Population pharmacokinetics. A regulatory perspective

Dose Individualisation of Antimicrobials from a Pharmacometric Standpoint: The Current Landscape

Successful antimicrobial therapy depends on achieving optimal drug concentrations within individual patients. Inter-patient variability in pharmacokinetics (PK) and differences in pathogen susceptibility (reflected in the minimum inhibitory concentration, [MIC]) necessitate personalised approaches. Dose individualisation strategies aim to address this challenge, improving treatment outcomes and minimising the risk of toxicity and antimicrobial resistance. Therapeutic drug monitoring (TDM), with the application of population pharmacokinetic (popPK) models, enables model-informed precision dosing (MIPD). PopPK models mathematically describe drug behaviour across populations and can be combined with patient-specific TDM data to optimise dosing regimens. The integration of machine learning (ML) techniques promises to further enhance dose individualisation by identifying complex patterns within extensive datasets. Implementing these approaches involves challenges, including rigorous model selection and validation to ensure suitability for target populations. Understanding the relationship between drug exposure and clinical outcomes is crucial, as is striking a balance between model complexity and clinical usability. Additionally, regulatory compliance, outcome measurement, and practical considerations for software implementation will be addressed. Emerging technologies, such as real-time biosensors, hold the potential for revolutionising TDM by enabling continuous monitoring, immediate and frequent dose adjustments, and near patient testing. The ongoing integration of TDM, advanced modelling techniques, and ML within the evolving digital health care landscape offers a potential for enhancing antimicrobial therapy. Careful attention to model development, validation, and ethical considerations of the applied techniques is paramount for successfully optimising antimicrobial treatment for the individual patient.

Application of machine learning techniques in population pharmacokinetics/pharmacodynamics modeling

Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates pharmacokinetic and pharmacodynamic data from many subjects to understand inter- and intra-individual variability due to patient backgrounds, including disease state and genetics. The typical workflow in pop-PK/PD analysis involves the determination of the structure model, selection of the error model, analysis based on the base model, covariate modeling, and validation of the final model. Machine learning is gaining considerable attention in the medical and various fields because, in contrast to traditional modeling, which often assumes linear or predefined relationships, machine learning modeling learns directly from data and accommodates complex patterns. Machine learning has demonstrated excellent capabilities for prescreening covariates and developing predictive models. This review introduces various applications of machine learning techniques in pop-PK/PD research.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

Modeling the protein binding non-linearity in population pharmacokinetic model of valproic acid in children with epilepsy: a systematic evaluation study

Background: Several studies have investigated the population pharmacokinetics (popPK) of valproic acid (VPA) in children with epilepsy. However, the predictive performance of these models in the extrapolation to other clinical environments has not been studied. Hence, this study evaluated the predictive abilities of pediatric popPK models of VPA and identified the potential effects of protein binding modeling strategies. Methods: A dataset of 255 trough concentrations in 202 children with epilepsy was analyzed to assess the predictive performance of qualified models, following literature review. The evaluation of external predictive ability was conducted by prediction- and simulation-based diagnostics as well as Bayesian forecasting. Furthermore, five popPK models with different protein binding modeling strategies were developed to investigate the discrepancy among the one-binding site model, Langmuir equation, dose-dependent maximum effect model, linear non-saturable binding equation and the simple exponent model on model predictive ability. Results: Ten popPK models were identified in the literature. Co-medication, body weight, daily dose, and age were the four most commonly involved covariates influencing VPA clearance. The model proposed by Serrano et al. showed the best performance with a median prediction error (MDPE) of 1.40%, median absolute prediction error (MAPE) of 17.38%, and percentages of PE within 20% (F20, 55.69%) and 30% (F30, 76.47%). However, all models performed inadequately in terms of the simulation-based normalized prediction distribution error, indicating unsatisfactory normality. Bayesian forecasting enhanced predictive performance, as prior observations were available. More prior observations are needed for model predictability to reach a stable state. The linear non-saturable binding equation had a higher predictive value than other protein binding models. Conclusion: The predictive abilities of most popPK models of VPA in children with epilepsy were unsatisfactory. The linear non-saturable binding equation is more suitable for modeling non-linearity. Moreover, Bayesian forecasting with prior observations improved model fitness.

Editorial for the Special Issue "A Themed Issue in Honor of Professor Hartmut Derendorf-Outstanding Contributions in the Fields of Quantitative Clinical Pharmacology"

Pharmacokinetics (PK) is the discipline investigating the absorption, distribution, metabolization and elimination of a drug in the body [...].

A multi-model approach to predict efficacious clinical dose for an anti-TGF-β antibody (GC2008) in the treatment of osteogenesis imperfecta

Osteogenesis imperfecta (OI) is a heterogeneous group of inherited bone dysplasias characterized by reduced skeletal mass and bone fragility. Although the primary manifestation of the disease involves the skeleton, OI is a generalized connective tissue disorder that requires a multidisciplinary treatment approach. Recent studies indicate that application of a transforming growth factor beta (TGF-β) neutralizing antibody increased bone volume fraction (BVF) and strength in an OI mouse model and improved bone mineral density (BMD) in a small cohort of patients with OI. In this work, we have developed a multitiered quantitative pharmacology approach to predict human efficacious dose of a new anti-TGF-β antibody drug candidate (GC2008). This method aims to translate GC2008 pharmacokinetic/pharmacodynamic (PK/PD) relationship in patients, using a number of appropriate mathematical models and available preclinical and clinical data. Compartmental PK linked with an indirect PD effect model was used to characterize both pre-clinical and clinical PK/PD data and predict a GC2008 dose that would significantly increase BMD or BVF in patients with OI. Furthermore, a physiologically-based pharmacokinetic model incorporating GC2008 and the body's physiological properties was developed and used to predict a GC2008 dose that would decrease the TGF-β level in bone to that of healthy individuals. By using multiple models, we aim to reveal information for different aspects of OI disease that will ultimately lead to a more informed dose projection of GC2008 in humans. The different modeling efforts predicted a similar range of pharmacologically relevant doses in patients with OI providing an informed approach for an early clinical dose setting.

Review: Role of Model-Informed Drug Development Approaches in the Lifecycle of Drug Development and Regulatory Decision-Making

Model-informed drug development (MIDD) is a powerful approach to support drug development and regulatory review. There is a rich history of MIDD applications at the U.S. Food and Drug Administration (FDA). MIDD applications span across the life cycle of the development of new drugs, generics, and biologic products. In new drug development, MIDD approaches are often applied to inform clinical trial design including dose selection/optimization, aid in the evaluation of critical regulatory review questions such as evidence of effectiveness, and development of policy. In the biopharmaceutics space, we see a trend for increasing role of computational modeling to inform formulation development and help strategize future in vivo studies or lifecycle plans in the post approval setting. As more information and knowledge becomes available pre-approval, quantitative mathematical models are becoming indispensable in supporting generic drug development and approval including complex generic drug products and are expected to help reduce overall time and cost. While the application of MIDD to inform the development of cell and gene therapy products is at an early stage, the potential for future application of MIDD include understanding and quantitative evaluation of information related to biological activity/pharmacodynamics, cell expansion/persistence, transgene expression, immune response, safety, and efficacy. With exciting innovations on the horizon, broader adoption of MIDD is poised to revolutionize drug development for greater patient and societal benefit.

Population Pharmacokinetic Evaluation with External Validation of Tacrolimus in Chinese Primary Nephrotic Syndrome Patients

PURPOSE

The generalizability of numerous tacrolimus population pharmacokinetic (popPK) models constructed to promote optimal tacrolimus dosing in patients with primary nephrotic syndrome (PNS) is unclear. This study aimed to evaluate the predictive performance of published tacrolimus popPK models for PNS patients with an external data set.

