Population Pharmacokinetic/Pharmacodynamic Modeling of Tumor Size Dynamics in Pembrolizumab-Treated Advanced Melanoma

Real-world overall survival after alternative dosing for pembrolizumab in the treatment of non-small cell lung cancer: A nationwide retrospective cohort study with a non-inferiority primary objective

BACKGROUND

High and increasing expenses on pembrolizumab ask for more cost-effective and sustainable treatment strategies to improve affordability of healthcare. Therefore, a part of the Dutch hospitals implemented an alternative, partially lower, weight-based dosing protocol for pembrolizumab. This provided the unique opportunity to compare the overall survival (OS) of the alternative pembrolizumab dosing protocol to standard dosing using a nationwide registry in non-small cell lung cancer (NSCLC) patients.

METHODS

This is a retrospective cohort study with a non-inferiority primary objective. Forty hospitals in the Dutch Medication Audit and Dutch Lung Cancer Audit treated 1966 patients with NSCLC with first line pembrolizumab (mono- or combination therapy) between Jan 1st 2021, and Mar 31st, 2023. Alternative weight-based pembrolizumab dosing (100/150/200 mg Q3W or 200/300/400 mg Q6W) was administered to 604 patients, and 1362 patients received standard pembrolizumab dosing (200 mg Q3W or 400 mg Q6W). A Cox proportional hazard model with selected covariates was used to compare the OS between alternative and standard dosing protocols. The non-inferiority margin was set at a hazard ratio (HR) of 1.2 for OS. Non-inferiority is established by showing that the upper limit of the 95 % confidence interval (CI) of the HR of OS is smaller or equal to 1.2.

RESULTS

Distribution of age (66.7 years +/-9.4), sex (45 % female) and treatment combinations were similar for both groups, comorbidity score was higher in the standard group. Median daily dose in the alternative dosing group was 22 % lower compared to the standard dosing group, 7.14 mg/day (interquartile range (IQR):5.48-8.04 mg/day) vs. 9.15 mg/day (IQR:8.33-9.52 mg/day), respectively. Alternative dosing was non-inferior to standard dosing regarding overall survival (adjusted HR 0.83, 95 %CI:0.69-1.003).

CONCLUSION

This large, retrospective real-world analysis supports the hypothesis that the alternative, partially lower pembrolizumab dosing protocol in NSCLC maintains treatment effectiveness while reducing treatment costs.

Bridging the gap between two-stage and joint models: The case of tumor growth inhibition and overall survival models

Many clinical trials generate both longitudinal biomarker and time-to-event data. We might be interested in their relationship, as in the case of tumor size and overall survival in oncology drug development. Many well-established methods exist for analyzing such data either sequentially (two-stage models) or simultaneously (joint models). Two-stage modeling (2stgM) has been challenged (i) for not acknowledging that biomarkers are endogenous covariable to the survival submodel and (ii) for not propagating the uncertainty of the longitudinal biomarker submodel to the survival submodel. On the other hand, joint modeling (JM), which properly circumvents both problems, has been criticized for being time-consuming, and difficult to use in practice. In this paper, we explore a third approach, referred to as a novel two-stage modeling (N2stgM). This strategy reduces the model complexity without compromising the parameter estimate accuracy. The three approaches (2stgM, JM, and N2stgM) are formulated, and a Bayesian framework is considered for their implementation. Both real and simulated data were used to analyze the performance of such approaches. In all scenarios, our proposal estimated the parameters approximately as JM but without being computationally expensive, while 2stgM produced biased results.

Disentangling Anti-Tumor Response of Immunotherapy Combinations: A Physiologically Based Framework for V937 Oncolytic Virus and Pembrolizumab

Immuno-oncology (IO) is a growing strategy in cancer treatment. Oncolytic viruses (OVs) can selectively infect cancer cells and lead to direct and/or immune-dependent tumor lysis. This approach represents an opportunity to potentiate the efficacy of immune checkpoint inhibitors (ICI), such as pembrolizumab. Currently, there is a lack of comprehensive quantitative models for the aforementioned scenarios. In this work, we developed a mechanistic framework describing viral kinetics, viral dynamics, and tumor response after intratumoral (i.t.) or intravenous (i.v.) administration of V937 alone or in combination with pembrolizumab. The model accounts for tumor shrinkage, in both injected and non-injected lesions, induced by: viral-infected tumor cell death and activated CD8 cells. OV-infected tumor cells enhanced the expansion of CD8 cells, whereas pembrolizumab inhibits their exhaustion by competing with PD-L1 in their binding to PD-1. Circulating viral levels and treatment effects on tumor volume were adequately characterized in all the different scenarios. This mechanistic-based model has been developed by combining top-down and bottom-up approaches and provides individual estimates of viral and ICI responses. The robustness of the model is reflected by the description of the tumor size time profiles in a variety of clinical scenarios. Additionally, this platform allows us to investigate not only the contribution of processes related to the viral kinetics and dynamics on tumor response, but also the influence of its interaction with an ICI. Additionally, the model can be used to explore different scenarios aiming to optimize treatment combinations and support clinical development.

Joint modeling of tumor dynamics and progression-free survival in advanced breast cancer: Leveraging data from amcenestrant early phase I-II trials

A joint modeling framework was developed using data from 75 patients of early amcenestrant phase I-II AMEERA-1-2 dose escalation and expansion cohorts. A semi-mechanistic tumor growth inhibition (TGI) model was developed. It accounts for the dynamics of sensitive and resistant tumor cells, an exposure-driven effect on tumor proliferation of sensitive cells, and a delay in the initiation of treatment effect to describe the time course of target lesion tumor size (TS) data. Individual treatment exposure overtime was introduced in the model using concentrations predicted by a population pharmacokinetic model of amcenestrant. This joint modeling framework integrated complex RECISTv1.1 criteria information, linked TS metrics to progression-free survival (PFS), and was externally evaluated using the randomized phase II trial AMEERA-3. We demonstrated that the instantaneous rate of change in TS (TS slope) was an important predictor of PFS and the developed joint model was able to predict well the PFS of amcenestrant phase II monotherapy trial using only early phase I-II data. This provides a good modeling and simulation tool to inform early development decisions.

Utility and impact of quantitative pharmacology on dose selection and clinical development of immuno-oncology therapy

Immuno-oncology (IO) therapies have changed the cancer treatment landscape. Immune checkpoint inhibitors (ICIs) have improved overall survival in 20-40% of patients with malignancies that were previously refractory. Due to the uniqueness in biology, modalities and patient responses, drug development strategies for IO differed from that traditionally used for cytotoxic and target therapies in oncology, and quantitative pharmacology utilizing modeling approach can be applied in all phases of the development process. In this review, we used case studies to showcase how various modeling methodologies were applied from translational science and dose selection through to label change, using examples that included anti-programmed-death-1 (anti-PD-1), anti-programmed-death ligand-1 (anti-PD-L1), anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4), and anti-glucocorticoid-induced tumor necrosis factor receptor-related protein (anti-GITR) antibodies. How these approaches were utilized to support phase I-III dose selection, the design of phase III trials, and regulatory decisions on label change are discussed to illustrate development strategies. Model-based quantitative approaches have positively impacted IO drug development, and a better understanding of the biology and exposure-response relationship may benefit the development and optimization of new IO therapies.

