Translational Pharmacokinetic/Pharmacodynamic Modeling of Tumor Growth Inhibition Supports Dose-Range Selection of the Anti-PD-1 Antibody Pembrolizumab

Using Pharmacokinetic and Pharmacodynamic Analysis to Optimize the Dosing Regimens of Fanastomig (EMB-02) in Patients With Advanced Solid Tumors

Fanastomig (also known as EMB-02) is a bispecific antibody targeting programmed cell death protein-1(PD-1) and lymphocyte activation gene-3 (LAG-3), developed for the treatment of advanced solid tumors. A first-in-human (FIH) Phase I study (NCT04618393) evaluated safety, tolerability, pharmacokinetics (PK), pharmacodynamics (PD), immunogenicity, and clinical efficacy of Fanastomig in patients with advanced solid tumors. To determine the recommended Phase II dose (RP2D), population pharmacokinetics (PopPK), and exposure and response analysis (E-R) were conducted. The PopPK model, demonstrating good performance, showed no clinically meaningful relationship between areas under the concentration-time curve (AUC) or maximum concentration (Cmax) of Fanastomig and selected covariates of interest. A nonlinear Emax model was fitted to Fanastomig PD-1 receptor occupancy (RO) in the peripheral blood compartment. The estimated half-maximal effective concentration (EC50) was 0.084 μg/mL (95% confidence interval [CI]: 0.0369-0.131). Assuming a threefold lower exposure in tumor tissue compared to that in serum, a target trough concentration of Fanastomig at ~2.27 μg/mL would be needed for 90% PD-1 RO in the tumor. Modeling and simulation indicated that a weekly dosing (QW) of 360 mg would achieve full peripheral blood RO in approximately 90% of patients. The incidence of anti-drug antibodies (ADAs) for Fanastomig was high (95.7%, 44/46), with a negative correlation between the ADA titer and dose levels; meanwhile, ADA minimally impacted PK exposure and efficacy. An inverse trend was observed between anaphylaxis and PK exposure. Fanastomig was well tolerated and had acceptable safety profiles up to 900 mg QW. Based on these findings, two dosing regimens have been selected for further clinical development. Trial Registration: ClinicalTrials.gov identifier: NCT04618393.

Model-Informed Selection of the Recommended Phase 2 Dosage for Anti-TIGIT Immunotherapy Leveraging co-Expressed PD-1 Inhibitor Target Engagement

Refining dose projections requires a deep understanding of drug-target relationships at the site of action, which is often challenging to achieve. Here we present a case study of how one can refine dose projections for a TIGIT-targeted immunotherapy by leveraging information from the well-studied PD-1 pathway since the co-expression of PD-1 and TIGIT on immune cells provides a unique opportunity to extrapolate data from one target to inform the dosing strategy for the other. We develop a fit-for-purpose mathematical model that captures the experimentally observed relationship between the concentration of a mouse PD-1 antagonist in the plasma and PD-1 target engagement within the tumor microenvironment (TME). We then assess the applicability of this PD-1 model to elucidate the relationship between drug concentration and target engagement for tiragolumab, an anti-TIGIT antibody, across various doses. This analysis aims to refine our understanding of the dose-response relationship for targeting TIGIT, a critical step in optimizing therapeutic efficacy, without conducting additional experiments. The approach is then extended to project efficacious doses for M6223, another anti-TIGIT antibody, using the established PD-1 model, by leveraging the M6223 clinical PK and PD data, as well as virtual population analysis. This work provides a case study of a possible framework for refining dose projections via quantitative estimation of drug-target relationship at the site of action by leveraging established drug-target relationships. Through extrapolating information from a well-characterized pathway, we offer a method to inform dose optimization strategies with limited data using model-informed drug development.

A Multiple-Model-Informed Drug-Development Approach for Optimal Regimen Selection of an Oncolytic Virus in Combination With Pembrolizumab

The antitumor efficacy of an intratumoral injection of a genetically engineered oncolytic vaccinia virus carrying human IL-7 and murine IL-12 genes (hIL-7/mIL-12-VV) was demonstrated in CT26.WT-bearing mice. In the CT26.WT-bearing mouse model, the efficacy of the combination of hIL-7/mIL-12-VV plus the anti-programmed cell death protein (PD)-1 antibody was determined to be correlated with the timing of administration: greater efficacy was observed when hIL-7/mIL-12-VV was administered before the anti-PD-1 agent instead of simultaneous administration. To identify an optimal dosing regimen for first-in-human clinical trials, a multiple model-informed drug-development (MIDD) approach was used through development of a quantitative systems pharmacology (QSP) model and an agent-based model (ABM). All models were built and verified using available literature and preclinical study data. Multiple dosing scenarios were explored using virtual populations by altering the interval between hIL-7/hIL-12-VV and pembrolizumab administration. In contrast with observations from preclinical studies, both the QSP and the ABM models demonstrated no antagonistic effect on the dose-dependent antitumor efficacy of hIL-7/hIL-12-VV by pembrolizumab in simulations of clinical therapy. Based on the MIDD strategy, it was recommended that the highest dose of hIL-7/hIL-12-VV and pembrolizumab should be administered on the same day, but with pembrolizumab administration following hIL-7/hIL-12-VV administration. Multiple different modeling approaches uniquely supported and informed the first-in-human clinical trial design by guiding the optimal dose and regimen selection.

A minimal physiologically-based pharmacokinetic modeling platform to predict intratumor exposure and receptor occupancy of an anti-LAG-3 monoclonal antibody

In oncology drug development, measuring drug concentrations at the tumor site and at the targeted receptor remains an ongoing challenge. Positron emission tomography (PET)-imaging is a promising noninvasive method to quantify intratumor exposure of a radiolabeled drug (biodistribution data) and target saturation by treatment doses in vivo. Here, we present the development and application of a minimal physiologically-based pharmacokinetic (mPBPK) modeling approach to integrate biodistribution data in a quantitative platform to characterize and predict intratumor exposure and receptor occupancy (RO) of BI 754111, an IgG-based anti-lymphocyte-activation gene 3 (LAG-3) monoclonal antibody (mAb). Specifically, calibration and qualification of the predictions were performed using 89Zr-labeled BI 754111 biodistribution data, that is, PET-derived intratumor drug concentration data, tumor-to-plasma ratios, and data from Patlak analyses. The model predictions were refined iteratively by the inclusion of additional biological processes into the model structure and the use of sensitivity analyses to assess the impact of model assumptions and parameter uncertainty on the predictions and model robustness. The developed mPBPK model allowed an adequate description of observed tumor radioactivity concentrations and tumor-to-plasma ratios leading to subsequent adequate prediction of LAG-3 RO at different dose levels. In the future, the developed model could be used as a platform for the prediction and analysis of biodistribution data for other mAbs and may ultimately support dose optimization by identifying dosages resulting in saturated RO.

