Phase I pharmacokinetic, safety, and preliminary efficacy study of tiragolumab in combination with atezolizumab in Chinese patients with advanced solid tumors.

PURPOSE: Tiragolumab is an immunoglobulin G1 monoclonal antibody targeting the immune checkpoint T cell immunoreceptor with immunoglobulin and immunoreceptor ITIM domains. Targeting multiple immune pathways may improve anti-tumor responses. The phase I YP42514 study assessed the pharmacokinetics (PK), safety, and preliminary efficacy of tiragolumab plus atezolizumab in Chinese patients with advanced solid tumors. METHODS: Adult patients from mainland China with Eastern Cooperative Oncology Group performance score 0/1, life expectancy of ≥ 12 weeks, and adequate hematologic/end organ function were eligible. Patients received tiragolumab 600 mg and atezolizumab 1200 mg intravenous every 3 weeks. Key endpoints were PK (serum concentrations of tiragolumab and atezolizumab) and safety. Results from this study were compared with the global phase I study, GO30103 (NCT02794571). RESULTS: In this study, 20 patients received a median of five doses of tiragolumab plus atezolizumab. Median age was 57.5 years, 85.0% of patients were male and the most common tumor type was non-small cell lung cancer. Exposures in Chinese patients were comparable to the global GO30103 population: geometric mean ratio was 1.07 for Cycle 1 tiragolumab area under the concentration-time curve0-21 and 0.92 and 0.93 for Cycle 1 peak and trough atezolizumab exposure, respectively. Treatment-related adverse events were consistent across the Chinese and global populations. Two patients (10.0%) in this study achieved a partial response. CONCLUSION: In this study, tiragolumab plus atezolizumab was tolerable and demonstrated preliminary anti-tumor activity. There were no meaningful differences in the PK or safety of tiragolumab plus atezolizumab between the Chinese and global populations. CLINICAL TRIAL REGISTRATION NUMBER: China Clinical Trial Registry Identifier CTR20210219/YP42514. Date of registration 16 March 2021.

Comparison of two-stage and joint TGI-OS modeling using data from six atezolizumab clinical studies in patients with metastatic non-small cell lung cancer.

Two-stage and joint modeling approaches are the two main approaches to investigate the link between longitudinal tumor size data and overall survival (OS) and anticipate clinical trial outcome. We here used a large database composed of one phase II and five phase III clinical trials evaluating atezolizumab (an immunotherapy) in monotherapy or in combination with chemotherapies in 3699 patients with non-small cell lung cancer to evaluate the differences between both approaches in terms of parameter estimates, magnitude of covariate effects, and ability to predict OS. Although the two-stage approach may underestimate the magnitude of the impact of tumor growth rate (KG ) on OS compared to joint modeling approach (hazard ratios [HRs] of 0.42-2.52 vs. 0.25-2.85, respectively, for individual KG varying from the 5th and 95th percentiles), this difference did not lead into poorer performance of the two-stage approach to describe the OS distribution in the six clinical studies. Overall, two-stage and joint modeling approaches accurately predicted OS HR with a median (range) difference with the observed OS HR of 0.02 (0.01-0.18) and 0.03 (0.00-0.19), in all cases considered, respectively (e.g., for IMpower150: 0.80 [0.66-0.95] vs. 0.82 [0.70-0.95], respectively, whereas the observed OS HR was 0.80). In our setting, the two-stage approach accurately predicted the benefit of atezolizumab on OS. Further work is needed to verify if similar results are achieved using phase Ib or phase II clinical trials where the number of patients and measurements is limited as well as in other cancer indications.

External validation of a tumor growth inhibition-overall survival model in non-small-cell lung cancer based on atezolizumab studies using alectinib data.

