Target-controlled Infusion of Remimazolam in Healthy Volunteers Shows Some Acute Tolerance.

BACKGROUND: Remimazolam exhibits sedative properties by binding to γ-aminobutyric acid type A receptors. Remimazolam is administered as a bolus dose or continuous infusion, but has not been studied using target-controlled infusion (TCI). The study quantified the relationship between the remimazolam concentration, Modified Observer's Assessment of Alertness and Sedation (MOAAS) score, and bispectral index (BIS) using TCI. METHODS: The authors performed a three-period, crossover, dose-ranging clinical trial in 24 healthy volunteers using age and sex stratification. Data collected in the first period, where remimazolam was administered alone using a step-up and step-down TCI protocol, were used for this analysis. Remimazolam concentrations, MOAAS scores, and BIS values were collected at each step at steady state. Data were analyzed using nonlinear mixed-effects modeling methodology. RESULTS: The relationship between remimazolam, BIS, and MOAAS differed between step-up and step-down infusions at similar remimazolam target concentrations. Tolerance, driven by remimazolam or CNS7054, significantly improved overall model fit (P < 0.01) for both BIS and MOAAS models. After 30 min of repeated bolus dosing, mimicking the regimen in the label for procedural sedation, the BIS and probability of MOAAS 2/3 were predicted to be 54 (95% prediction interval, 44 to 67) and 2% (95% prediction interval, 0 to 32%) versus 58 (95% prediction interval, 48 to 70) and 8% (95% prediction interval, 0 to 36%) in a model without and with tolerance, respectively. After 60 min of continuous infusion, mimicking the regimen in the label for general anesthesia, the BIS and probability of MOAAS 0 were predicted to be 40 (95% prediction interval, 33 to 50) and 87% (95% prediction interval, 18 to 100%) versus 50 (95% prediction interval, 41 to 60) and 59% (95% prediction interval, 6 to 99%) in a model without and with tolerance, respectively. CONCLUSIONS: In this study, it was shown that remimazolam-induced sedation is prone to tolerance development, which is potentially mediated by the CNS7054 concentration. The clinical consequences are, however, limited in situations where remimazolam is titrated to effect.

A Joint Pharmacokinetic Model for the Simultaneous Description of Plasma and Whole Blood Tacrolimus Concentrations in Kidney and Lung Transplant Recipients.

BACKGROUND AND OBJECTIVE: Historically, dosing of tacrolimus is guided by therapeutic drug monitoring (TDM) of the whole blood concentration, which is strongly influenced by haematocrit. The therapeutic and adverse effects are however expected to be driven by the unbound exposure, which could be better represented by measuring plasma concentrations. OBJECTIVE: We aimed to establish plasma concentration ranges reflecting whole blood concentrations within currently used target ranges. METHODS: Plasma and whole blood tacrolimus concentrations were determined in samples of transplant recipients included in the TransplantLines Biobank and Cohort Study. Targeted whole blood trough concentrations are 4-6 ng/mL and 7-10 ng/mL for kidney and lung transplant recipients, respectively. A population pharmacokinetic model was developed using non-linear mixed-effects modelling. Simulations were performed to infer plasma concentration ranges corresponding to whole blood target ranges. RESULTS: Plasma (n = 1973) and whole blood (n = 1961) tacrolimus concentrations were determined in 1060 transplant recipients. A one-compartment model with fixed first-order absorption and estimated first-order elimination characterised observed plasma concentrations. Plasma was linked to whole blood using a saturable binding equation (maximum binding 35.7 ng/mL, 95% confidence interval (CI) 31.0-40.4 ng/mL; dissociation constant 0.24 ng/mL, 95% CI 0.19-0.29 ng/mL). Model simulations indicate that patients within the whole blood target range are expected to have plasma concentrations (95% prediction interval) of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively. CONCLUSION: Whole blood tacrolimus target ranges, currently used to guide TDM, were translated to plasma concentration ranges of 0.06-0.26 ng/mL and 0.10-0.93 ng/mL for kidney and lung transplant recipients, respectively.

Development of a pharmacokinetic and pharmacodynamic model for intranasal administration of midazolam in older adults: a single-site two-period crossover study.

