Respiratory syncytial virus-A dynamics and the effects of lumicitabine, a nucleoside viral replication inhibitor, in experimentally infected humans.

Background: Respiratory syncytial virus (RSV) causes high morbidity, with mortality rates approaching or exceeding that of influenza in adult and infant patient populations, respectively. Lumicitabine (ALS-008176 or JNJ-64041575) is an oral nucleoside analogue prodrug in clinical development to treat RSV infections. This prodrug converts to plasma-circulating ALS-8112, and then to the 5'-active nucleoside triphosphate (NTP) form within host cells. We conducted an RSV-A challenge study in healthy adults to evaluate lumicitabine's activity during an active RSV infection. Objectives: To develop a semi-mechanistic mathematical model describing RSV kinetics, and the pharmacokinetics (PK) and pharmacodynamics (PD) of lumicitabine during treatment. Methods: Nasopharyngeal viral load and concentrations of ALS-8112 and ALS-8144 (uridine metabolite) were measured frequently over the study duration. Population viral kinetic and PK/PD models were developed using NONMEM. The RSV life-cycle was described using a target-cell-limited model that included a physiological delay. Results: The estimated clearances of ALS-8112 and ALS-8144 were 54.2 and 115 L/h/70 kg, respectively. A semi-physiological model was linked to predict ALS-8112 conversion to active intracellular NTP. Extensive and rapid RSV reduction occurred after lumicitabine treatment (EC50 = 1.79 µM), with >99% viral inhibition at 2 h after loading dose. Simulated NTP exposures and time to EC50 attainment suggested that rapid therapeutic effects and reduced dosing frequency are achievable in adult and paediatric patients. Conclusions: The semi-mechanistic model characterizes RSV kinetics and the antiviral effectiveness of lumicitabine in an adult challenge population. This model is applicable to guide dose selection in adult and paediatric patients.

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.

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.

Population Pharmacokinetic Modeling of JNJ-53718678, a Novel Fusion Inhibitor for the Treatment of Respiratory Syncytial Virus: Results from a Phase I, Double-Blind, Randomized, Placebo-Controlled First-in-Human Study in Healthy Adult Subjects.

BACKGROUND: JNJ-53718678 is a potent small-molecule inhibitor of the F-glycoprotein-mediated complex membrane fusion process of the respiratory syncytial virus. Here, we report the pharmacokinetics, the population pharmacokinetic modeling, and the safety and tolerability of JNJ-53718678 from the first-in-human, double-blind, randomized, placebo-controlled phase I study. METHODS: Healthy subjects were randomized (6:3) into five single-dose groups (25-1000 mg) or three multiple-dose groups [250 mg every 24 h (q24h), 500 mg q24h, 250 mg every 12 h; fed conditions for 8 days] to receive JNJ-53718678 or placebo. Blood and urine samples were collected at several timepoints up to 72 h after intake of JNJ-53718678 and analyzed using validated liquid chromatography-mass spectrometry methods. A population pharmacokinetic model was developed and validated. RESULTS: Peak plasma concentrations of JNJ-53718678 increased with increasing single (159 ± 54.9 to 6702 ± 1733 ng/mL) and multiple (on day 8, 1528 ± 256 to 2655 ± 591 ng/mL) doses. Steady-state conditions were reached on day 2 of the 8-day dosing regimen. Less than 4% of JNJ-53718678 was excreted in urine across all dose groups. Mean exposure of JNJ-53718678 was 7% lower in the fed state compared with the fasted state at the same dose. A two-compartment model with first-order absorption with parallel linear and non-linear elimination best described the pharmacokinetics of JNJ-53718678. No covariate effects were observed. CONCLUSIONS: A population pharmacokinetic model that describes the concentration data well with good precision of all parameter estimates was developed and validated. JNJ-53718678 was well tolerated at all single and multiple doses studied.

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.

Summary data of potency and parameter information from semi-mechanistic PKPD modeling of prolactin release following administration of the dopamine D2 receptor antagonists risperidone, paliperidone and remoxipride in rats.

We provide the reader with relevant data related to our recently published paper, comparing two mathematical models to describe prolactin turnover in rats following one or two doses of the dopamine D2 receptor antagonists risperidone, paliperidone and remoxipride, "A comparison of two semi-mechanistic models for prolactin release and prediction of receptor occupancy following administration of dopamine D2 receptor antagonists in rats" (Taneja et al., 2016) [1]. All information is tabulated. Summary level data on the in vitro potencies and the physicochemical properties is presented in Table 1. Model parameters required to explore the precursor pool model are presented in Table 2. In Table 3, estimated parameter comparisons for both models are presented, when separate potencies are estimated for risperidone and paliperidone, as compared to a common potency for both drugs. In Table 4, parameter estimates are compared when the drug effect is parameterized in terms of drug concentration or receptor occupancy.

