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Background: Vancomycin is a widely used antibiotic for the treatment of gram-positive bacterial infections, especially for methicillin-resistant Staphylococcus aureus (MRSA) infections. Due to a small therapeutic range and large inter-patient variability, therapeutic drug monitoring (TDM) of vancomycin is required to minimize toxicity and maximize treatment efficacy. Venous blood sampling is mostly applied for TDM of vancomycin, although this widely used sampling method is more invasive compared to less painful alternatives, such as the dried blood spot (DBS) method, which can be performed at home. Method: We developed an UPLC-MS/MS method for the quantification of vancomycin and creatinine in DBS. A fast sample preparation and short analysis run time of 5.2 min were applied, which makes this method highly suitable for clinical settings. Validation was performed according to international (FDA and EMA) guidelines. Results: The validated concentration range was found linear for creatinine from 41.8 µmol/L to 722 µmol/L and for vancomycin from 3.8 mg/L to 76.6 mg/L (r2 > 0.990) and the inaccuracies, imprecisions, hematocrit effects, and recoveries were < 15 % for both compounds. No significant carryover effect was observed. Conclusion: Hence, we successfully validated a quantification method for the simultaneous determination of creatinine and vancomycin in DBS.

Aims: Pregnancy after kidney transplantation is realistic but immunosuppressants should be continued to prevent rejection. Tacrolimus is safe during pregnancy and is routinely dosed based on whole-blood predose concentrations. However, maintaining these concentrations is complicated as physiological changes during pregnancy affect tacrolimus pharmacokinetics. The aim of this study was to describe tacrolimus pharmacokinetics throughout pregnancy and explain the changes by investigating covariates in a population pharmacokinetic model. Methods: Data of pregnant women using a twice-daily tacrolimus formulation following kidney transplantation were retrospectively collected from 6 months before conception, throughout gestation and up to 6 months postpartum. Pharmacokinetic analysis was performed using nonlinear mixed effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. The final model was evaluated using goodness-of-fit plots, visual predictive checks and a bootstrap analysis. Results: A total of 260 whole-blood tacrolimus predose concentrations from 14 pregnant kidney transplant recipients were included. Clearance increased during pregnancy from 34.5 to 41.7 L/h, by 15, 19 and 21% in the first, second and third trimester, respectively, compared to prior to pregnancy. This indicates a required increase in the tacrolimus dose by the same percentage to maintain the prepregnancy concentration. Haematocrit and gestational age were negatively correlated with tacrolimus clearance (P ≤ 0.01), explaining 18% of interindividual and 85% of interoccasion variability in oral clearance.Conclusions: Tacrolimus clearance increases during pregnancy, resulting in decreased exposure to tacrolimus, which is explained by gestational age and haematocrit. To maintain prepregnancy target whole-blood tacrolimus predose concentrations during pregnancy, increasing the dose is

BACKGROUND: Chemerin, an inflammatory adipokine, is upregulatefmed in preeclampsia, and its placental overexpression results in preeclampsia-like symptoms in mice. Statins may lower chemerin. METHODS: Chemerin was determined in a prospective cohort study in women suspected of preeclampsia and evaluated as a predictor versus the sFlt-1 (soluble fms-like tyrosine kinase-1)/PlGF (placental growth factor) ratio. Chemerin release was studied in perfused placentas and placental explants with or without the statins pravastatin and fluvastatin. We also addressed statin placental passage and the effects of chemerin in chorionic plate arteries. RESULTS: Serum chemerin was elevated in women with preeclampsia, and its addition to a predictive model yielded significant effects on top of the sFlt-1/PlGF ratio to predict preeclampsia and its fetal complications. Perfused placentas and explants of preeclamptic women released more chemerin and sFlt-1 and less PlGF than those of healthy pregnant women. Statins reversed this. Both statins entered the fetal compartment, and the fetal/maternal concentration ratio of pravastatin was twice that of fluvastatin. Chemerin constricted plate arteries, and this was blocked by a chemerin receptor antagonist and pravastatin. Chemerin did not potentiate endothelin-1 in chorionic plate arteries. In explants, statins upregulated low-density lipoprotein receptor expression, which relies on the same transcription factor as chemerin, and NO release.CONCLUSIONS: Chemerin is a biomarker for preeclampsia, and statins both prevent its placental upregulation and effects, in an NO and low-density lipoprotein receptor-dependent manner. Combined with their capacity to improve the sFlt-1/PlGF ratio, this offers an attractive mechanism by which statins may prevent or treat preeclampsia.