BACKGROUND: Intranasal midazolam can produce procedural sedation in frail older patients with dementia who are unable to tolerate necessary medical or dental procedures during domiciliary medical care. Little is known about the pharmacokinetics and pharmacodynamics of intranasal midazolam in older (>65 yr old) people. The aim of this study was to understand the pharmacokinetic/pharmacodynamic properties of intranasal midazolam in older people with the primary goal of developing a pharmacokinetic/pharmacodynamic model to facilitate safer domiciliary sedation care. METHODS: We recruited 12 volunteers: ASA physical status 1-2, aged 65-80 yr, and received midazolam 5 mg intravenously and 5 mg intranasally on two study days separated by a 6 day washout period. Concentrations of venous midazolam and 1'-OH-midazolam, Modified Observer's Assessment of Alertness/Sedation (MOAA/S) score, bispectral index (BIS), arterial pressure, ECG, and respiratory parameters were measured for 10 h. RESULTS: Time to peak effect of intranasal midazolam for BIS, MAP, and SpO2 were 31.9 (6.2), 41.0 (7.6), and 23.1 (3.0) min, respectively. Intranasal bioavailability was lower compared with intravenous administration (Fabs 95%; 95% confidence interval: 89-100%). A three-compartment model best described midazolam pharmacokinetics following intranasal administration. A separate effect compartment linked to the dose compartment best described an observed time-varying drug-effect difference between intranasal and intravenous midazolam, suggesting direct nose-to-brain transport. CONCLUSIONS: Intranasal bioavailability was high and sedation onset was rapid, with maximum sedative effects after 32 min. We developed a pharmacokinetic/pharmacodynamic model for intranasal midazolam for older persons and an online tool to simulate changes in MOAA/S, BIS, MAP, and SpO2 after single and additional intranasal boluses. CLINICAL TRIAL REGISTRATION: EudraCT (2019-004806-90).

Pharmacodynamic mechanism-based interaction model for the haemodynamic effects of remifentanil and propofol in healthy volunteers.

BACKGROUND: Propofol and remifentanil are frequently combined for the induction and maintenance of general anaesthesia. Both propofol and remifentanil cause vasodilation and potentially reduce arterial BP. We aimed to develop a mechanism-based model that characterises the haemodynamic interactions between remifentanil and propofol. METHODS: Data from two clinical trials in healthy volunteers were analysed using remifentanil-alone, propofol-alone, and combination groups. We evaluated remifentanil effects on haemodynamics using a previously developed mechanism-based haemodynamic model of propofol. The interaction between propofol and remifentanil was explored using the principles of the general pharmacodynamic interaction (GPDI) model. RESULTS: Remifentanil alone increased the dissipation rate of total peripheral resistance by 50% at 3.0 ng ml-1. Additionally, the dissipation rates of HR and stroke volume were attenuated by 4.8% and 4.9% per 1 ng ml-1 increase in remifentanil concentration, respectively. The maximal effect of propofol alone in decreasing the production rate of total peripheral resistance was 78%, which decreased to 32% when combined with remifentanil 4 ng ml-1. The effects of remifentanil on HR and stroke volume were attenuated by propofol with maximum decreases of 11.9% and 21.2%, respectively. Goodness-of-fit plots and prediction-corrected visual predictive check plots showed good predictive performance of the models. CONCLUSIONS: The structure of the previous mechanism-based haemodynamic model for propofol was able to describe the effects of remifentanil alone on haemodynamic variables. The GPDI model provided a good framework for characterising the pharmacodynamic interaction between remifentanil and propofol on haemodynamic properties. CLINICAL TRIAL REGISTRATION: NCT02043938; NCT03143972.

Gentamicin Administration in Dialysis Patients: Before or After Hemodialysis?