Quantitative Systems Pharmacology Modeling in Immuno-Oncology: Hypothesis Testing, Dose Optimization, and Efficacy Prediction

Despite an increasing number of clinical trials, cancer is one of the leading causes of death worldwide in the past decade. Among all complex diseases, clinical trials in oncology have among the lowest success rates, in part due to the high intra- and inter-tumoral heterogeneity. There are more than a thousand cancer drugs and treatment combinations being investigated in ongoing clinical trials for various cancer subtypes, germline mutations, metastasis, etc. Particularly, treatments relying on the (re)activation of the immune system have become increasingly present in the clinical trial pipeline. However, the complexities of the immune response and cancer-immune interactions pose a challenge to the development of these therapies. Quantitative systems pharmacology (QSP), as a computational approach to predict tumor response to treatments of interest, can be used to conduct in silico clinical trials with virtual patients (and emergent use of digital twins) in place of real patients, thus lowering the time and cost of clinical trials. In line with improved mechanistic understanding of the human immune system and promising results from recent cancer immunotherapy, QSP models can play critical roles in model-informed drug development in immuno-oncology. In this chapter, we discuss how QSP models were designed to serve different study objectives, including hypothesis testing, dose optimization, and efficacy prediction, via case studies in immuno-oncology.

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.

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.

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer

BACKGROUND

Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. Therapeutic targeting of miR-155 through its antagonist, anti-miR-155, has proven challenging due to its dual molecular effects.

METHODS

We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs.

RESULTS

Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimens to prevent antagonistic effects.

CONCLUSIONS

This work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

Development of a Multiscale Mechanistic Model for Predicting Tumor Response to Anti-miR-155

In this paper, we present a two-compartmental multiscale mechanistic model for investigating anti-miR-155 monotherapy for non-small cell lung cancer (NSCLC). The model was first quantified using in vivo data and subsequently extrapolated to human-scale for evaluating its translational potential in patients. Using the human-scale model, we explored the impact of dosing schedules on tumor response. The model demonstrated the efficacy of anti-miR-155 monotherapy in a virtual NSCLC patient, revealing treatment schedule-dependent suppression of tumor growth. Further analysis of the model will include testing the synergistic effects of anti-miR-155 with standard-of-care drugs, which will help design and optimize treatment schedules for combination therapy in NSCLC.

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.

Mannosylated STING Agonist Drugamers for Dendritic Cell-Mediated Cancer Immunotherapy

The Stimulator of Interferon Genes (STING) pathway is a promising target for cancer immunotherapy. Despite recent advances, therapies targeting the STING pathway are often limited by routes of administration, suboptimal STING activation, or off-target toxicity. Here, we report a dendritic cell (DC)-targeted polymeric prodrug platform (polySTING) that is designed to optimize intracellular delivery of a diamidobenzimidazole (diABZI) small-molecule STING agonist while minimizing off-target toxicity after parenteral administration. PolySTING incorporates mannose targeting ligands as a comonomer, which facilitates its uptake in CD206+/mannose receptor+ professional antigen-presenting cells (APCs) in the tumor microenvironment (TME). The STING agonist is conjugated through a cathepsin B-cleavable valine-alanine (VA) linker for selective intracellular drug release after receptor-mediated endocytosis. When administered intravenously in tumor-bearing mice, polySTING selectively targeted CD206+/mannose receptor+ APCs in the TME, resulting in increased cross-presenting CD8+ DCs, infiltrating CD8+ T cells in the TME as well as maturation across multiple DC subtypes in the tumor-draining lymph node (TDLN). Systemic administration of polySTING slowed tumor growth in a B16-F10 murine melanoma model as well as a 4T1 murine breast cancer model with an acceptable safety profile. Thus, we demonstrate that polySTING delivers STING agonists to professional APCs after systemic administration, generating efficacious DC-driven antitumor immunity with minimal side effects. This new polymeric prodrug platform may offer new opportunities for combining efficient targeted STING agonist delivery with other selective tumor therapeutic strategies.

Non-specific irreversible 89Zr-mAb uptake in tumours: evidence from biopsy-proven target-negative tumours using 89Zr-immuno-PET

BACKGROUND

Distribution of mAbs into tumour tissue may occur via different processes contributing differently to the 89Zr-mAb uptake on PET. Target-specific binding in tumours is of main interest; however, non-specific irreversible uptake may also be present, which influences quantification. The aim was to investigate the presence of non-specific irreversible uptake in tumour tissue using Patlak linearization on 89Zr-immuno-PET data of biopsy-proven target-negative tumours. Data of two studies, including target status obtained from biopsies, were retrospectively analysed, and Patlak linearization provided the net rate of irreversible uptake (Ki).

RESULTS

Two tumours were classified as CD20-negative and two as CD20-positive. Four tumours were classified as CEA-negative and nine as CEA-positive. Ki values of CD20-negative (0.43 µL/g/h and 0.92 µL/g/h) and CEA-negative tumours (mdn = 1.97 µL/g/h, interquartile range (IQR) = 1.50-2.39) were higher than zero. Median Ki values of target-negative tumours were lower than CD20-positive (1.87 µL/g/h and 1.90 µL/g/h) and CEA-positive tumours (mdn = 2.77 µL/g/h, IQR = 2.11-3.65).

CONCLUSION

Biopsy-proven target-negative tumours showed irreversible uptake of 89Zr-mAbs measured in vivo using 89Zr-immuno-PET data, which suggests the presence of non-specific irreversible uptake in tumours. Consequently, for 89Zr-immuno-PET, even if the target is absent, a tumour-to-plasma ratio always increases over time.

