A Model-Informed Method for the Purpose of Precision Dosing of Isoniazid in Pulmonary Tuberculosis.
Ontology highlight
ABSTRACT: Background and objective
This study aimed to develop and evaluate a population pharmacokinetic model and limited sampling strategy for isoniazid to be used in model-based therapeutic drug monitoring.Methods
A population pharmacokinetic model was developed based on isoniazid and acetyl-isoniazid pharmacokinetic data from seven studies with in total 466 patients from three continents. Three limited sampling strategies were tested based on the available sampling times in the dataset and practical considerations. The tested limited sampling strategies sampled at 2, 4, and 6 h, 2 and 4 h, and 2 h after dosing. The model-predicted area under the concentration-time curve from 0 to 24 h (AUC24) and the peak concentration from the limited sampling strategies were compared to predictions using the full pharmacokinetic curve. Bias and precision were assessed using the mean error (ME) and the root mean square error (RMSE), both expressed as a percentage of the mean model-predicted AUC24 or peak concentration on the full pharmacokinetic curve.Results
Performance of the developed model was acceptable and the uncertainty in parameter estimations was generally low (the highest relative standard error was 39% coefficient of variation). The limited sampling strategy with sampling at 2 and 4 h was determined as most suitable with an ME of 1.1% and RMSE of 23.4% for AUC24 prediction, and ME of 2.7% and RMSE of 23.8% for peak concentration prediction. For the performance of this strategy, it is important that data on both isoniazid and acetyl-isoniazid are used. If only data on isoniazid are available, a limited sampling strategy using 2, 4, and 6 h can be employed with an ME of 1.7% and RMSE of 20.9% for AUC24 prediction, and ME of 1.2% and RMSE of 23.8% for peak concentration prediction.Conclusions
A model-based therapeutic drug monitoring strategy for personalized dosing of isoniazid using sampling at 2 and 4 h after dosing was successfully developed. Prospective evaluation of this strategy will show how it performs in a clinical therapeutic drug monitoring setting.
SUBMITTER: van Beek SW
PROVIDER: S-EPMC8249295 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:There has been rising interest in using model-informed precision dosing to provide personalized medicine to patients at the bedside. This methodology utilizes population pharmacokinetic models, measured drug concentrations from individual patients, pharmacodynamic biomarkers, and Bayesian estimation to estimate pharmacokinetic parameters and predict concentration-time profiles in individual patients. Using these individualized parameter estimates and simulated drug exposure, dosing recommendations can be generated to maximize target attainment to improve beneficial effect and minimize toxicity. However, the accuracy of the output from this evaluation is highly dependent on the population pharmacokinetic model selected. This tutorial provides a comprehensive approach to evaluating, selecting, and validating a model for input and implementation into a model-informed precision dosing program. A step-by-step outline to validate successful implementation into a precision dosing tool is described using the clinical software platforms Edsim++ and MwPharm++ as examples.
| S-EPMC10725261 | biostudies-literature
Project description: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.
| S-EPMC7839485 | biostudies-literature
Project description:Optimal pharmacotherapy in pediatric patients with suspected infections requires understanding and integration of relevant data on the antibiotic, bacterial pathogen, and patient characteristics. Because of age-related physiological maturation and non-maturational covariates (e.g., disease state, inflammation, organ failure, co-morbidity, co-medication and extracorporeal systems), antibiotic pharmacokinetics is highly variable in pediatric patients and difficult to predict without using population pharmacokinetics models. The intra- and inter-individual variability can result in under- or overexposure in a significant proportion of patients. Therapeutic drug monitoring typically covers assessment of pharmacokinetics and pharmacodynamics, and concurrent dose adaptation after initial standard dosing and drug concentration analysis. Model-informed precision dosing (MIPD) captures drug, disease, and patient characteristics in modeling approaches and can be used to perform Bayesian forecasting and dose optimization. Incorporating MIPD in the electronic patient record system brings pharmacometrics to the bedside of the patient, with the aim of a consisted and optimal drug exposure. In this narrative review, we evaluated studies assessing optimization of antibiotic pharmacotherapy using MIPD in pediatric populations. Four eligible studies involving amikacin and vancomycin were identified from 418 records. Key articles, independent of year of publication, were also selected to highlight important attributes of MIPD. Although very little research has been conducted until this moment, the available data on vancomycin indicate that MIPD is superior compared to conventional dosing strategies with respect to target attainment. The utility of MIPD in pediatrics needs to be further confirmed in frequently used antibiotic classes, particularly aminoglycosides and beta-lactams.