Background:The aim of this open-label, multicenter, randomized controlled study was to investigate whether the life cycle pharma (LCP)-tacrolimus compared with the extended-release (ER)-tacrolimus formulation results in a difference in the prevalence of posttransplant diabetes, hypertension and chronic kidney disease (CKD) at 12 mo after liver transplantation.Methods:Patients were 1:1 randomized to either of the 2 tacrolimus formulations. The primary endpoint was defined as a composite endpoint of any of 3 events: sustained (>3 mo postrandomization) posttransplant diabetes, new-onset hypertension, and/or CKD, defined as estimated glomerular filtration rate <60 mL/min/1.73 m2 for >3 m during the follow-up.Results:In total, 105 patients were included. In the intention-to-treat analysis, a statistically significant lower proportion of liver transplant recipients in the LCP-tacrolimus group reached the composite primary endpoint at 12 mo compared with the ER-tacrolimus group (50.9% [27/53], 95% confidence interval [CI], 37.9%-63.9% versus 71.2% [37/52], 95% CI, 57.7%-81.7%; risk difference: 0.202; 95% CI, 0.002-0.382; P = 0.046). No significant difference was found in the per protocol analysis. In the intention-to-treat and per protocol population, fewer liver transplant recipients in the LCP-tacrolimus group developed CKD and new-onset hypertension compared with the ER-tacrolimus group. No differences in rejection rate, graft and patient survival were found. Conclusions:A statistically significant and clinically relevant reduction in the prevalence of the composite primary endpoint was found in the LCP-tacrolimus group compared with the ER-tacrolimus group in the first year after liver transplantation with comparable efficacy.

In order to prevent long-term immunity-related complications after lung transplantation, close monitoring of immunosuppressant levels using therapeutic drug monitoring (TDM) is paramount. Novel electronic nose (eNose) technology may be a non-invasive alternative to the current invasive procedures for TDM. We investigated the diagnostic and categorization capacity of eNose breathprints for Tacrolimus trough blood plasma levels (TAC trough) in lung transplant recipients (LTRs). We performed eNose measurements in stable LTR attending the outpatient clinic. We evaluated (1) the correlation between eNose measurements and TAC trough, (2) the diagnostic capacity of eNose technology for TAC trough, and (3) the accuracy of eNose technology for categorization of TAC trough into three clinically relevant categories (low: <7 µg ml −1, medium: 7-10 µg ml −1, and high: >10 µg ml −1 ). A total of 186 measurements from 86 LTR were included. There was a weak but statistically significant correlation (r = 0.21, p = 0.004) between the eNose measurements and TAC trough. The root mean squared error of prediction for the diagnostic capacity was 3.186 in the training and 3.131 in the validation set. The accuracy of categorization ranged between 45%-63% for the training set and 52%-69% in the validation set. There is a weak correlation between eNose breathprints and TAC trough in LTR. However, the diagnostic as well as categorization capacity for TAC trough using eNose breathprints is too inaccurate to be applicable in TDM.

