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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Assessing pharmacokinetic differences in Caucasian and East Asian (Japanese, Chinese and Korean) populations driven by CYP2C19 polymorphism using physiologically-based pharmacokinetic modelling.

Understanding the influence of ethnicity on drug exposure is key to patient safety and could minimize repetitive clinical studies. This analysis aimed to evaluate the ability of physiologically-based pharmacokinetic modelling to predict exposure of CYP2C19 substrates (lansoprazole, (es)citalopram, voriconazole) across Caucasian and East Asian populations. CYP2C19 abundance levels in Japanese and Chinese populations have been re-assessed based on clinical evidence. Model performance in each population was evaluated by predicted-over-observed AUC ratios and comparison of observed data with simulated plasma concentration profiles. Exposures in 84.4% (76 out of 90) of the clinical studies were predicted within 1.5-fold of observed values. The reported concentration-time profiles were well-captured within the 90% prediction intervals. With specified CYP2C19 phenotype, PBPK modelling is capable to predict systemic exposure of drugs largely metabolized by CYP2C19 in different ethnic populations. This study demonstrated PBPK modelling can be applied to assess genotype-dependent exposure difference across ethnicities.

Evaluation of the Drug-Drug Interaction Potential of Acalabrutinib and Its Active Metabolite, ACP-5862, Using a Physiologically-Based Pharmacokinetic Modeling Approach.

Acalabrutinib, a selective, covalent Bruton tyrosine kinase inhibitor, is a CYP3A substrate and weak CYP3A/CYP2C8 inhibitor. A physiologically-based pharmacokinetic (PBPK) model was developed for acalabrutinib and its active metabolite ACP-5862 to predict potential drug-drug interactions (DDIs). The model indicated acalabrutinib would not perpetrate a CYP2C8 or CYP3A DDI with the sensitive CYP substrates rosiglitazone or midazolam, respectively. The model reasonably predicted clinically observed acalabrutinib DDI with the CYP3A perpetrators itraconazole (4.80-fold vs. 5.21-fold observed) and rifampicin (0.21-fold vs. 0.23-fold observed). An increase of two to threefold acalabrutinib area under the curve was predicted for coadministration with moderate CYP3A inhibitors. When both the parent drug and active metabolite (total active components) were considered, the magnitude of the CYP3A DDI was much less significant. PBPK dosing recommendations for DDIs should consider the magnitude of the parent drug excursion, relative to safe parent drug exposures, along with the excursion of total active components to best enable safe and adequate pharmacodynamic coverage.

Population Pharmacokinetic Modeling and Probability of Target Attainment Analyses in Asian Patients With Community-Acquired Pneumonia Treated With Ceftaroline Fosamil.

Efficacy of ceftaroline fosamil, the prodrug of the active metabolite ceftaroline, was demonstrated in a phase 3 study of hospitalized Asian patients with Pneumonia Outcomes Research Team (PORT) risk class III-IV community-acquired pneumonia (NCT01371838). The objectives of the current analysis were to expand an existing ceftaroline and ceftaroline fosamil population pharmacokinetic (PK) model with data from this phase 3 study and a phase 1 study (NCT01458743) assessing ceftaroline PK in healthy Chinese volunteers and to evaluate the probability of PK/pharmacodynamic (PK/PD) target attainment (PTA) in Asian patients with community-acquired pneumonia (CAP) treated with ceftaroline fosamil. The ceftaroline plasma concentration-time course was simulated for 5000 Asian patients with CAP for different renal function subgroups using the final model. PTA was calculated for Streptococcus pneumoniae, Staphylococcus aureus, and non-extended-spectrum ß-lactamase-producing Enterobacteriaceae. PTA was also evaluated for ceftaroline MIC90 values of isolates collected from Asia-Pacific surveillance studies (2012-2014) and for EUCAST and FDA/CLSI ceftaroline susceptibility break points. The final model reasonably described the ceftaroline PK. Race was not found to be a significant covariate impacting ceftaroline PK, suggesting similar ceftaroline PK in Asian and Western populations when corrected for body weight. High PTAs (90%-100%) were predicted for Asian patients with CAP treated with ceftaroline fosamil, covering MIC90 values of target CAP pathogens from the region. Similarly, >90% PTAs were predicted at EUCAST and FDA/CLSI clinical break points for these pathogens. These results support the use of the ceftaroline fosamil dosing regimens approved in Europe and the United States in Asian patients with PORT III-IV CAP.

Resistance models to EGFR inhibition and chemotherapy in non-small cell lung cancer via analysis of tumour size dynamics.

PURPOSE: Imaging time-series data routinely collected in clinical trials are predominantly explored for covariates as covariates for survival analysis to support decision-making in oncology drug development. The key objective of this study was to assess if insights regarding two relapse resistance modes, de-novo (treatment selects out a pre-existing resistant clone) or acquired (resistant clone develops during treatment), could be inferred from such data. METHODS: Individual lesion size time-series data were collected from ten Phase III study arms where patients were treated with either first-generation EGFR inhibitors (erlotinib or gefitinib) or chemotherapy (paclitaxel/carboplatin combination or docetaxel). The data for each arm of each study were analysed via a competing models framework to determine which of the two mathematical models of resistance, de-novo or acquired, best-described the data. RESULTS: Within the first-line setting (treatment naive patients), we found that the de-novo model best-described the gefitinib data, whereas, for paclitaxel/carboplatin, the acquired model was preferred. In patients pre-treated with paclitaxel/carboplatin, the acquired model was again preferred for docetaxel (chemotherapy), but for patients receiving gefitinib or erlotinib, both the acquired and de-novo models described the tumour size dynamics equally well. Furthermore, in all studies where a single model was preferred, we found a degree of correlation in the dynamics of lesions within a patient, suggesting that there is a degree of homogeneity in pharmacological response. CONCLUSIONS: This analysis highlights that tumour size dynamics differ between different treatments and across lines of treatment. The analysis further suggests that these differences could be a manifestation of differing resistance mechanisms.