| S-EPMC7940353 | biostudies-literature
Project description:Background and objectiveThe immunosuppressant everolimus is increasingly applied in renal transplantation. Its extensive pharmacokinetic variability necessitates therapeutic drug monitoring, typically based on whole-blood trough concentrations (C0). Unfortunately, therapeutic drug monitoring target attainment rates are often unsatisfactory and patients with on-target exposure may still develop organ rejection. As everolimus displays erythrocyte partitioning, haematocrit-normalised whole-blood exposure has been suggested as a more informative therapeutic drug monitoring marker. Furthermore, model-informed precision dosing has introduced options for more sophisticated dose adaptation. We have previously developed a mechanistic population pharmacokinetic model, which described everolimus plasma pharmacokinetics and enabled estimation of haematocrit-normalised whole-blood exposure. Here, we externally evaluated this model for its utility for model-informed precision dosing.MethodsThe retrospective dataset included 4123 pharmacokinetic observations from routine clinical therapeutic drug monitoring in 173 renal transplant recipients. Model appropriateness was confirmed with a visual predictive check. A fit-for-purpose analysis was conducted to evaluate whether the model accurately and precisely predicted a future C0 or area under the concentration-time curve (AUC) from prior pharmacokinetic observations. Bias and imprecision were expressed as the mean percentage prediction error (MPPE) and mean absolute percentage prediction error (MAPE), stratified on 6 months post-transplant. Additionally, we compared dose adaptation recommendations of conventional C0-based therapeutic drug monitoring and C0- or AUC-based model-informed precision dosing, and assessed the percentage of differences between observed and haematocrit-normalised C0 (∆C0) and AUC (∆AUC) exceeding ± 20%.ResultsThe model showed adequate accuracy and precision for C0 and AUC prediction at ≤ 6 months (MPPEC0: 8.1 ± 2.5%, MAPEC0: 26.8 ± 2.1%; MPPEAUC: - 9.7 ± 5.1%, MAPEAUC: 13.3 ± 3.9%) and > 6 months post-transplant (MPPEC0: 4.7 ± 2.0%, MAPEC0: 25.4 ± 1.4%; MPPEAUC: - 0.13 ± 4.8%, MAPEAUC: 13.3 ± 2.8%). On average, dose adaptation recommendations derived from C0-based and AUC-based model-informed precision dosing were 2.91 ± 0.01% and 13.7 ± 0.18% lower than for conventional C0-based therapeutic drug monitoring at ≤ 6 months, and 0.93 ± 0.01% and 3.14 ± 0.04% lower at > 6 months post-transplant. The ∆C0 and ∆AUC exceeded ± 20% on 13.6% and 14.3% of occasions, respectively.ConclusionsWe demonstrated that our population pharmacokinetic model was able to accurately and precisely predict future everolimus exposure from prior pharmacokinetic measurements. In addition, we illustrated the potential added value of performing everolimus therapeutic drug monitoring with haematocrit-normalised whole-blood concentrations. Our results provide reassurance to implement this methodology in clinical practice for further evaluation.
