Engineering in Medicine To Address the Challenge of Cancer Drug Resistance: From Micro- and Nanotechnologies to Computational and Mathematical Modeling

Drug resistance has profoundly limited the success of cancer treatment, driving relapse, metastasis, and mortality. Nearly all anticancer drugs and even novel immunotherapies, which recalibrate the immune system for tumor recognition and destruction, have succumbed to resistance development. Engineers have emerged across mechanical, physical, chemical, mathematical, and biological disciplines to address the challenge of drug resistance using a combination of interdisciplinary tools and skill sets. This review explores the developing, complex, and under-recognized role of engineering in medicine to address the multitude of challenges in cancer drug resistance. Looking through the "lens" of intrinsic, extrinsic, and drug-induced resistance (also referred to as "tolerance"), we will discuss three specific areas where active innovation is driving novel treatment paradigms: (1) nanotechnology, which has revolutionized drug delivery in desmoplastic tissues, harnessing physiochemical characteristics to destroy tumors through photothermal therapy and rationally designed nanostructures to circumvent cancer immunotherapy failures, (2) bioengineered tumor models, which have benefitted from microfluidics and mechanical engineering, creating a paradigm shift in physiologically relevant environments to predict clinical refractoriness and enabling platforms for screening drug combinations to thwart resistance at the individual patient level, and (3) computational and mathematical modeling, which blends in silico simulations with molecular and evolutionary principles to map mutational patterns and model interactions between cells that promote resistance. On the basis that engineering in medicine has resulted in discoveries in resistance biology and successfully translated to clinical strategies that improve outcomes, we suggest the proliferation of multidisciplinary science that embraces engineering.

Characterizing Chemotherapy-Induced Neutropenia and Monocytopenia Through Mathematical Modelling

In spite of the recent focus on the development of novel targeted drugs to treat cancer, cytotoxic chemotherapy remains the standard treatment for the vast majority of patients. Unfortunately, chemotherapy is associated with high hematopoietic toxicity that may limit its efficacy. We have previously established potential strategies to mitigate chemotherapy-induced neutropenia (a lack of circulating neutrophils) using a mechanistic model of granulopoiesis to predict the interactions defining the neutrophil response to chemotherapy and to define optimal strategies for concurrent chemotherapy/prophylactic granulocyte colony-stimulating factor (G-CSF). Here, we extend our analyses to include monocyte production by constructing and parameterizing a model of monocytopoiesis. Using data for neutrophil and monocyte concentrations during chemotherapy in a large cohort of childhood acute lymphoblastic leukemia patients, we leveraged our model to determine the relationship between the monocyte and neutrophil nadirs during cyclic chemotherapy. We show that monocytopenia precedes neutropenia by 3 days, and rationalize the use of G-CSF during chemotherapy by establishing that the onset of monocytopenia can be used as a clinical marker for G-CSF dosing post-chemotherapy. This work therefore has important clinical applications as a comprehensive approach to understanding the relationship between monocyte and neutrophils after cyclic chemotherapy with or without G-CSF support.

The timing of cyclic cytotoxic chemotherapy can worsen neutropenia and neutrophilia

Despite recent advances in immunotherapies, cytotoxic chemotherapy continues to be a first-line treatment option for the majority of cancers. Unfortunately, a common side effect in patients undergoing chemotherapy treatment is neutropenia. To mitigate the risk of neutropenia and febrile neutropenia, prophylactic treatment with granulocyte-colony stimulating factor (G-CSF) is administered. Extensive pharmacokinetic/pharmacodynamic modelling of myelosuppression during chemotherapy has suggested avenues for therapy optimization to mitigate this neutropenia. However, the issue of resonance, whereby neutrophil oscillations are induced by the periodic administration of cytotoxic chemotherapy and the coadministration of G-CSF, potentially aggravating a patient's neutropenic/neutrophilic status, is not well-characterized in the clinical literature. Here, through analysis of neutrophil data from young acute lymphoblastic leukaemia patients, we find that resonance is occurring during cyclic chemotherapy treatment in 26% of these patients. Motivated by these data and our previous modelling studies on adult lymphoma patients, we examined resonance during treatment with or without G-CSF. Using our quantitative systems pharmacology model of granulopoiesis, we show that the timing of cyclic chemotherapy can worsen neutropenia or neutrophilia, and suggest clinically-actionable schedules to reduce the resonant effect. We emphasize that delaying supportive G-CSF therapy to 6-7 days after chemotherapy can mitigate myelosuppressive effects. This study therefore highlights the importance of quantitative systems pharmacology for the clinical practice for developing rational therapeutic strategies.

