Partha Nandy

Model-Based Approach To Characterize Efavirenz Autoinduction and Concurrent Enzyme Induction with Carbamazepine

ABSTRACT Characterization of the time course and magnitude of enzyme induction due to multiple inducers is important for interpretation of clinical data from drug-drug interaction studies. A population interaction model was developed to quantify efavirenz autoinduction and further induction with concurrent carbamazepine coadministration. Efavirenz concentration data in the absence and presence of carbamazepine following single- and multiple-dose oral administrations in healthy subjects were used for model development. The proposed model was able to describe the time-dependent efavirenz autoinduction and the further induction with carbamazepine when the agents were combined. The estimated population averages of efavirenz oral clearance were 5.5, 9.4, 14.4, and 16.7 liters/h on days 1, 14, and 35 and at steady state for the interaction, respectively, for efavirenz monotherapy for 2 weeks followed by the coadministration of carbamazepine for 3 weeks. The estimated times to 50% of the steady state for efavirenz autoinduction and for the induction resulting from the concurrent administration of efavirenz and carbamazepine were similar (around 10 to 12 days). With this model-based analysis, efavirenz exposures can be projected prior to and at the steady state of induction, allowing a better understanding of the time course and magnitude of enzyme induction.

Pharmacokinetic-Pharmacodynamic-Model-Guided Doripenem Dosing in Critically Ill Patients

ABSTRACT The growing number of infections caused by multidrug-resistant pathogens has prompted a more rational use of available antibiotics given the paucity of new, effective agents. Monte Carlo simulations were utilized to determine the appropriateness of several doripenem dosing regimens based on the probability of attaining the critical drug exposure metric of time that drug concentrations remain above the drug MIC (T>MIC) for 35% (and lower thresholds) of the dosing interval in >80 to 90% of the population (T>MIC 35% target). This exposure level generally correlates with in vivo efficacy for carbapenems. In patients with creatinine clearance of >50 ml/min, a 500-mg dose of doripenem infused over 1 h every 8 h is expected to be effective against bacilli with doripenem MICs of ≤1 μg/ml based on a T>MIC 35% target and MICs of ≤2 μg/ml based on lower targets. A longer, 4-hour infusion time improved target attainment in most cases, such that the T>MIC was adequate for pathogens with doripenem MICs as high as 4 μg/ml. Efficacy is expected for infections caused by pathogens with doripenem MICs of ≤2 μg/ml in patients with moderate renal impairment (creatinine clearance, 30 to 50 ml/min) who receive doripenem at 250 mg infused over 1 h every 8 h and in patients with severe impairment (creatinine clearance between 10 and 29 ml/min) who receive doripenem at 250 mg, infused over 1 h or 4 h, every 12 h. Results of pharmacokinetics/pharmacodynamics (PK/PD) modeling can guide dose optimization, thereby potentially increasing the clinical efficacy of doripenem against serious Gram-negative bacterial infections.

Population Pharmacokinetics of Doripenem Based on Data from Phase 1 Studies with Healthy Volunteers and Phase 2 and 3 Studies with Critically Ill Patients

ABSTRACT A population pharmacokinetic model of doripenem was constructed using data pooled from phase 1, 2, and 3 studies utilizing nonlinear mixed effects modeling. A 2-compartment model with zero-order input and first-order elimination best described the log-transformed concentration-versus-time profile of doripenem. The model was parameterized in terms of total clearance (CL), central volume of distribution (Vc), peripheral volume of distribution (Vp), and distribution clearance between the central and peripheral compartments (Q). The final model was described by the following equations (for jth subject): CLj (liters/h) = 13.6·(CLCRj/98 ml/min)0.659·(1 + CLracej [0 for Caucasian]); Vcj (liters) = 11.6·(weightj/73 kg)0.596; Qj (liters/h) = 4.74·(weightj/73)1.06; and Vpj (liters) = 6.04·(CLCRj/98 ml/min)0.417·(weightj/73 kg)0.840·(agej/40 years)0.307. According to the final model, population mean parameter estimates and interindividual variability (percent coefficient of variation [% CV]) for CL (liters/h), Vc (liters), Vp (liters), and Q (liters/h) were 13.6 (19%), 11.6 (19%), 6.0 (25%), and 4.7 (42%), respectively. Residual variability, estimated using three separate additive residual error models, was 0.17 standard deviation (SD), 0.55 SD, and 0.92 SD for phase 1, 2, and 3 data, respectively. Creatinine clearance was the most significant predictor of doripenem clearance. Mean Bayesian clearance was approximately 33%, 55%, and 76% lower for individuals with mild, moderate, or severe renal impairment, respectively, than for those with normal renal function. The population pharmacokinetic model based on healthy volunteer data and patient data informs us of doripenem disposition in a more general population as well as of the important measurable intrinsic and extrinsic factors that significantly influence interindividual pharmacokinetic differences.

