Metronomic reloaded: Theoretical models bringing chemotherapy into the era of precision medicine

Promotion of tumor angiogenesis and growth induced by low-dose antineoplastic agents via bone-marrow-derived cells in tumor tissues

Background

More research is needed to solidify the basis for reasonable metronomic chemotherapy regimens due to the inconsistent clinical outcomes from studies on metronomic chemotherapy with antineoplastic agents, along with signs of a nonlinear dose-response relationship at low doses. The present study therefore explored the dose-response relationships of representative antineoplastic agents in low dose ranges and their underlying mechanisms.

Methods

Cyclophosphamide (CPA) and 5-fluorouracil (5-Fu) were employed to observe the effects of the frequent administration of low-dose antineoplastic agents on tumor growth, tumor angiogenesis, and bone-marrow-derived cell (BMDC) mobilization in mouse models. The effects of antineoplastic agents on tumor and endothelial cell functions with or without BMDCs were analyzed in vitro.

Results

Tumor growth and metastasis were significantly promoted after the administration of CPA or 5-Fu at certain low dose ranges, and were accompanied by enhanced tumor angiogenesis and proangiogenic factor expression in tumor tissues, increased proangiogenic BMDC release in the circulating blood, and augmented proangiogenic BMDC retention in tumor tissues. Low concentrations of CPA or 5-Fu were found to significantly promote tumor cell migration and invasion, and enhance BMDC adhesion to endothelial cells in vitro.

Conclusion

These results suggest that there are risks in empirical metronomic chemotherapy using low-dose antineoplastic agents and the optimal dosage and administration schedule of antineoplastic agents need to be determined through further research.

Ultrasound-mediated nano-sized drug delivery systems for cancer treatment: Multi-scale and multi-physics computational modeling

Computational modeling enables researchers to study and understand various complex biological phenomena in anticancer drug delivery systems (DDSs), especially nano-sized DDSs (NSDDSs). The combination of NSDDSs and therapeutic ultrasound (TUS), that is, focused ultrasound and low-intensity pulsed ultrasound, has made significant progress in recent years, opening many opportunities for cancer treatment. Multiple parameters require tuning and optimization to develop effective DDSs, such as NSDDSs, in which mathematical modeling can prove advantageous. In silico computational modeling of ultrasound-responsive DDS typically involves a complex framework of acoustic interactions, heat transfer, drug release from nanoparticles, fluid flow, mass transport, and pharmacodynamic governing equations. Owing to the rapid development of computational tools, modeling the different phenomena in multi-scale complex problems involved in drug delivery to tumors has become possible. In the present study, we present an in-depth review of recent advances in the mathematical modeling of TUS-mediated DDSs for cancer treatment. A detailed discussion is also provided on applying these computational models to improve the clinical translation for applications in cancer treatment. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Addressing Genetic Tumor Heterogeneity, Post-Therapy Metastatic Spread, Cancer Repopulation, and Development of Acquired Tumor Cell Resistance

The concept of post-therapy metastatic spread, cancer repopulation and acquired tumor cell resistance (M-CRAC) rationalizes tumor progression because of tumor cell heterogeneity arising from post-therapy genetic damage and subsequent tissue repair mechanisms. Therapeutic strategies designed to specifically address M-CRAC involve tissue editing approaches, such as low-dose metronomic chemotherapy and the use of transcriptional modulators with or without targeted therapies. Notably, tumor tissue editing holds the potential to treat patients, who are refractory to or relapsing (r/r) after conventional chemotherapy, which is usually based on administering a maximum tolerable dose of a cytostatic drugs. Clinical trials enrolling patients with r/r malignancies, e.g., non-small cell lung cancer, Hodgkin's lymphoma, Langerhans cell histiocytosis and acute myelocytic leukemia, indicate that tissue editing approaches could yield tangible clinical benefit. In contrast to conventional chemotherapy or state-of-the-art precision medicine, tissue editing employs a multi-pronged approach targeting important drivers of M-CRAC across various tumor entities, thereby, simultaneously engaging tumor cell differentiation, immunomodulation, and inflammation control. In this review, we highlight the M-CRAC concept as a major factor in resistance to conventional cancer therapies and discusses tissue editing as a potential treatment.

Metronomic chemotherapy in ovarian cancer

Translational research and the development of targeted therapies have transformed the therapeutic landscape in epithelial ovarian cancer over the last decade. However, recurrent ovarian cancer continues to pose formidable challenges to therapeutic interventions, necessitating innovative strategies to optimize treatment outcomes. Current research focuses on the development of pharmaceuticals that target potential resistance pathways to DNA repair pathways. However, the cost and toxicity of some of these therapies are prohibitive and majority of patients lack access to clinical trials. Metronomic chemotherapy, characterized by the continuous administration of low doses of chemotherapeutic agents without long treatment breaks, has emerged as a promising approach with potential implications beyond recurrent setting. It acts primarily by inhibition of angiogenesis and activation of host immune system. We here review the mechanism of action of metronomic chemotherapy, as well as its current role, limitations, and avenues for further research in the management of epithelial ovarian cancer.

Adjusting the dose of traditional drugs combined with immunotherapy: reshaping the immune microenvironment in lung cancer

Immunotherapy is currently the most promising clinical treatment for lung cancer, not only revolutionizing second-line therapy but now also approved for first-line treatment. However, its clinical efficiency is not high and not all patients benefit from it. Thus, finding the best combination strategy to expand anti-PD-1/PD-L1-based immunotherapy is now a hot research topic. The conventional use of chemotherapeutic drugs and targeted drugs inevitably leads to resistance, toxic side effects and other problems. Recent research, however, suggests that by adjusting the dosage of drugs and blocking the activation of mutational mechanisms that depend on acquired resistance, it is possible to reduce toxic side effects, activate immune cells, and reshape the immune microenvironment of lung cancer. Here, we discuss the effects of different chemotherapeutic drugs and targeted drugs on the immune microenvironment. We explore the effects of adjusting the dosing sequence and timing, and the mechanisms of such responses, and show how the effectiveness and reliability of combined immunotherapy provide improved treatment outcomes.

