Population pharmacokinetic-pharmacodynamic model of propofol in adolescents undergoing scoliosis surgery with intraoperative wake-up test: a study using Bispectral index and composite auditory evoked potentials as pharmacodynamic endpoints.

BACKGROUND: In adolescents limited data are available on the pharmacokinetics (PK) and pharmacodynamics (PD) of propofol. In this study we derived a PK-PD model for propofol in adolescents undergoing idiopathic scoliosis surgery with an intraoperative wake-up test with reinduction of anesthesia using both Bispectral Index (BIS) and composite A-line ARX index (cAAI) as endpoints. METHODS: Fourteen adolescents (9.8-20.1 years) were evaluated during standardized propofol-remifentanil anesthesia for idiopathic scoliosis surgery with an intraoperative wake-up test with reinduction of anesthesia. BIS and cAAI were continuously measured and blood samples collected. A propofol PKPD model was developed using NONMEM. RESULTS: The time courses of propofol concentrations, BIS and cAAI values during anesthesia, intra-operative wakeup and reduction of anesthesia were best described by a two-compartment PK model linked to an inhibitory sigmoidal Emax PD model. For the sigmoidal Emax model, the propofol concentration at half maximum effect (EC50) was 3.51 and 2.14 mg/L and Hill coefficient 1.43 and 6.85 for BIS and cAAI, respectively. The delay in PD effect in relation to plasma concentration was best described by a two compartment effect-site model with a keo of 0.102 min- 1, ke12 of 0.121 min- 1 and ke21 of 0.172 min- 1. CONCLUSIONS: A population PKPD model for propofol in adolescents was developed that successfully described the time course of propofol concentration, BIS and cAAI in individuals upon undergoing scoliosis surgery with intraoperative wake-up test and reinduction of anesthesia. Large differences were demonstrated between both monitors. This may imply that BIS and cAAI measure fundamentally different endpoints in the brain.

Drug disposition in obesity: toward evidence-based dosing.

Obesity and morbid obesity are associated with many physiological changes affecting pharmacokinetics, such as increased blood volume, cardiac output, splanchnic blood flow, and hepatic blood flow. In obesity, drug absorption appears unaltered, although recent evidence suggests that this conclusion may be premature. Volume of distribution may vary largely, but the magnitude and direction of changes seem difficult to predict, with extrapolation on the basis of total body weight being the best approach to date. Changes in clearance may be smaller than in distribution, whereas there is growing evidence that the influence of obesity on clearance can be predicted on the basis of reported changes in the metabolic or elimination pathways involved. For obese children, we propose two methods to distinguish between developmental and obesity-related changes. Future research should focus on the characterization of physiological concepts to predict the optimal dose for each drug in the obese population.

Extending a Systems Model of the APP Pathway: Separation of ß- and γ-Secretase Sequential Cleavage Steps of APP.

The abnormal accumulation of amyloid-ß (Aß) in the brain parenchyma has been posited as a central event in the pathophysiology of Alzheimer's disease. Recently, we have proposed a systems pharmacology model of the amyloid precursor protein (APP) pathway, describing the Aß APP metabolite responses (Aß40, Aß42, sAPPα, and sAPPß) to ß-secretase 1 (BACE1) inhibition. In this investigation this model was challenged to describe Aß dynamics following γ-secretase (GS) inhibition. This led an extended systems pharmacology model, with separate descriptions to characterize the sequential cleavage steps of APP by BACE1 and GS, to describe the differences in Aß response to their respective inhibition. Following GS inhibition, a lower Aß40 formation rate constant was observed, compared with BACE1 inhibition. Both BACE1 and GS inhibition were predicted to lower Aß oligomer levels. Further model refinement and new data may be helpful to fully understand the difference in Aß dynamics following BACE1 versus GS inhibition.

Pharmacokinetic interactions and dosing rationale for antiepileptic drugs in adults and children.

