Tumor growth inhibition modeling in patients with second line biliary tract cancer and first line non-small cell lung cancer based on bintrafusp alfa trials.

This analysis aimed to quantify tumor dynamics in patients receiving either bintrafusp alfa (BA) or pembrolizumab, by population pharmacokinetic (PK)-pharmacodynamic modeling, and investigate clinical and molecular covariates describing the variability in tumor dynamics by pharmacometric and machine-learning (ML) approaches. Data originated from two clinical trials in patients with biliary tract cancer (BTC; NCT03833661) receiving BA and non-small cell lung cancer (NSCLC; NCT03631706) receiving BA or pembrolizumab. Individual drug exposure was estimated from previously developed population PK models. Population tumor dynamics models were developed for each drug-indication combination, and covariate evaluations performed using nonlinear mixed-effects modeling (NLME) and ML (elastic net and random forest models) approaches. The three tumor dynamics' model structures all included linear tumor growth components and exponential tumor shrinkage. The final BTC model included the effect of drug exposure (area under the curve) and several covariates (demographics, disease-related, and genetic mutations). Drug exposure was not significant in either of the NSCLC models, which included two, disease-related, covariates in the BA arm, and none in the pembrolizumab arm. The covariates identified by univariable NLME and ML highly overlapped in BTC but showed less agreement in NSCLC analyses. Hyperprogression could be identified by higher tumor growth and lower tumor kill rates and could not be related to BA exposure. Tumor size over time was quantitatively characterized in two tumor types and under two treatments. Factors potentially related to tumor dynamics were assessed using NLME and ML approaches; however, their net impact on tumor size was considered as not clinically relevant.

Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy.

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real-world setting. We developed a tumor growth inhibition model based on real-world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image-based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high-dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.

A multistate modeling and simulation framework to learn dose-response of oncology drugs: Application to bintrafusp alfa in non-small cell lung cancer.

The dose/exposure-efficacy analyses are often conducted separately for oncology end points like best overall response, progression-free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose-end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition-specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF-ß and PD-L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non-small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose-specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2-months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies.

Semiautomated Pipeline to Quantify Tumor Evolution From Real-World Positron Emission Tomography/Computed Tomography Imaging.

PURPOSE: A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology. METHODS: We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital. Cohort selection relied on semantically annotated electronic health records and was validated using process mining. The selected imaging examinations were segmented using an automatic commercial software prototype. A postprocessing algorithm enabled longitudinal lesion identification across imaging time points and consensus malignancy status prediction. Resulting data quality was evaluated against expert-annotated ground-truth and clinical outcomes obtained from radiology reports. RESULTS: The cohort included 108 patients with melanoma and 465 imaging examinations (median, 3; range, 1-15 per patient). Process mining was used to assess clinical data quality and revealed the diversity of care pathways encountered in a real-world setting. Longitudinal postprocessing greatly improved the consistency of image-derived data compared with single time point segmentation results (classification precision increased from 53% to 86%). Image-derived progression-free survival resulting from postprocessing was comparable with the manually curated clinical reference (median survival of 286 v 336 days, P = .89). CONCLUSION: We presented a general pipeline for the collection and curation of text- and image-based RWD, together with specific strategies to improve reliability. We showed that the resulting disease progression measures match reference clinical assessments at the cohort level, indicating that this strategy has the potential to unlock large amounts of actionable retrospective real-world evidence from clinical records.

Designing phase I oncology dose escalation using dose-exposure-toxicity models as a complementary approach to model-based dose-toxicity models.

