

BACKGROUND

Initial algorithm-based dosing appears to be effective in predicting tacrolimus dose requirement. However, achieving and maintaining the target concentrations is challenging. Model-based follow-up dosing, which considers patient characteristics and pharmacological data, may further personalize treatment. This study investigated whether model-based follow-up dosing could lead to more accurate tacrolimus exposure than standard therapeutic drug monitoring (TDM) in kidney transplant recipients after an initial algorithm-based dose.

METHODS

This simulation trial included patients from a prospective trial that received an algorithm-based tacrolimus starting dose followed by TDM. For every measured tacrolimus predose concentration (C 0,obs ), model-based dosing advice was simulated using the InsightRX software. Based on previous tacrolimus doses and C 0 , age, body surface area, CYP3A4 and CYP3A5 genotypes, hematocrit, albumin, and creatinine, the optimal next dose, and corresponding tacrolimus concentration (C 0,pred ) were predicted.

RESULTS

Of 190 tacrolimus C 0 values measured in 59 patients, 121 (63.7%; 95% CI 56.8-70.5) C 0,obs were within the therapeutic range (7.5-12.5 ng/mL) versus 126 (66.3%, 95% CI 59.6-73.0) for C 0,pred ( P = 0.89). The median absolute difference between the tacrolimus C 0 and the target tacrolimus concentration (10.0 ng/mL) was 1.9 ng/mL for C 0,obs versus 1.6 ng/mL for C 0,pred . In a historical cohort of 114 kidney transplant recipients who received a body weight-based starting dose followed by TDM, 172 of 335 tacrolimus C 0 (51.3%) were within the therapeutic range (10.0-15.0 ng/mL).

CONCLUSIONS

The combination of an algorithm-based tacrolimus starting dose with model-based follow-up dosing has the potential to minimize under- and overexposure to tacrolimus in the early posttransplant phase, although the additional effect of model-based follow-up dosing on initial algorithm-based dosing seems small.

Population Pharmacokinetic Model Development of Tacrolimus in Pediatric and Young Adult Patients Undergoing Hematopoietic Cell Transplantation

Background: With a notably narrow therapeutic window and wide intra- and interindividual pharmacokinetic (PK) variability, initial weight-based dosing along with routine therapeutic drug monitoring of tacrolimus are employed to optimize its clinical utilization. Both supratherapeutic and subtherapeutic tacrolimus concentrations can result in poor outcomes, thus tacrolimus PK variability is particularly important to consider in the pediatric population given the differences in absorption, distribution, metabolism, and excretion among children of various sizes and at different stages of development. The primary goals of the current study were to develop a population PK (PopPK) model for tacrolimus IV continuous infusion in the pediatric and young adult hematopoietic cell transplant (HCT) population and implement the PopPK model in a clinically available Bayesian forecasting tool.

Methods: A retrospective chart review was conducted of 111 pediatric and young adult patients who received IV tacrolimus by continuous infusion early in the post-transplant period during HCT from February 2016 to July 2020 at our institution. PopPK model building was performed in NONMEM. The PopPK model building process included identifying structural and random effects models that best fit the data and then identifying which patient-specific covariates (if any) further improved model fit.

Results: A total of 1,648 tacrolimus plasma steady-state trough concentrations were included in the PopPK modeling process. A 2-compartment structural model best fit the data. Allometrically-scaled weight was a covariate that improved estimation of both clearance and volume of distribution. Overall, model predictions only showed moderate bias, with minor under-prediction at lower concentrations and minor over-prediction at higher predicted concentrations. The model was implemented in a Bayesian dosing tool and made available at the point-of-care.

Discussion: Novel therapeutic drug monitoring strategies for tacrolimus within the pediatric and young adult HCT population are necessary to reduce toxicity and improve efficacy in clinical practice. The model developed presents clinical utility in optimizing the use of tacrolimus by enabling model-guided, individualized dosing of IV, continuous tacrolimus via a Bayesian forecasting platform.

A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors

Model‐informed precision dosing (MIPD) approaches typically apply maximum a posteriori (MAP) Bayesian estimation to determine individual pharmacokinetic (PK) parameters with the goal of optimizing future dosing regimens. This process combines knowledge about the individual, in the form of drug levels or pharmacodynamic biomarkers, with prior knowledge of the drug PK in the general population. Use of “flattened priors” (FPs), in which the weight of the model priors is reduced relative to observations about the patient, has been previously proposed to estimate individual PK parameters in instances where the patient is poorly described by the PK model. However, little is known about the predictive performance of FPs and when to apply FPs in MIPD. Here, FP is evaluated in a data set of 4679 adult patients treated with vancomycin. Depending on the PK model, prediction error could be reduced by applying FPs in 42–55% of PK parameter estimations. Machine learning (ML) models could identify instances where FPs would outperform MAPs with a specificity of 81–86%, reducing overall root mean squared error (RMSE) of PK model predictions by 12–22% (0.5–1.2 mg/L) relative to MAP alone. The factors most indicative of the use of FPs were past prediction residuals and bias in past PK predictions. A more clinically practical minimal model was developed using only these two features, reducing RMSE by 5–18% (0.20–0.93 mg/L) relative to MAP. This hybrid ML/PK approach advances the precision dosing toolkit by leveraging the power of ML while maintaining the mechanistic insight and interpretability of PK models.

Bayesian clinical decision support-guided versus clinician-guided vancomycin dosing in attainment of targeted pharmacokinetic parameters in a paediatric population

OBJECTIVES

To compare a Bayesian clinical decision support (CDS) dose-optimizing software program with clinician judgement in individualizing vancomycin dosing regimens to achieve vancomycin pharmacokinetic (PK)/pharmacodynamic (PD) targets in a paediatric population.

METHODS

A retrospective review combined with a model-based simulation of vancomycin dosing was performed on children aged 1 year to 18 years at the University of California, San Francisco Benioff Children's Hospital Mission Bay. Dosing regimens recommended by the clinical pharmacists, 'clinician-guided', were compared with alternative 'CDS-guided' dosing regimens. The primary outcome was the percentage of occasions predicted to achieve steady-state trough levels within the target range of 10-15 mg/L, with a secondary outcome of predicted attainment of AUC24 ≥400 mg·h/L. Statistical comparison between approaches was performed using a standard t-test.

