Towards Precision Dosing of Clozapine in Schizophrenia: External Evaluation of Population Pharmacokinetic Models and Bayesian Forecasting.

BACKGROUND: Therapeutic drug monitoring and treatment optimization of clozapine are recommended, owing to its narrow therapeutic range and pharmacokinetic (PK) variability. This study aims to assess the clinical applicability of published population PK models by testing their predictive performance in an external data set and to determine the effectiveness of Bayesian forecasting (BF) for clozapine treatment optimization. METHODS: Available models of clozapine were identified, and their predictive performance was determined using an external data set (53 patients, 151 samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated. The final concentration was reestimated for all patients using covariates or previously observed concentrations. RESULTS: The 7 included models presented limited predictive performance. Only 1 model met the acceptability criteria (median PE ≤ ±20% and median absolute PE ≤30%). There was no difference between the data used for building the models (therapeutic drug monitoring or PK study) or the number of compartments in the models. A tendency for higher inaccuracy at low concentrations during treatment initiation was observed. Heterogeneities were observed in the predictive performances between the subpopulations, especially in terms of smoking status and sex. For the models included, BF significantly improved their predictive performance. CONCLUSIONS: Our study showed that upon external evaluation, clozapine models provide limited predictive performance, especially in subpopulations such as nonsmokers. From the perspective of model-informed prediction dosing, model predictability should be improved using updating or metamodeling methods. Moreover, BF substantially improved model predictability and could be used for clozapine treatment optimization.

Assessment of the glomerular filtration rate (GFR) in kidney transplant recipients using Bayesian estimation of the iohexol clearance.

Background Plasma iohexol clearance (CLiohexol) is a reference technique for glomerular filtration rate (GFR) determination. In routine practice, CLiohexol is calculated using one of several formulas, which have never been evaluated in kidney transplant recipients. We aimed to model iohexol pharmacokinetics in this population, evaluate the predictive performance of three simplified formulas and evaluate whether a Bayesian algorithm improves CLiohexol estimation. Methods After administration of iohexol, six blood samples were drawn from 151 patients at various time points. The dataset was split into two groups, one to develop the population pharmacokinetic (POPPK) model (n = 103) and the other (n = 48) to estimate the predictive performances of the various GFR estimation methods. GFR reference values (GFRref) in the validation dataset were obtained by non-compartmental pharmacokinetic (PK) analysis. Predictive performances of each method were evaluated in terms of bias (ME), imprecision (root mean square error [RMSE]) and number of predictions out of the ±10% or 15% error interval around the GFRref. Results A two-compartment model best fitted the data. The Bayesian estimator with samples drawn at 30, 120 and 270 min allowed accurate prediction of GFRref (ME = 0.47%, RMSE = 3.42%), as did the Brøchner-Mortensen (BM) formula (ME = - 0.0425%, RMSE = 3.40%). With both methods, none of the CL estimates were outside the ±15% interval and only 2.4% were outside the ±10% for the BM formula (and none for the Bayesian estimator). In patients with GFR ≤30 mL/min/1.73 m2, the BM formula performed very well, while the Bayesian method could not be evaluated in depth due to too small a number of patients with adequate sampling times. Conclusions GFR can be estimated with acceptable accuracy in kidney transplant patients using the BM formula, but also using a Bayesian algorithm.

Population pharmacokinetic model for tumescent lidocaine in women undergoing breast cancer surgery.