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

Mathematical Modeling Support for Lung Cancer Therapy-A Short Review

The paper presents a review of models that can be used to describe dynamics of lung cancer growth and its response to treatment at both cell population and intracellular processes levels. To address the latter, models of signaling pathways associated with cellular responses to treatment are overviewed. First, treatment options for lung cancer are discussed, and main signaling pathways and regulatory networks are briefly reviewed. Then, approaches used to model specific therapies are discussed. Following that, models of intracellular processes that are crucial in responses to therapies are presented. The paper is concluded with a discussion of the applicability of the presented approaches in the context of lung cancer.

Metronomic Chemotherapy: Anti-Tumor Pathways and Combination with Immune Checkpoint Inhibitors

Increasing evidence pinpoints metronomic chemotherapy, a frequent and low dose drug administration with no prolonged drug-free intervals, as a potential tool to fight certain types of cancers. The primary identified targets of metronomic chemotherapy were the tumor endothelial cells involved in angiogenesis. After this, metronomic chemotherapy has been shown to efficiently target the heterogeneous population of tumor cells and, more importantly, elicit the innate and adaptive immune system reverting the "cold" to "hot" tumor immunologic phenotype. Although metronomic chemotherapy is primarily used in the context of a palliative setting, with the development of new immunotherapeutic drugs, a synergistic therapeutic role of the combined metronomic chemotherapy and immune checkpoint inhibitors has emerged at both the preclinical and clinical levels. However, some aspects, such as the dose and the most effective scheduling, still remain unknown and need further investigation. Here, we summarize what is currently known of the underlying anti-tumor effects of the metronomic chemotherapy, the importance of the optimal therapeutic dose and time-exposure, and the potential therapeutic effect of the combined administration of metronomic chemotherapy with checkpoint inhibitors in preclinical and clinical settings.

Assessing chemotherapy dosing strategies in a spatial cell culture model

Predicting patient responses to chemotherapy regimens is a major challenge in cancer treatment. Experimental model systems coupled with quantitative mathematical models to calculate optimal dose and frequency of drugs can enable improved chemotherapy regimens. Here we developed a simple approach to track two-dimensional cell colonies composed of chemo-sensitive and resistant cell populations via fluorescence microscopy and coupled this to computational model predictions. Specifically, we first developed multiple 4T1 breast cancer cell lines resistant to varying concentrations of doxorubicin, and demonstrated how heterogeneous populations expand in a two-dimensional colony. We subjected cell populations to varied dose and frequency of chemotherapy and measured colony growth. We then built a mathematical model to describe the dynamics of both chemosensitive and chemoresistant populations, where we determined which number of doses can produce the smallest tumor size based on parameters in the system. Finally, using an in vitro model we demonstrated multiple doses can decrease overall colony growth as compared to a single dose at the same total dose. In the future, this system can be adapted to optimize dosing strategies in the setting of heterogeneous cell types or patient derived cells with varied chemoresistance.

Revisiting metronomic vinorelbine with mathematical modelling: a Phase I trial in lung cancer

BACKGROUND

A phase Ia/Ib trial of metronomic oral vinorelbine (MOV) driven by a mathematical model was performed in heavily pretreated metastatic Non-Small Cell Lung Cancer or Pleural Mesothelioma patients. Disease Control Rate, progression free survival, toxicity and PK/PD were the main endpoints.

METHODS

Best MOV scheduling was selected using a simplified phenomenological, semi-mechanistic model with a total weekly dose of 150-mg vinorelbine. Computation of individual PK parameters was performed using population approach.

RESULTS

The mathematical model proposed the following metronomic schedule for a 150-mg weekly dose of vinorelbine: 60 mg D1, 30 mg D2, 60 mg D4. A total of 37 heavily pre-treated patients (30 evaluable) were enrolled. Grade III/IV neutropenia was observed in 30% patients. Median PFS was 11 weeks. Disease Control Rate was 73% (i.e.; 13% partial response and 60% stable disease). A large variability in drug exposure (AUC_0-24 h: 53%) and PK parameters (Cl: 83%) were observed among patients. Simulated trough levels after D2 and D4 showed similarly 56-73% variability among patients. Drug exposure was not associated with efficacy, but neutropenia was more frequent in patients with AUC > 250 ng/ml.h. Tumor burden, performance status and neutrophils-to-lymphocyte ratio (NLR) were associated with PFS, suggesting that MOV would be indicated in selected patients. We built a composite score to predict efficacy, mixing baseline tumor size and NLR showing 84% selectivity and 75% specificity.

CONCLUSIONS

MOV was characterized by important variability in drug exposure among patients. However, and despite being all heavily pre-treated, 73% of disease control rate and 11 weeks PFS were achieved with manageable toxicities. PK/PD relationships yielded conflicting results depending on the initial tumor burden and BSA, suggesting that patients should be carefully selected prior to be scheduled for metronomic regimen. Possible role NLR could play as a predictive marker suggests immunomodulating features with MOV.

In Silico Investigations of Multi-Drug Adaptive Therapy Protocols

The standard of care for cancer patients aims to eradicate the tumor by killing the maximum number of cancer cells using the maximum tolerated dose (MTD) of a drug. MTD causes significant toxicity and selects for resistant cells, eventually making the tumor refractory to treatment. Adaptive therapy aims to maximize time to progression (TTP), by maintaining sensitive cells to compete with resistant cells. We explored both dose modulation (DM) protocols and fixed dose (FD) interspersed with drug holiday protocols. In contrast to previous single drug protocols, we explored the determinants of success of two-drug adaptive therapy protocols, using an agent-based model. In almost all cases, DM protocols (but not FD protocols) increased TTP relative to MTD. DM protocols worked well when there was more competition, with a higher cost of resistance, greater cell turnover, and when crowded proliferating cells could replace their neighbors. The amount that the drug dose was changed, mattered less. The more sensitive the protocol was to tumor burden changes, the better. In general, protocols that used as little drug as possible, worked best. Preclinical experiments should test these predictions, especially dose modulation protocols, with the goal of generating successful clinical trials for greater cancer control.