RESULTS

A total of n=144 patient occasions were included. CDS-guided regimens were predicted to achieve vancomycin steady-state troughs in the target range on 70.8% (102/144) of occasions, as compared with 37.5% (54/144) in the clinician-guided arm (P<0.0001). An AUC24 of ≥400 mg·h/L was achieved on 93% (112/121) of occasions in the CDS-guided arm versus 72% (87/121) of occasions in the clinician-guided arm (P<0.0001).

CONCLUSIONS

In a simulated analysis, the use of a Bayesian CDS tool was better than clinician judgement in recommending vancomycin dosing regimens in which PK/PD targets would be attained in children.

The Relationship Between Busulfan Exposure and Achievement of Sustained Donor Myeloid Chimerism in Patients with Non-Malignant Disorders

The overall objective of allogeneic hematopoietic cell transplantation (HCT) in patients with non-malignant conditions involves replacing a dysfunctional or absent cell or gene product for disease correction. It is unclear whether lower busulfan exposure may be sufficient in this population to facilitate durable myeloid engraftment and limit toxicity. Given that neither the ideal level of mixed myeloid chimerism for specific non-malignant diseases nor how to condition a patient to achieve stable mixed myeloid chimerism is fully known, we sought to analyze the relationships among busulfan exposure, myeloid chimerism, and outcomes in patients with non-malignant conditions receiving busulfan as a part of combination pretransplant conditioning at our institution. This was a single-center, retrospective study including pediatric patients with a variety of non-malignant disorders who underwent allogeneic HCT at the University of California San Francisco Benioff Children's Hospital from March 2007 to June 2018. The busulfan cumulative area under the curve (cAUC) was estimated using a validated population pharmacokinetic model and nonlinear mixed effects modeling. Median busulfan cAUC for all patients was 70 mg·h/L (range, 53 to 108). All of the 29 patients with a busulfan cAUC of ≥70 mg·h/L achieved long-term disease correction with full or stable mixed (>20%) myeloid chimerism, compared to 78.5% (22/28) of patients with a cAUC of <70 mg·h/L (P = .01). Overall ksurvival was evaluated up to 3 years and was identical in patients with busulfan cAUC < 70 mg·h/L and patients with busulfan cAUC ≥70 mg·h/L (96% versus 93%; P = .92). Only three patients died, at days 65, 164 and 980 days post-HCT. Severe busulfan-related toxicities and graft-versus-host-disease (GVHD) were rare, with veno-occlusive disease occurring in four patients (7%), acute respiratory distress syndrome in three patients (5%), and GVHD in five patients (9%). These results demonstrate excellent outcomes and extremely low rates of toxicity across our entire cohort. Based on the results of this study, we recommend a busulfan exposure target of 75 mg·h/L (range, 70 to 80) in all non-malignant patients receiving allogeneic HCT to ensure optimal exposure for achievement of high-level stable myeloid chimerism.

Continuous Learning in Model-Informed Precision Dosing: A Case Study in Pediatric Dosing of Vancomycin

Model‐informed precision dosing (MIPD) leverages pharmacokinetic (PK) models to tailor dosing to an individual patient’s needs, improving attainment of therapeutic drug exposure targets and thus potentially improving drug efficacy or reducing adverse events. However, selection of an appropriate model for supporting clinical decision making is not trivial. Error or bias in dose selection may arise if the selected model was developed in a population not fully representative of the intended MIPD population. One previously proposed approach is continuous learning, in which an initial model is used in MIPD and then updated as additional data becomes available. In this case study of pediatric vancomycin MIPD, the potential benefits of the continuous learning approach are investigated. Five previously published models were evaluated and found to perform adequately in a data set of 273 pediatric patients in the intensive care unit. Additionally, two predefined simple PK models were fitted on separate populations of 50–350 patients in an approach mimicking clinical implementation of automated continuous learning. With these continuous learning models, prediction error using population PK parameters could be reduced by 2–13% compared with previously published models. Sample sizes of at least 200 patients were found suitable for capturing the interindividual variability in vancomycin at this institution, with limited benefits of larger data sets. Although comprised mostly of trough samples, these sparsely sampled routine clinical data allowed for reasonable estimation of simulated area under the curve (AUC). Together, these findings lay the foundations for a continuous learning MIPD approach.

Personalized Tuberculosis Treatment Through Model-Informed Dosing of Rifampicin

BACKGROUND AND OBJECTIVE

This study proposes a model-informed approach for therapeutic drug monitoring (TDM) of rifampicin to improve tuberculosis (TB) treatment.

METHODS

Two datasets from pulmonary TB patients were used: a pharmacokinetic study (34 patients, 373 samples), and TDM data (96 patients, 391 samples) collected at Radboud University Medical Center, The Netherlands. Nine suitable population pharmacokinetic models of rifampicin were identified in the literature and evaluated on the datasets. A model developed by Svensson et al. was found to be the most suitable based on graphical goodness of fit, residual diagnostics, and predictive performance. Prediction of individual area under the concentration-time curve from time zero to 24 h (AUC₂₄) and maximum concentration (C_max) employing various sampling strategies was compared with a previously established linear regression TDM strategy, using sampling at 2, 4, and 6 h, in terms of bias and precision (mean error [ME] and root mean square error [RMSE]).

RESULTS

A sampling strategy using 2- and 4-h blood collection was selected to be the most suitable. The bias and precision of the two strategies were comparable, except that the linear regression strategy was more biased in prediction of the AUC₂₄ than the model-informed approach (ME of 9.9% and 1.5%, respectively). A comparison of resulting dose advice, using predictions on a simulated dataset, showed no significant difference in sensitivity or specificity between the two methods. The model was successfully implemented in the InsightRX precision dosing platform.

CONCLUSION

Blood sampling at 2 and 4 h, combined with model-based prediction, can be used instead of the currently used linear regression strategy, shortening the sampling by 2 h and one sampling point without performance loss while simultaneously offering flexibility in sampling times.

Assessment of a Model-Informed Precision Dosing Platform Use in Routine Clinical Care for Personalized Busulfan Therapy in the Pediatric Hematopoietic Cell Transplantation (HCT) Population

Introduction

Population pharmacokinetic (PK) studies demonstrate model-based dosing for busulfan that incorporates body size and age improve clinical target attainment as compared to weight-based regimens. Recently, for clinical dosing of busulfan and TDM, our institution transitioned to a cloud-based clinical decision support tool (www.insight-rx.com). The goal of this study was to assess the dose decision tool for the achievement of target exposure of busulfan in children undergoing hematopoietic cell transplantation (HCT).

