In vitro PK/PD modeling of tyrosine kinase inhibitors in non-small cell lung cancer cell lines.

Tyrosine kinase inhibitors (TKIs) are routinely prescribed for the treatment of non-small cell lung cancer (NSCLC). As with all medications, patients can experience adverse events due to TKIs. Unfortunately, the relationship between many TKIs and the occurrence of certain adverse events remains unclear. There are limited in vivo studies which focus on TKIs and their effects on different regulation pathways. Many in vitro studies, however, that investigate the effects of TKIs observe additional changes, such as changes in gene activations or protein expressions. These studies could potentially help to gain greater understanding of the mechanisms for TKI induced adverse events. However, in order to utilize these pathways in a pharmacokinetic/pharmacodynamic (PK/PD) framework, an in vitro PK/PD model needs to be developed, in order to characterize the effects of TKIs in NSCLC cell lines. Through the use of ordinary differential equations, cell viability data and nonlinear mixed effects modeling, an in vitro TKI PK/PD model was developed with estimated PK and PD parameter values for the TKIs alectinib, crizotinib, erlotinib, and gefitinib. The relative standard errors for the population parameters are all less than 25%. The inclusion of random effects enabled the model to predict individual parameter values which provided a closer fit to the observed response. It is hoped that this model can be extended to include in vitro data of certain pathways that may potentially be linked with adverse events and provide a better understanding of TKI-induced adverse events.

Longitudinal Circulating Tumor DNA Modeling to Predict Disease Progression in First-Line Mutant Epidermal Growth Factor Receptor Non-Small Cell Lung Cancer.

This exploratory, post hoc analysis aimed to model circulating tumor DNA (ctDNA) dynamics and predict disease progression in patients with treatment-naïve locally advanced/metastatic epidermal growth factor receptor mutation (EGFRm)-positive non-small cell lung cancer, from the FLAURA trial (NCT02296125). Patients were randomized 1:1 and received osimertinib 80 mg once daily (q.d.) or comparator EGFR-TKIs (gefitinib 250 mg q.d. or erlotinib 150 mg q.d.). Plasma was collected at baseline and multiple timepoints until treatment discontinuation. Patients with Response Evaluation Criteria in Solid Tumors (RECIST) imaging data and detectable EGFR mutations (Ex19del/L858R) at baseline and ≥ 3 additional timepoints were evaluable. Joint modeling was conducted to characterize the relationship between longitudinal changes in ctDNA and probability of progression-free survival (PFS). A Bayesian joint model of ctDNA and PFS was developed solving differential equations with the ctDNA dynamics and the PFS time-to-event probability. Of 556 patients, 353 had detectable ctDNA at baseline. Evaluable patients (with available imaging and ≥ 3 additional timepoints, n = 320; ctDNA set) were divided into training (n = 259) and validation (n = 61) sets. In the validation set, the model predicted a median PFS of 17.7 months (95% confidence interval (CI): 11.9-28.3) for osimertinib (n = 23) and 9.1 months (95% CI: 6.3-14.8) for comparator (n = 38), consistent with observed RECIST PFS (16.4 months and 9.7, respectively). The model demonstrates that EGFRm ctDNA dynamics can predict the risk of disease progression in this patient population and could be used to predict RECIST-defined disease progression.

Pharmacokinetic/Pharmacodynamic Analysis of Savolitinib plus Osimertinib in an EGFR Mutation-Positive, MET-Amplified Non-Small Cell Lung Cancer Model.