BACKGROUND: Gentamicin is used to treat severe infections and has a small therapeutic window. This study aimed to optimize the dosing strategy of gentamicin in intermittently hemodialyzed patients by simulating concentration-time profiles during pre- and postdialysis dosing, based on a published pharmacokinetic model. METHODS: Pharmacokinetic simulations were performed with virtual patients, including septic patients, who were treated with gentamicin and received weekly hemodialysis with an interval of 48 h-48 h-72 h. The following dosing regimens were simulated: for nonseptic patients, 5 mg/kg gentamicin was given 1 h or 2 h before dialysis or a starting dose of 2.5 mg/kg and a maintenance dose of 1.5 mg/kg immediately after dialysis were given; for septic patients, 6 mg/kg gentamicin was given 1 h or 2 h before dialysis or a starting dose of 3 mg/kg and a maintenance dose of 1.8 mg/kg immediately were given after dialysis. The mean maximum concentration (C max ), area under the curve (AUC) 24 h , and target attainment (TA) of pharmacodynamic targets were calculated and compared. The following targets were adopted from the literature: C max >8 mg/L and <20 mg/L and AUC 24 h >70 mg·h/L and <120 mg·h/L. RESULTS: In nonseptic patients, postdialysis dosing resulted in a TA of 35% for C max of >8 mg/L, 100% for <20 mg/L and AUC 24 h >70 mg·h/L, and 45% for <120 mg·h/L. Dosing 2 h before dialysis resulted in a TA of 100% for C max of >8 mg/L, 40% for <20 mg/L, 65% for AUC 24 h >70 mg·h/L, and 77% for <120 mg·h/L. Simulations of septic patients resulted in comparable outcomes with higher TAs for C max <20 mg/L (96%), AUC 24 h >70 mg·h/L (90%), and AUC 24 h <120 mg·h/L (53%) for dosing 1 h before dialysis. CONCLUSIONS: Postdialysis dosing resulted in a low TA of C max >8 mg/L; however, predialysis dosing ensured a high TA of C max >8 mg/L and acceptable TA of C max <20 mg/L, AUC 24 h >70 mg·h/L, and AUC 24 h <120 mg·h/L, which could increase the efficacy of gentamicin. Therefore, clinicians should consider predialysis dosing of gentamicin in patients undergoing intermittent hemodialysis.

Moving Toward a Question-Centric Approach for Regulatory Decision Making in the Context of Drug Assessment.

The most intuitive question for market access for medicinal products is the benefit/risk (B/R) balance. The B/R assessment can conceptually be divided into subquestions related to establishing efficacy and safety. There are additional layers to the B/R ratio for medical products, including questions related to dose selection, clinical and nonclinical pharmacology, and drug quality. Explicitly stating the actual questions and how they contribute to the overall B/R provides a structure that fosters better informed cross-domain discussions. There is currently no systematic approach in the regulatory setting to assess and establish the acceptability of alternative methods and data sources. In most cases, the medicinal product sponsors tend to prioritize traditional data types and methods, which are well accepted by regulators for inclusion in regulatory submissions. This, in addition to the absence of rigor in the use and validation of new data types and methods, and the limited training of assessors in related fields can lead to increased regulatory skepticism toward new data types and methods. A data-knowledge backbone is needed to mitigate the uncertainty in efficacy and safety characterization. This white paper discusses the value of explicitly redefining and restructuring the regulatory scientific decision making around the scientific question to be addressed. The ecosystem proposed is based on three pillars: (i) a repository connecting questions, data, and methods; (ii) the development and validation of high-quality standards for data and methods; and (iii) credibility assessment. The ecosystem is applied to four use cases for illustration. The need for training and regulatory guidance is also discussed.

Dose assessment of melatonin in sepsis (DAMSEL2) study: Pharmacokinetics of two doses of oral melatonin in patients with sepsis.

Sepsis is defined as a dysregulated host response to infection, and high-dose melatonin has been proposed as a treatment due to its antioxidant and anti-inflammatory properties. However, there are no data describing the pharmacokinetics of high-dose oral melatonin in critically ill patients. We undertook an open-label trial to determine the tolerance of melatonin administration in these patients and pharmacokinetic analysis, to inform a planned randomised controlled trial. Two cohorts of critically ill patients with sepsis due to community-acquired pneumonia received either 20 or 50 mg oral melatonin liquid as a single dose. Blood samples and clinical measures were analysed over the next 24 h. Melatonin was well tolerated and there were no adverse events. Pharmacokinetic modelling showed that a semiphysiological model, which incorporates saturable first-pass hepatic extraction, was a good fit for our data. Maximum levels of melatonin were extremely high in patients receiving the 50 mg dose and levels of the major metabolite were much lower than expected and not different from those seen after 20 mg, suggesting saturation at the higher dose. We conclude that 20 mg seems a suitable dose of liquid melatonin in patients with sepsis.

Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients.

BACKGROUND: Beta-lactam antimicrobial concentrations are frequently suboptimal in critically ill patients. Population pharmacokinetic (PopPK) modeling is the golden standard to predict drug concentrations. However, currently available PopPK models often lack predictive accuracy, making them less suited to guide dosing regimen adaptations. Furthermore, many currently developed models for clinical applications often lack uncertainty quantification. We, therefore, aimed to develop machine learning (ML) models for the prediction of piperacillin plasma concentrations while also providing uncertainty quantification with the aim of clinical practice. METHODS: Blood samples for piperacillin analysis were prospectively collected from critically ill patients receiving continuous infusion of piperacillin/tazobactam. Interpretable ML models for the prediction of piperacillin concentrations were designed using CatBoost and Gaussian processes. Distribution-based Uncertainty Quantification was added to the CatBoost model using a proposed Quantile Ensemble method, useable for any model optimizing a quantile function. These models are subsequently evaluated using the distribution coverage error, a proposed interpretable uncertainty quantification calibration metric. Development and internal evaluation of the ML models were performed on the Ghent University Hospital database (752 piperacillin concentrations from 282 patients). Ensuing, ML models were compared with a published PopPK model on a database from the University Medical Centre of Groningen where a different dosing regimen is used (46 piperacillin concentrations from 15 patients.). RESULTS: The best performing model was the Catboost model with an RMSE and [Formula: see text] of 31.94-0.64 and 33.53-0.60 for internal evaluation with and without previous concentration. Furthermore, the results prove the added value of the proposed Quantile Ensemble model in providing clinically useful individualized uncertainty predictions and show the limits of homoscedastic methods like Gaussian Processes in clinical applications. CONCLUSIONS: Our results show that ML models can consistently estimate piperacillin concentrations with acceptable and high predictive accuracy when identical dosing regimens as in the training data are used while providing highly relevant uncertainty predictions. However, generalization capabilities to other dosing schemes are limited. Notwithstanding, incorporating ML models in therapeutic drug monitoring programs seems definitely promising and the current work provides a basis for validating the model in clinical practice.

Mechanism-based pharmacodynamic model for propofol haemodynamic effects in healthy volunteers☆.

BACKGROUND: The adverse haemodynamic effects of the intravenous anaesthetic propofol are well known, yet few empirical models have explored the dose-response relationship. Evidence suggests that hypotension during general anaesthesia is associated with postoperative mortality. We developed a mechanism-based model that quantitatively characterises the magnitude of propofol-induced haemodynamic effects during general anaesthesia. METHODS: Mean arterial pressure (MAP), heart rate (HR) and pulse pressure (PP) measurements were available from 36 healthy volunteers who received propofol in a step-up and step-down fashion by target-controlled infusion using the Schnider pharmacokinetic model. A mechanistic pharmacodynamic model was explored based on the Snelder model. To benchmark the performance of this model, we developed empirical models for MAP, HR, and PP. RESULTS: The mechanistic model consisted of three turnover equations representing total peripheral resistance (TPR), stroke volume (SV), and HR. Propofol-induced changes were implemented by Emax models on the zero-order production rates of the turnover equations for TPR and SV. The estimated 50% effective concentrations for propofol-induced changes in TPR and SV were 2.96 and 0.34 µg ml-1, respectively. The goodness-of-fit for the mechanism-based model was indistinguishable from the empirical models. Simulations showed that predictions from the mechanism-based model were similar to previously published MAP and HR observations. CONCLUSIONS: We developed a mechanism-based pharmacodynamic model for propofol-induced changes in MAP, TPR, SV, and HR as a potential approach for predicting haemodynamic alterations. CLINICAL TRIAL REGISTRATION: NCT02043938.