Robust estimation of quantitative perfusion from multi-phase pseudo-continuous arterial spin labeling

PURPOSE

Multi-phase PCASL has been proposed as a means to achieve accurate perfusion quantification that is robust to imperfect shim in the labeling plane. However, there exists a bias in the estimation process that is a function of noise in the data. In this work, this bias is characterized and then addressed in animal and human data.

METHODS

The proposed algorithm to overcome bias uses the initial biased voxel-wise estimate of phase tracking error to cluster regions with different off-resonance phase shifts, from which a high-SNR estimate of regional phase offset is derived. Simulations were used to predict the bias expected at typical SNR. Multi-phase PCASL in 3 rat strains (n = 21) at 9.4 T was considered, along with 20 human subjects previously imaged using ASL at 3 T. The algorithm was extended to include estimation of arterial blood flow velocity.

RESULTS

Based on simulations, a perfusion estimation bias of 6-8% was expected using 8-phase data at typical SNR. This bias was eliminated when a high-precision estimate of phase error was available. In the preclinical data, the bias-corrected measure of perfusion (107 ± 14 mL/100g/min) was lower than the standard analysis (116 ± 14 mL/100g/min), corresponding to a mean observed bias across strains of 8.0%. In the human data, bias correction resulted in a 15% decrease in the estimate of perfusion.

CONCLUSIONS

Using a retrospective algorithmic approach, it was possible to exploit common information found in multiple voxels within a whole region of the brain, offering superior SNR and thus overcoming the bias in perfusion quantification from multi-phase PCASL.

Determinants of combination GM-CSF immunotherapy and oncolytic virotherapy success identified through in silico treatment personalization

Oncolytic virotherapies, including the modified herpes simplex virus talimogene laherparepvec (T-VEC), have shown great promise as potent instigators of anti-tumour immune effects. The OPTiM trial, in particular, demonstrated the superior anti-cancer effects of T-VEC as compared to systemic immunotherapy treatment using exogenous administration of granulocyte-macrophage colony-stimulating factor (GM-CSF). Theoretically, a combined approach leveraging exogenous cytokine immunotherapy and oncolytic virotherapy would elicit an even greater immune response and improve patient outcomes. However, regimen scheduling of combination immunostimulation and T-VEC therapy has yet to be established. Here, we calibrate a computational biology model of sensitive and resistant tumour cells and immune interactions for implementation into an in silico clinical trial to test and individualize combination immuno- and virotherapy. By personalizing and optimizing combination oncolytic virotherapy and immunostimulatory therapy, we show improved simulated patient outcomes for individuals with late-stage melanoma. More crucially, through evaluation of individualized regimens, we identified determinants of combination GM-CSF and T-VEC therapy that can be translated into clinically-actionable dosing strategies without further personalization. Our results serve as a proof-of-concept for interdisciplinary approaches to determining combination therapy, and suggest promising avenues of investigation towards tailored combination immunotherapy/oncolytic virotherapy.