Patients and Methods

Patients (N = 188) were grouped into cohorts A, B, or C based on the method for initial dose calculation and estimation of AUC: Cohort A: Initial doses were based on the conventional dosing algorithm (as outlined in the manufacturers' package insert) and non-compartmental analysis (NCA) estimation using the trapezoidal rule for estimation of AUC following TDM. Cohort B: Initial doses for busulfan were estimated by a first-generation PK model and NCA estimation of AUC following TDM. Cohort C: Initial doses were calculated by an updated, second-generation PK model available in the dose decision tool with an estimation of AUC following TDM.

Results

The percent of individuals achieving the exposure target at the time of first PK collection was higher in subjects receiving initial doses provided by the model-informed precision dosing platform (cohort C, 75%) versus subjects receiving initial doses based on either of the two other approaches (conventional guidelines/cohort A, 25%; previous population PK model and NCA parameter estimation, cohort B, 50%). Similarly, the percent of subjects achieving the targeted cumulative busulfan exposure (cAUC) in cohort C was 100% vs. 66% and 88% for cohort A and B, respectively. For cAUC, the variability in the spread of target attainment (%CV) was low at 4.1% for cohort C as compared to cohort A (14.8%) and cohort B (17.1%).

Conclusion

Achievement of goal exposure early on in treatment was improved with the updated model for busulfan and the Bayesian platform. Model-informed dosing and TDM utilizing a Bayesian-based platform provides a significant advantage over conventional guidelines for the achievement of goal cAUC exposure.

Prospective validation of a model-informed precision dosing tool for vancomycin in intensive care patients

AIMS

Vancomycin is an important antibiotic for critically ill patients with Gram-positive bacterial infections. Critically ill patients typically have severely altered pathophysiology, which leads to inefficacy or toxicity. Model-informed precision dosing may aid in optimizing the dose, but prospectively validated tools are not available for this drug in these patients. We aimed to prospectively validate a population pharmacokinetic model for purpose model-informed precision dosing of vancomycin in critically ill patients.

METHODS

We first performed a systematic evaluation of various models on retrospectively collected pharmacokinetic data in critically ill patients and then selected the best performing model. This model was implemented in the Insight Rx clinical decision support tool and prospectively validated in a multicentre study in critically ill patients. The predictive performance was obtained as mean prediction error and relative root mean squared error.

RESULTS

We identified 5 suitable population pharmacokinetic models. The most suitable model was carried forward to a prospective validation. We found in a prospective multicentre study that the selected model could accurately and precisely predict the vancomycin pharmacokinetics based on a previous measurement, with a mean prediction error and relative root mean squared error of respectively 8.84% (95% confidence interval 5.72-11.96%) and 19.8% (95% confidence interval 17.47-22.13%).

CONCLUSION

Using a systematic approach, with a retrospective evaluation and prospective verification we showed the suitability of a model to predict vancomycin pharmacokinetics for purposes of model-informed precision dosing in clinical practice. The presented methodology may serve a generic approach for evaluation of pharmacometric models for the use of model-informed precision dosing in the clinic.

Model-Informed Precision Dosing of Vancomycin in Hospitalized Children: Implementation and Adoption at an Academic Children's Hospital

Background

Model-informed precision dosing (MIPD) can serve as a powerful tool during therapeutic drug monitoring (TDM) to help individualize dosing in populations with large pharmacokinetic variation. Yet, adoption of MIPD in the clinical setting has been limited. Overcoming technologic hurdles that allow access to MIPD at the point-of-care and placing it in the hands of clinical specialists focused on medication dosing may encourage adoption.

Objective

To describe the hospital implementation and usage of a MIPD clinical decision support (CDS) tool for vancomycin in a pediatric population.

Methods

Within an academic children’s hospital, MIPD for vancomycin was implemented via a commercial cloud-based CDS tool that utilized Bayesian forecasting. Clinical pharmacists were recognized as local champions to facilitate adoption of the tool and operated as end-users. Integration within the electronic health record (EHR) and automatic transmission of patient data to the tool were identified as important requirements. A web-link icon was developed within the EHR which when clicked sends users and needed patient-level clinical data to the CDS platform. Individualized pharmacokinetic predictions and exposure metrics for vancomycin are then presented in the form of a web-based dashboard. Use of the CDS tool as part of TDM was tracked and users were surveyed on their experience.

Results

After a successful pilot phase in the neonatal intensive care unit, implementation of MIPD was expanded to the pediatric intensive care unit, followed by availability to the entire hospital. During the first 2+ years since implementation, a total of 853 patient-courses (n = 96 neonates, n = 757 children) and 2,148 TDM levels were evaluated using the CDS tool. For the most recent 6 months, the CDS tool was utilized to support 79% (181/230) of patient-courses in which TDM was performed. Of 26 users surveyed, > 96% agreed or strongly agreed that automatic transmission of patient data to the tool was a feature that helped them complete tasks more efficiently; 81% agreed or strongly agreed that they were satisfied with the CDS tool.

Conclusions

Integration of a vancomycin CDS tool within the EHR, along with leveraging the expertise of clinical pharmacists, allowed for successful adoption of MIPD in clinical care.

Individualized Empiric Vancomycin Dosing in Neonates Using a Model-Based Approach

BACKGROUND

Vancomycin dosing in neonates is challenging because of the large variation in pharmacokinetics. Existing empiric dosing recommendations use table-based formats, within which a neonate is categorized on the basis of underlying characteristics. The ability to individualize dosing is limited because of the small number of "dose categories," and achieving narrow exposure targets is difficult. Our objective was to evaluate a model-based dosing approach (which we designated Neo-Vanco) designed to individualize empiric vancomycin dosing in neonates.

METHODS

Neo-Vanco was developed on the basis of a published, externally validated population pharmacokinetic model. Using a simulation-based methodology, individualized empiric doses that maximize the probability of attaining a 24-hour area under the curve/minimum inhibitory concentration ratio (AUC24/MIC) of >400 while minimizing troughs >20 mg/L are calculated. To evaluate the Neo-Vanco strategy, retrospective data from neonates treated with vancomycin at 2 healthcare systems were used, and empiric dose recommendations from the following 4 sources were examined: Neo-Vanco, Neofax, Red Book, and Lexicomp. Predicted AUC24 and troughs were calculated and compared.