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.

Outcomes of weight-based vs. fixed dose of Pembrolizumab among patients with non-small cell lung cancer

OBJECTIVE

This study aims to assess outcomes among patients with non-small cell lung cancer (NSCLC) who received treatment with pembrolizumab on a weight-based dose (WBD) or fixed-dose (FD) regimen using a non-inferiority (NI) analysis.

MATERIAL AND METHODS

This retrospective cohort study included adult patients with NSCLC weighing under 100 kg who received pembrolizumab between 1 January 2015 and 31 December 2020. Patients were grouped into either WBD or FD cohort based on the initial pembrolizumab dose and dosing regimen. The primary effectiveness outcome was overall survival (OS), analyzed using NI analysis with a lower margin of 10% comparing WBD to FD. Safety outcomes were all-cause emergency room visits or hospitalizations and incidence of selected immune-related adverse events (irAEs) and analyzed using NI analysis with an upper margin of 10%. All patients were followed until the end of health plan membership, death, or 30 June 2022, whichever occurred first.

RESULTS

A total of 1413 patients were evaluated. OS was observed in 36.6% of the FD group, and 37.7% in the WBD group (rate difference: 1%, 90% CI: -6%-8%, NI p-value < 0.01). NI was met in all three safety outcomes: proportion of all-cause emergency room visits (rate difference: 1.1%, NI p-value < 0.01); proportion of hospitalizations (rate difference: 2%, NI p-value < 0.01); and composite incidence of irAEs (rate difference: -2.2%, NI p-value = 0.03).

CONCLUSION

These findings suggest that WBD of pembrolizumab may be as appropriate as FD for the treatment of lung cancer.

Assessment of Clinical Response to V937 Oncolytic Virus After Intravenous or Intratumoral Administration Using Physiologically-Based Modeling

Oncolytic viruses (OVs) represent a potential therapeutic strategy in cancer treatment. However, there is currently a lack of comprehensive quantitative models characterizing clinical OV kinetics and distribution to the tumor. In this work, we present a mechanistic modeling framework for V937 OV, after intratumoral (i.t.) or intravascular (i.v.) administration in patients with cancer. A minimal physiologically-based pharmacokinetic model was built to characterize biodistribution of OVs in humans. Viral dynamics was incorporated at the i.t. cellular level and linked to tumor response, enabling the characterization of a direct OV killing triggered by the death of infected tumor cells and an indirect killing induced by the immune response. The model provided an adequate description of changes in V937 mRNA levels and tumor size obtained from phase I/II clinical trials after V937 administration. The model showed prominent role of viral clearance from systemic circulation and infectivity in addition to known tumor aggressiveness on clinical response. After i.v. administration, i.t. exposure of V937 was predicted to be several orders of magnitude lower compared with i.t. administration. These differences could be overcome if there is high virus infectivity and/or replication. Unfortunately, the latter process could not be identified at the current clinical setting. This work provides insights on selecting optimal OV considering replication rate and infectivity.

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.

Clinical benefit of pembrolizumab in treatment of first line non-small cell lung cancer: a systematic review and meta-analysis of clinical characteristics

OBJECTIVE

Pembrolizumab has become an integral first line therapeutic agent for non-small cell lung cancer (NSCLC), but its potential predictive role in clinical and molecular characteristics remains to be clarified. Accordingly, we performed a systematic review and meta-analysis to evaluate the clinical benefit of pembrolizumab in treatment of first line NSCLC and to select individuals with the greatest potential benefit from pembrolizumab therapy, in order to obtain a more accurate treatment of NSCLC in immunotherapy.

METHODS

Mainstream oncology datasets and conferences were searched for randomized clinical trials (RCTs) published before August 2022. RCTs involved individuals with first line NSCLC treated with pembrolizumab monotherapy or in combination with chemotherapy. Two authors independently selected the studies, extracted data, and assessed the risk of bias. The basic characteristics of the included studies were recorded, along with 95 percent confidence intervals (CI) and hazard ratios (HR) for all patients and subgroups. The primary endpoint was overall survival (OS), and secondary endpoints was progression-free survival (PFS). Pooled treatment data were estimated using the inverse variance-weighted method.

RESULTS

Five RCTs involving 2,877 individuals were included in the study. Pembrolizumab-based therapy significantly improved OS (HR 0.66; CI 95%, 0.55-0.79; p < 0.00001) and PFS (HR 0.60; CI 95%, 0.40-0.91; p = 0.02) compared with chemotherapy. OS was substantially enhanced in individuals aged < 65 years (HR 0.59; CI 95%, 0.42-0.82; p = 0.002), males (HR 0.74; CI 95%, 0.65-0.83; p < 0.00001), with a smoking history (HR 0.65; CI 95%, 0.52-0.82; p = 0.0003), with PD-L1 tumor proportion score (TPS) < 1% (HR 0.55; CI 95%, 0.41-0.73; p < 0.0001) and TPS ≥ 50% (HR 0.66; CI 95%, 0.56-0.76; p < 0.00001), but not in individuals aged ≥ 75 years (HR 0.82; CI 95%, 0.56-1.21; p = 0.32), females (HR 0.57; CI 95%, 0.31-1.06; p = 0.08), never smokers (HR 0.57; CI 95%, 0.18-1.80; p = 0.34), or with TPS 1-49% (HR 0.72; CI 95%, 0.52-1.01; p = 0.06). Pembrolizumab significantly prolonged OS in NSCLC patients, regardless of histology type (squamous or non-squamous NSCLC), performance status (PS) (0 or 1), and brain metastatic status (all p < 0.05). Subgroup analysis revealed that pembrolizumab combined with chemotherapy had more favorable HR values than pembrolizumab monotherapy in improving the OS of individuals with different clinical and molecular features.

CONCLUSION

Pembrolizumab-based therapy is a valuable option for first line treating advanced or metastatic NSCLC. Age, sex, smoking history and PD-L1 expression status can be used to predict the clinical benefit of pembrolizumab. Cautiousness was needed when using pembrolizumab in NSCLC patients aged ≥ 75 years, females, never smokers, or in patients with TPS 1-49%. Furthermore, pembrolizumab in combination with chemotherapy may be a more effective treatment regimen.

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.

REFINE-Lung implements a novel multi-arm randomised trial design to address possible immunotherapy overtreatment

Increasing evidence suggests that some immunotherapy dosing regimens for patients with advanced cancer could result in overtreatment. Given the high costs of these agents, and important implications for quality of life and toxicity, new approaches are needed to identify and reduce unnecessary treatment. Conventional two-arm non-inferiority designs are inefficient in this context because they require large numbers of patients to explore a single alternative to the standard of care. Here, we discuss the potential problem of overtreatment with anti-PD-1 directed agents in general and introduce REFINE-Lung (NCT05085028), a UK multicentre phase 3 study of reduced frequency pembrolizumab in advanced non-small-cell lung cancer. REFINE-Lung uses a novel multi-arm multi-stage response over continuous interventions (MAMS-ROCI) design to determine the optimal dose frequency of pembrolizumab. Along with a similarly designed basket study of patients with renal cancer and melanoma, REFINE-Lung and the MAMS-ROCI design could contribute to practice-changing advances in patient care and form a template for future immunotherapy optimisation studies across cancer types and indications. This new trial design is applicable to many new or existing agents for which optimisation of dose, frequency, or duration of therapy is desirable.

