Pharmacokinetic-pharmacodynamic modelling: history and perspectives

A physiologically-based pharmacokinetic/pharmacodynamic modeling approach for drug-drug-gene interaction evaluation of S-warfarin with fluconazole

Warfarin is a widely used anticoagulant, and its S-enantiomer has higher potency compared to the R-enantiomer. S-warfarin is mainly metabolized by cytochrome P450 (CYP) 2C9, and its pharmacological target is vitamin K epoxide reductase complex subunit 1 (VKORC1). Both CYP2C9 and VKORC1 have genetic polymorphisms, leading to large variations in the pharmacokinetics (PKs) and pharmacodynamics (PDs) of warfarin in the population. This makes dosage management of warfarin difficult, especially in the case of drug-drug interactions (DDIs). This study provides a whole-body physiologically-based pharmacokinetic/PD (PBPK/PD) model of S-warfarin for predicting the effects of drug-drug-gene interactions on S-warfarin PKs and PDs. The PBPK/PD model of S-warfarin was developed in PK-Sim and MoBi. Drug-dependent parameters were obtained from the literature or optimized. Of the 34 S-warfarin plasma concentration-time profiles used, 96% predicted plasma concentrations within twofold range compared to observed data. For S-warfarin plasma concentration-time profiles with CYP2C9 genotype, 364 of 386 predicted plasma concentration values (~94%) fell within the twofold of the observed values. This model was tested in DDI predictions with fluconazole as CYP2C9 perpetrators, with all predicted DDI area under the plasma concentration-time curve to the last measurable timepoint (AUClast) ratio within twofold of the observed values. The anticoagulant effect of S-warfarin was described using an indirect response model, with all predicted international normalized ratio (INR) within twofold of the observed values. This model also incorporates a dose-adjustment method that can be used for dose adjustment and predict INR when warfarin is used in combination with CYP2C9 perpetrators.

A tutorial for model-based evaluation and translation of cardiovascular safety in preclinical trials

Assessment of drug-induced effects on the cardiovascular (CV) system remains a critical component of the drug discovery process enabling refinement of the therapeutic index. Predicting potential drug-related unintended CV effects in the preclinical stage is necessary for first-in-human dose selection and preclusion of adverse CV effects in the clinical stage. According to the current guidelines for small molecules, nonclinical CV safety assessment conducted via telemetry analyses should be included in the safety pharmacology core battery studies. However, the manual for quantitative evaluation of the CV safety signals in animals is available only for electrocardiogram parameters (i.e., QT interval assessment), not for hemodynamic parameters (i.e., heart rate, blood pressure, etc.). Various model-based approaches, including empirical pharmacokinetic-toxicodynamic analyses and systems pharmacology modeling could be used in the framework of telemetry data evaluation. In this tutorial, we provide a comprehensive workflow for the analysis of nonclinical CV safety on hemodynamic parameters with a sequential approach, highlight the challenges associated with the data, and propose respective solutions, complemented with a reproducible example. The work is aimed at helping researchers conduct model-based analyses of the CV safety in animals with subsequent translation of the effect to humans seamlessly and efficiently.

Population PK/PD model of tacrolimus for exploring the relationship between accumulated exposure and quantitative scores in myasthenia gravis patients

Tacrolimus is an important immunosuppressant used in the treatment of myasthenia gravis (MG). However, the population pharmacokinetic (PK) characteristics together with the exposure-response of tacrolimus in the treatment of MG remain largely unknown. In this study, we aimed to develop a population PK/pharmacodynamic (PK/PD) model of tacrolimus in patients with MG, in order to explore the relationships among tacrolimus dose, exposure, and its therapeutic efficacy. The genotype of CYP3A5, Osserman's classification, and status of thymus, as well as demographic characteristics and other biomarkers from laboratory testing were tested as covariate, and simulations were performed based on the final model. The population PK model was described using a one-compartment model with first-order elimination and fixed absorption parameters. CYP3A5 genotype significantly influenced the apparent clearance, and total protein (TP) influenced the apparent volume of distribution as covariates. The quantitative MG scores were characterized by the cumulated area under curve of tacrolimus in a maximum effect function. Osserman's classification was a significant covariate on the initial score of patients with MG. The simulations demonstrated that tacrolimus showed an unsatisfying effect possibly due to insufficient exposure in some patients with MG. A starting dose of 2 mg/d and even higher dose for patients with CYP3A5 *1/*1 and *1/*3 and lower TP level were required for the rapid action of tacrolimus. The population PK/PD model quantitatively described the relationships among tacrolimus dose, exposure, and therapeutic efficacy in patients with MG, which could provide reference for the optimization of tacrolimus dosing regimen at the individual patient level.

Population Pharmacodynamic Analyses of Human Anti-Rabies Virus Monoclonal Antibody (Ormutivimab) in Healthy Adult Subjects

Ormutivimab is the first recombinant human anti-rabies monoclonal antibody (rhRIG) approved for clinical application in China. In this study, a population pharmacodynamic (PPD) model was established to compare the neutralizing antibody activities of Ormutivimab and human rabies immunoglobulin (HRIG), alone or combined with human rabies vaccine (Vero), in a phase II clinical trial, and to recommend a target dose for the phase III trial. The model was verified to fit the PPD data well. The stability of the model was verified by the bootstrap method. The level of neutralizing antibodies in vivo increased rapidly after administration of Ormutivimab or HRIG. Neutralizing antibodies with a strong activity were produced at 7 days (Ormutivimab + vaccine) or 10 days (HRIG + vaccine) after induction by the vaccine in vivo. Compared to that induced by HRIG + vaccine, the level of the neutralizing antibodies induced by Ormutivimab + vaccine peaked higher and faster. The levels of neutralizing antibodies induced by Ormutivimab + vaccine and HRIG + vaccine were similar within 21 days after administration. According to these results and the safety data, 20 IU·kg^-1 was recommended as the target dose in the confirmatory study of Ormutivimab. Registration: ClinicalTrials.gov #NCT02559921.