One of the objectives of oncology phase I dose-escalation studies has been to determine the maximum tolerated dose (MTD). Although MTD is no longer set as the dose for further development in contemporary oncology drug development, MTD determination is still important for informing the therapeutic index. Bayesian adaptive model-based designs are becoming mainstream in oncology first-in-human trials. Herein, we illustrate via simulations the use of systemic exposure in Bayesian adaptive dose-toxicity models to estimate MTD. We extend traditional dose-toxicity models to incorporate pharmacokinetic exposure, which provides information on exposure-toxicity relationships. We pursue dose escalation until the maximum tolerated exposure (corresponding to the MTD) is reached. By leveraging pharmacokinetics, dose escalation considers exposure and interindividual variability on a continuous rather than discrete domain, offering additional information for dose-escalation decisions. To demonstrate this, we generated 1000 simulations (starting dose of 1/25th the reference dose and six dose levels) for several different scenarios. Both rule-based and model-based designs were compared using metrics of potential safety, accuracy, and reliability. The mean results over simulations and different toxicity scenarios showed that model-based designs were better than rule-based methods and that exposure-toxicity model-based methods have the potential to valuably complement dose-toxicity model-based methods. Exposure-toxicity model-based methods had decreased underdose risk accompanied by a relatively smaller increase in overdose risk, resulting in improved net reliability. MTD estimation accuracy was compromised when exposure variability was large, emphasizing the importance of appropriate control of pharmacokinetic variability in phase I dose-escalation studies.

Exposure-response analyses for the MET inhibitor tepotinib including patients in the pivotal VISION trial: support for dosage recommendations.

PURPOSE: Tepotinib is a highly selective MET inhibitor approved for treatment of non-small cell lung cancer (NSCLC) harboring METex14 skipping alterations. Analyses presented herein evaluated the relationship between tepotinib exposure, and efficacy and safety outcomes. METHODS: Exposure-efficacy analyses included data from an ongoing phase 2 study (VISION) investigating 500 mg/day tepotinib in NSCLC harboring METex14 skipping alterations. Efficacy endpoints included objective response, duration of response, and progression-free survival. Exposure-safety analyses included data from VISION, plus four completed studies in advanced solid tumors/hepatocellular carcinoma (30-1400 mg). Safety endpoints included edema, serum albumin, creatinine, amylase, lipase, alanine aminotransferase, aspartate aminotransferase, and QT interval corrected using Fridericia's method (QTcF). RESULTS: Tepotinib exhibited flat exposure-efficacy relationships for all endpoints within the exposure range observed with 500 mg/day. Tepotinib also exhibited flat exposure-safety relationships for all endpoints within the exposure range observed with 30-1400 mg doses. Edema is the most frequently reported adverse event and the most frequent cause of tepotinib dose reductions and interruptions; however, the effect plateaued at low exposures. Concentration-QTc analyses using data from 30 to 1400 mg tepotinib resulted in the upper bounds of the 90% confidence interval being less than 10 ms for the mean exposures at the therapeutic (500 mg) and supratherapeutic (1000 mg) doses. CONCLUSIONS: These analyses provide important quantitative pharmacologic support for benefit/risk assessment of the 500 mg/day dosage of tepotinib as being appropriate for the treatment of NSCLC harboring METex14 skipping alterations. REGISTRATION NUMBERS: NCT01014936, NCT01832506, NCT01988493, NCT02115373, NCT02864992.

Population pharmacokinetic analysis of tepotinib, an oral MET kinase inhibitor, including data from the VISION study.

PURPOSE: Tepotinib is a highly selective, potent, mesenchymal-epithelial transition factor (MET) inhibitor, approved for the treatment of non-small cell lung cancer (NSCLC) harboring MET exon 14 skipping. Objectives of this population pharmacokinetic (PK) analysis were to evaluate the dose-exposure relationship of tepotinib and its major circulating metabolite, MSC2571109A, and to identify the intrinsic/extrinsic factors that are predictive of PK variability. METHODS: Data were included from 12 studies in patients with cancer and in healthy participants. A sequential modeling approach was used to analyze the parent and metabolite data, including covariate analyses. Potential associations between observed covariates and PK parameters were illustrated using bootstrap analysis-based forest plots. RESULTS: A two-compartment model with sequential zero- and first-order absorption, and a first-order elimination from the central compartment, best described the plasma PK of tepotinib in humans across the dose range of 30-1400 mg. The bioavailability of tepotinib was shown to be dose dependent, although bioavailability decreased primarily at doses above the therapeutic dose of 500 mg. The intrinsic factors of race, age, sex, body weight, mild/moderate hepatic impairment and mild/moderate renal impairment, along with the extrinsic factors of opioid analgesic and gefitinib intake, had no relevant effect on tepotinib PK. Tepotinib has a long effective half-life of ~ 32 h. CONCLUSIONS: Tepotinib shows dose proportionality up to at least the therapeutic dose, and time-independent clearance with a profile appropriate for once-daily dosing. None of the covariates identified had a clinically meaningful effect on tepotinib exposure or required dose adjustments.