Comparison of sequential and joint nonlinear mixed effects modeling of tumor kinetics and survival following Durvalumab treatment in patients with metastatic urothelial carcinoma

Standard endpoints such as objective response rate are usually poorly correlated with overall survival (OS) for treatment with immune checkpoint inhibitors. Longitudinal tumor size may serve as a more useful predictor of OS, and establishing a quantitative relationship between tumor kinetics (TK) and OS is a crucial step for successfully predicting OS based on limited tumor size measurements. This study aims to develop a population TK model in combination with a parametric survival model by sequential and joint modeling approaches to characterize durvalumab phase I/II data from patients with metastatic urothelial cancer, and to evaluate and compare the performance of the two modeling approaches in terms of parameter estimates, TK and survival predictions, and covariate identification. The tumor growth rate constant was estimated to be greater for patients with OS ≤ 16 weeks as compared to that for patients with OS > 16 weeks with the joint modeling approach (k_g= 0.130 vs. 0.0551 week^-1, p-value < 0.0001), but similar for both groups (k_g = 0.0624 vs.0.0563 week^-1, p-value = 0.37) with the sequential modeling approach. The predicted TK profiles by joint modeling appeared better aligned with clinical observations. Joint modeling also predicted OS more accurately than the sequential approach according to concordance index and Brier score. The sequential and joint modeling approaches were also compared using additional simulated datasets, and survival was predicted better by joint modeling in the case of a strong association between TK and OS. In conclusion, joint modeling enabled the establishment of a robust association between TK and OS and may represent a better choice for parametric survival analyses over the sequential approach.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

In Vitro/In Vivo Translation of Synergistic Combination of MDM2 and MEK Inhibitors in Melanoma Using PBPK/PD Modelling: Part III

The development of in vitro/in vivo translational methods and a clinical trial framework for synergistically acting drug combinations are needed to identify optimal therapeutic conditions with the most effective therapeutic strategies. We performed physiologically based pharmacokinetic-pharmacodynamic (PBPK/PD) modelling and virtual clinical trial simulations for siremadlin, trametinib, and their combination in a virtual representation of melanoma patients. In this study, we built PBPK/PD models based on data from in vitro absorption, distribution, metabolism, and excretion (ADME), and in vivo animals' pharmacokinetic-pharmacodynamic (PK/PD) and clinical data determined from the literature or estimated by the Simcyp simulator (version V21). The developed PBPK/PD models account for interactions between siremadlin and trametinib at the PK and PD levels. Interaction at the PK level was predicted at the absorption level based on findings from animal studies, whereas PD interaction was based on the in vitro cytotoxicity results. This approach, combined with virtual clinical trials, allowed for the estimation of PK/PD profiles, as well as melanoma patient characteristics in which this therapy may be noninferior to the dabrafenib and trametinib drug combination. PBPK/PD modelling, combined with virtual clinical trial simulation, can be a powerful tool that allows for proper estimation of the clinical effect of the above-mentioned anticancer drug combination based on the results of in vitro studies. This approach based on in vitro/in vivo extrapolation may help in the design of potential clinical trials using siremadlin and trametinib and provide a rationale for their use in patients with melanoma.

Case report: Pharmacokinetics of pembrolizumab in a patient with stage IV non-small cell lung cancer after a single 200 mg administration

Background

Pembrolizumab is a well-tolerated biologic agent with a potentially stable and durable anti-tumor response. Unfortunately, discontinuation of therapy can occur as a consequence of immune-related adverse effects (irAEs). These irAEs appear independent of dose and exposure. However, such irAEs might also result from pembrolizumab's highly specific mechanism of action and current dosing regimens. However, the currently available pharmacokinetic (PK) and pharmacodynamic (PD) data to reassess dosing strategies are insufficient.To highlight the importance of additional PK/PD studies, we present a case describing the complexity of pembrolizumab's PK/PD after a single 200 mg pembrolizumab dose in a treatment-naive patient with non-small cell lung cancer (NSCLC).

Case description

A 72-year-old man with stage IV NSCLC presented hepatotoxic symptoms 19 days after receiving the first 200 mg pembrolizumab dose. Hence, pembrolizumab therapy was paused, and prednisolone therapy was initiated, which successfully inhibited the toxic effect of pembrolizumab. However, repeated flare-ups due to prednisolone tapering suggest that the toxic effect of pembrolizumab outlasts the presence of pembrolizumab in the bloodstream. This further suggests that the T-cell-mediated immune response outlasts the programmed cell death protein 1 (PD-1) receptor occupancy by pembrolizumab, which challenges the need for the current fixed-interval strategies and their stop criteria.Furthermore, a validated ELISA quantified pembrolizumab levels in 15 samples within 123 days after administration. A shift in the pembrolizumab clearance rate was evident ensuing day 77 (0.6 µg/mL) after administration. Pembrolizumab levels up to day 77 (9.1-0.6 µg/mL) strongly exhibited a linear, first-order clearance (R² = 0.991), whereas after day 77, an accelerated non-linear clearance was observed. This transition from a linear to non-linear clearance was most likely a result of full target receptor saturation to non-full target receptor saturation, in which the added effect of target-mediated drug disposition occurs. This suggests that pembrolizumab's targets were fully saturated at levels above 0.6 µg/mL, which is 43 to 61 times lower than the steady-state trough levels (C_trough,ss) of the currently registered fixed-dosing regimens (3-5).

Virtual clinical trials: A tool for predicting patients who may benefit from treatment beyond progression with pembrolizumab in non-small cell lung cancer

Enrolling patients in immunotherapy clinical trials is becoming increasingly competitive. Virtual clinical trials can help investigators answer key questions despite this. For example, pembrolizumab is the recommended first-line treatment for non-small cell lung cancer (NSCLC) with no driver alterations and a programmed death ligand 1 (PD-L1) Tumor Proportion Score ≥50%. Salvage therapies for relapsed/refractory patients are limited. Retrospective studies suggest that a subset of patients may benefit from pembrolizumab beyond progression; these results have not been validated in a prospective study. We constructed digital twins of patients and simulated clinical trials to predict the best salvage therapy after progressive disease (PD) on pembrolizumab. Response dynamics were evaluated at the lesion level to represent patients who experience systemic PD while individual lesions continue shrinking. With >25,000 radiographic lesion measurements from >500 patients, we simulated responses to pembrolizumab, chemotherapy, and PD on pembrolizumab followed by either pembrolizumab beyond progression or salvage chemotherapy. Switching all progressors to salvage chemotherapy was suboptimal. Virtual trials predicted progression-free survival (PFS) from pembrolizumab beyond progression to be comparable with salvage chemotherapy in patients whose PD was due to nontarget progression. A PFS-optimized regimen may improve disease control rates ≥15%. Pembrolizumab beyond progression may benefit a subset of patients with PD-L1-high, driver alteration-free NSCLC, but prospective studies are warranted.

