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

Population pharmacokinetics of adebrelimab - Support of alternative flat dose regimen in extensive-stage small-cell lung cancer

Adebrelimab, a novel anti-PD-L1 antibody, has been approved by the National Medical Products Administration of China as an intravenous infusion for use in combination with carboplatin and etoposide as first-line treatment for extensive-stage small-cell lung cancer in 2023. A two-compartment model with empirical time-varying CL for adebrelimab was established based on data from 263 patients receiving body weight-based doses from two clinical studies. Significant covariate effects of baseline body weight, albumin levels, tumor size, neutrophil counts, and presence of anti-drug antibodies were identified on CL of debrelimab, none of which were clinically significant or warranted dose adjustment. The degree of decrease in CL was higher in patients who responded to treatment with adebrelimab than in non-responders. Adebrelimab exposures (AUC, Ctrough, or Cmax) were not identified as a statistically significant factor related to efficacy or safety endpoint in the exposure-response analysis. Distribution of simulated exposure metrics from the flat dose regimen (1200 mg q3w) was similar to the marketed weight-based dosing regimen (20 mg/kg q3w), supporting the alternative flat dose regimen in the clinic.

Predicting overall survival from tumor dynamics metrics using parametric statistical and machine learning models: application to patients with RET-altered solid tumors

In oncology drug development, tumor dynamics modeling is widely applied to predict patients' overall survival (OS) via parametric models. However, the current modeling paradigm, which assumes a disease-specific link between tumor dynamics and survival, has its limitations. This is particularly evident in drug development scenarios where the clinical trial under consideration contains patients with tumor types for which there is little to no prior institutional data. In this work, we propose the use of a pan-indication solid tumor machine learning (ML) approach whereby all three tumor metrics (tumor shrinkage rate, tumor regrowth rate and time to tumor growth) are simultaneously used to predict patients' OS in a tumor type independent manner. We demonstrate the utility of this approach in a clinical trial of cancer patients treated with the tyrosine kinase inhibitor, pralsetinib. We compared the parametric and ML models and the results showed that the proposed ML approach is able to adequately predict patient OS across RET-altered solid tumors, including non-small cell lung cancer, medullary thyroid cancer as well as other solid tumors. While the findings of this study are promising, further research is needed for evaluating the generalizability of the ML model to other solid tumor types.

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Quantitative Assessment of Drug Efficacy and Emergence of Resistance in Patients with Metastatic Renal Cell Carcinoma Using a Longitudinal Exposure-Tumor Growth Inhibition Model: Apitolisib (Dual PI3K/mTORC1/2 Inhibitor) Versus Everolimus (mTORC1 Inhibitor)

Cancer remains a significant global health challenge, and despite remarkable advancements in therapeutic strategies, poor tolerability of drugs (causing dose reduction/interruptions) and/or the emergence of drug resistance are major obstacles to successful treatment outcomes. Metastatic renal cell carcinoma (mRCC) accounts for 2% of global cancer diagnoses and deaths. Despite the initial success of targeted therapies in mRCC, challenges remain to overcome drug resistance that limits the long-term efficacy of these treatments. Our analysis aim was to develop a semi-mechanistic longitudinal exposure-tumor growth inhibition model for patients with mRCC to characterize and compare everolimus (mTORC1) and apitolisib's (dual PI3K/mTORC1/2) ability to inhibit tumor growth, and quantitate each drug's efficacy decay caused by emergence of tumor resistance over time. Model-estimated on-treatment tumor growth rate constant was 1.7-fold higher for apitolisib compared to everolimus. Estimated half-life for loss of treatment effect over time for everolimus was 16.1 weeks compared to 7.72 weeks for apitolisib, suggesting a faster rate of tumor re-growth for apitolisib patients likely due to the emergence of resistance. Goodness-of-fit plots including visual predictive check indicated a good model fit and the model was able to capture individual tumor size-time profiles. Based on our knowledge, this is the first clinical report to quantitatively assess everolimus (mTORC1) and apitolisib (PI3K/mTORC1/2) efficacy decay in patients with mRCC. These results highlight the difference in overall efficacy of 2 drugs due to the quantified efficacy decay caused by emergence of resistance, and emphasize the importance of model-informed drug development for targeted cancer therapy.