Pharmacometric modeling and machine learning analyses of prognostic and predictive factors in the JAVELIN Gastric 100 phase III trial of avelumab.

Avelumab (anti-PD-L1) is an approved anticancer treatment for several indications. The JAVELIN Gastric 100 phase III trial did not meet its primary objective of demonstrating superior overall survival (OS) with avelumab maintenance versus continued chemotherapy in patients with advanced gastric cancer/gastroesophageal junction cancer; however, the OS rate was numerically higher with avelumab at timepoints after 12 months. Machine learning (random forests, SIDEScreen, and variable-importance assessments) was used to build models to identify prognostic/predictive factors associated with long-term OS and tumor growth dynamics (TGDs). Baseline, re-baseline, and longitudinal variables were evaluated as covariates in a parametric time-to-event model for OS and Gompertzian population model for TGD. The final OS model incorporated a treatment effect on the log-logistic shape parameter but did not identify a treatment effect on OS or TGD. Variables identified as prognostic for longer OS included older age; higher gamma-glutamyl transferase (GGT) or albumin; absence of peritoneal carcinomatosis; lower neutrophil-lymphocyte ratio, lactate dehydrogenase, or C-reactive protein (CRP); response to induction chemotherapy; and Eastern Cooperative Oncology Group performance status of 0. Among baseline and time-varying covariates, the largest effects were found for GGT and CRP, respectively. Liver metastasis at re-baseline predicted higher tumor growth. Tumor size after induction chemotherapy was associated with number of metastatic sites and stable disease (vs. response). Asian region did not impact OS or TGD. Overall, an innovative workflow supporting pharmacometric modeling of OS and TGD was established. Consistent with the primary trial analysis, no treatment effect was identified. However, potential prognostic factors were identified.

Relevance of primary lesion location, tumour heterogeneity and genetic mutation demonstrated through tumour growth inhibition and overall survival modelling in metastatic colorectal cancer.

AIMS: The aims of this work were to build a semi-mechanistic tumour growth inhibition (TGI) model for metastatic colorectal cancer (mCRC) patients receiving either cetuximab + chemotherapy or chemotherapy alone and to identify early predictors of overall survival (OS). METHODS: A total of 1716 patients from 4 mCRC clinical studies were included in the analysis. The TGI model was built with 8973 tumour size measurements where the probability of drop-out was also included and modelled as a time-to-event variable using parametric survival models, as it was the case in the OS analysis. The effects of patient- and tumour-related covariates on model parameters were explored. RESULTS: Chemotherapy and cetuximab effects were included in an additive form in the TGI model. Development of resistance was found to be faster for chemotherapy (drug effect halved at wk 8) compared to cetuximab (drug effect halved at wk 12). KRAS wild-type status and presenting a right-sided primary lesion were related to a 3.5-fold increase in cetuximab drug effect and a 4.7× larger cetuximab resistance, respectively. The early appearance of a new lesion (HR = 4.14), a large tumour size at baseline (HR = 1.62) and tumour heterogeneity (HR = 1.36) were the main predictors of OS. CONCLUSIONS: Semi-mechanistic TGI and OS models have been developed in a large population of mCRC patients receiving chemotherapy in combination or not with cetuximab. Tumour-related predictors, including a machine learning derived-index of tumour heterogeneity, were linked to changes in drug effect, resistance to treatment or OS, contributing to the understanding of the variability in clinical response.

Model-Based Characterization of the Bidirectional Interaction Between Pharmacokinetics and Tumor Growth Dynamics in Patients with Metastatic Merkel Cell Carcinoma Treated with Avelumab.

