Target concentration intervention: beyond Y2K

Model-informed precision dosing: State of the art and future perspectives

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing, and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.

From Personalized to Precision Medicine in Oncology: A Model-Based Dosing Approach to Optimize Achievement of Imatinib Target Exposure

Imatinib is a targeted cancer therapy that has significantly improved the care of patients with chronic myeloid leukemia (CML) and gastrointestinal stromal tumor (GIST). However, it has been shown that the recommended dosages of imatinib are associated with trough plasma concentration (Cmin) lower than the target value in many patients. The aims of this study were to design a novel model-based dosing approach for imatinib and to compare the performance of this method with that of other dosing methods. Three target interval dosing (TID) methods were developed based on a previously published PK model to optimize the achievement of a target Cmin interval or minimize underexposure. We compared the performance of those methods to that of traditional model-based target concentration dosing (TCD) as well as fixed-dose regimen using simulated patients (n = 800) as well as real patients’ data (n = 85). Both TID and TCD model-based approaches were effective with about 65% of Cmin achieving the target imatinib Cmin interval of 1000–2000 ng/mL in 800 simulated patients and more than 75% using real data. The TID approach could also minimize underexposure. The standard 400 mg/24 h dosage of imatinib was associated with only 29% and 16.5% of target attainment in simulated and real conditions, respectively. Some other fixed-dose regimens performed better but could not minimize over- or underexposure. Model-based, goal-oriented methods can improve initial dosing of imatinib. Combined with subsequent TDM, these approaches are a rational basis for precision dosing of imatinib and other drugs with exposure–response relationships in oncology.

The Appropriately Designed TDM Clinical Trial: Endpoints, Pitfalls, and Perspectives

BACKGROUND

Appropriately designed clinical trials can provide the evidence needed to broadly implement therapeutic drug monitoring (TDM). In the past 30 years, some stunning successes but also some fascinating failures in demonstrating the benefits of TDM have been observed. Future TDM studies can be designed based on this experience.

METHODS

The manuscript is based on a combination of personal experience and published articles and discusses several aspects of the design and conduct of TDM studies.

RESULTS

Recommendations are provided to reduce the risk of protocol violations and to maximize the potential impact of a TDM study on clinical practice.

CONCLUSIONS

There are lessons that can be learned from previous experience, and this article gives an overview of potential TDM study designs, endpoints, pitfalls, and perspectives.

Hospital Pharmacometrics for Optimal Individual Administration of Antimicrobial Agents for Anti-methicillin-resistant Staphylococcus aureus Infected Patients

Therapeutic drug monitoring and target concentration intervention based on population pharmacokinetic and pharmacodynamic models has been strongly recommended for anti-methicillin-resistant Staphylococcus aureus (MRSA) agents in order to provide appropriate antimicrobial chemotherapy to each individual patient, and pharmacokinetic and pharmacodynamic analyses in hospitalized patients have been actively conducted, as evidenced with vancomycin. Teicoplanin, daptomycin, and linezolid have been the most studied antibiotics, using population pharmacokinetics of patients with MRSA. Infections caused by MRSA have higher severity and fatality rates than other antimicrobial-susceptible infections. Therefore, many medical facilities have been implementing infection control programs based on antimicrobial stewardship to prevent nosocomial infections and drug-resistant strains. Studies detailing pharmacometrics for these antibiotics have been reported to elucidate the pharmacokinetic and pharmacodynamic properties, to determine significant factors influencing variabilities between individuals, and to develop target concentration interventions and dosing regimens for adults, the elderly, patients with renal insufficiency including those on continuous renal replacement therapies, patients with low body weight, obese patients, and pediatric patients. This review presents the details of our recent research on the optimal dosing design of antimicrobial agents for the treatment of MRSA infection based on hospital pharmacometrics. In addition, the prospect of using modeling and simulation has shown major advantages in supporting dosing regimen selection.

Past, present and future perspectives of therapeutic drug monitoring in India

Therapeutic drug monitoring (TDM) is the clinical practice of performing drug assays and interpreting results to maintain constant therapeutic concentrations in patients' bloodstream. Conventional TDM was started way back in the 1960s and served to optimise pharmacotherapy by maximising therapeutic efficacy by evaluating efficacy failure and monitoring drug compliance, while minimising adverse events, in drugs with a narrow therapeutic range. Currently, the scope of TDM has been extended to additional indications which are of importance to India. Apart from the conventional indications, TDM can also help combat drug resistance amongst patients treated with antimicrobials, including anti-tubercular drugs and critically ill patients with compromised pharmacokinetics. TDM is also indicated for patients on antiretroviral drugs under specific clinical scenarios and is of high importance to India. Target concentration intervention (TCI) and apriori TDM (by merging TDM with pharmacogenomics) are emerging fields explored in developed nations. The authors sought to assess the evolution of TDM in India and evaluate the potential impact of newer indications in rationalising pharmacotherapy. In the mid-1980s, TDM was presented to India. Despite showing some initial progress, its use is limited to conventional indications. Its utility is also challenged by cost and higher reliance on conventional prescribing practices. However, the newer indications such as antimicrobial resistance, tuberculosis and HIV, with their high prevalence in developing nations, present an opportunity for the growth of TDM in these countries. Indian clinician's awareness and buoyant demands alongside expert contributions from clinical pharmacologists could widen its scope.