Pharmacometrics-Based Dose Selection of Levofloxacin as a Treatment for Postexposure Inhalational Anthrax in Children

ABSTRACT Levofloxacin was recently (May 2008) approved by the U.S. Food and Drug Administration as a treatment for children following inhalational exposure to anthrax. Given that no clinical trials to assess the efficacy of a chosen dose was conducted, the basis for the dose recommendation was based upon pharmacometric analyses. The objective of this paper is to describe the basis of the chosen pediatric dose recommended for the label. Pharmacokinetic (PK) data from 90 pediatric patients receiving 7 mg/kg of body weight levofloxacin and two studies of 47 healthy adults receiving 500 and 750 mg/kg levofloxacin were used for the pharmacometric analyses. Body weight was found to be a significant covariate for levofloxacin clearance and the volume of distribution. Consistently with developmental physiology, clearance also was found to be reduced in pediatric patients under 2 years of age due to immature renal function. Different dosing regimens were simulated to match adult exposure (area under the concentration-time curve from 0 to 24 h at steady state, maximum concentration of drug in serum at steady state, and minimum concentration of drug in serum at steady state) following the approved adult dose of 500 mg once a day. The recommended dose of 8 mg/kg twice a day was found to match the exposure of the dose approved for adults in a manner that permitted confidence that this dose in children would achieve efficacy comparable to that of adults.

Correlation between Prostate-Specific Antigen Kinetics and Overall Survival in Abiraterone Acetate–Treated Castration-Resistant Prostate Cancer Patients

Purpose: We constructed a biomarker-survival modeling framework to explore the relationship between prostate-specific antigen (PSA) kinetics and overall survival (OS) in metastatic castration-resistant prostate cancer (mCRPC) patients following oral administration of 1,000 mg/day of abiraterone acetate (AA). Experimental Design: The PSA-survival modeling framework was based on data from two phase III studies, COU-AA-301 (chemotherapy pretreated, n = 1,184) and COU-AA-302 (chemotherapy naïve, n = 1,081), and included a mixed-effects tumor growth inhibition model and a Cox proportional hazards survival model. Results: The effect of AA on PSA kinetics was significant (P < 0.0001) and comparable between the chemotherapy-naïve and -pretreated patients. PSA kinetics [e.g., PSA nadir, PSA response rate (≥30%, 50%, and 90%), time to PSA progression, PSA doubling time (PSADT)] were highly associated with OS in both populations. The model-based posttreatment PSADT had the strongest association with OS (HR ∼0.9 in both populations). The models could accurately predict survival outcomes. After adjusting for PSA kinetic endpoints, the treatment effect of AA on survival was no longer statistically significant in both studies, and the Prentice criteria of surrogacy were met for the PSA kinetic endpoints. A strong correlation was also observed between PSA and radiographic progression-free survival. Conclusions: The analysis revealed a consistent treatment effect of AA on PSA kinetics and strong associations between PSA kinetics and OS in chemotherapy-pretreated and -naïve patients, thereby providing a rationale to consider PSA kinetics as surrogacy endpoints to indicate clinical benefit in AA-treated patients with mCRPC regardless of chemotherapy treatment. Clin Cancer Res; 21(14); 3170–7. ©2015 AACR.

Population Pharmacokinetics of Tapentadol Immediate Release (IR) in Healthy Subjects and Patients with Moderate or Severe Pain

BackgroundTapentadol is a new, centrally active analgesic agent with two modes of action — ώ, opioid receptor agonism and norepinephrine reuptake inhibition — and the immediate-release (IR) formulation is approved in the US for the relief of moderate to severe acute pain. The aims of this analysis were to develop a population pharmacokinetic model to facilitate the understanding of the pharmacokinetics of tapentadol IR in healthy subjects and patients following single and multiple dosing, and to identify covariates that might explain variability in exposure following oral administration.MethodsThe analysis included pooled data from 11 385 serum pharmacokinetic samples from 1827 healthy subjects and patients with moderate to severe pain. Population pharmacokinetic modelling was conducted using nonlinear mixed-effects modelling (NONMEM®) software to estimate population pharmacokinetic parameters and the influence of the subjects’ demographic characteristics, clinical laboratory chemistry values and disease status on these parameters. Simulations were performed to assess the clinical relevance of the covariate effects on tapentadol exposure.ResultsA two-compartment model with zero-order release followed by first-order absorption and first-order elimination best described the pharmacokinetics of tapentadol IR following oral administration. The interindividual variability (coefficient of variation) in apparent oral clearance (CL/F) and the apparent central volume of distribution after oral administration were 30% and 29%, respectively. An additive error model was used to describe the residual variability in the log-transformed data, and the standard deviation values were 0.308 and 0.314 for intensively and sparsely sampled data, respectively. Covariate analysis showed that sex, age, bodyweight, race, body fat, hepatic function (using total bilirubin and total protein as surrogate markers), health status and creatinine clearance were statistically significant factors influencing the pharmacokinetics of tapentadol. Total bilirubin was a particularly important factor that influenced CL/F, which decreased by more than 60% in subjects with total bilirubin greater than 50 ώmol/L.ConclusionsThe population pharmacokinetic model for tapentadol IR identified the relationship between pharmacokinetic parameters and a wide range of covariates. The simulations of tapentadol exposure with identified, statistically significant covariates demonstrated that only hepatic function (as characterized by total bilirubin and total protein) may be considered a clinically relevant factor that warrants dose adjustment. None of the other covariates are of clinical relevance, nor do they necessitate dose adjustment.