PURPOSE: Empirical time-varying clearance models have been reported for several immune checkpoint inhibitors, including avelumab (anti-programmed death ligand 1). To investigate the exposure-response relationship for avelumab, we explored semimechanistic pharmacokinetic (PK)-tumor growth dynamics (TGD) models. PATIENTS AND METHODS: Plasma PK data were pooled from three phase I and II trials (JAVELIN Merkel 200, JAVELIN Solid Tumor, and JAVELIN Solid Tumor JPN); tumor size (TS) data were collected from patients with metastatic Merkel cell carcinoma (mMCC) enrolled in JAVELIN Merkel 200. A PK model was developed first, followed by TGD modeling to investigate interactions between avelumab exposure and TGD. A PK-TGD feedback loop was evaluated with simultaneous fitting of the PK and TGD models. RESULTS: In total, 1,835 PK observations and 338 TS observations were collected from 147 patients. In the final PK-TGD model, which included the bidirectional relationship between PK and TGD, avelumab PK was described by a two-compartment model with a positive association between clearance and longitudinal TS, with no additional empirical time-varying clearance identified. TGD was described by first-order tumor growth/shrinkage rates, with the tumor shrinkage rate decreasing exponentially over time; the exponential time-decay constant decreased with increasing drug concentration, representing the treatment effect through tumor shrinkage inhibition. CONCLUSIONS: We developed a TGD model that mechanistically captures the prevention of loss of antitumor immunity (i.e., T-cell suppression in the tumor microenvironment) by avelumab, and a bidirectional interaction between PK and TGD in patients with mMCC treated with avelumab, thus mechanistically describing previously reported time variance of avelumab elimination.

Optimization of dose selection using multiple surrogates of toxicity as a continuous variable in phase I cancer trial.

In phase I trials, it is the top priority of clinicians to effectively treat patients and minimize the chance of exposing them to subtherapeutic and overly toxic doses, while exploiting patient information. Motived by this practical consideration, we revive the one parameter linear dose-finder developed in 1970s to accommodate a continuous toxicity response in the phase I cancer clinical trials, which is called the two parameters linear dose-finder (2PLD). The 2PLD is a fully Bayesian model that assumes a linear relationship between toxicity response and dose. We suggest a dose search algorithm based on the 2PLD to exploit the grades of toxicities from multiple adverse events to align with Common Toxicity Criteria for Adverse Events provided by the National Cancer Institute. The proposed search procedure suggests an optimal dose to each patient by using accrued patients' information while controlling the posterior probability of overdose. The heterogeneity of patients in dose reaction is addressed by making a fully Bayesian inference about the standard deviation of toxicity responses. The 2PLD can be an attractive tool for clinical scientists due to its parsimonious description of a toxicity-dose curve and medical interpretation as well as an automatic posterior computation. We illustrate the performance of this design using simulation data to identify the maximum tolerated dose.

Translational pharmacokinetic-pharmacodynamic modeling of preclinical and clinical data of the oral MET inhibitor tepotinib to determine the recommended phase II dose.

Tepotinib is a highly selective and potent MET inhibitor in development for the treatment of patients with solid tumors. Given the favorable tolerability and safety profiles up to the maximum tested dose in the first-in-human (FIH) trial, an efficacy-driven translational modeling approach was proposed to establish the recommended phase II dose (RP2D). To study the in vivo pharmacokinetics (PKs)/target inhibition/tumor growth inhibition relationship, a subcutaneous KP-4 pancreatic cell-line xenograft model in mice with sensitivity to MET pathway inhibition was selected as a surrogate tumor model. Further clinical PK and target inhibition data (derived from predose and postdose paired tumor biopsies) from a FIH study were integrated with the longitudinal PKs and target inhibition profiles from the mouse xenograft study to establish a translational PK/pharmacodynamic (PD) model. Preclinical data showed that tumor regression with tepotinib treatment in KP-4 xenograft tumors corresponded to 95% target inhibition. We therefore concluded that a PD criterion of sustained, near-to-complete (>95%) phospho-MET inhibition in tumors should be targeted for tepotinib to be effective. Simulations of dose-dependent target inhibition profiles in human tumors that exceeded the PD threshold in more than 90% of patients established an RP2D of tepotinib 500 mg once daily. This translational mathematical modeling approach supports an efficacy-driven rationale for tepotinib phase II dose selection of 500 mg once daily. Tepotinib at this dose has obtained regulatory approval for the treatment of patients with non-small cell lung cancer harboring MET exon 14 skipping.