A recent development of new therapeutic agents and novel drug targets for cancer treatment

Despite recent advances in cancer diagnosis, prevention, detection, as well as management, the disease is expected to be the top cause of death globally. The chemotherapy approach for cancer has become more advanced in its design, yet no medication can cure enough against all types of cancer and its stage. Thus, this review aimed to summarize a recent development of new therapeutic agents and novel drug targets for the treatment of cancer. Several obstacles stand in the way of effective cancer treatment and drug development, including inaccessibility of tumor site by appropriate drug concentration, debilitating untoward effects caused by non-selective tissue distribution of chemotherapeutic agents, and occurrence of drug resistance, which leads to cross-resistance to a variety of drugs. Resistance to treatment with anticancer drugs results from multiple factors and the most common reason for acquiring drug resistance is marking and expelling drugs that prevent cancer cells to be targeted by chemotherapeutic agents. Moreover, insensitivity to drug-induced apoptosis, alteration, and mutation of drug target and interference/change of DNA replication are other main causes of treatment failure.

Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach

The development of an in silico approach that evaluates and identifies appropriate treatment protocols for individuals could help grow personalized treatment and increase cancer patient lifespans. With this motivation, the present study introduces a novel approach for sequential treatment cycles based on simultaneously examining drug delivery, tumor growth, and chemotherapy efficacy. This model incorporates the physical conditions of tumor geometry, including tumor, capillary network, and normal tissue assuming real circumstances, as well as the intravascular and interstitial fluid flow, drug concentration, chemotherapy efficacy, and tumor recurrence. Three treatment approaches-maximum tolerated dose (MTD), metronomic chemotherapy (MC), and chemo-switching (CS)-as well as different chemotherapy schedules are investigated on a real tumor geometry extracted from image. Additionally, a sensitivity analysis of effective parameters of drug is carried out to evaluate the potential of using different other drugs in cancer treatment. The main findings are: (i) CS, MC, and MTD have the best performance in reducing tumor cells, respectively; (ii) multiple doses raise the efficacy of drugs that have slower clearance, higher diffusivity, and lower to medium binding affinities; (iii) the suggested approach to eradicating tumors is to reduce their cells to a predetermined rate through chemotherapy and then apply adjunct therapy.

Drug-induced resistance evolution necessitates less aggressive treatment

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.

The Multiple Dimensions of Networks in Cancer: A Perspective

This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue “Networks in Cancer: From Symmetry Breaking to Targeted Therapy”. The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.

Minimal PK/PD model for simultaneous description of the maximal tolerated dose and metronomic treatment outcomes in mouse tumor models

PURPOSE

Metronomic chemotherapy (MC) is a promising approach where, in contrast to the conventional maximal tolerated dose (MTD) strategy, regular fractionated doses of the drug are used. This approach has proven its efficacy, although drug dosing and scheduling are often chosen empirically. Pharmacokinetic/pharmacodynamic (PK/PD) models provide a way to choose optimal protocols with computational methods. Existing models are usually too complicated and are valid for only a subset of drug schedules. To address this issue, we propose herein a simple model that can describe MC and MTD regimens simultaneously.

METHODS

The minimal model comprises tumor suppression due to antiangiogenic drug effect together with a cell-kill term, responsible for its cytotoxicity. The model was tested on data obtained on tumor-bearing mice treated with gemcitabine in ether MTD, MC, or combined (MTD + MC) regimens.

RESULTS

We conducted a number of tests in which data were divided in various ways into training and validation sets. The model successfully described different trends in the MTD and MC regimens. With parameters obtained by fitting the model to MTD data, the simulations correctly predicted trends in both the MC and combined therapy groups.

CONCLUSION

Our results demonstrate that the proposed model presents a minimal yet efficient tool for modeling outcomes in different treatment regimens in mice. We hope that this model has the potential for use in clinical practice in the development of patient-specific chemotherapy scheduling protocols based on observed treatment response.

Do mechanisms matter? Comparing cancer treatment strategies across mathematical models and outcome objectives

When eradication is impossible, cancer treatment aims to delay the emergence of resistance while minimizing cancer burden and treatment. Adaptive therapies may achieve these aims, with success based on three assumptions: resistance is costly, sensitive cells compete with resistant cells, and therapy reduces the population of sensitive cells. We use a range of mathematical models and treatment strategies to investigate the tradeoff between controlling cell populations and delaying the emergence of resistance. These models extend game theoretic and competition models with four additional components: 1) an Allee effect where cell populations grow more slowly at low population sizes, 2) healthy cells that compete with cancer cells, 3) immune cells that suppress cancer cells, and 4) resource competition for a growth factor like androgen. In comparing maximum tolerable dose, intermittent treatment, and adaptive therapy strategies, no therapeutic choice robustly breaks the three-way tradeoff among the three therapeutic aims. Almost all models show a tight tradeoff between time to emergence of resistant cells and cancer cell burden, with intermittent and adaptive therapies following identical curves. For most models, some adaptive therapies delay overall tumor growth more than intermittent therapies, but at the cost of higher cell populations. The Allee effect breaks these relationships, with some adaptive therapies performing poorly due to their failure to treat sufficiently to drive populations below the threshold. When eradication is impossible, no treatment can simultaneously delay emergence of resistance, limit total cancer cell numbers, and minimize treatment. Simple mathematical models can play a role in designing the next generation of therapies that balance these competing objectives.