Osimertinib is a third-generation, irreversible, oral EGFR tyrosine kinase inhibitor (TKI) recommended as first-line treatment for patients with locally advanced/metastatic EGFR mutation-positive (EGFRm) non-small cell lung cancer (NSCLC). However, MET amplification/overexpression is a common acquired osimertinib resistance mechanism. Savolitinib is an oral, potent, and highly selective MET-TKI; preliminary data suggest that combining osimertinib with savolitinib may overcome MET-driven resistance. A patient-derived xenograft (PDX) mouse model with EGFRm, MET-amplified NSCLC was tested with a fixed osimertinib dose [10 mg/kg for exposures equivalent to (≈)80 mg], combined with doses of savolitinib (0-15 mg/kg, ≈0-600 mg once daily), both with 1-aminobenzotriazole (to better match clinical half-life). After 20 days of oral dosing, samples were taken at various time points to follow the time course of drug exposure in addition to phosphorylated MET and EGFR (pMET and pEGFR) change. Population pharmacokinetics, savolitinib concentration versus percentage inhibition from baseline in pMET, and the relationship between pMET and tumor growth inhibition (TGI) were also modeled. As single agents, savolitinib (15 mg/kg) showed significant antitumor activity, reaching ∼84% TGI, and osimertinib (10 mg/kg) showed no significant antitumor activity (34% TGI, P > 0.05 vs. vehicle). Upon combination, at a fixed dose of osimertinib, significant savolitinib dose-related antitumor activity was shown, ranging from 81% TGI (0.3 mg/kg) to 84% tumor regression (15 mg/kg). Pharmacokinetic-pharmacodynamic modeling showed that the maximum inhibition of both pEGFR and pMET increased with increasing savolitinib doses. Savolitinib demonstrated exposure-related combination antitumor activity when combined with osimertinib in the EGFRm MET-amplified NSCLC PDX model.

Bioequivalence and Relative Bioavailability Studies to Assess a New Acalabrutinib Formulation That Enables Coadministration With Proton-Pump Inhibitors.

Acalabrutinib is a Bruton tyrosine kinase (BTK) inhibitor approved to treat adults with chronic lymphocytic leukemia, small lymphocytic lymphoma, or previously treated mantle cell lymphoma. As the bioavailability of the acalabrutinib capsule (AC) depends on gastric pH for solubility and is impaired by acid-suppressing therapies, coadministration with proton-pump inhibitors (PPIs) is not recommended. Three studies in healthy subjects (N = 30, N = 66, N = 20) evaluated the pharmacokinetics (PKs), pharmacodynamics (PDs), safety, and tolerability of acalabrutinib maleate tablet (AT) formulated with pH-independent release. Subjects were administered AT or AC (orally, fasted state), AT in a fed state, or AT in the presence of a PPI, and AT or AC via nasogastric (NG) route. Acalabrutinib exposures (geometric mean [% coefficient of variation, CV]) were comparable for AT versus AC (AUCinf 567.8 ng h/mL [36.9] vs 572.2 ng h/mL [38.2], Cmax 537.2 ng/mL [42.6] vs 535.7 ng/mL [58.4], respectively); similar results were observed for acalabrutinib's active metabolite (ACP-5862) and for AT-NG versus AC-NG. The geometric mean Cmax for acalabrutinib was lower when AT was administered in the fed versus the fasted state (Cmax 255.6 ng/mL [%CV, 46.5] vs 504.9 ng/mL [49.9]); AUCs were similar. For AT + PPI, geometric mean Cmax was lower (371.9 ng/mL [%CV, 81.4] vs 504.9 ng/mL [49.9]) and AUCinf was higher (AUCinf 694.1 ng h/mL [39.7] vs 559.5 ng h/mL [34.6]) than AT alone. AT and AC were similar in BTK occupancy. Most adverse events were mild with no new safety concerns. Acalabrutinib formulations were comparable and AT could be coadministered with PPIs, food, or via NG tube without affecting the PKs or PDs.

Bioavailability of acalabrutinib suspension delivered via nasogastric tube in the presence or absence of a proton pump inhibitor in healthy subjects.