AIMS: Population pharmacokinetic modelling has been widely used across many therapeutic areas to identify sources of variability, which are incorporated into models as covariate factors. Despite numerous publications on pharmacokinetic drug-drug interactions (DDIs) between antiepileptic drugs (AEDs), such data are not used to support the dose rationale for polytherapy in the treatment of epileptic seizures. Here we assess the impact of DDIs on plasma concentrations and evaluate the need for AED dose adjustment. METHODS: Models describing the pharmacokinetics of carbamazepine, clobazam, clonazepam, lamotrigine, levetiracetam, oxcarbazepine, phenobarbital, phenytoin, topiramate, valproic acid and zonisamide in adult and paediatric patients were collected from the published literature and implemented in NONMEM v7.2. Taking current clinical practice into account, we explore simulation scenarios to characterize AED exposure in virtual patients receiving mono- and polytherapy. Steady-state, maximum and minimum concentrations were selected as parameters of interest for this analysis. RESULTS: Our simulations show that DDIs can cause major changes in AED concentrations both in adults and children. When more than one AED is used, even larger changes are observed in the concentrations of the primary drug, leading to significant differences in steady-state concentration between mono- and polytherapy for most AEDs. These results suggest that currently recommended dosing algorithms and titration procedures do not ensure attainment of appropriate therapeutic concentrations. CONCLUSIONS: The effect of DDIs on AED exposure cannot be overlooked. Clinical guidelines must consider such covariate effects and ensure appropriate dosing recommendations for adult and paediatric patients who require combination therapy.

Correction to: Modelling the delay between pharmacokinetics and EEG effects of morphine in rats: binding kinetic versus effect compartment models.

The original version of this article was published open access. Unfortunately, due to a technical issue, the copyright holder name in the online version (HTML and XML) is incorrectly published as "Springer Science+Business Media, LLC, part of Springer Nature 2018". Instead, it should be "The Author(s) 2018".

Modelling the delay between pharmacokinetics and EEG effects of morphine in rats: binding kinetic versus effect compartment models.

Drug-target binding kinetics (as determined by association and dissociation rate constants, kon and koff) can be an important determinant of the kinetics of drug action. However, the effect compartment model is used most frequently instead of a target binding model to describe hysteresis. Here we investigate when the drug-target binding model should be used in lieu of the effect compartment model. The utility of the effect compartment (EC), the target binding kinetics (TB) and the combined effect compartment-target binding kinetics (EC-TB) model were tested on either plasma (ECPL, TBPL and EC-TBPL) or brain extracellular fluid (ECF) (ECECF, TBECF and EC-TBECF) morphine concentrations and EEG amplitude in rats. It was also analyzed when a significant shift in the time to maximal target occupancy (TmaxTO) with increasing dose, the discriminating feature between the TB and EC model, occurs in the TB model. All TB models assumed a linear relationship between target occupancy and drug effect on the EEG amplitude. All three model types performed similarly in describing the morphine pharmacodynamics data, although the EC model provided the best statistical result. The analysis of the shift in TmaxTO (∆TmaxTO) as a result of increasing dose revealed that ∆TmaxTO is decreasing towards zero if the koff is much smaller than the elimination rate constant or if the target concentration is larger than the initial morphine concentration. The results for the morphine PKPD modelling and the analysis of ∆TmaxTO indicate that the EC and TB models do not necessarily lead to different drug effect versus time curves for different doses if a delay between drug concentrations and drug effect (hysteresis) is described. Drawing mechanistic conclusions from successfully fitting one of these two models should therefore be avoided. Since the TB model can be informed by in vitro measurements of kon and koff, a target binding model should be considered more often for mechanistic modelling purposes.

Chronobiology and Pharmacologic Modulation of the Renin-Angiotensin-Aldosterone System in Dogs: What Have We Learned?