RESULTS

Overall, 492 neonates were evaluated (median postmenstrual age, 36 weeks [5th-95th percentiles (90% range), 25-47 weeks]; median weight, 2.4 kg [90% range, 0.6-4.8 kg]). The percentage of neonates predicted to achieve an AUC24/MIC of >400 was 94% with Neo-Vanco, 18% with Neofax, 23% with Red Book, and 55% with Lexicomp (all P < .0001 vs Neo-Vanco). Predicted troughs of >20 mg/L were infrequent and similar across the dosing approaches (Neo-Vanco, 2.8%; Neofax, 1.0% [P = .03]; Red Book, 2.6% [P = .99]; and Lexicomp, 4.1% [P = .27].

CONCLUSION

A model-based dosing approach that individualizes empiric vancomycin dosing was predicted to improve achievement of target exposure levels in neonates. Prospective clinical evaluation is warranted.

Model-Informed Precision Dosing at the Bedside: Scientific Challenges and Opportunities

The development of model-informed precision dosing (MIPD) tools, especially in the form of native or web-based applications to be used at the bedside, has garnered marked attention in recent years. Their potential clinical benefit can be large, but it should be ensured that such tools make optimal use of available clinical data and have adequate predictive ability. Unique scientific challenges specific to MIPD remain, which may require adaptation of commonly used diagnostics in pharmacometrics.

Heterogeneous drug penetrance of veliparib and carboplatin measured in triple negative breast tumors

BACKGROUND

Poly(ADP-ribose) polymerase inhibitors (PARPi), coupled to a DNA damaging agent is a promising approach to treating triple negative breast cancer (TNBC). However, not all patients respond; we hypothesize that non-response in some patients may be due to insufficient drug penetration. As a first step to testing this hypothesis, we quantified and visualized veliparib and carboplatin penetration in mouse xenograft TNBCs and patient blood samples.

METHODS

MDA-MB-231, HCC70 or MDA-MB-436 human TNBC cells were implanted in 41 beige SCID mice. Low dose (20 mg/kg) or high dose (60 mg/kg) veliparib was given three times daily for three days, with carboplatin (60 mg/kg) administered twice. In addition, blood samples were analyzed from 19 patients from a phase 1 study of carboplatin + PARPi talazoparib. Veliparib and carboplatin was quantified using liquid chromatography-mass spectrometry (LC-MS). Veliparib tissue penetration was visualized using matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) and platinum adducts (covalent nuclear DNA-binding) were quantified using inductively coupled plasma-mass spectrometry (ICP-MS). Pharmacokinetic modeling and Pearson's correlation were used to explore associations between concentrations in plasma, tumor cells and peripheral blood mononuclear cells (PBMCs).

RESULTS

Veliparib penetration in xenograft tumors was highly heterogeneous between and within tumors. Only 35% (CI 95% 26-44%), 74% (40-97%) and 46% (9-37%) of veliparib observed in plasma penetrated into MDA-MB-231, HCC70 and MDA-MB-436 cell-based xenografts, respectively. Within tumors, penetration heterogeneity was larger with the 60 mg/kg compared to the 20 mg/kg dose (RSD 155% versus 255%, P = 0.001). These tumor concentrations were predicted similar to clinical dosing levels, but predicted tumor concentrations were below half maximal concentration values as threshold of response. Xenograft veliparib concentrations correlated positively with platinum adduct formation (R 2 = 0.657), but no PARPi-platinum interaction was observed in patients' PBMCs. Platinum adduct formation was significantly higher in five gBRCA carriers (ratio of platinum in DNA in PBMCs/plasma 0.64% (IQR 0.60-1.16%) compared to nine non-carriers (ratio 0.29% (IQR 0.21-0.66%, P < 0.0001).

CONCLUSIONS

PARPi/platinum tumor penetration can be measured by MALDI-MSI and ICP-MS in PBMCs and fresh frozen, OCT embedded core needle biopsies. Large variability in platinum adduct formation and spatial heterogeneity in veliparib distribution may lead to insufficient drug exposure in select cell populations.

The Effect of Famotidine, a MATE1-Selective Inhibitor, on the Pharmacokinetics and Pharmacodynamics of Metformin

INTRODUCTION

Pharmacokinetic outcomes of transporter-mediated drug-drug interactions (TMDDIs) are increasingly being evaluated clinically. The goal of our study was to determine the effects of selective inhibition of multidrug and toxin extrusion protein 1 (MATE1), using famotidine, on the pharmacokinetics and pharmacodynamics of metformin in healthy volunteers.

METHODS

Volunteers received metformin alone or with famotidine in a crossover design. As a positive control, the longitudinal effects of famotidine on the plasma levels of creatinine (an endogenous substrate of MATE1) were quantified in parallel. Famotidine unbound concentrations in plasma reached 1 µM, thus exceeding the in vitro concentrations that inhibit MATE1 [concentration of drug producing 50 % inhibition (IC50) 0.25 µM]. Based on current regulatory guidance, these concentrations are expected to inhibit MATE1 clinically [i.e. maximum unbound plasma drug concentration (C max,u)/IC50 >0.1].

RESULTS

Consistent with MATE1 inhibition, famotidine administration significantly altered creatinine plasma and urine levels in opposing directions (p < 0.005). Interestingly, famotidine increased the estimated bioavailability of metformin [cumulative amount of unchanged drug excreted in urine from time zero to infinity (A e∞)/dose; p < 0.005] without affecting its systemic exposure [area under the plasma concentration-time curve (AUC) or maximum concentration in plasma (C max)] as a result of a counteracting increase in metformin renal clearance. Moreover, metformin-famotidine co-therapy caused a transient effect on oral glucose tolerance tests [area under the glucose plasma concentration-time curve between time zero and 0.5 h (AUCglu,0.5); p < 0.005].

CONCLUSIONS

These results suggest that famotidine may improve the bioavailability and enhance the renal clearance of metformin.

New Paradigm for Translational Modeling to Predict Long-term Tuberculosis Treatment Response

Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse‐to‐human translational pharmacokinetics (PKs) – pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, and rifampin concentrations accelerating bacterial death, clinical PK data, species‐specific protein binding, drug‐drug interactions, and patient‐specific pathology. Simulations of recent trials testing 4‐month regimens predicted 65% (95% confidence interval [CI], 55–74) relapse‐free patients vs. 80% observed in the REMox‐TB trial, and 79% (95% CI, 72–87) vs. 82% observed in the Rifaquin trial. Simulation of 6‐month regimens predicted 97% (95% CI, 93–99) vs. 92% and 95% observed in 2RHZE/4RH control arms, and 100% predicted and observed in the 35 mg/kg rifampin arm of PanACEA MAMS. These results suggest that the model can inform regimen optimization and predict outcomes of ongoing trials.