Physiologically based pharmacokinetic modelling to predict exposure differences in healthy volunteers and subjects with renal impairment: Ceftazidime case study.

Ceftazidime is a widely used ß-lactam antibiotic and almost entirely excreted via glomerular filtration in kidney. The objective of this analysis was to assess the ability of physiologically based pharmacokinetic (PBPK) model to predict ceftazidime exposure in healthy volunteers and subjects with renal impairment. A full PBPK model of ceftazidime was developed using physiochemical properties and clinical data. The total clearance of 115 mL/min and renal clearance of 100 mL/min were obtained from ceftazidime package insert. Healthy and chronic kidney disease (CKD) populations were applied for sampling of virtual subjects. The established PBPK model predicted mean plasma AUCinf were 138.5 ± 19.6, 230.7 ± 22.2, 369.3 ± 53.1 and 561.8 ± 92.4 h µg/mL in healthy, mild, moderate and severe renal impairment subjects, respectively, after administration of 1 g ceftazidime intravenous bolus dose. The predicted values were in close agreement with the weighted mean of the five reported clinical studies. The exposure was slightly under predicted in subjects with severely impaired renal function, but still within 1.5-fold range. The concentration-time profiles of ceftazidime were also well captured in healthy volunteers and subjects with renal impairment. The developed PBPK model along with systems pharmacokinetics (PK) (renal impaired populations) well predicted the ceftazidime exposure. PBPK models verified with clinical study in healthy volunteers could be potentially applied to predict PK and recommend dose adjustment for CKD patients.

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.

Population Pharmacokinetic and Exposure-Response Analysis of Selumetinib and Its N-desmethyl Metabolite in Patients With Non-Small Cell Lung Cancer.

Selumetinib (AZD6244, ARRAY-142886) is a mitogen-activated protein kinase kinase inhibitor that has been tested for treatment of non-small cell lung cancer (NSCLC). Selumetinib (75 mg twice daily) plus docetaxel in patients with advanced NSCLC has been assessed in phase 2 (SELECT-2) and phase 3 (SELECT-1) clinical trials. The objective of the current analysis was to investigate the exposure-response relationship of selumetinib in these 2 clinical trials, based on the development of a population pharmacokinetic (PopPK) model for selumetinib and its active metabolite, N-desmethyl selumetinib, in patients with NSCLC. A PopPK model using data from seven phase 1 studies was first developed and served as prior information for the development of the patient PopPK model. The pharmacokinetics (PK) of selumetinib and N-desmethyl selumetinib were modeled simultaneously. A two-compartment model with zero-first order absorption and first-order elimination reasonably described the selumetinib PK. The N-desmethyl metabolite of selumetinib was described by a one-compartment model with first-order elimination. The final PK parameter estimates were similar between patients with NSCLC and patients in the phase 1 population. Selumetinib apparent clearance and central volume of distribution were 11.9 L/h and 32.1 L, respectively, in patients. Individual selumetinib exposure metrics were estimated to investigate the correlation between exposure and efficacy/safety endpoints observed in NSCLC studies. There was no significant difference in progression-free survival (the primary endpoint) among the different quartiles of exposure. Similarly, no significant correlation was observed between selumetinib exposure and other secondary efficacy or safety endpoints. The conclusions are in accordance with the reported clinical findings.

Joint longitudinal and time-to-event models for multilevel hierarchical data.

Joint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper, we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progression-free survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level higher in the hierarchy than the patient-level, since this has implications for the model formulation.

Bridging Olaparib Capsule and Tablet Formulations Using Population Pharmacokinetic Meta-analysis in Oncology Patients.

BACKGROUND: Olaparib is a first-in-class potent oral poly(ADP-ribose) polymerase inhibitor. OBJECTIVES: The aims of this analysis were to establish an integrated population pharmacokinetic (PK) model of olaparib in patients with solid tumors and to bridge the PK of olaparib between capsule and tablet formulations. METHODS: The population PK model was developed using plasma concentration data from 659 patients in 11 phase I, II, and III studies of olaparib tablets/capsules monotherapy. Relative bioavailability between the tablet and capsule formulations was estimated and the relative exposure between olaparib tablet and capsule therapeutic doses was further assessed. RESULTS: The concentration-time profile was described using a two-compartment model with sequential zero- and first-order absorption and first-order elimination for both capsules and tablets with different absorption parameters. Multiple-dose clearance compared with single-dose clearance was reduced by approximately 15% (auto-inhibition). Disease severity had an impact on olaparib clearance, and tablet strength had an impact on Ka. The olaparib geometric mean area under the curve (AUC) and maximal concentration (Cmax) following a single 300 mg tablet were 42.1 µg h/mL and 5.8 µg/mL, respectively, and the steady-state geometric mean AUC and Cmax following a 300 mg tablet twice daily were 49.0 µg h/mL and 7.7 µg/mL, respectively. The relative exposure (AUC) of the 300 mg tablet formulation is 13% higher than the 400 mg capsule formulation. CONCLUSION: This analysis bridged the olaparib capsule and tablet formulation PK and provided key assessment to support the approval of the olaparib tablet formulation in patients with ovarian cancer, regardless of their BRCA mutation status.