Congestive heart failure (CHF) is a primary cause of morbidity and mortality with an increasing prevalence in human and canine populations. Recognition of the role of renin-angiotensin-aldosterone system (RAAS) overactivation in the pathophysiology of CHF has led to significant medical advances. By decreasing systemic vascular resistance and angiotensin II (AII) production, angiotensin-converting enzyme (ACE) inhibitors such as benazepril improve cardiac hemodynamics and reduce mortality in human and dog CHF patients. Although several experiments have pointed out that efficacy of ACE inhibitors depends on the time of administration, little attention is paid to the optimum time of dosing of these medications. A thorough characterization of the chronobiology of the renin cascade has the potential to streamline the therapeutic management of RAAS-related diseases and to help determining the optimal time of drug administration that maximizes efficacy of ACE inhibitors, while minimizing the occurrence of adverse effects. We have developed an integrated pharmacokinetic-pharmacodynamic model that adequately captures the disposition kinetics of the paradigm drug benazeprilat, as well as the time-varying changes of systemic renin-angiotensin-aldosterone biomarkers, without and with ACE inhibition therapy. Based on these chronobiological investigations, the optimal efficacy of ACE inhibitors is expected with bedtime dosing. The data further show that benazepril influences the dynamics of the renin-angiotensin-aldosterone cascade, resulting in a profound decrease in AII and aldosterone (ALD), while increasing renin activity for about 24 h. From the results of recent investigations in human, it is hypothesized that reduction of AII and ALD is one of the drivers of increased survival and improved quality of life in dogs receiving ACE inhibitors. To support and consolidate this hypothesis, additional efforts should be directed toward the collection of circulating RAAS peptides in spontaneous cases of canine CHF. If such a link could be established, profiling of these biomarkers could support determination of the severity of heart failure, complement clinical and echocardiographic findings, and be used for therapeutic drug monitoring purposes.

Characterization and Prediction of Cardiovascular Effects of Fingolimod and Siponimod Using a Systems Pharmacology Modeling Approach.

Sphingosine 1-phosphate (S1P) receptor agonists are associated with cardiovascular effects in humans. This study aims to develop a systems pharmacology model to identify the site of action (i.e., primary hemodynamic response variable) of S1P receptor agonists, and to predict, in a quantitative manner, the cardiovascular effects of novel S1P receptor agonists in vivo. The cardiovascular effects of once-daily fingolimod (0, 0.1, 0.3, 1, 3, and 10 mg/kg) and siponimod (3 and 15 mg/kg) were continuously recorded in spontaneously hypertensive rats and Wistar-Kyoto rats. The results were analyzed using a recently developed systems cardiovascular pharmacology model, i.e. the CVS model; total peripheral resistance and heart rate were identified as the site of action for fingolimod. Next, the CVS model was interfaced with an S1P agonist pharmacokinetic-pharmacodynamic (PKPD) model. This combined model adequately predicted, in a quantitative manner, the cardiovascular effects of siponimod using in vitro binding assays. In conclusion, the combined CVS and S1P agonist PKPD model adequately describes the hemodynamic effects of S1P receptor agonists in rats and constitutes a basis for the prediction, in a strictly quantitative manner, of the cardiovascular effects of novel S1P receptor agonists.

Translating QT interval prolongation from conscious dogs to humans.

AIM: In spite of screening procedures in early drug development, uncertainty remains about the propensity of new chemical entities (NCEs) to prolong the QT/QTc interval. The evaluation of proarrhythmic activity using a comprehensive in vitro proarrhythmia assay does not fully account for pharmacokinetic-pharmacodynamic (PKPD) differences in vivo. In the present study, we evaluated the correlation between drug-specific parameters describing QT interval prolongation in dogs and in humans. METHODS: Using estimates of the drug-specific parameter, data on the slopes of the PKPD relationships of nine compounds with varying QT-prolonging effects (cisapride, sotalol, moxifloxacin, carabersat, GSK945237, SB237376 and GSK618334, and two anonymized NCEs) were analysed. Mean slope estimates varied between -0.98 ms µM-1 and 6.1 ms µM-1 in dogs and -10 ms µM-1 and 90 ms µM-1 in humans, indicating a wide range of effects on the QT interval. Linear regression techniques were then applied to characterize the correlation between the parameter estimates across species. RESULTS: For compounds without a mixed ion channel block, a correlation was observed between the drug-specific parameter in dogs and humans (y = -1.709 + 11.6x; R2  = 0.989). These results show that per unit concentration, the drug effect on the QT interval in humans is 11.6-fold larger than in dogs. CONCLUSIONS: Together with information about the expected therapeutic exposure, the evidence of a correlation between the compound-specific parameter in dogs and in humans represents an opportunity for translating preclinical safety data before progression into the clinic. Whereas further investigation is required to establish the generalizability of our findings, this approach can be used with clinical trial simulations to predict the probability of QT prolongation in humans.