Evaluation of two software using Bayesian methods for monitoring exposure and dosing once-daily intravenous busulfan in paediatric patients receiving haematopoietic stem cell transplantation

AIM

To assess the ability of model-based personalised dosing tools to estimate busulfan exposure (i) in comparison to clinically used intensive sampling exposure estimation procedure, (ii) using limited sampling strategies and (iii) to predict changes in busulfan clearance during busulfan treatment.

METHODS

Data on intravenous busulfan dosing for patients with 4 consecutive days were entered into Bayesian forecasting software, InsightRX and NextDose. Prediction of busulfan cumulative exposure was compared to current clinical practice estimation, aiming for pre-defined individualised target of cumulative exposure. Estimation performance was tested given several limited sampling strategies.

RESULTS

Thirty-two paediatric patients (0.2-16.5 years) provided a total of 103 daily exposure measurements estimated using 7 samples taken per day (full sampling), with 19 patients having sampling following all doses administered. Both software tools utilising Bayesian methods provided acceptable relative bias and precision of cumulative exposure estimations under the tested sampling scenarios. Relative bias ranged from median RE of 0.1-14.6% using InsightRX and from 3.4-7.8% using NextDose. Precision ranged from median RMSE of 0.19-0.32 mg·h·L^-1 for InsightRX and 0.08-0.1 mg·h·L^-1 for NextDose. A median reduction in busulfan clearance from day 1 to day 4 was observed in the clinical data (-10.9%), when using InsightRX (-18.6%) and with NextDose (-14.7%).

CONCLUSION

Bayesian methods were shown to have relatively low bias and precisely estimate busulfan exposure using intensive sampling and several limited sampling strategies, which provides evidence for prospective studies to evaluate these tools in clinical practice. A trend to overestimation of exposure using Bayesian methods was observed compared to clinical practice. Reduction of busulfan clearance from day 1 to 4 of once daily dosing was confirmed and should be considered when adjusting doses.

Nonparametric Methods in Population Pharmacokinetics

Population pharmacokinetic (PK) modeling is a widely used approach to analyze PK data obtained from groups of individuals, in both industry and academic research. The approach can also be used to analyze pharmacodynamic (PD) data and pooled PK/PD data. There are 2 main families of population PK methods: parametric and nonparametric. The objectives of this article are to present an overview of nonparametric methods used in population pharmacokinetic modeling and to explain their specific characteristics to inform scientists and clinicians about their potential value for data analysis, simulation, dosage design, and therapeutic drug monitoring (TDM). Nonparametric methods have several interesting characteristics for population PK analysis, including computation of exact likelihoods, the ability to accommodate parameter probability distributions of any shape (eg, non-Gaussian), and to detect subpopulations and outliers. Nonparametric population methods are also highly relevant for model-based TDM and design of individualized drug dosage regimens. Several algorithms have been developed to estimate model parameter values within an individual and compute that individual's dosage to achieve target drug exposure with maximum precision and accuracy. Nonparametric modeling methods for both population and individual PK analysis are available under user-friendly packages.

On precision dosing of oral small molecule drugs in oncology

Personalization of oral small molecule anticancer drug doses based on individual patient blood drug levels, also known as therapeutic drug monitoring (TDM), has the potential to significantly improve the effectiveness of treatment by maximizing drug efficacy and minimize toxicity. However, this option has not yet been widely embraced by the oncology community. Some reasons for this include increased logistical complexity of dose individualization, the lack of clinical laboratories that measure small molecule drug concentrations in support of patient care, and the lack of reimbursement of costs. However, the main obstacle may be the lack of studies clearly demonstrating that monitoring of oral small molecule anticancer drug levels actually improves clinical outcomes. Without unequivocal evidence in support of TDM-guided dose individualization, especially demonstration of improved survival with TDM in randomized controlled trials, wide acceptance of this approach by oncologists and reimbursement by insurance companies is unlikely, and patients may continue to suffer as a result of receiving incorrect drug doses. This article reviews the current status of TDM of oral small molecule drugs in oncology and intends to provide strategic insights into the design of studies for evaluating the utility of TDM in this clinical context.

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.

A Fast Parameter Identification Framework for Personalized Pharmacokinetics

This paper introduces a novel framework for fast parameter identification of personalized pharmacokinetic problems. Given one sample observation of a new subject, the framework predicts the parameters of the subject based on prior knowledge from a pharmacokinetic database. The feasibility of this framework was demonstrated by developing a new algorithm based on the Cluster Newton method, namely the constrained Cluster Newton method, where the initial points of the parameters are constrained by the database. The algorithm was tested with the compartmental model of propofol on a database of 59 subjects. The average overall absolute percentage error based on constrained Cluster Newton method is 12.10% with the threshold approach, and 13.42% with the nearest-neighbor approach. The average computation time of one estimation is 13.10 seconds. Using parallel computing, the average computation time is reduced to 1.54 seconds, achieved with 12 parallel workers. The results suggest that the proposed framework can effectively improve the prediction accuracy of the pharmacokinetic parameters with limited observations in comparison to the conventional methods. Computation cost analyses indicate that the proposed framework can take advantage of parallel computing and provide solutions within practical response times, leading to fast and accurate parameter identification of pharmacokinetic problems.

