Safe and Effective Variability—A Criterion for Dose Individualization

Population pharmacokinetic analysis for dose regimen optimization of vancomycin in Southern Chinese children

Changes in physiological factors may result in large pharmacokinetic variability of vancomycin in pediatric patients, thereby leading to either supratherapeutic or subtherapeutic exposure and potentially affecting clinical outcomes. This study set out to characterize the disposition of vancomycin, quantify the exposure target and establish an optimal dosage regimen among the Southern Chinese pediatric population. Routine therapeutic drug monitoring data of 453 patients were available. We performed a retrospective population pharmacokinetic analysis of hospitalized children prescribed intravenous vancomycin using NONMEM® software. A one-compartment PPK model of vancomycin with body weight and renal functions as covariates based on a cutoff of 2 years old children was proposed in this study. Both internal and external validation showing acceptable and robust predictive performance of the model to estimate PK parameters. The value of area under the curve over 24 h to minimum inhibitory concentration ratio (AUC0-24/MIC) ≥ 260 was a significant predictor for therapeutic efficacy. Monte Carlo simulations served as a model-informed precision dosing approach and suggested that different optimal dose regimens in various scenarios should be considered rather than flat dosing. The evaluation of vancomycin exposure-efficacy relationship indicated that lower target level of AUC0-24/MIC may be needed to achieve clinical effectiveness in children, which was used to derive the recommended dosing regimen. Further prospective studies will be needed to corroborate and elucidate these results.

Heterogeneity in response to Elexacaftor/Tezacaftor/Ivacaftor in people with cystic fibrosis

BACKGROUND

Highly effective modulators of the CFTR channel have been demonstrated to dramatically impact disease progression and outcome. However, real-world data indicates that the magnitude of the clinical benefit is not equal among all patients receiving the treatment. We aimed to assess the variability in treatment response (as defined by the 6-month change in sweat chloride concentration, forced expiratory volume in one second [ppFEV1], body mass index [BMI], and CF Questionnaire-Revised [CFQ-R] respiratory domain score) and identify potential predictors in a group of patients receiving Elexacaftor-Tezacaftor-Ivacaftor (ETI) triple combination therapy.

METHODS

This was a single-center, prospective cohort study enrolling adults with CF at a major center in Italy. We used linear regression models to identify a set of potential predictors (including CFTR genotype, sex, age, and baseline clinical characteristics) and estimate the variability in treatment response.

RESULTS

The study included 211 patients (median age: 29 years, range: 12-58). Median changes (10-90th percentile) from baseline were: - 56 mEq/L (-76; -27) for sweat chloride concentration, +14.5 points (2.5; 32.0) for ppFEV1, +0.33 standard deviation scores (-0.13; 1.05) for BMI and +17 points (0; 39) for the CFQ-R respiratory domain score. The selected predictors explained 23 % of the variability in sweat chloride concentration changes, 18 % of the variability in ppFEV1 changes, 39 % of the variability in BMI changes, and 65 % of the variability in CFQ-R changes.

CONCLUSIONS

This study highlights a high level of heterogeneity in treatment response to ETI, which can only be partially explained by the baseline characteristics of the disease.

Medication use during pregnancy and the risk of gastroschisis: a systematic review and meta-analysis of observational studies

OBJECTIVES

The aetiology of gastroschisis is considered multifactorial. We conducted a systematic review and meta-analysis to assess whether the use of medications during pregnancy, is associated with the risk of gastroschisis in offspring.

METHODS

PubMed, EMBASE, and Scopus were searched from 1st January 1990 to 31st December 2020 to identify observational studies examining the association between medication use during pregnancy and the risk of gastroschisis. The Newcastle-Ottawa Scale was used for the quality assessment of the individual studies. We pooled adjusted measures using a random-effect model to estimate relative risk [RR] and the 95% confidence interval [CI]. I2 statistic for heterogeneity and publication bias was calculated.

RESULTS

Eighteen studies providing data on 751,954 pregnancies were included in the meta-analysis. Pooled RRs showed significant associations between aspirin (RR 1.66, 95% CI 1.16-2.38; I2 = 58.3%), oral contraceptives (RR 1.52, 95% CI 1.21-1.92; I2 = 22.0%), pseudoephedrine and phenylpropanolamine (RR 1.51, 95% CI 1.16-1.97; I2 = 33.2%), ibuprofen (RR 1.42, 95% CI 1.26-1.60; I2 = 0.0%), and gastroschisis. No association was observed between paracetamol and gastroschisis (RR 1.16, 95% CI 0.96-1.41; I2 = 39.4%).

CONCLUSIONS

These results suggest that the exposure in the first trimester of pregnancy to over the counter medications (OTC) such as aspirin, ibuprofen, pseudoephedrine and phenylpropanolamine as well as to oral contraceptives, was associated with an increased risk of gastroschisis. However, these associations are significant only in particular subgroups defined by geographic location, adjustment variables and type of control. Therefore, further research is needed to investigate them as potential risk factors for gastroschisis, to assess their safety in pregnancy and to develop treatment strategies to reduce the risk of gastroschisis in offspring. PROSPERO registration number: CRD42021287529.

Adaptation of a population pharmacokinetic model to inform tacrolimus therapy in heart transplant recipients

AIM

Existing tacrolimus population pharmacokinetic models are unsuitable for guiding tacrolimus dosing in heart transplant recipients. This study aimed to develop and evaluate a population pharmacokinetic model for tacrolimus in heart transplant recipients that considers the tacrolimus-azole antifungal interaction.

METHODS

Data from heart transplant recipients (n = 87) administered the oral immediate-release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of Ka , V2 /F, Q/F and V3 /F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat-free mass was implemented as a covariate on CL/F, V2 /F, V3 /F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction-corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion (n = 87) from one to three prior dosing occasions.

RESULTS

Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = -44, P < .001) was included in the final model. The pcVPC of the final model displayed good model adequacy. One recent drug concentration is sufficient for the model to guide tacrolimus dosing.

CONCLUSION

A population pharmacokinetic model that adequately describes tacrolimus pharmacokinetics in heart transplant recipients, considering the tacrolimus-azole antifungal interaction was developed. Prospective evaluation is required to assess its clinical utility to improve patient outcomes.