Metronomic vinorelbine is an excellent and safe treatment for advanced breast cancer: a retrospective, observational study

Advanced breast cancer (ABC) has become a chronic disease. In such a situation, an effective therapy with low toxicities and economically acceptable is needed. Metronomic vinorelbine (mVNR) has been proved to be effective on the control of MBC. The aim of this study is to evaluate the efficacy and safety of mVNR as the salvage therapy for patients with ABC. Oral vinorelbine (VNR) was administered at 70 mg/m2, fractionated on days 1, 3, and 5, for 3 weeks on and 1 week off. Once the mVNR was combined with trastuzumab, or was combined with bevacizumab, the schedule was changed to 2 weeks on and 1 week off. Clinical data of patients with ABC who had received treatment with mVNR and tumor characteristics were collected and analyzed. From Mar. 2013 to Dec, 2020, there were 90 patients with ABC received mVNR. The overall response rate was 53.3% and overall disease control rate (DCR) was 78.9% in this study, including 4 (4.4%) cases reached complete response, 44 (48.9%) cases reached partial response and 23 (25.6%) cases were table disease. The median time to treatment failure (TTF) of the Lumina A patients was 13.3 months, Lumina B patients was 9.1 months, Her-2 enrich patients was 8.9 months, and triple negative breast cancer (TNBC) patients was 5.6 months. Median overall survival time for Lumina A, Lumina B, Her-2 enrich and TNBC were 54.6 months, 53.3 months, 59.5 months and 24.5 months separately. Side effects were minimal and manageable. Metronomic VNR can be an effective treatment for ABC either works as a switch maintenance or salvage therapy. In combination with target therapy or hormonal therapy, mVNR can further improve TTF and DCR with minimal toxicities. Further study should focus on the optimal dosage, schedule and combination regimen.

How Should Cancer Models Be Constructed?

Choosing and optimizing treatment strategies for cancer requires capturing its complex dynamics sufficiently well for understanding but without being overwhelmed. Mathematical models are essential to achieve this understanding, and we discuss the challenge of choosing the right level of complexity to address the full range of tumor complexity from growth, the generation of tumor heterogeneity, and interactions within tumors and with treatments and the tumor microenvironment. We discuss the differences between conceptual and descriptive models, and compare the use of predator-prey models, evolutionary game theory, and dynamic precision medicine approaches in the face of uncertainty about mechanisms and parameter values. Although there is of course no one-size-fits-all approach, we conclude that broad and flexible thinking about cancer, based on combined modeling approaches, will play a key role in finding creative and improved treatments.

Metronomic Therapy in Oral Squamous Cell Carcinoma

Metronomic therapy is characterized by drug administration in a low-dose, repeated, and regular manner without prolonged drug-free interval. The two main anticancer mechanisms of metronomic therapy are antiangiogenesis and immunomodulation, which have been demonstrated in several delicate in vitro and in vivo experiments. In contrast to the traditional maximum tolerated dose (MTD) dosing of chemotherapy, metronomic therapy possesses comparative efficacy but greatlydecreases the incidence and severity of treatment side-effects. Clinical trials of metronomic anticancer treatment have revealed promising results in a variety cancer types and specific patient populations such as the elderly and pediatric malignancies. Oral cavity squamous cell carcinoma (OCSCC) is an important health issue in many areas around the world. Long-term survival is about 50% in locally advanced disease despite having high-intensity treatment combined surgery, radiotherapy, and chemotherapy. In this article, we review and summarize the essence of metronomic therapy and focus on its applications in OCSCC treatment.

Development and Validation of a Prediction Model of Overall Survival in High-Risk Neuroblastoma Using Mechanistic Modeling of Metastasis

Prognosis of high-risk neuroblastoma (HRNB) remains poor despite multimodal therapies. Better prediction of survival could help to refine patient stratification and better tailor treatments. We established a mechanistic model of metastasis in HRNB relying on two processes: growth and dissemination relying on two patient-specific parameters: the dissemination rate μ and the minimal visible lesion size S_vis. This model was calibrated using diagnosis values of primary tumor size, lactate dehydrogenase circulating levels, and the meta-iodobenzylguanidine International Society for Paediatric Oncology European (SIOPEN) score from nuclear imaging, using data from 49 metastatic patients. It was able to describe the data of total tumor mass (lactate dehydrogenase, R² > 0.99) and number of visible metastases (SIOPEN, R² = 0.96). A prediction model of overall survival (OS) was then developed using Cox regression. Clinical variables alone were not able to generate a model with sufficient OS prognosis ability (P = .507). The parameter μ was found to be independent of the clinical variables and positively associated with OS (P = .0739 in multivariable analysis). Critically, addition of this computational biomarker significantly improved prediction of OS with a concordance index increasing from 0.675 (95% CI, 0.663 to 0.688) to 0.733 (95% CI, 0.722 to 0.744, P < .0001), resulting in significant OS prognosis ability (P = .0422).

The Dichotomous Role of Bone Marrow Derived Cells in the Chemotherapy-Treated Tumor Microenvironment

Bone marrow derived cells (BMDCs) play a wide variety of pro- and anti-tumorigenic roles in the tumor microenvironment (TME) and in the metastatic process. In response to chemotherapy, the anti-tumorigenic function of BMDCs can be enhanced due to chemotherapy-induced immunogenic cell death. However, in recent years, a growing body of evidence suggests that chemotherapy or other anti-cancer drugs can also facilitate a pro-tumorigenic function in BMDCs. This includes elevated angiogenesis, tumor cell proliferation and pro-tumorigenic immune modulation, ultimately contributing to therapy resistance. Such effects do not only contribute to the re-growth of primary tumors but can also support metastasis. Thus, the delicate balance of BMDC activities in the TME is violated following tumor perturbation, further requiring a better understanding of the complex crosstalk between tumor cells and BMDCs. In this review, we discuss the different types of BMDCs that reside in the TME and their activities in tumors following chemotherapy, with a major focus on their pro-tumorigenic role. We also cover aspects of rationally designed combination treatments that target or manipulate specific BMDC types to improve therapy outcomes.