Mathematical Modeling for an MTT Assay in Fluorine-Containing Graphene Quantum Dots

The paper reports on a new mathematical model, starting with the original Hill equation which is derived to describe cell viability (V) while testing nanomaterials (NMs). Key information on the sample's morphology, such as mean size (⟨s⟩) and size dispersity (σ) is included in the new model via the lognormal distribution function. The new Hill-inspired equation is successfully used to fit MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) data from assays performed with the HepG2 cell line challenged by fluorine-containing graphene quantum dots (F:GQDs) under light (400-700 nm wavelength) and dark conditions. The extracted "biological polydispersity" (light: ⟨sMTT⟩=1.77±0.02 nm and σMTT=0.21±0.02); dark: ⟨sMTT⟩=1.87±0.02 nm and σMTT=0.22±0.01) is compared with the "morphological polydispersity" (⟨sTEM⟩=1.98±0.06 nm and σTEM=0.19±0.03), the latter obtained from TEM (transmission electron microscopy). The fitted data are then used to simulate a series of V responses. Two aspects are emphasized in the simulations: (i) fixing σ, one simulates V versus ⟨s⟩ and (ii) fixing ⟨s⟩, one simulates V versus σ. Trends observed in the simulations are supported by a phenomenological model picture describing the monotonic reduction in V as ⟨s⟩ increases (V~pa/(s)p-a; p and a are fitting parameters) and accounting for two opposite trends of V versus σ: under light (V~σ) and under dark (V~1/σ).

Limitations of Antibiotic MIC-Based PK-PD Metrics: Looking Back to Move Forward

Within a few years after the first successful clinical use of penicillin, investigations were conducted in animal infection models to explore a range of factors that were considered likely to influence the antibacterial response to the drug. Those studies identified that the response was influenced by not only the total daily dose but also the interval between individual doses across the day, and whether penicillin was administered in an intermittent or continuous manner. Later, as more antibiotics were discovered and developed, antimicrobial pharmacologists began to measure antibiotic concentrations in biological fluids. This enabled the linking of antibacterial response at a single time point in an animal or in vitro infection model with one of three summary pharmacokinetic (PK) measures of in vivo exposure to the antibiotic. The summary PK exposure measures were normalised to the minimum inhibitory concentration (MIC), an in vitro measure of the pharmacodynamic (PD) potency of the drug. The three PK-PD indices (ratio of maximum concentration to MIC, ratio of area under the concentration-time curve to MIC, time concentration is above MIC) have been used extensively since the 1980s. While these MIC-based summary PK-PD metrics have undoubtedly facilitated the development of new antibiotics and the clinical application of both new and old antibiotics, it is increasingly recognised that they have a number of substantial limitations. In this article we use a historical perspective to review the origins of the three traditional PK-PD indices before exploring in detail their limitations and the implications arising from those limitations. Finally, in the interests of improving antibiotic development and dosing in patients, we consider a model-based approach of linking the full time-course of antibiotic concentrations with that of the antibacterial response. Such an approach enables incorporation of other factors that can influence treatment outcome in patients and has the potential to drive model-informed precision dosing of antibiotics into the future.

A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges

Traditionally, in vitro and in vivo methods are useful for estimating human pharmacokinetics (PK) parameters; however, it is impractical to perform these complex and expensive experiments on a large number of compounds. The integration of publicly available chemical, or medical Big Data and artificial intelligence (AI)-based approaches led to qualitative and quantitative prediction of human PK of a candidate drug. However, predicting drug response with these approaches is challenging, partially because of the adaptation of algorithmic and limitations related to experimental data. In this report, we provide an overview of machine learning (ML)-based quantitative structure-activity relationship (QSAR) models used in the assessment or prediction of PK values as well as databases available for obtaining such data.

V2 ACHER: Visualization of complex trial data in pharmacometric analyses with covariates

Pharmacometric models can enhance clinical decision making, with covariates exposing potential contributions to variability of subpopulation characteristics, for example, demographics or disease status. Intuitive visualization of models with multiple covariates is needed because sparsity of data in visualizations trellised by covariate values can raise concerns about the credibility of the underlying model. V² ACHER, introduced here, is a stepwise transformation of data that can be applied to a variety of static (non-ordinary-differential-equation-based) pharmacometric analyses. This work uses four examples of increasing complexity to show how the transformation elucidates the relationship between observations and model results and how it can also be used in visual predictive checks to confirm the quality of a model. V² ACHER facilitates consistent, intuitive, single-plot visualization of a multicovariate model with a complex data set, thereby enabling easier model communication for modelers and for cross-functional development teams and facilitating confident use in support of decisions.

Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity

Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

Therapeutic Drug Monitoring of Targeted Anticancer Protein Kinase Inhibitors in Routine Clinical Use: A Critical Review

BACKGROUND

Therapeutic response to oral targeted anticancer protein kinase inhibitors (PKIs) varies widely between patients, with insufficient efficacy of some of them and unacceptable adverse reactions of others. There are several possible causes for this heterogeneity, such as pharmacokinetic (PK) variability affecting blood concentrations, fluctuating medication adherence, and constitutional or acquired drug resistance of cancer cells. The appropriate management of oncology patients with PKI treatments thus requires concerted efforts to optimize the utilization of these drug agents, which have probably not yet revealed their full potential.

METHODS

An extensive literature review was performed on MEDLINE on the PK, pharmacodynamics, and therapeutic drug monitoring (TDM) of PKIs (up to April 2019).

RESULTS

This review provides the criteria for determining PKIs suitable candidates for TDM (eg, availability of analytical methods, observational PK studies, PK-pharmacodynamics relationship analysis, and randomized controlled studies). It reviews the major characteristics and limitations of PKIs, the expected benefits of TDM for cancer patients receiving them, and the prerequisites for the appropriate utilization of TDM. Finally, it discusses various important practical aspects and pitfalls of TDM for supporting better implementation in the field of cancer treatment.