Preclinical InVivo Data Integrated in a Modeling Network Informs a Refined Clinical Strategy for a CD3 T-Cell Bispecific in Combination with Anti-PD-L1

TYRP1-TCB is a CD3 T-cell bispecific (CD3-TCB) antibody for the treatment of advanced melanoma. A tumor growth inhibition (TGI) model was developed using mouse xenograft data with TYRP1-TCB monotherapy or TYRP1-TCB plus anti-PD-L1 combination. The model was translated to humans to inform a refined clinical strategy. From xenograft mouse data, we estimated an EC₅₀ of 0.345 mg/L for TYRP1-TCB, close to what was observed in vitro using the same tumor cell line. The model showed that, though increasing the dose of TYRP1-TCB in monotherapy delays the time to tumor regrowth and promotes higher tumor cell killing, it also induces a faster rate of tumor regrowth. Combination with anti-PD-L1 extended the time to tumor regrowth by 25% while also decreasing the tumor regrowth rate by 69% compared to the same dose of TYRP1-TCB alone. The model translation to humans predicts that if patients' tumors were scanned every 6 weeks, only 46% of the monotherapy responders would be detected even at a TYRP1-TCB dose resulting in exposures above the EC₉₀. However, combination of TYRP1-TCB and anti-PD-L1 in the clinic is predicted to more than double the overall response rate (ORR), duration of response (DoR) and progression-free survival (PFS) compared to TYRP1-TCB monotherapy. As a result, it is highly recommended to consider development of CD3-TCBs as part of a combination therapy from the outset, without the need to escalate the CD3-TCB up to the Maximum Tolerated Dose (MTD) in monotherapy and without gating the combination only on RECIST-derived efficacy metrics.

Population pharmacokinetics and exposure-response of anti-programmed cell death protein-1 monoclonal antibody dostarlimab in advanced solid tumours

AIM

Develop a population pharmacokinetic (PopPK) model to characterise the pharmacokinetics (PK) of anti-programmed cell death protein-1 (PD-1) antibody dostarlimab, identify covariates of clinical relevance, and investigate efficacy/safety exposure-response (ER) relationships.

METHODS

A PopPK model was developed using Phase 1 GARNET (NCT02715284) trial data for dostarlimab (1, 3 or 10 mg kg-1 every 2 wk; 500 mg every 3 wk or 1000 mg every 6 wk; 500 mg every 3 wk × 4 then 1000 mg every 6 wk [recommended regimen]) serum concentrations over time. Concentration-time data were analysed using nonlinear mixed effects modelling with standard stepwise covariate modelling. ER was explored for treatment-related adverse events and overall response rate (ORR) using logistic regression.

RESULTS

PopPK model/adverse event ER analyses included 546 patients (ORR ER analysis n = 362). Dostarlimab PK was well described by a 2-compartment model with time-dependent linear elimination. Time-dependent clearance decreased over time to a maximum of 14.9%. At steady state, estimated dostarlimab geometric mean coefficient of variation % clearance was 0.179 (30.2%) L d-1 ; volume of distribution was 5.3 (14.2%) L; terminal elimination half-life was 23.5 (22.4%) days. Statistically significant covariates were age, body weight, sex, time-varying albumin and alanine aminotransferase for clearance; body weight, albumin and sex for volume of distribution of the central compartment. Hepatic or renal impairment did not affect PK. There were no clinically significant ER relationships.

CONCLUSION

Dostarlimab PK parameters are similar to other anti-programmed cell death protein-1 antibodies. The clinical impact of covariates on exposure was limited-to-moderate, supporting recommended dostarlimab monotherapy therapeutic dosing.

Review of the Clinical Pharmacokinetics, Efficacy and Safety of Pembrolizumab

BACKGROUND

Treatment of various types of cancer has been improved significantly with the discovery of biologic drugs that act as immune checkpoint inhibitors (ICIs). Pembrolizumab is a humanized monoclonal anti-PD-1 antibody currently approved for the treatment of a wide range of tumors, with more indications still being investigated in ongoing clinical trials.

OBJECTIVE

The aim of this paper is to present all currently available data regarding pembrolizumab pharmacokinetic and pharmacodynamic characteristics. Also, the possibility of using predicative biomarkers to monitor patients during cancer treatment is discussed.

METHODS

Database research was carried out (PubMed, ScienceDirect). Information was gathered from original articles, the European Medicines Agency datasheets and results from clinical trials.

RESULTS

This review summarizes present-day knowledge about the pharmacokinetics, different modeling approaches and dosage regimens, efficacy and safety of pembrolizumab and therapeutic monitoring of disease progression.

CONCLUSION

This review points out consistent pharmacokinetic characteristics of pembrolizumab in various cancer patients, the lack of pharmacokinetic-pharmacodynamic/outcome relationships, the need of adequate biomarkers predicting treatment success. Hence, there is a clear necessity for more data and experience in order to optimize pembrolizumab treatment for each individual patient.

Pembrolizumab Weight-Based Dosing: Conviction and Lacunae in Adopting a Cost-Saving Approach—A Survey Report

Introduction Use of immunotherapy drugs has increased leaps and bounds in the last decade with promising results in some of the cancers. The use is limited in low- and middle-income countries due to cost constraints. Weight-based dosing is one measure adopted by Canada and Israel to reduce cost burden and improve access to immunotherapeutic drugs.

Objective We conducted a survey among medical oncologists from India to understand challenges faced in accepting the weight-based dosing of pembrolizumab.

Materials and Methods Questionnaire covering various aspects related to use of immunotherapy drugs was made and it was circulated across various social media platforms. Medical oncologists practicing across India were invited to participate in this survey. The issues like access to drugs and awareness about weight-based dosing of pembrolizumab were covered in the survey. Also, the impact of international guidelines on accepting the weigh-based dosing was studied.

Results Ninety-nine medical oncologists across India participated in the survey. Only 60% medical oncologists are aware about weight-based dosing of pembrolizumab practiced in other countries. Further, 70% of medical oncologists could not prescribe immunotherapy due to cost factor in majority (90%) of their patients. More than 90% agreed that they will use weight-based dosing of pembrolizumab if the Drug Controller General of India, National Comprehensive Cancer Network, or European Society of Medical Oncologists guidelines endorses weight-based dosing.

Conclusion Weight-based dosing of pembrolizumab would be accepted if policy makers and Indian medical oncology societies come together and formulate guidelines. Such guidelines will improve accessibility to immunotherapy drugs and lead to huge cost savings.

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.

Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules

While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600–800 hours, and this data is used to calibrate a family of cancer growth models, each consisting of a system of ordinary differential equations, derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.

Acquired chemoresistance is a common cause of treatment failure in cancer. The scheduling of a multi-dose course of chemotherapeutic treatment may influence the dynamics of acquired chemoresistance, and drug schedule optimization may increase the duration of effectiveness of a particular chemotherapeutic agent for a particular patient. Here we present a method for experimentally optimizing an in vitro drug schedule through iterative rounds of experimentation and computational analysis, and demonstrate the method’s ability to improve the performance of doxorubicin treatment in three breast carcinoma cell lines. Specifically, we find that the interval between drug exposures can be optimized while holding drug concentration and number of treatments constant, suggesting that this may be a key variable to explore in future drug schedule optimization efforts. We further use this method’s model calibration and selection process to extract information about the underlying biology of the doxorubicin response, and find that the incorporation of delays on both cell death and regrowth are necessary for accurate parameterization of cell growth data.