BACKGROUND: A modeling framework was previously developed to simulate overall survival (OS) using tumor growth inhibition (TGI) data from six randomized phase 2/3 atezolizumab monotherapy or combination studies in non-small-cell lung cancer (NSCLC). We aimed to externally validate this framework to simulate OS in patients with treatment-naive advanced anaplastic lymphoma kinase (ALK)-positive NSCLC in the alectinib ALEX study. METHODS: TGI metrics were estimated from a biexponential model using longitudinal tumor size data from a Phase 3 study evaluating alectinib compared with crizotinib in patients with treatment-naive ALK-positive advanced NSCLC. Baseline prognostic factors and TGI metric estimates were used to predict OS. RESULTS: 286 patients were evaluable (at least baseline and one post-baseline tumor size measurements) out of 303 (94%) followed for up to 5 years (cut-off: 29 November 2019). The tumor growth rate estimate and baseline prognostic factors (inflammatory status, tumor burden, Eastern Cooperative Oncology Group performance status, race, line of therapy, and sex) were used to simulate OS in ALEX study. Observed survival distributions for alectinib and crizotinib were within model 95% prediction intervals (PI) for approximately 2 years. Predicted hazard ratio (HR) between alectinib and crizotinib was in agreement with the observed HR (predicted HR 0.612, 95% PI 0.480-0.770 vs. 0.625 observed HR). CONCLUSION: The TGI-OS model based on unselected or PD-L1 selected NSCLC patients included in atezolizumab trials is externally validated to predict treatment effect (HR) in a biomarker-selected (ALK-positive) population included in alectinib ALEX trial suggesting that TGI-OS models may be treatment independent.

Early Decision Making in a Randomized Phase II Trial of Atezolizumab in Biliary Tract Cancer Using a Tumor Growth Inhibition-Survival Modeling Framework.

We assess the longitudinal tumor growth inhibition (TGI) metrics and overall survival (OS) predictions applied to patients with advanced biliary tract cancer (BTC) enrolled in IMbrave151 a multicenter randomized phase II, double-blind, placebo-controlled trial evaluating the efficacy and safety of atezolizumab with or without bevacizumab in combination with cisplatin plus gemcitabine. Tumor growth rate (KG) was estimated for patients in IMbrave151. A pre-existing TGI-OS model for patients with hepatocellular carcinoma in IMbrave150 was modified to include available IMbrave151 study covariates and KG estimates and used to simulate IMbrave151 study outcomes. At the interim progression-free survival (PFS) analysis (98 patients, 27 weeks follow-up), clear separation in tumor dynamic profiles with a faster shrinkage rate and slower KG (0.0103 vs. 0.0117 week-1 ; tumor doubling time 67 vs. 59 weeks; KG geometric mean ratio of 0.84) favoring the bevacizumab containing arm was observed. At the first interim analysis for PFS, the simulated OS hazard ratio (HR) 95% prediction interval (PI) of 0.74 (95% PI: 0.58-0.94) offered an early prediction of treatment benefit later confirmed at the final analysis, observed HR of 0.76 based on 159 treated patients and 34 weeks of follow-up. This is the first prospective application of a TGI-OS modeling framework supporting gating of a phase III trial. The findings demonstrate the utility for longitudinal TGI and KG geometric mean ratio as relevant end points in oncology studies to support go/no-go decision making and facilitate interpretation of the IMbrave151 results to support future development efforts for novel therapeutics for patients with advanced BTC.

Tumor Dynamic Model-Based Decision Support for Phase Ib/II Combination Studies: A Retrospective Assessment Based on Resampling of the Phase III Study IMpower150.

PURPOSE: Model-based tumor growth inhibition (TGI) metrics are increasingly incorporated into go/no-go decisions in early clinical studies. To apply this methodology to new investigational combinations requires independent evaluation of TGI metrics in recently completed Phase III trials of effective immunotherapy. PATIENTS AND METHODS: Data were extracted from IMpower150, a positive, randomized, Phase III study of first-line therapy in 1,202 patients with non-small cell lung cancer. We resampled baseline characteristics and longitudinal sum of longest diameters of tumor lesions of patients from both arms, atezolizumab+ bevacizumab+chemotherapy (ABCP) versus BCP, to mimic Phase Ib/II studies of 15 to 40 patients/arm with 6 to 24 weeks follow-up. TGI metrics were estimated using a bi-exponential TGI model. Effect sizes were calculated as TGI metrics geometric mean ratio (GMR), objective response rate (ORR) difference (d), and progression-free survival (PFS), hazard ratio (HR) between arms. Correct and incorrect go decisions were evaluated as the probability to achieve desired effect sizes in ABCP versus BCP and BCP versus BCP, respectively, across 500 replicated subsamples for each design. RESULTS: For 40 patients/24 weeks follow-up, correct go decisions based on probability tumor growth rate (KG) GMR <0.90, dORR >0.10, and PFS HR <0.70 were 83%, 69%, and 58% with incorrect go decision rates of 4%, 12%, and 11%, respectively. For other designs, the ranking did not change with TGI metrics consistently overperforming RECIST endpoints. The predicted overall survival (OS) HR was around 0.80 in most of the scenarios investigated. CONCLUSIONS: Model-based estimate of KG GMR is an exploratory endpoint that informs early clinical decisions for combination studies.