Semimechanistic Clearance Models of Oncology Biotherapeutics and Impact of Study Design: Cetuximab as a Case Study.

This study aimed to explore the currently competing and new semimechanistic clearance models for monoclonal antibodies and the impact of clearance model misspecification on exposure metrics under different study designs exemplified for cetuximab. Six clearance models were investigated under four different study designs (sampling density and single/multiple-dose levels) using a rich data set from two cetuximab clinical trials (226 patients with metastatic colorectal cancer) and using the nonlinear mixed-effects modeling approach. A two-compartment model with parallel Michaelis-Menten and time-decreasing linear clearance adequately described the data, the latter being related to post-treatment response. With respect to bias in exposure metrics, the simplified time-varying linear clearance (CL) model was the best alternative. Time-variance of the linear CL component should be considered for biotherapeutics if response impacts pharmacokinetics. Rich sampling at steady-state was crucial for unbiased estimation of Michaelis-Menten elimination in case of the reference (parallel Michaelis-Menten and time-varying linear CL) model.

Comparison of Various Phase I Combination Therapy Designs in Oncology for Evaluation of Early Tumor Shrinkage Using Simulations.

There is still a lack of efficient designs for identifying the dose response in oncology combination therapies in early clinical trials. The concentration response relationship can be identified using the early tumor shrinkage time course, which has been shown to be a good early response marker of clinical efficacy. The performance of various designs using an exposure-tumor growth inhibition model was explored using simulations. Different combination effects of new drug M and cetuximab (reference therapy) were explored first assuming no effect of M on cetuximab (to investigate the type I error (α)), and subsequently assuming additivity or synergy between cetuximab and M. One-arm, two-arm, and four-arm designs were evaluated. In the one-arm design, 60 patients received cetuximab + M. In the two-arm design, 30 patients received cetuximab and 30 received cetuximab + M. In the four-arm design, in addition to cetuximab and cetuximab + M as standard doses, combination arms with lower doses of cetuximab were evaluated (15 patients/arm). Model-based predictions or "simulated observations" of early tumor shrinkage at week 8 (ETS8) were compared between the different arms. With the same number of individuals, the one-arm design showed better statistical power than other designs but led to strong inflation of α in case of misestimated reference for ETS8 value. The two-arm design protected against this misestimation and, with the same total number of subjects, would provide higher statistical power than a four-arm design. However, a four-arm design would be helpful for exploring more doses of cetuximab in combination with M to better understand the interaction.

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival.

Total tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We analyzed individual target lesions (iTLs) of patients with metastatic colorectal carcinoma (mCRC) to determine differences in TS dynamics by using the ClassIfication Clustering of Individual Lesions (CICIL) methodology. Results from subgroup analyses comparing genetic mutations and TS metrics were assessed and applied to survival analyses. Data from four mCRC clinical studies were analyzed (1781 patients, 6369 iTLs). CICIL was used to assess differences in lesion TS dynamics within a tissue (intra-class) or across different tissues (inter-class). First, lesions were automatically classified based on their location. Cross-correlation coefficients (CCs) determined if each pair of lesions followed similar or opposite dynamics. Finally, CCs were grouped by using the K-means clustering method. Heterogeneity in tumor dynamics was lower in the intra-class analysis than in the inter-class analysis for patients receiving cetuximab. More tumor heterogeneity was found in KRAS mutated patients compared to KRAS wild-type (KRASwt) patients and when using sum of longest diameters versus sum of products of diameters. Tumor heterogeneity quantified as the median patient's CC was found to be a predictor of overall survival (OS) (HR = 1.44, 95% CI 1.08-1.92), especially in KRASwt patients. Intra- and inter-tumor tissue heterogeneities were assessed with CICIL. Derived metrics of heterogeneity were found to be a predictor of OS time. Considering differences between lesions' TS dynamics could improve oncology models in favor of a better prediction of OS.