Tumor growth inhibition-overall survival modeling in non-small cell lung cancer: A case study from GEMSTONE-302

Overall survival is vital for approving new anticancer drugs but is often impractical for early-phase studies. The tumor growth inhibition-overall survival (TGI-OS) model could bridge the gap between early- and late-stage development. This study aimed to identify an appropriate TGI-OS model for patients with non-small cell lung cancer from the GEMSTONE-302 study of sugemalimab. We used three TGI models to delineate tumor trajectories and investigated three OS model for linking TGI metric to OS. All three TGI models accurately captured tumor profiles at the individual level. The published atezolizumab-based TGI-OS model predicted survival time satisfactorily through simulation-based evaluation, whereas the other published model built from multi-treatment underestimated OS. Our study-specific TGI-OS model identified time-to-growth as the most significant metric with the number of metastatic sites and neutrophil-to-lymphocyte ratio at baseline as covariates and exhibited robust OS predictability. Our findings demonstrated the effectiveness of the TGI-OS models in predicting phase III outcomes, which underpins their value as a powerful tool for antitumor drug development.

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.

Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE

While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data. We show that TDNODE overcomes a key limitation of existing models in its ability to make unbiased predictions from truncated data. The encoder-decoder architecture is designed to express an underlying dynamical law that possesses the fundamental property of generalized homogeneity with respect to time. Thus, the modeling formalism enables the encoder output to be interpreted as kinetic rate metrics, with inverse time as the physical unit. We show that the generated metrics can be used to predict patients' overall survival (OS) with high accuracy. The proposed modeling formalism provides a principled way to integrate multimodal dynamical datasets in oncology disease modeling.

C-Reactive Protein as an Early Predictor of Efficacy in Advanced Non-Small-Cell Lung Cancer Patients: A Tumor Dynamics-Biomarker Modeling Framework

In oncology, longitudinal biomarkers reflecting the patient's status and disease evolution can offer reliable predictions of the patient's response to treatment and prognosis. By leveraging clinical data in patients with advanced non-small-cell lung cancer receiving first-line chemotherapy, we aimed to develop a framework combining anticancer drug exposure, tumor dynamics (RECIST criteria), and C-reactive protein (CRP) concentrations, using nonlinear mixed-effects models, to evaluate and quantify by means of parametric time-to-event models the significance of early longitudinal predictors of progression-free survival (PFS) and overall survival (OS). Tumor dynamics was characterized by a tumor size (TS) model accounting for anticancer drug exposure and development of drug resistance. CRP concentrations over time were characterized by a turnover model. An x-fold change in TS from baseline linearly affected CRP production. CRP concentration at treatment cycle 3 (day 42) and the difference between CRP concentration at treatment cycles 3 and 2 were the strongest predictors of PFS and OS. Measuring longitudinal CRP allows for the monitoring of inflammatory levels and, along with its reduction across treatment cycles, presents a promising prognostic marker. This framework could be applied to other treatment modalities such as immunotherapies or targeted therapies allowing the timely identification of patients at risk of early progression and/or short survival to spare them unnecessary toxicities and provide alternative treatment decisions.

A prognostic baseline blood biomarker and tumor growth kinetics integrated model in paclitaxel/platinum treated advanced non-small cell lung cancer patients