METHODS

We prospectively collected 223 concentrations from 50 Chinese adult patients with PNS who were undergoing tacrolimus treatment. Data on published tacrolimus popPK models for adults and children with PNS were extracted from the literature. Model predictability was evaluated with prediction-based and simulation-based diagnostics and Bayesian forecasting.

RESULTS

In prediction-based evaluation, none of the 11 identified published popPK models of tacrolimus had met a predefined criteria of a mean prediction error ≤  ± 20%, and the prediction error within ± 30% of the identified models didn't exceed 50%. Simulation-based diagnostics also indicated unsatisfactory predictability. Bayesian forecasting demonstrated amelioration in the model predictability with the inclusion of 2-3 prior observations. Moreover, the predictive performance of nonlinear models was not better than that of one-compartment models.

CONCLUSIONS

The prediction of tacrolimus concentrations for patients with PNS remains challenging; published models are not applicable for extrapolation to other hospitals. Bayesian forecasting significantly improved model predictability and thereby helped to individualize tacrolimus dosing.

Validation of population pharmacokinetic models: a comparison of internal and external validation approaches for hydrochlorothiazide

1. Model evaluation is an important issue in population analyses. Our aim was to perform and illustrate metrics and techniques for internal and external evaluation with an application to population pharmacokinetics of hydrochlorothiazide (HCTZ).2. A nonlinear mixed effects model was used to study the pharmacokinetics of HCTZ. In addition, different types of internal assessment tools and external metrics were used for model evaluation. External evaluation was performed using an alternative dataset that included data from an independent group of subjects. For comparison, a previously published population pharmacokinetic model for HCTZ was applied to the same data.3. A two-compartment model with first-order oral absorption using a constant time delay between administration and absorption and first-order elimination best described HCTZ pharmacokinetics. Age had a statistically significant effect on HCTZ clearance. The final model performed adequately in the internal and external assessment tests. The final model showed better predictive performance than the other previously published HCTZ model.4. Finally, a robust population pharmacokinetic model for HCTZ in adults was constructed and validated internally and externally. Incorporating analytical assessment of nonlinear pharmacokinetics into the modelling may be a promising approach to improve the predictive power of the model.

Factors Affecting Time-Varying Clearance of Cyclosporine in Adult Renal Transplant Recipients: A Population Pharmacokinetic Perspective

AIM

The pharmacokinetic (PK) properties of cyclosporine (CsA) in renal transplant recipients are patient- and time-dependent. Knowledge of this time-related variability is necessary to maintain or achieve CsA target exposure. Here, we aimed to identify factors explaining variabilities in CsA PK properties and characterize time-varying clearance (CL/F) by performing a comprehensive analysis of CsA PK factors using population PK (popPK) modeling of long-term follow-up data from our institution.

METHODS

In total, 3674 whole-blood CsA concentrations from 183 patients who underwent initial renal transplantation were analyzed using nonlinear mixed-effects modeling. The effects of potential covariates were selected according to a previous study and well-accepted theoretical mechanisms. Model-informed individualized therapeutic regimens were also evaluated.

RESULTS

A two-compartment model adequately described the data and the estimated mean CsA CL/F was 32.6 L h^-1 (relative standard error: 5%). Allometrically scaled body size, hematocrit (HCT) level, CGC haplotype carrier status, and postoperative time may contribute to CsA PK variability. The CsA bioavailability in patients receiving a prednisolone dose (PD) of 80 mg was 20.6% lower than that in patients receiving 20 mg. A significant decrease (52.6%) in CL/F was observed as the HCT increased from 10.5% to 60.5%. The CL/F of the non-CGC haplotype carrier was 14.4% lower than that of the CGC haplotype carrier at 3 months post operation.

CONCLUSIONS

By monitoring body size, HCT, PD, and CGC haplotype, changes in CsA CL/F over time could be predicted. Such information could be used to optimize CsA therapy. CsA dose adjustments should be considered in different postoperative periods.

Creation of an inventory of quality markers used to evaluate pharmacokinetic literature: A systematic review

WHAT IS KNOWN AND OBJECTIVE

Robust critical appraisal tools for clinical pharmacokinetic studies are limited. Before development of such a tool is possible, quality markers (items deemed important for credibility of study results) must be identified. We aim to create an inventory of quality markers intended for the appraisal of clinical pharmacokinetic studies and to categorize identified markers into associated domains of study quality.

METHODS

Medline via ProQuest central (1946-Sep 2020, EMBASE (1974-Sep 2020), Cochrane database of systematic reviews, Google and Google Scholar were searched using the following search categories: pharmacokinetics, reporting guidelines and quality markers. Reference lists of the identified articles were searched manually. Any article (review, study or guideline) reporting quality markers related to the appraisal of pharmacokinetic literature was eligible for inclusion. Articles were further screened and limited to those reported in English on human subjects only. Cell-based and animal-based pharmacokinetic studies were excluded. Extracted data from included articles included identified or perceived markers of quality and baseline article data. Identified quality markers were then categorized according to manuscript reporting domains (abstract, introduction/background, methodology, results, discussion and conclusion).

RESULTS AND DISCUSSION

Of 789 studies identified, 17 articles were included for extraction of quality markers. A total of 35 quality markers were identified across eight categories. The most frequently reported quality markers were related to method (13/35) and result sections (6/35). Quality markers encompassed all aspects of study design and reporting and were both similar and different to established reporting checklists for clinical pharmacokinetic studies.

WHAT IS NEW AND CONCLUSION

The inventory of quality markers is now suitable to undergo further testing for inclusion in a tool designed for the appraisal of clinical pharmacokinetic studies.

A Bayesian Method for Population-wide Cardiotoxicity Hazard and Risk Characterization Using an In Vitro Human Model

Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes are an established model for testing potential chemical hazards. Interindividual variability in toxicodynamic sensitivity has also been demonstrated in vitro; however, quantitative characterization of the population-wide variability has not been fully explored. We sought to develop a method to address this gap by combining a population-based iPSC-derived cardiomyocyte model with Bayesian concentration-response modeling. A total of 136 compounds, including 54 pharmaceuticals and 82 environmental chemicals, were tested in iPSC-derived cardiomyocytes from 43 nondiseased humans. Hierarchical Bayesian population concentration-response modeling was conducted for 5 phenotypes reflecting cardiomyocyte function or viability. Toxicodynamic variability was quantified through the derivation of chemical- and phenotype-specific variability factors. Toxicokinetic modeling was used for probabilistic in vitro-to-in vivo extrapolation to derive population-wide margins of safety for pharmaceuticals and margins of exposure for environmental chemicals. Pharmaceuticals were found to be active across all phenotypes. Over half of tested environmental chemicals showed activity in at least one phenotype, most commonly positive chronotropy. Toxicodynamic variability factor estimates for the functional phenotypes were greater than those for cell viability, usually exceeding the generally assumed default of approximately 3. Population variability-based margins of safety for pharmaceuticals were correctly predicted to be relatively narrow, including some below 10; however, margins of exposure for environmental chemicals, based on population exposure estimates, generally exceeded 1000, suggesting they pose little risk at current general population exposures even to sensitive subpopulations. Overall, this study demonstrates how a high-throughput, human population-based, in vitro-in silico model can be used to characterize toxicodynamic population variability in cardiotoxic risk.