AIMS: Acalabrutinib, a selective Bruton tyrosine kinase inhibitor, is approved for the treatment of mantle cell lymphoma and chronic lymphocytic leukaemia. Many critically ill patients are unable to swallow and need oral medications to be delivered via a nasogastric (NG) tube. Furthermore, critically ill patients are typically administered proton-pump inhibitors (PPIs) to prevent stress ulcers. Concomitant administration with PPIs reduces acalabrutinib exposure and is not currently recommended. To evaluate acalabrutinib in subjects co-administered with PPIs who require NG delivery, a phase 1, open-label, randomized, crossover, single-dose study was conducted in healthy subjects. METHODS: The study assessed the relative bioavailability of an acalabrutinib suspension-in regular, degassed Coca-Cola-administered via NG tube (Acala-NG) versus the pharmacokinetics (PK) of an acalabrutinib capsule administered orally with water. In addition, the PPI effect was evaluated by comparing the PK following Acala-NG in the presence or absence of rabeprazole. RESULTS: Exposure of acalabrutinib and its active metabolite (ACP-5862) were comparable following administration of Acala-NG versus the oral capsule (Geo mean ratio, % ref [90% confidence interval, CI]: acalabrutinib AUCinf : 103 [93-113]; Cmax : 144 [120-173]). In addition, exposure was similar following administration of Acala-NG with and without a PPI (Geo mean ratio, % ref [90% CI]: acalabrutinib AUCinf : 105 [79-138]; Cmax : 95 [66-137]). No safety or tolerability concerns were observed, and all adverse events were mild and resolved without treatment. CONCLUSIONS: Acala-NG with or without a PPI is safe and well-tolerated without impeding bioavailability.

Concentration-QT modelling in early clinical oncology settings: Simulation evaluation of performance.

AIMS: Concentration-QT modelling (C-QTc) of first-in-human data has been rapidly adopted as the primary evaluation of QTc interval prolongation risk. Here, we evaluate the performance of C-QTc in early oncology settings (i.e., patients, no placebo or supratherapeutic dose, 3 + 3 designs). METHODS: C-QTc performance was evaluated across three oncology scenarios using a simulation-estimation approach: (scen1) typical dose-escalation testing six dose levels (n = 21); (scen2) small dose-escalation testing two dose levels (n = 9); (scen3) expansion cohorts at one dose level (n = 6-140). True ΔΔQTc effects ranged from 3 ms ("no effect") to 20 ms ("large effect"). Performance was assessed based on the upper limit of the ΔQTc two-sided 90% CI against a threshold of 10 or 20 ms. RESULTS: The performance against the 10 ms threshold was limited based on C-QTc data from typical dose escalation (scen1) and acceptable performance was observed only for relatively large expansions (n ≥ 45; scen3). Performance against the 20 ms threshold was acceptable based on C-QTc data from a typical dose escalation (scen1) or dose expansion cohort n > 10 (scen3). In general, pooling C-QTc data from dose escalation and expansion cohorts substantially improved the performance and reduced the ΔQTc 90% CI width. CONCLUSION: C-QTc performance appeared limited using a 10 ms threshold, but acceptable against a 20 ms threshold. Selection of threshold may be informed by the benefit-risk balance in a specific disease area. Acceptable precision (i.e., confidence intervals) of the estimated ΔQTc, regardless of its magnitude, can be facilitated by pooling data from dose escalation and expansion cohorts.

Exposure-response analysis of acalabrutinib and its active metabolite, ACP-5862, in patients with B-cell malignancies.

AIMS: Examine relationships between the systemic exposure of acalabrutinib, a highly selective, next-generation Bruton tyrosine kinase inhibitor, and its active metabolite (ACP-5862) vs. efficacy and safety responses in patients with B-cell malignancies who received acalabrutinib as monotherapy or in combination with obinutuzumab. METHODS: For exposure-efficacy analyses, patients with untreated chronic lymphocytic leukaemia were assessed for best overall response, progression-free survival and tumour regression. For exposure-safety analyses, incidences of grade ≥2 adverse events (AEs), grade ≥3 AEs and grade ≥2 events of clinical interest were assessed in patients with B-cell malignancies. Acalabrutinib and ACP-5862 pharmacokinetic (PK) parameter estimates were obtained from population PK modelling. Exposure calculations were based on study dosing regimens. Total active moieties were calculated to account for contributions of ACP-5862 to overall efficacy/safety. RESULTS: A total of 573 patients were included (exposure-efficacy analyses, n = 274; exposure-safety analyses, n = 573). Most patients (93%) received acalabrutinib 100 mg twice daily. Median total active area under the concentration-time curve (AUC24h,ss ) and total active maximal concentration at steady-state (Cmax,ss ) were similar for patients who received acalabrutinib as monotherapy or in combination with obinutuzumab, and for responders and nonresponders. No relationship was observed between AUC24h,ss /Cmax,ss and progression-free survival or tumour regression. Acalabrutinib AUC24h,ss and Cmax,ss were generally comparable across groups regardless of AE incidence. CONCLUSION: No clinically meaningful correlations between acalabrutinib PK exposure and efficacy and safety outcomes were observed. These data support the fixed acalabrutinib dose of 100 mg twice daily in the treatment of patients with B-cell malignancies.