Modelling the association between biomarkers and clinical outcome: an introduction to nonlinear joint models

Nonlinear joint models are a powerful tool to precisely analyze the association between a nonlinear biomarker and a time-to-event process, such as death. Here, we review the main methodological techniques required to build these models and to make inferences and predictions. We describe the main clinical applications and discuss the future developments of such models.

Optimized Dosing: The Next Step in Precision Medicine in Non-Small-Cell Lung Cancer

In oncology, and especially in the treatment of non-small-cell lung cancer (NSCLC), dose optimization is often a neglected part of precision medicine. Many drugs are still being administered in "one dose fits all" regimens or based on parameters that are often only minor determinants for systemic exposure. These dosing approaches often introduce additional pharmacokinetic variability and do not add to treatment outcomes. Fortunately, pharmacological knowledge is increasing, providing valuable information regarding the potential of, for example, therapeutic drug monitoring. This article focuses on the evidence for the most promising and easily implemented optimized dosing approaches for the small-molecule inhibitors, chemotherapeutic agents, and monoclonal antibodies as treatment options currently approved for NSCLC. Despite limitations such as investigations having been conducted in oncological diseases other than NSCLC or the retrospective origin of many analyses, an alternative dosing regimen could be beneficial for treatment outcomes, prescriber convenience, or financial burden on healthcare systems. This review of the literature provides recommendations on the implementation of dose optimization and advice regarding promising strategies that deserve further research in NSCLC.

Cross-Validation of a Multiplex LC-MS/MS Method for Assaying mAbs Plasma Levels in Patients with Cancer: A GPCO-UNICANCER Study

BACKGROUND

Different liquid chromatography tandem mass spectrometry (LC-MS/MS) methods have been published for quantification of monoclonal antibodies (mAbs) in plasma but thus far none allowed the simultaneous quantification of several mAbs, including immune checkpoint inhibitors. We developed and validated an original multiplex LC-MS/MS method using a ready-to-use kit to simultaneously assay 7 mAbs (i.e., bevacizumab, cetuximab, ipilimumab, nivolumab, pembrolizumab, rituximab and trastuzumab) in plasma. This method was next cross-validated with respective reference methods (ELISA or LC-MS/MS).

METHODS

The mAbXmise kit was used for mAb extraction and full-length stable-isotope-labeled antibodies as internal standards. The LC-MS/MS method was fully validated following current EMA guidelines. Each cross validation between reference methods and ours included 16-28 plasma samples from cancer patients.

RESULTS

The method was linear from 2 to 100 µg/mL for all mAbs. Inter- and intra-assay precision was <14.6% and accuracy was 90.1-111.1%. The mean absolute bias of measured concentrations between multiplex and reference methods was 10.6% (range 3.0-19.9%).

CONCLUSIONS

We developed and cross-validated a simple, accurate and precise method that allows the assay of up to 7 mAbs. Furthermore, the present method is the first to offer a simultaneous quantification of three immune checkpoint inhibitors likely to be associated in patients.

Regional Delivery of Anti-PD-1 Agent for Colorectal Liver Metastases Improves Therapeutic Index and Anti-Tumor Activity

Metastatic liver tumors have presented challenges with the use of checkpoint inhibitors (CPIs), with only limited success. We hypothesize that regional delivery (RD) of CPIs can improve activity in the liver and minimize systemic exposure, thereby reducing immune-related adverse events (irAE). Using a murine model of colorectal cancer liver metastases (LM), we confirmed high levels of PD-L1 expression on the tumor cells and liver myeloid-derived suppressor cells (L-MDSC). In vivo, we detected improved LM response at 3 mg/kg on PTD7 via portal vein (PV) regional delivery as compared to 3 mg/kg via tail vein (TV) systemic delivery (p = 0.04). The minimal effective dose at PTD7 was 5 mg/kg (p = 0.01) via TV and 0.3 mg/kg (p = 0.02) via PV. We detected 6.7-fold lower circulating CPI antibody levels in the serum using the 0.3 mg/kg PV treatment compared to the 5 mg/kg TV cohort (p < 0.001) without increased liver toxicity. Additionally, 3 mg/kg PV treatment resulted in increased tumor cell apoptotic signaling compared to 5 mg/kg TV (p < 0.05). Therefore, RD of an anti-PD-1 CPI therapy for CRCLM may improve the therapeutic index by reducing the total dose required and limiting the systemic exposure. These advantages could expand CPI indications for liver tumors.

Advances in the discovery and development of melanoma drug therapies

INTRODUCTION

Therapeutic strategies for melanoma have evolved significantly over the last decade shifting from cytotoxic chemotherapies like dacarbazine to targeted therapies and immunotherapies including immune checkpoint inhibitors. These new drug therapies have improved overall as well as progression-free survival, lowering the mortality of this cancer for melanoma patients with advanced disease. Newer strategies incorporate combination therapies that harness synergies between mechanisms of anticancer efficacy as well as help overcome resistance issues of monotherapies, which remain a challenge.

AREAS COVERED

This review looks at each class of drug therapy for melanoma and provides an overview of the preclinical mechanism of action, the clinical efficacy data, and their applications in combination therapy regimens. NCCN treatment guidelines, safety, toxicity, and immune-related adverse events are also described as well as a note on cost.

EXPERT OPINION

Numerous ongoing trials continue to evaluate the role of novel therapies and combinations for this challenging disease and understanding their mechanism of action, risks, benefits, and treatment guidelines can help care providers and patients have a more comprehensive and tailored discussion of treatment options and expectations.

The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives

Immuno-oncology (IO) focuses on the ability of the immune system to detect and eliminate cancer cells. Since the approval of the first immune checkpoint inhibitor, immunotherapies have become a major player in oncology treatment and, in 2021, represented the highest number of approved drugs in the field. In spite of this, there is still a fraction of patients that do not respond to these therapies and develop resistance mechanisms. In this sense, mathematical models offer an opportunity to identify predictive biomarkers, optimal dosing schedules and rational combinations to maximize clinical response. This work aims to outline the main therapeutic targets in IO and to provide a description of the different mathematical approaches (top-down, middle-out, and bottom-up) integrating the cancer immunity cycle with immunotherapeutic agents in clinical scenarios. Among the different strategies, middle-out models, which combine both theoretical and evidence-based description of tumor growth and immunological cell-type dynamics, represent an optimal framework to evaluate new IO strategies.