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.

A manual propofol infusion regimen for neonates and infants

AIMS

Manual propofol infusion regimens for neonates and infants have been determined from clinical observations in children under the age of 3 years undergoing anesthesia. We assessed the performance of these regimens using reported age-specific pharmacokinetic parameters for propofol. Where performance was poor, we propose alternative dosing regimens.

METHODS

Simulations using a reported general purpose pharmacokinetic propofol model were used to predict propofol blood plasma concentrations during manual infusion regimens recommended for children 0-3 years. Simulated steady state concentrations were 6-8 µg.mL^-1 in the first 30 minutes that were not sustained during 100 minutes infusions. Pooled clinical data (n = 161, 1902 plasma concentrations) were used to determine an alternative pharmacokinetic parameter set for propofol using nonlinear mixed effects models. A new manual infusion regimen for propofol that achieves a steady-state concentration of 3 µg.mL^-1 was determined using a heuristic approach.

RESULTS

A manual dosing regimen predicted to achieve steady-state plasma concentration of 3 µg.mL^-1 comprised a loading dose of 2 mg.kg^-1 followed by an infusion rate of 9 mg.kg^-1 .h^-1 for the first 15 minutes, 7 mg.kg^-1 .h^-1 from 15 to 30 minutes, 6 mg.kg^-1 .h^-1 from 30 to 60 minutes, 5 mg.kg^-1 .h^-1 from 1 to 2 hours in neonates (38-44 weeks postmenstrual age). Dose increased with age in those aged 1-2 years with a loading dose of 2.5 mg.kg^-1 followed by an infusion rate of 13 mg.kg^-1 .h^-1 for the first 15 minutes, 12 mg.kg^-1 .h^-1 from 15 to 30 minutes, 11 mg.kg^-1 .h^-1 from 30 to 60 minutes, and 10 mg.kg^-1 .h^-1 from 1 to 2 hours.

CONCLUSION

Propofol clearance increases throughout infancy to reach 92% that reported in adults (1.93 L.min.70 kg^-1 ) by 6 months postnatal age and infusion regimens should reflect clearance maturation and be cognizant of adverse effects from concentrations greater than the target plasma concentration. Predicted concentrations using a published general purpose pharmacokinetic propofol model were similar to those determined using a new parameter set using richer neonatal and infant 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.

Predicting the dose of vancomycin in ICU patients receiving different types of RRT therapy: a model-based meta-analytic approach

AIM

Previous pharmacokinetic (PK) studies have proposed various dosing regimens for vancomycin in intensive care unit (ICU) patients undergoing renal replacement therapy (RRT), but all are restricted to specific RRT modalities. To be useful in practice, a population PK model would need to predict vancomycin clearance during any RRT modality. Development of such a model is feasible using meta-analysis of published summarized estimates of vancomycin PK parameters. Our aims were: (i) to develop and validate a population PK model for vancomycin that takes into account any RRT modalities, and (ii) to predict vancomycin dosing for RRT patients in ICU.

METHODS

Vancomycin pharmacokinetics were assumed to be two-compartmental, total body clearance being the sum of non-RRT clearance and RRT-induced clearance. Drug disposition and non-RRT clearance parameters were estimated by systematic review and meta-analysis of previously published parameter estimates. The relationship between RRT-induced clearance and RRT flowrate settings was assessed using a model-based meta-analysis. Prediction performances of the PK model were assessed using external data.

RESULTS

The meta-analyses of disposition parameters, non-RRT clearance and RRT-induced clearance included 11, 6 and 38 studies (84 RRT clearance measurements) respectively. The model performed well in predicting external individual PK data. Individual vancomycin concentrations during RRT were accurately predicted using Bayesian estimation based solely on pre-RRT measurements.

CONCLUSIONS

The PK model allowed accurate prediction of the vancomycin pharmacokinetics during RRT in ICU patients. Based on the model of RRT-induced clearance, an appropriate adjustment of the vancomycin dosing regimen could be proposed for any kind of flowrate settings.

Population Pharmacokinetics and Pharmacodynamics of Teicoplanin and C-Reactive Protein in Hospitalized Patients With Gram-Positive Infections

Teicoplanin is an antibiotic agent used for the treatment of Gram-positive infections. The clinical benefit of teicoplanin is associated with its blood concentrations, but the optimal dosing regimen is not yet known. To explore the optimal individual dosing regimen, we performed a population pharmacokinetic (PK) and pharmacodynamic (PD) analysis targeting a large-scale population, including patients with a wide range of ages, body weights, and renal functions. The PK of teicoplanin was described with a 2-compartment model, and the PD of C-reactive protein (CRP) concentrations was described with a turnover maximum inhibition model. The elimination half-life of teicoplanin calculated from the final estimated parameters was 169 hours, and renal function was a significant covariate of teicoplanin clearance. The teicoplanin concentration producing 50% of the maximum inhibition of CRP production was estimated to be 2.66 mg/L. The minimum concentration of teicoplanin in patients with higher loading doses (15 mg/kg) reached the target range (15-30 mg/L) with a probability of >50% in the dosing simulation. We described the influence of body size, body composition, and renal function on the PK of teicoplanin. The population PKPD model of teicoplanin and CRP in this study should provide useful information for development of a dosing strategy including the sequential clinical benefit of teicoplanin.