Population pharmacokinetics of infliximab in patients with inflammatory bowel disease: potential implications for dosing in clinical practice

BACKGROUND

Infliximab (IFX) is effective in the treatment of inflammatory bowel diseases (IBD). Currently, IFX is administered at fixed doses and intervals; however, costs are high and optimisation is necessary. Several publications indicate that IFX should be dosed on trough levels ≥3.0 mg/L. For optimising IFX dosing, the use of a pharmacokinetic model is important. Population pharmacokinetics of IFX have been described earlier; however, these models were not used for dose optimising.

AIMS

To develop a pharmacokinetic model for IFX in IBD patients that can be used for dose-optimisation of IFX and to predict serum trough levels in this population.

METHODS

An observational retrospective study was performed in 42 IFX-treated IBD patients. Serum samples were drawn before infusion at T = 0, 2, 6, 14, 22 and 54 weeks and analysed for IFX and antibodies against IFX (ATI). Relevant covariates were recorded and a population pharmacokinetic model was developed.

RESULTS

Individual plots created using the final model showed good correspondence between observed and model predicted values. Serum levels were influenced by ATI, disease activity, sex and albumin. Our results show that in patients without ATI target trough levels ≥3.0 mg/L can be achieved by increasing dosing intervals from 8 to 12 weeks combined with a dose increase. This results in a reduction of 33% in concomitant costs.

CONCLUSIONS

In IBD patients without ATI, trough level dosing based on longer intervals can reduce IFX therapy-related visits to the hospital with one-third. Trough level based dose intensification should always be justified by disease activity parameters.

Incorporation of concentration data below the limit of quantification in population pharmacokinetic analyses

Handling of data below the lower limit of quantification (LLOQ), below the limit of quantification (BLOQ) in population pharmacokinetic (PopPK) analyses is important for reducing bias and imprecision in parameter estimation. We aimed to evaluate whether using the concentration data below the LLOQ has superior performance over several established methods. The performance of this approach (“All data”) was evaluated and compared to other methods: “Discard,” “LLOQ/2,” and “LIKE” (likelihood-based). An analytical and residual error model was constructed on the basis of in-house analytical method validations and analyses from literature, with additional included variability to account for model misspecification. Simulation analyses were performed for various levels of BLOQ, several structural PopPK models, and additional influences. Performance was evaluated by relative root mean squared error (RMSE), and run success for the various BLOQ approaches. Performance was also evaluated for a real PopPK data set. For all PopPK models and levels of censoring, RMSE values were lowest using “All data.” Performance of the “LIKE” method was better than the “LLOQ/2” or “Discard” method. Differences between all methods were small at the lowest level of BLOQ censoring. “LIKE” method resulted in low successful minimization (<50%) and covariance step success (<30%), although estimates were obtained in most runs (∼90%). For the real PK data set (7.4% BLOQ), similar parameter estimates were obtained using all methods. Incorporation of BLOQ concentrations showed superior performance in terms of bias and precision over established BLOQ methods, and shown to be feasible in a real PopPK analysis.

A time-to-event model for acute rejections in paediatric renal transplant recipients treated with ciclosporin A

AIMS

Ciclosporin A (CsA) dosing in immunosuppression after paediatric kidney transplantation remains challenging, and appropriate target CsA exposures (AUCs) are controversial. This study aimed to develop a time-to-first-acute rejection (AR) model and to explore predictive factors for therapy outcome.

METHODS

Patient records at the Children's Hospital in Helsinki, Finland, were analysed. A parametric survival model in NONMEM was used to describe the time to first AR. The influences of AUC and other covariates were explored using stepwise covariate modelling, bootstrap-stepwise covariate modelling and cross-validated stepwise covariate modelling. The clinical relevance of the effects was assessed with the time at which 90% of the patients were AR free (t90).

RESULTS

Data from 87 patients (0.7-19.8 years old, 54 experiencing an AR) were analysed. The baseline hazard was described with a function changing in steps over time. No statistically significant covariate effects were identified, a finding substantiated by all methods used. Thus, within the observed AUC range (90% interval 1.13-8.40 h mg l⁻¹), a rise in AUC was not found to increase protection from AR. Dialysis time, sex and baseline weight were potential covariates, but the predicted clinical relevance of their effects was low. For the strongest covariate, dialysis time, median t90 was 5.8 days (90% confidence interval 5.1-6.8) for long dialysis times (90th percentile) and 7.4 days (6.4-11.7) for short dialysis times (10th percentile).

CONCLUSIONS

A survival model with discrete time-varying hazards described the data. Within the observed range, AUC was not identified as a covariate. This feedback on clinical practice may help to avoid unnecessarily high CsA dosing in children.

Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose

Several software tools are available that facilitate the use of the NONMEM software and extend its functionality. This tutorial shows how three commonly used and freely available tools, Pirana, PsN, and Xpose, form a tightly integrated workbench for modeling and simulation with NONMEM. During the tutorial, we provide some guidance on what diagnostics we consider most useful in pharmacokinetic model development and how to construct them using these tools.

Model-based treatment optimization of a novel VEGFR inhibitor

AIM

To evaluate dosing and intervention strategies for the phase II programme of a VEGF receptor inhibitor using PK-PD modelling and simulation, with the aim of maximizing (i) the number of patients on treatment and (ii) the average dose level during treatment.

METHODS

A previously developed PK-PD model for lenvatinib (E7080) was updated and parameters were re-estimated (141 patients, once daily and twice daily regimens). Treatment of lenvatinib was simulated for 16 weeks, initiated at 25 mg once daily. Outcome measures included the number of patients on treatment and overall drug exposure. A hypertension intervention design proposed for phase II studies was evaluated, including antihypertensive treatment and dose de-escalation. Additionally, a within-patient dose escalation was investigated, titrating up to 50 mg once daily unless unacceptable toxicity occurred.

RESULTS

Using the proposed antihypertension intervention design, 82% of patients could remain on treatment, and the mean dose administered was 21.5 mg day⁻¹. The adverse event (AE) guided dose titration increased the average dose by 4.6 mg day⁻¹, while only marginally increasing the percentage of patients dropping out due to toxicity (from 18% to 20.8%).