Pharmacokinetic and Pharmacodynamic Modeling of Opioid-Induced Gastrointestinal Side Effects in Patients Receiving Tapentadol IR and Oxycodone IR

ABSTRACTPurposeTo understand the relationship between the risk of opioid-related gastrointestinal adverse effects (AEs) and exposure to tapentadol and oxycodone as well as its active metabolite, oxymorphone, using pharmacokinetic/pharmacodynamic models.MethodsThe analysis was based on a study in patients with moderate-to-severe pain following bunionectomy. Population PK modeling was conducted to estimate population PK parameters for tapentadol, oxycodone, and oxymorphone. Time to AEs was analyzed using Cox proportional-hazards models.ResultsRisk of nausea, vomiting, and constipation significantly increased with exposure to tapentadol or oxycodone/oxymorphone. However, elevated risk per drug exposure of AEs for tapentadol was ~3–4 times lower than that of oxycodone, while elevated AE risk per drug exposure of oxycodone was ~60 times lower than that for oxymorphone, consistent with reported in vitro receptor binding affinities for these compounds. Simulations show that AE incidence following administration of tapentadol IR is lower than that following oxycodone IR intake within the investigated range of analgesic noninferiority dose ratios.ConclusionsThis PK/PD analysis supports the clinical findings of reduced nausea, vomiting and constipation reported by patients treated with tapentadol, compared to patients treated with oxycodone.

Shrinkage in Nonlinear Mixed-Effects Population Models: Quantification, Influencing Factors, and Impact

Shrinkage of empirical Bayes estimates (EBEs) of posterior individual parameters in mixed-effects models has been shown to obscure the apparent correlations among random effects and relationships between random effects and covariates. Empirical quantification equations have been widely used for population pharmacokinetic/pharmacodynamic models. The objectives of this manuscript were (1) to compare the empirical equations with theoretically derived equations, (2) to investigate and confirm the influencing factor on shrinkage, and (3) to evaluate the impact of shrinkage on estimation errors of EBEs using Monte Carlo simulations. A mathematical derivation was first provided for the shrinkage in nonlinear mixed effects model. Using a linear mixed model, the simulation results demonstrated that the shrinkage estimated from the empirical equations matched those based on the theoretically derived equations. Simulations with a two-compartment pharmacokinetic model verified that shrinkage has a reversed relationship with the relative ratio of interindividual variability to residual variability. Fewer numbers of observations per subject were associated with higher amount of shrinkage, consistent with findings from previous research. The influence of sampling times appeared to be larger when fewer PK samples were collected for each individual. As expected, sample size has very limited impact on shrinkage of the PK parameters of the two-compartment model. Assessment of estimation error suggested an average 1:1 relationship between shrinkage and median estimation error of EBEs.

Impact of low percentage of data below the quantification limit on parameter estimates of pharmacokinetic models

The objectives of the simulation study were to evaluate the impact of BQL data on pharmacokinetic (PK) parameter estimates when the incidence of BQL data is low (e.g. ≤10%), and to compare the performance of commonly used modeling methods for handling BQL data such as data exclusion (M1) and likelihood-based method (M3). Simulations were performed by adapting the method of a recent publication by Ahn et al. (J Phamacokinet Pharmacodyn 35(4):401–421, 2008). The BQL data in the terminal elimination phase were created at frequencies of 1, 2.5, 5, 7.5, and 10% based on a one- and a two-compartment model. The impact of BQL data on model parameter estimates was evaluated based on bias and imprecision. The simulations demonstrated that for the one-compartment model, the impact of ignoring the low percentages of BQL data (≤10%) in the elimination phase was minimal. For the two-compartment model, when the BQL incidence was less than 5%, omission of the BQL data generally did not inflate the bias in the fixed-effect parameters, whereas more pronounced bias in the estimates of inter-individual variability (IIV) was observed. The BQL data in the elimination phase had the greatest impact on the volume of distribution estimate of the peripheral compartment of the two-compartment model. The M3 method generally provided better parameter estimates for both PK models than the M1 method. However, the advantages of the M3 over the M1 method varied depending on different BQL censoring levels, PK models and parameters. As the BQL percentages decreased, the relative gain of the M3 method based on more complex likelihood approaches diminished when compared to the M1 method. Therefore, it is important to balance the trade-off between model complexity and relative gain in model improvement when the incidence of BQL data is low. Understanding the model structure and the distribution of BQL data (percentage and location of BQL data) allows selection of an appropriate and effective modeling approach for handling low percentages of BQL data.

Population Pharmacokinetic Analysis of Ceftobiprole for Treatment of Complicated Skin and Skin Structure Infections

ABSTRACT Population pharmacokinetic analysis demonstrated that renal function, as assessed by creatinine clearance (CLCR), was the patient characteristic that had a clinically relevant impact on ceftobiprole pharmacodynamics. Dosing adjustments based on CLCR for subjects with renal impairment should provide ceftobiprole exposure similar to that in patients with normal renal function.