Software Tools for Model-Informed Precision Dosing: How Well Do They Satisfy the Needs?

Kantasiripitak, Wannee; Daele, Ruth Van; Gijsen, Matthias; Ferrante, Marc; Spriet, Isabel; Dreesen, Erwin

doi:10.3389/fphar.2020.00620

Cited by 83 publications

(87 citation statements)

References 35 publications

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“…To date, NONMEM ® is the gold standard to perform these analyses, but we acknowledge that its usage is not optimal for a routine estimation of AUC and potential dose adaptation. The development of model-informed precision dosing tools and the inclusion of ibrutinib into software libraries is encouraged [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Limited Sampling Strategy for Determination of Ibrutinib Plasma Exposure: Joint Analyses with Metabolite Data

et al. 2021

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Therapeutic drug monitoring of ibrutinib is based on the area under the curve of concentration vs. time (AUCIBRU) instead of trough concentration (Cmin,ss) because of a limited accumulation in plasma. Our objective was to identify a limited sampling strategy (LSS) to estimate AUCIBRU associated with Bayesian estimation. The actual AUCIBRU of 85 patients was determined by the Bayesian analysis of the full pharmacokinetic profile of ibrutinib concentrations (pre-dose T0 and 0.5, 1, 2, 4 and 6 h post-dose) and experimental AUCIBRU were derived considering combinations of one to four sampling times. The T0–1–2–4 design was the most accurate LSS (root-mean-square error RMSE = 11.0%), and three-point strategies removing the 1 h or 2 h points (RMSE = 22.7% and 14.5%, respectively) also showed good accuracy. The correlation between the actual AUCIBRU and Cmin,ss was poor (r2 = 0.25). The joint analysis of dihydrodiol-ibrutinib metabolite concentrations did not improve the predictive performance of AUCIBRU. These results were confirmed in a prospective validation cohort (n = 27 patients). At least three samples, within the pre-dose and 4 h post-dose period, are necessary to estimate ibrutinib exposure accurately.

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Section: Discussionmentioning

confidence: 99%

Limited Sampling Strategy for Determination of Ibrutinib Plasma Exposure: Joint Analyses with Metabolite Data

et al. 2021

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“…Although there is preliminary evidence that highlights the potential utility of AI software for optimising the treatment of patients in the ICU [107], evidence for AI software use for optimising the dosing of antimicrobials in the ICU is lacking. The dosing software InsightRx, which utilises both Bayesian forecasting and AI [108], has been observed to accurately predict the AUC of vancomycin based on one trough concentration (AUC InsightRx : AUC reference of 0.84 with 25th-75th median percentile of 0.77-0.88, accuracy was improved when two concentrations were used) [97]. In this study, InsightRx performed similarly well to other dosing software that utilise only Bayesian forecasting for predicting vancomycin AUC.…”

Section: Artificial Intelligence Softwarementioning

confidence: 57%

“…Currently, most dosing softwares are either web-based or standalone applications [5,108]. This potentially detracts from the usability of the dosing programs given clinicians will be required to manually extract data from hospital-based medical records, input data into the dosing program, generate the recommended doses and then return back to the medical records to amend the dose [108]. In health services where clinicians are often time-poor, this may pose a significant barrier to wide-spread adoption of this technology.…”

Section: Challenges Ahead For Widespread Dosing Softwarementioning

confidence: 99%

What Are the Current Approaches to Optimising Antimicrobial Dosing in the Intensive Care Unit?

et al. 2020

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Antimicrobial dosing in the intensive care unit (ICU) can be problematic due to various challenges including unique physiological changes observed in critically ill patients and the presence of pathogens with reduced susceptibility. These challenges result in reduced likelihood of standard antimicrobial dosing regimens achieving target exposures associated with optimal patient outcomes. Therefore, the aim of this review is to explore the various methods for optimisation of antimicrobial dosing in ICU patients. Dosing nomograms developed from pharmacokinetic/statistical models and therapeutic drug monitoring are commonly used. However, recent advances in mathematical and statistical modelling have resulted in the development of novel dosing software that utilise Bayesian forecasting and/or artificial intelligence. These programs utilise therapeutic drug monitoring results to further personalise antimicrobial therapy based on each patient’s clinical characteristics. Studies quantifying the clinical and cost benefits associated with dosing software are required before widespread use as a point-of-care system can be justified.

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“…This can be done using different modeling and Bayesian simulation software. A good overview of different available methods has recently been presented [ 170 ].…”

Section: Discussionmentioning

confidence: 99%

Population Pharmacokinetics and Bayesian Dose Adjustment to Advance TDM of Anti-TB Drugs

et al. 2021

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Tuberculosis (TB) is still the number one cause of death due to an infectious disease. Pharmacokinetics and pharmacodynamics of anti-TB drugs are key in the optimization of TB treatment and help to prevent slow response to treatment, acquired drug resistance, and adverse drug effects. The aim of this review was to provide an update on the pharmacokinetics and pharmacodynamics of anti-TB drugs and to show how population pharmacokinetics and Bayesian dose adjustment can be used to optimize treatment. We cover aspects on preclinical, clinical, and population pharmacokinetics of different drugs used for drug-susceptible TB and multidrug-resistant TB. Moreover, we include available data to support therapeutic drug monitoring of these drugs and known pharmacokinetic and pharmacodynamic targets that can be used for optimization of therapy. We have identified a wide range of population pharmacokinetic models for first- and second-line drugs used for TB, which included models built on NONMEM, Pmetrics, ADAPT, MWPharm, Monolix, Phoenix, and NPEM2 software. The first population models were built for isoniazid and rifampicin; however, in recent years, more data have emerged for both new anti-TB drugs, but also for defining targets of older anti-TB drugs. Since the introduction of therapeutic drug monitoring for TB over 3 decades ago, further development of therapeutic drug monitoring in TB next steps will again depend on academic and clinical initiatives. We recommend close collaboration between researchers and the World Health Organization to provide important guideline updates regarding therapeutic drug monitoring and pharmacokinetics/pharmacodynamics.

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Software Tools for Model-Informed Precision Dosing: How Well Do They Satisfy the Needs?

Cited by 83 publications

References 35 publications

Limited Sampling Strategy for Determination of Ibrutinib Plasma Exposure: Joint Analyses with Metabolite Data

Limited Sampling Strategy for Determination of Ibrutinib Plasma Exposure: Joint Analyses with Metabolite Data

What Are the Current Approaches to Optimising Antimicrobial Dosing in the Intensive Care Unit?

Population Pharmacokinetics and Bayesian Dose Adjustment to Advance TDM of Anti-TB Drugs

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