CONCLUSIONS

The proposed hypertension intervention design is expected to be effective in maintaining patients on treatment with lenvatinib. The AE-guided dose titration with blood pressure as a biomarker yielded a higher overall dose level, without relevant increases in toxicity. Since increased exposure to lenvatinib seems correlated with increased treatment efficacy, the adaptive treatment design may thus be a valid approach to improve treatment outcome.

Performance of methods for handling missing categorical covariate data in population pharmacokinetic analyses

In population pharmacokinetic analyses, missing categorical data are often encountered. We evaluated several methods of performing covariate analyses with partially missing categorical covariate data. Missing data methods consisted of discarding data (DROP), additional effect parameter for the group with missing data (EXTRA), and mixture methods in which the mixing probability was fixed to the observed fraction of categories (MIX(obs)), based on the likelihood of the concentration data (MIX(conc)), or combined likelihood of observed covariate data and concentration data (MIX(joint)). Simulations were implemented to study bias and imprecision of the methods in datasets with equal-sized and unbalanced category ratios for a binary covariate as well as datasets with non-random missingness (MNAR). Additionally, the performance and feasibility of implementation was assessed in two real datasets. At either low (10%) or high (50%) levels of missingness, all methods performed similarly well. Performance was similar for situations with unbalanced datasets (3:1 covariate distribution) and balanced datasets. In the MNAR scenario, the MIX methods showed a higher bias in the estimation of CL and covariate effect than EXTRA. All methods could be applied to real datasets, except DROP. All methods perform similarly at the studied levels of missingness, but the DROP and EXTRA methods provided less bias than the mixture methods in the case of MNAR. However, EXTRA was associated with inflated type I error rates of covariate selection, while DROP handled data inefficiently.

Decitabine triphosphate levels in peripheral blood mononuclear cells from patients receiving prolonged low-dose decitabine administration: a pilot study

PURPOSE

Decitabine is a nucleoside analog used in the treatment for myelodysplastic syndrome. The compound requires intracellular conversion to its triphosphate to become active. Decitabine triphosphate has, however, never been quantified in peripheral blood mononuclear cells (PBMCs) from patients.

METHOD

This article describes a method for the quantitative determination of decitabine triphosphate in PBMCs using liquid chromatography coupled to tandem mass spectrometry. The method was applied to ex vivo incubated whole blood samples and samples from three patients receiving prolonged low-dose decitabine treatment.

RESULTS

We successfully quantitated decitabine triphosphate in PBMCs. Considerable levels were detected in PBMCs from two patients that responded well to therapy, whereas only low levels were present in a non-responding patient. Moreover, the data show that, in contrast to plasma decitabine, intracellular decitabine triphosphate accumulates during a treatment cycle of nine infusions at a dose of 15 mg/m(2).

CONCLUSIONS

The results suggest a relationship between decitabine triphosphate levels and response to therapy. Based on the observed accumulation of decitabine triphosphate during a treatment cycle, a less intensive dose scheme could be feasible.

Pharmacodynamic biomarkers in model-based drug development in oncology

INTRODUCTION

Model-based drug development (MBDD) is recognized as an initiative able to improve success rates in the development of new anti-cancer agents. The use of pharmacodynamic (PD) biomarkers may be valuable in this context. The implementation of biomarkers in MBDD in oncology is the subject of this review.

METHODS

Literature was searched for articles and relevant conference abstract concerning application of biomarkers in MBDD in oncology. First, papers are discussed concerning the use of biomarkers in modeling and simulation analyses in preclinical and early clinical phases of drug development. Subsequently, articles concerning late-stage clinical drug development are discussed.

RESULTS

Only a limited set of articles and conference presentations were identified. As expected, the majority of publications are concerned with targeted anti-cancer drugs. In the early development of novel anti-cancer agents, most publications are concerned to the evaluation of dosing regimens for further clinical evaluation, or the identification of the required levels of target modulation. In general, combined analysis of clinical and preclinical data provide the most informative analyses. The use of biomarkers in late-stage drug development has mainly been confined to the prediction of phase III outcome on the basis of tumor growth data obtained from phase II trials, with tumor growth as biomarker for outcome.

CONCLUSION

The use of suitable biomarkers in MBDD, has shown its merits in oncology, especially in early clinical development. Considering the low number of reports in literature, we would propose a more active use of presented techniques during all developmental phases of new anticancer agents.

Two-stage model-based design of cancer phase I dose escalation trials: evaluation using the phase I program of barasertib (AZD1152)

Introduction Modeling and simulation of pharmacokinetics and pharmacodynamics has previously been shown to be potentially useful in designing Phase I programs of novel anti-cancer agents that show hematological toxicity. In this analysis, a two-stage model-based trial design was evaluated retrospectively using data from the Phase I program with the aurora kinase inhibitor barasertib. Methods Data from two Phase I trials and four regimens were used (n = 79). Using barasertib-hydroxy QPA plasma concentrations and neutrophil count data from only study 1A, a PKPD model was developed and subsequently used to predict the MTD and a safe starting dose for the other trials. Results The PKPD model based on data from the first study adequately described the time course of neutrophil count fluctuation. The two-stage model-based design provided safe starting doses for subsequent phase I trials for barasertib. Predicted safe starting dose levels were higher than those used in two subsequent trials, but lower than used in the other trial. Discussion The two-stage approach could have been applied safely to define starting doses for alternative dosing strategies with barasertib. The limited improvement in efficiency for the phase I program of barasertib may have been due to the fact that starting doses for the studied phase I trials were already nearly optimal. Conclusion Application of the two-stage model-based trial design in Phase I programs with novel anti-cancer drugs that cause haematological toxicity is feasible, safe, and may lead to a reduction in the number of patient treated at sub-therapeutic dose-levels.

