Application of a Two-Analyte Integrated Population Pharmacokinetic Model to Evaluate the Impact of Intrinsic and Extrinsic Factors on the Pharmacokinetics of Polatuzumab Vedotin in Patients with Non-Hodgkin Lymphoma.

PURPOSE: The established two-analyte integrated population pharmacokinetic model was applied to assess the impact of intrinsic/extrinsic factors on the pharmacokinetics (PK) of polatuzumab vedotin (pola) in patients with non-Hodgkin lymphoma (NHL) following bodyweight-based dosing. METHODS: Model simulations based on individual empirical Bayes estimates were used to evaluate the impact of intrinsic/extrinsic factors as patient subgroups on Cycle 6 exposures. Intrinsic factors included bodyweight, age, sex, hepatic and renal functions. Extrinsic factors included rituximab/obinutuzumab or bendamustine combination with pola and manufacturing process. The predicted impact on exposures along with the established exposure-response relationships were used to assess clinical relevance. RESULTS: No clinically meaningful differences in Cycle 6 pola exposures were found for the following subgroups: bodyweight 100-146 kg versus 38-<100 kg, age ≥ 65 years versus <65 years, female versus male, mild hepatic impairment versus normal, mild-to-moderate renal impairment versus normal. Co-administration of rituximab/obinutuzumab or bendamustine, and change in the pola manufacturing process, also had no meaningful impact on PK. CONCLUSIONS: In patients with NHL, bodyweight-based dosing is adequate, and no further dose adjustment is recommended for the heavier subgroup (100-146 kg). In addition, no dose adjustments are recommended for other subgroups based on intrinsic/extrinsic factors evaluated.

Model-based clinical pharmacology profiling and exposure-response relationships of the efficacy and biomarker of lebrikizumab in patients with moderate-to-severe asthma.

Lebrikizumab is a humanized monoclonal antibody that binds to interleukin-13 and has been evaluated as a treatment for moderate-to-severe asthma. Objectives of this work were to characterize lebrikizumab pharmacokinetics (PK), identify influential covariates, and graphically explore exposure-response relationships in moderate-to-severe asthmatics. Pooled PK data from 11 studies were used in the population PK model development. Full covariate modeling was used to evaluate the impact of pre-specified covariates. Response data (exacerbation rate, forced expiratory volume in 1 s [FEV1], and fractional exhaled nitric oxide [FeNO]) were obtained from moderate-to-severe asthmatics (n = 2148) who received placebo, lebrikizumab 37.5 mg or 125 mg every 4 weeks (Q4W) in two replicate phase 3 studies. Graphical exposure-response analyses were stratified by numerous covariates, including biomarker subgroups defined by serum periostin level and blood eosinophil count at baseline. Lebrikizumab PK was described by a two-compartment model with first-order absorption. Population typical values were estimated as 0.156 L/day for clearance (CL), 4.10 L for central volume (Vc), and 0.239 day-1 for absorption rate (ka), 85.6% for bioavailability (inter-subject variability: CL, 33.3%; Vc, 36.3%; ka, 40.8%). The estimated mean terminal half-life was 25.7 days. Body weight was the most influential covariate. Generally, the exposure-response analyses of FEV1 and FeNO showed increased response at higher exposure quartiles, while flat or unclear exposure-response relationships were observed in exacerbation rate. Lebrikizumab PK is as expected for a typical immunoglobulin G4 monoclonal antibody. Results from the exposure-response analyses suggested that, compared to 125 mg Q4W, the 37.5 mg Q4W dose did not achieve the maximum responses for FEV1 and FeNO, although it appeared to maximize the effect on exacerbation reduction. This suggests that the antibody levels needed to improve these outcomes may not be the same. In addition, the role of IL-13 in airflow obstruction/airway inflammation and asthma exacerbations might be different and targeting multiple pathways may be required to treat this heterogeneous disease and provide clinically meaningful benefits to asthma patients.

Population pharmacokinetics and exposure-response of trastuzumab emtansine in advanced breast cancer previously treated with ≥2 HER2-targeted regimens.