A comparison of two semi-mechanistic models for prolactin release and prediction of receptor occupancy following administration of dopamine D2 receptor antagonists in rats.

We compared the model performance of two semi-mechanistic pharmacokinetic-pharmacodynamic models, the precursor pool model and the agonist-antagonist interaction model, to describe prolactin response following the administration of the dopamine D2 receptor antagonists risperidone, paliperidone or remoxipride in rats. The time course of pituitary dopamine D2 receptor occupancy was also predicted. Male Wistar rats received a single dose (risperidone, paliperidone, remoxipride) or two consecutive doses (remoxipride). Population modeling was applied to fit the pool and interaction models to the prolactin data. The pool model was modified to predict the time course of pituitary D2 receptor occupancy. Unbound plasma concentrations of the D2 receptor antagonists were considered the drivers of the prolactin response. Both models were used to predict prolactin release following multiple doses of paliperidone. Both models described the data well and model performance was comparable. Estimated unbound EC50 for risperidone and paliperidone was 35.1nM (relative standard error 51%) and for remoxipride it was 94.8nM (31%). KI values for these compounds were 11.1nM (21%) and 113nM (27%), respectively. Estimated pituitary D2 receptor occupancies for risperidone and remoxipride were comparable to literature findings. The interaction model better predicted prolactin profiles following multiple paliperidone doses, while the pool model predicted tolerance better. The performance of both models in describing the prolactin profiles was comparable. The pool model could additionally describe the time course of pituitary D2 receptor occupancy. Prolactin response following multiple paliperidone doses was better predicted by the interaction model.

Population Pharmacokinetic Modeling of Tapentadol Extended Release (ER) in Healthy Subjects and Patients with Moderate or Severe Chronic Pain.

BACKGROUND AND OBJECTIVE: Tapentadol is a centrally acting analgesic with two mechanisms of action, µ-opioid receptor agonism and noradrenaline reuptake inhibition. The objectives were to describe the pharmacokinetic behavior of tapentadol after oral administration of an extended-release (ER) formulation in healthy subjects and patients with chronic pain and to evaluate covariate effects. METHODS: Data were obtained from 2276 subjects enrolled in five phase I and nine phase II and III studies. Nonlinear mixed-effects modeling was conducted using NONMEM. RESULTS: The population estimates of apparent oral clearance and apparent central volume of distribution were 257 L/h and 1870 L, respectively. The complex absorption was described with a transit compartment for the first input. The second input function embraces saturable "binding" in the "absorption compartment", and a time-varying rate constant. Covariate evaluation demonstrated that age, aspartate aminotransferase, and health (painful diabetic neuropathy or not) had a statistically significant effect on apparent clearance, and bioavailability appeared to be dependent on body weight. The pcVPC indicted that the model provided a robust and unbiased fit to the data. CONCLUSIONS: A one-compartment disposition model with two input functions and first-order elimination adequately described the pharmacokinetics of tapentadol ER. The dose-dependency in the pharmacokinetics of tapentadol ER is adequately described by the absorption model. None of the covariates were considered as clinically relevant factors that warrant dose adjustments.

Impact of chronic inflammation on the pharmacokinetic-pharmacodynamic relationship of naproxen.

OBJECTIVES: The use of biomarkers for predicting the clinical doses of analgesic drugs relies on the understanding of the relationship between drug exposure and response under disease conditions. In this study, we demonstrate the relevance of such a relationship for COX-inhibitors by modelling the effect of naproxen on prostaglandin E2 (PGE(2)) and thromboxane B2 (TXB(2)) in a chronic inflammation model in rats. METHODS: Rats were treated with Freund's complete adjuvant (FCA) by intraplantar injection. On post-inoculation days (PID) 7-21, animals received single or chronic (qd until day 21) doses of naproxen (10mg/kg). Blood samples were collected at various intervals after dosing to characterise naproxen pharmacokinetics and its effects on PGE(2) and TXB(2) production. PK-PD modelling was performed using nonlinear mixed effects in NONMEM. RESULTS: The inhibition of PGE(2) and TXB(2) could be described by a sigmoid E(max) model. A decrease in the potency estimates of both biomarkers was observed under chronic inflammation, as compared to healthy animals. IC(50) values for PGE(2) inhibition showed a shift from 2840+/-510 to 4000+/-677ng/ml(mean+/-SD), whilst IC(50) values for TXB(2) inhibition increased from 1180+/-323 to 3360+/-453ng/ml in healthy and FCA-inoculated animals, respectively. CONCLUSIONS: Our results show that chronic inflammation causes a significant change in the potency estimates for COX-inhibition. These findings illustrate the implications of pathophysiological processes on pharmacodynamics and consequently on the required exposure levels for achieving response during chronic treatment.

