Revisiting Bevacizumab + Cytotoxics Scheduling Using Mathematical Modeling: Proof of Concept Study in Experimental Non-Small Cell Lung Carcinoma

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.

Overcoming immuno-resistance by rescheduling anti-VEGF/cytotoxics/anti-PD-1 combination in lung cancer model

Background: Many tumors are refractory to immune checkpoint inhibitors, but their combination with cytotoxics is expected to improve sensitivity. Understanding how and when cytotoxics best re-stimulate tumor immunity could help overcome resistance to immune checkpoint inhibitors. Methods: In vivo studies were performed in C57BL/6 mice grafted with immune-refractory LL/2 lung cancer model. A longitudinal immunomonitoring study on tumor, spleen, and blood after multiple treatments including Cisplatin, Pemetrexed, and anti-VEGF, either alone or in combination, was performed, spanning a period of up to 21 days, to determine the optimal time window during which immune checkpoint inhibitors should be added. Finally, an efficacy study was conducted comparing the antiproliferative performance of various schedules of anti-VEGF, Pemetrexed-Cisplatin doublet, plus anti-PD-1 (i.e., immunomonitoring-guided scheduling, concurrent dosing or a random sequence), as well as single agent anti-PD1. Results: Immunomonitoring showed marked differences between treatments, organs, and time points. However, harnessing tumor immunity (i.e., promoting CD8 T cells or increasing the T CD8/Treg ratio) started on D7 and peaked on D14 with the anti-VEGF followed by cytotoxics combination. Therefore, a 14-day delay between anti-VEGF/cytotoxic and anti-PD1 administration was considered the best sequence to test. Efficacy studies then confirmed that this sequence achieved higher antiproliferative efficacy compared to other treatment modalities (i.e., -71% in tumor volume compared to control). Conclusions: Anti-VEGF and cytotoxic agents show time-dependent immunomodulatory effects, suggesting that sequencing is a critical feature when combining these agents with immune checkpoint inhibitors. An efficacy study confirmed that sequencing treatments further enhance antiproliferative effects in lung cancer models compared to concurrent dosing and partly reverse the resistance to cytotoxics and anti-PD1.

Harnessing tumor immunity with cytotoxics: T cells monitoring in mice bearing lung tumors treated with anti-VEGF and pemetrexed-cisplatin doublet

BACKGROUND

Successful immunotherapy is restricted to some cancers only, and combinatorial strategies with other drugs could help to improve their efficacy. Here, we monitor T cells in NSCLC model after treatment with cytotoxics (CT) and anti-VEGF drugs, to understand when immune checkpoint inhibitors should be best associated next.

METHODS

In vivo study was performed on BALB/c mice grafted with KLN205 cells. Eight treatments were tested including control, cisplatin and pemetrexed as low (LD CT) and full (MTD CT) dose as single agents, flat dose anti-VEGF and the association anti-VEGF + CT. Full immunomonitoring was performed by flow cytometry on tumor, spleen and blood over 3 weeks.

RESULTS

Immunomodulatory effect was dependent upon both treatments and time. In tumors, combination groups shown numerical lower Treg cells on Day 21. In spleen, anti-VEGF and LD CT group shown higher CD8/Treg ratio on Day 7; on Day 14, higher T CD4 were observed in both combination groups. Finally, in blood, Tregs were lower and CD8/Treg ratio higher, on Day 14 in both combination groups. On Day 21, CD4 and CD8 T cells were higher in the anti-VEGF + MTD CT group.

CONCLUSIONS

Anti-VEGF associated to CT triggers notable increase in CD8/Tregs ratio. Regarding the scheduling, a two-week delay after using anti-VEGF and CT could be the best sequence to optimize antitumor 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.