AIMS: We conducted population pharmacokinetic (PopPK) and exposure-response analyses for trastuzumab emtansine (T-DM1), to assess the need for T-DM1 dose optimization in patients with low exposure by using TH3RESA [A Study of Trastuzumab Emtansine in Comparison With Treatment of Physician's Choice in Patients With human epidermal growth factor receptor 2 (HER2)-positive Breast Cancer Who Have Received at Least Two Prior Regimens of HER2-directed Therapy] study data (NCT01419197). The randomized phase III TH3RESA study investigated T-DM1 vs. treatment of physician's choice (TPC) in patients with heavily pretreated HER2-positive advanced breast cancer. METHODS: We compared a historical T-DM1 PopPK model with T-DM1 pharmacokinetics in TH3RESA and performed exposure-response analyses using model-predicted cycle 1 maximum concentration (Cmax ), cycle 1 minimum concentration (Cmin ) and area under the concentration-time curve at steady state (AUCss ). Kaplan-Meier analyses [overall survival (OS), progression-free survival (PFS)] and logistic regression [overall response rate (ORR), safety] were stratified by T-DM1 exposure metrics. Survival hazard ratios (HRs) in the lowest exposure quartile (Q1) of cycle 1 Cmin were compared with matched TPC-treated patients. RESULTS: T-DM1 concentrations in TH3RESA were described well by the historical PopPK model. Patients with higher cycle 1 Cmin and AUCss exhibited numerically longer median OS and PFS and higher ORR than patients with lower exposure. Exposure-response relationships were less evident for cycle 1 Cmax . No relationship between exposure and safety was identified. HRs for the comparison of T-DM1-treated patients in the Q1 subgroup with matched TPC-treated patients were 0.96 [95% confidence interval (CI) 0.63, 1.47] for OS and 0.92 (95% CI 0.64, 1.32) for PFS. CONCLUSIONS: Exposure-response relationships for efficacy were inconsistent across exposure metrics. HRs for survival in patients in the lowest T-DM1 exposure quartile vs. matched TPC-treated patients suggest that, compared with TCP, the approved T-DM1 dose is unlikely to be detrimental to patients with low exposure.

Semi-mechanistic Multiple-Analyte Pharmacokinetic Model for an Antibody-Drug-Conjugate in Cynomolgus Monkeys.

PURPOSE: A semi-mechanistic multiple-analyte population pharmacokinetics (PK) model was developed to describe the complex relationship between the different analytes of monomethyl auristatin E (MMAE) containing antibody-drug conjugates (ADCs) and to provide insight regarding the major pathways of conjugate elimination and unconjugated MMAE release in vivo. METHODS: For an anti-CD79b-MMAE ADC the PK of total antibody (Tab), conjugate (evaluated as antibody conjugated MMAE or acMMAE), and unconjugated MMAE were quantified in cynomolgus monkeys for single (0.3, 1, or 3 mg/kg), and multiple doses (3 or 5 mg/kg, every-three-weeks for 4 doses). The PK data of MMAE in cynomolgus monkeys, after intravenous administration of MMAE at single doses (0.03 or 0.063 mg/kg), was included in the analysis. A semi-mechanistic model was developed and parameter estimates were obtained by simultaneously fitting the model to all PK data using a hybrid ITS-MCPEM method. RESULTS: The final model well described the observed Tab, acMMAE and unconjugated MMAE concentration-time profiles. Analysis suggested that conjugate is lost via both proteolytic degradation and deconjugation, while unconjugated MMAE in systemic circulation appears to be mainly released via proteolytic degradation of the conjugate. CONCLUSIONS: Our model improves the understanding of ADC catabolism, which may provide useful insights when designing future ADCs.

Daily dosing of vismodegib to steady state does not prolong the QTc interval in healthy volunteers.

INTRODUCTION: Vismodegib was assessed as being of low risk for QT interval prolongation based on prior nonclinical and clinical experience. A dedicated study was conducted to further assess the potential for vismodegib to prolong the QTc interval. METHODS AND RESULTS: Given the nonlinear pharmacokinetics of vismodegib, a thorough QTc study as is typically designed was not possible, and an innovative design was employed. This dedicated QTc study was powered to exclude a 20-millisecond change from the baseline QTc interval. The subjects were administered daily oral 150 mg of vismodegib for 7 days, or a single dose of 400 mg of moxifloxacin, with corresponding matching placebos. The upper limits of the 90% confidence intervals for the difference in ΔQTcF between vismodegib and placebo at steady state were <20 milliseconds at all timepoints with a maximum of 10 milliseconds at 12 hours postdose. Exposure-response analysis yielded an estimated slope equal to 0.11 ms/µM, which was not statistically significant. After a single dose of moxifloxacin was administered, the lower limits of the 90% confidence interval of the difference in ΔQTcF between moxifloxacin and placebo were >5 milliseconds from 1-12 hours postdose, thereby establishing assay sensitivity. CONCLUSIONS: There was no effect of vismodegib on the QTc interval when dosed daily at 150 mg to steady state.