Differences in the sensitivity of behavioural measures of pain to the selectivity of cyclo-oxygenase inhibitors.

OBJECTIVES: Freund's complete adjuvant (FCA) is an animal model of inflammatory pain commonly used in the screening of COX-inhibitors. However, there is little understanding of how behavioural measures of the anti-inflammatory effect in the FCA model correlate to differences in mechanism of action and whether such endpoints equally reflect drug activity in humans. In the current investigation we evaluate the time course of the analgesic effect for different endpoints after treatment with drugs with varying degrees of selectivity for COX-1 and COX-2. We also assess prostaglandin (PGE(2)) and thromboxane (TXB(2)) inhibition to establish the correlation between behavioural measures and the degree of selectivity for COX-1 and COX-2. METHODS: Sprague-Dawley rats were treated with FCA by intra-plantar injection. On post-inoculation day (PID) 7, rats received a single oral dose of naproxen, diclofenac, ketorolac or rofecoxib. Drug treatment continued until PID 21. A control group received placebo only. Behavioural endpoints for inflammatory pain and blood samples for biomarkers were obtained at various time points before and after dosing to characterise the time course of drug effect and disease progression. RESULTS: COX-inhibitors showed no effect on the dynamic plantar test. In contrast, full analgesia was observed after drug administration for weight bearing capacity (WBC) and paw pressure (PP), with varying duration of the effect for each of the endpoints. No tolerance to drug effect was observed up to 14 days of chronic treatment. Rofecoxib showed an increase in baseline pain threshold values after chronic treatment, which may be related to its pharmacokinetic characteristics. CONCLUSIONS: Changes in paw pressure threshold seem to best reflect the anti-hyperalgesic properties of COX-inhibitors with enough sensitivity to enable estimation of the dose-exposure-response curve.

Correlation between in vitro and in vivo concentration-effect relationships of naproxen in rats and healthy volunteers.

Understanding the mechanisms underlying the analgesic effect of new cyclooxygenase inhibitors is essential to identify dosing requirements in early stages of drug development. Accurate extrapolation to humans of in vitro and in vivo findings in preclinical species is needed to optimise dosing regimen in inflammatory conditions. The current investigation characterises the inhibition of prostaglandin E2 (PGE(2)) and thromboxane B2 (TXB(2)) by naproxen in vitro and in vivo in rat and human blood. The inhibition of PGE(2) in the absence or presence of increasing concentrations of naproxen (10(-8)-10(-1) M) was measured by ex vivo whole blood stimulation with LPS, whereas inhibition of TXB(2) was measured in serum following blood clotting. In further experiments, inhibition of PGE(2) and TXB(2) levels was also assessed ex vivo in animals treated with naproxen (2.5, 10, 25 mg kg(-1)). Subsequently, pharmacokinetic (PK)/pharmacodynamics (PD) modelling of in vitro and in vivo data was performed using nonlinear mixed effects in NONMEM (V). Inhibition of PGE(2) and TXB(2) was characterised by a sigmoid E(max) model. The exposure-response relationships in vitro and in vivo were of the same order of magnitude in both species. IC(80) estimates obtained in vitro were similar for PGE(2) inhibition (130.8 +/- 11 and 131.9 +/- 19 10(-6) M, mean +/- s.d. for humans and rats, respectively), but slightly different for TXB(2) inhibition (103.9+/-15 and 151.4 +/- 40 10(-6) M, mean +/- s.d. for humans and rats, respectively, P < 0.05). These differences, however, may not be biologically relevant. The results confirm the value of exposure-effect relationships determined in vitro as a means to predict the pharmacological activity in vivo. This analysis also highlights the need to parameterise concentration-effect relationships in early drug development, as indicated by the estimates of IC(80) for PGE(2) and TXB(2) inhibition.