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

First-line oxaliplatin-based chemotherapy and nivolumab for metastatic microsatellite-stable colorectal cancer-the randomised METIMMOX trial

BACKGROUND

We evaluated first-line treatment of metastatic microsatellite-stable colorectal cancer with short-course oxaliplatin-based chemotherapy alternating with immune checkpoint blockade.

METHODS

Patients were randomly assigned to chemotherapy (the FLOX regimen; control group) or alternating two cycles each of FLOX and nivolumab (experimental group). Radiographic response assessment was done every eight weeks with progression-free survival (PFS) as the primary endpoint. Cox proportional-hazards regression models estimated associations between PFS and relevant variables. A post hoc analysis explored C-reactive protein as signal of responsiveness to immune checkpoint blockade.

RESULTS

Eighty patients were randomised and 38 in each group received treatment. PFS was comparable-control group: median 9.2 months (95% confidence interval (CI), 6.3-12.7); experimental group: median 9.2 months (95% CI, 4.5-15.0). The adjusted Cox model revealed that experimental-group subjects aged ≥60 had significantly lowered progression risk (p = 0.021) with hazard ratio 0.17 (95% CI, 0.04-0.76). Experimental-group patients with C-reactive protein <5.0 mg/L when starting nivolumab (n = 17) reached median PFS 15.8 months (95% CI, 7.8-23.7). One-sixth of experimental-group cases (all KRAS/BRAF-mutant) achieved complete response.

CONCLUSIONS

The investigational regimen did not improve the primary outcome for the intention-to-treat population but might benefit small subgroups of patients with previously untreated, metastatic microsatellite-stable colorectal cancer.

TRIAL REGISTRATION

ClinicalTrials.gov number, NCT03388190 (02/01/2018).

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 modeling of tumor dynamics and development of drug resistance in non-small cell lung cancer patients treated with erlotinib

Insight into the development of treatment resistance can support the optimization of anticancer treatments. This study aims to characterize the tumor dynamics and development of drug resistance in patients with non-small cell lung cancer treated with erlotinib, and investigate the relationship between baseline circulating tumor DNA (ctDNA) data and tumor dynamics. Data obtained for the analysis included (1) intensively sampled erlotinib concentrations from 29 patients from two previous pharmacokinetic (PK) studies, and (2) tumor sizes, ctDNA measurements, and sparsely sampled erlotinib concentrations from 18 patients from the START-TKI study. A two-compartment population PK model was first developed which well-described the PK data. The PK model was subsequently applied to investigate the exposure-tumor dynamics relationship. To characterize the tumor dynamics, models accounting for intra-tumor heterogeneity and acquired resistance with or without primary resistance were investigated. Eventually, the model assumed acquired resistance only resulted in an adequate fit. Additionally, models with or without exposure-dependent treatment effect were explored, and no significant exposure-response relationship for erlotinib was identified within the observed exposure range. Subsequently, the correlation of baseline ctDNA data on EGFR and TP53 variants with tumor dynamics' parameters was explored. The analysis indicated that higher baseline plasma EGFR mutation levels correlated with increased tumor growth rates, and the inclusion of ctDNA measurements improved model fit. This result suggests that quantitative ctDNA measurements at baseline have the potential to be a predictor of anticancer treatment response. The developed model can potentially be applied to design optimal treatment regimens that better overcome resistance.

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.

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.