Applying interpretable machine learning workflow to evaluate exposure-response relationships for large-molecule oncology drugs.

The application of logistic regression (LR) and Cox Proportional Hazard (CoxPH) models are well-established for evaluating exposure-response (E-R) relationship in large molecule oncology drugs. However, applying machine learning (ML) models on evaluating E-R relationships has not been widely explored. We developed a workflow to train regularized LR/CoxPH and tree-based XGboost (XGB) models, and derive the odds ratios for best overall response and hazard ratios for overall survival, across exposure quantiles to evaluate the E-R relationship using clinical trial datasets. The E-R conclusions between LR/CoxPH and XGB models are overall consistent, and largely aligned with historical pharmacometric analyses findings. Overall, applying this interpretable ML workflow provides a promising alternative method to assess E-R relationships for impacting key dosing decisions in drug development.

Asian race and origin have no clinically meaningful effects on polatuzumab vedotin pharmacokinetics in patients with relapsed/refractory B-cell non-Hodgkin lymphoma.

PURPOSE: The CD79b-targeted antibody-drug conjugate polatuzumab vedotin (pola), alone and with chemoimmunotherapy, has clinical efficacy and a tolerable safety profile in B-cell non-Hodgkin lymphoma (B-NHL). We assessed (a) whether exposure from global studies of pola is comparable to Asian patients, and (b) if the recommended pola dose is appropriate in Asian patients based on exposure. METHODS: The pharmacokinetics (PK) of pola in Asian and global populations was characterized for three analytes (antibody-conjugated monomethyl auristatin E (MMAE) [acMMAE], total antibody, and unconjugated MMAE) in five phase 1b/2 single-agent and combination studies in B-NHL patients (JO29138 [JAPICCTI-142580], DCS4968g [NCT01290549], GO27834 [NCT01691898], GO29044 [NCT01992653], and GO29365 [NCT02257567]). PK data were compared between Japanese phase 1 JO29138 (JAPICCTI-142580) and global phase 1 DCS4968g (NCT01290549) studies and between Asian and non-Asian patients in the randomized relapsed/refractory B-NHL cohorts of the phase 1b/2 study GO29365 (NCT02257567). A population PK (popPK) model was used to assess the effects of Asian race and region on acMMAE and unconjugated MMAE exposure. RESULTS: PK non-compartmental analysis (NCA) parameters for the key analyte acMMAE in the Japanese JO29138 (JAPICCTI-142580) and global phase 1 DCS4968g (NCT01290549) studies were similar. In GO29365 (NCT02257567), the phase 1b/2 combination study, mean exposure to the analytes was generally lower in Asian patients (by ~ 9.9 to 17.5%), but not to a clinically meaningful extent. Overall, the popPK model further suggested comparable PK in Asian patients with B-NHL (race or region) versus non-Asian patients. CONCLUSION: Race has no clinically meaningful effect on pola PK. These results (and observations from efficacy/safety exposure-response analyses) support no pola dose adjustments are warranted for Asian patients with DLBCL.

Exposure-safety and exposure-efficacy analyses of polatuzumab vedotin in patients with relapsed or refractory diffuse large B-cell lymphoma.