| S-EPMC7862213 | biostudies-literature
Project description:BackgroundPharmacokinetic nomograms, equations, and software are considered the main tools available for Therapeutic Drug Monitoring (TDM). Model-informed precision dosing (MIPD) is an advanced discipline of TDM that allows dose individualization, and requires a software for knowledge integration and statistical calculations. Due to its precision and extensive applicability, the use of these software is widespread in clinical practice. However, the currently available evidence on these tools remains scarce.ObjectivesTo review and summarize the available evidence on MIPD software tools to facilitate its identification, evaluation, and selection by users.MethodsAn electronic literature search was conducted in MEDLINE, EMBASE, OpenAIRE, and BASE before July 2022. The PRISMA-ScR was applied. The main inclusion criteria were studies focused on developing software for use in clinical practice, research, or modelling.ResultsTwenty-eight software were classified as MIPD software. Ten are currently unavailable. The remaining 18 software were described in depth. It is noteworthy that all MIPD software used Bayesian statistical methods to estimate drug exposure and all provided a population model by default, except NONMEN.ConclusionsPharmacokinetic software have become relevant tools for TDM. MIPD software have been compared, facilitating its selection for use in clinical practice. However, it would be interesting to standardize the quality and validate the software tools.
| S-EPMC10386689 | biostudies-literature
Project description: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.
| S-EPMC7224248 | biostudies-literature
Project description:Background:Routine clinical TDM data is often used to develop population pharmacokinetic (PK) models, which are applied in turn for model-informed precision dosing. The impact of uncertainty in documented sampling and infusion times in population PK modeling and model-informed precision dosing have not yet been systematically evaluated. The aim of this study was to investigate uncertain documentation of (i) sampling times and (ii) infusion rate exemplified with two anti-infectives. Methods:A stochastic simulation and estimation study was performed in NONMEM® using previously published population PK models of meropenem and caspofungin. Uncertainties, i.e. deviation between accurate and planned sampling and infusion times (standard deviation (SD) ± 5 min to ± 30 min) were added randomly in R before carrying out the simulation step. The estimation step was then performed with the accurate or planned times (replacing real time points by scheduled study values). Relative bias (rBias) and root mean squared error (rRMSE) were calculated to determine accuracy and precision of the primary and secondary PK parameters on the population and individual level. The accurate and the misspecified (using planned sampling times) model were used for Bayesian forecasting of meropenem to assess the impact on PK/PD target calculations relevant to dosing decisions. Results:On the population level, the estimates of the proportional residual error (prop.-err.) and the interindividual variability (IIV) on the central volume of distribution (V1) were most affected by erroneous records in the sampling and infusion time (e.g. rBias of prop.-err.: 75.5% vs. 183% (meropenem) and 10.1% vs. 109% (caspofungin) for ± 5 vs. ± 30 min, respectively). On the individual level, the rBias of the planned scenario for the typical values V1, Q and V2 increased with increasing uncertainty in time, while CL, AUC and elimination half-life were least affected. Meropenem as a short half-life drug (~1 h) was more affected than caspofungin (~ 9-11 h). The misspecified model provided biased PK/PD target information (e.g. falsely overestimated time above MIC (T > MIC) when true T > MIC was <0.4 and thus patients at risk of undertreatment), while the accurate model gave precise estimates of the indices across all simulated patients. Conclusions:Even 5-minute-uncertainties caused bias and significant imprecision of primary population and individual PK parameters. Thus, our results underline the importance of accurate documentation of time.
| S-EPMC7063976 | biostudies-literature
Project description:Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to also include altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, because only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step toward building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.