Development of a pediatric physiologically-based pharmacokinetic model to support recommended dosing of atezolizumab in children with solid tumors.

Background: Atezolizumab has been studied in multiple indications for both pediatric and adult patient populations. Generally, clinical studies enrolling pediatric patients may not collect sufficient pharmacokinetic data to characterize the drug exposure and disposition because of operational, ethical, and logistical challenges including burden to children and blood sample volume limitations. Therefore, mechanistic modeling and simulation may serve as a tool to predict and understand the drug exposure in pediatric patients. Objective: To use mechanistic physiologically-based pharmacokinetic (PBPK) modeling to predict atezolizumab exposure at a dose of 15 mg/kg (max 1,200 mg) in pediatric patients to support dose rationalization and label recommendations. Methods: A minimal mechanistic PBPK model was used which incorporated age-dependent changes in physiology and biochemistry that are related to atezolizumab disposition such as endogenous IgG concentration and lymph flow. The PBPK model was developed using both in vitro data and clinically observed data in adults and was verified across dose levels obtained from a phase I and multiple phase III studies in both pediatric patients and adults. The verified model was then used to generate PK predictions for pediatric and adult subjects ranging from 2- to 29-year-old. Results: Individualized verification in children and in adults showed that the simulated concentrations of atezolizumab were comparable (76% within two-fold and 90% within three-fold, respectively) to the observed data with no bias for either over- or under-prediction. Applying the verified model, the predicted exposure metrics including Cmin, Cmax, and AUCtau were consistent between pediatric and adult patients with a geometric mean of pediatric exposure metrics between 0.8- to 1.25-fold of the values in adults. Conclusion: The results show that a 15 mg/kg (max 1,200 mg) atezolizumab dose administered intravenously in pediatric patients provides comparable atezolizumab exposure to a dose of 1,200 mg in adults. This suggests that a dose of 15 mg/kg will provide adequate and effective atezolizumab exposure in pediatric patients from 2- to 18-year-old.

C-Terminal Lysine Processing of IgG in Human Suction Blister Fluid: Implications for Subcutaneous Administration.

C-terminal lysine (CTK) is often classified as a potential critical quality attribute for therapeutic antibodies being developed for subcutaneous (SC) administration because of its potential to impact antibody SC bioavailability/pharmacokinetics (PK). This classification both inflates development costs and increases comparability risks for SC administration of antibodies. However, prior risk assessments of CTK have not fully considered biotransformation of CTK in the SC space, which may play an important role given that circulating carboxypeptidases in humans rapidly process CTK on intravenously administered antibodies. Here, CTK biotransformation in biofluid derived from human SC space was investigated. The representative fluid from the human SC space was sampled from 10 healthy human subjects using the suction blister method. Glycosylated antibody containing high levels of CTK (expressed using a carboxypeptidase D CRISPR/Cas9 knockout CHO cell line) was incubated in the collected suction blister fluids (SBFs), recovered using cognate antigen pulldowns, and characterized for remaining CTK levels using intact and reduced liquid chromatography-mass spectrometry (LC-MS) analysis. CTK processing (i.e., carboxypeptidase activity) was evident in all SBF and exhibited first-order kinetics with rate constants of 2.18 ± 0.57 d-1 (at 37 °C). PK simulations that integrated CTK processing pathways and their associated rate constants were subsequently performed using a range of clinically observed PK parameters for therapeutic antibodies, including atezolizumab- and pertuzumab-specific parameters. The impact of CTK content (even up to 100%) on SC PK outcomes such as bioavailability and Ctrough were modest (<14%) for all combinations of PK parameters tested in the sensitivity analysis. This study forms the cornerstone data package for derisking CTK as a PK liability for antibody SC programs and highlights the usefulness of fully considering biotransformation during product quality criticality assessments.

Effectiveness of mindfulness parent training on parenting stress and children's ADHD-related behaviors: A systematic review and meta-analysis.