The advent of biological therapies for anti-cancer treatment has had a significant impact on patient outcomes. Targeted xenobiotics, including oncolytic viruses, in combination with existing, more general, immunotherapies like exogenous cytokines show great promise for continuing to improve cancer care. However, determining optimal combination regimens can be difficult, given that testing proposed schedules would require large cohorts of patients enrolled in clinical trials. Fortunately, computational biology can help to address treatment scheduling while simultaneously helping to unravel the mechanisms driving therapeutic responses. In this work, we integrate a mathematical model of GM-CSF and talimogene laherparepvec (T-VEC) oncolytic virotherapy into a virtual clinical trial to optimize their administration in combination. Using this platform, we inferred a clinically-actionable combination schedule for patients with late-stage melanoma that significantly improved virtual patient outcome when compared to GM-CSF and T-VEC monotherapies, and a standard combination strategy. Our results outline a rational approach to therapy optimization with meaningful consequences for how we effectively design and implement clinical trials to maximize their success, and how we treat melanoma with combined immuno- and virotherapy.

Equivalences between age structured models and state dependent distributed delay differential equations

We use the McKendrick equation with variable ageing rate and randomly distributed mat-uration time to derive a state dependent distributed delay differential equation. We show that the resulting delay differential equation preserves non-negativity of initial conditions and we characterise local stability of equilibria. By specifying the distribution of maturation age, we recover state depen-dent discrete, uniform and gamma distributed delay differential equations. We show how to reduce the uniform case to a system of state dependent discrete delay equations and the gamma distributed case to a system of ordinary differential equations. To illustrate the benefits of these reductions, we convert previously published transit compartment models into equivalent distributed delay differential equations.

Chaos synchronization and Nelder-Mead search for parameter estimation in nonlinear pharmacological systems: Estimating tumor antigenicity in a model of immunotherapy

In mathematical pharmacology, models are constructed to confer a robust method for optimizing treatment. The predictive capability of pharmacological models depends heavily on the ability to track the system and to accurately determine parameters with reference to the sensitivity in projected outcomes. To closely track chaotic systems, one may choose to apply chaos synchronization. An advantageous byproduct of this methodology is the ability to quantify model parameters. In this paper, we illustrate the use of chaos synchronization combined with Nelder-Mead search to estimate parameters of the well-known Kirschner-Panetta model of IL-2 immunotherapy from noisy data. Chaos synchronization with Nelder-Mead search is shown to provide more accurate and reliable estimates than Nelder-Mead search based on an extended least squares (ELS) objective function. Our results underline the strength of this approach to parameter estimation and provide a broader framework of parameter identification for nonlinear models in pharmacology.

Towards Quantitative Systems Pharmacology Models of Chemotherapy-Induced Neutropenia

Neutropenia is a serious toxic complication of chemotherapeutic treatment. For years, mathematical models have been developed to better predict hematological outcomes during chemotherapy in both the traditional pharmaceutical sciences and mathematical biology disciplines. An increasing number of quantitative systems pharmacology (QSP) models that combine systems approaches, physiology, and pharmacokinetics/pharmacodynamics have been successfully developed. Here, I detail the shift towards QSP efforts, emphasizing the importance of incorporating systems-level physiological considerations in pharmacometrics.

A Mathematical Model of Granulopoiesis Incorporating the Negative Feedback Dynamics and Kinetics of G-CSF/Neutrophil Binding and Internalization

We develop a physiological model of granulopoiesis which includes explicit modelling of the kinetics of the cytokine granulocyte colony-stimulating factor (G-CSF) incorporating both the freely circulating concentration and the concentration of the cytokine bound to mature neutrophils. G-CSF concentrations are used to directly regulate neutrophil production, with the rate of differentiation of stem cells to neutrophil precursors, the effective proliferation rate in mitosis, the maturation time, and the release rate from the mature marrow reservoir into circulation all dependent on the level of G-CSF in the system. The dependence of the maturation time on the cytokine concentration introduces a state-dependent delay into our differential equation model, and we show how this is derived from an age-structured partial differential equation model of the mitosis and maturation and also detail the derivation of the rest of our model. The model and its estimated parameters are shown to successfully predict the neutrophil and G-CSF responses to a variety of treatment scenarios, including the combined administration of chemotherapy and exogenous G-CSF. This concomitant treatment was reproduced without any additional fitting to characterize drug-drug interactions.