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.

Inter occasion variability in individual optimal design

Inter occasion variability (IOV) is of importance to consider in the development of a design where individual pharmacokinetic or pharmacodynamic parameters are of interest. IOV may adversely affect the precision of maximum a posteriori (MAP) estimated individual parameters, yet the influence of inclusion of IOV in optimal design for estimation of individual parameters has not been investigated. In this work two methods of including IOV in the maximum a posteriori Fisher information matrix (FIM_MAP) are evaluated: (i) MAP_occ—the IOV is included as a fixed effect deviation per occasion and individual, and (ii) POP_occ—the IOV is included as an occasion random effect. Sparse sampling schedules were designed for two test models and compared to a scenario where IOV is ignored, either by omitting known IOV (Omit) or by mimicking a situation where unknown IOV has inflated the IIV (Inflate). Accounting for IOV in the FIM_MAP markedly affected the designs compared to ignoring IOV and, as evaluated by stochastic simulation and estimation, resulted in superior precision in the individual parameters. In addition MAP_occ and POP_occ accurately predicted precision and shrinkage. For the investigated designs, the MAP_occ method was on average slightly superior to POP_occ and was less computationally intensive.

Population pharmacokinetics of imatinib in Iranian patients with chronic-phase chronic myeloid leukemia

PURPOSE

We evaluated the population pharmacokinetics (PPK) and exposure-response relationship of imatinib mesylate in Iranian patients with chronic myeloid leukemia (CML).This study was designed to assess steady state (SS) imatinib trough concentrations (Cmin) and pharmacokinetics parameters of imatinib in patients with CML in chronic phase after at least 12-month treatment.

METHODS

Plasma concentrations from a randomized controlled trial consist of 61 patients who received oral imatinib at doses ranged between 300 and 800 mg in various dosing interval, which were quantified using a validated reversed-phase high-performance liquid chromatographic method with UV detection method on different occasions at SS and evaluated using PPK model.

RESULTS

A one-compartment model with zero-order absorption and a lag time was sufficient in describing the concentration-time profile. Inter-individual variability (IIV) was modeled for all parameters. Oral clearance (CL/F) and the volume of distribution (V/F) were estimated to 10.8 L/h with 30 % IIV and 265 L with 53 % IIV, respectively. Inter-occasion variability (IOV) was included in CL/F (17 %) and V/F (22 %).The proportional residual error of the model was 8 %.

CONCLUSIONS

Simulation analysis from individual parameters shows exposure to imatinib is highly variable among patients. Imatinib trough plasma levels <1,257 ng/mL were associated with lower rates of major molecular response. Because of the wide IIV compared with IOV with imatinib in our study, trough levels may play a role in investigating instances of suboptimal response.

Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs

Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation has evolved as an important tool for rational drug development and drug use, where developed models characterize both the typical trends in the data and quantify the variability in relationships between dose, concentration, and desired effects and side effects. In parallel, rapid emergence of antibiotic-resistant bacteria imposes new challenges on modern health care. Models that can characterize bacterial growth, bacterial killing by antibiotics and immune system, and selection of resistance can provide valuable information on the interactions between antibiotics, bacteria, and host. Simulations from developed models allow for outcome predictions of untested scenarios, improved study designs, and optimized dosing regimens. Today, much quantitative information on antibiotic PKPD is thrown away by summarizing data into variables with limited possibilities for extrapolation to different dosing regimens and study populations. In vitro studies allow for flexible study designs and valuable information on time courses of antibiotic drug action. Such experiments have formed the basis for development of a variety of PKPD models that primarily differ in how antibiotic drug exposure induces amplification of resistant bacteria. The models have shown promise for efficacy predictions in patients, but few PKPD models describe time courses of antibiotic drug effects in animals and patients. We promote more extensive use of modeling and simulation to speed up development of new antibiotics and promising antibiotic drug combinations. This review summarizes the value of PKPD modeling and provides an overview of the characteristics of available PKPD models of antibiotics based on in vitro, animal, and patient data.