Dexmedetomidine Clearance Decreases with Increasing Drug Exposure: Implications for Current Dosing Regimens and Target-controlled Infusion Models Assuming Linear Pharmacokinetics.

BACKGROUND: Numerous pharmacokinetic models have been published aiming at more accurate and safer dosing of dexmedetomidine. The vast majority of the developed models underpredict the measured plasma concentrations with respect to the target concentration, especially at plasma concentrations higher than those used in the original studies. The aim of this article was to develop a dexmedetomidine pharmacokinetic model in healthy adults emphasizing linear versus nonlinear kinetics. METHODS: The data of two previously published clinical trials with stepwise increasing dexmedetomidine target-controlled infusion were pooled to build a pharmacokinetic model using the NONMEM software package (ICON Development Solutions, USA). Data from 48 healthy subjects, included in a stratified manner, were utilized to build the model. RESULTS: A three-compartment mamillary model with nonlinear elimination from the central compartment was superior to a model assuming linear pharmacokinetics. Covariates included in the final model were age, sex, and total body weight. Cardiac output did not explain between-subject or within-subject variability in dexmedetomidine clearance. The results of a simulation study based on the final model showed that at concentrations up to 2 ng · ml-1, the predicted dexmedetomidine plasma concentrations were similar between the currently available Hannivoort model assuming linear pharmacokinetics and the nonlinear model developed in this study. At higher simulated plasma concentrations, exposure increased nonlinearly with target concentration due to the decreasing dexmedetomidine clearance with increasing plasma concentrations. Simulations also show that currently approved dosing regimens in the intensive care unit may potentially lead to higher-than-expected dexmedetomidine plasma concentrations. CONCLUSIONS: This study developed a nonlinear three-compartment pharmacokinetic model that accurately described dexmedetomidine plasma concentrations. Dexmedetomidine may be safely administered up to target-controlled infusion targets under 2 ng · ml-1 using the Hannivoort model, which assumed linear pharmacokinetics. Consideration should be taken during long-term administration and during an initial loading dose when following the dosing strategies of the current guidelines.

Comment on Morse et al. A Universal Pharmacokinetic Model for Dexmedetomidine in Children and Adults. J. Clin. Med. 2020, 9, 3480.

We read with interest the recently published manuscript [...].

Pharmacokinetic properties of remimazolam in subjects with hepatic or renal impairment.

BACKGROUND: Remimazolam is a new benzodiazepine for procedural sedation and general anaesthesia. The aim of this study was to characterise its pharmacokinetic properties and safety in renally and hepatically impaired subjects. METHODS: Two separate trials were conducted in patients with hepatic (n=11) or renal impairment (n=11) compared with matched healthy subjects (n=9 and n=12, respectively). The hepatic impairment trial was an open-label adaptive 'Reduced Design' trial, using a single bolus of remimazolam 0.1 mg kg-1 i.v., whereas the renal impairment trial was an open-label trial of a single bolus dose of remimazolam 1.5 mg i.v. Remimazolam plasma concentrations over time were analysed by population pharmacokinetic modelling. RESULTS: Remimazolam pharmacokinetic properties were adequately described by a three-compartment, recirculatory model. Exposure in subjects with severe hepatic impairment was 38.1% higher (i.e. clearance was 38.1% lower) compared with healthy volunteers. This increase caused a slightly delayed recovery (8.0 min for healthy, 12.1 min for moderate, and 16.7 min for severe hepatic impairment). With renal impairment, plasma clearance was comparable with that measured in healthy subjects. Simulations of Cmax after a bolus dose of 10 mg showed no relevant impact of hepatic or renal impairment. The overall incidence of adverse events was low, and all adverse events were mild. CONCLUSIONS: As Cmax after a remimazolam bolus i.v. was not affected by hepatic or renal impairment, no dose adjustments are required. No unexpected adverse events related to remimazolam were seen in subjects with renal or hepatic impairment. CLINICAL TRIAL REGISTRATION: Hepatic impairment trial: ClinicalTrials.gov, NCT01790607 (https://clinicaltrials.gov/ct2/show/NCT01790607). Renal impairment trial: EudraCT Number: 2014-004575-23.

