Population pharmacokinetic analysis of esomeprazole in Japanese subjects with various CYP2C19 phenotypes.

WHAT IS KNOWN AND OBJECTIVE: Esomeprazole, the S-isomer of omeprazole, is a proton pump inhibitor which has been approved by over 125 countries, also known as NEXIUM® . Esomeprazole was developed to provide further improvement on efficacy for acid-related diseases with higher systemic bioavailability due to the less first-pass metabolism and lower plasma clearance. Esomeprazole is primarily metabolized by CYP2C19. Approximately <1% of Caucasians and 5%-10% of Asians have absent CYP2C19 enzyme activity. Although the influence of various CYP2C19 phenotypes on esomeprazole pharmacokinetics has been studied, this is the first report in the Japanese population where 27 low CYP2C19 metabolizers were included. METHODS: In this study, a population PK model describing the PK of esomeprazole was developed to understand the difference of CYP2C19 phenotypes on clearance in the Japanese population. The model quantitatively assessed the influence of CYP2C19 phenotype on esomeprazole PK in healthy Japanese male subjects after receiving repeated oral dosing. The inhibition mechanism of esomeprazole on CYP2C19 activity was also included in the model. RESULTS AND DISCUSSION: CYP2C19 phenotype and dose were found as statistically significant covariates on esomeprazole clearance. The apparent clearance at 10-mg dose was 17.32, 9.77 and 7.37 (L/h) for homozygous extensive metabolizer, heterozygous extensive metabolizer and poor metabolizer subjects, respectively. And the apparent clearance decreased as dose increased. WHAT IS NEW AND CONCLUSION: The established population PK model well described the esomeprazole PK and model-predicted esomeprazole PK was in good agreement with external clinical data, suggesting the robustness and applicability of the current model for predicting esomeprazole PK.

Optimal designs for regional bridging studies using the Bayesian power prior method.

As described in the ICH E5 guidelines, a bridging study is an additional study executed in a new geographical region or subpopulation to link or "build a bridge" from global clinical trial outcomes to the new region. The regulatory and scientific goals of a bridging study is to evaluate potential subpopulation differences while minimizing duplication of studies and meeting unmet medical needs expeditiously. Use of historical data (borrowing) from global studies is an attractive approach to meet these conflicting goals. Here, we propose a practical and relevant approach to guide the optimal borrowing rate (percent of subjects in earlier studies) and the number of subjects in the new regional bridging study. We address the limitations in global/regional exchangeability through use of a Bayesian power prior method and then optimize bridging study design with a return on investment viewpoint. The method is demonstrated using clinical data from global and Japanese trials in dapagliflozin for type 2 diabetes.

Population pharmacokinetic analysis of danvatirsen supporting flat dosing switch.

Danvatirsen is a Generation 2.5 antisense oligonucleotide under clinical development. Population PK modelling was conducted using data from 3 available danvatirsen Phase I/II studies in oncology patients to investigate the impact of flat dosing on exposure compared to ideal body weight-based dosing. A total of 126 patients who received danvatirsen doses ranging from 1 to 4 mg/kg as monotherapy or in combination with durvalumab, most at 3 mg/kg (n = 70), was used in the danvatirsen population PK analysis. A 2-compartment model with linear elimination described the data well. Covariate analysis revealed ideal body weight was not a significant covariate on the PK of danvatirsen; nor was age, sex or race. The model-based simulation suggested that steady state weekly AUC and Cmax were very similar between 3 mg/kg and 200 mg flat dosing (geometric mean of AUC: 62.5 vs. 63.4 mg h/L and Cmax: 26.2 vs. 26.5 mg/L for two dose groups) with slightly less overall between-subject variability in the flat dosing regimen. The switch to flat dosing was approved by multiple regulatory agencies, including FDA, EMA, PMDA and ANSM. Several ongoing studies have been evaluating flat dosing. Interim analysis from an ongoing study (D5660C00016, NCT03421353) has shown the observed steady state concentration from 200 mg flat dose is in agreement with the model predictions. The population PK model could be further utilized in subsequent exposure-response efficacy and safety modelling.

Simplified Aztreonam Dosing in Patients with End-Stage Renal Disease: Results of a Monte Carlo Simulation.