Mitigating non-genetic resistance to checkpoint inhibition based on multiple states of immune exhaustion

Despite the revolutionary impact of immune checkpoint inhibition on cancer therapy, the lack of response in a subset of patients, as well as the emergence of resistance, remain significant challenges. Here we explore the theoretical consequences of the existence of multiple states of immune cell exhaustion on response to checkpoint inhibition therapy. In particular, we consider the emerging understanding that T cells can exist in various states: fully functioning cytotoxic cells, reversibly exhausted cells with minimal cytotoxicity, and terminally exhausted cells. We hypothesize that inflammation augmented by drug activity triggers transitions between these phenotypes, which can lead to non-genetic resistance to checkpoint inhibitors. We introduce a conceptual mathematical model, coupled with a standard 2-compartment pharmacometric (PK) model, that incorporates these mechanisms. Simulations of the model reveal that, within this framework, the emergence of resistance to checkpoint inhibitors can be mitigated through altering the dose and the frequency of administration. Our analysis also reveals that standard PK metrics do not correlate with treatment outcome. However, we do find that levels of inflammation that we assume trigger the transition from the reversibly to terminally exhausted states play a critical role in therapeutic outcome. A simulation of a population that has different values of this transition threshold reveals that while the standard high-dose, low-frequency dosing strategy can be an effective therapeutic design for some, it is likely to fail a significant fraction of the population. Conversely, a metronomic-like strategy that distributes a fixed amount of drug over many doses given close together is predicted to be effective across the entire simulated population, even at a relatively low cumulative drug dose. We also demonstrate that these predictions hold if the transitions between different states of immune cell exhaustion are triggered by prolonged antigen exposure, an alternative mechanism that has been implicated in this process. Our theoretical analyses demonstrate the potential of mitigating resistance to checkpoint inhibitors via dose modulation.

Minimally sufficient experimental design using identifiability analysis

Mathematical models are increasingly being developed and calibrated in tandem with data collection, empowering scientists to intervene in real time based on quantitative model predictions. Well-designed experiments can help augment the predictive power of a mathematical model but the question of when to collect data to maximize its utility for a model is non-trivial. Here we define data as model-informative if it results in a unique parametrization, assessed through the lens of practical identifiability. The framework we propose identifies an optimal experimental design (how much data to collect and when to collect it) that ensures parameter identifiability (permitting confidence in model predictions), while minimizing experimental time and costs. We demonstrate the power of the method by applying it to a modified version of a classic site-of-action pharmacokinetic/pharmacodynamic model that describes distribution of a drug into the tumor microenvironment (TME), where its efficacy is dependent on the level of target occupancy in the TME. In this context, we identify a minimal set of time points when data needs to be collected that robustly ensures practical identifiability of model parameters. The proposed methodology can be applied broadly to any mathematical model, allowing for the identification of a minimally sufficient experimental design that collects the most informative data.

Guiding model-driven combination dose selection using multi-objective synergy optimization

Despite the growing appreciation that the future of cancer treatment lies in combination therapies, finding the right drugs to combine and the optimal way to combine them remains a nontrivial task. Herein, we introduce the Multi-Objective Optimization of Combination Synergy - Dose Selection (MOOCS-DS) method for using drug synergy as a tool for guiding dose selection for a combination of preselected compounds. This method decouples synergy of potency (SoP) and synergy of efficacy (SoE) and identifies Pareto optimal solutions in a multi-objective synergy space. Using a toy combination therapy model, we explore properties of the MOOCS-DS algorithm, including how optimal dose selection can be influenced by the metric used to define SoP and SoE. We also demonstrate the potential of our approach to guide dose and schedule selection using a model fit to preclinical data of the combination of the PD-1 checkpoint inhibitor pembrolizumab and the anti-angiogenic drug bevacizumab on two lung cancer cell lines. The identification of optimally synergistic combination doses has the potential to inform preclinical experimental design and improve the success rates of combination therapies. Jel classificationDose Finding in Oncology.

Killing a fly with a sledgehammer: Atezolizumab exposure in real-world lung cancer patients

Atezolizumab is an anti-PDL1 approved for treating lung cancer. A threshold of 6 μg/mL in plasma has been associated with target engagement. The extent to which patients could be overexposed with the standard 1200 mg q3w dosing remains unknown. Here, we monitored atezolizumab peak and trough levels in 27 real-world patients with lung cancer as part of routine therapeutic drug monitoring. Individual pharmacokinetic (PK) parameters were calculated using a population approach and optimal dosing-intervals were simulated with respect to the target trough levels. No patient had plasma levels below 6 μg/mL. The results showed that the mean trough level after the first treatment was 78.3 ± 17 μg/mL, that is, 13 times above the target concentration. The overall response rate was 55.5%. Low-grade immune-related adverse events was observed in 37% of patients. No relationship was found between exposure metrics of atezolizumab (i.e., minimum plasma concentration, maximum plasma concentration, and area under the curve) and pharmacodynamic end points (i.e., efficacy and toxicity). Further simulations suggest that the dosing interval could be extended from 21 days to 49 up to 136 days (mean: 85.7 days, i.e., q12w), while ensuring plasma levels still above the 6 μg/mL target threshold. This observational, real-world study suggests that the standard 1200 mg q3w fixed-dose regimen of atezolizumab results in significant overexposure in all the patients. This was not associated with increased side effects. As plasma levels largely exceed pharmacologically active concentrations, interindividual variability in PK parameters did not impact efficacy. Our data suggest that dosing intervals could be markedly extended with respect to the target threshold associated with efficacy.