Aims: Aripiprazole is one of the most commonly prescribed antipsychotic drugs to children and adolescents worldwide, but it is associated with serious side-effects, including weight gain. This study assessed the population pharmacokinetics of aripiprazole and its active metabolite and investigated the relationship between pharmacokinetic parameters and body mass index (BMI) in children and adolescents with autism spectrum disorder (ASD) and behavioural problems. Secondary outcomes were metabolic, endocrine, extrapyramidal and cardiac side-effects and drug effectiveness. Methods: Twenty-four children and adolescents (15 males, 9 females) aged 6–18 years were included in a 24-week prospective observational trial. Drug plasma concentrations, side-effects and drug effectiveness were measured at several time points during follow-up. Relevant pharmacokinetic covariates, including CYP2D6, CYP3A4, CYP3A5 and P-glycoprotein (ABCB1) genotypes, were determined. Nonlinear mixed-effects modelling (NONMEM®) was used for a population pharmacokinetic analysis with 92 aripiprazole and 91 dehydro-aripiprazole concentrations. Subsequently, model-based trough concentrations, maximum concentrations and 24-h area under the curves (AUCs) were analysed to predict outcomes using generalized and linear mixed-effects models. Results: For both aripiprazole and dehydro-aripiprazole, one-compartment models best described the measured concentrations, with albumin and BMI as significant covariates. Of all the pharmacokinetic parameters, higher sum (aripiprazole plus dehydro-aripiprazole) trough concentrations best predicted higher BMI z-scores (P <.001) and higher Hb1Ac levels (P =.03) during follow-up. No significant association was found between sum concentrations and effectiveness. Conclusions: Our results indicate a threshold with regard to safety, which suggests that therapeutic drug monitoring of aripiprazole

Introduction: Tacrolimus is a potent immunosuppressive drug with many side effects including nephrotoxicity and post-transplant diabetes mellitus. To limit its toxicity, therapeutic drug monitoring (TDM) is performed. However, tacrolimus’ pharmacokinetics are highly variable within and between individuals, which complicates their clinical management. Despite TDM, many kidney transplant recipients will experience under- or overexposure to tacrolimus. Therefore, dosing algorithms have been developed to limit the time a patient is exposed to off-target concentrations. Areas Covered: Tacrolimus starting dose algorithms and models for follow-up doses developed and/or tested since 2015, encompassing both adult and pediatric populations. Literature was searched in different databases, i.e. Embase, PubMed, Web of Science, Cochrane Register, and Google Scholar, from inception to February 2023 Expert Opinion: Many algorithms have been developed, but few have been prospectively evaluated. These performed better than bodyweight-based starting doses, regarding the time a patient is exposed to off-target tacrolimus concentrations. No benefit in reduced tacrolimus toxicity has yet been observed. Most algorithms were developed from small datasets, contained only a few tacrolimus concentrations per person, and were not externally validated. Moreover, other matrices should be considered which might better correlate with tacrolimus toxicity than the whole-blood concentration, e.g. unbound plasma or intra-lymphocytic tacrolimus concentrations.

Background: Pentobarbital pharmacokinetics (PK) remain elusive and the therapeutic windows narrow. Administration is frequent in critically ill children with refractory status epilepticus (SE) and severe traumatic brain injury (sTBI). Objectives: To investigate pentobarbital PK in SE and sTBI patients admitted to the paediatric intensive care unit (PICU) with population-based PK (PopPK) modelling and dosing simulations. Methods: Develop a PopPK model with non-linear mixed-effects modelling (NONMEM®) with retrospective data (n = 36; median age 1.3 years; median weight 10 kg; 178 blood samples) treated with continuous intravenous pentobarbital. An independent dataset was used for external validation (n = 9). Dosing simulations with the validated model evaluated dosing regimens. Results: A one-compartment PK model with allometrically scaled weight on clearance (CL; 0.75) and volume of distribution (V d; 1) captured data well. Typical CL and V d values were 3.59 L/70 kg/h and 142 L/70 kg, respectively. Elevated creatinine and C-reactive protein (CRP) levels significantly correlated to decreased CL, explaining 84% of inter-patient variability, and were incorporated in the final model. External validation using stratified visual predictive checks showed good results. Simulations demonstrated patients with elevated serum creatinine and CRP failed to achieve steady state yet progressed to toxic levels with current dosing regimens. Conclusions: The one-compartment PK model of intravenous pentobarbital described data well whereby serum creatinine and CRP significantly correlated with pentobarbital CL. Dosing simulations formulated adjusted dosing advice in patients with elevated creatinine and/or CRP. Prospective PK studies with pharmacodynamic endpoints, are imperative to optimise pentobarbital dosing in terms of safety and clinical efficacy in critically ill children.