Improved characterization of the pharmacokinetics of acalabrutinib and its pharmacologically active metabolite, ACP-5862, in patients with B-cell malignancies and in healthy subjects using a population pharmacokinetic approach.

This analysis aimed to describe the pharmacokinetics (PK) of acalabrutinib and its active metabolite, ACP-5862. A total of 8935 acalabrutinib samples from 712 subjects and 2394 ACP-5862 samples from 304 subjects from 12 clinical studies in patients with B-cell malignancies and healthy subjects were analysed by nonlinear mixed-effects modelling. Acalabrutinib PK was characterized by a 2-compartment model with first-order elimination. The large variability in absorption was adequately described by transit compartment chain and first-order absorption, with between-occasion variability on the mean transit time and relative bioavailability. The PK of ACP-5862 was characterized by a 2-compartment model with first-order elimination, and the formation rate was defined as the acalabrutinib clearance multiplied by the fraction metabolized. Health status, Eastern Cooperative Oncology Group performance status, and coadministration of proton-pump inhibitors were significant covariates. However, none of the investigated covariates led to clinically meaningful changes in exposure, supporting a flat dosing of acalabrutinib.

Imputing Biomarker Status from RWE Datasets-A Comparative Study.

Missing data is a universal problem in analysing Real-World Evidence (RWE) datasets. In RWE datasets, there is a need to understand which features best correlate with clinical outcomes. In this context, the missing status of several biomarkers may appear as gaps in the dataset that hide meaningful values for analysis. Imputation methods are general strategies that replace missing values with plausible values. Using the Flatiron NSCLC dataset, including more than 35,000 subjects, we compare the imputation performance of six such methods on missing data: predictive mean matching, expectation-maximisation, factorial analysis, random forest, generative adversarial networks and multivariate imputations with tabular networks. We also conduct extensive synthetic data experiments with structural causal models. Statistical learning from incomplete datasets should select an appropriate imputation algorithm accounting for the nature of missingness, the impact of missing data, and the distribution shift induced by the imputation algorithm. For our synthetic data experiments, tabular networks had the best overall performance. Methods using neural networks are promising for complex datasets with non-linearities. However, conventional methods such as predictive mean matching work well for the Flatiron NSCLC biomarker dataset.

Physiologically Based Pharmacokinetic Modeling for Selumetinib to Evaluate Drug-Drug Interactions and Pediatric Dose Regimens.

Selumetinib (ARRY-142886), an oral, potent and highly selective allosteric mitogen-activated protein kinase kinase 1/2 inhibitor, is approved by the US Food and Drug Administration for the treatment of pediatric patients aged ≥2 years with neurofibromatosis type 1 with symptomatic, inoperable plexiform neurofibromas. A physiologically based pharmacokinetic (PBPK) model was constructed to predict plasma concentration-time profiles of selumetinib, and to evaluate the impact of coadministering moderate cytochrome P450 (CYP) 3A4/2C19 inhibitors/inducers. The model was also used to extrapolate pharmacokinetic exposures from older children with different body surface area to guide dosing in younger children. This model was built based on physiochemical data and clinical in vivo drug-drug interaction (DDI) studies with itraconazole and fluconazole, and verified against data from an in vivo rifampicin DDI study and an absolute bioavailability study. The pediatric model was updated by changing system-specific input parameters using the Simcyp pediatric module. The model captured the observed selumetinib pharmacokinetic profiles and the interactions with CYP inhibitors/inducers. The predictions from the PBPK model showed a DDI effect of 30% to 40% increase or decrease in selumetinib exposure when coadministered with moderate CYP inhibitors or inducers, respectively, which was used to inform dose management and adjustments. The pediatric PBPK model was applied to simulate exposures in specific body surface area brackets that matched those achieved with a 25 mg/m2 dose in SPRINT clinical trials. The pediatric PBPK model was used to guide the dose for younger patients in a planned pediatric clinical study.

