A Pediatric Covariate Function for CYP3A-Mediated Midazolam Clearance Can Scale Clearance of Selected CYP3A Substrates in Children.

Recently a framework was presented to assess whether pediatric covariate models for clearance can be extrapolated between drugs sharing elimination pathways, based on extraction ratio, protein binding, and other drug properties. Here we evaluate when a pediatric covariate function for midazolam clearance can be used to scale clearance of other CYP3A substrates. A population PK model including a covariate function for clearance was developed for midazolam in children aged 1-17 years. Commonly used CYP3A substrates were selected and using the framework, it was assessed whether the midazolam covariate function accurately scales their clearance. For eight substrates, reported pediatric clearance values were compared numerically and graphically with clearance values scaled using the midazolam covariate function. For sildenafil, clearance values obtained with population PK modeling based on pediatric concentration-time data were compared with those scaled with the midazolam covariate function. According to the framework, a midazolam covariate function will lead to systemically accurate clearance scaling (absolute prediction error (PE) < 30%) for CYP3A substrates binding to albumin with an extraction ratio between 0.35 and 0.65 when binding < 10% in adults, between 0.05 and 0.55 when binding > 90%, and with an extraction ratio ranging between these values when binding between 10 and 90%. Scaled clearance values for eight commonly used CYP3A substrates were reasonably accurate (PE < 50%). Scaling of sildenafil clearance was accurate (PE < 30%). We defined for which CYP3A substrates a pediatric covariate function for midazolam clearance can accurately scale plasma clearance in children. This scaling approach may be useful for CYP3A substrates with scarce or no available pediatric PK information.

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.

Sacubitril/valsartan (LCZ696) significantly reduces aldosterone and increases cGMP circulating levels in a canine model of RAAS activation.

Simultaneous blockade of angiotensin receptors and enhancement of natriuretic peptides (NP) by the first-in-class angiotensin receptor neprilysin (NEP) inhibitor sacubitril/valsartan constitutes an effective approach to treating heart failure. This study examined the effects of sacubitril/valsartan (225 and 675 mg/day) vs. placebo, sacubitril (360 mg/day), valsartan (900 mg/day), and benazepril (5 mg/day) on the dynamics of the renin-angiotensin-aldosterone system (RAAS) and the NP system in dogs. Beagle dogs (n = 18) were fed a low-salt diet (0.05% Na) for 15 days to model RAAS activation observed in clinical heart failure. Drugs were administered once daily during the last 10 days, while the effects on the RAAS and NPs were assessed on Day 1, 5, and 10. Steady-state pharmacokinetics of the test agents were evaluated on Day 5. Compared with placebo, sacubitril/valsartan (675 mg) substantially increased cGMP circulating levels, while benazepril and valsartan showed no effect. Additionally, sacubitril/valsartan (675 mg) and valsartan significantly increased plasma renin activity, angiotensin I and angiotensin II concentrations. Finally, sacubitril/valsartan (both doses), and valsartan significantly decreased plasma aldosterone vs. placebo. Systemic exposure to valsartan following sacubitril/valsartan 675 mg administration was similar to that observed with valsartan 900 mg administration alone. Sacubitril/valsartan favorably modulates the dynamics of the renin and NP cascades through complementary NEP and RAAS inhibition.

The future of drug development: the paradigm shift towards systems therapeutics.

Progress in cell biology, genetics, molecular, and systems pharmacology is the driving force behind a current paradigm shift in drug research. This paradigm shift shapes new avenues for advanced treatments that are commonly referred to as 'systems therapeutics'. Systems therapeutics differ in many ways from current drugs because they target biological networks rather than single transduction pathways, and affect disease processes rather than physiological processes. Here, we examine how the paradigm shift towards systems therapeutics will change current scientific concepts of the interactions between drugs and diseases, the organization of research and development, as well as the clinical use and therapeutic evaluations of therapeutic interventions.

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".

In vitro and in silico analysis of the effects of D2 receptor antagonist target binding kinetics on the cellular response to fluctuating dopamine concentrations.

BACKGROUND AND PURPOSE: Target binding kinetics influence the time course of the drug effect (pharmacodynamics) both (i) directly, by affecting the time course of target occupancy, driven by the pharmacokinetics of the drug, competition with endogenous ligands and target turnover, and (ii) indirectly, by affecting signal transduction and homeostatic feedback. For dopamine D2 receptor antagonists, it has been hypothesized that fast receptor binding kinetics cause fewer side effects, because part of the dynamics of the dopaminergic system is preserved by displacement of these antagonists. EXPERIMENTAL APPROACH: Target binding kinetics of D2 receptor antagonists and signal transduction after dopamine and D2 receptor antagonist exposure were measured in vitro. These data were integrated by mechanistic modelling, taking into account competitive binding of endogenous dopamine and the antagonist, the turnover of the second messenger cAMP and negative feedback by PDE turnover. KEY RESULTS: The proposed signal transduction model successfully described the cellular cAMP response for 17 D2 receptor antagonists with widely different binding kinetics. Simulation of the response to fluctuating dopamine concentrations revealed that a significant effect of the target binding kinetics on the dynamics of the signalling only occurs at endogenous dopamine concentration fluctuations with frequencies below 1 min-1 . CONCLUSIONS AND IMPLICATIONS: Signal transduction and feedback are important determinants of the time course of drug effects. The effect of the D2 receptor antagonist dissociation rate constant (koff ) is limited to the maximal rate of fluctuations in dopamine signalling as determined by the dopamine koff and the cAMP turnover.