The manufacturer-recommended aztreonam dosing for patients with creatinine clearance values of <10 ml/min/1.73 m2 is complex. It is not known whether simpler posthemodialysis dosing administered once daily or thrice weekly can reliably achieve pharmacodynamic goals. We found that 1 or 2 g administered once daily after hemodialysis had >90% probability of target attainment up to MICs of 4 or 8 mg/liter, respectively. Thrice-weekly dosing should generally be avoided, except in nonsevere infections with MICs of ≤0.5 mg/liter.

Population exposure-safety analysis of cediranib for Phase I and II studies in patients with cancer.

AIMS: A multistudy analysis of cediranib, a potent, selective inhibitor of all three vascular endothelial growth factor receptors (VEGFR-1, -2 and -3), was conducted to establish population exposure-safety models for the relationship of cediranib exposure to the safety endpoints, diastolic and systolic blood pressure (DBP and SBP) and diarrhoea in cancer patients. These models were applied to predict safety outcomes for different cediranib dose regimens. METHODS: Models for hypertension and diarrhoea were constructed based on data from 10 Phase I and three Phase II studies comprising 631 cancer patients following cediranib once-daily oral dosing. Daily DBP and SBP were simultaneously characterized using indirect response models for predicted cediranib concentration-time courses, while daily diarrhoea events were modelled as ordered categorical variables with a proportional odds model with a Markov element for predicted average cediranib concentrations. RESULTS: For 20 mg cediranib once-daily oral administration, the mean increase in DBP and SBP was predicted to be 7 (95% CI 3-13) and 8 mmHg (95% CI 3-16), respectively, while the probability of mild diarrhoea, but not the severity, was predicted to increase over time. Severe diarrhoea was predicted to be resolved rapidly upon discontinuation of cediranib treatment. CONCLUSIONS: Maximum blood pressure increase was observed within the first few days of cediranib treatment, consistent with the pharmacokinetic profile of cediranib reaching steady state in about 5 days. The probability of diarrhoea increased with cediranib concentration but was far more dependent on the status of diarrhoea predicted on the previous day.

Assessing QT/QTc interval prolongation with concentration-QT modeling for Phase I studies: impact of computational platforms, model structures and confidence interval calculation methods.

Modeling the relationship between drug concentrations and heart rate corrected QT interval (QTc) change from baseline (C-∆QTc), based on Phase I single ascending dose (SAD) or multiple ascending dose (MAD) studies, has been proposed as an alternative to thorough QT studies (TQT), in assessing drug-induced QT prolongation risk. The present analysis used clinical SAD, MAD and TQT study data of an experimental compound, AZD5672, to evaluate the performance of: (i) three computational platforms (linear mixed-effects modeling implemented via PROC MIXED in SAS, as well as in R using LME4 package and linear quantile mixed models (LQMM) implemented via LQMM package; (ii) different model structures with and without treatment- or time-specific intercepts; and (iii) three methods for calculating the confidence interval (CI) of QTc prolongation (analytical and bootstrap methods with fixed or varied geometric mean concentrations). We show that treatment- and time-specific intercepts may need to be included into C-∆QTc modeling through PROC MIXED or LME4, regardless of their statistical significance. With the intersection union test (IUT) in the TQT study as a reference for comparison, inclusion of these intercepts increased the feasibility for C-∆QTc modelling of SAD or MAD to reach the same conclusion as the IUT analysis based on TQT study. Compared to PROC MIXED or LME4, the LQMM method is less dependent on inclusion of treatment- or time-specific intercepts, and the bootstrap CI calculation methods provided higher likelihood for C-∆QTc modeling of SAD and MAD studies to reach the same conclusion as the IUT based on the TQT study.

Integrating dose estimation into a decision-making framework for model-based drug development.

Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response. Doses which are too high or low pose risk of clinical or commercial failure. This paper addresses this issue and continues the evolution of formal decision frameworks in drug development. Here, we consider the integration of both efficacy and dose-response estimation accuracy into the go/no-go decision process, using a model-based approach. Using prespecified target and lower reference values associated with both efficacy and dose accuracy, we build a decision framework to more completely characterize development risk. Given the limited knowledge of dose response in early development, our approach incorporates a set of dose-response models and uses model averaging. The approach and its operating characteristics are illustrated through simulation. Finally, we demonstrate the decision approach on a post hoc analysis of the phase 2 data for naloxegol (a drug approved for opioid-induced constipation).