Mechanistic characterization of oscillatory patterns in unperturbed tumor growth dynamics: The interplay between cancer cells and components of tumor microenvironment

Mathematical modeling of unperturbed and perturbed tumor growth dynamics (TGD) in preclinical experiments provides an opportunity to establish translational frameworks. The most commonly used unperturbed tumor growth models (i.e. linear, exponential, Gompertz and Simeoni) describe a monotonic increase and although they capture the mean trend of the data reasonably well, systematic model misspecifications can be identified. This represents an opportunity to investigate possible underlying mechanisms controlling tumor growth dynamics through a mathematical framework. The overall goal of this work is to develop a data-driven semi-mechanistic model describing non-monotonic tumor growth in untreated mice. For this purpose, longitudinal tumor volume profiles from different tumor types and cell lines were pooled together and analyzed using the population approach. After characterizing the oscillatory patterns (oscillator half-periods between 8-11 days) and confirming that they were systematically observed across the different preclinical experiments available (p<10-9), a tumor growth model was built including the interplay between resources (i.e. oxygen or nutrients), angiogenesis and cancer cells. The new structure, in addition to improving the model diagnostic compared to the previously used tumor growth models (i.e. AIC reduction of 71.48 and absence of autocorrelation in the residuals (p>0.05)), allows the evaluation of the different oncologic treatments in a mechanistic way. Drug effects can potentially, be included in relevant processes taking place during tumor growth. In brief, the new model, in addition to describing non-monotonic tumor growth and the interaction between biological factors of the tumor microenvironment, can be used to explore different drug scenarios in monotherapy or combination during preclinical drug development.

Immune-checkpoint inhibitor therapy response evaluation using oncophysics-based mathematical models

The field of oncology has transformed with the advent of immunotherapies. The standard of care for multiple cancers now includes novel drugs that target key checkpoints that function to modulate immune responses, enabling the patient's immune system to elicit an effective anti-tumor response. While these immune-based approaches can have dramatic effects in terms of significantly reducing tumor burden and prolonging survival for patients, the therapeutic approach remains active only in a minority of patients and is often not durable. Multiple biological investigations have identified key markers that predict response to the most common form of immunotherapy-immune checkpoint inhibitors (ICI). These biomarkers help enrich patients for ICI but are not 100% predictive. Understanding the complex interactions of these biomarkers with other pathways and factors that lead to ICI resistance remains a major goal. Principles of oncophysics-the idea that cancer can be described as a multiscale physical aberration-have shown promise in recent years in terms of capturing the essence of the complexities of ICI interactions. Here, we review the biological knowledge of mechanisms of ICI action and how these are incorporated into modern oncophysics-based mathematical models. Building on the success of oncophysics-based mathematical models may help to discover new, rational methods to engineer immunotherapy for patients in the future. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Advances in pharmacokinetics and pharmacodynamics of PD-1/PD-L1 inhibitors

Immune checkpoint inhibitors (ICIs) are a group of drugs designed to improve the therapeutic effects on various types of malignant tumors. Irrespective of monotherapy or combinational therapies as first-line and later-line therapy, ICIs have achieved benefits for various tumors. Programmed cell death protein-1 (PD-1) / ligand 1 (PD-L1) is an immune checkpoint that suppresses antitumor immunity, especially in the tumor microenvironment (TME). PD-1/PD-L1 immune checkpoint inhibitors block tumor-related downregulation of the immune system, thereby enhancing antitumor immunity. In comparison with traditional small-molecule drugs, ICIs exhibit pharmacokinetic characteristics owing to their high molecular weight. Furthermore, different types of ICIs exhibit different pharmacodynamic characteristics. Hence, ICIs have been approved for different indications by the Food and Drug Administration (FDA) and National Medical Products Administration (NMPA). This review summarizes pharmacokinetic and pharmacodynamic studies of PD-1/ PD-L1 inhibitors to provide a reference for rational clinical application.

Interspecies and in vitro-in vivo scaling for quantitative modeling of whole-body drug pharmacokinetics in patients: Application to the anticancer drug oxaliplatin

Quantitative systems pharmacology holds the promises of integrating results from laboratory animals or in vitro human systems into the design of human pharmacokinetic/pharmacodynamic (PK/PD) models allowing for precision and personalized medicine. However, reliable and general in vitro-to-in vivo extrapolation and interspecies scaling methods are still lacking. Here, we developed a translational strategy for the anticancer drug oxaliplatin. Using ex vivo PK data in the whole blood of the mouse, rat, and human, a model representing the amount of platinum (Pt) in the plasma and in the red blood cells was designed and could faithfully fit each dataset independently. A "purely physiologically-based (PB)" scaling approach solely based on preclinical data failed to reproduce human observations, which were then included in the calibration. Investigating approaches in which one parameter was set as species-specific, whereas the others were computed by PB scaling laws, we concluded that allowing the Pt binding rate to plasma proteins to be species-specific permitted to closely fit all data, and guaranteed parameter identifiability. Such a strategy presenting the drawback of including all clinical datasets, we further identified a minimal subset of human data ensuring accurate model calibration. Next, a "whole body" model of oxaliplatin human PK was inferred from the ex vivo study. Its three remaining parameters were estimated, using one third of the available patient data. Remarkably, the model achieved a good fit to the training dataset and successfully reproduced the unseen observations. Such validation endorsed the legitimacy of our scaling methodology calling for its testing with other drugs.

Mathematical modeling of cancer immunotherapy for personalized clinical translation

Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.

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.

Early change in the clearance of pembrolizumab reflects the survival and therapeutic response: A population pharmacokinetic analysis in real-world non-small cell lung cancer patients

OBJECTIVES

The dosing pattern of pembrolizumab is based on population pharmacokinetic (Pop-PK) analysis of clinical trials. Data for Japanese patients or patient populations with poor conditions such as cachexia are scarce. In this study, we performed a Pop-PK analysis of Japanese non-small cell lung cancer patients and analyzed the relationship between exposure, treatment effect, and survival.

MATERIALS AND METHODS

A total of 270 blood samples from 76 patients who received 200 mg pembrolizumab every 3 weeks between March 2017 and December 2018 were included. Blood concentrations of pembrolizumab were measured using mass spectrometry, and Pop-PK analysis was conducted using the Phoenix NLME software with a one-compartment model.

RESULTS

The estimated median of clearance (CL) in this analysis population was 0.104 L/day, about half of the historical data for Western data. Overall, pembrolizumab CL decreased over time, with some populations showing increased CL early in the treatment and others showing decreased CL over time. When the time-varying CL was stratified by quartile, the group with decreasing CL showed significantly better treatment response and survival than the group with increasing CL, even though the group included more patients with cachexia. Detailed analysis suggested that the patient population that responded to pembrolizumab treatment had an improved general condition and reduced protein catabolism, further decreasing CL.

