Pembrolizumab: Role of Modeling and Simulation in Bringing a Novel Immunotherapy to Patients With Melanoma

Aiding early clinical drug development by elucidation of the relationship between tumor growth inhibition and survival in relapsed/refractory multiple myeloma patients

Early prognosis of clinical efficacy is an urgent need for oncology drug development. Herein, we systemically examined the quantitative approach of tumor growth inhibition (TGI) and survival modeling in the space of relapsed and refractory multiple myeloma (MM), aiming to provide insights into clinical drug development. Longitudinal serum M-protein and progression-free survival (PFS) data from three phase III studies (N = 1367) across six treatment regimens and different patient populations were leveraged. The TGI model successfully described the longitudinal M-protein data in patients with MM. The tumor inhibition and growth parameters were found to vary as per each study, likely due to the patient population and treatment regimen difference. Based on a parametric time-to-event model for PFS, M-protein reduction at week 4 was identified as a significant prognostic factor for PFS across the three studies. Other factors, including Eastern Cooperative Oncology Group performance status, prior anti-myeloma therapeutics, and baseline serum ß2-microglobulin level, were correlated with PFS as well. In conclusion, patient disease characteristics (i.e., baseline tumor burden and treatment lines) were important determinants of tumor inhibition and PFS in MM patients. M-protein change at week 4 was an early prognostic biomarker for PFS.

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.

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.

Progress in Research on Tumor Metastasis Inhibitors

Tumor metastasis is a significant cause of malignant cancer-related death. Therefore, inhibiting tumor metastasis is an effective means of treating malignant tumors. Increasing our understanding of the molecular mechanisms that govern tumor metastasis can reveal new anti-cancer targets. This article will discuss the breakthroughs in this area and the corresponding recent developments in anti-cancer drug discovery.

Novel strategies in immune checkpoint inhibitor drug development: How far are we from the paradigm shift?

The development of immune checkpoint inhibitors (ICI) represents a major milestone in immune-oncology. Over the years these agents have demonstrated efficacy in an increasing array of malignancies. Despite this success however, significant challenges remain. Novel approaches to both drug development and trial design are required to incorporate the unique pharmacokinetic and pharmacodynamic properties of ICIs. Further, it has also been established that the benefit of ICIs is limited to only a subset of patients. The molecular interactions between native immune cells and tumorigenesis and progression represent an active area of biomarker research, and elucidating the mechanisms of response and resistance is crucial to develop rational trial designs for the next wave of immune-oncology (IO) clinical trials, particularly in patients with primary and/or acquired resistance. Efforts are now being made to integrate both biological and clinical information using novel multi-omic approaches which are now being developed to further elucidate the molecular signatures associated with IO treatment response and resistance and enable rational drug development and trial design processes. As such, precision IO and the ability to deliver patient-specific choices for ICI monotherapies or combination therapies has become an increasingly tangible goal. We herein describe the current landscape in ICI drug development and discuss the challenges and future directions in this exciting and evolving era in immune-oncology.

Opportunities for Quantitative Translational Modeling in Oncology

A 2-day meeting was held by members of the UK Quantitative Systems Pharmacology Network () in November 2018 on the topic of Translational Challenges in Oncology. Participants from a wide range of backgrounds were invited to discuss current and emerging modeling applications in nonclinical and clinical drug development, and to identify areas for improvement. This resulting perspective explores opportunities for impactful quantitative pharmacology approaches. Four key themes arose from the presentations and discussions that were held, leading to the following recommendations: Evaluate the predictivity and reproducibility of animal cancer models through precompetitive collaboration. Apply mechanism of action (MoA) based mechanistic models derived from nonclinical data to clinical trial data. Apply MoA reflective models across trial data sets to more robustly quantify the natural history of disease and response to differing interventions. Quantify more robustly the dose and concentration dependence of adverse events through mathematical modelling techniques and modified trial design.

Optimizing drug development in oncology by clinical trial simulation: Why and how?