Personalized medicine is currently hampered by the lack of flexible drug formulations. Especially for pediatric patients, manual compounding of personalized drug formulations by pharmacists is required. Three-Dimensional (3D) printing of medicines, which enables small-scale manufacturing at the point-of-care, can fulfill this unmet clinical need. This study investigates the feasibility of developing a 3D-printed tablet formulation at the point-of-care which complies to quality requirements for clinical practice, including bioequivalence. Development, manufacturing, and quality control of the 3D-printed tablets was performed at the manufacturing facility and laboratory of the department of Clinical Pharmacy and Toxicology at Leiden University Medical Center. Sildenafil was used as a model drug for the tablet formulation. Along with the 3D-printed tablets a randomized, an open-label, 2-period, crossover, single-dose clinical trial to assess bioequivalence was performed in healthy adults. Bioequivalence was established if areas under the plasma concentration curve from administration to the time of the last quantifiable concentration (AUC0-t) and maximum plasma concentration (Cmax) ratios were within the limits of 80.00–125.00%. The manufacturing process provided reproducible 3D-printed tablets that adhered to quality control requirements and were consequently used in the clinical trial. The clinical trial was conducted in 12 healthy volunteers. The 90% confidence intervals (CIs) of both AUC0-t and Cmax ratios were within bioequivalence limits (AUC0-t 90% CI: 87.28–104.14; Cmax 90% CI: 80.23–109.58). For the first time, we demonstrate the development of a 3D-printed tablet formulation at the point-of-care that is bioequivalent to its marketed originator. The 3D printing of personalized formulations is a disruptive technology for compounding, bridging the gap toward personalized medicine.

The number of pharmacokinetic (PK) models of meropenem is increasing. However, the daily role of these PK models in the clinic remains unclear, especially for critically ill patients. Therefore, we evaluated the published meropenem models on real-world ICU data to assess their suitability for use in clinical practice. All models were built in NONMEM and evaluated using prediction and simulation-based diagnostics for the ability to predict the subsequent meropenem concentrations without plasma concentrations (a priori), and with plasma concentrations (a posteriori), for use in therapeutic drug monitoring (TDM). Eighteen PopPK models were included for evaluation. The a priori fit of the models, without the use of plasma concentrations, was poor, with a prediction error (PE)% of the interquartile range (IQR) exceeding the ±30% threshold. The fit improved when one to three concentrations were used to improve model predictions for TDM purposes. Two models were in the acceptable range with an IQR PE% within ±30%, when two or three concentrations were used. The role of PK models to determine the starting dose of meropenem in this population seems limited. However, certain models might be suitable for TDM-based dose adjustment using two to three plasma concentrations.

Temocillin is used for the treatment of various infections caused by Enterobacterales. The pharmacokinetic (PK)/pharmacodynamic (PD) index that is best correlated with the activity of beta-lactams is the percentage of time that the unbound concentration exceeds the MIC (%fT.MIC). However, the %fT.MIC needed for a bacteriostatic or killing effect of temocillin is unknown in thigh and lung infection models. In the present study, we studied the temocillin PK in plasma and epithelial lining fluid (ELF) of infected neutropenic mice and determined the plasma exposure-response relationships for Escherichia coli and Klebsiella pneumoniae. Neutropenic murine thigh and lung infection models were used. The bacterial loads in the thighs or lungs were determined. A sigmoid maximum-effect model was used to fit the plasma exposure-response relationship. A one-compartment model with first-order absorption best described temocillin PK (clearance [CL], 1.03 L/h/kg; volume of distribution [V], 0.457 L/kg). Protein binding was 78.2% 6 1.3% across different plasma concentrations. A static effect was achieved for all strains in both the thigh and lung infection models. However, the median %fT.MIC needed for a static effect was much lower in the lung infection model (27.8% for E. coli and 38.2% for K. pneumoniae) than in the thigh infection model (65.2% for E. coli and 64.9% for K. pneumoniae). A 1-log kill was reached for all strains in the lung infection model (median % fT.MIC values of 42.1% for E. coli and 44.1% for K. pneumoniae) and 7 out of 8 strains in the thigh infection model (median %fT.MIC values of 85.4% for E. coli and 74.5% for K. pneumoniae). These data support the use of temocillin in patients with pneumonia.