Simultaneous Quantification of Ivacaftor, Tezacaftor, and Elexacaftor in Cystic Fibrosis Patients' Plasma by a Novel LC-MS/MS Method

The new breakthrough cystic fibrosis (CF) drug combination of ivacaftor (IVA), tezacaftor (TEZ), and elexacaftor (ELX), namely "caftor" drugs, directly modulates the activity and trafficking of the defective CF transmembrane conductance regulator protein (CFTR) underlying the CF disease. The role of therapeutic drug monitoring (TDM) of caftor drugs in clinical settings has recently been established. The availability of reliable and robust analytical methods for the quantification of IVA, TEZ, and ELX is essential to support dose-concentration-effect studies. We have developed and validated a new liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the rapid and simultaneous quantification of IVA, TEZ, and ELX from the plasma of CF patients. The method was based on a rapid extraction protocol from 50 μL human plasma and separation on a reversed-phase C-18 HPLC column after the addition of deuterated internal standards. Accurate analyte quantification using multiple reaction monitoring (MRM) detection was then obtained using a Thermofisher Quantiva triple-quadrupole MS coupled to an Ultimate 3000 UHPLC. The method has been validated following international (EMA) guidelines for bioanalytical method validation and has been tested on plasma samples from 62 CF patients treated with the three-drug combination IVA/TEZ/ELX, marketed as Kaftrio^® or Trikafta^®, in steady-state condition. The assay was linear over wide concentration ranges (0.008-12 mg/L) in plasma for IVA, TEZ, and ELX, suitable for a broad range of plasma concentrations, and accurate and reproducible in the absence of matrix effects. The stability of analytes for at least 30 days at room temperature could allow for cost-effective shipment and storage. On the same day of sample collection, a sweat test was evaluated for 26 associated patients' samples, FEV1 (%) for 58, and BMI was calculated for 62. However, Spearman correlation showed no correlation between C_through plasma concentrations of analytes (IVA, TEZ, ELX) and sweat test, FEV1 (%), or BMI. Our method proved to be suitable for TDM and could be helpful in assessing dose-concentration-response correlations in larger studies.

Optimal dosing of enoxaparin in overweight and obese children

AIM

Current enoxaparin dosing guidelines in children are based on total body weight. This is potentially inappropriate in obese children as it may overestimate the drug clearance. Current evidence suggests that obese children may require lower initial doses of enoxaparin, therefore the aim of this work was to characterise the pharmacokinetics of enoxaparin in obese children and to propose a more appropriate dosing regimen.

METHODS

Data from 196 unique encounters of 160 children who received enoxaparin treatment doses were analysed. Enoxaparin concentration was quantified using the chromogenic anti factor Xa (anti-Xa) assay. Patients provided a total of 552 anti-Xa samples. Existing published pharmacokinetic (PK) models were fitted and evaluated against our dataset using prediction-corrected visual predictive check plots (pcVPCs). A PK model was fitted using a nonlinear mixed-effects modelling approach. The fitted model was used to evaluate the current standard dosing and identify an optimal dosing regimen for obese children.

RESULTS

Published models of enoxaparin pharmacokinetics in children did not capture the pharmacokinetics of enoxaparin in obese children as shown by pcVPCs. A one-compartment model with linear elimination best described the pharmacokinetics of enoxaparin. Allometrically scaled fat-free mass with an estimated exponent of 0.712 (CI 0.66-0.76) was the most influential covariate on clearance while linear fat-free mass was selected as the covariate on volume. Simulations from the model showed that fat-free mass-based dosing could achieve the target anti-Xa activity at steady state in 77.5% and 78.2% of obese and normal-weight children, respectively, compared to 65.2% and 75.5% for standard total body weight-based dosing.

CONCLUSIONS

A population PK model that describes the time course of anti-Xa activity of enoxaparin was developed in a paediatric population. Based on this model, a unified dosing regimen was proposed that will potentially improve the success rate of target attainment in overweight/obese patients without the need for patient body size categorisation. Therefore, prospective validation of the proposed approach is warranted.

Interactions of the protease inhibitor, ritonavir, with common anesthesia drugs

The protease inhibitor, ritonavir, is a strong inhibitor of CYP 3A. The drug is used for management of the human immunovirus and is currently part of an oral antiviral drug combination (nirmatrelvir-ritonavir) for the early treatment of SARS-2 COVID-19-positive patients aged 12 years and over who have recognized comorbidities. The CYP 3A enzyme system is responsible for clearance of numerous drugs used in anesthesia (e.g., alfentanil, fentanyl, methadone, rocuronium, bupivacaine, midazolam, ketamine). Ritonavir will have an impact on drug clearances that are dependent on ritonavir concentration, anesthesia drug intrinsic hepatic clearance, metabolic pathways, concentration-response relationship, and route of administration. Drugs with a steep concentration-response relationship (ketamine, midazolam, rocuronium) are mostly affected because small changes in concentration have major changes in effect response. An increase in midazolam concentration is observed after oral administration because CYP 3A in the gastrointestinal wall is inhibited, causing a large increase in relative bioavailability. Fentanyl infusion may be associated with a modest increase in plasma concentration and effect, but the large between subject variability of pharmacokinetic and pharmacodynamic concentration changes suggests it will have little impact on an individual patient, especially when used with adverse effect monitoring. It has been proposed that drugs that have no or only a small metabolic pathway involving the CYP 3A enzyme be used during anesthesia, for example, propofol, atracurium, remifentanil, and the volatile agents. That anesthesia approach denies children of drugs with considerable value. It is better that the inhibitory changes in clearance of these drugs are understood so that rational drug choices can be made to tailor drug use to the individual patient. Altered drug dose, anticipation of duration of effect, timing of administration, use of reversal agents and perioperative monitoring would better behoove children undergoing anesthesia.

Population pharmacokinetic and pharmacodynamic analysis of rivaroxaban in Chinese patients with non-valvular atrial fibrillation

Rivaroxaban, a direct factor Xa inhibitor, is widely used for stroke prevention in patients with non-valvular atrial fibrillation (NVAF). The aim of this study was to conduct a population pharmacokinetic-pharmacodynamic (PK-PD) analysis of rivaroxaban in Chinese patients with NVAF to assess ethnic differences and provide model-based precision dosing. A total of 256 rivaroxaban plasma concentrations and 244 prothrombin time (PT) measurements were obtained from 195 Chinese NVAF patients from a prospective clinical trial. The population PK-PD model was developed using nonlinear mixed effects modeling (NONMEM) software. The PK of rivaroxaban was adequately described using a one-compartment model with first-order adsorption and elimination. Estimated glomerular filtration rate (eGFR) was identified as a major covariate for apparent clearance. No single nucleotide polymorphism was identified as a significant covariate. PT exhibited a linear relationship with rivaroxaban concentration. Total bilirubin (TBIL) and eGFR were identified as significant covariates for baseline PT. According to the Monte Carlo simulation, 15 mg for Chinese patients with eGFR ≥50 mL/min and normal liver function yielded an exposure comparable to 20 mg for Caucasian patients. Patients with moderately impaired renal function may require a lower dose of rivaroxaban to avoid overexposure. Moreover, there was an approximate 26% increase in PT levels in patients with TBIL of 34 μmol/L and eGFR of 30 mL/min, which could increase the risk of major bleeding. The established population PK-PD model could inform individualized dosing for Chinese NVAF patients who are administered rivaroxaban.

Therapeutic Drug Monitoring of Ivacaftor, Lumacaftor, Tezacaftor, and Elexacaftor in Cystic Fibrosis: Where Are We Now?