Population pharmacokinetics and exposure-response of selumetinib and its N-desmethyl metabolite in pediatric patients with neurofibromatosis type 1 and inoperable plexiform neurofibromas.

PURPOSE: Selumetinib (ARRY-142886) is a potent, selective, MEK1/2 inhibitor approved in the US for the treatment of children (≥ 2 years) with neurofibromatosis type 1 (NF1) and symptomatic, inoperable plexiform neurofibromas (PN). We characterized population pharmacokinetics (PK) of selumetinib and its active N-desmethyl metabolite, evaluated exposure-safety/efficacy relationships, and assessed the proposed therapeutic dose of 25 mg/m2 bid based on body surface area (BSA) in this patient population. METHODS: Population PK modeling and covariate analysis (demographics, formulation, liver enzymes, BSA, patients/healthy volunteers) were based on pooled PK data from adult healthy volunteers (n = 391), adult oncology patients (n = 83) and pediatric patients with NF1-PN (n = 68). Longitudinal selumetinib/metabolite exposures were predicted with the final model. Exposure-safety/efficacy analyses were applied to pediatric patients (dose levels: 20, 25, 30 mg/m2 bid). RESULTS: Selumetinib and metabolite concentration-time courses were modeled using a joint compartmental model. Typical selumetinib plasma clearance was 11.6 L/h (95% CI 11.0-12.2 L/ h). Only BSA had a clinically relevant (> 20%) impact on exposure, supporting BSA-based administration in children. Selumetinib and metabolite exposures in responders (≥ 20% PN volume decrease from baseline) and non-responders were largely overlapping, with medians numerically higher in responders. No clear relationships between exposure and safety events were established; exposure was not associated with key adverse events (AEs) including rash acneiform, diarrhea, vomiting, and nausea. CONCLUSION: Findings support continuous selumetinib 25 mg/m2 bid in pediatric patients. Importantly, the updated dosing nomogram ensures that patients will receive a clinically active, yet tolerable, dose regardless of differences in BSA and allows dose reductions, if necessary.

QT Prolongation Risk Assessment in Oncology: Lessons Learned From Small-Molecule New Drug Applications Approved During 2011-2019.

The International Conference on Harmonisation (ICH) E14 guidance provides recommendations to assess the potential of a drug to delay cardiac repolarization (QT prolongation), including general guidelines for cases in which a conventional thorough QT study (TQT) might not be feasible. These guidelines have been updated through the ICH question-and-answer process, with the last revision in 2015. We conducted a comprehensive analysis of QT prolongation evaluation of small-molecule new drug applications (NDAs) approved in oncology between 2011 and 2019 to extract learning experience. The following information was analysed: (1) methods to assess QT prolongation, (2) electrocardiogram data collection, (3) QT-related label language, and (4) postmarketing requirements. Overall, every NDA included a QT assessment. The concentration-QTc modeling approach (studies in which QT was not the primary objective) was the most common approach (59%), followed by the TQT and the dedicated QT studies (20% and 21%, respectively). The quality and quantity of the QT assessments were different across NDAs, which suggested relatively large flexibility in the designs and approaches to characterizing QT liability. The QT-related label language reflected the QT results, but also the safety events and the study design limitations because of the oncology settings. There was no delay in approval because of less robust QTc studies as long as the benefit-to-risk ratio of the drug was acceptable, and the implications were reflected in the label. This work offers a structured understanding of the QT evaluation criteria by the Food and Drug Administration and can assist in planning QT prolongation assessments in oncology settings.

Gefitinib exposure and occurrence of interstitial lung disease in Japanese patients with non-small-cell lung cancer.