| S-EPMC8846635 | biostudies-literature
Project description:IntroductionAt tamoxifen standard dosing, ∼20% of breast cancer patients do not reach proposed target endoxifen concentrations >5.97 ng/mL. Thus, better understanding the large interindividual variability in tamoxifen pharmacokinetics (PK) is crucial. By applying non-linear mixed-effects (NLME) modeling to a pooled 'real-world' clinical PK database, we aimed to (i) dissect several levels of variability and identify factors predictive for endoxifen exposure and (ii) assess different tamoxifen dosing strategies for their potential to increase the number of patients reaching target endoxifen concentrations.MethodsTamoxifen and endoxifen concentrations with genetic and demographic data of 468 breast cancer patients from six reported studies were used to develop a NLME parent-metabolite PK model. Different levels of variability on model parameters or measurements were investigated and the impact of covariates thereupon explored. The model was subsequently applied in a simulation-based comparison of three dosing strategies with increasing degree of dose individualization for a large virtual breast cancer population. Interindividual variability of endoxifen concentrations and the fraction of patients at risk for not reaching target concentrations were assessed for each dosing strategy.Results and conclusionsThe integrated NLME model enabled to differentiate and quantify four levels of variability (interstudy, interindividual, interoccasion, and intraindividual). Strong influential factors, i.e., CYP2D6 activity score, drug-drug interactions with CYP3A and CYP2D6 inducers/inhibitors and age, were reliably identified, reducing interoccasion variability to <20% CV. Yet, unexplained interindividual variability in endoxifen formation remained large (47.2% CV). Hence, therapeutic drug monitoring seems promising for achieving endoxifen target concentrations. Three tamoxifen dosing strategies [standard dosing (20 mg QD), CYP2D6-guided dosing (20, 40, and 60 mg QD) and individual model-informed precision dosing (MIPD)] using three therapeutic drug monitoring samples (5-120 mg QD) were compared, leveraging the model. The proportion of patients at risk for not reaching target concentrations was 22.2% in standard dosing, 16.0% in CYP2D6-guided dosing and 7.19% in MIPD. While in CYP2D6-guided- and standard dosing interindividual variability in endoxifen concentrations was high (64.0% CV and 68.1% CV, respectively), it was considerably reduced in MIPD (24.0% CV). Hence, MIPD demonstrated to be the most promising strategy for achieving target endoxifen concentrations.
| S-EPMC7136483 | biostudies-literature
Project description:Rifampicin exhibits complexities in its pharmacokinetics (PK), including high inter-occasion variability (IOV), which is challenging for dose individualization. Model-informed precision dosing (MIPD) can be used to optimize individual doses. In this simulation-based study we investigated the magnitude of IOV in rifampicin PK on an exposure level, the impact of not acknowledging IOV when performing MIPD, and the number of sampling occasions needed to forecast the dose. Subjects with drug-susceptible tuberculosis (TB) were simulated from a previously developed population PK model. To explore the magnitude of IOV, the area under the plasma concentration-time curve from time zero up to 24 h (AUC0-24h) after 35 mg/kg in the typical individual was simulated for 1,000 sampling occasions at steady-state. The impact of ignoring IOV for dose predictions was investigated by comparing the prediction error of a MIPD approach including IOV to an approach ignoring IOV. Furthermore, the number of sampling occasions needed to predict individual doses using a MIPD approach was assessed. The AUC0-24h in the typical individual varied substantially between simulated sampling occasions [95% prediction interval (PI): 122.2 to 331.2 h mg/L], equivalent to an IOV in AUC0-24h of 25.8%, compared to an inter-individual variability of 25.4%. The median of the individual prediction errors using a MIPD approach incorporating IOV was 0% (75% PI: -14.6% to 0.0%), and the PI for the individual prediction errors was narrower with than without IOV (median: 0%, 75% PI: -14.6% to 20.0%). The most common target dose in this population was forecasted correctly in 95% of the subjects when IOV was included in MIPD. In subjects where doses were not predicted optimally, a lower dose was predicted compared to the target, which is favorable from a safety perspective. Moreover, the imprecision (relative root mean square error) and bias in predicted doses using MIPD with IOV decreased statistically significant when a second sampling occasion was added (difference in imprecision: -9.1%, bias: -7.7%), but only marginally including a third (difference in imprecision: -0.1%, bias: -0.1%). In conclusion, a large variability in exposure of rifampicin between occasions was shown. In order to forecast the individual dose correctly, IOV must be acknowledged which can be achieved using a MIPD approach with PK information from at least two sampling occasions.
| S-EPMC7266983 | biostudies-literature