Drugs modulating the cystic fibrosis transmembrane conductance regulator (CFTR) protein, namely ivacaftor, lumacaftor, tezacaftor, and elexacaftor, are currently revolutionizing the management of patients with cystic fibrosis (CF), particularly those with at least one F508del variant (up to 85% of patients). These “caftor” drugs are mainly metabolized by cytochromes P450 3A, whose enzymatic activity is influenced by environmental factors, and are sensitive to inhibition and induction. Hence, CFTR modulators are characterized by an important interindividual pharmacokinetic variability and are also prone to drug–drug interactions. However, these CFTR modulators are given at standardized dosages, while they meet all criteria for a formal therapeutic drug monitoring (TDM) program that should be considered in cases of clinical toxicity, less-than-expected clinical response, drug or food interactions, distinct patient subgroups (i.e., pediatrics), and for monitoring short-term adherence. While the information on CFTR drug exposure–clinical response relationships is still limited, we review the current evidence of the potential interest in the TDM of caftor drugs in real-life settings.

Population Pharmacokinetic Modelling of Acetaminophen and Ibuprofen: the Influence of Body Composition, Formulation and Feeding in Healthy Adult Volunteers

BACKGROUND AND OBJECTIVE

Combined acetaminophen and ibuprofen are common antipyretic and analgesic drugs. Formulation and feeding affect drug absorption. Drug clearance has a nonlinear relationship with total body weight. The covariate effect of fat mass on acetaminophen and ibuprofen pharmacokinetics remains unexplored. This study sought to quantify acetaminophen and ibuprofen pharmacokinetics with intravenous, tablet, sachet and oral suspension formulations in fed and fasted states.

METHODS

Pooled time-concentration data for acetaminophen and ibuprofen were available from fasting and fed healthy adults. Data from intravenous, tablet, sachet and suspension formulations were analysed using nonlinear mixed-effects models. Body composition was considered as a covariate on clearances and volumes of distribution (V_d). Size metrics investigated were total body weight, fat and fat-free mass. Theory-based allometry was used to scale pharmacokinetic parameters to a 70 kg individual. A factor on absorption half-life and lag time quantified delays due to feeding for oral formulations. Pharmacokinetic-pharmacodynamic simulations were used to explore the time courses of pain response for acetaminophen and ibuprofen for each formulation.

RESULTS

Pooled data included 116 individuals (18-49 years, 49-116 kg) with 6095 acetaminophen and 6046 ibuprofen concentrations available for analysis. A two-compartment pharmacokinetic model with first-order elimination described disposition for both drugs. Normal fat mass was the best covariate to describe acetaminophen clearance (CL), with a factor for fat contribution (FFATCL) of 0.816. Acetaminophen volume of distribution was described using total body weight. Normal fat mass was the best covariate to describe ibuprofen clearance (FFATCL = 0.863) and volume of distribution: (FFATV = 0.718). Clearance and central volume of distribution were 24.0 L/h/70 kg and 43.5 L/h/70 kg for acetaminophen. Ibuprofen clearance and central volume of distribution were 3.79 L/h/70 kg and 10.5 L/h/70 kg. Bioavailability and absorption half-life were 86% and 12 min for acetaminophen and 94% and 27 min for ibuprofen. Absorption lag times were 5.3 min and 6.7 min for acetaminophen and ibuprofen, respectively. Feeding increased both absorption half-life and absorption lag time when compared to the tablet formulation under fasting conditions. Feeding had the most pronounced effect on the lag time associated with tablet formulation for both drugs. Time to a pain score reduction of 2 points (visual analogue score, 0-10) differed by only 5-10 min across all formulations for acetaminophen and ibuprofen.

CONCLUSION

Fat mass was an important covariate to describe acetaminophen and ibuprofen pharmacokinetics. The absorption half-lives of acetaminophen and ibuprofen were increased in fed states. The delay in absorption, quantified by a lag time, was protracted for both drugs.

Dose Banding – weighing up benefits, risks and therapeutic failure

Aims

Dose banding is a method of dose individualisation in which all patients with similar characteristics are allocated to the same dose. Dose banding results in some patients receiving less intensive treatment which risks a reduction in therapeutic benefit (iatrogenic therapeutic failure) because of variability not predicted by dose banding. This study aims to explore the effects of dose banding on therapeutic success and failure.

Methods

This was a simulation study. Virtual patients were simulated under a simple pharmacokinetic model where the response of interest is the steady‐state average concentration. Clearance was correlated with a covariate used for dose banding. Dose individualisation was based on: one‐dose‐fits‐all, covariate‐based dosing, empirical dose banding, dose banding optimised for net therapeutic benefit and optimised for both benefit and minimising iatrogenic therapeutic failure.

Results

The lowest and highest probability of target attainment (PTA) were 44% for one‐dose‐fits‐all and 72% for covariate‐based dosing. Neither dosing approach would result in iatrogenic therapeutic failure as lower dose intensities do not occur. Empirical dose banding performed better than one‐dose‐fits‐all with 59% PTA but not as good as either optimised method (64–69% PTA) while carrying a risk of iatrogenic therapeutic failure in 25% of patients. Optimising for benefit (only) improved PTA but carried a risk of iatrogenic therapeutic failure of up to 10%. Optimising for benefit and minimising iatrogenic therapeutic failure provided the best balance.

Conclusion

Future application of dose banding needs to consider both the probability of benefit as well the risk of causing iatrogenic therapeutic failure.

Predicting model‐informed precision dosing: A test‐case in tacrolimus dose adaptation for kidney transplant recipients

Before investing resources into the development of a precision dosing (model‐informed precision dosing [MIPD]) tool for tacrolimus, the performance of the tool was evaluated in silico. A retrospective dataset of 315 de novo kidney transplant recipients was first used to identify a one‐compartment pharmacokinetic (PK) model with time‐dependent clearance. MIPD performance was subsequently evaluated by calculating errors to predict future concentrations, which is directly related to dosing precision and probability of target attainment (PTA). Based on the identified model residual error, the theoretical upper limit was 45% PTA for a target of 13.5 ng/ml and an acceptable range of 12–15 ng/ml. Using empirical Bayesian estimation, this limit was reached on day 5 post‐transplant and beyond. By incorporating correlated within‐patient variability when predicting future individual concentrations, PTA improved beyond the theoretical upper limit. This yielded a Bayesian feedback dosing algorithm accurately predicting future trough concentrations and adapting each dose to reach a target concentration. Simulated concentration‐time profiles were then used to quantify MIPD‐based improvement on three end points: average PTA increased from 28% to 39%, median time to three concentrations in target decreased from 10 to 7 days, and mean log‐squared distance to target decreased from 0.080 to 0.055. A study of 200 patients was predicted to have sufficient power to demonstrate these nuanced PK end points reliably. These simulations supported our decision to develop a precision dosing tool for tacrolimus and test it in a prospective trial.

Recent advancements in clearance monitoring of monoclonal antibodies in patients with inflammatory bowel diseases

INTRODUCTION

Less than 50% of patients with inflammatory bowel diseases (IBD) receiving monoclonal antibody (mAb) therapy achieve endoscopic remission. Poor outcomes may indicate a need for dose optimization. During therapeutic drug monitoring (TDM), drug concentrations are measured, and when found too low, dosage regimen escalations are performed. To date, benefits of TDM of mAbs in patients with IBD are uncertain.