Population pharmacokinetic and exposure simulation analysis for cediranib (AZD2171) in pooled Phase I/II studies in patients with cancer.

AIMS: A population pharmacokinetic (PK) model was developed for cediranib to simulate cediranib exposure for different doses, including comedication with strong uridine glucuronosyl transferase/P-glycoprotein inducers such as rifampicin, in cancer patients. METHODS: Plasma concentrations and covariates from 625 cancer patients after single or multiple oral cediranib administrations ranging from 0.5 to 90 mg in 19 Phase I and II studies were included in the analysis. Stepwise covariate modelling was used to develop the population PK model. The final model was used to simulate cediranib exposure in cancer patients to evaluate cediranib target coverage and the need for dose adjustment for covariates or coadministration with rifampicin. RESULTS: A two-compartment model with sequential zero- and first-order absorption and first-order elimination adequately described the cediranib concentration-time courses. Body weight and age were identified as having statistically significant impact on cediranib PK, but only <21% impact on AUC and maximum concentrations. Simulated lower bounds of 90% prediction interval or median of unbound cediranib concentrations after cediranib 15 or 20 mg exceeded the IC50 for vascular endothelial growth factor receptors-1, -2 and -3. Exposures of cediranib 20 or 30 mg with coadministration of rifampicin were comparable to those of 15 or 20 mg, respectively, without coadministration. CONCLUSIONS: No covariate was identified to require dose adjustment for cediranib. Cediranib exposure following 15 or 20 mg daily dose administration is adequate overall for inhibition of in vitro estimated vascular endothelial growth factor receptor-1, -2 and -3 activities. An increase in cediranib dose may be needed for cediranib coadministered with strong uridine glucuronosyl transferase/P-glycoprotein inducers such as rifampicin.

Development of a physiologically based pharmacokinetic model to predict the effects of flavin-containing monooxygenase 3 (FMO3) polymorphisms on itopride exposure.

Itopride, a substrate of FMO3, has been used for the symptomatic treatment of various gastrointestinal disorders. Physiologically based pharmacokinetic (PBPK) modeling was applied to evaluate the impact of FMO3 polymorphism on itopride pharmacokinetics (PK). The Asian populations within the Simcyp simulator were updated to incorporate information on the frequency, activity and abundance of FMO3 enzyme with different phenotypes. A meta-analysis of relative enzyme activities suggested that FMO3 activity in subjects with homozygous Glu158Lys and Glu308Gly mutations (Lys158 and Gly308) in both alleles is ~47% lower than those carrying two wild-type FMO3 alleles. Individuals with homozygous Lys158 and Gly308 mutations account for about 5% of the total population in Asian populations. A CLint of 9 µl/min/pmol was optimised for itopride via a retrograde approach as human liver microsomal results would under-predict its clearance by ~7.9-fold. The developed itopride PBPK model was first verified with three additional clinical studies in Korean and Japanese subjects resulting in a predicted clearance of 52 to 69 l/h, which was comparable to those observed (55 to 88 l/h). The model was then applied to predict plasma concentration-time profiles of itopride in Chinese subjects with wild type or homozygous Lys158 and Gly308 FMO3 genotypes. The ratios of predicted to observed AUC of itopride in subjects with each genotype were 1.23 and 0.94, respectively. In addition, the results also suggested that for FMO3 metabolised drugs with a safety margin of 2 or more, proactive genotyping FMO3 to exclude subjects with homozygous Lys158/Gly308 alleles may not be necessary.

Population pharmacokinetics of naloxegol in a population of 1247 healthy subjects and patients.