Background/Objective: Literature shows that there is a circular relationship between children's ADHD-related behaviors and parenting stress. This systematic review and meta-analysis aimed to understand if mindfulness parent trainings have benefits for both parenting stress and the problem behaviors in children with ADHD. Methods: Five databases, CINAHL, Embase, PsycINFO, PubMED, and Web of Science, were searched. Within-group effects at post-treatment and follow-up assessment, and between-group effects at post-treatment were analyzed. Effect sizes (Hedges' g) were also calculated. Results: Ten studies (5 RCTs and 5 non-RCTs) met the selection criteria and were selected for systematic review, and nine of them were included for meta-analysis. Among these 10 studies, five studies involved mindfulness training for both parents and children, while the other five studies involved mindfulness training for parents only. Within-group effects at post-treatment were small-to-large for all outcomes. Hedges' g ranged between -0.17 [95% CI (-0.98, 0.64)] and 4.70 [95% CI (3.59, 5.81)] for parenting stress; 0.17 [95% CI (-0.03, 0.37)] and 4.03 [95% CI (2.97, 5.09)] for children's problem behaviors; and 0.20 [95% CI (-0.10, 0.50)] and 2.98 [95% CI (2.16, 3.80)] for children's ADHD symptoms. Between-group comparisons showed mindfulness parent training was superior to other active controls on all outcomes. Conclusion: Findings suggest that mindfulness parent training may be beneficial for parenting stress and children's ADHD-related behaviors, and due to the small number of studies reviewed, cautions should be taken when interpreting the results.

Extension of the Alternative Intravenous Dosing Regimens of Atezolizumab into Combination Settings through Modeling and Simulation.

Atezolizumab is approved as an intravenous (IV) infusion for use as a single agent and in combination with other therapies in a number of indications. The objectives of this publication are to characterize atezolizumab pharmacokinetics (PK) across indications with the available clinical data from one phase I and eight phase III studies, to determine the exposure-response (ER) relationships in combination settings across a variety of tumor types, and to provide the clinical safety to support the extension of the 840 mg q2w, 1200 mg q3w, and 1680 mg q4w IV dosing regimens across various indications in combination settings. Across all clinical studies, atezolizumab PK remained in the dose-linear range and were similar across tumor types when used in combination therapy or as a monotherapy. In the combination studies, efficacy was independent of the exposures tested and there was no significant increase in adverse events with increasing atezolizumab exposure (flat ER). The safety profile of atezolizumab in the individual combination studies was generally consistent with the established safety profile of atezolizumab, the combination partners, and the disease under study. The similar atezolizumab PK across monotherapy and combination therapy settings as well as the flat ER in new tumor types and combination therapies support the use of the 3 interchangeable atezolizumab dosing regimens in the combination setting. Atezolizumab is now approved with 3 interchangeable IV dosing regimens of 840 mg q2w, 1200 mg q3w, and 1680 mg q4w for single-agent and combination therapy use in the USA and EU.

Tumor Growth Inhibition-Overall Survival (TGI-OS) Model for Subgroup Analysis Based on Post-Randomization Factors: Application for Anti-drug Antibody (ADA) Subgroup Analysis of Atezolizumab in the IMpower150 Study.

Longitudinal changes of tumor size or tumor-associated biomarkers have been receiving growing attention as early markers of treatment benefits. Tumor growth inhibition-overall survival (TGI-OS) models represent mathematical frameworks used to establish a link from tumor size trajectory to survival outcome with the aim of predicting survival benefit with tumor data from a small number of subjects with a short follow-up time. In the present study, we applied the TGI-OS model to assess treatment benefit in the IMpower150 study for patients who exhibited development of anti-drug antibodies (ADA). Direct comparison between subgroups of the active arm [ADA positive (ADA +) and negative (ADA -) groups] to the entire control group is not appropriate, due to potential imbalances of baseline prognostic factors between ADA + and ADA - patients. Thus, the TGI-OS modeling framework was employed to adjust for differences in prognostic factors between the ADA subgroups to more accurately estimate the treatment benefits. After adjustment, the TGI-OS model predicted comparable hazard ratios (HRs) of OS between ADA + and ADA - subgroups, suggesting that the development of ADA does not have a clinically significant impact on the treatment benefit of atezolizumab.

Applications of Model-Based Meta-Analysis in Drug Development.