CONCLUSIONS

Adaptation of PKIs dosage regimens at the individual patient level, through a rational TDM approach, could prevent oncology patients from being exposed to ineffective or unnecessarily toxic drug concentrations in the era of personalized medicine.

Plasma and intracerebral pharmacokinetics and pharmacodynamics modeling for the acetylcholine releasing effect of ginsenoside Rg1 in mPFC of Aβ model rats

Ginsenoside Rg1 is a major bioactive component of ginseng. Limited information is available regarding Rg1 concentrations in the central neural system and the corresponding relationship of plasma/intracerebral concentrations, and intracerebral effects of Rg1. Awake Aβ model rats received a single subcutaneous administration of Rg1. Concentrations of unbound Rg1 and acetylcholine in the brain extracellular fluid and Rg1 in plasma were then determined. An E_max-two compartment pharmacokinetic/pharmacodynamics (PK/PD) model without effect compartment was finally obtained by evaluating three mechanism-based models. The corresponding relationship between the plasma PK and PD of Rg1 can be described as E = 119.05•C/(73.42 + C).[Formula: see text].

Accelerated treatment of endocarditis-The POET II trial: Rationale and design of a randomized controlled trial

BACKGROUND

The optimal antibiotic treatment length for infective endocarditis (IE) is uncertain. International guidelines recommend treatment duration of up to 6 weeks for patients with left-sided IE but are primarily based on historical data and expert opinion. Efficacies of modern therapies, fast recovery seen in many patients with IE, and complications to long hospital stays challenge the rationale for fixed treatment durations in all patients.

OBJECTIVE

The objective was to conduct a noninferiority randomized controlled trial (acronym POET II) investigating the safety of accelerated (shortened) antibiotic therapy as compared to standard duration in patients with left-sided IE.

METHODS

The POET II trial is a multicenter, multinational, open-label, noninferiority randomized controlled trial. Patients with definite left-sided IE due to Streptococcus spp, Staphylococcus aureus, or Enterococcus faecalis will be eligible for enrolment. Each patient will be randomized to accelerated antibiotic treatment or standard-length treatment (1:1) following clinical stabilization as defined by clinical parameters, laboratory values, and transesophageal echocardiography findings. Accelerated treatment will be between 2 and 4 weeks, whereas standard-length treatment will be between 4 and 6 weeks, depending on microbiologic etiology, complications, need for valve surgery, and prosthetic versus native valve endocarditis. The primary outcome is a composite of all-cause mortality, unplanned cardiac surgery, relapse of bacteremia, or embolization within 6 months of randomization.

CONCLUSIONS

The POET II trial will investigate the safety of accelerated antibiotic therapy for patients with left-sided IE caused by Streptococcus spp, Staphylococcus aureus, or Enterococcus faecalis. The results of the POET II trial will improve the evidence base of treatment recommendations, and clinical practice may be altered.

Executable cancer models: successes and challenges

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field.

The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib

Pharmacometric methods have hugely benefited from progress in analytical and computer sciences during the past decades, and play nowadays a central role in the clinical development of new medicinal drugs. It is time that these methods translate into patient care through therapeutic drug monitoring (TDM), due to become a mainstay of precision medicine no less than genomic approaches to control variability in drug response and improve the efficacy and safety of treatments. In this review, we make the case for structuring TDM development along five generic questions: 1) Is the concerned drug a candidate to TDM? 2) What is the normal range for the drug's concentration? 3) What is the therapeutic target for the drug's concentration? 4) How to adjust the dosage of the drug to drive concentrations close to target? 5) Does evidence support the usefulness of TDM for this drug? We exemplify this approach through an overview of our development of the TDM of imatinib, the very first targeted anticancer agent. We express our position that a similar story shall apply to other drugs in this class, as well as to a wide range of treatments critical for the control of various life-threatening conditions. Despite hurdles that still jeopardize progress in TDM, there is no doubt that upcoming technological advances will shape and foster many innovative therapeutic monitoring methods.

Multi-scale modeling of drug binding kinetics to predict drug efficacy

Optimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials.

A Population Pharmacokinetic and Pharmacodynamic Analysis of RP5063 Phase 2 Study Data in Patients with Schizophrenia or Schizoaffective Disorder

Background and Objective

RP5063 is a novel multimodal dopamine (D)–serotonin (5-HT) stabilizer possessing partial agonist activity for D_2/3/4 and 5-HT_1A/2A, antagonist activity for 5-HT_2B/2C/7, and moderate affinity for the serotonin transporter. Phase 2 trial data analysis of RP5063 involving patients with schizophrenia and schizoaffective disorder defined: (1) the pharmacokinetic profile; and (2) the pharmacokinetic/pharmacodynamic relationships.

Methods

Pharmacokinetic sample data (175 patients on RP5063; 28 doses/patient) were analyzed, utilized one- and two-compartment models, and evaluated the impact of covariates. Pharmacodynamic analysis involved development of an E_max model.

Results

The pharmacokinetic analysis identified a one-compartment model incorporating body mass index influence on volume as the optimum construct, with fixed-effect parameters: (1) oral clearance (Cl/F), 5.11 ± 0.11 L/h; (2) volume of distribution (V_c/F), 328.00 ± 31.40 L; (3) absorption constant (ka) 0.42 ± 0.17 h⁻¹; (4) lag time (t lag) of 0.41 ± 0.02 h; and (5) a calculated half-life of 44.5 h. Pharmacokinetics were linear related to dose. An E_max model for total Positive and Negative Syndrome Scale (PANSS) scores as the response factor against cumulative area under the curve (AUC) provided fixed-effect estimates: (1) E_o = 87.3 ± 0.71 (PANSS Units; pu); (2) E_max = − 31.60 ± 4.05 (pu); and (3) AUC₅₀ = 89.60 ± 30.10 (µg·h/mL). The predicted PANSS improvement reflected a clinical dose range of 5–30 mg.