Background: Antipsychotic drugs are an important part of the treatment of irritability and aggression in children with an autism spectrum disorder (ASD). However, significant weight gain and metabolic disturbances are clinically relevant side effects of antipsychotic use in children. In the SPACe study, we showed positive correlations between both risperidone and aripiprazole plasma trough concentrations and weight gain over a 6-month period. The trial SPACe 2: STAR is designed as a follow-up study, in which we aim to research whether therapeutic drug monitoring in clinical practice can prevent severe weight gain, while retaining clinical effectiveness. Methods: SPACe 2: STAR is an international, multicentre, randomised controlled trial (RCT). One hundred forty children aged 6 to 18 who are about to start risperidone or aripiprazole treatment for ASD related behavioural problems will be randomised into one of two groups: a therapeutic drug monitoring (TDM) group, and a care as usual (CAU) group. Participants will be assessed at baseline and 4, 10, 24, and 52 weeks follow-up. In the TDM group, physicians will receive dosing advice based on plasma levels of risperidone and aripiprazole and its metabolites at 4 and 10 weeks. Plasma levels will be measured in dried blood spots (DBS). The primary outcome will be BMI z-score at 24 weeks after start of antipsychotic treatment. Among the secondary outcomes are effectiveness, metabolic laboratory measurements, levels of prolactin, leptin and ghrelin, extrapyramidal side effects, and quality of life. Discussion: This will be the first RCT evaluating the effect of TDM of antipsychotic drugs in children and adolescents. Thus, findings from SPACe 2: STAR will be of great value in optimising treatment in this vulnerable population.

Quantification of beta-lactam antibiotics can be performed by using liquid chromatography in combination with tandem mass spectrometry (MS/MS) or ultraviolet (UV) detection. Since beta-lactam antibiotics are known as highly polar analytes, using standard reversed phase chromatography will result in very early elution, which is often not desirable. Some retention is preferred to reduce matrix effects, because a high amount of non-retained molecular matrix species elute early from the column. For highly polar analytes, ultra-performance convergence chromatography (UPC2) may be a suitable alternative. This method is based on supercritical fluid chromatography. To our knowledge, we developed the first UPC2-MS/MS method for the determination of amoxicillin, benzylpenicillin, flucloxacillin, piperacillin, cefotaxime, cefuroxime, ceftazidime, imipenem, meropenem, and the free fraction of cefuroxime and flucloxacillin in human plasma. The method was validated according to the Food and Drug Administration guidelines. The method was found linear (r2 >0.990) for all analytes. The inaccuracies and imprecisions were < 15% for all analytes. The matrix effect and recovery were nearly all consistent with coefficient of variation of less than 15% and no significant carryover effect was observed. Furthermore, this method was found to be suitable for daily routine analysis in hospital settings, requiring only 50 µL of plasma.