A hybrid modeling approach for assessing mechanistic models of small molecule partitioning in vivo using a machine learning-integrated modeling platform

Prediction of the first-in-human dosing regimens is a critical step in drug development and requires accurate quantitation of drug distribution. Traditional in vivo studies used to characterize clinical candidate’s volume of distribution are error-prone, time- and cost-intensive and lack reproducibility in clinical settings. The paper demonstrates how a computational platform integrating machine learning optimization with mechanistic modeling can be used to simulate compound plasma concentration profile and predict tissue-plasma partition coefficients with high accuracy by varying the lipophilicity descriptor logP. The approach applied to chemically diverse small molecules resulted in comparable geometric mean fold-errors of 1.50 and 1.63 in pharmacokinetic outputs for direct tissue:plasma partition and hybrid logP optimization, with the latter enabling prediction of tissue permeation that can be used to guide toxicity and efficacy dosing in human subjects. The optimization simulations required to achieve these results were parallelized on the AWS cloud and generated outputs in under 5 h. Accuracy, speed, and scalability of the framework indicate that it can be used to assess the relevance of other mechanistic relationships implicated in pharmacokinetic-pharmacodynamic phenomena with a lower risk of overfitting datasets and generate large database of physiologically-relevant drug disposition for further integration with machine learning models.

Modeling and simulation to predict the degree of disability over time in acute ischemic stroke patients

Disability in patients with acute stroke varies over time, with the prediction of outcomes being critical for proper management. This study aimed to develop a model to predict the cumulative probability of each modified Rankin Scale (mRS) score over time with inclusion of significant covariates. Longitudinal data obtained from 193 patients, 1–24 months after onset of acute ischemic stroke, were included for a modeling analysis using nonlinear mixed‐effect modeling (NONMEM). After selecting a model that best described the time course of the probability of different mRS scores, potential covariates were tested. Visual predicted check plots, parameter estimates, and decreases in minimum objective function values were used for model evaluation. The inclusion of disease progression (DP) in the baseline proportional odds cumulative logit model significantly improved the model compared to the baseline model without DP. An inhibitory maximum effect (E_max) model was determined to be the best DP model for describing the probability of specific mRS scores over time. In the final model, DP was multiplied with the baseline cumulative logit probability with a baseline adjustment. In addition to differences in lesion volume (DLV), the final model included comorbid diabetes mellitus (DM) and baseline National Institutes of Health Stroke Scale (NIHSS) scores on E_max as statistically significant covariates. This study developed a model including DLV, NIHSS score, and comorbid DM for predicting the disability time course in patients with acute ischemic stroke. This model may help to predict disease outcomes and to develop more appropriate management plans for patients with acute stroke.

Validation of a perioperative population factor VIII pharmacokinetic model with a large cohort of pediatric hemophilia a patients

AIMS

Population pharmacokinetic (PK) models are increasingly applied to perform individualized dosing of factor VIII (FVIII) concentrates in haemophilia A patients. To guarantee accurate performance of a population PK model in dose individualization, validation studies are of importance. However, external validation of population PK models requires independent data sets and is, therefore, seldomly performed. Therefore, this study aimed to validate a previously published population PK model for FVIII concentrates administrated perioperatively.

METHODS

A previously published population PK model for FVIII concentrate during surgery was validated using independent data from 87 children with severe haemophilia A with a median (range) age of 2.6 years (0.03-15.2) and body weight of 14 kg (4-57). First, the predictive performance of the previous model was evaluated with MAP Bayesian analysis using NONMEM v7.4. Subsequently, the model parameters were (re)estimated using a combined dataset consisting of the previous modelling data and the data available for the external validation.

RESULTS

The previous model underpredicted the measured FVIII levels with a median of 0.17 IU mL^-1 . Combining the new, independent and original data, a dataset comprising 206 patients with a mean age of 7.8 years (0.03-77.6) and body weight of 30 kg (4-111) was obtained. Population PK modelling provided estimates for CL, V1, V2, and Q: 171 mL h^-1  68 kg^-1 , 2930 mL 68 kg^-1 , 1810 mL 68 kg^-1 , and 172 mL h^-1  68 kg^-1 , respectively. This model adequately described all collected FVIII levels, with a slight median overprediction of 0.02 IU mL^-1 .

CONCLUSIONS

This study emphasizes the importance of external validation of population PK models using real-life data.

Machine learning in pharmacometrics: Opportunities and challenges

The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.

Population Pharmacokinetics of PEGylated Asparaginase in Children with Acute Lymphoblastic Leukemia: Treatment Phase Dependency and Predictivity in Case of Missing Data

BACKGROUND AND OBJECTIVES

The pharmacokinetics of polyethylene glycol-conjugated asparaginase (PEG-ASNase) are characterized by an increase in elimination over time, a marked increase in ASNase activity levels from induction to reinduction, and high inter- and intraindividual variability. A population pharmacokinetic (PopPK) model is required to estimate individual dose intensity, despite gaps in monitoring compliance.

METHODS

In the AIEOP-BFM ALL 2009 trial, two PEG-ASNase administrations (2500 U/m² intravenously) during induction (14-day interval) and one administration during reinduction were administered in children with acute lymphoblastic leukemia. ASNase activity levels were monitored weekly. A PopPK model was used for covariate modeling and external validation. The predictivity of the model in case of missing data was tested for observations, as well as for the derived parameters of the area under the concentration time curve (AUC_0-∞) and time above different thresholds.

RESULTS

Compared to the first administration in induction (1374 patients, 6069 samples), the initial clearance and volume of distribution decreased by 11.0% and 15.9%, respectively, during the second administration during induction and by 41.2% and 28.4% during reinduction. Furthermore, the initial clearance linearly increased for children aged > 8 years and was 7.1% lower for females. The model was successfully externally validated (1253 patients, 5523 samples). In case of missing data, > 52% of the predictions for observations and > 82% for derived parameters were within ± 20% of the nominal value.

CONCLUSION

A PopPK model that describes the complex pharmacokinetics of PEG-ASNase was successfully externally validated. AUC_0-∞ or time above different thresholds, which are parameters describing dose intensity, can be estimated with high predictivity, despite missing data. ( www.clinicaltrials.gov , NCT01117441, first submitted date: May 3, 2010).

Antibiotics in Adult Cystic Fibrosis Patients: A Review of Population Pharmacokinetic Analyses

BACKGROUND

Lower respiratory tract infections are common in adult patients with cystic fibrosis (CF) and are frequently caused by Pseudomonas aeruginosa, resulting in chronic lung inflammation and fibrosis. The progression of multidrug-resistant strains of P. aeruginosa and alterations in the pharmacokinetics of many antibiotics in CF make optimal antimicrobial therapy a challenge, as reflected by high between- and inter-individual variability (IIV).

OBJECTIVES

This review provides a synthesis of population pharmacokinetic models for various antibiotics prescribed in adult CF patients, and aims at identifying the most reported structural models, covariates and sources of variability influencing the dose-concentration relationship.

METHODS

A literature search was conducted using the PubMed database, from inception to August 2020, and articles were retained if they met the inclusion/exclusion criteria.

RESULTS

A total of 19 articles were included in this review. One-, two- and three-compartment models were reported to best describe the pharmacokinetics of various antibiotics. The most common covariates were lean body mass and creatinine clearance. After covariate inclusion, the IIV (range) in total body clearance was 27.2% (10.40-59.7%) and 25.9% (18.0-33.9%) for β-lactams and aminoglycosides, respectively. IIV in total body clearance was estimated at 36.3% for linezolid and 22.4% for telavancin. The IIV (range) in volume of distribution was 29.4% (8.8-45.9%) and 15.2 (11.6-18.0%) for β-lactams and aminoglycosides, respectively, and 26.9% for telavancin. The median (range) of residual variability for all studies, using a combined (proportional and additive) model, was 12.7% (0.384-30.80%) and 0.126 mg/L (0.007-1.88 mg/L), respectively.