Conclusions

Pharmacokinetics of RP5063 behaved predictably and consistently. Pharmacodynamics were characterized using an E_max model, reflecting total PANSS score as a function of cumulative AUC, that showed high predictability and low variability when correlated with actual observations.

Electronic supplementary material

The online version of this article (10.1007/s13318-018-0472-z) contains supplementary material, which is available to authorized users.

Acute vs cumulative benefits of metformin use in patients with type 2 diabetes and heart failure

AIMS

To evaluate the association between metformin use and heart failure (HF) exacerbation in people with type 2 diabetes (T2D) and pre-existing HF using alternative exposure models.

MATERIALS AND METHODS

We analysed data for patients with T2D and incident HF from a national US insurance claims database. We compared the results of several multivariable Cox models where time-varying use of metformin was modelled as: (1) current use; (2) total duration of past use; and (3) use within the past 30 days or 10 days. The outcome was defined as time to HF-related hospitalization. We then re-analysed the data using flexible weighted cumulative exposure (WCE) models.

RESULTS

A total of 7620 patients with diabetes and incident HF were analysed. The mean (SD) patient age was 54 (8) years, and 58% (n = 4440) were men. In all, 3799 individuals (50%) were exposed to metformin, and 837 HF hospitalizations (11%) occurred (mean follow-up 1.7 years). Results of conventional models suggested potential acute benefits in reducing HF exacerbation with metformin use in the past 10 days (adjusted hazard ratio [aHR] 0.76, 95% confidence interval [CI] 0.60-0.97), while WCE models, which provided a better fit for the data, suggested lack of a systematic effect (aHR 0.91, 95% CI 0.69-1.20).

CONCLUSIONS

Our results suggest that cumulative metformin exposure does not decrease the risk of HF-related exacerbation. Use of other anti-hyperglycaemic agents with proven efficacy in patients with HF should also be considered as treatment options in this population.

Model selection for clustering of pharmacokinetic responses

BACKGROUND AND OBJECTIVE

Pharmacokinetics comprises the study of drug absorption, distribution, metabolism and excretion over time. Clinical pharmacokinetics, focusing on therapeutic management, offers important insights towards personalised medicine through the study of efficacy and toxicity of drug therapies. This study is hampered by subject's high variability in drug blood concentration, when starting a therapy with the same drug dosage. Clustering of pharmacokinetics responses has been addressed recently as a way to stratify subjects and provide different drug doses for each stratum. This clustering method, however, is not able to automatically determine the correct number of clusters, using an user-defined parameter for collapsing clusters that are closer than a given heuristic threshold. We aim to use information-theoretical approaches to address parameter-free model selection.

METHODS

We propose two model selection criteria for clustering pharmacokinetics responses, founded on the Minimum Description Length and on the Normalised Maximum Likelihood.

RESULTS

Experimental results show the ability of model selection schemes to unveil the correct number of clusters underlying the mixture of pharmacokinetics responses.

CONCLUSIONS

In this work we were able to devise two model selection criteria to determine the number of clusters in a mixture of pharmacokinetics curves, advancing over previous works. A cost-efficient parallel implementation in Java of the proposed method is publicly available for the community.

Spatiotemporal pharmacodynamics of meropenem- and tobramycin-treated Pseudomonas aeruginosa biofilms

Objectives

The selection and dose of antibiotic therapy for biofilm-related infections are based on traditional pharmacokinetic studies using planktonic bacteria. The objective of this study was to characterize the time course and spatial activity of human exposure levels of meropenem and tobramycin against Pseudomonas aeruginosa biofilms grown in an in vitro flow-chamber model.

Methods

Pharmacokinetic profiles of meropenem and tobramycin used in human therapy were administered to GFP-labelled P. aeruginosa PAO1 grown in flow chambers for 24 or 72 h. Images were acquired using confocal laser scanning microscopy throughout antibiotic treatment. Bacterial biomass was measured using COMSTAT and pharmacokinetic/pharmacodynamic models were fitted using NONMEM7.

Results

Meropenem treatment resulted in more rapid and sustained killing of both the 24 and 72 h PAO1 biofilm compared with tobramycin. Biofilm regrowth after antibiotic treatment occurred fastest with tobramycin. Meropenem preferentially killed subpopulations within the mushroom cap of the biofilms, regardless of biofilm maturity. The spatial killing by tobramycin varied with biofilm maturity. A tobramycin-treated 24 h biofilm resulted in live and dead cells detaching from the biofilm, while treatment of a 72 h biofilm preferentially killed subpopulations on the periphery of the mushroom stalk. Regrowth occurred primarily on the mushroom caps. Combination meropenem and tobramycin therapy resulted in rapid and efficient killing of biofilm cells, with a spatial pattern similar to meropenem alone.

Conclusions

Simulated human concentrations of meropenem and tobramycin in young and mature PAO1 biofilms exhibited differences in temporal and spatial patterns of killing and antibiotic tolerance development.

Useful pharmacodynamic endpoints in children: selection, measurement, and next steps

Pharmacodynamic (PD) endpoints are essential for establishing the benefit-to-risk ratio for therapeutic interventions in children and neonates. This article discusses the selection of an appropriate measure of response, the PD endpoint, which is a critical methodological step in designing pediatric efficacy and safety studies. We provide an overview of existing guidance on the choice of PD endpoints in pediatric clinical research. We identified several considerations relevant to the selection and measurement of PD endpoints in pediatric clinical trials, including the use of biomarkers, modeling, compliance, scoring systems, and validated measurement tools. To be useful, PD endpoints in children need to be clinically relevant, responsive to both treatment and/or disease progression, reproducible, and reliable. In most pediatric disease areas, this requires significant validation efforts. We propose a minimal set of criteria for useful PD endpoint selection and measurement. We conclude that, given the current heterogeneity of pediatric PD endpoint definitions and measurements, both across and within defined disease areas, there is an acute need for internationally agreed, validated, and condition-specific pediatric PD endpoints that consider the needs of all stakeholders, including healthcare providers, policy makers, patients, and families.