The variability in beta-cell function in placebo-treated subjects with type 2 diabetes: application of the weight-HbA1c-insulin-glucose (WHIG) model.

AIM: The weight-glycosylated haemoglobin (HbA1C)-insulin-glucose (WHIG) model describes the effects of changes in weight on insulin sensitivity (IS) in newly diagnosed, obese subjects receiving placebo treatment. This model was applied to a wider population of placebo-treated subjects, to investigate factors influencing the variability in IS and ß-cell function. METHODS: The WHIG model was applied to the WHIG dataset (Study 1) and two other placebo datasets (Studies 2 and 3). Studies 2 and 3 consisted of nonobese subjects and subjects with advanced type 2 diabetes mellitus (T2DM). Body weight, fasting serum insulin (FSI), fasting plasma glucose (FPG) and HbA1c were used for nonlinear mixed-effects modelling (using NONMEM v7.2 software). Sources of interstudy variability (ISV) and potential covariates (age, gender, diabetes duration, ethnicity, compliance) were investigated. RESULTS: An ISV for baseline parameters (body weight and ß-cell function) was required. The baseline ß-cell function was significantly lower in subjects with advanced T2DM (median difference: Study 2: 15.6%, P < 0.001; Study 3: 22.7%, P < 0.001) than in subjects with newly diagnosed T2DM (Study 1). A reduction in the estimated insulin secretory response in subjects with advanced T2DM was observed but diabetes duration was not a significant covariate. CONCLUSION: The WHIG model can be used to describe the changes in weight, IS and ß-cell function in the diabetic population. IS remained relatively stable between subjects but a large ISV in ß-cell function was observed. There was a trend towards decreasing ß-cell responsiveness with diabetes duration, and further studies, incorporating subjects with a longer history of diabetes, are required.

A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations.

PURPOSE: Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human brain disposition. METHODS: A mathematical model consisting of several physiological brain compartments in the rat was developed using rich concentration-time profiles from nine structurally diverse drugs in plasma, brain extracellular fluid, and two cerebrospinal fluid compartments. The effect of active drug transporters was also accounted for. Subsequently, the model was translated to predict human concentration-time profiles for acetaminophen and morphine, by scaling or replacing system- and drug-specific parameters in the model. RESULTS: A common model structure was identified that adequately described the rat pharmacokinetic profiles for each of the nine drugs across brain compartments, with good precision of structural model parameters (relative standard error <37.5%). The model predicted the human concentration-time profiles in different brain compartments well (symmetric mean absolute percentage error <90%). CONCLUSIONS: A multi-compartmental brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations.

Systems Pharmacology Analysis of the Amyloid Cascade after ß-Secretase Inhibition Enables the Identification of an Aß42 Oligomer Pool.

The deposition of amyloid-ß (Aß) oligomers in brain parenchyma has been implicated in the pathophysiology of Alzheimer's disease. Here we present a systems pharmacology model describing the changes in the amyloid precursor protein (APP) pathway after administration of three different doses (10, 30, and 125 mg/kg) of the ß-secretase 1 (BACE1) inhibitor MBi-5 in cisterna magna ported rhesus monkeys. The time course of the MBi-5 concentration in plasma and cerebrospinal fluid (CSF) was analyzed in conjunction with the effect on the concentrations of the APP metabolites Aß42, Aß40, soluble ß-amyloid precursor protein (sAPP) α, and sAPPß in CSF. The systems pharmacology model contained expressions to describe the production, elimination, and brain-to-CSF transport for the APP metabolites. Upon administration of MBi-5, a dose-dependent increase of the metabolite sAPPα and dose-dependent decreases of sAPPß and Aß were observed. Maximal inhibition of BACE1 was close to 100% and the IC50 value was 0.0256 µM (95% confidence interval, 0.0137-0.0375). A differential effect of BACE1 inhibition on Aß40 and Aß42 was observed, with the Aß40 response being larger than the Aß42 response. This enabled the identification of an Aß42 oligomer pool in the systems pharmacology model. These findings indicate that decreases in monomeric Aß responses resulting from BACE1 inhibition are partially compensated by dissociation of Aß oligomers and suggest that BACE1 inhibition may also reduce the putatively neurotoxic oligomer pool.