CONCLUSION

This is the first review that highlights key aspects of different population pharmacokinetic models of antibiotics prescribed in adult CF patients, effectively proposing relevant information for clinicians and researchers to optimize antibiotic therapy in CF.

Can Population Pharmacokinetics of Antibiotics be Extrapolated? Implications of External Evaluations

BACKGROUND AND OBJECTIVE

External evaluation is an important issue in the population pharmacokinetic analysis of antibiotics. The purpose of this review was to summarize the current approaches and status of external evaluations and discuss the implications of external evaluation results for the future individualization of dosing regimens.

METHODS

We systematically searched the PubMed and EMBASE databases for external evaluation studies of population analysis and extracted the relevant information from these articles. A total of 32 studies were included in this review.

RESULTS

Vancomycin was investigated in 17 (53.1%) articles and was the most studied drug. Other studied drugs included gentamicin, tobramycin, amikacin, amoxicillin, ceftaroline, meropenem, fluconazole, voriconazole, and rifampicin. Nine (28.1%) studies were prospective, and the sample size varied widely between studies. Thirteen (40.6%) studies evaluated the population pharmacokinetic models by systematically searching for previous studies. Seven (21.9%) studies were multicenter studies, and 27 (84.4%) adopted the sparse sampling strategy. Almost all external evaluation studies of antibiotics (93.8%) used metrics for prediction-based diagnostics, while relatively fewer studies were based on simulations (46.9%) and Bayesian forecasting (25.0%).

CONCLUSION

The results of external evaluations in previous studies revealed the poor extrapolation performance of existing models of prediction- and simulation-based diagnostics, whereas the posterior Bayesian method could improve predictive performance. There is an urgent need for the development of standards and guidelines for external evaluation studies.

Population Pharmacokinetics of Clotting Factor Concentrates and Desmopressin in Hemophilia

Hemophilia A and B are bleeding disorders caused by a deficiency of clotting factor VIII and IX, respectively. Patients with severe hemophilia (< 0.01 IU mL⁻¹) and some patients with moderate hemophilia (0.01–0.05 IU mL⁻¹) administer clotting factor concentrates prophylactically. Desmopressin (d-amino d-arginine vasopressin) can be applied in patients with non-severe hemophilia A. The aim of administration of factor concentrates or desmopressin is the prevention or cessation of bleeding. Despite weight-based dosing, it has been demonstrated that factor concentrates still exhibit considerable pharmacokinetic variability. Population pharmacokinetic analyses, in which this variability is quantified and explained, are increasingly performed in hemophilia research. These analyses can assist in the identification of important patient characteristics and can be applied to perform patient-tailored dosing. This review aims to present and discuss the population pharmacokinetic analyses that have been conducted to develop population pharmacokinetic models describing factor levels after administration of factor VIII or factor IX concentrates or d-amino d-arginine vasopressin. In total, 33 publications were retrieved from the literature. Two approaches were applied to perform population pharmacokinetic analyses, the standard two-stage approach and non-linear mixed-effect modeling. Using the standard two-stage approach, four population pharmacokinetic models were established describing factor VIII levels. In the remaining 29 analyses, the non-linear mixed-effect modeling approach was applied. NONMEM was the preferred software to establish population pharmacokinetic models. In total, 18 population pharmacokinetic analyses were conducted on the basis of data from a single product. From all available population pharmacokinetic analyses, 27 studies also included data from pediatric patients. In the majority of the population pharmacokinetic models, the population pharmacokinetic parameters were allometrically scaled using actual body weight. In this review, the available methods used for constructing the models, key features of these models, patient population characteristics, and established covariate relationships are described in detail.

Targeting drug delivery in the vascular system: Focus on endothelium

The bloodstream is the main transporting pathway for drug delivery systems (DDS) from the site of administration to the intended site of action. In many cases, components of the vascular system represent therapeutic targets. Endothelial cells, which line the luminal surface of the vasculature, play a tripartite role of the key target, barrier, or victim of nanomedicines in the bloodstream. Circulating DDS may accumulate in the vascular areas of interest and in off-target areas via mechanisms bypassing specific molecular recognition, but using ligands of specific vascular determinant molecules enables a degree of precision, efficacy, and specificity of delivery unattainable by non-affinity DDS. Three decades of research efforts have focused on specific vascular targeting, which have yielded a multitude of DDS, many of which are currently undergoing a translational phase of development for biomedical applications, including interventions in the cardiovascular, pulmonary, and central nervous systems, regulation of endothelial functions, host defense, and permeation of vascular barriers. We discuss the design of endothelial-targeted nanocarriers, factors underlying their interactions with cells and tissues, and describe examples of their investigational use in models of acute vascular inflammation with an eye on translational challenges.

Vascular Drug Delivery Using Carrier Red Blood Cells: Focus on RBC Surface Loading and Pharmacokinetics

Red blood cells (RBC) have great potential as drug delivery systems, capable of producing unprecedented changes in pharmacokinetics, pharmacodynamics, and immunogenicity. Despite this great potential and nearly 50 years of research, it is only recently that RBC-mediated drug delivery has begun to move out of the academic lab and into industrial drug development. RBC loading with drugs can be performed in several ways-either via encapsulation within the RBC or surface coupling, and either ex vivo or in vivo-depending on the intended application. In this review, we briefly summarize currently used technologies for RBC loading/coupling with an eye on how pharmacokinetics is impacted. Additionally, we provide a detailed description of key ADME (absorption, distribution, metabolism, elimination) changes that would be expected for RBC-associated drugs and address unique features of RBC pharmacokinetics. As thorough understanding of pharmacokinetics is critical in successful translation to the clinic, we expect that this review will provide a jumping off point for further investigations into this area.

Pharmacokinetic studies in pregnancy

The effects of the many biochemical and physiologic changes of pregnancy on the dose-response relationship of drugs administered to pregnant women are poorly understood. The dose-response relationship is affected by pharmacokinetics, or what the body does to a drug, and pharmacodynamics, or what a drug does to the body. Insights into the potential effects of the changes of pregnancy on one aspect of the dose-response relationship of a drug can be obtained by studying the pharmacokinetics of the drug in the various stages of pregnancy and the postpartum period. There are several available approaches to studying pharmacokinetic changes in pregnancy. Single trough screening studies can provide qualitative estimates of elimination clearance, which with the dosing rate determines the steady-state drug concentration, throughout pregnancy and into the postpartum period. Population pharmacokinetic studies such as two stage pharmacokinetic studies and studies using a nonlinear mixed effects pharmacokinetic modeling approach can characterize pharmacokinetic changes more rigorously.

Population pharmacokinetic analysis of intravenous busulfan: GSTA1 genotype is not a predictive factor of initial dose in Chinese adult patients undergoing hematopoietic stem cell transplantation

PURPOSE

This study aimed to develop a population pharmacokinetic (PPK) model to investigate the impact of GSTA1, GSTP1, and GSTM1 genotypes on busulfan pharmacokinetic (PK) variability in Chinese adult patients.

METHODS

Forty-three and 19 adult patients who underwent hematopoietic stem cell transplantation (HSCT) were enrolled for modeling group and validation group, respectively. All patients received twice-daily intravenous busulfan as part of conditioning regimen before HSCT. The PPK model was developed by nonlinear mixed-effect modeling. Covariates investigated were age, sex, actual body weight, body surface area, diagnoses, hepatic function markers, GST genotypes and conditioning regimen.