PURPOSE: A prospective, multicenter, large-scale cohort with a nested case-control study (NCT00252759) was conducted to identify and quantify risk factors for interstitial lung disease (ILD) in Japanese patients with non-small-cell lung cancer who received gefitinib. This study reports the association between gefitinib exposure and the occurrence of ILD. METHODS: A total of 1891 gefitinib plasma concentrations from 336 patients were measured after first dose, at steady state, and at time of ILD occurrence. Influences of demographic and pathophysiological factors on pharmacokinetics were investigated by non-linear mixed-effect modeling. The exposure to gefitinib was compared between patients without and with ILD occurrence to explore risks associated with gefitinib-induced ILD. Intra-patient comparison of exposure was also conducted between times at ILD development and normal states. RESULTS: In the population pharmacokinetic analysis for gefitinib, α1-acid glycoprotein (AGP), age, body weight, and concomitant use of cytochrome P450 3A4 inducers were significant covariates on oral clearance (CL/F). AGP and body weight were also identified as factors affecting the volume of distribution. CL/F was significantly lower at the time of ILD occurrence than normal states. Patients who developed ILD tended to show higher exposure to gefitinib than those without ILD; however, these differences were not statistically significant. On the other hand, exposure at the time of ILD occurrence was significantly elevated compared to the time of normal state within the same patients. CONCLUSIONS: Significant elevation of exposure of gefitinib was observed at the time of ILD occurrence, suggesting reduction of CL/F could be associated with ILD-induced AGP elevation. Increase in exposure of gefitinib is unlikely to be a robust predictor of ILD and does not warrant any dose modifications.

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.

Food Effect Study Design With Oral Drugs: Lessons Learned From Recently Approved Drugs in Oncology.

Evaluation of the effect of food on the pharmacokinetics of oral oncology drugs is critical to drug development, as food can mitigate or exacerbate toxicities and alter systemic exposure. Our aim is to expand on current US Food and Drug Administration (FDA) guidance and provide data-driven food-effect study design recommendations specific to the oncology therapeutic area. Data for recently approved small-molecule oncology drugs was extracted from the clinical pharmacology review in the sponsor's FDA submission package. Information on subject selection, meal types, timing of the study relative to the pivotal trial, and study outcomes was analyzed. The number of subjects enrolled ranged from 12 to 60, and the majority of studies (19 of 29) were conducted in healthy volunteers. Using AstraZeneca cost data, healthy volunteer studies were estimated to cost 10-fold less than cancer patient studies. Nine of 29 (31%) studies included meals with multiple levels of fat content. Analysis of a subset of 16 drugs revealed that final results for the food-effect study were available before the start of the pivotal trial for only 2 drugs. Conducting small food-effect studies powered to estimate effect, rather than confirm no effect, with only a standardized high-fat meal according to FDA guidance may eliminate unnecessary studies, reduce cost, and improve efficiency in oncology drug development. Starting food-effect studies as early as possible is key to inform dosing in pivotal trials.

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.

Efficacy and Safety Exposure-Response Analyses of Olaparib Capsule and Tablet Formulations in Oncology Patients.

Olaparib is a poly ADP-ribose polymerase inhibitor that induces synthetic lethality in tumors with deficient homologous recombination repair. Population exposure-response analyses were performed to evaluate the efficacy and safety of olaparib exposure in patients with cancer. Data from multiple phase I/II/III clinical studies from both capsule and tablet formulations were combined for efficacy (N = 410) and safety (N = 757) analyses. Exposure-progression-free survival (Cox proportional hazards model indicated that a 300 mg b.i.d. tablet was statistically superior to the 200 mg b.i.d. tablet dose (hazard ratio of 0.96), although the difference was small. Exposure-safety logistic regression models and hemoglobin models predicted similar probability of safety events or hemoglobin decrease with largely overlapping 95% confidence intervals at 300 mg b.i.d. tablet, 200 mg b.i.d. tablet, and 400 mg b.i.d. capsule. The analyses provided key assessments to support the approval of olaparib 300 mg tablet therapeutic dose in patients with ovarian and breast cancer, regardless of their breast cancer (BRCA) mutation status.

Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model.

BACKGROUND: Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies. METHODS: A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx. RESULTS: The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1. CONCLUSIONS: This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.

Harnessing Meta-analysis to Refine an Oncology Patient Population for Physiology-Based Pharmacokinetic Modeling of Drugs.