Benchmarking therapeutic drug monitoring software: a review of available computer tools

Therapeutic drug monitoring (TDM) aims to optimize treatments by individualizing dosage regimens based on the measurement of blood concentrations. Dosage individualization to maintain concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculations currently represent the gold standard TDM approach but require computation assistance. In recent decades computer programs have been developed to assist clinicians in this assignment. The aim of this survey was to assess and compare computer tools designed to support TDM clinical activities. The literature and the Internet were searched to identify software. All programs were tested on personal computers. Each program was scored against a standardized grid covering pharmacokinetic relevance, user friendliness, computing aspects, interfacing and storage. A weighting factor was applied to each criterion of the grid to account for its relative importance. To assess the robustness of the software, six representative clinical vignettes were processed through each of them. Altogether, 12 software tools were identified, tested and ranked, representing a comprehensive review of the available software. Numbers of drugs handled by the software vary widely (from two to 180), and eight programs offer users the possibility of adding new drug models based on population pharmacokinetic analyses. Bayesian computation to predict dosage adaptation from blood concentration (a posteriori adjustment) is performed by ten tools, while nine are also able to propose a priori dosage regimens, based only on individual patient covariates such as age, sex and bodyweight. Among those applying Bayesian calculation, MM-USC*PACK© uses the non-parametric approach. The top two programs emerging from this benchmark were MwPharm© and TCIWorks. Most other programs evaluated had good potential while being less sophisticated or less user friendly. Programs vary in complexity and might not fit all healthcare settings. Each software tool must therefore be regarded with respect to the individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Computer-assisted TDM is gaining growing interest and should further improve, especially in terms of information system interfacing, user friendliness, data storage capability and report generation.

A pharmacokinetic standard for babies and adults

The pharmacokinetic behavior of medicines used in humans follows largely predictable patterns across the human age range from premature babies to elderly adults. Most of the differences associated with age are in fact due to differences in size. Additional considerations are required to describe the processes of maturation of clearance processes and postnatal changes in body composition. Application of standard approaches to reporting pharmacokinetic parameters is essential for comparative human pharmacokinetic studies from babies to adults. A standardized comparison of pharmacokinetic parameters obtained in children and adults is shown for 46 drugs. Appropriate size scaling shows that children (over 2 years old) are similar to adults. Maturation changes are generally completed within the first 2 years of postnatal life; consequently babies may be considered as immature children, whereas children are just small adults.

Prediction of morphine dose in humans

BACKGROUND

Morphine is widely used throughout the human life span. Several pharmacokinetic models have been proposed to predict how morphine clearance changes with weight and age. This study uses a large external data set to evaluate the ability of pharmacokinetic models to predict morphine doses.

METHODS

A data set of morphine clearance estimates was created from published reports in premature neonates, full-term neonates, infants, children, and adults. This external data set was used to evaluate published models for morphine clearance as well as other models proposed for use in neonates and infants. Morphine clearance predictions were used to predict morphine dose rates to achieve similar target concentrations in all age groups.

RESULTS

An allometric ¾ power model using weight combined with a sigmoid maturation model using postmenstrual age successfully predicted the morphine dose rate (within 25% of target) in all age groups except infants [predicted dose 30% under target (95% CI, 7-46%)]. Other published models based on empirical allometric scaling all made unacceptable predictions (>100% of target) in at least one age group.

CONCLUSIONS

Clearance based on empirical allometric scaling predicted unacceptable doses. Theory-based allometric scaling combined with a maturation function has been confirmed by external evaluation to provide a sound basis for describing clearance and predicting morphine doses in humans of all ages.

Predictability of individualized dosage regimens of carbamazepine and valproate mono- and combination therapy

WHAT IS KNOWN AND OBJECTIVE

Many investigators agree that appropriate rational utilization of therapeutic drug monitoring (TDM) with Bayesian feedback dosage adjustment facilitates epilepsy treatment with carbamazepine (CBZ) and/or valproate (VPA) by increasing the seizure control and safety, as well as by reducing treatment costs. In previous works we have developed and used in clinical practice population pharmacokinetic (PK) models of different dosage forms for VPA and post-induction CBZ behaviour, as well as for combined therapy with CBZ plus another 'old' antiepileptic drug (AED). An important step of external validation is to evaluate how well a procedure of Bayesian individualizing AED dosage regimens based on a proposed population PK model and sparse TDM data 'works', and how helpful it is in real practical clinical settings. The aim of this study was to evaluate the predictability of individualized dosage regimens for monotherapy with CBZ in the post-induction period or with VPA, as well as for CBZ and VPA given as combination therapy based on TDM data of epileptic patients and the earlier developed population models.

METHODS

Four groups of TDM data were analysed using the USC*PACK software for PK/PD analysis: 556 predictions for adult epileptic patients on CBZ monotherapy, 662 predictions for VPA monotherapy, 402 predictions of CBZ serum levels and 430 predictions of VPA serum levels for adult epileptic patients on CBZ+VPA combination therapy. Statistical characteristics of the prediction errors (PE) and weighted PE were used to estimate bias and precision of predictions. Intraindividual and interoccasional variability of predictions were also estimated.

RESULTS AND DISCUSSION

This study demonstrated that in most cases of CBZ and VPA monotherapy and combination therapy, predictions of future AED concentrations based on the earlier developed population PK models, TDM data and patient-specific maximum a posteriori probability Bayesian posterior parameter values provided clinically acceptable estimates. Statistical analysis of the residuals demonstrated that the distributions of residual and weighted residual were close to the normal distribution (Kolmogorov-Smirnov test, P > 0·05) and their mean values did not differ statistically significant from zero (no statistically significant bias, P > 0·05) for all groups of predictions. The observed decreased quality of predictions of VPA concentrations during VPA+CBZ combination therapy, especially when CBZ dosages were changed, might well be explained by their PK interactions. For all groups, in linear regression analysis, the observed trend of decreasing of the prediction quality over various future prediction time horizons was considered statistically significant (P < 0·05). Prediction of serum levels further in future was less precise than those closer to the present for a 1·5- to 3·5-year observation period. No bias in predictions was associated with the time horizons.