Changing Body Weight-Based Dosing to a Flat Dose for Avelumab in Metastatic Merkel Cell and Advanced Urothelial Carcinoma.

Avelumab, an anti-programmed death-ligand 1 monoclonal antibody approved for the treatment of metastatic Merkel cell carcinoma and platinum-treated urothelial carcinoma, was initially approved with a 10 mg/kg weight-based dose. We report pharmacokinetic (PK)/pharmacodynamic analyses for avelumab comparing weight-based dosing and a flat 800 mg dose, developed using data from 1,827 patients enrolled in 3 clinical trials (NCT01772004, NCT01943461, and NCT02155647). PK metrics were simulated for weight-based and flat-dosing regimens and summarized by quartiles of weight. Derived exposure metrics were used in simulations of exposure-safety (various tumors) and exposure-efficacy (objective responses; Merkel cell or urothelial carcinoma). Flat dosing was predicted to provide similar exposure to weight-based dosing, with slightly lower variability. Exposure-safety and exposure-efficacy simulations suggested similar benefit:risk profiles for the two dosing regimens. These pharmacometric analyses provided the basis for the US Food and Drug Administration approval of a flat dose of avelumab 800 mg every 2 weeks in approved indications.

First-in-Man Phase I Trial of the Selective MET Inhibitor Tepotinib in Patients with Advanced Solid Tumors.

PURPOSE: Tepotinib is an oral, potent, highly selective MET inhibitor. This first-in-man phase I trial investigated the MTD of tepotinib to determine the recommended phase II dose (RP2D). PATIENTS AND METHODS: Patients received tepotinib orally according to one of three dose escalation regimens (R) on a 21-day cycle: R1, 30-400 mg once daily for 14 days; R2, 30-315 mg once daily 3 times/week; or R3, 300-1,400 mg once daily. After two cycles, treatment could continue in patients with stable disease until disease progression or unacceptable toxicity. The primary endpoint was incidence of dose-limiting toxicity (DLT) and treatment-emergent adverse events (TEAE). Secondary endpoints included safety, tolerability, pharmacokinetics, pharmacodynamics, and antitumor effects. RESULTS: One hundred and forty-nine patients received tepotinib (R1: n = 42; R2: n = 45; R3: n = 62). Although six patients reported DLTs [one patient in R1 (115 mg), three patients in R2 (60, 100, 130 mg), two patients in R3 (1,000, 1,400 mg)], the MTD was not reached at the highest tested dose of 1,400 mg daily. The RP2D of tepotinib was established as 500 mg once daily, supported by translational modeling data as sufficient to achieve ≥95% MET inhibition in ≥90% of patients. Treatment-related TEAEs were mostly grade 1 or 2 fatigue, peripheral edema, decreased appetite, nausea, vomiting, and lipase increase. The best overall response in R3 was partial response in two patients, both with MET overexpression. CONCLUSIONS: Tepotinib was well tolerated with clinical activity in MET-dysregulated tumors. The RP2D of tepotinib was established as 500 mg once daily. MET abnormalities can drive tumorigenesis. This first-in-man trial demonstrated that the potent, highly selective MET inhibitor tepotinib can reduce or stabilize tumor burden and is well tolerated at doses up to 1,400 mg once daily. An RP2D of 500 mg once daily, as determined from translational modeling and simulation integrating human population pharmacokinetic and pharmacodynamic data in tumor biopsies, is being used in ongoing clinical trials.

Population Pharmacokinetic Analysis of Bintrafusp Alfa in Different Cancer Types.