CONCLUSION

In populations that benefit from pembrolizumab treatment, CL may be reduced early in their treatment, which may be a predictive and prognostic factor. However, further prospective validation of our findings is needed.

Model-informed drug development for immuno-oncology agonistic anti-GITR antibody GWN323: Dose selection based on MABEL and biologically active dose

GWN323, an agonistic human anti-GITR (glucocorticoid-induced TNFR-related protein) IgG1 antibody, was studied clinically as an immuno-oncology therapeutic agent. A model-based minimum anticipated biological effect level (MABEL) approach integrating in vitro and in vivo data informed dose selection for the first-in-human (FIH) study. Data evaluated included pharmacokinetics (PK) of DTA-1.mIgG2a (mouse surrogate GITR antibody for GWN323), target-engagement pharmacodynamic (PD) marker soluble GITR (sGITR), tumor shrinkage in Colon26 syngeneic mice administered with DTA-1.mIgG2a, cytokine release of GWN323 in human peripheral blood mononuclear cells, and GITR binding affinity. A PK model was developed to describe DTA-1.mIgG2a PK, and its relationship with sGITR was also modeled. Human GWN323 PK was predicted by allometric scaling of mouse PK. Based on the totality of PK/PD modeling and in vitro and in vivo pharmacology and toxicology data, MABEL was estimated to be 3-10 mg once every 3 weeks (Q3W), which informed the starting dose selection of the FIH study. Based on tumor kinetic PK/PD modeling of tumor inhibition by DTA-1.mIgG2a in Colon26 mice and the predicted human PK of GWN323, the biologically active dose of GWN323 was predicted to be 350 mg Q3W, which informed the dose escalation of the FIH study. GWN323 PK from the FIH study was described by a population PK model; the relationship with ex vivo interleukin-2 release, a target-engagement marker, was also modeled. The clinical PK/PD modeling data supported the biological active dose projected from the translational PK/PD modeling in a "learn and confirm" paradigm of model-informed drug development of GWN323.

Preclinical characterization and clinical translation of pharmacodynamic markers for MK-5890: a human CD27 activating antibody for cancer immunotherapy

Background

Immune checkpoint inhibitors (ICI) have radically changed cancer therapy, but most patients with cancer are unresponsive or relapse after treatment. MK-5890 is a CD27 agonist antibody intended to complement ICI therapy. CD27 is a member of the tumor necrosis factor receptor superfamily that plays a critical role in promoting responses of T cells, B cells and NK cells.

Methods

Anti-CD27 antibodies were generated and selected for agonist activity using NF-кB luciferase reporter assays. Antibodies were humanized and characterized for agonism using in vitro T-cell proliferation assays. The epitope recognized on CD27 by MK-5890 was established by X-ray crystallography. Anti-tumor activity was evaluated in a human CD27 knock-in mouse. Preclinical safety was tested in rhesus monkeys. Pharmacodynamic properties were examined in mouse, rhesus monkeys and a phase 1 dose escalation clinical study in patients with cancer.

Results

Humanized anti-CD27 antibody MK-5890 (hIgG1) was shown to bind human CD27 on the cell surface with sub-nanomolar potency and to partially block binding to its ligand, CD70. Crystallization studies revealed that MK-5890 binds to a unique epitope in the cysteine-rich domain 1 (CRD1). MK-5890 activated CD27 expressed on 293T NF-κB luciferase reporter cells and, conditional on CD3 stimulation, in purified CD8+ T cells without the requirement of crosslinking. Functional Fc-receptor interaction was required to activate CD8+ T cells in an ex vivo tumor explant system and to induce antitumor efficacy in syngeneic murine subcutaneous tumor models. MK-5890 had monotherapy efficacy in these models and enhanced efficacy of PD-1 blockade. MK-5890 reduced in an isotype-dependent and dose-dependent manner circulating, but not tumor-infiltrating T-cell numbers in these mouse models. In rhesus monkey and human patients, reduction in circulating T cells was transient and less pronounced than in mouse. MK-5890 induced transient elevation of chemokines MCP-1, MIP-1α, and MIP-1β in the serum of mice, rhesus monkeys and patients with cancer. MK-5890 was well tolerated in rhesus monkeys and systemic exposure to MK-5890 was associated with CD27 occupancy at all doses.

Conclusions

MK-5890 is a novel CD27 agonistic antibody with the potential to complement the activity of PD-1 checkpoint inhibition in cancer immunotherapy and is currently undergoing clinical evaluation.

Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer

Quantitative systems pharmacology (QSP) modeling is an emerging mechanistic computational approach that couples drug pharmacokinetics/pharmacodynamics and the course of disease progression. It has begun to play important roles in drug development for complex diseases such as cancer, including triple-negative breast cancer (TNBC). The combination of the anti-PD-L1 antibody atezolizumab and nab-paclitaxel has shown clinical activity in advanced TNBC with PD-L1-positive tumor-infiltrating immune cells. As tumor-associated macrophages (TAMs) serve as major contributors to the immuno-suppressive tumor microenvironment, we incorporated the dynamics of TAMs into our previously published QSP model to investigate their impact on cancer treatment. We show that through proper calibration, the model captures the macrophage heterogeneity in the tumor microenvironment while maintaining its predictive power of the trial results at the population level. Despite its high mechanistic complexity, the modularized QSP platform can be readily reproduced, expanded for new species of interest, and applied in clinical trial simulation.

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A mechanistic model of quantitative systems pharmacology in immuno-oncology

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Dynamics of tumor-associated macrophages are integrated into our previous work

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Conducting in silico clinical trials to predict clinical response to cancer therapy

Optimized scaling of translational factors in oncology: from xenografts to RECIST

Purpose

Tumor growth inhibition (TGI) models are regularly used to quantify the PK–PD relationship between drug concentration and in vivo efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response.

Method

To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data.

Results

The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of − 0.25.

Conclusions

We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04458-8.