AREAS COVERED

This review presents an overview of what clearance monitoring is, how it can be performed, and why and when it may be valuable in treating patients with IBD. Virtual patients were used for illustration. A literature search was performed to summarize current evidence for clearance monitoring in IBD and other disease settings.

EXPERT OPINION

During clearance monitoring, mAb clearance is calculated and monitored over time. Higher mAb clearance in patients with IBD has been associated with higher target load (target-mediated drug disposition), protein-losing enteropathy (fecal drug loss), and immunogenicity. Although not prospectively confirmed, clearance monitoring might facilitate identification of (yet) asymptomatic disease flares or presence of (yet) undetectable anti-drug antibodies. Furthermore, clearance monitoring may be used to predict treatment outcomes. Whether dosage regimen adjustments can modify the clearance time course and the treatment outcome is to be determined.

Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective

This collection of contributions from the European Network on Understanding Gastrointestinal Absorption-related Processes (UNGAP) community assembly aims to provide information on some of the current and newer methods employed to study the behaviour of medicines. It is the product of interactions in the immediate pre-Covid period when UNGAP members were able to meet and set up workshops and to discuss progress across the disciplines. UNGAP activities are divided into work packages that cover special treatment populations, absorption processes in different regions of the gut, the development of advanced formulations and the integration of food and pharmaceutical scientists in the food-drug interface. This involves both new and established technical approaches in which we have attempted to define best practice and highlight areas where further research is needed. Over the last months we have been able to reflect on some of the key innovative approaches which we were tasked with mapping, including theoretical, in silico, in vitro, in vivo and ex vivo, preclinical and clinical approaches. This is the product of some of us in a snapshot of where UNGAP has travelled and what aspects of innovative technologies are important. It is not a comprehensive review of all methods used in research to study drug dissolution and absorption, but provides an ample panorama of current and advanced methods generally and potentially useful in this area. This collection starts from a consideration of advances in a priori approaches: an understanding of the molecular properties of the compound to predict biological characteristics relevant to absorption. The next four sections discuss a major activity in the UNGAP initiative, the pursuit of more representative conditions to study lumenal dissolution of drug formulations developed independently by academic teams. They are important because they illustrate examples of in vitro simulation systems that have begun to provide a useful understanding of formulation behaviour in the upper GI tract for industry. The Leuven team highlights the importance of the physiology of the digestive tract, as they describe the relevance of gastric and intestinal fluids on the behaviour of drugs along the tract. This provides the introduction to microdosing as an early tool to study drug disposition. Microdosing in oncology is starting to use gamma-emitting tracers, which provides a link through SPECT to the next section on nuclear medicine. The last two papers link the modelling approaches used by the pharmaceutical industry, in silico to Pop-PK linking to Darwich and Aarons, who provide discussion on pharmacometric modelling, completing the loop of molecule to man.

Oxycodone target concentration dosing for acute pain in children

BACKGROUND

Oxycodone pharmacokinetics have been described in premature neonates through to obese adults. Covariate influences have been accounted for using allometry (size) and maturation of oxycodone clearance with age. The target concentration is dependent on pain intensity that may differ over pain duration or between individuals.

METHODS

We assumed a target concentration of 35 mcg.L^-1 (acceptable range ±20%) to be associated with adequate analgesia without increased risk of adverse effects from respiratory depression. Pharmacokinetic simulation was used to estimate dose in neonates through to obese adults given intravenous or parenteral oxycodone.

RESULTS

There were 84% of simulated oxycodone concentrations within the acceptable range during maintenance dosing. Variability around the simulated target concentration decreased with age. The maturation of oxycodone clearance is reflected in changes to context-sensitive halftime where clearance is immature in neonates compared with older children and adults. The intravenous loading and maintenance doses for a typical 5-year-old child are 100 mcg.kg^-1 and 33 mcg.kg^-1 .h^-1 . In a typical adult, the loading dose is 100 mcg.kg^-1 and maintenance dose 23 mcg.kg^-1 .h^-1 .

CONCLUSION

Simulation was used to suggest loading and maintenance doses to attain an oxycodone concentration of 35 mcg.L^-1 predicted in adults. Although the covariates age and weight contribute 92% variability for clearance, there remains variability accounting for 16% of concentrations outside the target range. Duration of analgesic effect after ceasing infusion is anticipated to be longer in neonates where context-sensitive halftime is greater than older children and adults.

PBPK Modeling and Simulation and Therapeutic Drug Monitoring: Possible Ways for Antibiotic Dose Adjustment

Pharmacokinetics (PK) is a branch of pharmacology present and of vital importance for the research and development (R&D) of new drugs, post-market monitoring, and continued optimizations in clinical contexts. Ultimately, pharmacokinetics can contribute to improving patients’ clinical outcomes, helping enhance the efficacy of treatments, and reducing possible adverse side effects while also contributing to precision medicine. This article discusses the methods used to predict and study human pharmacokinetics and their evolution to the current physiologically based pharmacokinetic (PBPK) modeling and simulation methods. The importance of therapeutic drug monitoring (TDM) and PBPK as valuable tools for Model-Informed Precision Dosing (MIPD) are highlighted, with particular emphasis on antibiotic therapy since dosage adjustment of antibiotics can be vital to ensure successful clinical outcomes and to prevent the spread of resistant bacterial strains.

Evaluation of published population pharmacokinetic models to inform tacrolimus dosing in adult heart transplant recipients

BACKGROUND AND AIM

Identification of the most appropriate population pharmacokinetic model-based Bayesian estimation is required prior to its implementation in routine clinical practice to inform tacrolimus dosing decisions. This study aimed to determine the predictive performances of relevant population pharmacokinetic models of tacrolimus developed from various solid organ transplant recipient populations in adult heart transplant recipients, stratified based on concomitant azole antifungal use. Concomitant azole antifungal therapy alters tacrolimus pharmacokinetics substantially, necessitating dose adjustments.

METHODS

Population pharmacokinetic models of tacrolimus were selected (n = 17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate-release formulation of tacrolimus (Prograf) were obtained up to 391 days post-transplant. The performance of each model was evaluated using: (i) prediction-based assessment (bias and imprecision) of the individual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL = 0, FOCE-I) from 1-3 prior dosing occasions; and (ii) simulation-based assessment (prediction-corrected visual predictive check). Both assessments were stratified based on concomitant azole antifungal use.

RESULTS

Regardless of the number of prior dosing occasions (1-3) and concomitant azole antifungal use, all models demonstrated unacceptable individual predicted tacrolimus concentration of the fourth dosing occasion (n = 152). The prediction-corrected visual predictive check graphics indicated that these models inadequately predicted observed tacrolimus concentrations.

CONCLUSION

All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.

Pharmacokinetics of perioperative FVIII in adult patients with haemophilia A: An external validation and development of an alternative population pharmacokinetic model

INTRODUCTION

Haemophilia A patients require perioperative clotting factor replacement to limit excessive bleeding. Weight-based dosing of Factor VIII (FVIII) does not account for inter-individual pharmacokinetic (PK) variability, and may lead to suboptimal FVIII exposure.