Certain oncology compounds exhibit fundamental pharmacokinetic (PK) disparities between healthy and malignant conditions. Given the effects of tumor-associated inflammation on enzyme and transporter expression, we performed a meta-analysis of CYP- and transporter-sensitive substrate clinical PK to quantitatively compare enzyme and transporter abundances between healthy volunteers (HV) and cancer patients (CP). Hepatic and intestinal CYP1A2, CYP2C19, and CYP3A4 abundance were subsequently adjusted via Simcyp's sensitivity analysis tool. Of the 11 substrates we investigated, seven displayed marked exposure differences >1.25-fold between CP and HV. Although CP studies are limited, meta-analysis-based reduction in CYP1A2, CYP2C19, and CYP3A4 enzyme abundances in a virtual oncology population effectively captures CP-PK for caffeine, theophylline, midazolam, simvastatin, omeprazole, and a subset of oncology compounds. These changes allow extrapolation from HV to CP, enhancing predictive capability; therefore, conducting simulations in this CYP-modified oncology (MOD-CP) population provides a more relevant characterization of CP-PK.

Comparison of the Pharmacokinetics of the Phase II and Phase III Capsule Formulations of Selumetinib and the Effects of Food on Exposure: Results From Two Randomized Crossover Trials in Healthy Male Subjects.

PURPOSE: Selumetinib (AZD6244, ARRY-142886), an oral, potent, and highly selective mitogen-activated protein kinase 1/2 inhibitor with a short half-life, has shown activity across various tumor types. Before initiation of Phase III trials, the site, scale, and color (hypromellose shell from white [Phase II] to blue [Phase III]) of the selumetinib 25mg capsule manufacture was changed. We present 2 crossover trials evaluating Phase III capsules in healthy subjects. METHODS: The relative bioavailability trial was a Phase I, open-label, randomized, 3-treatment, 4-period, 6-sequence crossover trial in healthy male subjects (aged 18-55 years). Subjects received selumetinib 75mg (3 × 25 mg) Phase II or Phase III capsules, or a 35mg oral solution, during 4 dosing periods in 1 of 6 randomized treatment sequences. The food effect trial was a Phase I, open-label, randomized, 2-period crossover trial in healthy male subjects (aged 18-45 years). Subjects were randomized to 1 of 2 sequences to receive selumetinib 75mg (3 × 25 mg) Phase III capsules. In sequence 1, subjects received selumetinib after 10 hours of fasting. Following a washout period, selumetinib was administered after a high-fat meal. In sequence 2, subjects received selumetinib in the fed state, before the fasted state. Pharmacokinetic parameters were determined from serial blood sampling. FINDINGS: Twenty-seven subjects were randomized to the relative bioavailability trial; 26 completed all dosing periods. Mean selumetinib AUC was unchanged (geometric least squares mean ratio [GLSMR], 90.01% [90% CI, 81.74-99.11]). Cmax was 18% lower with the Phase III capsules (GLSMR, 81.97% [90% CI, 69.01-97.36]). A post hoc exploratory statistical analysis excluding outlying observations with later Tmax showed that Phase II and III capsules produced similar exposure in terms of Cmax and AUC. High intrasubject variability for Cmax attributed to the pharmacokinetic sampling schedule was judged to have impacted on the estimated GLSMR. In the food effect trial, 34 subjects completed both study periods. A high-fat meal reduced selumetinib Cmax compared with the fasted state (GLSMR, 49.76% [90% CI, 43.82-56.51]); AUC was minimally changed (GLSMR, 84.08% [90% CI, 80.72-87.59]). Median Tmax was prolonged by 1.49 hours. No deaths or serious adverse events were reported. IMPLICATIONS: Selumetinib 75mg (3 × 25 mg) Phase III capsules are being used in ongoing pivotal Phase III trials and should be administered in the fasted state. Based on findings from the relative bioavailability trial, pharmacokinetic sampling frequency was increased for healthy subject trials, including the food effect trial. ClinicalTrials.gov identifiers: NCT01635023 (relative bioavailability) and NCT01974349 (food effect).