An Algorithm and R Program for Fitting and Simulation of Pharmacokinetic and Pharmacodynamic Data

BACKGROUND AND OBJECTIVES

Pharmacokinetic/pharmacodynamic link models are widely used in dose-finding studies. By applying such models, the results of initial pharmacokinetic/pharmacodynamic studies can be used to predict the potential therapeutic dose range. This knowledge can improve the design of later comparative large-scale clinical trials by reducing the number of participants and saving time and resources. However, the modeling process can be challenging, time consuming, and costly, even when using cutting-edge, powerful pharmacological software. Here, we provide a freely available R program for expediently analyzing pharmacokinetic/pharmacodynamic data, including data importation, parameter estimation, simulation, and model diagnostics.

METHODS

First, we explain the theory related to the establishment of the pharmacokinetic/pharmacodynamic link model. Subsequently, we present the algorithms used for parameter estimation and potential therapeutic dose computation. The implementation of the R program is illustrated by a clinical example. The software package is then validated by comparing the model parameters and the goodness-of-fit statistics generated by our R package with those generated by the widely used pharmacological software WinNonlin.

RESULTS

The pharmacokinetic and pharmacodynamic parameters as well as the potential recommended therapeutic dose can be acquired with the R package. The validation process shows that the parameters estimated using our package are satisfactory.

CONCLUSIONS

The R program developed and presented here provides pharmacokinetic researchers with a simple and easy-to-access tool for pharmacokinetic/pharmacodynamic analysis on personal computers.

Experimental Studies and Modeling of the Drying Kinetics of Multicomponent Polymer Films

The process of drying thin polymer films is an important operation that influences the film structure and solid state, and the stability of the product. The purpose of this work was to study and model the drying kinetics of multicomponent films based on two polymers: hydroxypropyl methylcellulose (HPMC, amorphous) and polyvinyl alcohol (PVA, semicrystalline). The isothermal drying kinetics of the films at different temperatures (40, 60, and 80°C) were studied using thermo-gravimetric analysis (TGA) and convection oven methods. Solid-state characterization tools used in the study included polarization and hot-stage microscopy, scanning electron microscopy (SEM), and differential scanning calorimetry (DSC). The drying kinetics of HPMC and PVA films in the TGA apparatus and convection oven were comparable. The three-parameter (W _max, τ, n) Hill equation successfully modeled the experimental drying kinetics. The time factor τ in the Hill equation nicely explained two drying phases in the films. Solid-state phase changes occurring in the films during dehydration had a bearing on the drying kinetics and mechanisms. TGA can be used as a simple tool to determine the end points in drying processes using ovens or tunnels. The three-parameter Hill equation explained the drying kinetics and diffusion mechanisms of the solvent through the polymer films for the first time. This study advances our understanding of film drying, in particular for pharmaceutically relevant thin films.

Mathematical Modeling of Biofilm Structures Using COMSTAT Data

Mathematical modeling holds great potential for quantitatively describing biofilm growth in presence or absence of chemical agents used to limit or promote biofilm growth. In this paper, we describe a general mathematical/statistical framework that allows for the characterization of complex data in terms of few parameters and the capability to (i) compare different experiments and exposures to different agents, (ii) test different hypotheses regarding biofilm growth and interaction with different agents, and (iii) simulate arbitrary administrations of agents. The mathematical framework is divided to submodels characterizing biofilm, including new models characterizing live biofilm growth and dead cell accumulation; the interaction with agents inhibiting or stimulating growth; the kinetics of the agents. The statistical framework can take into account measurement and interexperiment variation. We demonstrate the application of (some of) the models using confocal microscopy data obtained using the computer program COMSTAT.

Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination

BACKGROUND

The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Nevertheless, Cox-MSM performance in the case of multiple treatments has not been fully explored under different degree of time-dependent confounding for treatments or in case of interaction between treatments. We aimed to evaluate and compare the performance of the marginal structural Cox model (Cox-MSM) to the standard Cox model in estimating the treatment effect in the case of multiple treatments under different scenarios of time-dependent confounding and when an interaction between treatment effects is present.

METHODS

We specified a Cox-MSM with two treatments including an interaction term for situations where an adverse event might be caused by two treatments taken simultaneously but not by each treatment taken alone. We simulated longitudinal data with two treatments and a time-dependent confounder affected by one or the two treatments. To fit the Cox-MSM, we used the inverse probability weighting method. We illustrated the method to evaluate the specific effect of protease inhibitors combined (or not) to other antiretroviral medications on the anal cancer risk in HIV-infected individuals, with CD4 cell count as time-dependent confounder.

RESULTS

Overall, Cox-MSM performed better than the standard Cox model. Furthermore, we showed that estimates were unbiased when an interaction term was included in the model.

CONCLUSION

Cox-MSM may be used for accurately estimating causal individual and joined treatment effects from a combination therapy in presence of time-dependent confounding provided that an interaction term is estimated.

Using Multiple Pharmacovigilance Models Improves the Timeliness of Signal Detection in Simulated Prospective Surveillance

INTRODUCTION

Prospective pharmacovigilance aims to rapidly detect safety concerns related to medical products. The exposure model selected for pharmacovigilance impacts the timeliness of signal detection. However, in most real-life pharmacovigilance studies, little is known about which model correctly represents the association and there is no evidence to guide the selection of an exposure model. Different exposure models reflect different aspects of exposure history, and their relevance varies across studies. Therefore, one potential solution is to apply several alternative exposure models simultaneously, with each model assuming a different exposure-risk association, and then combine the model results.