AIMS: Naloxegol, a polyethylene glycol conjugated derivative of the opioid antagonist naloxone, is in clinical development for treatment of opioid-induced constipation (OIC). The aim of the study was to develop a population pharmacokinetic model describing the concentration vs. time profile of orally administered naloxegol, and determine the impact of pre-specified demographic and clinical factors and concomitant medication on population estimates of apparent clearance (CL/F) and apparent central compartment volume of distribution (Vc /F). METHODS: Analysis included 12,844 naloxegol plasma concentrations obtained from 1247 healthy subjects, patients with non-OIC and patients with OIC in 14 phase 1, 2b and 3 clinical studies. Pharmacokinetic analysis used the non-linear mixed effects modelling program. Goodness of fit plots and posterior predictive checks were conducted to confirm concordance with observed data. RESULTS: The final model was a two compartment disposition model with dual absorptions, comprising one first order absorption (ka1 4.56 h(-1) ) and one more complex absorption with a transit compartment (ktr 2.78 h(-1) ). Mean (SE) parameter estimates for CL/F and Vc /F, the parameters assessed for covariate effects, were 115 (3.41) l h(-1) and 160 (27.4) l, respectively. Inter-individual variability was 48% and 51%, respectively. Phase of study, gender, race, concomitant strong or moderate CYP3A4 inhibitors, strong CYP3A4 inducers, P-glycoprotein inhibitors or inducers, naloxegol formulation, baseline creatinine clearance and baseline opioid dose had a significant effect on at least one pharmacokinetic parameter. Simulations indicated concomitant strong CYP3A4 inhibitors or inducers had relevant effects on naloxegol exposure. CONCLUSIONS: Administration of strong CYP3A4 inhibitors or inducers had a clinically relevant influence on naloxegol pharmacokinetics.

Reporting guidelines for population pharmacokinetic analyses.

The purpose of this work was to develop a consolidated set of guiding principles for reporting of population pharmacokinetic (PK) analyses based on input from a survey of practitioners as well as discussions between industry, consulting and regulatory scientists. The survey found that identification of population covariate effects on drug exposure and support for dose selection (where population PK frequently serves as preparatory analysis to exposure-response modeling) are the main areas of influence for population PK analysis. The proposed guidelines consider two main purposes of population PK reports (1) to present key analysis findings and their impact on drug development decisions, and (2) as documentation of the analysis methods for the dual purpose of enabling review of the analysis and facilitating future use of the models. This work also identified two main audiences for the reports: (1) a technically competent group responsible for in-depth review of the data, methodology, and results, and (2) a scientifically literate, but not technically adept group, whose main interest is in the implications of the analysis for the broader drug development program. We recommend a generalized question-based approach with six questions that need to be addressed throughout the report. We recommend eight sections (Synopsis, Introduction, Data, Methods, Results, Discussion, Conclusions, Appendix) with suggestions for the target audience and level of detail for each section. A section providing general expectations regarding population PK reporting from a regulatory perspective is also included. We consider this an important step towards industrialization of the field of pharmacometrics such that non-technical audience also understands the role of pharmacometrics analyses in decision making. Population PK reports were chosen as representative reports to derive these recommendations; however, the guiding principles presented here are applicable for all pharmacometric reports including PKPD and simulation reports.

Population pharmacokinetics modeling and exposure-response analyses of cemiplimab in patients with recurrent or metastatic cervical cancer.

A population pharmacokinetic (PopPK) model was previously developed for cemiplimab in patients with solid tumors, including advanced cutaneous squamous cell carcinoma (CSCC). Here, we update the existing PopPK model and characterize exposure-response relationships using efficacy and safety data obtained in patients with recurrent or metastatic cervical cancer (R/M CC). To improve model stability and robustness of the existing PopPK model in 1062 patients, the random-effect error model was revised, and structural covariates were removed from the base model to be tested in the covariate analysis. The updated model was used for external validation of cemiplimab pharmacokinetics (PK) in patients with R/M CC on cemiplimab monotherapy (350 mg every 3 weeks intravenously) from a phase III study (NCT03257267). Exposure-response relationships for cemiplimab efficacy (overall survival [OS], progression-free survival [PFS], duration of response [DOR], objective response rate [ORR]), and safety (immune-related adverse events [irAEs]) were analyzed in 295 patients with R/M CC from the aforementioned study. The updated PopPK model showed improved stability with 94.8% successful bootstrap runs vs. 47.6% in the prior model. Cemiplimab exposure was similar across tumor types, including basal cell carcinoma, CSCC, and non-small cell lung cancer. External validation showed the updated model adequately described cemiplimab PK in patients with R/M CC. In exposure-response efficacy analyses, Cox proportional hazard modeling (CPHM) showed no trend between exposure and OS, Kaplan-Meier plots showed no trend between exposure and PFS or DOR, and logistic regression analyses conducted on ORR showed no exposure-response relationship. In exposure-response safety analyses, CPHM showed no trend between exposure and irAEs.