Metronomic chemotherapy in non-small cell lung cancer

Metronomic chemotherapy (MCT) is defined as the rhythmic chemotherapy of low-dose cytotoxic drugs with short or no drug-free breaks over prolonged periods. MCT affects tumor cells and the tumor microenvironment. Particularly, the low-dose schedule impairs the repair process of endothelial cells, resulting in an anti-angiogenesis effect. By stimulating the immune system to eliminate tumor cells, MCT induces immunological activation. Furthermore, combined with targeted therapy, anti-angiogenic drugs enhance the efficacy of MCT. The present review is an overview of phase I, II and III clinical trials focusing on the efficacy, toxicity and mechanism of MCT in patients with non-small cell lung cancer (NSCLC). Furthermore, the prospects of MCT in NSCLC have been discussed. The present review indicated that MCT is an efficacious treatment for selected patients with NSCLC, with acceptable systemic side effects and economic viability for public health.

Towards Rational Cancer Therapeutics: Optimizing Dosing, Delivery, Scheduling, and Combinations

The current trend to personalize anticancer therapies mostly relies on selecting the best drug or combination of drugs to achieve optimal efficacy in patients. In addition to the comprehensive genetic and molecular knowledge of each tumor before choosing the drugs to be given, there is probably much room left for improvement by further personalizing the very modes by which the drugs are given, once they have been carefully selected. In particular, shifting from standard dosing to tailored dosing should help in maintaining drug exposure levels in the right therapeutic window, thus ensuring that the efficacy/toxicity balance is optimal. This paper covers the current knowledge regarding pharmacokinetic/pharmacodynamic relationships of anticancer agents, from decades-old cytotoxics to the latest immune checkpoint inhibitors, the most frequent sources for long-neglected interpatient variability impacting on drug exposure levels, and what could be done to achieve real personalized medicine in oncology such as implementing therapeutic drug monitoring with adaptive dosing strategies or using model-driven modalities for personalized dosing and scheduling.

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."

The rationale of dose-response curves in selecting cancer drug dosing

Drug development for cancer chemotherapy has an interesting history. A mix of serendipity, animal, cell line, and standard pharmacological principles of dose, dose-response, dose-concentration, dose intensity and combination therapies have been used to develop optimal dosing schedules. However in practice, significant gaps in the translation of preclinical to clinical dosing schedules persist, and clinical development has instead moved to new drug development. A older chemotherapies are still the backbone of most solid tumour schedules, therapeutic drug monitoring has emerged as a method for optimising the dose for individual patients.

Optimization of Cancer Treatment in the Frequency Domain

Thorough exploration of alternative dosing frequencies is often not performed in conventional pharmacometrics approaches. Quantitative systems pharmacology (QSP) can provide novel insights into optimal dosing regimen and drug behaviors which could add a new dimension to the design of novel treatments. However, methods for such an approach are currently lacking. Recently, we illustrated the utility of frequency-domain response analysis (FdRA), an analytical method used in control engineering, using several generic pharmacokinetic-pharmacodynamic case studies. While FdRA is not applicable to models harboring ever increasing variables such as those describing tumor growth, studying such models in the frequency domain provides valuable insight into optimal dosing frequencies. Through the analysis of three distinct tumor growth models (cell cycle-specific, metronomic, and acquired resistance), we demonstrate the application of a simulation-based analysis in the frequency domain to optimize cancer treatments. We study the response of tumor growth to dosing frequencies while simultaneously examining treatment safety, and found for all three models that above a certain dosing frequency, tumor size is insensitive to an increase in dosing frequency, e.g., for the cell cycle-specific model, one dose per 3 days, and an hourly dose yield the same reduction of tumor size to 3% of the initial size after 1 year of treatment. Additionally, we explore the effect of drug elimination rate changes on the tumor growth response. In summary, we show that the frequency-domain view of three models of tumor growth dynamics can help in optimizing drug dosing regimen to improve treatment success.

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.

Multidrug Cancer Therapy in Metastatic Castrate-Resistant Prostate Cancer: An Evolution-Based Strategy

PURPOSE

Integration of evolutionary dynamics into systemic therapy for metastatic cancers can prolong tumor control compared with standard maximum tolerated dose (MTD) strategies. Prior investigations have focused on monotherapy, but many clinical cancer treatments combine two or more drugs. Optimizing the evolutionary dynamics in multidrug therapy is challenging because of the complex cellular interactions and the large parameter space of potential variations in drugs, doses, and treatment schedules. However, multidrug therapy also represents an opportunity to further improve outcomes using evolution-based strategies.

EXPERIMENTAL DESIGN

We examine evolution-based strategies for two-drug therapy and identify an approach that divides the treatment drugs into primary and secondary roles. The primary drug has the greatest efficacy and/or lowest toxicity. The secondary drug is applied solely to reduce the resistant population to the primary drug.

RESULTS

Simulations from the mathematical model demonstrate that the primary-secondary approach increases time to progression (TTP) compared with conventional strategies in which drugs are administered without regard to evolutionary dynamics. We apply our model to an ongoing adaptive therapy clinical trial of evolution-based administration of abiraterone to treat metastatic castrate-resistant prostate cancer. Model simulations, parameterized with data from individual patients who progressed, demonstrate that strategic application of docetaxel during abiraterone therapy would have significantly increased their TTP.

CONCLUSIONS

Mathematical models can integrate evolutionary dynamics into multidrug cancer clinical trials. This has the potential to improve outcomes and to develop clinical trials in which these mathematical models are also used to estimate the mechanism(s) of treatment failure and explore alternative strategies to improve outcomes in future trials.