Exposure-response relationships were investigated to assess the risk/benefit of polatuzumab vedotin (pola) + bendamustine-rituximab (pola + BR) in relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL). Analyses were conducted in pivotal study GO29365 (NCT02257567; BR/pola + BR/pola + BG [BG: bendamustine-obinutuzumab]; 1.8 mg/kg pola, every 3 weeks [Q3W], six cycles), and supportive studies DCS4968g (NCT01290549) and GO27834 (NCT01691898) (pola/pola + R/pola + G; 0.1-2.4 mg/kg pola Q3W; eight-cycle landmark), separately. Exposure was characterized as simulated cycle-6 AUC and Cmax for antibody-conjugated mono-methyl auristatin E (acMMAE) and unconjugated MMAE. Supportive studies showed response rate and safety risk (grade ≥2 peripheral neuropathy; grade ≥3 anemia) increased with exposure, suggesting not to dose below 1.8 mg/kg (up to eight-cycle) for balancing safety and efficacy. Pivotal study with limited exposure range showed no exposure-safety relationship and slightly positive exposure (acMMAE)-efficacy relationship for overall survival. The exposure-response analyses and the observed risk/benefit characteristics in pivotal study supported pola (1.8 mg/kg) +BR Q3W for six cycles in R/R DLBCL patients.

Physiologically-Based Pharmacokinetic Model-Informed Drug Development for Fenebrutinib: Understanding Complex Drug-Drug Interactions.

Fenebrutinib is a CYP3A substrate and time-dependent inhibitor, as well as a BCRP and OATP1B transporter inhibitor in vitro. Physiologically-based pharmacokinetic (PBPK) modeling strategies with the ultimate goal of understanding complex drug-drug interactions (DDIs) and proposing doses for untested scenarios were developed. The consistency in the results of two independent approaches, PBPK simulation and endogenous biomarker measurement, supported that the observed transporter DDI is primarily due to fenebrutinib inhibition of intestinal BCRP, rather than hepatic OATP1B. A mechanistic-absorption model accounting for the effects of excipient complexation with fenebrutinib was used to rationalize the unexpected observation of itraconazole-fenebrutinib DDI (maximum plasma concentration (Cmax ) decreased, and area under the curve (AUC) increased). The totality of the evidence from sensitivity analysis and clinical and nonclinical data suggested that fenebrutinib is likely a sensitive CYP3A substrate. This advanced PBPK application allowed the use of model-informed approach to facilitate the development of concomitant medication recommendations for fenebrutinib without requiring additional clinical DDI studies.

Integrated Two-Analyte Population Pharmacokinetic Model of Polatuzumab Vedotin in Patients With Non-Hodgkin Lymphoma.

A two-analyte integrated population pharmacokinetic (PK) model that simultaneously describes concentrations of antibody-conjugated monomethyl auristatin E (acMMAE) and unconjugated MMAE following repeated administrations of polatuzumab vedotin (pola) was developed based on data from four clinical studies of pola in patients with non-Hodgkin lymphoma. A two-compartment model with a nonspecific, time-dependent linear clearance, a linear time-dependent exponentially declining clearance, and a Michaelis-Menten clearance provided a good fit of the acMMAE plasma PK profiles. All three acMMAE elimination pathways contributed to the input to the central compartment of unconjugated MMAE, which was also described by a two-compartment model. Population PK parameters, covariate effects, and interindividual variability of model parameters were estimated. The impact of clinically relevant covariates on PK exposures of each analyte were quantified and reported to support key label claims.

Pharmacokinetics of trastuzumab emtansine (T-DM1) as a single agent or in combination with pertuzumab in HER2-positive breast cancer patients with recurrent or locally advanced metastatic breast cancer.

PURPOSE: The phase III MARIANNE study investigated single-agent trastuzumab emtansine (T-DM1) and combination T-DM1 plus pertuzumab as the first-line treatment for human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (MBC). Pharmacokinetic properties of T-DM1 and pertuzumab in these patients and the potential for drug-drug interactions (DDIs) were assessed. METHODS: Pharmacokinetic samples of T-DM1-related analytes (T-DM1 conjugate, total trastuzumab, DM1) and pertuzumab were analyzed. Observed pharmacokinetic data were summarized for all analytes. Historical population pharmacokinetic models for T-DM1 conjugate and pertuzumab in HER2-positive MBC were used to derive empirical Bayes estimates of pharmacokinetic parameters. RESULTS: In MARIANNE (N = 375), mean ± standard deviation population pharmacokinetic model-predicted Cycle 1 Cmax for T-DM1 conjugate was 74.4 ± 10.1 µg/mL, Cycle 1 Ctrough was 1.34 ± 0.802 µg/mL, and area under the concentration-time curve from time zero to infinity after first dose (AUCinf) was 338 ± 69.5 µg*day/mL. These values were similar to other T-DM1 studies. Pharmacokinetics of T-DM1 conjugate and other analytes (total trastuzumab, DM1) were similar with or without pertuzumab. In the pertuzumab plus T-DM1 arm, mean model-predicted Cycle 1 pertuzumab Cmax, Ctrough, and AUCinf were 276 ± 50.0 µg/mL, 64.8 ± 17.9 µg/mL, and 4470 ± 1360 µg*day/mL, respectively. These values were similar to other pertuzumab studies. CONCLUSIONS: Based on the population pharmacokinetic analysis of T-DM1 conjugate and pertuzumab, pharmacokinetics are similar across different lines of treatment and stages of disease including previously untreated MBC patients, and no DDIs were identified for combined use of T-DM1 and pertuzumab.