In therapeutic research, the safety and efficacy of pharmaceutical products are necessarily tested on humans via clinical trials after an extensive and expensive preclinical development period. Methodologies such as computer modeling and clinical trial simulation (CTS) might represent a valuable option to reduce animal and human assays. The relevance of these methods is well recognized in pharmacokinetics and pharmacodynamics from the preclinical phase to postmarketing. However, they are barely used and are poorly regarded for drug approval, despite Food and Drug Administration and European Medicines Agency recommendations. The generalization of CTS could be greatly facilitated by the availability of software for modeling biological systems, by clinical trial studies and hospital databases. Data sharing and data merging raise legal, policy and technical issues that will need to be addressed. Development of future molecules will have to use CTS for faster development and thus enable better patient management. Drug activity modeling coupled with disease modeling, optimal use of medical data and increased computing speed should allow this leap forward. The realization of CTS requires not only bioinformatics tools to allow interconnection and global integration of all clinical data but also a universal legal framework to protect the privacy of every patient. While recognizing that CTS can never replace 'real-life' trials, they should be implemented in future drug development schemes to provide quantitative support for decision-making. This in silico medicine opens the way to the P4 medicine: predictive, preventive, personalized and participatory.

Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.

Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology

Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment-with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges.

Early-drug development in the era of immuno-oncology: are we ready to face the challenges?

The classical development of drugs has progressively faded away, and we are currently in an era of seamless drug-development, where first-in-human trials include unusually big expansion cohorts in the search for early signs of activity and rapid regulatory approval. The fierce competition between different pharmaceutical companies and the hype for immune combinations obliges us to question the current way in which we are evaluating these drugs. In this review, we discuss critical issues and caveats in immunotherapy development. A particular emphasis is put on the limitations of pre-clinical toxicology studies, where both murine models and cynomolgus monkeys have underpredicted toxicity in humans. Moreover, relevant issues surrounding dose determination during phase I trials, such as dose-escalation methods or flat versus body-weight dosing, are discussed. A proposal of how to face these different challenges is offered, in order to achieve maximum efficacy with minimum toxicity for our patients.

Pharmacometric Applications and Challenges in the Development of Therapeutic Antibodies in Immuno-Oncology

Purpose of review

Monoclonal antibodies targeting key checkpoints in immune stimulatory pathways have over the last years become the mainstay of cancer immunotherapy. This article provides a brief review of the application and key impact of pharmacometrics and quantitative clinical pharmacology approaches in the development of these novel biologics.

Recent findings

The clinical development and selection of optimal dosing regimens for monoclonal antibodies used in immune-oncology has been facilitated by an extensive application of pharmacometric approaches to characterize the exposure-response relationship for major efficacy and safety endpoints. These analysis techniques were applied for the anti CTLA-4 antibody ipilimumab, as well as the anti PD1/PD-L1 antibodies nivolumab, pembrolizumab, avelumab, atezolizumab and durvalumab. The utilization of quantitative clinical pharmacology, including model-based analyses, did not only support the identification of efficacious doses with acceptable safety limits, but was also able to address complicating challenges such as time- and response-dependent changes in antibody clearance as observed for most compounds.

Summary

A widespread and systematic application of pharmacometric approaches has provided key aspects in elucidating, interpreting and integrating preclinical, biochemical and clinical data in support of the development of safe and efficacious dosing regimens of monoclonal antibodies used in immuno-oncology, thereby facilitating the clinical use of this promising new class of biologics in cancer patients with unmet medical needs.

Integrative Pharmacology: Advancing Development of Effective Immunotherapies

With the recent advances in cancer immunotherapy, it is now evident that the antigen-specific activation of the patients' immune responses can be utilized for achieving significant therapeutic benefits. Novel molecules have been developed and promising advances have been achieved in cancer therapy. The recent success of cancer immunotherapy clearly reflects the novelty of the approach and importance of this class of therapeutics. Due to the nature of immunotherapy, i.e., harnessing the patient's immune system, it becomes critical to evaluate the important variables that can guide preclinical development, translational strategies, patient selection, and effective clinical dosing paradigms following single and combination therapies. To further boost the durability and efficacy profiles of IO (immuno-oncology) drugs following single agent therapy, novel combination therapies are being sought. Combination strategies have become critical for enhancing the anti-tumor immunity in broader cancer indications. Comprehensive methods are being developed to quantify the synergistic combination effect profiles at various development phases. Further evaluation of the signaling and pathway components can potentially establish a unique "signature" characteristic for specific combination therapies following modulation of various immunomodulatory pathways. In this article, critical topics related to preclinical, translational, and clinical development of IO agents are discussed.