RESULTS

A total of 488 busulfan concentrations from 43 patients were obtained for the PPK model. The PK of intravenous busulfan was described by one-compartment model with first-order elimination with estimated clearance (CL) of 14.2 L/h and volume of distribution of 64.1 L. Inclusion of GSTA1 genotype as a covariate accounted for 1.1% of the inter-individual variability of busulfan CL (from 17.8% in the basic model to 16.7% in the final model). The accuracy and applicability of the final model were externally validated in the independent group. The difference of busulfan PK between Chinese patients and Caucasian patients existed because of the rarity of haplotype *B in Chinese population.

CONCLUSIONS

Although the GSTA1 genotype-based PPK model of intravenous busulfan was successfully developed and externally validated, the GSTA1 genotype was not considered to be clinically relevant to busulfan CL. We did not suggest the guidance of GSTA1 genotype on initial busulfan dose in Chinese adult patients.

Antiplatelet Effect of a Pulaimab [Anti-GPIIb/IIIa F(ab)2 Injection] Evaluated by a Population Pharmacokinetic-pharmacodynamic Model

BACKGROUND

Cardiovascular disease has one of the highest mortality rates among all the diseases. Platelets play an important role in the pathogenesis of cardiovascular diseases. Platelet membrane glycoprotein GPIIb/IIIa antagonists are the most effective antiplatelet drugs, and pulaimab is one of these. The study aims to promote individual medication of pulaimab [anti-GPIIb/IIIa F(ab)2 injection] by discovering the pharmacological relationship among the dose, concentration, and effects. The goal of this study is to establish a population pharmacokineticpharmacodynamic model to evaluate the antiplatelet effect of intravenous pulaimab injection.

METHODS

Data were collected from 59 healthy subjects who participated in a Phase-I clinical trial. Plasma concentration was used as the pharmacokinetic index, and platelet aggregation inhibition rate was used as the pharmacodynamic index. The basic pharmacokinetics model was a two-compartment model, whereas the basic pharmacodynamics model was a sigmoid-EMAX model with a direct effect. The covariable model was established by a stepwise method. The final model was verified by a goodness-of-fit method, and predictive performance was assessed by a Bootstrap (BS) method.

RESULTS

In the final model, typical population values of the parameters were as follows: central distribution Volume (V1), 183 L; peripheral distribution Volume (V2), 349 L; Central Clearance (CL), 31 L/h; peripheral clearance(Q), 204 L/h; effect compartment concentration reaching half of the maximum effect (EC50), 0.252 mg/L; maximum effect value (EMAX), 54.0%; and shape factor (γ), 0.42. In the covariable model, thrombin time had significant effects on CL and EMAX. Verification by the goodness-of-fit and BS methods showed that the final model was stable and reliable.

CONCLUSION

A model was successfully established to evaluate the antiplatelet effect of intravenous pulaimab injection that could provide support for the clinical therapeutic regimen.

Population Pharmacokinetics of Diazoxide in Children with Hyperinsulinemic Hypoglycemia

BACKGROUND

Diazoxide is the first-line treatment for pediatric hyperinsulinemic hypoglycemia (HI). This study aimed to elucidate the pharmacokinetics of diazoxide in children with HI.

METHODS

We obtained 81 blood samples from 22 children with HI. Measured serum diazoxide concentrations were used for population pharmacokinetic analysis. Patient factors influencing pharmacokinetics were estimated using nonlinear mixed-effects model analysis. Relationships between drug exposure and adverse drug reactions were also investigated.

RESULTS

Diazoxide disposition in the body was described by a 1-compartment model. Oral clearance (CL/F) and the volume of distribution were proportional to body weight (WT), as expressed by CL/F in males (liters/h) = 0.0358 + 0.00374 × WT (kg). CL/F in females was 39% greater than that in males. Steady-state concentrations of diazoxide were similar following twice- and 3 times-daily dosing when the total daily doses were comparable. A patient whose serum diazoxide concentration exceeded 100 μg/mL over a 4-month period developed hyperglycemia. No significant correlation was observed between severity of hirsutism and diazoxide concentration.

CONCLUSION

We have proposed for the first time a population pharmacokinetic model for diazoxide in children with HI. The potential risk of diabetes mellitus and/or hyperglycemia increases when serum concentrations of diazoxide exceed 100 μg/mL.

In silico evaluation of limited blood sampling strategies for individualized recombinant factor IX prophylaxis in hemophilia B patients

Essentials Individual pharmacokinetic (PK) parameters can be obtained by limited sampling strategies (LSSs). Following 100 IU kg-1 rFIX, LSSs with 1 to 3 samples were evaluated in 5000 simulated subjects. For all LSSs, estimated individual PK parameters showed acceptable bias and precision. One sample between 10 min-3 h and two between 48 h-56 h showed best predictive performance.

SUMMARY

Background Patients with severe hemophilia B regularly administer prophylactic intravenous doses of clotting factor IX concentrate to maintain a trough level of at least 0.01 IU mL-1 in order to prevent joint bleeds. Assessment of individual pharmacokinetic (PK) parameters allows individualization of the recombinant factor IX (rFIX) dose. Aim To evaluate the predictive performance of limited sampling strategies (LSSs) with one to three samples to estimate individual PK parameters of rFIX. Methods Monte Carlo simulations were performed to obtain 5000 concentration-time profiles by the use of population PK parameters for rFIX from literature. Eleven LSSs were developed with one, two or three samples taken within an 80-h interval following administration of 100 IU kg-1 rFIX. Clearance (CL), half-life (t1/2 ), time to 1% and steady-state distribution volume (Vss ) were estimated for each simulated individual by the use of Bayesian analysis. Results For each LSS, average bias was small for CL (range - 1.5% to 1.4%), t1/2 (range - 4.5% to - 0.7%), time to 1% (range - 2.9% to 0%), and Vss (range - 3.7% to 0.3%). Imprecision for these parameters ranged from 6.4% to 11.9%, from 10.3% to 15.6%, from 7.3% to 10.9%, and from 9% to 20.1%, respectively. The best predictive performance was achieved with one sample taken between 10 min and 3 h and two samples taken between 48 h and 56 h after administration of rFIX. Conclusions This study demonstrates that limited sampling strategies, used for individualized dosing of rFIX in hemophilia B patients, can be developed and evaluated by in silico simulation.

Safety and pharmacokinetics of multiple dose myo-inositol in preterm infants

BACKGROUND

Preterm infants with respiratory distress syndrome (RDS) given inositol had reduced bronchopulmonary dysplasia (BPD), death and severe retinopathy of prematurity (ROP). We assessed the safety and pharmacokinetics of daily inositol to select a dose providing serum levels previously associated with benefit, and to learn if accumulation occurred when administered throughout the normal period of retinal vascularization.

METHODS

Infants ≤ 29 wk GA (n = 122, 14 centers) were randomized and treated with placebo or inositol at 10, 40, or 80 mg/kg/d. Intravenous administration converted to enteral when feedings were established, and continued to the first of 10 wk, 34 wk postmenstrual age (PMA) or discharge. Serum collection employed a sparse sampling population pharmacokinetics design. Inositol urine losses and feeding intakes were measured. Safety was prospectively monitored.

RESULTS

At 80 mg/kg/d mean serum levels reached 140 mg/l, similar to Hallman's findings. Levels declined after 2 wk, converging in all groups by 6 wk. Analyses showed a mean volume of distribution 0.657 l/kg, clearance 0.058 l/kg/h, and half-life 7.90 h. Adverse events and comorbidities were fewer in the inositol groups, but not significantly so.

CONCLUSION

Multiple dose inositol at 80 mg/kg/d was not associated with increased adverse events, achieves previously effective serum levels, and is appropriate for investigation in a phase III trial.