For decades, flucloxacillin has been used to treat methicillin-susceptible Staphylococcus aureus (MSSA). Little is still known about its pharmacodynamics (PD). The present study aimed to determine the pharmacokinetic (PK)/PD index and the PD-index value minimally required for efficacy. MICs of 305 MSSA isolates were measured to determine the wild-type distribution. The PD of 8 S. aureus, 1 S. pyogenes, and 1 S. agalactiae isolates were evaluated in a neutropenic murine thigh infection model. Two S. aureus isolates were used in a dose-fractionation study and a dose–response analysis was performed additionally in the in vivo model. Data were analyzed with a population PK and sigmoid maximum effect model. The end of the wild-type distribution was 1 mg/L. The percentage of time the unbound concentration was above MIC (%fT > MIC) was best correlated with efficacy. For S. aureus, median %fT > 0.25 × MIC required for 1-log reduction was 15%. The value for S. pyogenes was 10%fT > MIC and for S. agalactiae 22%fT > 0.25xMIC for a 1-log reduction. The effect of flucloxacillin reached a 2-log reduction of S. aureus at 20%fT > 0.25xMIC and also for S. pyogenes and S. agalactiae, a reduction was reached. These data may serve to optimize dosing regimens currently used in humans.

Background and Objective: Intravenous immunoglobulin (IVIg) at a standard dosage is the treatment of choice for Guillain–Barré syndrome. The pharmacokinetics, however, is highly variable between patients, and a rapid clearance of IVIg is associated with poor recovery. We aimed to develop a model to predict the pharmacokinetics of a standard 5-day IVIg course (0.4 g/kg/day) in patients with Guillain–Barré syndrome. Methods: Non-linear mixed-effects modelling software (NONMEM®) was used to construct a pharmacokinetic model based on a model-building cohort of 177 patients with Guillain–Barré syndrome, with a total of 589 sequential serum samples tested for total immunoglobulin G (IgG) levels, and evaluated on an independent validation cohort that consisted of 177 patients with Guillain–Barré syndrome with 689 sequential serum samples. Results: The final two-compartment model accurately described the daily increment in serum IgG levels during a standard IVIg course; the initial rapid fall and then a gradual decline to steady-state levels thereafter. The covariates that increased IgG clearance were a more severe disease (as indicated by the Guillain–Barré syndrome disability score) and concomitant methylprednisolone treatment. When the current dosing regimen was simulated, the percentage of patients who reached a target ∆IgG > 7.3 g/L at 2 weeks decreased from 74% in mildly affected patients to only 33% in the most severely affected and mechanically ventilated patients (Guillain–Barré syndrome disability score of 5). Conclusions: This is the first population-pharmacokinetic model for standard IVIg treatment in Guillain–Barré syndrome. The model provides a new tool to predict the pharmacokinetics of alternative regimens of IVIg in Guillain–Barré syndrome to design future trials and personalise treatment.

Introduction: Kidney transplant rejection remains an important clinical problem despite the development of effective immunosuppressive therapy. Two major types of rejection are recognized, T-cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR), which have a different pathophysiology and are treated differently. Unfortunately, long-term outcomes of both TCMR and ABMR remain unsatisfactory despite current therapy. Hence, alternative therapeutic drugs are urgently needed. Areas covered: This review covers novel and investigational drugs for the pharmacological treatment of kidney transplant rejection. Potential therapeutic strategies and future directions are discussed. Expert opinion: The development of alternative pharmacologic treatment of rejection has focused mostly on ABMR, since this is the leading cause of kidney allograft loss and currently lacks an effective, evidence-based therapy. At present, there is insufficient high-quality evidence for any of the covered investigational drugs to support their use in ABMR. However, with the emergence of targeted therapies, the potential arises for individualized treatment strategies. In order to generate more high-quality evidence for such strategies and overcome the obstacles of classic randomized controlled trials, we advocate the implementation of adaptive trial designs and surrogate clinical endpoints. We believe such adaptive trial designs could help to understand the risks and benefits of promising drugs such as tocilizumab, clazakizumab, belimumab, and imlifidase.