Pharmacology-based toxicity assessment: towards quantitative risk prediction in humans.

Despite ongoing efforts to better understand the mechanisms underlying safety and toxicity, ~30% of the attrition in drug discovery and development is still due to safety concerns. Changes in current practice regarding the assessment of safety and toxicity are required to reduce late stage attrition and enable effective development of novel medicines. This review focuses on the implications of empirical evidence generation for the evaluation of safety and toxicity during drug development. A shift in paradigm is needed to (i) ensure that pharmacological concepts are incorporated into the evaluation of safety and toxicity; (ii) facilitate the integration of historical evidence and thereby the translation of findings across species as well as between in vitro and in vivo experiments and (iii) promote the use of experimental protocols tailored to address specific safety and toxicity questions. Based on historical examples, we highlight the challenges for the early characterisation of the safety profile of a new molecule and discuss how model-based methodologies can be applied for the design and analysis of experimental protocols. Issues relative to the scientific rationale are categorised and presented as a hierarchical tree describing the decision-making process. Focus is given to four different areas, namely, optimisation, translation, analytical construct and decision criteria. From a methodological perspective, the relevance of quantitative methods for estimation and extrapolation of risk from toxicology and safety pharmacology experimental protocols, such as points of departure and potency, is discussed in light of advancements in population and Bayesian modelling techniques (e.g. non-linear mixed effects modelling). Their use in the evaluation of pharmacokinetics (PK) and pharmacokinetic-pharmacodynamic relationships (PKPD) has enabled great insight into the dose rationale for medicines in humans, both in terms of efficacy and adverse events. Comparable benefits can be anticipated for the assessment of safety and toxicity profile of novel molecules.

Pooled population pharmacokinetic model of imipenem in plasma and the lung epithelial lining fluid.

AIMS: Several clinical trials have confirmed the therapeutic benefit of imipenem for treatment of lung infections. There is however no knowledge of the penetration of imipenem into the lung epithelial lining fluid (ELF), the site of action relevant for lung infections. Furthermore, although the plasma pharmacokinetics (PK) of imipenem has been widely studied, most studies have been based on selected patient groups. The aim of this analysis was to characterize imipenem plasma PK across populations and to quantify imipenem ELF penetration. METHODS: A population model for imipenem plasma PK was developed using data obtained from healthy volunteers, elderly subjects and subjects with renal impairment, in order to identify predictors for inter-individual variability (IIV) of imipenem PK. Subsequently, a clinical study which measured plasma and ELF concentrations of imipenem was included in order to quantify lung penetration. RESULTS: A two compartmental model best described the plasma PK of imipenem. Creatinine clearance and body weight were included as subject characteristics predictive for IIV on clearance. Typical estimates for clearance, central and peripheral volume, and inter-compartmental clearance were 11.5 l h(-1) , 9.37 l, 6.41 l, 13.7 l h(-1) , respectively (relative standard error (RSE) <8%). The distribution of imipenem into ELF was described using a time-independent penetration coefficient of 0.44 (RSE 14%). CONCLUSION: The identified lung penetration coefficient confirms the clinical relevance of imipenem for treatment of lung infections, while the population PK model provided insights into predictors of IIV for imipenem PK and may be of relevance to support dose optimization in various subject groups.

Exploring genetic and non-genetic risk factors for delayed graft function, acute and subclinical rejection in renal transplant recipients.