Influence of the cytochrome P450 2D6 *10/*10 genotype on the pharmacokinetics of paroxetine in Japanese patients with major depressive disorder: a population pharmacokinetic analysis

OBJECTIVE

Although the reduced function of the cytochrome P450 2D6*10 (CYP2D6*10) allele is common among Asian populations, existing evidence does not support paroxetine therapy adjustments for patients who have the CYP2D6*10 allele. In this study, we attempted to evaluate the degree of the impact of different CYP2D6 genotypes on the pharmacokinetic (PK) variability of paroxetine in a Japanese population using a population PK approach.

METHODS

This retrospective study included 179 Japanese patients with major depressive disorder who were being treated with paroxetine. CYP2D6*1, *2, *5, *10, and *41 polymorphisms were observed. A total of 306 steady-state concentrations for paroxetine were collected from the patients. A nonlinear mixed-effects model identified the apparent Michaelis-Menten constant (Km) and the maximum velocity (Vmax) of paroxetine; the covariates included CYP2D6 genotypes, patient age, body weight, sex, and daily paroxetine dose.

RESULTS

The allele frequencies of CYP2D6*1, *2, *5, *10, and *41 were 39.4, 14.5, 4.5, 41.1, and 0.6%, respectively. There was no poor metabolizer who had two nonfunctional CYP2D6*5 alleles. A one-compartment model showed that the apparent Km value was decreased by 20.6% in patients with the CYP2D6*10/*10 genotype in comparison with the other CYP2D6 genotypes. Female sex also influenced the apparent Km values. No PK parameters were affected by the presence of one CYP2D6*5 allele.

CONCLUSION

Unexpectedly, elimination was accelerated in individuals with the CYP2D6*10/*10 genotype. Our results show that the presence of one CYP2D6*5 allele or that of any CYP2D6*10 allele may have no major effect on paroxetine PKs in the steady state.

Gap analysis of pharmacokinetics and pharmacodynamics in burn patients: a review

Severe burn injury results in a multifaceted physiological response that significantly alters drug pharmacokinetics and pharmacodynamics (PK/PD). This response includes hypovolemia, increased vascular permeability, increased interstitial hydrostatic pressure, vasodilation, and hypermetabolism. These physiologic alterations impact drug distribution and excretion-thus varying the drug therapeutic effect on the body or microorganism. To this end, in order to optimize critical care for the burn population it is essential to understand how burn injury alters PK/PD parameters. The purpose of this article is to describe the relationship between burn injury and drug PK/PD. We conducted a literature review via PubMed and Google to identify burn-related PK/PD studies. Search parameters included "pharmacokinetics," "pharmacodynamics," and "burns." Based on our search parameters, we located 38 articles that studied PK/PD parameters specifically in burns. Twenty-seven articles investigated PK/PD of antibiotics, 10 assessed analgesics and sedatives, and one article researched an antacid. Out of the 37 articles, there were 19 different software programs used and eight different control groups. The mechanisms behind alterations in PK/PD in burns remain poorly understood. Dosing techniques must be adapted based on burn injury-related changes in PK/PD parameters in order to ensure drug efficacy. Although several PK/PD studies have been undertaken in the burn population, there is wide variation in the analytical techniques, software, and study sample sizes used. In order to refine dosing techniques in burns and consequently improve patient outcomes, there must be harmonization among PK/PD analyses.

External evaluation of published population pharmacokinetic models of tacrolimus in adult renal transplant recipients

AIM

Several tacrolimus population pharmacokinetic models in adult renal transplant recipients have been established to facilitate dose individualization. However, their applicability when extrapolated to other clinical centres is not clear. This study aimed to (1) evaluate model external predictability and (2) analyze potential influencing factors.

METHODS

Published models were screened from the literature and were evaluated using an external dataset with 52 patients (609 trough samples) collected by postoperative day 90 via methods that included (1) prediction-based prediction error (PE%), (2) simulation-based prediction- and variability-corrected visual predictive check (pvcVPC) and normalized prediction distribution error (NPDE) tests and (3) Bayesian forecasting to assess the influence of prior observations on model predictability. The factors influencing model predictability, particularly the impact of structural models, were evaluated.

RESULTS

Sixteen published models were evaluated. In prediction-based diagnostics, the PE% within ±30% was less than 50% in all models, indicating unsatisfactory predictability. In simulation-based diagnostics, both the pvcVPC and the NPDE indicated model misspecification. Bayesian forecasting improved model predictability significantly with prior 2-3 observations. The various factors influencing model extrapolation included bioassays, the covariates involved (CYP3A5*3 polymorphism, postoperative time and haematocrit) and whether non-linear kinetics were used.

CONCLUSIONS

The published models were unsatisfactory in prediction- and simulation-based diagnostics, thus inappropriate for direct extrapolation correspondingly. However Bayesian forecasting could improve the predictability considerably with priors. The incorporation of non-linear pharmacokinetics in modelling might be a promising approach to improving model predictability.

Population Pharmacokinetics of Boosted-Elvitegravir in HIV-Infected Patients

Elvitegravir (EVG) is an HIV strand transfer integrase inhibitor approved for the treatment of HIV infection as a part of antiretroviral regimens containing cobicistat (COBI) or ritonavir (RTV) as a booster. The population pharmacokinetics of EVG in treatment-naive and -experienced HIV patients was determined, and the effects of demographic, biometric, and formulation covariates on EVG pharmacokinetics (PK) were evaluated. Data from 31 clinical studies (25 in healthy subjects, 6 phase 1b to phase 3 in HIV-1-infected patients) with COBI-boosted EVG studies (as EVG/co or EVG/COBI/FTC/TDF single-tablet regimen) or RTV-boosted EVG studies (EVG/r) were analyzed using NONMEM. The effect of the covariates age, sex, race, health status (healthy volunteers vs HIV patients), weight, body mass index (BMI), body surface area (BSA), creatinine clearance (estimated GFR), and formulation were evaluated. EVG PK, with COBI or RTV, was described by a 2-compartment model, with first-order absorption and elimination and an absorption lag time. A statistically significant, but not clinically relevant, effect of BSA on EVG clearance (CL) was observed. Coadministration of atazanavir or lopinavir with EVG/r had an effect on EVG CL consistent with the known interaction with these agents. No other covariate had a meaningful effect on EVG PK. EVG PK was well described in a population PK model with HIV-infected patients, with low PK variability and no relevant effect of demographic or biometric covariates.

Controlled cellular uptake and drug efficacy of nanotherapeutics

Cellular uptake pathway of nanoparticle (NP) is different from that of free drugs. Therefore, NP-mediated nanotherapeutics can be designed to overcome the adverse effects of free drugs. However, synthetic NPs are typically trapped in the endosome and have difficulty to reach the cytosol because of the characteristic endocytosis, where the endosomal membranes wrap-up the introduced NPs. In this study, the Spacer molecules linking the apoptotic anticancer drug and the gold NP (AuNP) are designed and cellular uptake procedure and drug deployment in the cancer cells are controlled. X-ray nanoscopy and two-photon microscopy are employed to observe the AuNPs in a cell in-situ without additional dye molecule or imaging agent introduction on an AuNP. We confirm that the effective design of the Spacer molecules importantly control the cellular interaction of the AuNPs. This technology can be generalized to broad biomedical applications utilizing nanotherapeutics-mediated diagnosis and new-concepted disease treatment technologies.