PURPOSE: Early initiation of antibiotics is essential for ameliorating infections in critically ill patients. The correct dosage of antibiotics is imperative to ensure their adequate exposure. Critically ill patients have altered pharmacokinetic parameters and are often infected by less susceptible microorganisms. Differences in drug disposition are not considered with standard doses of antibiotics. This can lead to suboptimal antibiotic exposure in critically ill patients. To overcome this problem of suboptimal dosing, therapeutic drug monitoring (TDM) is a strategy commonly used to support individualized dosing of antibiotics. It is routinely used for vancomycin and aminoglycosides in clinical practice. In recent years, it has become apparent that TDM may also be used in other antibiotics. METHODS: This review summarizes the evidence for TDM of antibiotics in critically ill patients, focuses on clinical outcomes, and summarizes possibilities for optimized TDM in the future. RESULTS AND CONCLUSION: After reviewing the literature, we can conclude that general TDM implementation is advised for glycopeptides and aminoglycosides, as evidence of the relationship between TDM and clinical outcome is present. For antibiotics, such as beta-lactams, fluoroquinolones, and linezolid, it seems rational to perform TDM in specific patient cases. TDM involving other antibiotics is supported by individual cases, specifically to decrease toxicity. When focusing on future possibilities to improve TDM of antibiotics in critically ill patients, implementation of model-informed precision dosing should be investigated because it can potentially streamline the TDM process. The logistics of TDM, such as turnaround time and available equipment, are challenging but may be overcome by rapid bioanalytical techniques or real-time monitoring of drug concentrations through biosensors in the future. Education, clinical information on targets, and clinical outcome studies are other important fac

Aim: Risperidone is the most commonly prescribed antipsychotic drug to children and adolescents worldwide, but it is associated with serious side effects, including weight gain. This study assessed the relationship of risperidone and 9-hydroxyrisperidone trough concentrations, maximum concentrations and 24-hour area under the curves (AUCs) with body mass index (BMI) z-scores in children and adolescents with autism spectrum disorder (ASD) and behavioural problems. Secondary outcomes were metabolic, endocrine, extrapyramidal and cardiac side effects and effectiveness. Methods: Forty-two children and adolescents (32 males) aged 6-18 years were included in a 24-week prospective observational trial. Drug plasma concentrations, side effects and effectiveness were measured at several time points during follow-up. Relevant pharmacokinetic covariates, including medication adherence and CYP2D6, CYP3A4, CYP3A5 and P-glycoprotein (ABCB1) genotypes, were measured. Nonlinear mixed-effects modelling (NONMEM®) was used for a population pharmacokinetic analysis with 205 risperidone and 205 9-hydroxyrisperidone concentrations. Subsequently, model-based trough concentrations, maximum concentrations and 24-hour AUCs were analysed to predict outcomes using generalized and linear mixed-effects models. Results: A risperidone two-compartment model combined with a 9-hydroxyrisperidone one-compartment model best described the measured concentrations. Of all the pharmacokinetic parameters, higher risperidone sum trough concentrations best predicted higher BMI z-scores during follow-up (P <.001). Higher sum trough concentrations also predicted more sedation (P <.05), higher prolactin levels (P <.001) and more effectiveness measured with Aberrant Behavior Checklist irritability score (P <.01). Conclusion: Our results indicate a therapeutic window exists, which suggests that therapeutic drug monitoring of risperidone might increase safety and effectiveness in children and adolesce