WHAT IS NEW AND CONCLUSION

Our validation results suggest good predictive performance of the population models developed earlier, and quite acceptable predictions of future AED serum levels for individualized dosage regimens of CBZ and VPA therapy in real clinical settings.

Nonlinear pharmacokinetics of piperacillin in healthy volunteers--implications for optimal dosage regimens

AIMS

(i) To describe the first-order and mixed-order elimination pathways of piperacillin, (ii) to determine the between occasion variability (BOV) of pharmacokinetic parameters and (iii) to propose optimized dosage regimens.

METHODS

We performed a five-period replicate dose study in four healthy volunteers. Each subject received 4g piperacillin as a single 5min intravenous infusion in each study period. Drug analysis was performed by HPLC. We used NONMEM and S-ADAPT for population pharmacokinetic analysis and Monte Carlo simulation to predict the probability of target attainment (PTA) with a target time of non-protein bound concentration above MIC >50% of the dosing interval.

RESULTS

A model with first-order nonrenal elimination and parallel first-order and mixed-order renal elimination had the best predictive performance. For a 70kg subject we estimated 4.40lh(-1) for nonrenal clearance, 5.70lh(-1) for first-order renal clearance, 170mgh(-1) for V(max) , and 49.7mgl(-1) for K(m) for the mixed-order renal elimination. The BOV was 39% for V(max) , 117% for K(m) , and 8.5% for total clearance. A 30min infusion of 4g every 6h achieved robust (≥90%) PTAs for MICs ≤12mgl(-1) . As an alternative mode of administration, a 5h infusion of 6g every 8h achieved robust PTAs for MICs ≤48mgl(-1) .

CONCLUSIONS

Part of the renal elimination of piperacillin is saturable at clinically used doses. The BOV of total clearance and volume of distribution were low. Prolonged infusions achieved better PTAs compared with shorter infusions at similar daily doses. This benefit was most pronounced for MICs between 12 and 48mgl(-1) .

Population pharmacokinetic and pharmacodynamic modelling of the effects of nicorandil in the treatment of acute heart failure

AIMS

The aims of the study were 1) to evaluate the pharmacokinetics of nicorandil in healthy subjects and acute heart failure (AHF) patients and 2) to evaluate the exposure-response relationship with pulmonary arterial wedge pressure (PAWP) in AHF patients and to predict an appropriate dosing regimen for nicorandil.

METHODS

Based on the data from two healthy volunteer and three AHF patient studies, models were developed to characterize the pharmacokinetics and pharmacodynamics of nicorandil. PAWP was used as the pharmacodynamic variable. An asymptotic exponential disease progression model was used to account for time dependent changes in PAWP that were not explained by nicorandil exposure. The modelling was performed using NONMEM version V.

RESULTS

The pharmacokinetics of nicorandil were characterized by a two-compartment model with linear elimination. CL, V1 and V2 in AHF patients were 1.96, 1.39 and 4.06 times greater than in healthy subjects. Predicted plasma concentrations were assumed to have an immediate concentration effect relationship on PAWP. An inhibitory E(max) model with E(max) of -11.7 mmHg and EC(50) of 423 microg l(-1) was considered the best relationship between nicorandil concentrations and PAWP. PAWP decreased independently of nicorandil exposure. This drug independent decline was described by an asymptotic decrease of 6.1 mmHg with a half-life of 5.3 h.

CONCLUSIONS

AHF patients have higher clearance and initial distribution volume of nicorandil compared with healthy subjects. The median target nicorandil concentration to decrease PAWP by 30% is predicted to be 748 microg l(-1), indicating that a loading dose of 200 microg kg(-1) and a maintenance dose of 400 microg kg(-1) h(-1) would be appropriate for the initial treatment of AHF.

Multiple-pool cell lifespan models for neutropenia to assess the population pharmacodynamics of unbound paclitaxel from two formulations in cancer patients

PURPOSE

Our objective was to build a mechanism-based pharmacodynamic model for the time course of neutropenia in cancer patients following paclitaxel treatment with a tocopherol-based Cremophor-free formulation (Tocosol Paclitaxel) and Cremophor EL-formulated paclitaxel (Taxol).

METHODS

A randomized two-way crossover trial was performed with 35 adult patients who received 175 mg/m(2) paclitaxel as either 15 min (Tocosol Paclitaxel) or 3 h (Taxol) intravenous infusions. Paclitaxel concentrations were measured by LC-MS/MS. NONMEM VI was used for population pharmacodynamics.