Pembrolizumab in the treatment of advanced urothelial cancer

Pembrolizumab is a humanized monoclonal antibody that targets PD-1. In the Phase III trial KEYNOTE-045, pembrolizumab was associated with a significant overall survival benefit when compared with docetaxel, paclitaxel and vinflunine in second line metastatic urothelial carcinoma (UC). Additionally, in the first line, early results from an interim analysis of the Phase II trial Keynote-052 study indicated that pembrolizumab is efficacious for cisplatin-ineligible patients. Based on data from these trials, pembrolizumab was the most recent among the five checkpoint inhibitors tested in UC to be approved by the US FDA in May 2017. It was granted regular approval for patients with advanced-stage UC who progress after receiving platinum-based chemotherapy and accelerated approval in the first line for patients who are ineligible to receive cisplatin.

A Phamacoeconomic Analysis of Personalized Dosing vs Fixed Dosing of Pembrolizumab in Firstline PD-L1-Positive Non-Small Cell Lung Cancer

Background

In October 2016, pembrolizumab became the new standard of care for firstline treatment of patients with metastatic non-small cell lung cancer (mNSCLC) whose tumors express programmed death ligand 1 in at least 50% of cells. The US Food and Drug Administration-recommended dose is 200 mg every three weeks. Multiple studies have demonstrated equivalent efficacy with weight-based doses between 2 mg/kg and 10 mg/kg. The objective of this study was to compare the economic impact of using personalized dosing (2 mg/kg) vs fixed dosing (200 mg) in the firstline setting of mNSCLC.

Methods

We performed a budget impact analysis from the US societal perspective to compare fixed dosing with personalized dosing. We calculated the target population and weight of patients who would be treated with pembrolizumab annually in the firstline setting. Using survival curves from the KEYNOTE 024 trial with Weibull extrapolation, we estimated the mean number of cycles that patients would receive. Using the Medicare average sales price, we calculated the difference in cost between personalized and fixed dosing.

Results

Our base case model demonstrates that the total annual cost of pembrolizumab with fixed dosing is US $3 440 127 429, and with personalized dosing it is US $2 614 496 846. The use of personalized dosing would lead to a 24.0% annual savings of US $825 630 583 in the United States.

Conclusions

Personalized dosing of pembrolizumab may have the potential to save approximately $0.825 billion annually in the United States, likely without impacting outcomes. This option should be considered for the firstline management of PD-L1-positive advanced lung cancer.

Time dependent pharmacokinetics of pembrolizumab in patients with solid tumor and its correlation with best overall response

Pembrolizumab is a monoclonal antibody that targets the programmed death-1 receptor to induce immune-mediated clearance (CL) of tumor cells. Originally approved by the US Food and Drug Administration in 2014 for treating patients with unresectable or metastatic melanoma, pembrolizumab is now also used to treat patients with non-small-cell lung cancer, classical Hodgkin lymphoma, head and neck cancer, and urothelial cancer. This paper describes the recently identified feature of pembrolizumab pharmacokinetics, the time-dependent or time-varying CL. Overall results indicate that CL decreases over the treatment period of a typical patient in a pattern well described by a sigmoidal function of time with three parameters: the maximum proportion change in CL from baseline (approximately I_max or exactly e^Imax - 1), the time to reach I_max/2 (TI₅₀), and a Hill coefficient. Best overall response per response evaluation criteria in solid tumor category was found to be associated with the magnitude of I_max.

Commentary on Pharmacometrics for Immunotherapy

This commentary provides an overview of recent examples of pharmacometrics applied during the clinical development of two antagonists of the programmed death‐1 (PD‐1) cell surface receptor, pembrolizumab and nivolumab. Despite the remarkable achievements obtained in predicting the correct dosing schedule from different quantitative approaches, data indicated a great degree of heterogeneity in tumor response. To achieve therapeutic goals the search for predictive biomarkers associated with a lack of response and mechanism‐based combination studies are warranted.