Approaching Pharmacometrics as a Paleontologist Would: Recovering the Links Between Drugs and the Body Through Reconstruction

Our knowledge of dinosaurs comes primarily from the fossil record. Notwithstanding the condition of these vestiges, paleontologists reconstruct early reptilian life by comparison to previously discovered specimens. When relics are missing, reasonable deductions are used to fill in the gaps.

Neutrophil dynamics during concurrent chemotherapy and G-CSF administration: Mathematical modelling guides dose optimisation to minimise neutropenia

The choice of chemotherapy regimens is often constrained by the patient's tolerance to the side effects of chemotherapeutic agents. This dose-limiting issue is a major concern in dose regimen design, which is typically focused on maximising drug benefits. Chemotherapy-induced neutropenia is one of the most prevalent toxic effects patients experience and frequently threatens the efficient use of chemotherapy. In response, granulocyte colony-stimulating factor (G-CSF) is co-administered during chemotherapy to stimulate neutrophil production, increase neutrophil counts, and hopefully avoid neutropenia. Its clinical use is, however, largely dictated by trial and error processes. Based on up-to-date knowledge and rational considerations, we develop a physiologically realistic model to mathematically characterise the neutrophil production in the bone marrow which we then integrate with pharmacokinetic and pharmacodynamic (PKPD) models of a chemotherapeutic agent and an exogenous form of G-CSF (recombinant human G-CSF, or rhG-CSF). In this work, model parameters represent the average values for a general patient and are extracted from the literature or estimated from available data. The dose effect predicted by the model is confirmed through previously published data. Using our model, we were able to determine clinically relevant dosing regimens that advantageously reduce the number of rhG-CSF administrations compared to original studies while significantly improving the neutropenia status. More particularly, we determine that it could be beneficial to delay the first administration of rhG-CSF to day seven post-chemotherapy and reduce the number of administrations from ten to three or four for a patient undergoing 14-day periodic chemotherapy.

Neuraxial analgesia effects on labour progression: facts, fallacies, uncertainties and the future

Approximately 60% of women who labour in the USA receive some form of neuraxial analgesia, but concerns have been raised regarding whether it negatively impacts the labour and delivery process. In this review, we attempt to clarify what has been established as truths, falsities and uncertainties regarding the effects of this form of pain relief on labour progression, negative and/or positive. Additionally, although the term 'epidural' has become synonymous with neuraxial analgesia, we discuss two other techniques, combined spinal-epidural and continuous spinal analgesia, that are gaining popularity, as well as their effects on labour progression.

An integrated pressure and pathway approach to the spatial analysis of groundwater nitrate: a case study from the southeast of Ireland

Excess nitrogen in soil, aquatic and atmospheric environments is an escalating global problem. Eutrophication is the principal threat to surface water quality in the Republic of Ireland. European Union Water Framework Directive (2000/60/EC) water quality status assessments found that 16% of Irish groundwater bodies were 'at risk' of poor status due to the potential deterioration of associated estuarine and coastal water quality by nitrate from groundwater. This paper presents a methodology for evaluating pressure and pathway parameters affecting the spatial distribution of groundwater nitrate, investigated at a regional scale using existing national spatial datasets. The potential for nitrate transfer to groundwater was rated based on the introduced concepts of Pressure Loading and Pathway Connectivity Rating, each based on a combination of selected pressure and pathway parameters respectively. In the region studied, the South Eastern River Basin District of Ireland, this methodology identified that pathway parameters were more important than pressure parameters in understanding the spatial distribution of groundwater nitrate. Statistical analyses supported these findings and further demonstrated that the proportion of poorly drained soils, arable land, karstic flow regimes, regionally important bedrock aquifers and high vulnerability groundwater within the zones of contribution of the monitoring points are statistically significantly related to groundwater nitrate concentrations. Soil type was found to be the most important parameter. Analysis of variance showed that a number of the pressure and pathway parameters are interrelated. The parameters identified by the presented methodology may provide useful insights into the best way to manage and mitigate the influence of nitrate contamination of groundwater in this region. It is suggested that the identification of critical source areas based on the identified parameters would be an appropriate management tool, enabling planning and enforcement resources to be focussed on areas which will yield most benefit.