Lowering Etoposide Doses Shifts Cell Demise From Caspase-Dependent to Differentiation and Caspase-3-Independent Apoptosis via DNA Damage Response, Inducing AML Culture Extinction

Cytotoxic chemotherapy, still the most widely adopted anticancer treatment, aims at eliminating cancer cells inducing apoptosis with DNA damaging agents, exploiting the differential replication rate of cancer vs. normal cells; efficiency is evaluated in terms of extent of induced apoptosis, which depends on the individual cell sensitivity to a given drug, and on the dose. In this in vitro study, we report that the concentration of etoposide, a topoisomerase II poison widely used in clinics, determines both the kinetics of cell death, and the type of apoptosis induced. We observed that on a set of myeloid leukemia cell lines, etoposide at high (50 uM) dose promoted a rapid caspase-3-mediated apoptosis, whereas at low (0.5 uM) dose, it induced morphological and functional granulocytic differentiation and caspase-2-dependent, but caspase-3-independent, cell death, displaying features consistent with apoptosis. Both differentiation and caspase-2- (but not 3)-mediated apoptosis were contrasted by caffeine, a well-known inhibitor of the cellular DNA damage response (DDR), which maintained cell viability and cycling, indicating that the effects of low etoposide dose are not the immediate consequence of damage, but the result of a signaling pathway. DDR may be thus the mediator responsible for translating a mere dosage-effect into different signal transduction pathways, highlighting a strategic action in regulating timing and mode of cell death according to the severity of induced damage. The evidence of different molecular pathways induced by high vs. low drug doses may possibly contribute to explain the different effects of cytotoxic vs. metronomic therapy, the latter achieving durable clinical responses by treating cancer patients with stable, low doses of otherwise canonical cytotoxic drugs; intriguingly caspase-3, a major promoter of wounded tissue regeneration, is also a key factor of post-therapy cancer repopulation. All this suggests that cancer control in response to cytotoxic drugs arises from complex reprogramming mechanisms in tumor tissue, recently described as anakoinosis.

Optimal dynamic regimens with artificial intelligence: The case of temozolomide

We determine an optimal protocol for temozolomide using population variability and dynamic optimization techniques inspired by artificial intelligence. We use a Pharmacokinetics/Pharmacodynamics (PK/PD) model based on Faivre and coauthors (Faivre, et al., 2013) for the pharmacokinetics of temozolomide, as well as the pharmacodynamics of its efficacy. For toxicity, which is measured by the nadir of the normalized absolute neutrophil count, we formalize the myelosuppression effect of temozolomide with the physiological model of Panetta and coauthors (Panetta, et al., 2003). We apply the model to a population with variability as given in Panetta and coauthors (Panetta, et al., 2003). Our optimization algorithm is a variant in the class of Monte-Carlo tree search algorithms. We do not impose periodicity constraint on our solution. We set the objective of tumor size minimization while not allowing more severe toxicity levels than the standard Maximum Tolerated Dose (MTD) regimen. The protocol we propose achieves higher efficacy in the sense that –compared to the usual MTD regimen– it divides the tumor size by approximately 7.66 after 336 days –the 95% confidence interval being [7.36–7.97]. The toxicity is similar to MTD. Overall, our protocol, obtained with a very flexible method, gives significant results for the present case of temozolomide and calls for further research mixing operational research or artificial intelligence and clinical research in oncology.

Seek and destroy: improving PK/PD profiles of anticancer agents with nanoparticles

INTRODUCTION

The Pharmacokinetics/pharmacodynamics (PK/PD) relationships with cytotoxics are usually based on a steepening concentration-effect relationship; the greater the drug amount, the greater the effect. The Maximum Tolerated Dose paradigm, finding the balance between efficacy, while keeping toxicities at their manageable level, has been the rule of thumb for the last 50-years. Developing nanodrugs is an appealing strategy to help broaden this therapeutic window. The fact that efficacy and toxicity with cytotoxics are intricately linked is primarily due to the complete lack of specificity toward the tumor tissue during their distribution phase. Because nanoparticles are expected to better target tumor tissue while sparing healthy cells, accumulating large amounts of cytotoxics in tumors could be achieved in a safer way. Areas covered: This review aims at presenting how nanodrugs present unique features leading to reconsidering PK/PD relationships of anticancer agents. Expert commentary: The constant interplay between carrier PK, interactions with cancer cells, payload release, payload PK, target expression and target engagement, makes picturing the exact PK/PD relationships of nanodrugs particularly challenging. However, those improved PK/PD relationships now make the once contradictory higher efficacy and lower toxicities requirement an achievable goal in cancer patients.

Emerging roles for clinical pharmacometrics in cancer precision medicine

Purpose of review

Although significant progress has been made in cancer research, there exist unmet needs in patient care as reflected by the 'Cancer Moonshot' goals. This review appreciates the potential utility of quantitative pharmacology in cancer precision medicine.

Recent findings

Precision oncology has received federal funding largely due to 'The Precision Medicine Initiative'. Precision medicine takes into account the inter-individual variability, and allows for tailoring the right medication or the right dose of drug to the best subpopulation of patients who will likely respond to the intervention, thus enhancing therapeutic success and reducing "financial toxicity" to patients, families and caregivers. The National Cancer Institute (NCI) committed US$ 70 million from its fiscal year 2016 budget to advance precision oncology research. Through the 'Critical Path Initiative', pharmacometrics has gained an important role in drug development; however, it is yet to find widespread clinical applicability.