AIMS: This study aimed at identifying pharmacological factors such as pharmacogenetics and drug exposure as new predictive biomarkers for delayed graft function (DGF), acute rejection (AR) and/or subclinical rejection (SCR). METHODS: Adult renal transplant recipients (n = 361) on cyclosporine-based immunosuppression were followed for the first 6 months after transplantation. The incidence of DGF and AR were documented as well as the prevalence of SCR at 6 months in surveillance biopsies. Demographic, transplant-related factors, pharmacological and pharmacogenetic factors (ABCB1, CYP3A5, CYP3A4, CYP2C8, NR1I2, PPP3CA and PPP3CB) were analysed in a combined approach in relation to the occurrence of DGF, AR and prevalence of SCR at month 6 using a proportional odds model and time to event model. RESULTS: Fourteen per cent of the patients experienced at least one clinical rejection episode and only DGF showed a significant effect on the time to AR. The incidence of DGF correlated with a deceased donor kidney transplant (27% vs. 0.6% of living donors). Pharmacogenetic factors were not associated with risk for DGF, AR or SCR. A deceased donor kidney and acute rejection history were the most important determinants for SCR, resulting in a 52% risk of SCR at 6 months (vs. 11% average). In a sub-analysis of the patients with AR, those treated with rejection treatment including ATG, significantly less frequent SCR was found in the 6-month biopsy (13% vs. 50%). CONCLUSIONS: Transplant-related factors remain the most important determinants of DGF, AR and SCR. Furthermore, rejection treatment with depleting antibodies effectively prevented SCR in 6-month surveillance biopsies.

Model-Based Optimisation of Deferoxamine Chelation Therapy.

PURPOSE: Here we show how a model-based approach may be used to provide further insight into the role of clinical and demographic covariates on the progression of iron overload. The therapeutic effect of deferoxamine is used to illustrate the application of disease modelling as a means to characterising treatment response in individual patients. METHODS: Serum ferritin, demographic characteristics and individual treatment data from clinical routine practice on 27 patients affected by ß-thalassaemia major were used for the purposes of this analysis. The time course of serum ferritin was described by a hierarchical nonlinear mixed effects model, in which compliance was parameterised as a covariate factor. Modelling and simulation procedures were implemented in NONMEM (7.2.0). RESULTS: A turnover model best described serum ferritin changes over time, with the effect of blood transfusions introduced on the ferritin conversion rate and the effect of deferoxamine on the elimination parameter (Kout) in a proportional manner. The results of the simulations showed that poor quality of execution is preferable over drug holidays; and that independently of the compliance pattern, the therapeutic intervention is not effective if >60% of the doses are missed. CONCLUSIONS: Modelling of ferritin response enables characterisation of the dynamics of iron overload due to chronic transfusion. The approach can be used to support decision making in clinical practice, including personalisation of the dose for existing and novel chelating agents.

Structure-Based Prediction of Anti-infective Drug Concentrations in the Human Lung Epithelial Lining Fluid.

PURPOSE: Obtaining pharmacologically relevant exposure levels of antibiotics in the epithelial lining fluid (ELF) is of critical importance to ensure optimal treatment of lung infections. Our objectives were to develop a model for the prediction of the ELF-plasma concentration ratio (EPR) of antibiotics based on their chemical structure descriptors (CSDs). METHODS: EPR data was obtained by aggregating ELF and plasma concentrations from historical clinical studies investigating antibiotics and associated agents. An elastic net regularized regression model was used to predict EPRs based on a large number of CSDs. The model was tuned using leave-one-drug-out cross validation, and the predictions were further evaluated using a test dataset. RESULTS: EPR data of 56 unique compounds was included. A high degree of variability in EPRs both between- and within drugs was apparent. No trends related to study design or pharmacokinetic factors could be identified. The model predicted 80% of the within-drug variability (R(2) WDV) and 78.6% of drugs were within 3-fold difference from the observations. Key CSDs were related to molecular size and lipophilicity. When predicting EPRs for a test dataset the R(2) WDV was 75%. CONCLUSIONS: This model is of relevance to inform dose selection and optimization during antibiotic drug development of agents targeting lung infections.

Translational Modeling in Schizophrenia: Predicting Human Dopamine D2 Receptor Occupancy.