A drug-disease model for predicting survival in an Ebola outbreak.

REGN-EB3 (Inmazeb) is a cocktail of three human monoclonal antibodies approved for treatment of Ebola infection. This paper describes development of a mathematical model linking REGN-EB3's inhibition of Ebola virus to survival in a non-human primate (NHP) model, and translational scaling to predict survival in humans. Pharmacokinetic/pharmacodynamic data from single- and multiple-dose REGN-EB3 studies in infected rhesus macaques were incorporated. Using discrete indirect response models, the antiviral mechanism of action was used as a forcing function to drive the reversal of key Ebola disease hallmarks over time, for example, liver and kidney damage (elevated alanine [ALT] and aspartate aminotransferases [AST], blood urea nitrogen [BUN], and creatinine), and hemorrhage (decreased platelet count). A composite disease characteristic function was introduced to describe disease severity and integrated with the ordinary differential equations estimating the time course of clinical biomarkers. Model simulation results appropriately represented the concentration-dependence of the magnitude and time course of Ebola infection (viral and pathophysiological), including time course of viral load, ALT and AST elevations, platelet count, creatinine, and BUN. The model estimated the observed survival rate in rhesus macaques and the dose of REGN-EB3 required for saturation of the pharmacodynamic effects of viral inhibition, reversal of Ebola pathophysiology, and survival. The model also predicted survival in clinical trials with appropriate scaling to humans. This mathematical investigation demonstrates that drug-disease modeling can be an important translational tool to integrate preclinical data from an NHP model recapitulating disease progression to guide future translation of preclinical data to clinical study design.

Assessing the Combined Public Health Impact of Pharmaceutical Interventions on Pandemic Transmission and Mortality: An Example in SARS CoV-2.

To assess the combined role of anti-viral monoclonal antibodies (mAbs) and vaccines in reducing severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) transmission and mortality in the United States, an agent-based model was developed that accounted for social contacts, movement/travel, disease progression, and viral shedding. The model was calibrated to coronavirus disease 2019 (COVID-19) mortality between October 2020 and April 2021 (aggressive pandemic phase), and projected an extended outlook to estimate mortality during a less aggressive phase (April-August 2021). Simulated scenarios evaluated mAbs for averting infections and deaths in addition to vaccines and aggregated non-pharmaceutical interventions. Scenarios included mAbs as a treatment of COVID-19 and for passive immunity for postexposure prophylaxis (PEP) during a period when variants were susceptible to the mAbs. Rapid diagnostic testing paired with mAbs was evaluated as an early treatment-as-prevention strategy. Sensitivity analyses included increasing mAb supply and vaccine rollout. Allocation of mAbs for use only as PEP averted up to 14% more infections than vaccine alone, and targeting individuals ≥ 65 years averted up to 37% more deaths. Rapid testing for earlier diagnosis and mAb use amplified these benefits. Doubling the mAb supply further reduced infections and mortality. mAbs provided benefits even as proportion of the immunized population increased. Model projections estimated that ~ 42% of expected deaths between April and August 2021 could be averted. Assuming sensitivity to mAbs, their use as early treatment and PEP in addition to vaccines would substantially reduce SARS-CoV-2 transmission and mortality even as vaccination increases and mortality decreases. These results provide a template for informing public health policy for future pandemic preparedness.

Population pharmacokinetics and exposure-response modeling for evinacumab in homozygous familial hypercholesterolemia.