Model-based meta-analysis (MBMA) is a quantitative approach that leverages published summary data along with internal data and can be applied to inform key drug development decisions, including the benefit-risk assessment of a treatment under investigation. These risk-benefit assessments may involve determining an optimal dose compared against historic external comparators of a particular disease indication. MBMA can provide a flexible framework for interpreting aggregated data from historic reference studies and therefore should be a standard tool for the model-informed drug development (MIDD) framework.In addition to pairwise and network meta-analyses, MBMA provides further contributions in the quantitative approaches with its ability to incorporate longitudinal data and the pharmacologic concept of dose-response relationship, as well as to combine individual- and summary-level data and routinely incorporate covariates in the analysis.A common application of MBMA is the selection of optimal dose and dosing regimen of the internal investigational molecule to evaluate external benchmarking and to support comparator selection. Two case studies provided examples in applications of MBMA in biologics (durvalumab + tremelimumab for safety) and small molecule (fenebrutinib for efficacy) to support drug development decision-making in two different but well-studied disease areas, i.e., oncology and rheumatoid arthritis, respectively.Important to the future directions of MBMA include additional recognition and engagement from drug development stakeholders for the MBMA approach, stronger collaboration between pharmacometrics and statistics, expanded data access, and the use of machine learning for database building. Timely, cost-effective, and successful application of MBMA should be part of providing an integrated view of MIDD.

Evaluation of atezolizumab immunogenicity: Clinical pharmacology (part 1).

Baseline patient characteristics and prognostic factors are important considerations in oncology when evaluating the impact of immunogenicity on pharmacokinetics (PK) and efficacy. Here, we assessed the impact of anti-drug antibodies (ADA) on the PK of the immune checkpoint inhibitor atezolizumab (an anti-PD-L1 monoclonal antibody). We evaluated data from ≈ 4500 patients from 12 clinical trials across different tumor types, treatment settings, and dosing regimens. In our dataset, ~ 30% of patients (range, 13-54%) developed treatment-emergent ADA, and in vitro neutralizing antibodies (NAb) were seen in ~ 50% of ADA-positive (+) patients. Pooled time course data showed a trend toward lower atezolizumab exposure in ADA+ patients, which was more pronounced in ADA+/NAb+ patients. However, the atezolizumab concentration distributions overlapped, and drug concentrations exceeded 6 µg/ml, the target concentration required for receptor saturation, in greater than 95% of patients. Patients had sufficient exposure regardless of ADA status. The dose selected to allow for dosing over effects from ADA resulted in a flat exposure-response relationship. Analysis of study results by ADA titer showed that exposure and overall survival were not affected in a clinically meaningful way. High tumor burden, low albumin, and high CRP at baseline showed the greatest association with ADA development but not with subsequent NAb development. These imbalanced factors at baseline can confound analysis of ADA impact. ADA increases atezolizumab clearance minimally (9%), and its impact on exposure based on the totality of the clinical pharmacology assessment does not appear to be clinically meaningful.

Atezolizumab and Bevacizumab in Patients with Unresectable Hepatocellular Carcinoma: Pharmacokinetic and Safety Assessments Based on Hepatic Impairment Status and Geographic Region.

INTRODUCTION: Phase 1b GO30140 and phase 3 IMbrave150 studies evaluated first-line atezolizumab + bevacizumab for unresectable hepatocellular carcinoma (HCC). Here, we evaluated pharmacokinetics (PK) and safety by hepatic impairment status and geographic region. METHODS: Patients received atezolizumab 1,200 mg + bevacizumab 15 mg/kg IV every 3 weeks. Drug concentrations were evaluated by descriptive statistics and population PK. PK and adverse event frequencies were evaluated by hepatic impairment status and region. RESULTS: 323 IMbrave150 patients and 162 GO30140 patients were PK evaluable. Compared with IMbrave150 patients who had normal hepatic function per the National Cancer Institute Organ Dysfunction Working Group (NCI-ODWG) criteria (n = 123), patients with mild impairment (n = 171) had a geometric mean ratio (GMR) of 0.92 for cycle 1 atezolizumab area under the concentration-time curve (AUC); patients with moderate impairment (n = 27) had a GMR of 0.88. Patients in Asia ([n = 162] vs. outside [n = 161]) had a GMR of 1.25 for cycle 1 atezolizumab AUC. Compared with GO30140 patients who had normal hepatic function (NCI-ODWG [n = 61]), patients with mild impairment (n = 92) had a GMR of 0.97 for cycle 1 peak bevacizumab concentrations; those with moderate impairment (n = 9) had a GMR of 0.94. Patients in Asia (n = 111) versus outside Asia (n = 51) had a GMR of 0.94 for cycle 1 peak bevacizumab concentration. PK results were generally comparable when evaluated based on additional hepatic functional definitions (Child-Pugh or albumin/bilirubin criteria) or study enrollment in Japan. No associations between atezolizumab PK and HCC etiology were seen. Adverse event frequencies were similar across evaluated groups. CONCLUSIONS: IMbrave150 and GO30140 patients with unresectable HCC had varying baseline hepatic impairment and high enrollment from Asia. PK data demonstrated considerable exposure overlap across groups. Treatment was tolerable across groups. No need for dose adjustment based on mild or moderate hepatic impairment or region is recommended based on this analysis.

Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition-overall survival modeling framework.

The objectives of the study were to use tumor size data from 10 phase II/III atezolizumab studies across five solid tumor types to estimate tumor growth inhibition (TGI) metrics and assess the impact of TGI metrics and baseline prognostic factors on overall survival (OS) for each tumor type. TGI metrics were estimated from biexponential models and posttreatment longitudinal data of 6699 patients. TGI-OS full models were built using parametric survival regression by including all significant baseline covariates from the Cox univariate analysis followed by a backward elimination step. The model performance was evaluated for each trial by 1000 simulations of the OS distributions and hazard ratios (HR) of the atezolizumab-containing arms versus the respective controls. The tumor growth rate estimate was the most significant predictor of OS across all tumor types. Several baseline prognostic factors, such as inflammatory status (C-reactive protein, albumin, and/or neutrophil-to-lymphocyte ratio), tumor burden (sum of longest diameters, number of metastatic sites, and/or presence of liver metastases), Eastern Cooperative Oncology Group performance status, and lactate dehydrogenase were also highly significant across multiple studies in the final multivariate models. TGI-OS models adequately described the OS distribution. The model-predicted HRs indicated good model performance across the 10 studies, with observed HRs within the 95% prediction intervals for all study arms versus controls. Multivariate TGI-OS models developed for different solid tumor types were able to predict treatment effect with various atezolizumab monotherapy or combination regimens and could be used to support design and analysis of future studies.

Translational and pharmacokinetic-pharmacodynamic application for the clinical development of GDC-0334, a novel TRPA1 inhibitor.

GDC-0334 is a novel small molecule inhibitor of transient receptor potential cation channel member A1 (TRPA1), a promising therapeutic target for many nervous system and respiratory diseases. The pharmacokinetic (PK) profile and pharmacodynamic (PD) effects of GDC-0334 were evaluated in this first-in-human (FIH) study. A starting single dose of 25 mg was selected based on integrated preclinical PK, PD, and toxicology data following oral administration of GDC-0334 in guinea pigs, rats, dogs, and monkeys. Human PK and PK-PD of GDC-0334 were characterized after single and multiple oral dosing using a population modeling approach. The ability of GDC-0334 to inhibit dermal blood flow (DBF) induced by topical administration of allyl isothiocyanate (AITC) was evaluated as a target-engagement biomarker. Quantitative models were developed iteratively to refine the parameter estimates of the dose-concentration-effect relationships through stepwise estimation and extrapolation. Human PK analyses revealed that bioavailability, absorption rate constant, and lag time increase when GDC-0334 was administered with food. The inhibitory effect of GDC-0334 on the AITC-induced DBF biomarker exhibited a clear sigmoid-Emax relationship with GDC-0334 plasma concentrations in humans. This study leveraged emerging preclinical and clinical data to enable iterative refinement of GDC-0334 mathematical models throughout the FIH study for dose selection in subsequent cohorts throughout the study. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? GDC-0334 is a novel, small molecule TRPA1 inhibitor and a pharmacokinetic-pharmacodynamic (PK-PD) modeling strategy could be implemented in a systematic and step-wise manner to build and learn from emerging data for early clinical development. WHAT QUESTION DID THIS STUDY ADDRESS? Can noncompartmental and population-based analyses be used to describe the PK and PD characteristics of GDC-0334 in preclinical and clinical studies? WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? GDC-0334 exposure generally increased with dose in rats, dogs, and monkeys. The starting dose (25 mg) in the clinical study was determined based on the preclinical data. GDC-0334 exhibited linear PK in humans and the bioavailability was increased with food. The inhibitory effect of GDC-0334 on dermal blood flow induced by the TRPA1 agonist allyl isothiocyanate in humans indicates a clear PK-PD relationship. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? The models developed based on TRPA1 agonist-induced dermal blood flow inhibition data can be used to predict PK-PD relationships in future preclinical and clinical studies evaluating new drug entities that target TRPA1.