Do Vancomycin Pharmacokinetics Differ Between Obese and Non-obese Patients? Comparison of a General-Purpose and Four Obesity-Specific Pharmacokinetic Models.

BACKGROUND: Over the past decade, numerous obesity-specific pharmacokinetic (PK) models and dosage regimens have been developed. However, it is unclear whether vancomycin PKs differ between obese and other patients after accounting for weight, age, and kidney function. In this study, the authors investigated whether using obesity-specific population PK models for vancomycin offers any advantage in accuracy and precision over using a recently developed general-purpose model. METHODS: Vancomycin plasma concentrations in a cohort of 49 obese patients (body mass index [BMI] >30 kg/m2), not previously used in the development of any of the evaluated models, were used to validate the performance of 4 obesity-specific models and a general model. Bias and imprecision were calculated for the a priori and a posteriori predictive performance. RESULTS: The bias of the a priori prediction was lowest for one of the obesity-specific models (-1.40%) and that of the general model was a close second (-7.0%). The imprecision was lowest for the general model (4.34 mg/L). The predictive performance for the a posteriori predictions was best for the general model, both for bias (1.96%) and imprecision (2.75 mg/L). CONCLUSIONS: The results of the external validation of vancomycin PK in obese patients showed that currently available obesity-specific models do not necessarily outperform a broadly supported general-purpose model. Based on these results, the authors conclude that there is no advantage in using vancomycin PK models specifically tailored to obese patients over the general-purpose model reported by Colin et al.

Influence of an "Electroencephalogram-Based" Monitor Choice on the Delay Between the Predicted Propofol Effect-Site Concentration and the Measured Drug Effect.

BACKGROUND: Clinicians can optimize propofol titration by using 2 sources of pharmacodynamic (PD) information: the predicted effect-site concentration for propofol (Ceprop) and the electroencephalographically (EEG) measured drug effect. Relation between these sources should be time independent, that is, perfectly synchronized. In reality, various issues corrupt time independency, leading to asynchrony or, in other words, hysteresis. This asynchrony can lead to conflicting information, making effective drug dosing challenging. In this study, we tried to quantify and minimize the hysteresis between the Ceprop (calculated using the Schnider model for propofol) and EEG measured drug effect, using nonlinear mixed-effects modeling (NONMEM). Further, we measured the influence of EEG-based monitor choice, namely Bispectral index (BIS) versus qCON index (qCON) monitor, on propofol PD hysteresis. METHODS: We analyzed the PD data from 165 patients undergoing propofol-remifentanil anesthesia for outpatient surgery. Drugs were administered using target-controlled infusion (TCI) pumps. Pumps were programmed with Schnider model for propofol and Minto model for remifentanil. We constructed 2 PD models (direct models) relating the Schnider Ceprop to the measured BIS and qCON monitor values. We quantified the models' misspecification due to hysteresis, on an individual level, using the root mean squared errors (RMSEs). Subsequently, we optimized the PD models' predictions by adding a lag term to both models (lag-time PD models) and quantified the optimization using the RMSE. RESULTS: There is a counterclockwise hysteresis between Ceprop and BIS/qCON values. Not accounting for this hysteresis results in a direct PD model with an effect-site concentration which produces 50% of the maximal drug effect (Ce50) of 6.24 and 8.62 µg/mL and RMSE (median and interquartile range [IQR]) of 9.38 (7.92-11.23) and 8.41(7.04-10.2) for BIS and qCON, respectively. Adding a modeled lag factor of 49 seconds to the BIS model and 53 seconds to the qCON model improved both models' prediction, resulting in similar Ce50 (3.66 and 3.62 µg/mL for BIS and qCON) and lower RMSE (median (IQR) of 7.87 (6.49-9.90) and 6.56 (5.28-8.57) for BIS and qCON. CONCLUSIONS: There is a significant "Ceprop versus EEG measured drug effect" hysteresis. Not accounting for it leads to conflicting PD information and false high Ce50 for propofol in both monitors. Adding a lag term improved the PD model performance, improved the "pump-monitor" synchrony, and made the estimates of Ce50 for propofol more realistic and less monitor dependent.

PKPD Modeling and Dosing Considerations in Advanced Ovarian Cancer Patients Treated with Cisplatin-Based Intraoperative Intraperitoneal Chemotherapy.