Exposure-Response-Based Product Profile-Driven Clinical Utility Index for Ipatasertib Dose Selection in Prostate Cancer.

The aims of this work were to characterize ipatasertib exposure-response (E-R) relationships in a phase II study and to quantitatively assess benefit-risk using a clinical utility index approach to support ipatasertib phase III dose selection in patients with metastatic castration-resistant prostate cancer. Logistic regression and Cox proportional-hazards models characterized E-R relationships for safety and efficacy endpoints, respectively. Exposure metrics with and without considering dose interruptions/reductions (modifications) were tested in the E-R models. Despite a steeper E-R relationship when accounting for dose modifications, similar dose-response projections were generated. The clinical utility index analysis assessed important attributes, weights, and clinically meaningful cutoff/tradeoff values based on predefined minimal, target, and optimistic product profiles. Ipatasertib 400 mg daily, showing the highest probability of achieving the minimal product profiles and better benefit-risk balance than other doses (200-500 mg daily), was selected for further development in metastatic castration-resistant prostate cancer.

Assessment of Correlation Between Early and Late Efficacy Endpoints to Identify Potential Surrogacy Relationships in Non-Hodgkin Lymphoma: a Literature-Based Meta-analysis of 108 Phase II and Phase III Studies.

Correlations between early and late efficacy endpoints were assessed to identify potential surrogate endpoints for overall survival (OS) or progression-free survival (PFS) with clinical trial-level data in three non-Hodgkin lymphoma (NHL) subtypes: diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). One hundred and eight phase II-III trials (129 trial arms) in DLBCL, FL, and MCL were identified and included in the database. Correlations between efficacy endpoints were analyzed using weighted linear regression and Pearson's coefficient of determination (R 2). In newly diagnosed DLBCL, 6-month PFS was strongly correlated with 2-year OS (R 2 = 0.81, 95% confidence interval [CI] 0.51-0.96). Six-month PFS was strongly correlated with 3-year PFS (R 2 = 0.89, 95% CI 0.62-0.96) in FL and was moderately correlated with 2-year OS (R 2 = 0.69, 95% CI 0.40-0.91) in MCL trials. Linear regression determined that a 10% increase in 6-month PFS would yield a 13% ± 1.2% increase in 2-year OS in DLBCL, a 23% ± 1.1% increase in 3-year PFS in FL, or a 6.7% ± 1.0% increase in 2-year OS in MCL. Both 6-month PFS and complete response (CR) rate were moderately correlated with median PFS in FL trials with R 2 = 0.66 (95% CI 0.52-0.98) and R 2 = 0.69 (95% CI 0.22-0.89), respectively. Six-month PFS is a potential surrogate endpoint for 2-year OS in newly diagnosed DLBCL and MCL and for 3-year PFS in FL. Both 6-month PFS and CR rate are potential surrogate endpoints for median PFS in FL patients. Confirmation and validation of these correlations may facilitate early interpretation of NHL trials.

A Phase I Pharmacokinetic Study of Trastuzumab Emtansine (T-DM1) in Patients with Human Epidermal Growth Factor Receptor 2-Positive Metastatic Breast Cancer and Normal or Reduced Hepatic Function.

