CD19-CD28: an affinity-optimized CD28 agonist for combination with glofitamab (CD20-TCB) as off-the-shelf immunotherapy.

ABSTRACT: Effective T-cell responses not only require the engagement of T-cell receptors (TCRs; "signal 1"), but also the availability of costimulatory signals ("signal 2"). T-cell bispecific antibodies (TCBs) deliver a robust signal 1 by engaging the TCR signaling component CD3ε, while simultaneously binding to tumor antigens. The CD20-TCB glofitamab redirects T cells to CD20-expressing malignant B cells. Although glofitamab exhibits strong single-agent efficacy, adding costimulatory signaling may enhance the depth and durability of T-cell-mediated tumor cell killing. We developed a bispecific CD19-targeted CD28 agonist (CD19-CD28), RG6333, to enhance the efficacy of glofitamab and similar TCBs by delivering signal 2 to tumor-infiltrating T cells. CD19-CD28 distinguishes itself from the superagonistic antibody TGN1412, because its activity requires the simultaneous presence of a TCR signal and CD19 target binding. This is achieved through its engineered format incorporating a mutated Fc region with abolished FcγR and C1q binding, CD28 monovalency, and a moderate CD28 binding affinity. In combination with glofitamab, CD19-CD28 strongly increased T-cell effector functions in ex vivo assays using peripheral blood mononuclear cells and spleen samples derived from patients with lymphoma and enhanced glofitamab-mediated regression of aggressive lymphomas in humanized mice. Notably, the triple combination of glofitamab with CD19-CD28 with the costimulatory 4-1BB agonist, CD19-4-1BBL, offered substantially improved long-term tumor control over glofitamab monotherapy and respective duplet combinations. Our findings highlight CD19-CD28 as a safe and highly efficacious off-the-shelf combination partner for glofitamab, similar TCBs, and other costimulatory agonists. CD19-CD28 is currently in a phase 1 clinical trial in combination with glofitamab. This trial was registered at www.clinicaltrials.gov as #NCT05219513.

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.

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.

Bayesian forecasting of tumor size metrics and overall survival.

The tumor size ratio (TSR), time-to-tumor growth (TTG), and tumor growth rate (kG) are frequently suggested as model-based predictors of overall survival (OS) for different types of tumors. When the tumor metrics are applied in forecasting of the outcome for individual patients at an early stage, the tumor data might be sparse resulting in imprecise prediction. This simulation study aimed to investigate how the tumor follow-up data and estimation approaches influence the accuracy in the tumor size metrics and the predicted hazard of death for individual patients. Longitudinal tumor size and OS data were simulated using tumor growth inhibition and Weibull distribution models, respectively. Based on the model and increasing measurement durations, the accuracy (defined as 80-125% of the simulated "true" value) in individual metrics and hazard was computed. TSR week 6 (TSRw6) accuracy was adequate for 91% of the patients when tumor size was measured up to 12 weeks. For TTG and kG metrics, the highest accuracy observed was lower (43 and 77%, respectively) and occurred later (42 and 60 weeks, respectively). The simultaneous (joint) and sequential estimation approaches resulted in similar accuracies, however, in general, the sequential approach where individual tumor size parameters are fixed, demonstrated inferior estimation properties. The TSRw6 and the model-predicted tumor time course (absolute or relative change) had better forecasting properties than TTG or kG. The population pharmacokinetic (PK) parameters and data approach performed similarly or better than the simultaneous approach and had a better accuracy in estimating individuals' hazard of death than the individual PK parameters method.

Tumor growth inhibition modeling of individual lesion dynamics and interorgan variability in HER2-negative breast cancer patients treated with docetaxel.