RESULTS

The cytotoxic effect on neutrophils was described by four mechanism-based models predicated on known properties of paclitaxel that used unbound concentrations in the central, deep peripheral or an intracellular compartment as forcing functions. Tocosol Paclitaxel was estimated to release 9.8% of the dose directly into the deep peripheral compartment (DPC). All models provided reasonable fitting of neutropenic effects. The model with the best predictive performance assumed that this dose fraction was released into 22.5% of the DPC which included the site of toxicity. The second-order cytotoxic rate constant was 0.00211 mL/ng per hour (variability: 52% CV). The relative exposure at the site of toxicity was 2.21 +/- 0.41 times (average +/- SD) larger for Tocosol Paclitaxel compared to Taxol. Lifespan was 11.0 days for progenitor cells, 1.95 days for maturating cells, and 4.38 days for neutrophils. Total drug exposure in blood explained half of the variance in nadir to baseline neutrophil count ratio.

CONCLUSIONS

The relative exposure of unbound paclitaxel at the site of toxicity was twice as large for Tocosol Paclitaxel compared to Taxol. The proposed mechanism-based models explained the extent and time course of neutropenia jointly for both formulations.

Target concentration intervention is needed for tobramycin dosing in paediatric patients with cystic fibrosis--a population pharmacokinetic study

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

* Two recent papers have been published which have attempted to build a full population pharmacokinetic model for tobramycin in children with cystic fibrosis. * However, neither study was able to provide any information about between-subject variability (BSV) and between-occasion variability (BOV), which is necessary to justify and draw conclusions about the use of target concentration intervention (TCI). * In the publications no simulations were provided to show any new directions or evaluate current therapy.

WHAT THIS STUDY ADDS

* This study provides sound evidence that TCI must be undertaken in this patient group, as the BOV is significantly less than the BSV. * The simulations from this model clearly show that current dosing and monitoring methods will not achieve the necessary targets to maximize the pharmacokinetic-pharmacodynamic relationships of aminoglycosides in this patient group. * The model presented is able to be easily incorporated into Bayesian dose individualization software, e.g. Abbottbase or TCIworks, to achieve dosing targets more accurately.

AIM

The primary aim was to estimate the population pharmacokinetic parameters of once-daily intravenous (i.v.) tobramycin in paediatric cystic fibrosis (CF) patients and to investigate the influence of covariates. The second aim was to assess the need for target concentration intervention (TCI) for tobramycin in this patient group.

METHODS

Retrospective demographic, dosing and concentration data were collected from 35 CF patients (21 female, 14 male) aged 0.5-17.8 years, from whom 318 tobramycin plasma concentrations were available. NONMEM was used to estimate the population pharmacokinetics of tobramycin. Simulations were performed using weight-based dosing with a weight from a covariate distribution model to evaluated current dosing schedules and monitoring practices.

RESULTS

A two-compartment model best described the data with population parameter estimates for clearance of central compartment (CL) of 6.37 l h(-1) per 70 kg; volume of central compartment (V(c)) of 18.7 l per 70 kg; intercompartmental clearance (Q) of 0.393 l h(-1); and volume of peripheral compartment (V(per)) of 1.32 l. The inclusion of total body weight as covariate reduced the random component of between-subject variability in CL from 50.1% to 11.7% and in V(c) from 62.2% to 11.6%. The between-occasion variability on CL was estimated in the final model as 6.5%. Simulations show that one dose does not fit all and TCI and dose adjustment are required.

CONCLUSIONS

This study provides the first pharmacokinetic model of once-daily i.v. tobramycin for the use of target concentration intervention in paediatric CF patients.

Limited predictability of amikacin clearance in extreme premature neonates at birth

AIM

Identify and quantify factors describing variability of amikacin clearance in preterm neonates at birth.

METHODS

Population pharmacokinetics of amikacin were estimated in a cohort of 205 extreme preterm neonates [post conception age (PCA) 27.8, SD 1.8, range 24-30 weeks; weight 1.07, SD 0.34, range 0.45-1.98 kg, postnatal age < 72 h]. Covariate analysis included weight, PCA, Apgar score, prophylactic administration of a nonsteroidal anti-inflammatory drug (NSAID) to the neonate, maternal indomethacin and betamethasone administration, and chorioamnionitis.

RESULTS

A one-compartment linear disposition model with zero order input (0.3 h i.v. infusion) and first-order elimination was used. The population parameter estimate for volume of distribution (V) was 40.2 l per 70 kg. Clearance (CL) increased from 0.486 l h(-1) per 70 kg at 24 weeks PCA to 0.940 l h(-1) per 70 kg by 30 weeks PCA. The population parameter variability (PPV) for CL and V was 0.336 and 0.451. The use of a NSAID (either aspirin or ibuprofen) in the first day of life reduced amikacin clearance by 22%. Overall 65% of the variability of CL was predictable. Weight explained 48%, PCA 15% and NSAIDs 2%.

CONCLUSIONS

Size and post-conception age are the major contributors to clearance variability in extreme premature neonates (<31 weeks PCA). The large (35% of total) unexplained variability in clearance reinforces the need for target concentration intervention to reduce variability in exposure to a safe and effective range.

Quantitative justification for target concentration intervention--parameter variability and predictive performance using population pharmacokinetic models for aminoglycosides

AIMS

[1] To quantify the random and predictable components of variability for aminoglycoside clearance and volume of distribution [2] To investigate models for predicting aminoglycoside clearance in patients with low serum creatinine concentrations [3] To evaluate the predictive performance of initial dosing strategies for achieving an aminoglycoside target concentration.