OBJECTIVE: The aim of this study was to evaluate the pharmacokinetics (PK) of trastuzumab emtansine (T-DM1) and relevant analytes in patients with human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer and hepatic impairment. METHODS: Patients were enrolled in three independent parallel cohorts based on hepatic function per Child-Pugh criteria: normal hepatic function, mild hepatic impairment, and moderate hepatic impairment. Patients received T-DM1 3.6 mg/kg intravenously every 3 weeks. PK samples were collected during cycles 1 and 3, and the PK of T-DM1 and relevant analytes were characterized and compared across cohorts. RESULTS: Compared with patients with normal hepatic function (n = 10), T-DM1 clearance at cycle 1 was 1.8- and 4.0-fold faster in the mild (n = 10) and moderate (n = 8) cohorts, respectively. The trend of faster clearance was less apparent in cycle 3, with similar T-DM1 clearance across cohorts (mean ± standard deviation 8.16 ± 3.27 [n = 9], 9.74 ± 3.62 [n = 7], and 8.99 and 10.2 [individual values, n = 2] mL/day/kg for the normal, mild, and moderate cohorts, respectively). T-DM1 clearance at cycle 1 correlated significantly with baseline albumin, aspartate aminotransferase, and HER2 extracellular domain concentrations (p < 0.05). Plasma concentrations of DM1 and DM1-containing catabolites were low and were comparable across cohorts. CONCLUSIONS: No increase in systemic DM1 concentration was observed in patients with mild or moderate hepatic impairment versus those with normal hepatic function. The faster T-DM1 clearance observed at cycle 1 in patients with hepatic impairment appeared to be transient. After repeated dosing (three cycles), T-DM1 exposure in patients with mild and moderate hepatic impairment was within the range seen in those with normal hepatic function.

Development of a Physiologically Based Pharmacokinetic Model for Itraconazole Pharmacokinetics and Drug-Drug Interaction Prediction.

BACKGROUND AND OBJECTIVES: Physiologically based pharmacokinetic (PBPK) modeling for itraconazole has been challenging due to highly variable in vitro d ata used for 'bottom-up' model building. Under-prediction of pharmacokinetics and drug-drug interactions (DDIs) following multiple doses of itraconazole has limited the use of PBPK model simulation to aid an itraconazole clinical DDI study design. The aim of this work is to develop an itraconazole PBPK model predominantly using a 'top-down' approach to enable a more accurate pharmacokinetic and DDI prediction. METHODS: An itraconazole PBPK model describing itraconazole and hydroxyl-itraconazole (OH-ITZ) was constructed in Simcyp(®). The key parameters that govern the pharmacokinetic profile, including non-linear clearance (i.e., maximum rate of reaction [V max] and the Michaelis-Menten constant [K m]) and volume of distribution for both itraconazole and OH-ITZ, were redefined by leveraging existing in vivo data. Model verification was performed by comparing the simulated itraconazole and OH-ITZ pharmacokinetic profiles with the observed clinical data. Finally, the model was used to simulate clinical DDIs between itraconazole and midazolam. RESULTS: The developed PBPK model well-described the pharmacokinetics of itraconazole and OH-ITZ, and particularly captured their accumulation after repeated doses of itraconazole. This was verified with the observed data from 29 clinical studies where itraconazole solution or capsule was given as a single or multiple dose. The predicted DDI between itraconazole and midazolam was within 1.25-fold of the observed data for seven of ten studies and within 1.5-fold for nine of ten studies. CONCLUSION: The improvement of the itraconazole PBPK model increased our confidence in using PBPK model simulations to optimize clinical itraconazole DDI study design.

Physiologically based pharmacokinetic modeling as a tool to predict drug interactions for antibody-drug conjugates.

BACKGROUND AND OBJECTIVES: Monomethyl auristatin E (MMAE, a cytotoxic agent), upon releasing from valine-citrulline-MMAE (vc-MMAE) antibody-drug conjugates (ADCs), is expected to behave like small molecules. Therefore, evaluating the drug-drug interaction (DDI) potential associated with MMAE is important in the clinical development of ADCs. The objective of this work was to build a physiologically based pharmacokinetic (PBPK) model to assess MMAE-drug interactions for vc-MMAE ADCs. METHODS: A PBPK model linking antibody-conjugated MMAE (acMMAE) to its catabolite unconjugated MMAE associated with vc-MMAE ADCs was developed using a mixed 'bottom-up' and 'top-down' approach. The model was developed using in silico and in vitro data and in vivo pharmacokinetic data from anti-CD22-vc-MMAE ADC. Subsequently, the model was validated using clinical pharmacokinetic data from another vc-MMAE ADC, brentuximab vedotin. Finally, the verified model was used to simulate the results of clinical DDI studies between brentuximab vedotin and midazolam, ketoconazole, and rifampicin. RESULTS: The pharmacokinetic profile of acMMAE and unconjugated MMAE following administration of anti-CD22-vc-MMAE was well described by simulations using the developed PBPK model. The model's performance in predicting unconjugated MMAE pharmacokinetics was verified by successful simulation of the pharmacokinetic profile following brentuximab vedotin administration. The model simulated DDIs, expressed as area under the concentration-time curve (AUC) and maximum concentration (C max) ratios, were well within the two-fold of the observed data from clinical DDI studies. CONCLUSIONS: This work is the first demonstration of the use of PBPK modelling to predict MMAE-based DDI potential. The described model can be extended to assess the DDI potential of other vc-MMAE ADCs.