Angiogenic inhibitor pre-administration improves the therapeutic effects of immunotherapy

In lung cancer, immune checkpoint inhibitors (ICIs) are often inadequate for tumor growth inhibition. Angiogenic inhibitors (AIs) are required to normalize tumor vasculature for improved immune cell infiltration. However, in clinical practice, ICIs and cytotoxic antineoplastic agents are simultaneously administered with an AI when tumor vessels are abnormal. Therefore, we examined the effects of pre-administering an AI for lung cancer immunotherapy in a mouse lung cancer model. Using DC101, an anti-vascular endothelial growth factor receptor 2 (VEGFR2) monoclonal antibody, a murine subcutaneous Lewis lung cancer (LLC) model was used to determine the timing of vascular normalization. Microvessel density (MVD), pericyte coverage, tissue hypoxia, and CD8-positive cell infiltration were analyzed. The effects of an ICI and paclitaxel after DC101 pre-administration were investigated. On Day 3, increased pericyte coverage and alleviated tumor hypoxia represented the highest vascular normalization. CD8+ T-cell infiltration was also highest on Day 3. When combined with an ICI, DC101 pre-administration significantly reduced PD-L1 expression. When combined with an ICI and paclitaxel, only DC101 pre-administration significantly inhibited tumor growth, but simultaneous administration did not. AI pre-administration, and not simultaneous administration, may increase the therapeutic effects of ICIs due to improved immune cell infiltration.

Efficient simulation of clinical target response surfaces

Simulation of combination therapies is challenging due to computational complexity. Either a simple model is used to simulate the response for many combinations of concentration to generate a response surface but parameter variability and uncertainty are neglected and the concentrations are constant—the link to the doses to be administered is difficult to make—or a population pharmacokinetic/pharmacodynamic model is used to predict the response to combination therapy in a clinical trial taking into account the time‐varying concentration profile, interindividual variability (IIV), and parameter uncertainty but simulations are limited to only a few selected doses. We devised new algorithms to efficiently search for the combination doses that achieve a predefined efficacy target while taking into account the IIV and parameter uncertainty. The result of this method is a response surface of confidence levels, indicating for all dose combinations the likelihood of reaching the specified efficacy target. We highlight the importance to simulate across a population rather than focus on an individual. Finally, we provide examples of potential applications, such as informing experimental design.

Metronomic Anti-Cancer Therapy: A Multimodal Therapy Governed by the Tumor Microenvironment

Simple Summary

Metronomic chemotherapy with different mechanisms of action against cancer cells and their microenvironment represents an exceptional holistic cancer treatment. Each type of tumor has its own characteristics, including each individual tumor in each patient. Understanding the complexity of the dynamic interactions that take place between tumor and stromal cells and the microenvironment in tumor progression and metastases, as well as the response of the host and the tumor itself to anticancer therapy, will allow therapeutic actions with long-lasting effects to be implemented using metronomic regimens. This study aims to highlight the complexity of cellular interactions in the tumor microenvironment and summarize some of the preclinical and clinical results that explain the multimodality of metronomic therapy, which, together with its low toxicity, supports an inhibitory effect on the primary tumor and metastases. We also highlight the possible use of nano-therapeutic agents as good partners for metronomic chemotherapy.

Abstract

The concept of cancer as a systemic disease, and the therapeutic implications of this, has gained special relevance. This concept encompasses the interactions between tumor and stromal cells and their microenvironment in the complex setting of primary tumors and metastases. These factors determine cellular co-evolution in time and space, contribute to tumor progression, and could counteract therapeutic effects. Additionally, cancer therapies can induce cellular and molecular responses in the tumor and host that allow them to escape therapy and promote tumor progression. In this study, we describe the vascular network, tumor-infiltrated immune cells, and cancer-associated fibroblasts as sources of heterogeneity and plasticity in the tumor microenvironment, and their influence on cancer progression. We also discuss tumor and host responses to the chemotherapy regimen, at the maximum tolerated dose, mainly targeting cancer cells, and a multimodal metronomic chemotherapy approach targeting both cancer cells and their microenvironment. In a combination therapy context, metronomic chemotherapy exhibits antimetastatic efficacy with low toxicity but is not exempt from resistance mechanisms. As such, a better understanding of the interactions between the components of the tumor microenvironment could improve the selection of drug combinations and schedules, as well as the use of nano-therapeutic agents against certain malignancies.