Aims: Tacrolimus is a critical dose drug and to avoid under- and overexposure, therapeutic drug monitoring is standard practice. However, rejection and drug-related toxicity occur despite whole-blood tacrolimus pre-dose concentrations ([Tac]blood) being on target. Monitoring tacrolimus concentrations at the target site (within peripheral blood mononuclear cells; [Tac]cells) may better correlate with drug-efficacy. The aim of this study was to (1) investigate the relationship between [Tac]blood and [Tac]cells, (2) identify factors affecting the tacrolimus distribution in cells and whole-blood, and (3) study the relationship between [Tac]cells and clinical outcomes after kidney transplantation. Methods: A total of 175 renal transplant recipients were prospectively followed. [Tac]blood and [Tac]cells were determined at Months 3, 6 and 12 post-transplantation. Patients were genotyped for ABCB1 1199G>A and 3435C>T, CYP3A4 15389C>T, and CYP3A5 6986G>A. Data on rejection and tacrolimus-related nephrotoxicity and post-transplant diabetes mellitus were collected. Results: Correlations between [Tac]blood and [Tac]cells were moderate to poor (Spearman's r = 0.31; r = 0.41; r = 0.61 at Months 3, 6 and 12, respectively). The [Tac]cells/[Tac]blood ratio was stable over time in most patients (median intra-patient variability 39.0%; range 3.5%–173.2%). Age, albumin and haematocrit correlated with the [Tac]cells/[Tac]blood ratio. CYP3A5 and CYP3A4 genotype combined affected both dose-corrected [Tac]blood and [Tac]cells. ABCB1 was not significantly related to tacrolimus distribution. Neither [Tac]blood nor [Tac]cells correlated with clinical outcomes. Conclusions: The correlation between [Tac]blood and [Tac]cells is poor. Age, albumin and haematocrit correlate wit

Population pharmacokinetic modeling and simulation (M&S) are used to improve antibiotic dosing. Little is known about the differences in parametric and nonparametric M&S. Our objectives were to compare (1) the external validation of parametric and nonparametric models of imipenem in critically ill patients and (2) the probability of target attainment (PTA) calculations using simulations of both models. The M&S software used was NONMEM 7.2 (parametric) and Pmetrics 1.5.2 (nonparametric). The external predictive performance of both models was adequate for eGFRs ≥ 78 mL/min but insufficient for lower eGFRs, indicating that the models (developed using a population with eGFR ≥ 60 mL/min) could not be extrapolated to lower eGFRs. Simulations were performed for three dosing regimens and three eGFRs (90, 120, 150 mL/min). Fifty percent of the PTA results were similar for both models, while for the other 50% the nonparametric model resulted in lower MICs. This was explained by a higher estimated between-subject variability of the nonparametric model. Simulations indicated that 1000 mg q6h is suitable to reach MICs of 2 mg/L for eGFRs of 90–120 mL/min. For MICs of 4 mg/L and for higher eGFRs, dosing recommendations are missing due to largely different PTA values per model. The consequences of the different modeling approaches in clinical practice should be further investigated.

Bodyweight-based tacrolimus dosing followed by therapeutic drug monitoring is standard clinical care after renal transplantation. However, after transplantation, a meager 38% of patients are on target at first steady-state and it can take up to 3 weeks to reach the target tacrolimus predose concentration (C0). Tacrolimus underexposure and overexposure is associated with an increased risk of rejection and drug-related toxicity, respectively. To minimize subtherapeutic and supratherapeutic tacrolimus exposure in the immediate post-transplant phase, a previously developed dosing algorithm to predict an individual’s tacrolimus starting dose was tested prospectively. In this single-arm, prospective, therapeutic intervention trial, 60 de novo kidney transplant recipients received a tacrolimus starting dose based on a dosing algorithm instead of a standard, bodyweight-based dose. The algorithm included cytochrome P450 (CYP)3A4 and CYP3A5 genotype, body surface area, and age as covariates. The target tacrolimus C0, measured for the first time at day 3, was 7.5–12.5 ng/mL. Between February 23, 2019, and July 7, 2020, 60 patients were included. One patient was excluded because of a protocol violation. On day 3 post-transplantation, 34 of 59 patients (58%, 90% CI 47–68%) had a tacrolimus C0 within the therapeutic range. Markedly subtherapeutic (< 5.0 ng/mL) and supratherapeutic (> 20 ng/mL) tacrolimus concentrations were observed in 7% and 3% of the patients, respectively. Biopsy-proven acute rejection occurred in three patients (5%). In conclusion, algorithm-based tacrolimus dosing leads to the achievement of the tacrolimus target C0 in as many as 58% of the patients on day 3 after kidney transplantation.

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