Population pharmacokinetics of trastuzumab emtansine (T-DM1), a HER2-targeted antibody-drug conjugate, in patients with HER2-positive metastatic breast cancer: clinical implications of the effect of covariates.

PURPOSE: Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate comprising the humanized monoclonal antibody trastuzumab linked to DM1, a highly potent cytotoxic agent. A population pharmacokinetic (PK) analysis was performed to estimate typical values and interindividual variability of T-DM1 PK parameters and the effects of clinically relevant covariates. METHODS: Serum samples were collected from 671 patients with human epidermal growth factor receptor 2-positive locally advanced or metastatic breast cancer (MBC) who received single-agent T-DM1 in five phase I to phase III studies. Nonlinear mixed-effects modeling with the first-order conditional estimation method was used. RESULTS: A linear two-compartment model with first-order elimination from the central compartment described T-DM1 PKs in the clinical dose range. T-DM1 elimination clearance was 0.676 L/day, volume of distribution in the central compartment (V c) was 3.127 L, and terminal elimination half-life was 3.94 days. Age, race, region, and renal function did not influence T-DM1 PK. Given the low-to-moderate effect of all statistically significant covariates on T-DM1 exposure, none of these covariates is expected to result in a clinically meaningful change in T-DM1 exposure. CONCLUSIONS: T-DM1 PK properties are consistent and predictable in patients. A further refinement of dose based on baseline covariates other than body weight for the current 3.6 mg/kg regimen would not yield clinically meaningful reductions in interindividual PK variability in patients with MBC.

Pharmacokinetics of hedgehog pathway inhibitor vismodegib (GDC-0449) in patients with locally advanced or metastatic solid tumors: the role of alpha-1-acid glycoprotein binding.

PURPOSE: In a phase I trial for patients with refractory solid tumors, hedgehog pathway inhibitor vismodegib (GDC-0449) showed little decline in plasma concentrations over 7 days after a single oral dose and nonlinearity with respect to dose and time after single and multiple dosing. We studied the role of GDC-0449 binding to plasma protein alpha-1-acid glycoprotein (AAG) to better understand these unusual pharmacokinetics. EXPERIMENTAL DESIGN: Sixty-eight patients received GDC-0449 at 150 (n = 41), 270 (n = 23), or 540 (n = 4) mg/d, with pharmacokinetic (PK) sampling at multiple time points. Total and unbound (dialyzed) GDC-0449 plasma concentrations were assessed by liquid chromatography/tandem mass spectrometry, binding kinetics by surface plasmon resonance-based microsensor, and AAG levels by ELISA. RESULTS: A linear relationship between total GDC-0449 and AAG plasma concentrations was observed across dose groups (R(2) = 0.73). In several patients, GDC-0449 levels varied with fluctuations in AAG levels over time. Steady-state, unbound GDC-0449 levels were less than 1% of total, independent of dose or total plasma concentration. In vitro, GDC-0449 binds AAG strongly and reversibly (K(D) = 13 µmol/L) and human serum albumin less strongly (K(D) = 120 µmol/L). Simulations from a derived mechanistic PK model suggest that GDC-0449 pharmacokinetics are mediated by AAG binding, solubility-limited absorption, and slow metabolic elimination. CONCLUSIONS: GDC-0449 levels strongly correlated with AAG levels, showing parallel fluctuations of AAG and total drug over time and consistently low, unbound drug levels, different from previously reported AAG-binding drugs. This PK profile is due to high-affinity, reversible binding to AAG and binding to albumin, in addition to solubility-limited absorption and slow metabolic elimination properties.