METHODS

We simulated alternative clinically plausible associations between time-varying drug exposure and the hazard of an adverse event. Prospective surveillance was conducted on the simulated data by estimating parametric and semi-parametric exposure-risk models at multiple times during follow-up. For each model separately, and using combined evidence from different subsets of models, we compared the time to signal detection.

RESULTS

Timely detection across the simulated associations was obtained by fitting a set of pharmacovigilance models. This set included alternative parametric models that assumed different exposure-risk associations and flexible models that made no assumptions regarding the form/shape of the association. Times to detection generated using a simple combination of evidence from multiple models were comparable to those observed under the ideal, but unrealistic, scenario where pharmacovigilance relied on the single 'true' model used for data generation.

CONCLUSIONS

Simulation results indicate that, if the true model is not known, an association can be detected in a more timely manner by first fitting a carefully selected set of exposure-risk models and then generating a signal as soon as any of the models considered yields a test statistic value below a predetermined testing threshold.

Modelling Drug Abuse Epidemics in the Presence of Limited Rehabilitation Capacity

The abuse of drugs is now an epidemic globally whose control has been mainly through rehabilitation. The demand for drug abuse rehabilitation has not been matched with the available capacity resulting in limited placement of addicts into rehabilitation. In this paper, we model limited rehabilitation through the Hill function incorporated into a system of nonlinear ordinary differential equations. Not every member of the community is equally likely to embark on drug use, risk structure is included to help differentiate those more likely (high risk) to abuse drugs and those less likely (low risk) to abuse drugs. It is shown that the model has multiple equilibria, and using the centre manifold theory, the model exhibits the phenomenon of backward bifurcation whose implications to rehabilitation are discussed. Sensitivity analysis and numerical simulations are performed. The results show that saturation in rehabilitation will in the long run lead to the escalation of drug abuse. This means that limited access to rehabilitation has negative implications in the fight against drug abuse where rehabilitation is the main form of control. This suggests that increased access to rehabilitation is likely to lower the drug abuse epidemic.

Pharmacotherapy in pediatric epilepsy: from trial and error to rational drug and dose selection - a long way to go

INTRODUCTION

Whereas ongoing efforts in epilepsy research focus on the underlying disease processes, the lack of a physiologically based rationale for drug and dose selection contributes to inadequate treatment response in children. In fact, limited information on the interindividual variation in pharmacokinetics and pharmacodynamics of anti-epileptic drugs (AEDs) in children drive prescription practice, which relies primarily on dose regimens according to a mg/kg basis. Such practice has evolved despite advancements in pediatric pharmacology showing that growth and maturation processes do not correlate linearly with changes in body size.

AREAS COVERED

In this review we aim to provide 1) a comprehensive overview of the sources of variability in the response to AEDs, 2) insight into novel methodologies to characterise such variation and 3) recommendations for treatment personalisation.

EXPERT OPINION

The use of pharmacokinetic-pharmacodynamic principles in clinical practice is hindered by the lack of biomarkers and by practical constraints in the evaluation of polytherapy. The identification of biomarkers and their validation as tools for drug development and therapeutics will require some time. Meanwhile, one should not miss the opportunity to integrate the available pharmacokinetic data with modeling and simulation concepts to prevent further delays in the development of personalised treatments for pediatric patients.

Multiscale modelling approaches for assessing cosmetic ingredients safety

The European Union's ban on animal testing for cosmetic ingredients and products has generated a strong momentum for the development of in silico and in vitro alternative methods. One of the focus of the COSMOS project was ab initio prediction of kinetics and toxic effects through multiscale pharmacokinetic modeling and in vitro data integration. In our experience, mathematical or computer modeling and in vitro experiments are complementary. We present here a summary of the main models and results obtained within the framework of the project on these topics. A first section presents our work at the organelle and cellular level. We then go toward modeling cell levels effects (monitored continuously), multiscale physiologically based pharmacokinetic and effect models, and route to route extrapolation. We follow with a short presentation of the automated KNIME workflows developed for dissemination and easy use of the models. We end with a discussion of two challenges to the field: our limited ability to deal with massive data and complex computations.

Quantitative Systems Pharmacology: A Framework for Context

Quantitative Systems Pharmacology (QSP) is receiving increased attention. As the momentum builds and the expectations grow it is important to (re)assess and formalize the basic concepts and approaches. In this short review, I argue that QSP, in addition to enabling the rational integration of data and development of complex models, maybe more importantly, provides the foundations for developing an integrated framework for the assessment of drugs and their impact on disease within a broader context expanding the envelope to account in great detail for physiology, environment and prior history. I articulate some of the critical enablers, major obstacles and exciting opportunities manifesting themselves along the way. Charting such overarching themes will enable practitioners to identify major and defining factors as the field progressively moves towards personalized and precision health care delivery.

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.

Quantitative Systems Pharmacology Approaches Applied to Microphysiological Systems (MPS): Data Interpretation and Multi-MPS Integration

Our goal in developing Microphysiological Systems (MPS) technology is to provide an improved approach for more predictive preclinical drug discovery via a highly integrated experimental/computational paradigm. Success will require quantitative characterization of MPSs and mechanistic analysis of experimental findings sufficient to translate resulting insights from in vitro to in vivo. We describe herein a systems pharmacology approach to MPS development and utilization that incorporates more mechanistic detail than traditional pharmacokinetic/pharmacodynamic (PK/PD) models. A series of studies illustrates diverse facets of our approach. First, we demonstrate two case studies: a PK data analysis and an inflammation response--focused on a single MPS, the liver/immune MPS. Building on the single MPS modeling, a theoretical investigation of a four-MPS interactome then provides a quantitative way to consider several pharmacological concepts such as absorption, distribution, metabolism, and excretion in the design of multi-MPS interactome operation and experiments.