Information on individual lesion dynamics and organ location are often ignored in pharmacometric modeling analyses of tumor response. Typically, the sum of their longest diameters is utilized. Herein, a tumor growth inhibition model was developed for describing the individual lesion time-course data from 183 patients with metastatic HER2-negative breast cancer receiving docetaxel. The interindividual variability (IIV), interlesion variability (ILV), and interorgan variability of parameters describing the lesion time-courses were evaluated. Additionally, a model describing the probability of new lesion appearance and a time-to-event model for overall survival (OS), were developed. Before treatment initiation, the lesions were largest in the soft tissues and smallest in the lungs, and associated with a significant IIV and ILV. The tumor growth rate was 2.6 times higher in the breasts and liver, compared with other metastatic sites. The docetaxel drug effect in the liver, breasts, and soft tissues was greater than or equal to 1.2 times higher compared with other organs. The time-course of the largest lesion, the presence of at least 3 liver lesions, and the time since study enrollment, increased the probability of new lesion appearance. New lesion appearance, along with the time to growth and time-course of the largest lesion at baseline, were identified as the best predictors of OS. This tumor modeling approach, incorporating individual lesion dynamics, provided a more complete understanding of heterogeneity in tumor growth and drug effect in different organs. Thus, there may be potential to tailor treatments based on lesion location, lesion size, and early lesion response to provide better clinical outcomes.

Multistate model for pharmacometric analyses of overall survival in HER2-negative breast cancer patients treated with docetaxel.

The aim of this study was to develop a multistate model for overall survival (OS) analysis, based on parametric hazard functions and combined with an investigation of predictors derived from a longitudinal tumor size model on the transition hazards. Different states - stable disease, tumor response, progression, second-line treatment, and death following docetaxel treatment initiation (stable state) in patients with HER2-negative breast cancer (n = 183) were used in model building. Past changes in tumor size prospectively predicts the probability of state changes. The hazard of death after progression was lower for subjects who had longer treatment response (i.e., longer time-to-progression). Young age increased the probability of receiving second-line treatment. The developed multistate model adequately described the transitions between different states and jointly the overall event and survival data. The multistate model allows for simultaneous estimation of transition rates along with their tumor model derived metrics. The metrics were evaluated in a prospective manner so not to cause immortal time bias. Investigation of predictors and characterization of the time to develop response, the duration of response, the progression-free survival, and the OS can be performed in a single multistate modeling exercise. This modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, thereby facilitating early clinical interventions to improve anticancer therapy.

Model-based Dose Individualization of Sunitinib in Gastrointestinal Stromal Tumors.

PURPOSE: Various biomarkers have been proposed for sunitinib therapy in gastrointestinal stromal tumor (GIST). However, the lack of "real-life" comparative studies hampers the selection of the most appropriate one. We, therefore, set up a pharmacometric simulation framework to compare each proposed biomarker. EXPERIMENTAL DESIGN: Models describing relations between sunitinib exposure, adverse events (hand-foot syndrome, fatigue, hypertension, and neutropenia), soluble VEGFR (sVEGFR)-3, and overall survival (OS) were connected to evaluate the differences in survival and adverse events under different dosing algorithms. Various fixed dosing regimens [4/2 (weeks on/weeks off) or 2/1 (50 mg), and continuous daily dosing (37.5 mg)] and individualization approaches [concentration-adjusted dosing (CAD), toxicity-adjusted dosing (TAD), and sVEGFR-3-adjusted dosing (VAD)] were explored following earlier suggested blood sampling schedules and dose-reduction criteria. Model-based forecasts of biomarker changes were evaluated for predictive accuracy and the advantage of a model-based dosing algorithm was evaluated for clinical implementation. RESULTS: The continuous daily dosing regimen was predicted to result in the longest survival. TAD (24.5 months) and VAD (25.5 months) increased median OS as compared with a fixed dose schedule (19.9 and 21.5 months, respectively) and CAD (19.7 and 21.3 months, respectively), without markedly raising the risk of intolerable toxicities. Changes in neutrophil count and sVEGFR-3 were accurately forecasted in the majority of subjects (>65%), based on biweekly blood sampling. CONCLUSIONS: Dose adjustments based on the pharmacodynamic biomarkers neutrophil count and sVEGFR-3 can increase OS while retaining drug safety. Future efforts could explore the possibility of incorporating a model-based dose approach in clinical practice to increase dosing accuracy for these biomarkers.