Pivotal Dose of Pembrolizumab: A Dose-Finding Strategy for Immuno-Oncology

Despite numerous publications emphasizing the value of dose finding, drug development in oncology is dominated by the mindset that higher dose provides higher efficacy. Examples of dose finding implemented by biopharmaceutical firms can change this mindset. The purpose of this article is to outline a pragmatic dose selection strategy for immuno-oncology (IO) and other targeted monoclonal antibodies (mAbs). The approach was implemented for pembrolizumab. Selecting a recommended phase II dose (RP2D) with a novel mechanism of action is often challenging due to uncertain relationships between pharmacodynamics measurements and clinical end points. Additionally, phase I efficacy and safety data are generally inadequate for RP2D selection for IO mAbs. Here, the RP2D was estimated based on phase I (clinical study KN001 A and A2) pharmacokinetics data as the dose required for target saturation, which represents a surrogate for maximal pharmacological effect for antagonist mAbs. Due to limitations associated with collecting and analyzing tumor biopsies, characterizing intratumoral target engagement (TE) is challenging. To overcome this gap, a physiologically-based pharmacokinetic model was implemented to predict intratumoral TE. As tumors are spatially heterogeneous, TE was predicted in well-vascularized and poorly vascularized tumor regions. Additionally, impact of differences in target expression, for example, due to interindividual variability and cancer type, was simulated. Simulations showed that 200 mg every 3 weeks can achieve ≥ 90% TE in clinically relevant scenarios, resulting in the recommendation of 200 mg every 3 weeks as the RP2D. Randomized dose comparison studies (KN001 B2 and D) showing similar efficacy over a fivefold dose/exposure range confirmed the RP2D as the pivotal dose.

Low-dose pembrolizumab in the treatment of advanced non-small cell lung cancer

A dose of 200 mg 3‐weekly of pembrolizumab was approved by the Food and Drug Administration (FDA) as treatment for advanced non‐small cell lung cancer (NSCLC) without oncogenic drivers. This is despite evidence showing no difference in efficacy with 2 mg/kg. Our study aimed to assess the efficacy of a lower fixed dose of 100 mg, which is closer to 2 mg/kg weight‐based dose in an average‐sized Asian patient. All patients receiving pembrolizumab for advanced NSCLC from January 2016 to March 2020 in National University Hospital, Singapore, were included in this retrospective observational study. The effect of pembrolizumab 100 mg (Pem100) vs 200 mg (Pem200) upon survival outcomes, toxicity and cost were examined. One hundred fourteen patients received pembrolizumab. Sixty‐five (57%) and 49 (43%) received Pem100 and Pem200, respectively. There was no difference in progression‐free survival (PFS) and overall survival (OS) between Pem100 vs Pem200 as a single agent (PFS: 6.8 vs 4.2 months, hazard ratio [HR] 0.72, 95% confidence interval [CI] 0.36‐1.46, P = .36; 9 month OS: 58% vs 63%, HR 1.08, 95% CI 0.48‐2.41, P = .86) and when combined with chemotherapy (9‐month PFS: 60% vs 50%, HR0.84, 95% CI 0.34‐2.08, P = .71; 9‐month OS: 85% vs 58%, HR 0.27, 95% CI 0.062‐1.20, P = .09). No significant difference in response rate or ≥G3 immune‐related toxicities between Pem100 and Pem200 was observed. A cost minimisation analysis evaluating the degree of cost savings related to drug costs estimated a within study cost saving of SGD4,290,912 and cost saving per patient of SGD39,942 in the Pem100 group. A 100 mg of pembrolizumab appears to be effective with reduction in cost. A randomised trial should be done to investigate a lower dose of pembrolizumab.

What's new?

Pembrolizumab, a monoclonal antibody directed against the PD‐1 receptor, has received FDA approval for the treatment of lung cancer at a fixed dose of 200 mg every 3 weeks. However, doses above 2 mg/kg show a lack of benefit, calling for further evaluation in Asian populations. This retrospective observational study demonstrates the efficacy of a lower fixed dose of pembrolizumab (100 mg every 3 weeks) compared with standard‐dose pembrolizumab. The results also confirm the clinical activity of pembrolizumab at a lower dose than 2 mg/kg every 3 weeks, which could provide considerable cost savings to patients and the health system.

Immune-Related Adverse Events (irAE) in Cancer Immune Checkpoint Inhibitors (ICI) and Survival Outcomes Correlation: To Rechallenge or Not?

Simple Summary

This study examined the real-world experience and occurrence of immune-related adverse events (irAEs) under cancer checkpoint immunotherapy, and the relationship between its treatment rechallenge status and their impact on clinical outcomes. The current study demonstrates that immune checkpoint inhibitors (ICI) reinitiation after an irAE-related interruption in the setting of cancer immunotherapy was not associated with significantly improved survival outcome when compared with those without ICI treatment reinitiation after irAE-related therapy interruption.

Abstract

Introduction: There is growing recognition of immune related adverse events (irAEs) from immune checkpoint therapies being correlated with treatment outcomes in certain malignancies. There are currently limited data or consensus to guide management of irAEs with regards to treatment rechallenge. Methods: We conducted a retrospective analysis with an IRB-approved protocol of adult patients seen at the WVU Cancer Institute between 2011–2019 with a histopathologic diagnosis of active cancers and were treated with immune checkpoint inhibitors (ICI) therapy. Results: Demographics were similar between the ICI interrupted irAE groups within cancer types. Overall, out of 548 patients who received ICI reviewed, there were 133 cases of ≥1 irAE found of any grade. Being treated with anti-CTLA-4 inhibitor ICI was associated with lower risk of death compared to anti-PD-1 ICI. The overall survival difference observed for irAE positive patients, between rechallenged (37.8 months, reinitiated with/without interruption; 38.6 months, reinitiated after interruption) and interrupted/non-reinitiated (i.e., discontinued) groups (24.9 months) was not statistically significant, with a numerical trend favoring the former. Conclusions: Our exploratory study did not identify significantly different survival outcomes among the Appalachian West Virginia adult cancer patients treated with ICI who developed irAE and had treatment reinitiated after interruption, when compared with those not reinitiated.

Dosage of anti-PD-1 monoclonal antibodies: a cardinal open question

Discovery and clinical development of monoclonal antibodies with the ability to interfere in the regulation of the immune response have significantly changed the landscape of oncology in recent years. Among the active agents licensed by the regulatory agencies, nivolumab and pembrolizumab are paradigmatic as the most relevant ones according to the magnitude of available data derived from the extensive preclinical and clinical experience. Although in both cases the respective data sheets indicate well-defined dosage regimens, a review of the literature permits to verify the existence of many issues still unresolved about dosing the two agents, so it must be considered an open question of potentially important consequences, in which to work to improve the effectiveness and efficiency of use.

Computational modelling of modern cancer immunotherapy

Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change the approach to cancer treatment. However, numerous questions remain to be answered to understand immunotherapy response better and further improve the benefit for future cancer patients. Computational models are promising tools that can contribute to accelerated immunotherapy research by providing new clues and hypotheses that could be tested in future trials, based on preceding simulations in addition to the empirical rationale. In this topical review, we briefly summarise the history of cancer immunotherapy, including computational modelling of traditional cancer immunotherapy, and comprehensively review computational models of modern cancer immunotherapy, such as immune checkpoint inhibitors (as monotherapy and combination treatment), co-stimulatory agonistic antibodies, bispecific antibodies, and chimeric antigen receptor T cells. The modelling approaches are classified into one of the following categories: data-driven top-down vs mechanistic bottom-up, simplistic vs detailed, continuous vs discrete, and hybrid. Several common modelling approaches are summarised, such as pharmacokinetic/pharmacodynamic models, Lotka-Volterra models, evolutionary game theory models, quantitative systems pharmacology models, spatio-temporal models, agent-based models, and logic-based models. Pros and cons of each modelling approach are critically discussed, particularly with the focus on the potential for successful translation into immuno-oncology research and routine clinical practice. Specific attention is paid to calibration and validation of each model, which is a necessary prerequisite for any successful model, and at the same time, one of the main obstacles. Lastly, we provide guidelines and suggestions for the future development of the field.