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

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

Quantitative mathematical modeling of clinical brain metastasis dynamics in non-small cell lung cancer

Brain metastases (BMs) are associated with poor prognosis in non-small cell lung cancer (NSCLC), but are only visible when large enough. Therapeutic decisions such as whole brain radiation therapy would benefit from patient-specific predictions of radiologically undetectable BMs. Here, we propose a mathematical modeling approach and use it to analyze clinical data of BM from NSCLC. Primary tumor growth was best described by a gompertzian model for the pre-diagnosis history, followed by a tumor growth inhibition model during treatment. Growth parameters were estimated only from the size at diagnosis and histology, but predicted plausible individual estimates of the tumor age (2.1-5.3 years). Multiple metastatic models were further assessed from fitting either literature data of BM probability (n = 183 patients) or longitudinal measurements of visible BMs in two patients. Among the tested models, the one featuring dormancy was best able to describe the data. It predicted latency phases of 4.4-5.7 months and onset of BMs 14-19 months before diagnosis. This quantitative model paves the way for a computational tool of potential help during therapeutic management.

Optimal Scheduling of Bevacizumab and Pemetrexed/Cisplatin Dosing in Non-Small Cell Lung Cancer

Bevacizumab-pemetrexed/cisplatin (BEV-PEM/CIS) is a first-line therapeutic for advanced nonsquamous non-small cell lung cancer. Bevacizumab potentiates PEM/CIS cytotoxicity by inducing transient tumor vasculature normalization. BEV-PEM/CIS has a narrow therapeutic window. Therefore, it is an attractive target for administration schedule optimization. The present study leverages our previous work on BEV-PEM/CIS pharmacodynamic modeling in non-small cell lung cancer-bearing mice to estimate the optimal gap in the scheduling of sequential BEV-PEM/CIS. We predicted the optimal gap in BEV-PEM/CIS dosing to be 2.0 days in mice and 1.2 days in humans. Our simulations suggest that the efficacy loss in scheduling BEV-PEM/CIS at too great of a gap is much less than the efficacy loss in scheduling BEV-PEM/CIS at too short of a gap.

Pharmacodynamic Drug-Drug Interactions

Pharmacodynamic drug-drug interactions (DDIs) occur when the pharmacological effect of one drug is altered by that of another drug in a combination regimen. DDIs often are classified as synergistic, additive, or antagonistic in nature, albeit these terms are frequently misused. Within a complex pathophysiological system, the mechanism of interaction may occur at the same target or through alternate pathways. Quantitative evaluation of pharmacodynamic DDIs by employing modeling and simulation approaches is needed to identify and optimize safe and effective combination therapy regimens. This review investigates the opportunities and challenges in pharmacodynamic DDI studies and highlights examples of quantitative methods for evaluating pharmacodynamic DDIs, with a particular emphasis on the use of mechanism-based modeling and simulation in DDI studies. Advancements in both experimental and computational techniques will enable the application of better, model-informed assessments of pharmacodynamic DDIs in drug discovery, development, and therapeutics.

Dose- and time-dependence of the host-mediated response to paclitaxel therapy: a mathematical modeling approach

It has recently been suggested that pro-tumorigenic host-mediated processes induced in response to chemotherapy counteract the anti-tumor activity of therapy, and thereby decrease net therapeutic outcome. Here we use experimental data to formulate a mathematical model describing the host response to different doses of paclitaxel (PTX) chemotherapy as well as the duration of the response. Three previously described host-mediated effects are used as readouts for the host response to therapy. These include the levels of circulating endothelial progenitor cells in peripheral blood and the effect of plasma derived from PTX-treated mice on migratory and invasive properties of tumor cells in vitro. A first set of mathematical models, based on basic principles of pharmacokinetics/pharmacodynamics, did not appropriately describe the dose-dependence and duration of the host response regarding the effects on invasion. We therefore provide an alternative mathematical model with a dose-dependent threshold, instead of a concentration-dependent one, that describes better the data. This model is integrated into a global model defining all three host-mediated effects. It not only precisely describes the data, but also correctly predicts host-mediated effects at different doses as well as the duration of the host response. This mathematical model may serve as a tool to predict the host response to chemotherapy in cancer patients, and therefore may be used to design chemotherapy regimens with improved therapeutic outcome by minimizing host mediated effects.