[Application of MIDD in Clinical Research of Antitumor Drugs]

The antitumor drug has become one of the focused areas in new drug research and development. Their clinical research generally consumes a long period of time, with high cost and high risk. Model-informed drug development (MIDD) integrates and quantitatively analyzes physiological, pharmacological, and disease progression information through modeling and simulation, which can reduce the cost of drug development and improve the efficiency of clinical research. In this essay, Osimertinib and Pembrolizumab are given as examples to illustrate the specific application of MIDD in different phases of clinical research, aiming to provide references for the application of MIDD to guide the clinical research of antitumor drugs.
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Evolution of Preclinical Characterization and Insights into Clinical Pharmacology of Checkpoint Inhibitors Approved for Cancer Immunotherapy

Cancer immunotherapy has significantly advanced the treatment paradigm in oncology, with approvals of immuno‐oncology agents for over 16 indications, many of them first line. Checkpoint inhibitors (CPIs) are recognized as an essential backbone for a successful anticancer therapy regimen. This review focuses on the US Food and Drug Administration (FDA) regulatory approvals of major CPIs and the evolution of translational advances since their first approval close to a decade ago. In addition, critical preclinical and clinical pharmacology considerations, an overview of the pharmacokinetic and dose/regimen aspects, and a discussion of the future of CPI translational and clinical pharmacology as combination therapy becomes a mainstay of industrial immunotherapy development and in clinical practice are also discussed.

Applications of Quantitative System Pharmacology Modeling to Model-Informed Drug Development

Significant advances in analytical technologies have dramatically improved our ability to deconvolute disease biology at molecular, cellular, and tissue levels. Quantitative system pharmacology (QSP) modeling is a computational framework to systematically integrate pharmaceutical properties of a drug candidate with scientific understanding of that deeper disease etiology, target expression, genetic variability, and human physiological processes, thus enabling more insightful drug development decisions related to efficacy and safety. In this chapter, we discuss the key attributes of QSP models in comparison to traditional models. We discuss a recommended four-step process to construct a QSP model to support drug development decisions. A number of illustrative QSP examples related to high-value drug development questions and decisions impacting target identification, lead generation and optimization, first in human studies, and clinical dose and schedule optimization are covered in the chapter. The future perspectives of QSP in the context of potential regulatory acceptance are also discussed.

Predictive Simulations in Preclinical Oncology to Guide the Translation of Biologics

Preclinical in vivo studies form the cornerstone of drug development and translation, bridging in vitro experiments with first-in-human trials. However, despite the utility of animal models, translation from the bench to bedside remains difficult, particularly for biologics and agents with unique mechanisms of action. The limitations of these animal models may advance agents that are ineffective in the clinic, or worse, screen out compounds that would be successful drugs. One reason for such failure is that animal models often allow clinically intolerable doses, which can undermine translation from otherwise promising efficacy studies. Other times, tolerability makes it challenging to identify the necessary dose range for clinical testing. With the ability to predict pharmacokinetic and pharmacodynamic responses, mechanistic simulations can help advance candidates from in vitro to in vivo and clinical studies. Here, we use basic insights into drug disposition to analyze the dosing of antibody drug conjugates (ADC) and checkpoint inhibitor dosing (PD-1 and PD-L1) in the clinic. The results demonstrate how simulations can identify the most promising clinical compounds rather than the most effective in vitro and preclinical in vivo agents. Likewise, the importance of quantifying absolute target expression and antibody internalization is critical to accurately scale dosing. These predictive models are capable of simulating clinical scenarios and providing results that can be validated and updated along the entire development pipeline starting in drug discovery. Combined with experimental approaches, simulations can guide the selection of compounds at early stages that are predicted to have the highest efficacy in the clinic.

Translational Modeling Identifies Synergy between Nanoparticle-Delivered miRNA-22 and Standard-of-Care Drugs in Triple-Negative Breast Cancer

PURPOSE

Downregulation of miRNA-22 in triple-negative breast cancer (TNBC) is associated with upregulation of eukaryotic elongation 2 factor kinase (eEF2K) protein, which regulates tumor growth, chemoresistance, and tumor immunosurveillance. Moreover, exogenous administration of miRNA-22, loaded in nanoparticles to prevent degradation and improve tumor delivery (termed miRNA-22 nanotherapy), to suppress eEF2K production has shown potential as an investigational therapeutic agent in vivo.

METHODS

To evaluate the translational potential of miRNA-22 nanotherapy, we developed a multiscale mechanistic model, calibrated to published in vivo data and extrapolated to the human scale, to describe and quantify the pharmacokinetics and pharmacodynamics of miRNA-22 in virtual patient populations.

RESULTS

Our analysis revealed the dose-response relationship, suggested optimal treatment frequency for miRNA-22 nanotherapy, and highlighted key determinants of therapy response, from which combination with immune checkpoint inhibitors was identified as a candidate strategy for improving treatment outcomes. More importantly, drug synergy was identified between miRNA-22 and standard-of-care drugs against TNBC, providing a basis for rational therapeutic combinations for improved response CONCLUSIONS: The present study highlights the translational potential of miRNA-22 nanotherapy for TNBC in combination with standard-of-care drugs.

Pharmacokinetics and pharmacodynamics of MEDI0680, a fully human anti-PD-1 monoclonal antibody, in patients with advanced malignancies

MEDI0680 is a humanized immunoglobulin monoclonal antibody that targets human programmed cell death protein 1 (PD-1) for the treatment of cancer. A population two-compartmental pharmacokinetic (PK) model and a sequential direct maximal effective drug concentration receptor occupancy (RO) model with baseline parameters were developed to quantify PK variability, identify significant covariates, and characterize the relationship between the PK and the RO of MEDI0680. A total of 58 patients with advanced malignancies received MEDI0680 by intravenous infusion at a dose of 0.1-20 mg/kg in a phase 1 study. The clearance was 0.27 L per day and the central volume of distribution (V₁) was 3.14 L, with a modest between-subject variability of 30 and 19%, respectively. None of the evaluated covariates showed any impact on PK parameters except for a nonclinically meaningful relevant impact of body weight on V₁. The estimated half-maximal effective concentration for MEDI0680 binding to the PD-1 antigen was approximately 1.88 µg/mL. Visual predictive check results demonstrated good predictability of the final population PK-RO model. PK-RO simulations demonstrated that > 90% RO could be maintained in all subjects after a 20-mg/kg dose every 2 weeks (Q2W). Therefore, 20 mg/kg Q2W and an equivalently fixed dose of 1500 mg was recommended for phase 2 studies.