Establishing Good Practices for Exposure-Response Analysis of Clinical Endpoints in Drug Development

This tutorial aims at promoting good practices for exposure–response (E-R) analyses of clinical endpoints in drug development. The focus is on practical aspects of E-R analyses to assist modeling scientists with a process of performing such analyses in a consistent manner across individuals and projects and tailored to typical clinical drug development decisions. This includes general considerations for planning, conducting, and visualizing E-R analyses, and how these are linked to key questions.

Bacterial fitness shapes the population dynamics of antibiotic-resistant and -susceptible bacteria in a model of combined antibiotic and anti-virulence treatment

Bacterial resistance to antibiotic treatment is a huge concern: introduction of any new antibiotic is shortly followed by the emergence of resistant bacterial isolates in the clinic. This issue is compounded by a severe lack of new antibiotics reaching the market. The significant rise in clinical resistance to antibiotics is especially problematic in nosocomial infections, where already vulnerable patients may fail to respond to treatment, causing even greater health concern. A recent focus has been on the development of anti-virulence drugs as a second line of defence in the treatment of antibiotic-resistant infections. This treatment, which weakens bacteria by reducing their virulence rather than killing them, should allow infections to be cleared through the body׳s natural defence mechanisms. In this way there should be little to no selective pressure exerted on the organism and, as such, a predominantly resistant population should be less likely to emerge. However, before the likelihood of resistance to these novel drugs emerging can be predicted, we must first establish whether such drugs can actually be effective. Many believe that anti-virulence drugs would not be powerful enough to clear existing infections, restricting their potential application to prophylaxis. We have developed a mathematical model that provides a theoretical framework to reveal the circumstances under which anti-virulence drugs may or may not be successful. We demonstrate that by harnessing and combining the advantages of antibiotics with those provided by anti-virulence drugs, given infection-specific parameters, it is possible to identify treatment strategies that would efficiently clear bacterial infections, while preventing the emergence of antibiotic-resistant subpopulations. Our findings strongly support the continuation of research into anti-virulence drugs and demonstrate that their applicability may reach beyond infection prevention.

Using physiologically-based pharmacokinetic-guided "body-on-a-chip" systems to predict mammalian response to drug and chemical exposure

The continued development of in vitro systems that accurately emulate human response to drugs or chemical agents will impact drug development, our understanding of chemical toxicity, and enhance our ability to respond to threats from chemical or biological agents. A promising technology is to build microscale replicas of humans that capture essential elements of physiology, pharmacology, and/or toxicology (microphysiological systems). Here, we review progress on systems for microscale models of mammalian systems that include two or more integrated cellular components. These systems are described as a "body-on-a-chip", and utilize the concept of physiologically-based pharmacokinetic (PBPK) modeling in the design. These microscale systems can also be used as model systems to predict whole-body responses to drugs as well as study the mechanism of action of drugs using PBPK analysis. In this review, we provide examples of various approaches to construct such systems with a focus on their physiological usefulness and various approaches to measure responses (e.g. chemical, electrical, or mechanical force and cellular viability and morphology). While the goal is to predict human response, other mammalian cell types can be utilized with the same principle to predict animal response. These systems will be evaluated on their potential to be physiologically accurate, to provide effective and efficient platform for analytics with accessibility to a wide range of users, for ease of incorporation of analytics, functional for weeks to months, and the ability to replicate previously observed human responses.

Dose-dependent model of caffeine effects on human vigilance during total sleep deprivation

Caffeine is the most widely consumed stimulant to counter sleep-loss effects. While the pharmacokinetics of caffeine in the body is well-understood, its alertness-restoring effects are still not well characterized. In fact, mathematical models capable of predicting the effects of varying doses of caffeine on objective measures of vigilance are not available. In this paper, we describe a phenomenological model of the dose-dependent effects of caffeine on psychomotor vigilance task (PVT) performance of sleep-deprived subjects. We used the two-process model of sleep regulation to quantify performance during sleep loss in the absence of caffeine and a dose-dependent multiplier factor derived from the Hill equation to model the effects of single and repeated caffeine doses. We developed and validated the model fits and predictions on PVT lapse (number of reaction times exceeding 500 ms) data from two separate laboratory studies. At the population-average level, the model captured the effects of a range of caffeine doses (50-300 mg), yielding up to a 90% improvement over the two-process model. Individual-specific caffeine models, on average, predicted the effects up to 23% better than population-average caffeine models. The proposed model serves as a useful tool for predicting the dose-dependent effects of caffeine on the PVT performance of sleep-deprived subjects and, therefore, can be used for determining caffeine doses that optimize the timing and duration of peak performance.

Preclinical PK/PD modeling and human efficacious dose projection for a glucokinase activator in the treatment of diabetes

Human Hexokinase IV, or glucokinase (GK), is a regulator of glucose concentrations in the body. It plays a key role in pancreatic insulin secretion as well as glucose biotransformation in the liver, making it a potentially viable target for treatment of Type 2 diabetes. Allosteric activators of GK have been shown to decrease blood glucose concentrations in both animals and humans. Here, the development of a mathematical model is presented that describes glucose modulation in an ob/ob mouse model via administration of a potent GK activator, with the goal of projecting a human efficacious dose and plasma exposure. The model accounts for the allosteric interaction between GK, the activator, and glucose using a modified Hill function. Based on model simulations using data from the ob/ob mouse and in vitro studies, human projections of glucose response to the GK activator are presented, along with dose and regimen predictions to maintain clinically significant decreases in blood glucose in a Type 2 diabetic patient. This effort serves as a basis to build a detailed mechanistic understanding of GK and its role as a therapeutic target for Type 2 diabetes, and it highlights the benefits of using such an approach in a drug discovery setting.