Extended follow-up of an antibiotic cycling program for the management of febrile neutropenia in a hematologic malignancy and hematopoietic cell transplantation unit

BACKGROUND

Febrile neutropenia is a common complication during treatment of hematological malignancies and hematopoietic cell transplantation. Empiric antibiotic therapy in this setting, while standard of care, commonly leads to microbial resistance. We have previously shown that cycling antibiotics in this patient population is feasible. This report provides long-term follow-up of cycling antibiotics in this patient population.

METHODS

In a prospective cohort of hematological malignancy patients with neutropenic fever, we sought to evaluate the role of empiric antibiotic cycling in preventing antibiotic resistance. Antibiotic cycling was initiated in March 2002 and, until June 2005, antibiotics were cycled every 8 months (Cycling Period A). From July 2005 to December 2009, antibiotics were cycled every 3 months (Cycling Period B). The rates of bacteremia, resistance, and complications were compared to a retrospective cohort (Pre-cycling Period).

RESULTS

The rate of gram-negative bacteremia decreased when compared to Cycling Periods A and B (5.3 vs. 2.1 and 3.3 episodes/1000 patient-days, respectively, P < 0.0001), most likely owing to implementation of quinolone prophylaxis. The resistance profile of the gram-negative organisms isolated remained stable over the 3 time periods, with the exception of an increase in quinolone resistance during the cycling periods. Gram-positive bacteremia rates remained stable, but vancomycin-resistant Enterococcus (VRE) increased significantly (0.1 vs. 1.0 and 1.6 episodes/1000 patient-days, respectively, P = 0.005) during cycling periods. Mortality rates were comparable.

CONCLUSIONS

Antibiotic cycling for neutropenic fever was effectively implemented and followed over an extended time period. Gram-negative resistance remained stable, but there is some concern for selection of resistant gram-positive bacteria, specifically VRE. Although antibiotic cycling did not seem to cause resistance in our study, further study is necessary to clarify the effect of cycling on antibiotic resistance, patient outcomes, and hospital cost.

Onset and complications of varicella zoster reactivation in the autologous hematopoietic cell transplant population

BACKGROUND

Varicella zoster virus (VZV) infections are a common complication in patients receiving autologous or allogeneic hematopoietic cell transplant (HCT). Recent guideline revisions suggest extending VZV prophylaxis to 1 year after autologous HCT. We retrospectively evaluated reactivation at our center, before implementation of extended acyclovir prophylaxis, to determine onset and outcome in the autologous HCT population.

METHODS

Inclusion criteria consisted of adult patients who received an autologous HCT with documentation for at least 1 year post transplant. Those excluded from review were patients who received acyclovir prophylaxis for >30 days post transplant or subsequently received an allogeneic transplant within 1 year. For patients in whom reactivation occurred, the severity of infection, the timing of onset, treatment of the reactivation, and any complications were recorded.

RESULTS

In the final analysis, 56 patients were assessed. Reactivation of zoster occurred in 16% of recipients with a median onset of 4.5 months post transplant. Complications that were observed include postherpetic neuralgia, severe pain, scarring, and motor weakness. Two patients required hospitalization for treatment, with 1 patient requiring 6 months of rehabilitation for motor weakness following the infection.

CONCLUSIONS

Our study revealed a 16% incidence of VZV reactivation in our autologous HCT population. The onset of these occurrences ranged from 2 to 10 months post transplant, with significant VZV-associated complications. We consider VZV reactivation a serious concern in the autologous transplant setting, requiring extended prophylaxis.