An affinity threshold for maximum efficacy in anti-PD-1 immunotherapy

Monoclonal antibodies targeting the programmed cell death protein 1 (PD-1) remain the most prevalent cancer immunotherapy both as a monotherapy and in combination with additional therapies. Despite the extensive success of anti-PD-1 monoclonal antibodies in the clinic, the experimental relationship between binding affinity and functional potency for anti-PD-1 antibodies in vivo has not been reported. Anti-PD-1 antibodies with higher and lower affinity than nivolumab or pembrolizumab are entering the clinic and show varied preclinical efficacy. Here, we explore the role of broad-ranging affinity variation within a single lineage in a syngeneic immunocompetent mouse model. By developing a panel of murine anti-PD-1 antibodies with varying affinity (ranging from KD = 20 pM - 15 nM), we find that there is a threshold affinity required for maximum efficacy at a given dose in the treatment of the MC38 adenocarcinoma model with anti-PD-1 immunotherapy. Physiologically based pharmacokinetic modeling complements interpretation of the experimental results and highlights the direct relationship between dose, affinity, and PD-1 target saturation in the tumor.

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 biological 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 predictive 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, and the need for adequate biomarkers to predict treatment success. Hence, there is a clear necessity for more data and experience in order to optimize pembrolizumab treatment for each individual patient.

Drug delivery strategies in maximizing anti-angiogenesis and anti-tumor immunity

Metronomic chemotherapy has been shown to elicit anti-tumor immune response and block tumor angiogenesis distinct from that observed with maximal tolerated dose (MTD) therapy. This review delves into the mechanisms behind anti-tumor immunity and seeks to identify the differential effect of dosing regimens, including daily low-dose and medium-dose intermittent chemotherapy (MEDIC), on both innate and adaptive immune populations involved in observed anti-tumor immune response. Given reports of VEGF/VEGFR blockade antagonizing anti-tumor immunity, drug choice, dose, and selective delivery determined by advanced formulations/vehicles are highlighted as potential sources of innovation for identifying anti-angiogenic modalities that may be combined with metronomic regimens without interrupting key immune players in the anti-tumor response. Engineered drug delivery mechanisms that exhibit extended and local release of anti-angiogenic agents both alone and in combination with chemotherapeutic treatments have also been demonstrated to elicit a potent and potentially systemic anti-tumor immune response, favoring tumor regression and stasis over progression. This review examines this interplay between various cancer models, the host immune response, and select anti-cancer agents depending on drug dosing, scheduling/regimen, and delivery modality.

A Single Animal Species-Based Prediction of Human Clearance and First-in-Human Dose of Monoclonal Antibodies: Beyond Monkey

These days, there is a lot of emphasis on the prediction of human clearance (CL) from a single species for monoclonal antibodies (mabs). Many studies indicate that monkey is the most suitable species for the prediction of human clearance for mabs. However, it is not well established if rodents (mouse or rat) can also be used to predict human CL for mabs. The objectives of this study were to predict and compare human CL as well as first-in-human dose of mabs from mouse or rat, ormonkey. Four methods were used for the prediction of human CL of mabs. These methods were: use of four allometric exponents (0.75, 0.80, 0.85, and 0.90), a minimal physiologically based pharmacokinetics method (mPBPK), lymph flow rate, and liver blood flow rate. Based on the predicted CL, first-in-human dose of mabs was projected using either exponent 1.0 (linear scaling) or exponent 0.85, and human-equivalent dose (HED) from each of these species. The results of the study indicated that rat or mouse could provide a reasonably accurate prediction of human CL as well as first-in-human dose of mabs. When exponent 0.85 was used for CL prediction, there were 78%, 95%, and 92% observations within a 2-fold prediction error for mouse, rat, and monkey, respectively. Predicted human dose fell within the observed human dose range (administered to humans) for 10 out of 13 mabs for mouse, 11 out of 12 mabs for rat, and 12 out of 15 mabs for monkey. Overall, the clearance and first-in-human dose of mabs were predicted reasonably well by all three species (a single species). On average, monkey may be the best species for the prediction of human clearance and human dose but mouse or rat especially; rat can be a very useful species for conducting the aforementioned studies.

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.

Murine cancer cachexia models replicate elevated catabolic pembrolizumab clearance in humans

BACKGROUND

Monoclonal antibody (mAb) immune checkpoint inhibitor (ICI) therapies have dramatically impacted oncology this past decade. However, only about one-third of patients respond to treatment, and biomarkers to predict responders are lacking. Recent ICI clinical pharmacology data demonstrate high baseline drug clearance (CL0) significantly associates with shorter overall survival, independent of ICI exposure, in patients receiving ICI mAb therapies. This suggests CL0 may predict outcomes from ICI therapy, and cachectic signalling may link elevated CL0 and poor response. Our aim was to determine if mouse models of cancer cachexia will be useful for studying these phenomena and their underlying mechanisms.

METHODS

We evaluated pembrolizumab CL in the C26 and Lewis lung carcinoma mouse models of cancer cachexia. A single treatment of vehicle or pembrolizumab, at a dose of 2 or 10 mg/kg, was administered intravenously by tail vein injection. Pembrolizumab was quantified by an ELISA in serial plasma samples, and FcRn gene (Fcgrt) expression was assessed in liver using real-time quantitative reverse transcription PCR. Non-compartmental and mixed-effects pharmacokinetics analyses were performed.

RESULTS

We observed higher pembrolizumab CL0 and decreased Fcgrt expression in whole liver tissue from tumour-bearing vs. tumour-free mice. In multivariate analysis, presence of tumour, total murine IgG, muscle weight and Fcgrt expression were significant covariates on CL, and total murine IgG was a significant covariate on V1 and Q.

CONCLUSIONS

These data demonstrate increases in catabolic clearance of monoclonal antibodies observed in humans can be replicated in cachectic mice, in which Fcgrt expression is also reduced. Notably, FcRn activity is essential for proper antigen presentation and antitumour immunity, which may permit the study of cachexia's impact on FcRn-mediated clearance and efficacy of ICI therapies.