Population pharmacometrics in support of analgesics studies

Population pharmacometric modeling is used to explain both population trends as well as the sources and magnitude of variability in pharmacokinetic and pharmacodynamics data; the later, in part, by taking into account patient characteristics such as weight, age, renal function and genetics. The approach is best known for its ability to analyze sparse data, i.e. when only a few measurements have been collected from each subject, but other benefits include its flexibility and the potential to construct more detailed models than those used in the traditional individual curve fitting approach. This review presents the basic concepts of population pharmacokinetic and pharmacodynamic modeling and includes several analgesic drug examples. In addition, the use of these models to design and optimize future studies is discussed. In this context, finding the best design factors, such as the sampling times or the dose, for future studies within pre-defined criteria using a previously constructed population pharmacokinetic model can help researchers acquire clinically meaningful data without wasting resources and unnecessarily exposing vulnerable patient groups to study drugs and additional blood sampling.

Understanding the linked kinetics of benzo(a)pyrene and 3-hydroxybenzo(a)pyrene biomarker of exposure using physiologically-based pharmacokinetic modelling in rats

3-hydroxybenzo(a)pyrene (3-OHBaP) in urine has been proposed as a biomarker of occupational exposure to polycyclic aromatic hydrocarbons. However, to reconstruct exposure doses in workers from biomarker measurements, a thorough knowledge of the kinetics of the benzo(a)pyrene (BaP) and 3-OHBaP given different routes of exposure is needed. A rat physiologically-based pharmacokinetic model of BaP and 3-OHBaP was built. Organs (tissues) represented as compartments were based on in vivo experimental data in rats. Tissue: blood partition coefficients, permeability coefficients, metabolism rates, excretion parameters, and absorption fractions and rates for different routes-of-entry were obtained directly from published in vivo time courses of BaP and 3-OHBaP in blood, various tissues and excreta of rats. The latter parameter values were best-fitted by least square procedures and Monte Carlo simulations. Sensitivity analyses were then carried out to ensure the stability of the model and the key parameters driving the overall modeled kinetics. This modeling pointed out critical determinants of the kinetics: (1) hepatic metabolism of BaP and 3-OHBaP elimination rate as the most sensitive parameters; (2) the strong partition of BaP in lungs compared to other tissues, followed by adipose tissues and liver; (3) the strong partition of 3-OHBaP in kidneys; (4) diffusion-limited tissue transfers of BaP in lungs and 3-OHBaP in lungs, adipose tissues and kidneys; (5) significant entero-hepatic recycling of 3-OHBaP. Very good fits to various sets of experimental data in rats from four different routes-of-entry (intravenous, oral, dermal and inhalation) were obtained with the model.

Mephedrone pharmacokinetics after intravenous and oral administration in rats: relation to pharmacodynamics

RATIONALE

Mephedrone (4-methylmethcathinone) is a still poorly known drug of abuse, alternative to ecstasy or cocaine.

OBJECTIVE

The major aims were to investigate the pharmacokinetics and locomotor activity of mephedrone in rats and provide a pharmacokinetic/pharmacodynamic model.

METHODS

Mephedrone was administered to male Sprague-Dawley rats intravenously (10 mg/kg) and orally (30 and 60 mg/kg). Plasma concentrations and metabolites were characterized using LC/MS and LC-MS/MS fragmentation patterns. Locomotor activity was monitored for 180-240 min.

RESULTS

Mephedrone plasma concentrations after i.v. administration fit a two-compartment model (α = 10.23 h(-1), β = 1.86 h(-1)). After oral administration, peak mephedrone concentrations were achieved between 0.5 and 1 h and declined to undetectable levels at 9 h. The absolute bioavailability of mephedrone was about 10% and the percentage of mephedrone protein binding was 21.59 ± 3.67%. We have identified five phase I metabolites in rat blood after oral administration. The relationship between brain levels and free plasma concentration was 1.85 ± 0.08. Mephedrone induced a dose-dependent increase in locomotor activity, which lasted up to 2 h. The pharmacokinetic-pharmacodynamic model successfully describes the relationship between mephedrone plasma concentrations and its psychostimulant effect.

CONCLUSIONS

We suggest a very important first-pass effect for mephedrone after oral administration and an easy access to the central nervous system. The model described might be useful in the estimation and prediction of the onset, magnitude, and time course of mephedrone pharmacodynamics as well as to design new animal models of mephedrone addiction and toxicity.

An integrated pharmacokinetic and pharmacodynamic study of a new drug of abuse, methylone, a synthetic cathinone sold as "bath salts"

INTRODUCTION

Methylone (3,4-methylenedioxymethcathinone) is a new psychoactive substance and an active ingredient of "legal highs" or "bath salts". We studied the pharmacokinetics and locomotor activity of methylone in rats at doses equivalent to those used in humans.

MATERIAL AND METHODS

Methylone was administered to male Sprague-Dawley rats intravenously (10mg/kg) and orally (15 and 30 mg/kg). Plasma concentrations and metabolites were characterized by LC/MS and LC-MS/MS fragmentation patterns. Locomotor activity was monitored for 180-240 min.

RESULTS

Oral administration of methylone induced a dose-dependent increase in locomotor activity in rats. The plasma concentrations after i.v. administration were described by a two-compartment model with distribution and terminal elimination phases of α=1.95 h(-1) and β=0.72 h(-1). For oral administration, peak methylone concentrations were achieved between 0.5 and 1h and fitted to a flip-flop model. Absolute bioavailability was about 80% and the percentage of methylone protein binding was of 30%. A relationship between methylone brain levels and free plasma concentration yielded a ratio of 1.42 ± 0.06, indicating access to the central nervous system. We have identified four Phase I metabolites after oral administration. The major metabolic routes are N-demethylation, aliphatic hydroxylation and O-methylation of a demethylenate intermediate.

DISCUSSION

Pharmacokinetic and pharmacodynamic analysis of methylone showed a correlation between plasma concentrations and enhancement of the locomotor activity. A contribution of metabolites in the activity of methylone after oral administration is suggested. Present results will be helpful to understand the time course of the effects of this drug of abuse in humans.