OBJECTIVES: To assess the ability of a previously developed hybrid physiology-based pharmacokinetic-pharmacodynamic (PBPKPD) model in rats to predict the dopamine D2 receptor occupancy (D2RO) in human striatum following administration of antipsychotic drugs. METHODS: A hybrid PBPKPD model, previously developed using information on plasma concentrations, brain exposure and D2RO in rats, was used as the basis for the prediction of D2RO in human. The rat pharmacokinetic and brain physiology parameters were substituted with human population pharmacokinetic parameters and human physiological information. To predict the passive transport across the human blood-brain barrier, apparent permeability values were scaled based on rat and human brain endothelial surface area. Active efflux clearance in brain was scaled from rat to human using both human brain endothelial surface area and MDR1 expression. Binding constants at the D2 receptor were scaled based on the differences between in vitro and in vivo systems of the same species. The predictive power of this physiology-based approach was determined by comparing the D2RO predictions with the observed human D2RO of six antipsychotics at clinically relevant doses. RESULTS: Predicted human D2RO was in good agreement with clinically observed D2RO for five antipsychotics. Models using in vitro information predicted human D2RO well for most of the compounds evaluated in this analysis. However, human D2RO was under-predicted for haloperidol. CONCLUSIONS: The rat hybrid PBPKPD model structure, integrated with in vitro information and human pharmacokinetic and physiological information, constitutes a scientific basis to predict the time course of D2RO in man.

Model-based evaluation of drug-induced QTc prolongation for compounds in early development.

AIMS: Significant differences between dogs and humans have been observed in the concentration-QTc effect relationship of compounds with known pro-arrhythmic properties. These findings suggest that interspecies differences must be considered when evaluating drug effects. The aim of this study was to evaluate the performance of a model-based approach to assess the risk of QTc prolongation for three investigational compounds (NCE01, NCE02 and NCE03). METHODS: Pharmacokinetic and pharmacodynamic data from experiments in conscious dogs and healthy subjects were included in this analysis. Pharmacokinetic modelling and deconvolution methods were applied to derive drug concentrations at the time of each QT measurement. An integrated pharmacokinetic-pharmacodynamic (PKPD) model was then used to describe QT prolongation. A threshold of ≥10 ms was used to characterize the probability of QTc prolongation. RESULTS: The PKPD relationships of all three compounds were successfully described in both species. A strong effect was observed after administration of NCE01 to dogs and humans, with a slope of 0.0061 and 0.0662 ms nm(-1), respectively, and maximal probability of QTc prolongation ≥10 ms at peak concentration. For NCE02 and NCE03, QTc-shortening and borderline QT effects were observed both in dogs and humans, as described by negative or very shallow slopes (NCE02, -0.00098 and -0.01 ms nm(-1); NCE03, 0.00064 and -0.0002 ms nm(-1)). CONCLUSIONS: Whilst NEC01 shows clear pro-arrhythmic effects, the liability for QT/QTc prolongation for NCE02 and NCE03 can be deemed low at the expected therapeutic exposure. Moreover, our results show the advantages of an integrated PKPD approach as the basis for translating pro-arrhythmic effects from dogs to humans.

Integration of PKPD relationships into benefit-risk analysis.

AIM: Despite the continuous endeavour to achieve high standards in medical care through effectiveness measures, a quantitative framework for the assessment of the benefit-risk balance of new medicines is lacking prior to regulatory approval. The aim of this short review is to summarise the approaches currently available for benefit-risk assessment. In addition, we propose the use of pharmacokinetic-pharmacodynamic (PKPD) modelling as the pharmacological basis for evidence synthesis and evaluation of novel therapeutic agents. METHODS: A comprehensive literature search has been performed using MESH terms in PubMed, in which articles describing benefit-risk assessment and modelling and simulation were identified. In parallel, a critical review of multi-criteria decision analysis (MCDA) is presented as a tool for characterising a drug's safety and efficacy profile. RESULTS: A definition of benefits and risks has been proposed by the European Medicines Agency (EMA), in which qualitative and quantitative elements are included. However, in spite of the value of MCDA as a quantitative method, decisions about benefit-risk balance continue to rely on subjective expert opinion. By contrast, a model-informed approach offers the opportunity for a more comprehensive evaluation of benefit-risk balance before extensive evidence is generated in clinical practice. CONCLUSIONS: Benefit-risk balance should be an integral part of the risk management plan and as such considered before marketing authorisation. Modelling and simulation can be incorporated into MCDA to support the evidence synthesis as well evidence generation taking into account the underlying correlations between favourable and unfavourable effects. In addition, it represents a valuable tool for the optimization of protocol design in effectiveness trials.