AIM

To perform an external validation of a previously developed population PK (popPK) model of perioperative FVIII in haemophilia A patients.

METHODS

A retrospective chart review identified perioperative haemophilia A patients at the University of North Carolina (UNC) between April 2014 and November 2019. Patient data was used to externally validate a previously published popPK model proposed by Hazendonk. Based on these validation results, a modified popPK model was developed to characterize FVIII PK in our patients. Dosing simulations were performed using this model to compare FVIII target attainment between intermittent bolus (IB) and continuous infusion (CI) administration methods.

RESULTS

A total of 521 FVIII concentrations, drawn from 34 patients, were analysed. Validation analyses revealed that the Hazendonk model did not fully capture FVIII PK in the UNC cohort. Therefore, a modified one-compartment model, with weight and age as covariates on clearance (CL), was developed. Dosing simulations revealed that CI resulted in improved target attainment by 16%, with reduced overall FVIII usage by 58 IU/kg, compared to IB.

CONCLUSION

External validation revealed a previously published popPK model of FVIII did not adequately characterize UNC patients, likely due to differences in patient populations. Future prospective studies are needed to evaluate our model prior to implementation into clinical practice.

Evaluating the utility of therapeutic drug monitoring in the clinical use of small molecule kinase inhibitors: a review of the literature

Introduction: Orally administered small molecule kinase inhibitors (KI) are a key class of targeted anti-cancer medicines that have contributed substantially to improved survival outcomes in patients with advanced disease. Since the introduction of KIs in 2001, there has been a building body of evidence that the benefit derived from these drugs may be further enhanced by individualizing dosing on the basis of concentration.Areas covered: This review considers the rationale for individualized KI dosing and the requirements for robust therapeutic drug monitoring (TDM). Current evidence supporting TDM-guided KI dosing is presented and critically evaluated, and finally potential approaches to address translational challenges for TDM-guided KI dosing and alternate approaches to support individualization of KI dosing are discussed.Expert opinion: Intuitively, the individualization of KI dosing through an approach such as TDM-guided dosing has great potential to enhance the effectiveness and tolerability of these drugs. However, based on current literature evidence it is unrealistic to propose that TDM-guided KI dosing should be routinely implemented into clinical practice.

Status Toward the Implementation of Precision Dosing in Children

Precision dosing is progressing beyond the conceptual and proof-of-concept stages toward implementation. As the availability of dosing algorithms, tools, and platforms increases, so do the investment in technology services and actual implementation of clinical services offering these solutions to patients. Nowhere is this needed more than in pediatric populations, which are still reliant on adult drug development and bridging strategies to support dosing, often in the absence of actual dose-finding studies in the target pediatric population. Still, there is more work to be done to ensure that proper governance of these services is maintained, and that sustainability of these early implementations is guided by new science as it evolves and meaningful outcome data to confirm that such services deliver on both clinical and economic return on investment. In addition, the field should ensure that all approaches beyond a therapeutic drug monitoring-driven, pharmacokinetic-centric approach should be considered as the tools and services evolve, especially when pediatric-specific pharmacokinetic/pharmacodyamic and pharmacogenetic data are available and shown to be useful to guide dosing. This review evaluates current pediatric precision dosing efforts, highlighting their utility, longevity, and sustainability and assesses the current process for implementing such approaches examining current barriers that stand in the way of broader implementation and the stakeholders that must engage to ensure its ultimate success.

Development and Validation of a Population-Pharmacokinetic Model for Rurioctacog Alfa Pegol (Adynovate®): A Report on Behalf of the WAPPS-Hemo Investigators Ad Hoc Subgroup

BACKGROUND AND OBJECTIVE

Rurioctacog alfa pegol (Adynovate) is a modified recombinant factor VIII concentrate used for treating hemophilia A. Aiming to improve treatment tailoring on the Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) platform for patients of all ages treated with Adynovate, we have developed and evaluated a population pharmacokinetic (PopPK) model. On the platform, PopPK models are used as priors for Bayesian forecasting that derive individual PK of hemophilia patients and are subsequently used for personalized dose regimen design.

METHODS

Factor activity measurements and demographic covariate data from patients infused with Adynovate were extracted from the WAPPS-Hemo database. Evaluations testing the appropriateness of Bayesian forecasting included 10-fold cross validation, a limited sampling analysis (LSA), and an external evaluation using additional independent data extracted from the WAPPS-Hemo database at a later date.

RESULTS

The model was constructed using 650 plasma factor activity observations (555 one stage assay and 95 chromogenic assay - 4.6% below limit of quantification) measured in 154 patients from 36 hemophilia centres. A two-compartment model including between subject variability on clearance and central volume was selected as the base model. Covariates were fat free mass on clearance and central volume, age on clearance and assay type on activity. The final model was well-suited to predict PK parameters of new individuals (n = 26) from sparse observations.

CONCLUSIONS

The development of a PopPK model for Adynovate using real-world data increases the covariate space (e.g. age) beyond what is possible from clinical trial data. This model is available on the WAPPS-Hemo platform for tailoring treatment in hemophilia A patients.

Therapeutic drug monitoring of oral targeted antineoplastic drugs

PURPOSE

This review provides an overview of the current challenges in oral targeted antineoplastic drug (OAD) dosing and outlines the unexploited value of therapeutic drug monitoring (TDM). Factors influencing the pharmacokinetic exposure in OAD therapy are depicted together with an overview of different TDM approaches. Finally, current evidence for TDM for all approved OADs is reviewed.

METHODS

A comprehensive literature search (covering literature published until April 2020), including primary and secondary scientific literature on pharmacokinetics and dose individualisation strategies for OADs, together with US FDA Clinical Pharmacology and Biopharmaceutics Reviews and the Committee for Medicinal Products for Human Use European Public Assessment Reports was conducted.

RESULTS

OADs are highly potent drugs, which have substantially changed treatment options for cancer patients. Nevertheless, high pharmacokinetic variability and low treatment adherence are risk factors for treatment failure. TDM is a powerful tool to individualise drug dosing, ensure drug concentrations within the therapeutic window and increase treatment success rates. After reviewing the literature for 71 approved OADs, we show that exposure-response and/or exposure-toxicity relationships have been established for the majority. Moreover, TDM has been proven to be feasible for individualised dosing of abiraterone, everolimus, imatinib, pazopanib, sunitinib and tamoxifen in prospective studies. There is a lack of experience in how to best implement TDM as part of clinical routine in OAD cancer therapy.

CONCLUSION

Sub-therapeutic concentrations and severe adverse events are current challenges in OAD treatment, which can both be addressed by the application of TDM-guided dosing, ensuring concentrations within the therapeutic window.