The association between adverse events and outcome under checkpoint inhibitors: Where is the deal?

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A review which lays out different potential contributions which can help to understand the IRAEs-outcome link.

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There is a possibility to compute a multifactorial index to characterise patients as ICI sensitive or ICI unsensitive.

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Prospective trails with ICIs are now fesaible to shape patient care beyond high -dose steroids.

Recent reports have put into evidence the possibility of a link between immune-related adverse events (IRAEs) and treatment outcome, patients drawing a benefit from treatment being also exposed to the risk to develop toxicity.

A still unanswered question remains the biological origin(s) which can sustain and explain such a relationship.

The purpose of this review paper is to lay out different potential contributions which can help to understand the IRAEs-outcome link and to propose clinical perspectives taking advantage of this association.

In this respect, pharmacokinetics aspects, immunological and immunogenetics implications have been taken into consideration.

Bayesian inference using Hamiltonian Monte-Carlo algorithm for nonlinear joint modeling in the context of cancer immunotherapy

Treatment evaluation in advanced cancer mainly relies on overall survival and tumor size dynamics. Both markers and their association can be simultaneously analyzed by using joint models, and these approaches are supported by many softwares or packages. However, these approaches are essentially limited to linear models for the longitudinal part, which limit their biological interpretation. More biological models of tumor dynamics can be obtained by using nonlinear models, but they are limited by the fact that parameter identifiability require rich dataset. In that context Bayesian approaches are particularly suited to incorporate the biological knowledge and increase the information available, but they are limited by the high computing cost of Monte-Carlo by Markov Chains algorithms. Here, we aimed to assess the performances of the Hamiltonian Monte-Carlo (HMC) algorithm implemented in Stan for inference in a nonlinear joint model. The method was validated on simulated data where HMC provided proper posterior distributions and credibility intervals in a reasonable computational time. Then the association between tumor size dynamics and survival was assessed in patients with advanced or metastatic bladder cancer treated with atezolizumab, an immunotherapy agent. HMC confirmed limited sensitivity to prior distributions. A cross-validation approach was developed and identified the current slope of tumor size dynamics as the most relevant driver of survival. In summary, HMC is an efficient approach to perform nonlinear joint models in a Bayesian framework, and opens the way for the use of nonlinear models to characterize both the rapid dynamics and the intersubject variability observed during cancer immunotherapy treatment.

Enhanced Detection of Treatment Effects on Metastatic Colorectal Cancer with Volumetric CT Measurements for Tumor Burden Growth Rate Evaluation

PURPOSE

Mathematical models combined with new imaging technologies could improve clinical oncology studies. To improve detection of therapeutic effect in patients with cancer, we assessed volumetric measurement of target lesions to estimate the rates of exponential tumor growth and regression as treatment is administered.

EXPERIMENTAL DESIGN

Two completed phase III trials were studied (988 patients) of aflibercept or panitumumab added to standard chemotherapy for advanced colorectal cancer. Retrospectively, radiologists performed semiautomated measurements of all metastatic lesions on CT images. Using exponential growth modeling, tumor regression (d) and growth (g) rates were estimated for each patient's unidimensional and volumetric measurements.

RESULTS

Exponential growth modeling of volumetric measurements detected different empiric mechanisms of effect for each drug: panitumumab marginally augmented the decay rate [tumor half-life; d [IQR]: 36.5 days (56.3, 29.0)] of chemotherapy [d: 44.5 days (67.2, 32.1), two-sided Wilcoxon P = 0.016], whereas aflibercept more significantly slowed the growth rate [doubling time; g = 300.8 days (154.0, 572.3)] compared with chemotherapy alone [g = 155.9 days (82.2, 347.0), P ≤ 0.0001]. An association of g with overall survival (OS) was observed. Simulating clinical trials using volumetric or unidimensional tumor measurements, fewer patients were required to detect a treatment effect using a volumetric measurement-based strategy (32-60 patients) than for unidimensional measurement-based strategies (124-184 patients).

CONCLUSIONS

Combined tumor volume measurement and estimation of tumor regression and growth rate has potential to enhance assessment of treatment effects in clinical studies of colorectal cancer that would not be achieved with conventional, RECIST-based unidimensional measurements.

Immune Checkpoint Inhibitors in Melanoma: A Review of Pharmacokinetics and Exposure-Response Relationships

Immune checkpoint inhibitors are a new class of monoclonal antibodies that amplify T-cell-mediated immune responses against cancer cells. The introduction of these new drugs, first anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4) and then anti-programmed death-1 (anti-PD1), was a major improvement in the treatment of advanced or metastatic melanoma, a highly immunogenic tumour. The development strategy for immune checkpoint immunotherapies differed from that traditionally used for cytotoxic therapies in oncology. The choices of doses at which to conduct clinical trials, and subsequently the choice of doses at which to use these new therapies, were not based on the identification of a maximum tolerated dose from dose-escalation studies; thus, pharmacokinetic and pharmacokinetic-pharmacodynamic modelling was essential. The studies conducted have shown that the pharmacokinetics of ipilimumab were linear and not time-dependent. In addition, there was a correlation between the trough concentrations of ipilimumab and its therapeutic efficacy. On the contrary, the anti-PD1 immunotherapies nivolumab and pembrolizumab had time-dependent pharmacokinetics. Their therapeutic efficacy was not related to their trough concentration, but there was a correlation between the clearance of anti-PD1 and the survival of melanoma patients. This review highlights the complexity of interpreting the exposure-response relationships of these agents. Further studies are needed to assess the value of therapeutic drug monitoring of immune checkpoint inhibitors in the treatment of melanoma.

Clinical Pharmacology and Interplay of Immune Checkpoint Agents: A Yin-Yang Balance

T cells have a central role in immune system balance. When activated, they may lead to autoimmune diseases. When too anergic, they contribute to infection spread and cancer proliferation. Immune checkpoint proteins regulate T cell function, including cytotoxic T lymphocyte antigen-4 (CTLA-4) and programmed cell death-1 (PD-1) and its ligand (PD-L1). These nodes of self-tolerance may be exploited pharmacologically to downregulate (CTLA-4 agonists) and activate [CTLA-4 and PD-1/PD-L1 antagonists, also called immune checkpoint inhibitors (ICIs)] the immune system.CTLA-4 agonists are used to treat rheumatologic immune disorders and graft rejection. CTLA-4, PD-1, and PD-L1 antagonists are approved for multiple cancer types and are being investigated for chronic viral infections. Notably, ICIs may be associated with immune-related adverse events (irAEs), which can be highly morbid or fatal. CTLA-4 agonism has been a promising method to reverse such life-threatening irAEs. Herein, we review the clinical pharmacology of these immune checkpoint agents with a focus on their interplay in human diseases.