Evaluation of α2-integrin expression as a biomarker for tumor growth inhibition for the investigational integrin inhibitor E7820 in preclinical and clinical studies

E7820 is an orally active inhibitor of α(2)-integrin mRNA expression, currently tested in phases I and II. We aimed to evaluate what levels of inhibition of integrin expression are needed to achieve tumor stasis in mice, and to compare this to the level of inhibition achieved in humans. Tumor growth inhibition was measured in mice bearing a pancreatic KP-1 tumor, dosed at 12.5-200 mg/kg over 21 days. In the phase I study, E7820 was administered daily for 28 days over a range of 0-200 mg, followed by a 7-day washout period. PK-PD models were developed in NONMEM. α(2)-Integrin expression measured on platelets, corresponding to tumor stasis at t = 21 in 50% and 90% of the mice (I(int,50), I(int,90)) were calculated. It was evaluated if these levels of inhibition could be achieved in patients at tolerable doses. One hundred nineteen α(2)-Integrin measurements and 210 tumor size measurements were available from mice. The relationship between PK and α(2)-integrin expression was modeled using an indirect-effect model, subsequently linked to an exponential tumor growth model. I(inh,50) and I(inh,90) were 14.7% (RSE 7%) and 17.9% (RSE 8%). Four hundred sixty two α(2)-integrin measurements were available from 29 patients. Using the schedule of 100 mg qd (MTD), α(2)-integrin expression was inhibited more strongly than the I(int,50) and I(int,90) in greater than 95% and greater than 50% of patients, respectively. Moderate inhibition of α(2)-integrin expression corresponded to tumor stasis in mice, and similar levels could be reached in patients with the dose level of 100 mg qd.

Piraña and PCluster: a modeling environment and cluster infrastructure for NONMEM

Pharmacokinetic-pharmacodynamic modeling using non-linear mixed effects modeling (NONMEM) is a powerful yet challenging technique, as the software is generally accessed from the command line. A graphical user interface, Piraña, was developed that offers a complete modeling environment for NONMEM, enabling both novice and advanced users to increase efficiency of their workflow. Piraña provides features for the management and creation of model files, the overview of modeling results, creation of run reports and handling of datasets and output tables, and the running of custom R scripts on model output. Through the secure shell (SSH) protocol, Piraña can also be used to connect to Linux clusters (SGE, MOSIX) for distribution of workload. Modeling with NONMEM is computationally burdensome, which may be alleviated by distributing runs to computer clusters. A solution to this problem is offered here, called PCluster. This platform is easy to set up, runs in standard network environments, and can be extended with additional nodes if needed. The cluster supports the modeling toolkit Perl speaks NONMEM (PsN), and can include dedicated or non-dedicated PCs. A daemon script, written in Perl, was designed to run in the background on each node in the cluster, and to manage job distribution. The PCluster can be accessed from Piraña, and both software products have extensively been tested on a large academic network. The software is available under an open-source license.

An integrated pharmacokinetic model for the influence of CYP3A4 expression on the in vivo disposition of lopinavir and its modulation by ritonavir

Lopinavir, a human immunodeficiency virus protease inhibitor, has a very low oral bioavailability, which can be enhanced with a low dose of the CYPA4 inhibitor ritonavir. Our aim was to separately quantify the role of intestinal and hepatic cytochrome P450 3A (CYP3A4) expression on lopinavir disposition in a novel mouse model. Lopinavir and ritonavir were administered to mice selectively expressing human CYP3A4 in the intestine and/or liver. Using nonlinear mixed-effects modeling, we could separately quantify the effects of intestinal CYP3A4 expression, hepatic CYP3A4 expression, and the presence of ritonavir on both the absorption and elimination of lopinavir, which was previously not possible using noncompartmental methods. Intestinal, but not hepatic, CYP3A4-related first-pass metabolism was the major barrier for systemic entry of lopinavir. Relative oral bioavailability of lopinavir in mice expressing both hepatic and intestinal CYP3A4 was only 1.3% when compared with mice that were CYP3A deficient. In presence of ritonavir, relative bioavailability increased to 9.5% due to inhibiton of intestinal, but not due to inhibition of hepatic first-pass metabolism. Hepatic CYP3A4 related systemic clearance was inversely related to ritonavir exposure and not only hepatic but also intestinal CYP3A4 expression contributed to systemic clearance of lopinavir.

Clinical pharmacokinetics of therapeutic monoclonal antibodies

Monoclonal antibodies (mAbs) have been used in the treatment of various diseases for over 20 years and combine high specificity with generally low toxicity. Their pharmacokinetic properties differ markedly from those of non-antibody-type drugs, and these properties can have important clinical implications. mAbs are administered intravenously, intramuscularly or subcutaneously. Oral administration is precluded by the molecular size, hydrophilicity and gastric degradation of mAbs. Distribution into tissue is slow because of the molecular size of mAbs, and volumes of distribution are generally low. mAbs are metabolized to peptides and amino acids in several tissues, by circulating phagocytic cells or by their target antigen-containing cells. Antibodies and endogenous immunoglobulins are protected from degradation by binding to protective receptors (the neonatal Fc-receptor [FcRn]), which explains their long elimination half-lives (up to 4 weeks). Population pharmacokinetic analyses have been applied in assessing covariates in the disposition of mAbs. Both linear and nonlinear elimination have been reported for mAbs, which is probably caused by target-mediated disposition. Possible factors influencing elimination of mAbs include the amount of the target antigen, immune reactions to the antibody and patient demographics. Bodyweight and/or body surface area are generally related to clearance of mAbs, but clinical relevance is often low. Metabolic drug-drug interactions are rare for mAbs. Exposure-response relationships have been described for some mAbs. In conclusion, the parenteral administration, slow tissue distribution and long elimination half-life are the most pronounced clinical pharmacokinetic characteristics of mAbs.

A model of hypertension and proteinuria in cancer patients treated with the anti-angiogenic drug E7080

Hypertension and proteinuria are commonly observed side-effects for anti-angiogenic drugs targeting the VEGF pathway. In most cases, hypertension can be controlled by prescription of anti-hypertensive (AH) therapy, while proteinuria often requires dose reductions or dose delays. We aimed to construct a pharmacokinetic–pharmacodynamic (PK–PD) model for hypertension and proteinuria following treatment with the experimental VEGF-inhibitor E7080, which would allow optimization of treatment, by assessing the influence of anti-hypertensive medication and dose reduction or dose delays in treating and avoiding toxicity. Data was collected from a phase I study of E7080 (n = 67), an inhibitor of multiple tyrosine kinases, among which VEGF. Blood pressure and urinalysis data were recorded weekly. Modeling was performed in NONMEM, and direct and indirect response PK–PD models were evaluated. A previously developed PK model was used. An indirect response PK–PD model described the increase in BP best, while the probability of developing proteinuria toxicity in response to exposure to E7080, was best described by a Markov transition model. This model may guide clinical interventions and provide treatment recommendations for E7080, and may serve as a template model for other drugs in this class.