Summary

Stakeholders including clinicians and pharmacometricians need to work in concert to ensure that benefits of model-based approaches are harnessed to personalize cancer care to the individual needs of the patient via better dosing strategies, companion diagnostics, and predictive biomarkers. In medical oncology, where immediate patient care is the clinician's primary concern, pharmacometric approaches can be tailored to build models that rely on patient data already digitally available in the Electronic Health Record (EHR) to facilitate quick collaboration and avoid additional funding needs. Taken together, we offer a roadmap for the future of precision oncology which is fraught with both challenges and opportunities for pharmacometricians and clinicians alike.

Administration of temozolomide: Comparison of conventional and metronomic chemotherapy regimens

PURPOSE

We compare the Maximum Tolerated Dose (MTD) and Metronomic Chemotherapy (MC) protocols for temozolomide administration. We develop an innovative methodology for characterizing optimal chemotherapy regimens.

METHODS

We use a PK/PD model based on Faivre et al. (2013) for the pharmacokinetics of temozolomide, as well as the pharmacodynamics of its efficacy. For toxicity, which is measured by the nadir of the normalized absolute neutrophil count, we formalize the myelosuppression effect of temozolomide with the physiological model of Panetta et al. (2003b). We introduce a multi-criteria tool for comparing protocols along their efficacy and toxicity dimensions.

RESULTS

We show that the toxicity of the MC regimen proposed by Faivre et al. (2013) can greatly be reduced without affecting its efficacy, while the standard MTD protocol efficacy cannot be improved without impairing its toxicity. We also show that for any acceptable toxicity level, the optimal protocol remains closely related to standard MTD.

CONCLUSIONS

Overall, our new method enables a rich comparison between protocols along multiple dimensions. We can rank protocols for temozolomide administration. It is a first step toward building optimal individual protocols.

Model driven optimization of antiangiogenics + cytotoxics combination: application to breast cancer mice treated with bevacizumab + paclitaxel doublet leads to reduced tumor growth and fewer metastasis

Bevacizumab is the first-in-class antiangiogenic drug and is almost always administrated in combination with cytotoxics. Reports have shown that bevacizumab could induce a transient phase of vascular normalization, thus ensuring a better drug delivery when cytotoxics administration is adjuvant. However, determining the best sequence remains challenging. We have developed a mathematical model describing the impact of antiangiogenics on tumor vasculature. A 3.4 days gap between bevacizumab and paclitaxel was first proposed by our model. To test its relevance, 84 mice were orthotopically xenografted with human MDA-231^Luc+ refractory breast cancer cells. Two sets of experiments were performed, based upon different bevacizumab dosing (10 or 20 mg/kg) and inter-cycle intervals (7 or 10 days), comprising several combinations with paclitaxel. Results showed that scheduling bevacizumab 3 days before paclitaxel improved antitumor efficacy (48% reduction in tumor size compared with concomitant dosing, p < 0.05) and reduced metastatic spreading. Additionally, bevacizumab alone could lead to more aggressive metastatic disease with shorter survival in animals. Our model was able to fit the experimental data and provided insights on the underlying dynamics of the vasculature's ability to deliver the cytotoxic agent. Final simulations suggested a new, data-informed optimal gap of 2.2 days. Our experimental data suggest that current concomitant dosing between bevacizumab and paclitaxel could be a sub-optimal strategy at bedside. In addition, this proof of concept study suggests that mathematical modelling could help to identify the optimal interval among a variety of possible alternate treatment modalities, thus refining the way experimental or clinical studies are conducted.

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.

Chemotherapeutic Dose Scheduling Based on Tumor Growth Rates Provides a Case for Low-Dose Metronomic High-Entropy Therapies

We extended the classical tumor regression models such as Skipper's laws and the Norton-Simon hypothesis from instantaneous regression rates to the cumulative effect over repeated cycles of chemotherapy. To achieve this end, we used a stochastic Moran process model of tumor cell kinetics coupled with a prisoner's dilemma game-theoretic cell-cell interaction model to design chemotherapeutic strategies tailored to different tumor growth characteristics. Using the Shannon entropy as a novel tool to quantify the success of dosing strategies, we contrasted MTD strategies as compared with low-dose, high-density metronomic strategies (LDM) for tumors with different growth rates. Our results show that LDM strategies outperformed MTD strategies in total tumor cell reduction. This advantage was magnified for fast-growing tumors that thrive on long periods of unhindered growth without chemotherapy drugs present and was not evident after a single cycle of chemotherapy but grew after each subsequent cycle of repeated chemotherapy. The evolutionary growth/regression model introduced in this article agrees well with murine models. Overall, this model supports the concept of designing different chemotherapeutic schedules for tumors with different growth rates and develops quantitative tools to optimize these schedules for maintaining low-volume tumors. Cancer Res; 77(23); 6717-28. ©2017 AACR.

Mathematical modeling for Phase I cancer trials: A study of metronomic vinorelbine for advanced non-small cell lung cancer (NSCLC) and mesothelioma patients

INTRODUCTION

Using mathematical modelling allows to select a treatment's regimen across infinite possibilities. Here, we report the phase I assessment of a new schedule for metronomic vinorelbine in treating refractory advanced NSCLC and mesothelioma patients.

RESULTS

Overall, 13 patients were screened and 12 were treated (50% male, median age: 68yrs), including 9 NSCLC patients. All patients received at least one week (3 doses) of treatment. At data cut-off, the median length of treatment was 6.5 weeks (1-32+). All the patients presented with at least one adverse event (AE) and six patients with a severe AE (SAE). One partial response and 5 stable diseases were observed. The median OS was 6.4 months (95% CI, 4.8 to 12 months). The median and mean vinorelbine's AUC were 122 ng/ml*h and 159 ng/ml*h, respectively, with the higher plasmatic vinorelbine exposure associated with the best ORR (difference of AUC comparison between responders and non-responders, p-value 0.017).

MATERIALS AND METHODS

The mathematical modelling determined the administration of vinorelbine, 60 mg on Day 1, 30 mg on Day 2 and 60 mg on Day 4 weekly until progression, as the best schedule. Advanced NSCLC or mesothelioma patients progressing after standard treatment were eligible for the trial. NCT02555007.