Single-cell and subcellular pharmacokinetic imaging allows insight into drug action in vivo

Pharmacokinetic analysis at the organ level provides insight into how drugs distribute throughout the body, but cannot explain how drugs work at the cellular level. Here we demonstrate in vivo single-cell pharmacokinetic imaging of PARP-1 inhibitors and model drug behaviour under varying conditions. We visualize intracellular kinetics of the PARP-1 inhibitor distribution in real time, showing that PARP-1 inhibitors reach their cellular target compartment, the nucleus, within minutes in vivo both in cancer and normal cells in various cancer models. We also use these data to validate predictive finite element modelling. Our theoretical and experimental data indicate that tumour cells are exposed to sufficiently high PARP-1 inhibitor concentrations in vivo and suggest that drug inefficiency is likely related to proteomic heterogeneity or insensitivity of cancer cells to DNA-repair inhibition. This suggests that single-cell pharmacokinetic imaging and derived modelling improve our understanding of drug action at single-cell resolution in vivo.

Contributing factors to the apparent clearance of bepridil in patients with paroxysmal or persistent atrial fibrillation: analysis using population pharmacokinetics

BACKGROUND

Bepridil is used as an antiarrhythmic drug due to its class I, class III, and class IV electrophysiological properties, but it has serious adverse effects such as QT prolongation and torsade de pointes. Bepridil has complex pharmacokinetic (PK) properties with large interindividual differences in plasma concentrations. The aim of this study was to evaluate the contributing factors to changes in the dose-concentration relationship of bepridil and the risk factors for excessive QT prolongation in patients with paroxysmal or persistent atrial fibrillation (AF).

METHODS

A population PK analysis was performed by using NONMEM based on 425 concentration points from 76 patients receiving bepridil. A 1-compartment model approximating an intravenous model was used to examine the interindividual variability of the apparent systematic clearance (CL/F) of bepridil. A population PK-pharmacodynamic analysis was performed using the linear regression.

RESULTS

Age was a contributing factor to the CL/F of bepridil in AF patients. The QTc interval increased as the area under the plasma bepridil concentration time curve (AUC) increased. The AUC in patients without a bundle branch block, the baseline QT interval, and the existence of structural heart disease in patients with a bundle branch block were explanatory variables of excessive QTc prolongation (QTc > 500 ms) during bepridil therapy.

CONCLUSIONS

Using population PK methodology, this study showed that age was a contributing factor to the apparent clearance of bepridil in Japanese patients with AF and that excessive QT prolongation might be related to a larger AUC.

Gastric secretion does not affect the reliability of the 13C-acetate breath test: A validation of the 13C-acetate breath test by magnetic resonance imaging

BACKGROUND

(13)C-Acetate labeled meals are widely used to determine meal emptying by means of analyzing resulting (13)CO(2) exhalation dynamics. In contrast to the underlying metabolic processes, only few (13)C breath test meal emptying studies have focused on intragastric processes that may alter (13)CO(2) exhalation. This work assessed the effect of enhanced gastric secretion on the reliability of half emptying time (t50) measurements by (13)C-acetate breath test.

METHODS

(13)CO(2) exhalation data were acquired in a double-blind, randomized, cross-over gastric emptying study in 12 healthy volunteers receiving either pentagastrin or placebo intravenously. The standard method proposed by Ghoos et al. was applied to calculate t50 (t50_Ghoos) from (13)CO(2) exhalation data, which were compared and tested for agreement to meal half emptying times (t50_MV) from concurrent recorded MRI (magnetic resonance imaging) volume data. In addition, the accumulated gastric secretion volumes during infusion as detected by MRI (AUC_SV(60)) were correlated with the corresponding cumulative percent (13)C doses recovered (cPDR(60)).

KEY RESULTS

t50_Ghoos and t50_MV showed a linear correlation with a slope of 1.1 ± 0.3 (r(2) = 0.67), however, a positive offset of 136 min for t50_Ghoos. No correlation was detected between AUC_SV(60) and cPDR(60) (r(2) = 0.11). Both, breath test and MRI, revealed a prolonged t50 under pentagastrin infusion with median differences in t50_Ghoos of 45[28-84] min (P = 0.002) and t50_MV of 39[28-52] min (P = 0.002).

CONCLUSIONS & INFERENCES

This study suggests that (13)CO(2) exhalation after ingestion of a (13) C-labeled liquid test meal is not affected by stimulated gastric secretion, but is rather reflecting the dynamics of meal or caloric emptying from the stomach.

Fundamentals of population pharmacokinetic modelling: validation methods

Population pharmacokinetic modelling is widely used within the field of clinical pharmacology as it helps to define the sources and correlates of pharmacokinetic variability in target patient populations and their impact upon drug disposition; and population pharmacokinetic modelling provides an estimation of drug pharmacokinetic parameters. This method's defined outcome aims to understand how participants in population pharmacokinetic studies are representative of the population as opposed to the healthy volunteers or highly selected patients in traditional pharmacokinetic studies. This review focuses on the fundamentals of population pharmacokinetic modelling and how the results are evaluated and validated. This review defines the common aspects of population pharmacokinetic modelling through a discussion of the literature describing the techniques and placing them in the appropriate context. The concept of validation, as applied to population pharmacokinetic models, is explored focusing on the lack of consensus regarding both terminology and the concept of validation itself. Population pharmacokinetic modelling is a powerful approach where pharmacokinetic variability can be identified in a target patient population receiving a pharmacological agent. Given the lack of consensus on the best approaches in model building and validation, sound fundamentals are required to ensure the selected methodology is suitable for the particular data type and/or patient population. There is a need to further standardize and establish the best approaches in modelling so that any model created can be systematically evaluated and the results relied upon.

Basic concepts in population modeling, simulation, and model-based drug development

Modeling is an important tool in drug development; population modeling is a complex process requiring robust underlying procedures for ensuring clean data, appropriate computing platforms, adequate resources, and effective communication. Although requiring an investment in resources, it can save time and money by providing a platform for integrating all information gathered on new therapeutic agents. This article provides a brief overview of aspects of modeling and simulation as applied to many areas in drug development.

Measuring the interaction of meal and gastric secretion: a combined quantitative magnetic resonance imaging and pharmacokinetic modeling approach

BACKGROUND

The stimulation and intragastric accumulation of gastric secretion has been recognized as an important factor in gastroesophageal reflux disease. However, the interaction of gastric secretion and meal emptying has not been fully understood. Current methods to assess gastric secretion are either invasive or unable to provide information on its volume, distribution and dynamics. The aim of this study was to quantify the interaction between meal emptying and meal induced gastric secretion by using quantitative magnetic resonance imaging (MRI) and pharmacokinetic analysis.

METHODS

A chocolate test meal was developed which is secretion stimulating and MRI compatible. Meal emptying and gastric secretion were assessed in fourteen healthy volunteers using a validated quantitative MRI technique. A population based pharmacokinetic model was developed and applied to the extracted volume data, assessing the meal emptying rate, rate of secretion and their interaction.

KEY RESULTS

The test meal continuously induced gastric secretion in all subjects, which partly accumulated at the meal-air interface, forming a 'secretion layer' in the proximal stomach. Traditional fitting detected a significant correlation between meal emptying rate and rate of secretion. The pharmacokinetic model quantified this interaction and estimated a 2.3 ± 1 fold higher effect of meal on secretion than vice versa. The efficacy of the emptied meal to produce gastric secretion was 61%.

CONCLUSIONS & INFERENCES

The combined quantitative MRI and pharmacokinetic model approach allows for the quantification of gastric secretion volume and its interaction on meal emptying. The observed secretion layer might explain previous findings postulating the presence of an intragastric 'acid pocket'.

