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 singleand 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.Drug-drug interaction studies are an important aspect of clinical pharmacology research and can be a critical step to optimize the use of selected medicines. In the treatment of human immunodeficiency virus (HIV) infection and HIV complications, polypharmacy is compulsory, and this necessitates the conduct of numerous interaction studies (3). Interpretation of results from interaction studies may be challenging when multiple enzyme inducers or inhibitors are involved while study duration is restricted. In some cases, it may be difficult to know when steady state is achieved or only a "pseudo"-steady state may be achievable during a controlled study period.Enzyme induction has important clinical implications when enhanced drug metabolism results in lower drug concentrations, which leads to a suboptimal efficacious response or, even worse, the development of drug resistance. Enzyme induction can be due to (i) a drug affecting its own metabolism (autoinduction), (ii) comedication(s) with induction capability, or (iii) both, such as the concomitant use of efavirenz and carbamazepine.Efavirenz is a potent nonnucleoside reverse transcriptase inhibitor approved for the treatment of HIV-1 infection in combination with other antiretroviral agents (efavirenz [Sustiva] package insert for capsules and tablets; Bristol-Myers Squibb Co., Princeton, NJ) (1). It is metabolized mainly by cytochrome P450 2B6 (CYP2B6) and possibly CYP3A4 or other CYP isoforms to a less extent (efavirenz package insert for capsules and tablets; Bristol-Myers Squibb Co.) (1, 29). Thus, drugs that induce CYP2B6/3A4 (e.g., rifampin, carbamazepine, and phenobarbital) may increase the clearance of efavirenz, resulting...

No 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 (V c ), peripheral volume of distribution (V p ), and distribution clearance between the central and peripheral compartments (Q). The final model was described by the following equations (for jth subject): CL j (liters/h) ‫؍‬ 13. interindividual variability (percent coefficient of variation [% CV]) for CL (liters/h), V c (liters), V p (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.

SUMMARYPurpose: To identify and validate the efficacious monotherapy dosing regimen for topiramate in children aged 2 to <10 years with newly diagnosed epilepsy using pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation bridging. Methods: Several models were developed in pediatric and adult populations to relate steady-state trough plasma concentrations (C MIN ) of topiramate to the magnitude of clinical effect in monotherapy and adjunctive settings. These models were integrated to derive and support the monotherapy dosing regimen for pediatric patients. Key Findings: A two-compartmental population PK model with first-order absorption described the time course of topiramate C MIN as a function of dosing regimen. Disposition of topiramate was related to age, body weight, and use of various concomitant antiepileptic drugs. The PK-PD model for monotherapy indicated that the hazard of time to first seizure decreased with increasing C MIN and time since randomization. Higher baseline seizure frequency increased risk for seizures. Age did not significantly influence hazard of time to first seizure after randomization to monotherapy. For adjunctive therapy, the distribution of drug and placebo responses was not significantly different among age groups. Based on the available PK-PD modeling data, the dosing regimen expected to achieve a 65-75% seizure freedom rate after 1 year for pediatric patients age 2-10 years is approximately 6-9 mg/kg per day. Significance: This analysis indicated no difference in PK-PD of topiramate between adult and pediatric patients. Effects of indication and body weight on PK were adequately integrated into the model, and monotherapy dosing regimens were identified for children 2-10 years of age.

BackgroundThe objective of this analysis was to develop a nonlinear disease progression model, using an expanded set of covariates that captures the longitudinal Clinical Dementia Rating Scale–Sum of Boxes (CDR–SB) scores. These were derived from the Alzheimer’s Disease Neuroimaging Initiative ADNI-1 study, of 301 Alzheimer’s disease and mild cognitive impairment patients who were followed for 2–3 years.MethodsThe model describes progression rate and baseline disease score as a function of covariates. The covariates that were tested fell into five groups: a) hippocampal volume; b) serum and cerebrospinal fluid (CSF) biomarkers; c) demographics and apolipoprotein Epsilon 4 (ApoE4) allele status; d) baseline cognitive tests; and e) disease state and comedications.ResultsCovariates associated with baseline disease severity were disease state, hippocampal volume, and comedication use. Disease progression rate was influenced by baseline CSF biomarkers, Trail-Making Test part A score, delayed logical memory test score, and current level of impairment as measured by CDR–SB. The rate of disease progression was dependent on disease severity, with intermediate scores around the inflection point score of 10 exhibiting high disease progression rate. The CDR–SB disease progression rate in a typical patient, with late mild cognitive impairment and mild Alzheimer’s disease, was estimated to be approximately 0.5 and 1.4 points/year, respectively.ConclusionsIn conclusion, this model describes disease progression in terms of CDR–SB changes in patients and its dependency on novel covariates. The CSF biomarkers included in the model discriminate mild cognitive impairment subjects as progressors and nonprogressors. Therefore, the model may be utilized for optimizing study designs, through patient population enrichment and clinical trial simulations.