Time-dependent population PK models of single-agent atezolizumab in patients with cancer.

PURPOSE: The time-varying clearance (CL) of the PD-L1 inhibitor atezolizumab was assessed on a population of 1519 cancer patients (primarily with non-small-cell lung cancer or metastatic urothelial carcinoma) from three clinical studies. METHODS: The first step was to identify the baseline covariates affecting atezolizumab CL without including time-varying components (stationary covariate model). Two time-varying models were then investigated: (1) a model allowing baseline covariates to vary over time (time-varying covariate model), (2) a model with empirical time-varying Emax CL function. RESULTS: The final stationary covariate model included main effects of body weight, albumin levels, tumor size, anti-drug antibodies (ADA) and gender on atezolizumab CL. Both time-varying models resulted in a clear improvement of the data fit and visual predictive checks over the stationary model. The time-varying covariate model provided the best fit of the data. In this model, the main driver for change in CL over time was variations in albumin level with an increase in serum albumin (improvement in a patient's status) mirroring a decrease in CL. Time-varying ADAs had a small impact (9% increase in CL). None of the covariates impacted atezolizumab CL by more than ± 30% from median. The estimated maximum decrease in CL with time was 22% with the Emax model. CONCLUSION: The overall impact of covariates on atezolizumab CL did not warrant any change in atezolizumab dosing recommendations. The results support the hypothesis that variation in atezolizumab CL over time is associated with patients' disease status, as shown with other checkpoint inhibitors.

Results of a Dose-Finding Phase 1b Study of Subcutaneous Atezolizumab in Patients With Locally Advanced or Metastatic Non-Small Cell Lung Cancer.

Intravenous (IV) atezolizumab is approved for non-small cell lung and other cancers. Subcutaneous (SC) atezolizumab coformulated with recombinant human hyaluronidase, a permeation enhancer for SC dispersion and absorption, is being developed to improve treatment options, reduce burden, and increase efficiency for patients and practitioners. IMscin001 (NCT03735121), a 2-part, open-label, global, multicenter, phase 1b/3 study, is evaluating the pharmacokinetics (PK), safety, and efficacy of SC atezolizumab. The part 1 (phase 1b) objective was determination of an SC atezolizumab dose yielding a serum trough concentration (Ctrough ) comparable with IV. Patients enrolled in 3 cohorts received SC atezolizumab 1800 mg (thigh) once (cohort 1), 1200 mg (thigh) every 2 weeks for 3 cycles (cohort 2), or 1800 mg (abdomen) every 3 weeks cycle 1, then cycles 2 and 3 (thigh) every 3 weeks (cohort 3). In subsequent cycles, IV atezolizumab 1200 mg every 3 weeks was administered until loss of clinical benefit. SC atezolizumab 1800 mg every 3 weeks and 1200 mg every 2 weeks provided similar Ctrough and area under the curve values in cycle 1 to the corresponding IV atezolizumab reference, was well tolerated, and exhibited a safety profile consistent with the established IV formulation. Exposure following SC injection in the abdomen was lower (20%, 28%, and 27% for Ctrough , maximum concentration, and area under the concentration-time curve from time 0 to day 21, respectively) than in the thigh. Part 1 SC and IV PK data were analyzed using a population PK modeling approach, followed by simulations. Part 2 (phase 3) will now be initiated to demonstrate that SC atezolizumab PK exposure is not lower than that of IV.

Application of Machine Learning for Tumor Growth Inhibition - Overall Survival Modeling Platform.

Machine learning (ML) was used to leverage tumor growth inhibition (TGI) metrics to characterize the relationship with overall survival (OS) as a novel approach and to compare with traditional TGI-OS modeling methods. Historical dataset from a phase III non-small cell lung cancer study (OAK, atezolizumab vs. docetaxel, N = 668) was used. ML methods support the validity of TGI metrics in predicting OS. With lasso, the best model with TGI metrics outperforms the best model without TGI metrics. Boosting was the best linear ML method for this dataset with reduced estimation bias and lowest Brier score, suggesting better prediction accuracy. Random forest did not outperform linear ML methods despite hyperparameter optimization. Kernel machine was marginally the best nonlinear ML method for this dataset and uncovered nonlinear and interaction effects. Nonlinear ML may improve prediction by capturing nonlinear effects and covariate interactions, but its predictive performance and value need further evaluation with larger datasets.