Intraperitoneal chemoperfusion (IPEC) of cisplatin is a popular treatment for advanced ovarian cancer, typically under hyperthermia (HIPEC). The use of cisplatin under (H)IPEC is off-label, and the role of hyperthermia is unknown. The aim of this study was to characterize the pharmacokinetic/pharmacodynamic (PKPD) properties of cisplatin under (H)IPEC and to predict the optimal treatment regimen. Using a randomized design, data on intact cisplatin perfusate and plasma concentrations, leukocyte counts-a hematotoxicity marker-and serum creatinine-a nephrotoxicity marker-were collected from 50 patients treated with a combination of cytoreductive surgery (CRS) and either normothermic or hyperthermic IPEC of cisplatin dosed at 75, 100, and 120 mg/m2. The non-linear mixed effects modeling technique was used to construct the PKPD models. The PK of intact cisplatin was characterized by a two-compartment model. A semi-physiological myelosuppression model for the leukopenia was modified to account for the CRS-induced leukocytosis and the residual myelosuppression effect of neoadjuvant chemotherapy. The incidence and severity of nephrotoxicity were described by a discrete-time Markov model. Hyperthermia increased the absorption rate of cisplatin by 16.3% but did not show a clinically relevant impact on the investigated toxicities compared with normothermia. Leukopenia was not severe, but nephrotoxicity can become severe or life-threatening and was affected by the dose and IPEC duration. The model predicted that nephrotoxicity is minimal at a cisplatin dose of 75 mg/m2 with an IPEC duration of 1-2 h and an 1-h duration is favored for doses between 100 and 120 mg/m2. Graphical abstract.

Target-controlled-infusion models for remifentanil dosing consistent with approved recommendations.

BACKGROUND: Target-controlled infusion (TCI) systems use pharmacokinetic (PK) models to predict the drug infusion rates necessary to achieve a desired target plasma or effect-site concentration. As new PK models are developed and implemented in TCI systems, there can be uncertainty as to which target concentrations are appropriate. Existing dose recommendations can serve as a point of reference to identify target concentrations suitable for clinical applications. METHODS: Simulations of remifentanil TCI were performed using three PK models (Minto, Eleveld, and Kim). We sought to identify models and target concentrations for remifentanil administration in children, adult, older people, and severely obese individuals, consistent with the remifentanil product label. In a typical adult this is an induction dose of 0.5-1 µg kg-1 and starting maintenance infusion rate of 0.25 µg kg-1 min-1. RESULTS: For the Minto, Eleveld, and Kim remifentanil models, a plasma target concentration of ∼ 4 ng ml-1 achieves drug administration consistent with product label recommended initial doses for all groups with minor exceptions. With effect-site targeting in older individuals, a target concentration of ∼2 ng ml-1 is required for induction and ∼4 ng ml-1 for starting maintenance to achieve drug dosages close to product label recommendations. CONCLUSIONS: We identified remifentanil TCI target concentrations that resulted in drug administration similar to product label dosing recommendations. This approach did not necessarily identify target concentrations that achieve desired clinical effect, only those that are consistent with the product label recommended doses. We estimate that plasma target concentrations of 3.1-5.3 ng ml-1 are suitable for initial dosing.

Genetic Algorithms as a Tool for Dosing Guideline Optimization: Application to Intermittent Infusion Dosing for Vancomycin in Adults.

This paper demonstrates the use of a genetic algorithm (GA) for the optimization of a dosing guideline. GAs are well-suited to derive combinations of doses and dosing intervals that go into a dosing guideline when the number of possible combinations rule out the calculation of all possible outcomes. GAs also allow for different constraints to be imposed on the optimization process to safeguard the clinical feasibility of the dosing guideline. In this work, we demonstrate the use of a GA for the optimization of intermittent vancomycin administration in adult patients. Constraints were placed on the dose strengths, the length of the dosing intervals, and the maximum infusion rate. In addition, flexibility with respect to the timing of the first maintenance dose was included in the optimization process. The GA-based optimal solution is compared with the Scottish Antimicrobial Prescribing Group vancomycin guideline.