Evinacumab, an angiopoietin-like protein 3 (ANGPTL3) inhibitor, has been shown to significantly reduce low-density lipoprotein cholesterol (LDL-C) in patients with homozygous familial hypercholesterolemia (HoFH). This work characterized the population pharmacokinetics (PK)/pharmacodynamics (PD) of evinacumab using pooled phase III clinical data. Total evinacumab PK were described by a two-compartment model with combined linear and saturable (Michaelis-Menten) elimination, and first-order absorption. At clinically relevant concentrations, plasma drug concentrations were mainly influenced by the linear clearance pathway. Although the maximum target-mediated rate of elimination (Vmax ) parameter for the saturable pathway was found to be positively related to baseline ANGPLTL3, variability in body weight contributed more to the variability in evinacumab exposure than variability in ANGPTL3. An effect of HoFH versus healthy volunteers on Vmax was also identified. Weight-based dosing regimens resulted in consistent evinacumab exposure across weight ranges. An indirect exposure-response model adequately described the relationship between evinacumab and LDL-C, where drug concentration is assumed to inhibit LDL-C production. The final population PK/PD model included two nonclinically significant covariates (race and baseline body weight) on the maximum drug-induced inhibitory effect (Imax ) and one (baseline LDL-C) on the evinacumab concentration inducing 50% of Imax (IC50 ). A smaller IC50 was observed in patients with higher baseline LDL-C, suggesting greater sensitivity to treatment. Population exposure-response analysis permitted estimation of derived PD parameters and individual LDL-C levels over time for patients with HoFH. The model accurately predicted the proportion of patients with HoFH achieving prespecified LDL-C goals with evinacumab during the ELIPSE HoFH study, further supporting a dosing strategy.

A quantitative systems pharmacology modeling platform for evaluating triglyceride profiles in patients with high triglycerides receiving evinacumab.

A model to quantitatively characterize the effect of evinacumab, an investigational monoclonal antibody against angiopoietin-like protein 3 (ANGPTL3) on lipid trafficking is needed. A quantitative systems pharmacology (QSP) approach was developed to predict the transient responses of different triglyceride (TG)-rich lipoprotein particles in response to evinacumab administration. A previously published hepatic lipid model was modified to address specific queries relevant to the mechanism of evinacumab and its effect on lipid metabolism. Modifications included the addition of intermediate-density lipoprotein and low-density lipoprotein compartments to address the modulation of lipoprotein lipase (LPL) activity by evinacumab, ANGPTL3 biosynthesis and clearance, and a target-mediated drug disposition model. A sensitivity analysis guided the creation of virtual patients (VPs). The drug-free QSP model was found to agree well with clinical data published with the initial hepatic liver model over simulations ranging from 20 to 365 days in duration. The QSP model, including the interaction between LPL and ANGPTL3, was validated against clinical data for total evinacumab, total ANGPTL3, and TG concentrations as well as inhibition of apolipoprotein CIII. Free ANGPTL3 concentration and LPL activity were also modeled. In total, seven VPs were created; the lipid levels of the VPs were found to match the range of responses observed in evinacumab clinical trial data. The QSP model results agreed with clinical data for various subjects and was shown to characterize known TG physiology and drug effects in a range of patient populations with varying levels of TGs, enabling hypothesis testing of evinacumab effects on lipid metabolism.

The Posology of Dupilumab in Pediatric Patients With Atopic Dermatitis.

Dupilumab demonstrated efficacy with an acceptable safety profile in two randomized, double-blind, placebo-controlled, parallel-group, phase III trials in adolescents (12-17 years; LIBERTY AD ADOL) and children (6-11 years; LIBERTY AD PEDS) with atopic dermatitis (AD) treated for 16 weeks. Here, we present the pharmacokinetic profiles and exposure-response (E-R) relationships of dupilumab that guided the posology in these populations. A total of 251 adolescent patients with moderate-to-severe AD were randomized to subcutaneous dupilumab monotherapy every 2 weeks (q2w; 200 mg q2w, baseline weight < 60 kg; 300 mg q2w, ≥ 60 kg), dupilumab 300 mg every 4 weeks (q4w; non-weight tiered), or placebo; 367 children with severe AD were randomized to dupilumab q2w (100 mg q2w, baseline weight < 30 kg; 200 mg q2w, ≥ 30 kg), dupilumab 300 mg q4w, or placebo. Children received concomitant topical corticosteroids in addition to dupilumab, and loading doses were administered at the start of therapy. Mean dupilumab trough concentrations at week 16 for weight subcategories in each dosing regimen were compared with adult exposures for the approved dupilumab 300 mg q2w regimen. Positive E-R relationships were demonstrated between dupilumab trough concentrations and AD outcome measures across patient populations and regimens; no relationship was observed with treatment-emergent conjunctivitis. Based on these analyses, a weight-tiered posology was proposed for adolescents (200/300 mg q2w in patients 30-< 60 kg/≥ 60 kg) and children (300 mg q4w in patients 15-< 30 kg, 200 mg q2w in patients 30-< 60 kg) with moderate-to-severe AD.