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.

Correction to: Effect of Age-Related Factors on the Pharmacokinetics of Lamotrigine and Potential Implications for Maintenance Dose Optimisation in Future Clinical Trials.

Effect of Age-Related Factors on the Pharmacokinetics of Lamotrigine and Potential Implications for Dose Optimisation in Epilepsy Patients should read.

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.

Drugs Being Eliminated via the Same Pathway Will Not Always Require Similar Pediatric Dose Adjustments.

For scaling drug plasma clearance (CLp) from adults to children, extrapolations of population pharmacokinetic (PopPK) covariate models between drugs sharing an elimination pathway have enabled accelerated development of pediatric models and dosing recommendations. This study aims at identifying conditions for which this approach consistently leads to accurate pathway specific CLp scaling from adults to children for drugs undergoing hepatic metabolism. A physiologically based pharmacokinetic (PBPK) simulation workflow utilizing mechanistic equations defining hepatic metabolism was developed. We found that drugs eliminated via the same pathway require similar pediatric dose adjustments only in specific cases, depending on drugs extraction ratio, unbound fraction, type of binding plasma protein, and the fraction metabolized by the isoenzyme pathway for which CLp is scaled. Overall, between-drug extrapolation of pediatric covariate functions for CLp is mostly applicable to low and intermediate extraction ratio drugs eliminated by one isoenzyme and binding to human serum albumin in children older than 1 month.

Effect of Age-Related Factors on the Pharmacokinetics of Lamotrigine and Potential Implications for Maintenance Dose Optimisation in Future Clinical Trials.

BACKGROUND AND AIMS: In this study, we evaluate the performance of allometric concepts to predict the implications of age and size on the pharmacokinetics of lamotrigine, and assess the dose rationale across different age groups from 0.2 to 91 years. METHODS: An allometrically scaled pharmacokinetic model was developed using adolescent and adult data, taking into account the effect of comedications. Model parameters were then used to extrapolate lamotrigine pharmacokinetics to older adults (> 65 years), children (4-12 years) and infants and toddlers (0.2-2.0 years). In addition, simulations were performed to identify the implication of different doses and dosing regimens for each population, so as to ensure steady-state concentrations within a predefined reference range. RESULTS: The pharmacokinetics of lamotrigine was best described using a one-compartment model with first-order absorption and elimination. Carbamazepine, phenytoin, and valproic acid changed systemic clearance (CL) by + 76.5, + 129, and - 47.4%, respectively. Allometric principles allowed accurate extrapolation of disposition parameters to older adults and children older than 4 years of age. A maturation function was required to describe changes in exposure in younger patients. Compared with adults, a child aged 1.7 years has a 31.5% higher CL, after correcting for body weight. Patients > 65 years of age showed a decrease in CL of approximately 15%. CONCLUSION: Population pharmacokinetic models are usually limited to a subgroup of patients, which may mask the identification of factors contributing to interindividual variability. The availability of an integrated model including the whole patient population provides insight into the role of age-related changes in the disposition of lamotrigine, and potential implications for maintenance dose optimisation in any future trials. TRIAL REGISTRATION: According to GlaxoSmithKline's Clinical Trial Register, data from the GlaxoSmithKline studies LAM100034 and LEP103944, corresponding to ClinicalTrials.gov identifiers NCT00113165 and NCT00264615, used in this work, have been used in previous publications (doi: https://doi.org/10.1212/01.wnl.0000277698.33743.8b , https://doi.org/10.1111/j.1528-1167.2007.01274.x ).

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.

Individualized Dosing Algorithms and Therapeutic Monitoring for Antiepileptic Drugs.

Pharmacokinetic (PK) models exist for most antiepileptic drugs (AEDs). Yet their use in clinical practice to assess interindividual differences and derive individualized doses has been limited. Here we show how model-based dosing algorithms can be used to ensure attainment of target exposure and improve treatment response in patients. Using simulations, different treatment scenarios were explored for 11 commonly used AEDs. For each drug, five scenarios were considered: 1) all patients receive the same dose. 2) Individual clearance (CL), as predicted by population PK models, is used to personalize treatment. 3-5) Individual CL, obtained by therapeutic drug monitoring (TDM) according to different sampling schemes, is used to personalize treatment. Attainment of steady-state target exposure was used as the performance criterion to rank each scenario. In contrast to current clinical guidelines, our results show that patient demographic and clinical characteristics should be used in conjunction with TDM to personalize the treatment of seizures.

Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach.

Knowledge of drug concentration-time profiles at the central nervous system (CNS) target-site is critically important for rational development of CNS targeted drugs. Our aim was to translate a recently published comprehensive CNS physiologically-based pharmacokinetic (PBPK) model from rat to human, and to predict drug concentration-time profiles in multiple CNS compartments on available human data of four drugs (acetaminophen, oxycodone, morphine and phenytoin). Values of the system-specific parameters in the rat CNS PBPK model were replaced by corresponding human values. The contribution of active transporters for the four selected drugs was scaled based on differences in expression of the pertinent transporters in both species. Model predictions were evaluated with available pharmacokinetic (PK) data in human brain extracellular fluid and/or cerebrospinal fluid, obtained under physiologically healthy CNS conditions (acetaminophen, oxycodone, and morphine) and under pathophysiological CNS conditions where CNS physiology could be affected (acetaminophen, morphine and phenytoin). The human CNS PBPK model could successfully predict their concentration-time profiles in multiple human CNS compartments in physiological CNS conditions within a 1.6-fold error. Furthermore, the model allowed investigation of the potential underlying mechanisms that can explain differences in CNS PK associated with pathophysiological changes. This analysis supports the relevance of the developed model to allow more effective selection of CNS drug candidates since it enables the prediction of CNS target-site concentrations in humans, which are essential for drug development and patient treatment.

Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ8-tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

Modeling of prolactin response following dopamine D2 receptor antagonists in rats: can it be translated to clinical dosing?

Prolactin release is a side effect of antipsychotic therapy with dopamine antagonists, observed in rats as well as humans. We examined whether two semimechanistic models could describe prolactin response in rats and subsequently be translated to predict pituitary dopamine D2 receptor occupancy and plasma prolactin concentrations in humans following administration of paliperidone or remoxipride. Data on male Wistar rats receiving single or multiple doses of risperidone, paliperidone, or remoxipride was described by two semimechanistic models, the precursor pool model and the agonist-antagonist interaction model. Using interspecies scaling approaches, human D2 receptor occupancy and plasma prolactin concentrations were predicted for a range of clinical paliperidone and remoxipride doses. The predictions were compared with corresponding observations described in literature as well as with predictions from published models developed on human data. The pool model could predict D2 receptor occupancy and prolactin response in humans following single doses of paliperidone and remoxipride. Tolerance of prolactin release was predicted following multiple doses. The interaction model underpredicted both D2 receptor occupancy and prolactin response. Prolactin elevation may be deployed as a suitable biomarker for interspecies translation and can inform the clinical safe and effective dose range of antipsychotic drugs. While the pool model was more predictive than the interaction model, it overpredicted tolerance on multiple dosing. Shortcomings of the translations reflect the need for better mechanistic models.

Predicting Drug Concentration-Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically-Based Pharmacokinetic Model.

Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS compartments. System-specific and drug-specific model parameters were derived from literature and in silico predictions. The model was validated using detailed concentration-time profiles from 10 drugs in rat plasma, brain extracellular fluid, 2 cerebrospinal fluid sites, and total brain tissue. These drugs, all small molecules, were selected to cover a wide range of physicochemical properties. The concentration-time profiles for these drugs were adequately predicted across the CNS compartments (symmetric mean absolute percentage error for the model prediction was <91%). In conclusion, the developed PBPK model can be used to predict temporal concentration profiles of drugs in multiple relevant CNS compartments, which we consider valuable information for efficient CNS drug development.

Characterizing QT interval prolongation in early clinical development: a case study with methadone.

Recently, we have shown how pharmacokinetic-pharmacodynamic (PKPD) modeling can be used to assess the probability of QT interval prolongation both in dogs and humans. A correlation between species has been identified for a drug-specific parameter, making it possible to prospectively evaluate nonclinical signals. Here, we illustrate how nonclinical data on methadone can be used to support the evaluation of dromotropic drug effects in humans. ECG and drug concentration data from a safety pharmacology study in dogs were analyzed using nonlinear mixed effects modeling. The slope of the PKPD model describing the probability of QT interval prolongation was extrapolated from dogs to humans and subsequently combined with methadone pharmacokinetic data as input for clinical trial simulations. Concentration versus time profiles were simulated for doses between 5 and 500 mg. Predicted peak concentrations in humans were then used as reference value to assess the probability of an increase in QT interval of ≥5 and ≥10 ms. Point estimates for the slope in dogs suggested low probability of ≥10 ms prolongation in humans, whereas an effect of approximately 5 ms increase is predicted when accounting for the 90% credible intervals of the drug-specific parameter in dogs. Interspecies differences in drug disposition appear to explain the discrepancies between predicted and observed QT prolonging effects in humans. Extrapolation of the effects of racemic compound may not be sufficient to describe the increase in QT interval observed after administration of methadone to patients. Assessment of the contribution of enantioselective metabolism and active metabolites is critical.