PURPOSE: Tumescent lidocaine anesthesia (TLA) is an opportunity to perform mastectomy for breast cancer without general anesthesia in elderly women. Few reports are available on the pharmacokinetics of lidocaine in a context of TLA during a unilateral mastectomy. The aim of this study was to describe lidocaine pharmacokinetics in elderly women undergoing breast cancer surgery after TLA and to explore the risk of the toxicity of this technique. METHODS: A prospective study was conducted to examine the pharmacokinetics of lidocaine in women undergoing TLA. TLA consists of an intradermal lidocaine instillation (20 mL, 1% [200 mg]) followed by a tumescent lidocaine infiltration (100 mL of 1% lidocaine [1000 mg] and 0.5 mg epinephrine to 1 L Ringer's lactate) via an infusion pump. A population pharmacokinetic (popPK) analysis was performed using the nonlinear mixed effects model (NONMEM). RESULTS: The analysis included 116 observations from 17 women with a median (range) age of 83.4 (60.5-90.0). The median tumescent lidocaine dose was 800 mg (range 375-1000 mg) infused over 48.0 ± 11.0 min. A one-compartment disposition model with first order absorption, two input compartments, and a central elimination best described the pharmacokinetics of lidocaine. The estimates (between subject variability; relative standard error, %) of apparent volume, apparent clearance, tumescent absorption rate, and instillation absorption rate were 195.0 (46.3; 14.5%) L, 24.7 (48.9; 13.3%) L h-1, 0.28 (39.6; 13.8%) h-1, and 2.56 (135.3; 44.9%) h-1, respectively. CONCLUSIONS: This is the first popPK model developed to describe kinetic profiles of TLA. These findings confirm the slow diffusion of lidocaine from the tumescent deposit.

Population pharmacokinetic model and Bayesian estimator for 2 tacrolimus formulations in adult liver transplant patients.

AIMS: Tacrolimus is a narrow therapeutic range drug that requires fine dose adjustment, for which pharmacokinetic (PK) models have been amply proposed in renal, but not in liver, transplant recipients. This study aimed to build population PK models and Bayesian estimators (BEs) in adult de novo liver transplant patients receiving either the immediate-release (Prograf, twice daily, TD) or prolonged-release (Advagraf, once daily, OD) forms to help PK-guided dose individualization. METHODS: In total, 160 tacrolimus concentration-time profiles (1654 samples) were collected from 80 patients, at day 7 (D7) and week 6 (W6) post-transplant. Four population PK models were developed using in-parallel parametric and nonparametric approaches for each formulation and period post-transplant. The best limited sampling strategies for estimating the area-under-the-curve (AUC) were selected by comparing predicted values to an independent dataset. Finally, the doses required to reach AUC targets were estimated using each BE and compared to the doses obtained using the trapezoidal AUC. RESULTS: Tacrolimus PK was best described using a 1-compartmental model with first-order elimination and 2 γ-distributions to describe the absorption. In the validation datasets, Bayesian AUC estimates yielded mean bias/root mean squared prediction error of −5.06%/13.43% (OD D7), 2.25%/8.51% (OD W6), −2.36%/7.27% (TD D7) and 0.87%/9.07% (TD W6) for the in-parallel parametric approach; and 8.95%/17.84% (OD D7), −0.11%/10.13% (OD W6), 3.57%/18.40% (TD D7) and 4.48%/12.59% (TD W6) for the nonparametric approach. CONCLUSION: The BEs and limited sampling strategies proposed here are able to predict accurately and precisely tacrolimus AUC in liver patients using only 3 plasma concentrations. The dosing methods are available on our ImmunoSuppressive Bayesian dose Adjustment website (www.pharmaco.chu-limoges.fr).

Local Anesthetic Plasma Concentrations as a Valuable Tool to Confirm the Diagnosis of Local Anesthetic Systemic Toxicity? A Report of 10 Years of Experience.

BACKGROUND: Local anesthetic systemic toxicity (LAST) has been reported as a serious complication of local anesthetic (LA) peripheral injection. The signs and symptoms of LAST are highly variable, and the challenge remains to confirm its diagnosis. In this context, the determination of LA plasma concentration appears as a valuable tool to confirm LAST diagnosis. The aims of this study were to describe observed LA concentrations in patients suspected with LAST and their contribution to diagnostic confirmation. METHODS: We retrospectively reported suspected LAST in patients for which at least one plasma LA concentration was determined to confirm diagnosis of LAST. Data collection came from our pharmacological laboratory's database. Clinical signs and symptoms of toxicity, their onset time and observed LA concentrations were used to confirm LAST diagnosis. RESULTS: 33 patients who presented with suspected LAST after ropivacaine and/or lidocaine administration were included. Prodromal symptoms were observed in 13 patients. Isolated central nervous system (CNS) toxicity occurred in 11 patients, and combined CNS and cardiovascular toxicity occurred in 12. One, two or three venous plasma samples were performed in 11, 3 and 19 patients, respectively. Toxic plasma LA concentrations were observed in three patients, receiving peripheral LA injection using lidocaine (16.1 µg/mL) and ropivacaine (4.2 and 4.8 µg/mL). CONCLUSION: This study presents an important biological and clinical dataset of patients who presented with suspected LAST. Plasma LA concentrations could bring valuable information in the diagnosis of LAST but requires rigorous sample protocols.