CONCLUSIONS

Responses with acceptable safety profile were observed in heavily pretreated NSCLC and mesothelioma patients using oral vinorelbine at this metronomic dosage based on a mathematic modeling. This study demonstrates the feasibility of this new type of approach, as mathematical modeling may help to rationally decide the better regimen to be clinically tested across infinite possibilities.

Metronomic and metronomic-like therapies in neuroendocrine tumors - Rationale and clinical perspectives

Metronomic therapy is characterized by the administration of regular low doses of certain drugs with very low toxicity. There have been numerous debates over the empirical approach of this regimen, but fewest side effects are always something to consider in order to improve patients' quality of life. Neuroendocrine tumors (NETs) are rare malignancies relatively slow-growing; therefore their treatment is often chronic, involving several different therapies for tumor growth control. Knowing that these tumors are highly vascularized, the anti-angiogenic aspect is highly regarded as something to be targeted in all patients harboring NETs. Additionally the metronomic schedule has proved to be effective on an immunological level, rendering this approach as a multi-targeted therapy. Rationalizing that advanced NETs are in many cases a chronic disease, with which patients can live for as long as possible, a systemic therapy with regular low doses and a very low toxicity is in many cases a judicious manner of pursuing stabilization. Metronomic schedule is usually correlated with chemotherapy in oncology, but other therapies, such as radiotherapy and biotherapy can be delivered in a metronomic like manner. This review describes clinical trials and case series involving metronomic therapies alone or in combination in patients with advanced NETs. Nowadays level of evidence about metronomic therapy in NETs is quite low, therefore future prospective clinical studies are needed to validate the metronomic approach in specific clinical settings.

Next generation metronomic chemotherapy-report from the Fifth Biennial International Metronomic and Anti-angiogenic Therapy Meeting, 6-8 May 2016, Mumbai

The 5^th Biennial Metronomic and Anti-angiogenic Therapy Meeting was held on 6^th – 8^th May in the Indian city of Mumbai. The meeting brought together a wide range of clinicians and researchers interested in metronomic chemotherapy, anti-angiogenics, drug repurposing and combinations thereof. Clinical experiences, including many from India, were reported and discussed in three symposia covering breast cancer, head and neck cancers and paediatrics. On the pre-clinical side research into putative mechanisms of action, and the interactions between low dose metronomic chemotherapy and angiogenesis and immune responses, were discussed in a number of presentations. Drug repurposing was discussed both in terms of clinical results, particularly with respect to angiosarcoma and high-risk neuroblastoma, and in pre-clinical settings, particularly the potential for peri-operative interventions. However, it was clear that there remain a number of key areas of challenge, particularly in terms of definitions, perceptions in the wider oncological community, mechanisms of action and predictive biomarkers. While the potential for metronomics and drug repurposing in low and middle income countries remains a key theme, it is clear that there is also considerable potential for clinically relevant improvements in patient outcomes even in high income economies.

Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations

Background

A thorough understanding of the ecological and evolutionary mechanisms that drive the phenotypic evolution of neoplastic cells is a timely and key challenge for the cancer research community. In this respect, mathematical modelling can complement experimental cancer research by offering alternative means of understanding the results of in vitro and in vivo experiments, and by allowing for a quick and easy exploration of a variety of biological scenarios through in silico studies.

Results

To elucidate the roles of phenotypic plasticity and selection pressures in tumour relapse, we present here a phenotype-structured model of evolutionary dynamics in a cancer cell population which is exposed to the action of a cytotoxic drug. The analytical tractability of our model allows us to investigate how the phenotype distribution, the level of phenotypic heterogeneity, and the size of the cell population are shaped by the strength of natural selection, the rate of random epimutations, the intensity of the competition for limited resources between cells, and the drug dose in use.

Conclusions

Our analytical results clarify the conditions for the successful adaptation of cancer cells faced with environmental changes. Furthermore, the results of our analyses demonstrate that the same cell population exposed to different concentrations of the same cytotoxic drug can take different evolutionary trajectories, which culminate in the selection of phenotypic variants characterised by different levels of drug tolerance. This suggests that the response of cancer cells to cytotoxic agents is more complex than a simple binary outcome, i.e., extinction of sensitive cells and selection of highly resistant cells. Also, our mathematical results formalise the idea that the use of cytotoxic agents at high doses can act as a double-edged sword by promoting the outgrowth of drug resistant cellular clones. Overall, our theoretical work offers a formal basis for the development of anti-cancer therapeutic protocols that go beyond the ‘maximum-tolerated-dose paradigm’, as they may be more effective than traditional protocols at keeping the size of cancer cell populations under control while avoiding the expansion of drug tolerant clones.

Reviewers

This article was reviewed by Angela Pisco, Sébastien Benzekry and Heiko Enderling.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-016-0143-4) contains supplementary material, which is available to authorized users.

Metronomic oral vinorelbine in advanced breast cancer and non-small-cell lung cancer: current status and future development

Metronomic chemotherapy (mCT), a frequent administration of low-dose chemotherapy, allows prolonged treatment duration and minimizes the toxicity of standard-dose chemotherapy. mCT has multiple actions against cancer cells including inhibition of angiogenesis and modulation of the immune system. A number of studies lend support to the clinical efficacy of mCT in advanced breast cancer and non-small-cell lung cancer. However, further evidence is necessary to describe the optimal use of mCT and to identify suitable patients. Oral vinorelbine has emerged as a promising metronomic treatment in patients with metastatic breast cancer and non-small-cell lung cancer and is the only orally available microtubule-targeting agent. This paper reviews current evidence on metronomic oral vinorelbine, discusses its management and defines a suitable patient profile on the basis of a workshop of Italian experts.