Opportunities for using in silico-based extended dosing regimens for monoclonal antibody immune checkpoint inhibitors

Therapeutic drug monitoring (TDM) involves frequent measurements of drug concentrations to ensure levels remain within a therapeutic window, and it is especially useful for drugs with narrow therapeutic indices or extensive interindividual pharmacokinetic variability. This technique has never been applied to immuno-oncology drugs, but, given recent examinations of clinical data (both exposure and response) on a number of these drugs, further investigations into TDM may be justified to reduce costs as well as potentially reducing the severity and/or duration of immune-related adverse events. Specifically, all but one of the approved PD-1 and PD-L1 inhibitors (pembrolizumab, nivolumab, cemiplimab-rwlc, atezolizumab, avelumab, durvalumab) have been shown to exhibit a plateaued exposure-response (E-R) curve at doses evaluated extensively to date, as well as time-dependent changes in drug exposure. Furthermore, responders have a greater decrease in drug clearance over time and would, therefore, have supratherapeutic serum concentrations. With frequent trough measurements, it is possible to use pharmacokinetic modelling and simulation to estimate drug clearance via Bayesian methods. Based on patient-specific estimates for clearance, optimal alternative dosing strategies can be simulated to lower drug and cost burden yet maintain therapeutic levels, especially as the clearance of the drug decreases over time. This review will comprehensively discuss each of the FDA approved PD-1, PD-L1/2 and CTLA-4 inhibitors regarding their indications and current recommended dosing, with evidence supporting the investigation of these types of TDM strategies.

Model-Informed Drug Development of the Masked Anti-PD-L1 Antibody CX-072

CX-072 is an anti-PD-L1 (programmed death ligand 1) Probody therapeutic (Pb-Tx) designed to be preferentially activated by proteases in the tumor microenvironment and not in healthy tissue. Here, we report the model-informed drug development of CX-072. A quantitative systems pharmacology (QSP) model that captured known mechanisms of Pb-Tx activation, biodistribution, elimination, and target engagement was used to inform clinical translation. The QSP model predicted that a trough level of masked CX-072 (intact CX-072) of 13-99 nM would correspond to a targeted, 95% receptor occupancy in the tumor. The QSP model predictions appeared consistent with preliminary human single-dose pharmacokinetic (PK) data following CX-072 0.03-30.0 mg/kg as monotherapy: CX-072 circulated predominantly as intact CX-072 with minimal evidence of target-mediated drug disposition. A preliminary population PK (POPPK) analysis based upon 130 subjects receiving 0.03-30.0 mg/kg as monotherapy included a provision for a putative time-dependent and dose-dependent antidrug antibody (ADA) effect on clearance (CL) with a mixture model. Preliminary POPPK estimates for intact CX-072 time-invariant CL and volume of distribution were 0.306 L/day and 4.84 L, respectively. Exposure-response analyses did not identify statistically significant relationships with best change from baseline sum of measurements and either adverse events of grade ≥ 3 or of special interest. Simulations suggested that > 95% of patients receiving CX-072 10 mg/kg every two weeks would exceed the targeted trough level regardless of ADA, and that dose adjustment by body weight was not necessary, supporting a fixed 800 mg dose for evaluation in phase II.

Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors

Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical impact in improving overall survival of several malignancies associated with poor outcomes; however, only 20–40% of patients will show long-lasting survival. Further clarification of factors related to treatment response can support improvements in clinical outcome and guide the development of novel immune checkpoint therapies. In this article, we have provided an overview of the pharmacokinetic (PK) aspects related to current ICIs, which include target-mediated drug disposition and time-varying drug clearance. In response to the variation in treatment exposure of ICIs and the significant healthcare costs associated with these agents, arguments for both dose individualization and generalization are provided. We address important issues related to the efficacy and safety, the pharmacodynamics (PD), of ICIs, including exposure–response relationships related to clinical outcome. The unique PK and PD aspects of ICIs give rise to issues of confounding and suboptimal surrogate endpoints that complicate interpretation of exposure–response analysis. Biomarkers to identify patients benefiting from treatment with ICIs have been brought forward. However, validated biomarkers to monitor treatment response are currently lacking.

Safety of pembrolizumab in recurrent or advanced gastric cancer expressing PD-L1 refractory to platinum and fluoropyrimidine

INTRODUCTION

Pembrolizumab is a highly selective fully human immunoglobulin 4 monoclonal antibody against programmed death 1 (PD-1). Phase II and III trials have demonstrated that pembrolizumab has antitumor activity in previously treated patients with advanced gastric cancer (AGC).

AREA COVERED

Pembrolizumab was approved by the US Food and Drug Administration in September 2017 as third-line or later treatment of AGC in patients positive for programmed death ligand 1 (PD-L1). In this article, we review the development of pembrolizumab, its pharmacology, and its safety profile as monotherapy in patients with PD-L1 positive previous treated AGC.

EXPERT OPINION

Pembrolizumab has demonstrated durable response and acceptable safety profile in patients receiving two previous courses of systemic chemotherapy. It has become a treatment option in patients with AGC. Currently, there are several ongoing clinical trials of pembrolizumab in combination with cytotoxic chemotherapy or molecular targeting agents AGC.

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

The amount of "big" data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development, or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging, or electronic health records), pharmacometrics and quantitative systems pharmacology, as well as tumor kinetics and metastasis modeling. Focus is set on studies with high potential of clinical translation, and particular attention is given to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: "mechanistic learning."

Extended-Interval Dosing Strategy of Immune Checkpoint Inhibitors in Lung Cancer: Will it Outlast the COVID-19 Pandemic?

Patients with lung cancer are particularly vulnerable to complications from coronavirus disease-2019 (COVID-19). Recurrent hospital visits and hospital admission are potential risk factors for acquiring infection with its causative pathogen, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). As immune checkpoint inhibitors (ICIs) constitute the therapeutic backbone for the vast majority of patients with advanced lung cancer in the absence of actionable driver oncogenes, there have been intense discussions within the oncology community regarding risk-benefit of delaying these treatments or use of alternative extended-interval treatment strategies to minimize the risk of viral transmission secondary to unintended nosocomial exposures. In the midst of the COVID-19 pandemic, the U.S. Food and Drug Administration (FDA) granted accelerated approval for extended-interval strategy of pembrolizumab at a dose of 400 mg every 6 weeks for all already approved oncologic indications. Herein, we summarize the evidence from the in silico pharmacokinetic modeling/simulation studies supporting extended-interval dosing strategies for the ICIs used in lung cancer. We further review the evolving clinical evidence behind these approaches and predict that they will continue to be used in routine practice even long after the pandemic, particularly for patients with durable disease control.

Immune Checkpoint Inhibitor Dosing: Can We Go Lower Without Compromising Clinical Efficacy?

In just a few years, immune checkpoint inhibitors have dramatically changed the landscape in oncology, offering durable responses and improved survival for many patients across several tumor types. With more than 3,300 new agents in the immuno-oncology pipeline plus a wide array of combinations being studied, it seems this new era is just getting started. These advances come with a significant caveat: most of the world population does not have access to their benefits, because the yearly cost of a novel anticancer medication can routinely exceed $100,000. There is a large amount of data showing that checkpoint inhibitors have significant activity at doses much lower than those currently approved. We review the evidence for reduced drug dosing as a strategy to increase the number of patients who can be treated and what would be needed to further validate this approach.