Modeling Tumor Growth and Treatment Resistance Dynamics Characterizes Different Response to Gefitinib or Chemotherapy in Non-Small Cell Lung Cancer.

Differences in the effect of gefitinib and chemotherapy on tumor burden in non-small cell lung cancer remain to be fully understood. Using a Bayesian hierarchical model of tumor size dynamics, we estimated the rates of tumor growth and treatment resistance for patients in the Iressa Pan-Asia Study study (NCT00322452). The following relationships characterize greater efficacy of gefitinib in epidermal growth factor receptor (EGFR) positive tumors: Maximum drug effect is, in decreasing order, gefitinib in EGFR-positive, chemotherapy in EGFR-positive, chemotherapy in EGFR-negative, and gefitinib in EGFR-negative tumors; the rate of resistance emergence is, in increasing order: gefitinib in EGFR positive, chemotherapy in EGFR positive, while each is plausibly similar to the rate in EGFR negative tumors, which are estimated with less certainty. The rate of growth is smaller in EGFR-positive than in EGFR-negative fully resistant tumors, regardless of treatment. The model can be used to compare treatment effects and resistance dynamics among different drugs.

Tumor Growth Dynamic Modeling in Oncology Drug Development and Regulatory Approval: Past, Present, and Future Opportunities.

Model-informed drug development (MIDD) approaches have rapidly advanced in drug development in recent years. Additionally, the Prescription Drug User Fee Act (PDUFA) VI has specific commitments to further enhance MIDD. Tumor growth dynamic (TGD) modeling, as one of the commonly utilized MIDD approaches in oncology, fulfills the purposes to accelerate the drug development, to support new drug and biologics license applications, and to guide the market access. Increasing knowledge of TGD modeling methodologies, encouraging applications in clinical setting for patients' survival, and complementing assessment of regulatory review for submissions, together fueled promising potentials for imminent enhancement of TGD in oncology. This review is to comprehensively summarize the history of TGD, and present case examples of the recent advance of TGD modeling (mixture model and joint model), as well as the TGD impact on regulatory decisions, thus illustrating challenges and opportunities. Additionally, this review presents the future perspectives for TGD approach.

Single-dose pharmacokinetics and pharmacodynamics of anacetrapib, a potent cholesteryl ester transfer protein (CETP) inhibitor, in healthy subjects.

AIMS: Anacetrapib is an orally active and potent inhibitor of CETP in development for the treatment of dyslipidaemia. These studies endeavoured to establish the safety, tolerability, pharmacokinetics and pharmacodynamics of rising single doses of anacetrapib, administered in fasted or fed conditions, and to preliminarily assess the effect of food, age, gender and obesity on the single-dose pharmacokinetics and pharmacodynamics of anacetrapib. METHODS: Safety, tolerability, anacetrapib concentrations and CETP activity were evaluated. RESULTS: Anacetrapib was rapidly absorbed, with peak concentrations occurring at approximately 4 h post-dose and an apparent terminal half-life ranging from approximately 9 to 62 h in the fasted state and from approximately 42 to approximately 83 h in the fed state. Plasma AUC and C(max) appeared to increase in a less than approximately dose-dependent manner in the fasted state, with an apparent plateau in absorption at higher doses. Single doses of anacetrapib markedly and dose-dependently inhibited serum CETP activity with peak effects of approximately 90% inhibition at t(max) and approximately 58% inhibition at 24 h post-dose. An E(max) model best described the plasma anacetrapib concentration vs CETP activity relationship with an EC(50) of approximately 22 nm. Food increased exposure to anacetrapib; up to approximately two-three-fold with a low-fat meal and by up to approximately six-eight fold with a high-fat meal. Anacetrapib pharmacokinetics and pharmacodynamics were similar in elderly vs young adults, women vs men, and obese vs non-obese young adults. Anacetrapib was well tolerated and was not associated with any meaningful increase in blood pressure. CONCLUSIONS: Whereas food increased exposure to anacetrapib significantly, age, gender and obese status did not meaningfully influence anacetrapib pharmacokinetics and pharmacodynamics.