Paclitaxel/platinum chemotherapy, the backbone of standard first-line treatment of advanced non-small cell lung cancer (NSCLC), exhibits high interpatient variability in treatment response and high toxicity burden. Baseline blood biomarker concentrations and tumor size (sum of diameters) at week 8 relative to baseline (RS8) are widely investigated prognostic factors. However, joint analysis of data on demographic/clinical characteristics, blood biomarker levels, and chemotherapy exposure-driven early tumor response for improved prediction of overall survival (OS) is clinically not established. We developed a Weibull time-to-event model to predict OS, leveraging data from 365 patients receiving paclitaxel/platinum combination chemotherapy once every three weeks for ≤six cycles. A developed tumor growth inhibition model, combining linear tumor growth and first-order paclitaxel area under the concentration-time curve-induced tumor decay, was used to derive individual RS8. The median model-derived RS8 in all patients was a 20.0% tumor size reduction (range from -78% to +15%). Whereas baseline carcinoembryonic antigen, cytokeratin fragments, and thyroid stimulating hormone levels were not significantly associated with OS in a subset of 221 patients, and lactate dehydrogenase, interleukin-6 and neutrophil-to-lymphocyte ratio levels were significant only in univariate analyses (p value < 0.05); C-reactive protein (CRP) in combination with RS8 most significantly affected OS (p value < 0.01). Compared to the median population OS of 11.3 months, OS was 128% longer at the 5th percentile levels of both covariates and 60% shorter at their 95th percentiles levels. The combined paclitaxel exposure-driven RS8 and baseline blood CRP concentrations enables early individual prognostic predictions for different paclitaxel dosing regimens, forming the basis for treatment decision and optimizing paclitaxel/platinum-based advanced NSCLC chemotherapy.

Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models

Longitudinal models of biomarkers such as tumour size dynamics capture treatment efficacy and predict treatment outcome (overall survival) of a variety of anticancer therapies, including chemotherapies, targeted therapies, immunotherapies and their combinations. These pharmacological endpoints like tumour dynamic (tumour growth inhibition) metrics have been proposed as alternative endpoints to complement the classical RECIST endpoints (objective response rate, progression-free survival) to support early decisions both at the study level in drug development as well as at the patients level in personalised therapy with checkpoint inhibitors. This perspective paper presents recent developments and future directions to enable wider and robust use of model-based decision frameworks based on pharmacological endpoints.

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.

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.

Multistate Pharmacometric Model to Define the Impact of Second-Line Immunotherapies on the Survival Outcome of the IMpower131 Study

Overall survival is defined as the time since randomization into the clinical trial to event of death or censor (end of trial or follow-up), and is considered to be the most reliable cancer end point. However, the introduction of second-line treatment after disease progression could influence survival and be considered a confounding factor. The aim of the current study was to set up a multistate model framework, using data from the IMpower131 study, to investigate the influence of second-line immunotherapies on overall survival analysis. The model adequately described the transitions between different states in patients with advanced squamous non-small cell lung cancer treated with or without atezolizumab plus nab-paclitaxel and carboplatin, and characterized the survival data. High PD-L1 expression at baseline was associated with a decreased hazard of progression, while the presence of liver metastasis at baseline was indicative of a high risk of disease progression after initial response. The hazard of death after progression was lower for participants who had longer treatment response, i.e., longer time to progression. The simulations based on the final multistate model showed that the addition of atezolizumab to the nab-paclitaxel and carboplatin regimen had significant improvement in the patients' survival (hazard ratio = 0.75, 95% prediction interval: 0.61-0.90 favoring the atezolizumab + nab-paclitaxel and carboplatin arm). The developed modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, and investigate the benefit of primary therapy in survival while accounting for the switch to alternative treatment in the case of disease progression.

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.

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

Atezolizumab is approved as an intravenous infusion for use as a single agent and in combination with other therapies in a number of indications.1 The objectives of this publication are to characterize atezolizumab pharmacokinetics (PK) across indications with available clinical data from one Phase I and eight Phase III studies, to determine the exposure-response (ER) relationships in the 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 the 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 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 setting as well as the flat ER in new tumor types and combination therapies support the use of the three atezolizumab dosing regimens to be used interchangeably in the combination setting. Atezolizumab is now approved with three interchangeable dosing regimens of 840 mg q2w, 1200 mg q3w, and 1680 mg q4w for single-agent and combination therapy use in the US and EU. This article is protected by copyright. All rights reserved.

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.