From Therapeutic Drug Monitoring to Model-Informed Precision Dosing for Antibiotics

Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have evolved as important tools to inform rational dosing of antibiotics in individual patients with infections. In particular, critically ill patients display altered, highly variable pharmacokinetics and often suffer from infections caused by less susceptible bacteria. Consequently, TDM has been used to individualize dosing in this patient group for many years. More recently, there has been increasing research on the use of MIPD software to streamline the TDM process, which can increase the flexibility and precision of dose individualization but also requires adequate model validation and re-evaluation of existing workflows. In parallel, new minimally invasive and noninvasive technologies such as microneedle-based sensors are being developed, which-together with MIPD software-have the potential to revolutionize how patients are dosed with antibiotics. Nonetheless, carefully designed clinical trials to evaluate the benefit of TDM and MIPD approaches are still sparse, but are critically needed to justify the implementation of TDM and MIPD in clinical practice. The present review summarizes the clinical pharmacology of antibiotics, conventional TDM and MIPD approaches, and evidence of the value of TDM/MIPD for aminoglycosides, beta-lactams, glycopeptides, and linezolid, for which precision dosing approaches have been recommended.

Estimation of Body Fat Percentage for Clinical Pharmacokinetic Studies in Children

Obesity is a prevalent childhood condition and the degree of adiposity appears likely to be an important covariate in the pharmacokinetics (PKs) of many drugs. We undertook these studies to facilitate the evaluation and, where appropriate, quantification of the covariate effect of body fat percentage (BF%) on PK parameters in children. We examined two large databases to determine the values and variabilities of BF% in children with healthy body weights and in those with obesity, comparing the accuracy and precision of BF% estimation by both clinical methods and demographically derived techniques. Additionally, we conducted simulation studies to evaluate the utility of the several methods for application in clinical trials. BF% was correlated with body mass index (BMI), but was highly variable among both children with healthy body weights and those with obesity. Bio-impedance and several demographically derived techniques produced mean estimates of BF% that differed from dual x-ray absorptiometry by < 1% (accuracy) and a SD of 5% or less (precision). Simulation studies confirmed that when the differences in precision among the several methods were small compared with unexplained between-subject variability of a PK parameter, the techniques were of similar value in assessing the contribution of BF%, if any, as a covariate for that PK parameter. The combination of sex and obesity stage explained 68% of the variance of BF% with BMI. The estimation of BF% from sex and obesity stage can routinely be applied to PK clinical trials to evaluate the contribution of BF% as a potential covariate.

A Critique of Pharmacokinetic Calculators for Drug Dosing Individualization

The 'one-dose-fits-all' approach where drug dosing regimen is prescribed according to recommendations from a summary of product characteristics is not appropriate for many patients whose clinical characteristics significantly differ from the most frequent ones in a population, as it cannot guarantee optimal exposure of target tissues to the drug. Our aim here is to provide a concise review of pharmacokinetic calculators currently available for clinical use and, at the same time, to suggest the minimum standards that they should satisfy to be routinely used in clinical practice. A systematic search of Medline, Ebsco, Scopus, Scindeks, Cochrane Library and Google Scholar was performed to find publications about available pharmacokinetic calculators for drug dose individualization. Theoretically well-founded and mathematically correct calculators for many drugs are available, but only a few calculators for specific drugs have been validated in clinical practice or through clinical trials, and the results published in peer-reviewed journals. The majority of available pharmacokinetic calculators for drug dosing individualization remain unvalidated, i.e., there is no evidence of their efficacy and safety in real-life clinical settings. Pharmacokinetic calculators for drug dose individualization are irreplaceable tools for achieving precision medicine, where dosing regimens are tailored to the needs and personal characteristics of each patient, maximizing efficacy and minimizing toxicity.

Population pharmacokinetics of azathioprine active metabolite in patients with inflammatory bowel disease and dosage regimens optimisation

Azathioprine is a first-line drug used to maintain the remission of inflammatory bowel disease (IBD). As a prodrug, azathioprine is metabolised to produce active 6-thioguanine nucleotides (6-TGN). There are large individual variations in the pharmacokinetics/pharmacodynamics of 6-TGN in patients with IBD. Here, we aimed to develop a model to quantitatively investigate factors that affect 6-TGN pharmacokinetics to formulate a dosage guideline for azathioprine. Data were collected prospectively from 100 adult patients with IBD who were receiving azathioprine. Patients were genotyped for two single-nucleotide polymorphisms (TPMT*3C c.719A > G and NUDT15 c.415C > T). Using high-performance liquid chromatography, we measured 156 steady-state trough concentrations of 6-TGN within the range 0.09 to 1.16 mg/L (ie 133-1733 pmol per 8 × 10⁸ RBC). The covariates analysed included sex, age, body-weight, laboratory tests and concomitant medications. A population pharmacokinetic model was established using "non-linear mixed-effects modelling" software and the "first-order conditional estimation method with interaction." Body-weight, TPMT*3C polymorphisms and co-therapy with mesalazine were found to be important factors influencing the clearance of 6-TGN. A dosage guideline for azathioprine was developed based on the PPK model that enables individualised azathioprine dosing in adult patients with different body-weights, TPMT*3C genotypes and co-administration with mesalazine.

Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

Ontogeny of Phase I Metabolism of Drugs

Capturing ontogeny of enzymes involved in phase I metabolism is crucial to improve prediction of dose-concentration and concentration-effect relationships throughout infancy and childhood. Once captured, these patterns can be integrated in semiphysiologically or physiology-based pharmacokinetic models to support predictions in specific pediatric settings or to support pediatric drug development. Although these translational efforts are crucial, isoenzyme-specific ontogeny-based models should also incorporate data on variability of maturational and nonmaturational covariates (eg, disease, treatment modalities, pharmacogenetics). Therefore, this review provides a summary of the ontogeny of phase I drug-metabolizing enzymes, indicating current knowledge gaps and recent progresses. Furthermore, we tried to illustrate that straightforward translation of isoenzyme-specific ontogeny to predictions does not allow full exploration of scenarios of potential variability related to maturational (non-age-related variability, other isoenzymes or transporters) or nonmaturational (disease, pharmacogenetics) covariates, and necessitates integration in a "systems" concept.

Model-Informed Precision Dosing of Everolimus: External Validation in Adult Renal Transplant Recipients

BACKGROUND AND OBJECTIVE

The 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.

METHODS

The 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%.

RESULTS

The model showed adequate accuracy and precision for C₀ and AUC prediction at ≤ 6 months (MPPE_C0: 8.1 ± 2.5%, MAPE_C0: 26.8 ± 2.1%; MPPE_AUC: - 9.7 ± 5.1%, MAPE_AUC: 13.3 ± 3.9%) and > 6 months post-transplant (MPPE_C0: 4.7 ± 2.0%, MAPE_C0: 25.4 ± 1.4%; MPPE_AUC: - 0.13 ± 4.8%, MAPE_AUC: 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.

CONCLUSIONS

We 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.

TDM is dead. Long live TCI!

Twenty years ago, target concentration intervention (TCI) was distinguished from therapeutic drug monitoring (TDM). It was proposed that TCI would bring more clinical benefit because of the precision of the approach and the ability to link TCI to principles of pharmacokinetics and pharmacodynamics to predict the dose required by an individual (1). We examine the theory and clinical trial evidence supporting the benefits of TCI over TDM and conclude that in the digital age TDM should be abandoned and replaced by TCI.