METHODS

Aminoglycoside demographic, dosing and concentration data were collected from 697 adult patients (> or =20 years old) as part of standard clinical care using a target concentration intervention approach for dose individualization. It was assumed that aminoglycoside clearance had a renal and a nonrenal component, with the renal component being linearly related to predicted creatinine clearance.

RESULTS

A two compartment pharmacokinetic model best described the aminoglycoside data. The addition of weight, age, sex and serum creatinine as covariates reduced the random component of between subject variability (BSVR) in clearance (CL) from 94% to 36% of population parameter variability (PPV). The final pharmacokinetic parameter estimates for the model with the best predictive performance were: CL, 4.7 l h(-1) 70 kg(-1); intercompartmental clearance (CLic), 1 l h(-1) 70 kg(-1); volume of central compartment (V1), 19.5 l 70 kg(-1); volume of peripheral compartment (V2) 11.2 l 70 kg(-1).

CONCLUSIONS

Using a fixed dose of aminoglycoside will achieve 35% of typical patients within 80-125% of a required dose. Covariate guided predictions increase this up to 61%. However, because we have shown that random within subject variability (WSVR) in clearance is less than safe and effective variability (SEV), target concentration intervention can potentially achieve safe and effective doses in 90% of patients.

Plasma concentrations of the enantiomers of fluoxetine and norfluoxetine: sources of variability and preliminary observations on relations with clinical response

Factors affecting the plasma concentrations of the R- and S-enantiomers of fluoxetine and norfluoxetine were investigated in 131 adult patients receiving long-term fluoxetine, of 10 to 60 mg/d (mean, 24 +/- 10 mg/d). Plasma concentration values (geometric means, CI 95%) in these patients were 186 (156, 223) nmol/L for S-fluoxetine, 67 (58, 77) nmol/L for R-fluoxetine, 247 (212, 287) nmol/L for S-norfluoxetine, and 118 (102, 137) nmol/L for R-norfluoxetine. The difference between the concentrations of the respective R- and S-enantiomers was statistically significant ( P< 0.0001) for both the parent drug and the demethylated metabolite. A significant correlation was found between the concentrations of each enantiomer and the prescribed daily dosage (r = 0.44, P< 0.0001 for S-fluoxetine; r = 0.48, P < 0.0001 for R-fluoxetine; r = 0.36, < 0.0001 for S-norfluoxetine; r = 0.32, P = 0.0003 for R-norfluoxetine), but the variability in concentration at any given dosage was considerable. When an iterative model based on multiple polynomial regressions was applied to determine the potential contributions of dosage, age, gender, body weight, and concomitant medication to the variability in the plasma concentration of the enantiomers, dosage was consistently found to provide the greatest predictive value. The predictive value of the model could be consistently improved when concentrations of other enantiomers were included as covariates. Of 58 patients with depressive symptoms for whom evaluation of clinical response (CGI scale) was available, 33 (57%) responded favorably to treatment. The plasma levels of individual enantiomers and of the active moiety (ActM, sum of the concentrations of R-fluoxetine, S-fluoxetine, and S-norfluoxetine) in these patients did not differ significantly from those found in patients with unsatisfactory therapeutic response. Likewise, the concentrations of individual enantiomers and of the ActM were similar in patients with or without adverse effects. Overall, these results demonstrate that the pharmacokinetics of fluoxetine and norfluoxetine exhibit marked stereoselectivity and considerable interpatient variability, which could not be explained by differences in gender, age, or comedication. In addition, a considerable variability was found in the enantiomers' concentrations associated with a favorable therapeutic response.

Clinical pharmacology: principles and practice of drug therapy in medical education

Educational reform has taken place in many Medical Schools. A traditional passive approach has been replaced by a more active, student-centred approach, founded on Problem-Based Learning. This has not been without risk because many well-structured courses have been abandoned, and this is of particular significance to the principles and practice of drug therapy. Here we outline an approach which could be incorporated into a medical curriculum and suggest some guidelines and a list of questions that should be asked in clinical situations involving drug therapy.

Relationship between clinical effects, serum drug concentration, and concurrent drug interactions in depressed patients treated with citalopram, fluoxetine, clomipramine, paroxetine or venlafaxine

The relationship between clinical effects and plasma concentrations of citalopram, fluoxetine, clomipramine, paroxetine and venlafaxine was studied in 119 cases of major depression. Clinical effects were evaluated using the Clinical Global Impression (CGI) improvement scale. Antidepressants were quantified by a separative chromatographic methodology. Plasma concentrations in responder patients were compared with the plasma concentrations proposed in literature as effective values. We found that the usual therapeutic window is convenient for citalopram and clomipramine, but could be reduced for fluoxetine and increased for venlafaxine and paroxetine. Concurrent drug interactions were also evaluated and clomipramine or citalopram plasma levels were found to be influenced by the presence of associated drugs. A larger study is needed, taking into account not only plasma concentrations and clinical effects, but also some pharmacokinetic data, especially the metabolic activity characterising the patient, and the presence or not of associated drugs. Copyright 2000 John Wiley & Sons, Ltd.