Ropivacaine Wound Infiltration for Pain Management After Breast Cancer Mastectomy: A Population Pharmacokinetic Analysis.

Ropivacaine continuous wound infusions (CWIs) are extensively used as a component of multimodal analgesia. The rational application of CWI of ropivacaine requires a thorough understanding of its pharmacokinetics to investigate the risk of potential systemic toxicity. A population pharmacokinetic (popPK) study was undertaken to describe the pharmacokinetics of ropivacaine CWI during 75 hours. Women undergoing a unilateral mastectomy were scheduled to receive CWI for 40 hours for postoperative analgesia. A 10-mL ropivacaine 0.75% bolus followed by continuous infusion (400 mL of 0.2% ropivacaine at a flow rate of 10 mL/h) was administered via a multihole catheter placed on the major pectoral muscle. PopPK analysis was performed using the nonlinear mixed-effects model. A 1-compartment disposition model with an absorption compartment and a transit compartment for the infusion best describes the data (67 observations from 10 women). Population parameter estimates (between-subject variability, %) are apparent central volume (V/F) 269 L (39.1%), apparent clearance (CL/F) 18.8 h-1 (74.9%), and absorption rate (K12) 0.406 h-1 . The model predicted Cmax as 1.45 ± 0.80 µg/mL, which occurred in the 42.4th hour (39-45.9 hours). This popPK model describes the pharmacokinetics of ropivacaine during continuous wound infusion and confirms the safety profile of the present technique.

Amikacin in Critically Ill Patients: A Review of Population Pharmacokinetic Studies.

BACKGROUND: Amikacin is an aminoglycoside commonly used in intensive care units for the treatment of patients with life-threatening Gram-negative infections. Although aminoglycosides are extensively used, the accurate determination of their optimal dosage is complicated by marked intra- and interindividual variability in intensive care unit patients. Amikacin pharmacokinetics have been described in numerous studies over the past 25 years. OBJECTIVE: This review presents a synthesis of the population pharmacokinetic models for amikacin described in critically ill patients. The objective was to determine whether there was a consensus on a structural model and which covariates had been identified. METHODS: A literature search was conducted from the PubMed database, from its inception up until December 2015, using the following terms: 'amikacin', 'pharmacokinetic(s)', 'population', 'model(ling)' and 'nonlinear mixed effect'. Articles were excluded if they were not pertinent. The reference lists of all selected articles were also evaluated. RESULTS: Ten articles were included in this review: pharmacokinetics of amikacin were described by a one-compartment or a two-compartment model. Various covariates were tested, but only two (creatinine clearance and total body weight) were included in almost all of the described models. After inclusion of these covariates, the interindividual variability (range) in clearance and the volume of distribution were 44.4 % (28.2-69.4 %) and 31.3 % (8.1-44.7 %), respectively. The residual variability (range) was around 21.0 % (9.0-31.0 %), using a proportional model, and for a combined model (proportional/additive), the median (range) values were 0.615 mg/L (0.2-1.03 mg/L) and 29.2 % (26.8-31.6 %). CONCLUSION: This review highlights the different population pharmacokinetic models for amikacin developed in critically ill patients over the past decades and proposes relevant information for clinicians and researchers. To optimize amikacin dosage, this review points out the relevant covariates according to the target population. In a population of critically ill patients, dose optimization mainly depends on creatinine clearance and total body weight. New pharmacokinetic population studies could be considered, with new covariates of interest to be tested in model building and to further explain variability. Another future perspective could be external evaluation of previously published models.