Individualized Dosing With High Inter-Occasion Variability Is Correctly Handled With Model-Informed Precision Dosing-Using Rifampicin as an Example

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 (AUC_0–24h) 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 AUC_0–24h 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 AUC_0–24h 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.

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

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.

Precision Dosing Priority Criteria: Drug, Disease, and Patient Population Variables

The administered dose of a drug modulates whether patients will experience optimal effectiveness, toxicity including death, or no effect at all. Dosing is particularly important for diseases and/or drugs where the drug can decrease severe morbidity or prolong life. Likewise, dosing is important where the drug can cause death or severe morbidity. Since we believe there are many examples where more precise dosing could benefit patients, it is worthwhile to consider how to prioritize drug-disease targets. One key consideration is the quality of information available from which more precise dosing recommendations can be constructed. When a new more precise dosing scheme is created and differs significantly from the approved label, it is important to consider the level of proof necessary to either change the label and/or change clinical practice. The cost and effort needed to provide this proof should also be considered in prioritizing drug-disease precision dosing targets. Although precision dosing is being promoted and has great promise, it is underutilized in many drugs and disease states. Therefore, we believe it is important to consider how more precise dosing is going to be delivered to high priority patients in a timely manner. If better dosing schemes do not change clinical practice resulting in better patient outcomes, then what is the use? This review paper discusses variables to consider when prioritizing precision dosing candidates while highlighting key examples of precision dosing that have been successfully used to improve patient care.

Pharmacokinetic and pharmacodynamic considerations of general anesthesia in pediatric subjects

Introduction: The target concentration strategy uses PKPD information for dose determination. Models have also quantified exposure-response relationships, improved understanding of developmental pharmacokinetics, rationalized dose prescription, provided insight into the importance of covariate information, explained drug interactions and driven decision-making and learning during drug development.Areas covered: The prime PKPD consideration is parameter estimation and quantification of variability. The main sources of variability in children are age (maturation) and weight (size). Model use is mostly confined to pharmacokinetics, partly because anesthesia effect models in the young are imprecise. Exploration of PK and PD covariates and their variability hold potential to better individualize treatment.Expert opinion: The ability to model drugs using computer-based technology is hindered because covariate data required to individualize treatment using these programs remain lacking. Target concentration intervention strategies remain incomplete because covariate information that might better predict individualization of dose is absent. Pharmacogenomics appear a valuable area for investigation for pharmacodynamics and pharmacodynamics. Effect measures in the very young are imprecise. Assessment of the analgesic component of anesthesia is crude. While neuromuscular monitoring is satisfactory, depth of anaesthesia EEG interpretation is inadequate. Closed loop anesthesia is possible with better understanding of EEG changes.

The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib

Pharmacometric methods have hugely benefited from progress in analytical and computer sciences during the past decades, and play nowadays a central role in the clinical development of new medicinal drugs. It is time that these methods translate into patient care through therapeutic drug monitoring (TDM), due to become a mainstay of precision medicine no less than genomic approaches to control variability in drug response and improve the efficacy and safety of treatments. In this review, we make the case for structuring TDM development along five generic questions: 1) Is the concerned drug a candidate to TDM? 2) What is the normal range for the drug's concentration? 3) What is the therapeutic target for the drug's concentration? 4) How to adjust the dosage of the drug to drive concentrations close to target? 5) Does evidence support the usefulness of TDM for this drug? We exemplify this approach through an overview of our development of the TDM of imatinib, the very first targeted anticancer agent. We express our position that a similar story shall apply to other drugs in this class, as well as to a wide range of treatments critical for the control of various life-threatening conditions. Despite hurdles that still jeopardize progress in TDM, there is no doubt that upcoming technological advances will shape and foster many innovative therapeutic monitoring methods.

Proposed Therapeutic Range of Treosulfan in Reduced Toxicity Pediatric Allogeneic Hematopoietic Stem Cell Transplant Conditioning: Results From a Prospective Trial

Treosulfan is given off‐label in pediatric allogeneic hematopoietic stem cell transplant. This study investigated treosulfan's pharmacokinetics (PKs), efficacy, and safety in a prospective trial. Pediatric patients (n = 87) receiving treosulfan‐fludarabine conditioning were followed for at least 1 year posttransplant. PKs were described with a two‐compartment model. During follow‐up, 11 of 87 patients died and 12 of 87 patients had low engraftment (≤ 20% myeloid chimerism). For each increase in treosulfan area under the curve from zero to infinity (AUC_(0‐∞)) of 1,000 mg hour/L the hazard ratio (95% confidence interval) for mortality increase was 1.46 (1.23–1.74), and the hazard ratio for low engraftment was 0.61 (0.36–1.04). A cumulative AUC_(0‐∞) of 4,800 mg hour/L maximized the probability of success (> 20% engraftment and no mortality) at 82%. Probability of success with AUC_(0‐∞) between 80% and 125% of this target were 78% and 79%. Measuring PK at the first dose and individualizing the third dose may be required in nonmalignant disease.

Optimizing Mycophenolic Acid Exposure in Kidney Transplant Recipients: Time for Target Concentration Intervention

The immunosuppressive agent mycophenolate is used extensively in kidney transplantation, yet dosing strategy applied varies markedly from fixed dosing ("one-dose-fits-all"), to mycophenolic acid (MPA) trough concentration monitoring, to dose optimization to an MPA exposure target (as area under the concentration-time curve [MPA AUC0-12]). This relates in part to inconsistent results in prospective trials of concentration-controlled dosing (CCD). In this review, the totality of evidence supporting mycophenolate CCD is examined: pharmacological characteristics, observational data linking exposure to efficacy and toxicities, and randomized controlled trials of CCD, with attention to dose optimization method and exposure achieved. Fixed dosing of mycophenolate consistently leads to underexposure associated with rejection, as well as overexposure associated with toxicities. When CCD is driven by pharmacokinetic calculation to a target concentration (target concentration intervention), MPA exposure is successfully controlled and clinical benefits are seen. There remains a need for consensus on practical aspects of mycophenolate target concentration intervention in contemporary tacrolimus-containing regimens and future research to define maintenance phase exposure targets. However, given ongoing consequences of both overimmunosuppression and underimmunosuppression in kidney transplantation, impacting short- and long-term outcomes, these should be a priority. The imprecise "one-dose-fits-all" approach should be replaced by the clinically proven MPA target concentration strategy.