Predictive ability of a semi-mechanistic model for neutropenia in the development of novel anti-cancer agents: two case studies

In cancer chemotherapy neutropenia is a common dose-limiting toxicity. An ability to predict the neutropenic effects of cytotoxic agents based on proposed trial designs and models conditioned on previous studies would be valuable. The aim of this study was to evaluate the ability of a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model for myelosuppression to predict the neutropenia observed in Phase I clinical studies, based on parameter estimates obtained from prior trials. Pharmacokinetic and neutropenia data from 5 clinical trials for diflomotecan and from 4 clinical trials for indisulam were used. Data were analyzed and simulations were performed using the population approach with NONMEM VI. Parameter sets were estimated under the following scenarios: (a) data from each trial independently, (b) pooled data from all clinical trials and (c) pooled data from trials performed before the tested trial. Model performance in each of the scenarios was evaluated by means of predictive (visual and numerical) checks. The semi-mechanistic PK/PD model for neutropenia showed adequate predictive ability for both anti-cancer agents. For diflomotecan, similar predictions were obtained for the three scenarios. For indisulam predictions were better when based on data from the specific study, however when the model parameters were conditioned on data from trials performed prior to a specific study, similar predictions of the drug related-neutropenia profiles and descriptors were obtained as when all data were used. This work provides further indication that modeling and simulation tools can be applied in the early stages of drug development to optimize future trials.

'Flooding' of the lungs and severe dyspnea in a patient with bronchoalveolar carcinoma

In this case report, we describe a patient with bronchoalveolar carcinoma that experienced severe bronchorrhea and dyspnea after inhalation of N-acetylcysteine. The adverse reactions occurred both after oral and nebulized administration of N-acetylcysteine, resulting in severe dyspnea and the feeling of 'drowning'. Bronchorrhea has previously been reported as an uncommon but serious complication of bronchoalveolar carcinoma. We strongly suspect the administration of N-acetylcysteine to be implicated, as the complications occurred immediately after administration of this drug. As the patient suffered from hyperhomocysteinemia, we speculate that an additive or synergistic interaction with homocysteine may have been involved as well.

Application of population pharmacokinetic modeling in early clinical development of the anticancer agent E7820

The aim of this study was to assess the population pharmacokinetics (PopPK) of the novel oral anti-cancer agent E7820. Both a non-linear mixed effects modeling analysis and a non-compartmental analysis (NCA) were performed and results were compared. Data were obtained from a phase I dose escalation study in patients with malignant solid tumors or lymphomas. E7820 was administered daily for 28 days, followed by a washout period of 7 days prior to the start of subsequent cycles. A one compartment model with linear elimination from the central compartment was shown to give adequate fit, while absorption was described using a turnover model. Final population parameter estimates of basic PK parameters obtained with the PopPK method were (RSE): clearance, 6.24 L/h (7.1%), volume of distribution, 66.0 L (8.5%), mean transit time to the absorption compartment, 0.638 h (6.5%). The intake of food prior to dose administration slowed absorption (2.8-fold, RSE 13%) and increased relative bioavailability of E7820 by 36% (RSE 14%), although the effect on C (max) and AUC was not significant. Comparison with the NCA approach showed approximately equal PK parameter estimates and food effect measures, although specific advantages of PopPK included efficiency in use of data and more appropriate assessment of variability.

[The pharmacokinetics of monoclonal antibodies]

Monoclonal antibodies (MOABs) are, due to their specificity, increasingly being deployed for therapeutic purposes. MOABs are derived from immunoglobulins and are fully or partially of murine or human origin. They are administered parenterally: mostly intravenously, but subcutaneous or intramuscular administration is also possible, in which case absorption probably occurs through the lymphatic system. The distribution of MOABs from the bloodstream into the tissues is slow and is hampered by the high molecular size of the MOABs, which is a lesser problem for fragments of antibodies (Fab fragments). MOABs are metabolised to peptides and amino acids. This process takes place in many tissues of the body, but probably predominantly in epithelial cells. As a consequence of the saturable binding of the MOAB to its target, a dose-dependent (non-linear) elimination is often observed. Immune reactions can accelerate the elimination of antibodies, partially depending on the degree ofhumanisation of the antibody. Antibodies and endogenous immunoglobulins are protected from elimination by binding to protective receptors (neonatal Fc-receptor; FcRn), which explains their long half-lives (up to 4 weeks). Metabolic pharmacokinetic interactions with other drugs have not been reported and are not expected. It is expected that in the years to come, new MOABs directed towards new targets will appear on the market, as well as existing antibodies with improved pharmacokinetic properties.

Use of double-blind placebo-controlled N-of-1 trials among stimulant-treated youths in The Netherlands: a descriptive study

OBJECTIVES

An N-of-1 trial is a double-blind placebo-controlled randomized trial to objectively and systematically evaluate the individual's response. This approach seems extraordinarily suitable for assessing the efficacy of stimulants in the treatment of attention deficit hyperactivity disorder (ADHD). The aim is to examine the use of N-of-1 trials among youths in the Netherlands, the protocols used, and the continuation of stimulant treatment thereafter.

METHODS

Physicians requesting N-of-1 trials with stimulants were interviewed about their rationale and protocol. Prevalence and continuation were investigated by extracting N-of-1 trials among youths <20 years of age from a large pharmacy dispensing database for 2000-2004.

RESULTS

The main purpose of N-of-1 trials mentioned by physicians was the assessing of individuals' response and dose-finding. Trial length, dosing schedule and efficacy assessment differed per physician. Trials consisted of a maximum of two treatment periods per dose. The annual percentage of youths starting stimulant treatment with an N-of-1 trial fluctuated between 0.6% (3/462) and 3.3% (10/301). No statistical significant difference could be detected between the continuation of stimulant treatment with or without an N-of-1 trial (p = 0.71).

CONCLUSIONS

N-of-1 trials with stimulants are infrequently and not optimally used in the Netherlands. The results of N-of-1 protocols described by physicians are of questionable value, due to the small number of treatment periods per dose. More uniformity in the protocols would make it easier to encompass the N-of-1 methodology in physicians' daily practice.

Evidence for a two component magnetic response in UPt3

The magnetic response of the heavy fermion superconductor UPt3 has been investigated on a microscopic scale by muon Knight shift studies. Two distinct and isotropic Knight shifts have been found for the field in the basal plane. While the volume fractions associated with the two Knight shifts are approximately equal at low and high temperatures, they show a dramatic and opposite temperature dependence around T(N). Our results are independent on the precise muon localization site. We conclude that UPt3 is characterized by a two component magnetic response.