PKgraph: an R package for graphically diagnosing population pharmacokinetic models

Population pharmacokinetic (PopPK) modeling has become increasing important in drug development because it handles unbalanced design, sparse data and the study of individual variation. However, the increased complexity of the model makes it more of a challenge to diagnose the fit. Graphics can play an important and unique role in PopPK model diagnostics. The software described in this paper, PKgraph, provides a graphical user interface for PopPK model diagnosis. It also provides an integrated and comprehensive platform for the analysis of pharmacokinetic data including exploratory data analysis, goodness of model fit, model validation and model comparison. Results from a variety of modeling fitting software, including NONMEM, Monolix, SAS and R, can be used. PKgraph is programmed in R, and uses the R packages lattice, ggplot2 for static graphics, and rggobi for interactive graphics.

PKreport: report generation for checking population pharmacokinetic model assumptions

BACKGROUND

Graphics play an important and unique role in population pharmacokinetic (PopPK) model building by exploring hidden structure among data before modeling, evaluating model fit, and validating results after modeling.

RESULTS

The work described in this paper is about a new R package called PKreport, which is able to generate a collection of plots and statistics for testing model assumptions, visualizing data and diagnosing models. The metric system is utilized as the currency for communicating between data sets and the package to generate special-purpose plots. It provides ways to match output from diverse software such as NONMEM, Monolix, R nlme package, etc. The package is implemented with S4 class hierarchy, and offers an efficient way to access the output from NONMEM 7. The final reports take advantage of the web browser as user interface to manage and visualize plots.

CONCLUSIONS

PKreport provides 1) a flexible and efficient R class to store and retrieve NONMEM 7 output, 2) automate plots for users to visualize data and models, 3) automatically generated R scripts that are used to create the plots; 4) an archive-oriented management tool for users to store, retrieve and modify figures, 5) high-quality graphs based on the R packages, lattice and ggplot2. The general architecture, running environment and statistical methods can be readily extended with R class hierarchy. PKreport is free to download at http://cran.r-project.org/web/packages/PKreport/index.html.

Integrated analysis of preclinical data to support the design of the first in man study of LY2181308, a second generation antisense oligonucleotide

AIMS

To predict the concentration and target inhibition profiles of the survivin inhibitor antisense oligonucleotide LY2181308 in humans.

METHODS

An indirect pharmacokinetic/pharmacodynamic (PK/PD) model was built to predict the inhibition of survivin mRNA and protein in humans following LY2181308 dosing. Plasma and tissue PK data from cynomolgus monkeys were analyzed by non-linear mixed effect modelling techniques. Human PK parameters were predicted using allometric scaling. Assumptions about the pharmacodynamic parameters were made based upon the target and tumour growth inhibition data from mouse xenograft models. This enabled the prediction of the clinical PK/PD profiles.

RESULTS

Following a 750 mg dose, LY2181308 tumour concentrations ranging from 18.8 to 54µgg(-1) were predicted to lead to 50 to 90% target inhibition. In humans, LY2181308 tumour concentrations fro 13.9 to 52.8µgg(-1) (n=4, LY2181308 750mg) were observed associated with a median survivin mRNA and protein inhibition of 20%±34 (SD) (n=9) and 23%±63 (SD) (n=10), respectively. The human PK parameters were adequately estimated: central V(d) , 4.09 l (90% CI, 3.6, 4.95), distribution clearances, 2.54 (2.36, 2.71), 0.0608 (0.033, 0.6) and 1.67 (1.07, 2.00)lh(-1) , peripheral V(d) s, 25 900 (19 070, 37 200), 0.936 (0.745, 2.07) and 2.51 (1.01, 2.922)l, mean elimination clearance 23.1lh(-1) (5.6, 33.4) and mean terminal half-life, 32.7 days (range 22-52 days).

CONCLUSION

The model reasonably predicted LY2181308 PK in humans. Overall, the integration of preclinical PK/PD data enabled to appropriately predict dose and dosing regimen of LY2181308 in humans with pharmacologically relevant survivin inhibition achieved at 750mg.

Prediction tacrolimus blood levels based on the Bayesian method in adult kidney transplant patients

The use of tacrolimus is complicated by its narrow therapeutic index and wide intra- and interpatient variability. We have previously described a tacrolimus population pharmacokinetics model obtained in an adult kidney transplant cohort. The aims of the present study were (1) to validate that model using an external dataset and (2) to evaluate the prediction using a Bayesian method. Data were retrospectively collected from 34 adult patients receiving kidney transplantation. Trough blood concentrations of tacrolimus were predicted using the empirical Bayesian method with sparse samples obtained during the previous week. The system performance was evaluated by the mean prediction error (ME), mean absolute prediction error (MAE). and root mean square error (RMSE). Patients were administrated oral or intravenous tacrolimus as part of a triple immunosuppressive regimen with mycophenolate mofetil and corticosteroids. Subsequent doses were adjusted on the basis of clinical evidence of efficacy and toxicity and by routine therapeutic drug monitoring. In our previous model, clearance increased with post transplantation days and with prednisone dosage. Concentrations predicted by the population mean pharmacokinetic parameter values match well with observed concentrations during oral therapy. Bayesian prediction using trough concentrations obtained after 21 days of treatment significantly decreased ME, MAE, and RMSE compared with predictions from data including this period. After 21 days of treatment, there was an insignificant bias ME (0.22 ± 2.59 ng/ml), a reasonable precision MAE (1.97 ± 1.69 ng/ml) and RMSE (1.28 ± 0.58 ng/ml). The present study demonstrates the suitability of the Bayesian method for the prediction of trough blood concentrations of tacrolimus using only few samples in adult kidney transplantation recipients.

Customized in silico population mimics actual population in docetaxel population pharmacokinetic analysis

Population pharmacokinetic (PK) analyses have been successfully incorporated into drug dosing optimization; however, these analyses necessitate relatively large patient cohorts that many clinical trials do not have the luxury of affording. To address this problem, we developed an approach that utilizes physiologically based pharmacokinetic (PBPK) modeling coupled with Monte Carlo simulation to generate a virtual population, complete with associated patient characteristics and PK data, for population PK analysis. For this work, we used a previously published PBPK model for docetaxel and found that the systemic clearance of this drug was significantly affected by blood volume, slowly perfused tissue volume, and two liver metabolic parameters--the maximum rate of liver metabolism and the Michaelis constant for liver metabolism. These findings, as well as the PK variability predictions, are consistent with those previously associated with docetaxel clearance in population PK analyses performed with actual patient populations, namely plasma protein levels, body size, and hepatic function. Thus, this in silico exercise demonstrates the utility of simulation modeling coupled to population PK analysis for the estimation of PK variability and the identification of patient characteristics that affect a drug's PK in the absence of data assembled from large clinical trials.

Evaluation of population pharmacokinetic models for amikacin dosage individualization in critically ill patients

Objectives

The aim of this study was to evaluate the reliability for dosage individualization and Bayesian adaptive control of several literature-retrieved amikacin population pharmacokinetic models in patients who were critically ill.

Methods

Four population pharmacokinetic models, three of them customized for critically-ill patients, were applied using pharmacokinetic software to fifty-one adult patients on conventional amikacin therapy admitted to the intensive care unit. An estimation of patient-specific pharmacokinetic parameters for each model was obtained by retrospective analysis of the amikacin serum concentrations measured (n = 162) and different clinical covariates. The model performance for a priori estimation of the area under the serum concentration-time curve (AUC) and maximum serum drug concentration (Cmax) targets was obtained.

Key findings

Our results provided valuable confirmation of the clinical importance of the choice of population pharmacokinetic models when selecting amikacin dosages for patients who are critically ill. Significant differences in model performance were especially evident when only information concerning clinical covariates was used for dosage individualization and over the two most critical determinants of clinical efficacy of amikacin i.e. the AUC and Cmax values.

Conclusions

Only a single amikacin serum level seemed necessary to diminish the influence of population model on dosage individualization.