Pharmacokinetic modelling and Bayesian estimation-assisted decision tools to optimize vancomycin dosage in neonates: only one piece of the puzzle

Introduction: Vancomycin is commonly administered to neonates, while observational data on therapeutic drug monitoring (TDM, trough levels) suggest that vancomycin exposure and dosage remain substandard. Area covered: Data on vancomycin pharmacokinetics (PK) and its covariates are abundant. Consequently, modeling is an obvious tool to improve targeted exposure, with a shift from TDM trough levels to area under the curve (AUC_24h) targets, as in adults. Continuous administration appeared as a practice to facilitate AUC_24h target attainment, while Bayesian model-supported targeting emerged as a novel tool. However, the AUC_24h/MIC (minimal inhibitory concentration) target itself should consider neonate-specific aspects (bloodstream infections, coagulase-negative staphylococci, protein binding, underexplored causes of variability, like assays, preparation and administration inaccuracies, or missing covariates). Expert opinion: To improve targeted exposure in neonates, initial vancomycin prescription should be based on 'a priori model-based individual dosing' using validated dosing regimens, followed by further tailoring by dosing optimization applying Bayesian estimation-assisted TDM. Future research should focus on the feasibility to integrate these tools (individualized dosing, Bayesian models) in clinical practice, and to perform PK/PD studies in the relevant animal models and human neonatal setting (coagulase-negative staphylococci, bloodstream infections).

Routine clinical care data for population pharmacokinetic modeling: the case for Fanhdi/Alphanate in hemophilia A patients

Fanhdi/Alphanate is a plasma derived factor VIII concentrate used for treating hemophilia A, for which there has not been any dedicated model describing its pharmacokinetics (PK). A population PK model was developed using data extracted from the Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) project. WAPPS-Hemo provided individual PK profiles for hemophilia patients using sparse observations as provided in routine clinical care by hemophilia centers. Plasma factor activity measurements and covariate data from hemophilia A patients on Fanhdi/Alphanate were extracted from the WAPPS-Hemo database. A population PK model was developed using NONMEM and evaluated for suitability for Bayesian forecasting using prediction-corrected visual predictive check (pcVPC), cross validation, limited sampling analysis and external evaluation against a population PK model developed on rich sampling data. Plasma factor activity measurements from 92 patients from 12 centers were used to derive the model. The PK was best described by a 2-compartment model including between subject variability on clearance and central volume, fat free mass as a covariate on clearance, central and peripheral volumes, and age as covariate on clearance. Evaluations showed that the developed population PK model could predict the PK parameters of new individuals based on limited sampling analysis and cross and external evaluations with acceptable precision and bias. This study shows the feasibility of using real-world data for the development of a population PK model. Evaluation and comparison of the model for Bayesian forecasting resulted in similar results as a model developed using rich sampling data.

Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data

AIMS

This study aims to assess approaches to handle interoccasion variability (IOV) in a model-based therapeutic drug monitoring (TDM) context, using a population pharmacokinetic model of coagulation factor VIII as example.

METHODS

We assessed 5 model-based TDM approaches: empirical Bayes estimates (EBEs) from a model including IOV, with individualized doses calculated based on individual parameters either (i) including or (ii) excluding variability related to IOV; and EBEs from a model excluding IOV by (iii) setting IOV to zero, (iv) summing variances of interindividual variability (IIV) and IOV into a single IIV term, or (v) re-estimating the model without IOV. The impact of varying IOV magnitudes (0-50%) and number of occasions/observations was explored. The approaches were compared with conventional weight-based dosing. Predictive performance was assessed with the prediction error percentiles.

RESULTS

When IOV was lower than IIV, the accuracy was good for all approaches (50^th percentile of the prediction error [P50] <7.4%), but the precision varied substantially between IOV magnitudes (P97.5 61-528%). Approach (ii) was the most precise forecasting method across a wide range of scenarios, particularly in case of sparse sampling or high magnitudes of IOV. Weight-based dosing led to less precise predictions than the model-based TDM approaches in most scenarios.

CONCLUSIONS

Based on the studied scenarios and theoretical expectations, the best approach to handle IOV in model-based dose individualization is to include IOV in the generation of the EBEs but exclude the portion of unexplained variability related to IOV in the individual parameters used to calculate the future dose.

Pharmacodynamic principles and target concentration intervention

This tutorial reviews the principles of dose individualisation with an emphasis on target concentration intervention (TCI). Once a target effect is chosen then pharmacodynamics can predict the target concentration and pharmacokinetics can predict the target dose to achieve the required response. Dose individualisation can be considered at three levels: population, group and individual. Population dosing, also known as fixed dosing or "one size fits all" is often used but is poor clinical pharmacology; group dosing uses patient features such as weight, organ function and co-medication to adjust the dose for a typical patient; individual dosing uses observations of patient response to inform about pharmacokinetic and pharmacodynamics in the individual and use these individual differences to individualise dose.

Performing and interpreting individual pharmacokinetic profiles in patients with Hemophilia A or B: Rationale and general considerations

OBJECTIVES

In a separate document, we have provided specific guidance on performing individual pharmacokinetic (PK) studies using limited samples in persons with hemophilia with the goal to optimize prophylaxis with clotting factor concentrates. This paper, intended for clinicians, aims to describe how to interpret and apply PK properties obtained in persons with hemophilia.

METHODS

The members of the Working Party on population PK (PopPK) of the ISTH SSC Subcommittee on Factor VIII and IX and rare bleeding disorders, together with additional hemophilia and PK experts, completed a survey and ranking exercise whereby key areas of interest in the field were identified. The group had regular web conferences to refine the manuscript's scope and structure, taking into account comments from the external feedback to the earlier document.

RESULTS

Many clinical decisions in hemophilia are based on some form of explicit or implicit PK assessment. Individual patient PK profiles can be analyzed through traditional or PopPK methods, with the latter providing the advantage of fewer samples needing to be collected on any prophylaxis regimen, and without the need the for a washout period. The most useful presentation of PK results for clinical decision making are a curve of the factor activity level over time, the time to achieve a certain activity level, or related parameters like half-life or exposure (AUC). Software platforms have been developed to deliver this information to clinicians at the point of care. Key characteristics of studies measuring average PK parameters were reviewed, outlining what makes a credible head-to-head comparison among different concentrates. Large data collections of PK and treatment outcomes currently ongoing will advance care in the future.

CONCLUSIONS

Traditionally used to compare different concentrates, PK can support tailoring of hemophilia treatment by individual profiling, which is greatly simplified by adopting a PopPK/Bayesian method and limited sampling protocol.

Mathematical modeling and simulation in animal health. Part III: Using nonlinear mixed-effects to characterize and quantify variability in drug pharmacokinetics

A common feature of human and veterinary pharmacokinetics is the importance of identifying and quantifying the key determinants of between-patient variability in drug disposition and effects. Some of these attributes are already well known to the field of human pharmacology such as bodyweight, age, or sex, while others are more specific to veterinary medicine, such as species, breed, and social behavior. Identification of these attributes has the potential to allow a better and more tailored use of therapeutic drugs both in companion and food-producing animals. Nonlinear mixed effects (NLME) have been purposely designed to characterize the sources of variability in drug disposition and response. The NLME approach can be used to explore the impact of population-associated variables on the relationship between drug administration, systemic exposure, and the levels of drug residues in tissues. The latter, while different from the method used by the US Food and Drug Administration for setting official withdrawal times (WT) can also be beneficial for estimating WT of approved animal drug products when used in an extralabel manner. Finally, NLME can also prove useful to optimize dosing schedules, or to analyze sparse data collected in situations where intensive blood collection is technically challenging, as in small animal species presenting limited blood volume such as poultry and fish.