Current Treatment for Cervical Cancer: An Update

Cervical cancer is the leading gynecologic health problem which is considered as the 4th most widespread tumour in women. The prevalence of this fatal ailment is emerging gradually across the globe as about 18.1 million new cancer cases have been reported in 2018. The predominance of cervical cancer has been significantly found in low and middle-income countries as cervical cancer ranks fourth for both incidence and mortality, conversely, there are no effective screening systems available. This mortal state is certainly influenced by exposure of human papillomavirus, dysregulation of caspase enzyme, elevated expression of Inhibitor Apoptotic Protein (IAP), overexpression of Vascular Endothelial Growth Factors (VEGF), active/passive smoking, and dysfunction of the immune system. Generally, the clinical trial on pipeline drugs leads to the development of some promising new therapies that are more effective than standard approaches and often unavailable outside of the clinical setting. Indeed, several biological interventions that can modulate the pathological cascade of cervical cancer are still under investigation. Thus, there is a need to further summarise the promising therapies for cervical cancer as we have accomplished in HER2-positive breast cancer by targeting HER2 therapies and immune checkpoint inhibitors in melanoma. The present report revealed the pharmacokinetic/ pharmacodynamics aspects of various pipeline drugs that are promising for the treatment of cervical cancer. Moreover, the study revealed the possible mechanism, adverse drug reaction, combined therapy and pleiotropic action of these under investigational drugs, which can further improve the therapeutic efficacy and restrict the imaginable harmful effects.

The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development

Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use.

A mechanistic systems pharmacology modeling platform to investigate the effect of PD-L1 expression heterogeneity and dynamics on the efficacy of PD-1 and PD-L1 blocking antibodies in cancer

Tumors have developed multitude of ways to evade immune response and suppress cytotoxic T cells. Programed cell death protein 1 (PD-1) and programed cell death ligand 1 (PD-L1) are immune checkpoints that when activated, rapidly inactivate the cytolytic activity of T cells. Expression heterogeneity of PD-L1 and the surface receptor dynamics of both PD-1 and PD-L1 may be important parameters in modulating the immune response. PD-L1 is expressed on both tumor and non-tumor immune cells and this differential expression reflects different aspects of anti-tumor immunity. Here, we developed a mechanistic computational model to investigate the role of PD-1 and PD-L1 dynamics in modulating the efficacy of PD-1 and PD-L1 blocking antibodies. Our model incorporates immunological synapse restricted interaction of PD-1 and PD-L1, basal parameters for receptor dynamics, and T cell interaction with tumor and non-tumor immune cells. Simulations predict the existence of a threshold in PD-1 expression above which there is no efficacy for both anti-PD-1 and anti-PD-L1. Model also predicts that anti-tumor response is more sensitive to PD-L1 expression on non-tumor immune cells than tumor cells. New combination strategies are suggested that may enhance efficacy in resistant cases such as combining anti-PD-1 with a low dose of anti-PD-L1 or with inhibitors of PD-L1 recycling and synthesis. Another combination strategy suggested by the model is the combination of anti-PD-1 and anti-PD-L1 with enhancers of PD-L1 degradation rate. Virtual patients are then generated to test specific biomarkers of response. Intriguing predictions that emerge from the virtual patient simulations are that PD-1 blocking antibody results in higher response rate than PD-L1 blockade and that PD-L1 expression density on non-tumor immune cells rather than tumor cells is a predictor of response.

Current methods in translational cancer research

Recent developments in pre-clinical screening tools, that more reliably predict the clinical effects and adverse events of candidate therapeutic agents, has ushered in a new era of drug development and screening. However, given the rapid pace with which these models have emerged, the individual merits of these translational research tools warrant careful evaluation in order to furnish clinical researchers with appropriate information to conduct pre-clinical screening in an accelerated and rational manner. This review assesses the predictive utility of both well-established and emerging pre-clinical methods in terms of their suitability as a screening platform for treatment response, ability to represent pharmacodynamic and pharmacokinetic drug properties, and lastly debates the translational limitations and benefits of these models. To this end, we will describe the current literature on cell culture, organoids, in vivo mouse models, and in silico computational approaches. Particular focus will be devoted to discussing gaps and unmet needs in the literature as well as current advancements and innovations achieved in the field, such as co-clinical trials and future avenues for refinement.

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.

Model Informed Dosing Regimen and Phase I Results of the Anti-PD-1 Antibody Budigalimab (ABBV-181)

Budigalimab is a humanized, recombinant, Fc mutated IgG1 monoclonal antibody targeting programmed cell death 1 (PD‐1) receptor, currently in phase I clinical trials. The safety, efficacy, pharmacokinetics (PKs), pharmacodynamics (PDs), and budigalimab dose selection from monotherapy dose escalation and multihistology expansion cohorts were evaluated in patients with previously treated advanced solid tumors who received budigalimab at 1, 3, or 10 mg/kg intravenously every 2 weeks (Q2W) in dose escalation, including Japanese patients that received 3 and 10 mg/kg Q2W. PK modeling and PK/PD assessments informed the dosing regimen in expansion phase using data from body‐weight‐based dosing in the escalation phase, based on which patients in the multihistology expansion cohort received flat doses of 250 mg Q2W or 500 mg every four weeks (Q4W). Immune‐related adverse events (AEs) were reported in 11 of 59 patients (18.6%), of which 1 of 59 (1.7%) was considered grade ≥ 3 and the safety profile of budigalimab was consistent with other PD‐1 targeting agents. No treatment‐related grade 5 AEs were reported. Four responses per Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 were reported in the dose escalation cohort and none in the multihistology expansion cohort. PK of budigalimab was approximately dose proportional and sustained > 99% peripheral PD‐1 receptor saturation was observed by 2 hours postdosing, across doses. PK/PD and safety profiles were comparable between Japanese and Western patients, and exposure‐safety analyses did not indicate any trends. Observed PK and PD‐1 receptor saturation were consistent with model predictions for flat doses and less frequent regimens, validating the early application of PK modeling and PK/PD assessments to inform the recommended dose and regimen, following dose escalation.