INTRODUCTION: Bintrafusp alfa, an innovative first-in-class bifunctional fusion protein composed of the extracellular domain of the TGF-ßRII receptor (a TGF-ß "trap") fused to a human IgG1 monoclonal antibody blocking programmed death ligand 1, has shown promising antitumor activity and manageable safety. METHODS: To support the dosing strategy for bintrafusp alfa, we developed a population pharmacokinetics model using a full covariate modeling approach, based on pharmacokinetic and covariate data from 644 patients with various solid tumors who received bintrafusp alfa intravenously in two clinical studies. RESULTS: A two-compartmental linear model best described bintrafusp alfa concentrations, and no time-varying clearance was identified. Using this model, the estimated clearance was 0.0158 l/h (relative standard error, 4.1%), and the central and peripheral volume of distribution were 3.21 l (relative standard error, 3.2%) and 0.483 l (relative standard error, 9.8%), respectively. The estimated mean elimination half-life of bintrafusp alfa was 6.93 days (95% CI 4.69-9.65 days). Several intrinsic factors (bodyweight, albumin, sex, and tumor type) were found to influence bintrafusp alfa pharmacokinetics, but none of these covariate effects was considered clinically meaningful and no dosage adjustments are recommended. Notably, simulations from the model suggested less variability in exposure metrics with flat dosing versus weight-based dosing. CONCLUSIONS: Pharmacokinetic analysis of bintrafusp alfa supports the use of a flat dose regimen in further clinical trials (recommended phase 2 dose: 1200 mg every 2 weeks). TRIAL REGISTRATION: ClinicalTrials.gov identifiers: NCT02517398 and NCT02699515. FUNDING: Merck Healthcare KGaA as part of an alliance between Merck Healthcare KGaA and GlaxoSmithKline.

Time-Varying Clearance and Impact of Disease State on the Pharmacokinetics of Avelumab in Merkel Cell Carcinoma and Urothelial Carcinoma.

Avelumab, a human anti-programmed death ligand 1 immunoglobulin G1 antibody, has shown efficacy and manageable safety in multiple tumors. A two-compartment population pharmacokinetic model for avelumab incorporating intrinsic and extrinsic covariates and time-varying clearance (CL) was identified based on data from 1,827 patients across three clinical studies. Of 14 tumor types, a decrease in CL over time was more notable in metastatic Merkel cell carcinoma and squamous cell carcinoma of the head and neck, which had maximum decreases of 32.1% and 24.7%, respectively. The magnitude of reduction in CL was higher in responders than in nonresponders. Significant covariate effects of baseline weight, baseline albumin, and sex were identified on both CL and central distribution volume. Significant covariate effects of black/African American race, C-reactive protein, and immunogenicity were found on CL. None of the covariate or time-dependent effects were clinically important or warranted dose adjustment.

Over-adherence to capecitabine: a potential safety issue in breast and colorectal cancer patients.

PURPOSE: The aim of the OCTO clinical study was to measure patients' adherence to capecitabine-based treatment. METHODS: A cohort of ambulatory patients treated with capecitabine monotherapy for either locally advanced or metastatic, breast or colorectal cancer was monitored for 6 cycles. Adherence was assessed in all patients by self-completed questionnaires on disease, pill-count and pharmacological dosage of FBAL (metabolite of capecitabine); and in half of the cohort by electronic medication event monitoring systems (MEMS™) recording the opening times of the device. RESULTS: Forty patients were enrolled between November 2008 and September 2011 and treated by capecitabine for an average of 4.75 cycles (range 1-6). Hand-foot syndrome (HFS) was the most frequently reported toxicity (35% patients), and to a lesser extent fatigue and/or asthenia (21%), nausea and/or vomiting (13%) and diarrhea (11%). In the MEMS™ cohort, 20 patients were included. Patients' adherence was excellent with very few missing occasions (23/2272 records). Close analysis of MEMS™ data revealed unexpected medication patterns, such as patients taking extra days of medication beyond planned cycle, patients taking extra doses per day and patients missing a day of dosing and "compensating" by taking extra the following day (N = 7, 18%). A trend was found between over-adherence and high-grade toxicity (grades 3 and/or 4): OR 4.74 [0.65-45.2], p = 0.13 and higher AUC (p = 0.16). There was a trend towards increased AUC of FBAL in over-adherent patients (p = 0.16). CONCLUSION: Adherence to oral anticancer chemotherapy was found excellent in this population suggesting over-adherence to capecitabine and potential safety implications for outpatients' drugs.