Understanding the Connection between Nanoparticle Uptake and Cancer Treatment Efficacy using Mathematical Modeling

ROS mediated Cu[Fe(CN)5NO] nanoparticles for triple negative breast cancer: A detailed study in preclinical mouse model

Triple negative breast cancer (TNBC) is an aggressive form of tumor, more prevalent in younger women resulting in poor survival rate (2nd in cancer deaths) because of its asymptomatic existence. The most popular and convenient approach for the treatment of TNBC is chemotherapy which is associated with several limitations. Considering the importance of nanotechnology in health care system, in the present manuscript, we have designed and developed a simple, efficient, cost effective, and ecofriendly method for the synthesis of copper nitroprusside analogue nanoparticles (Cu[Fe(CN)5NO] which is abbreviated as CuNPANP that may be the potential anti-cancer nanomedicine for the treatment of TNBC. Copper (present in CuNPANP) is used because of its affordability, nutritional value and various biomedical applications. The CuNPANP are thoroughly characterized using several analytical techniques. The in vitro cell viability (in normal cells) and the ex vivo hemolysis assay reveal the biocompatible nature of CuNPANP. The anti-cancer activity of the CuNPANP is established in TNBC cells (MDA-MB-231 and 4T1) through several in vitro assays along with plausible mechanisms. The intraperitoneal administration of CuNPANP in orthotopic breast tumor model by transplanting 4T1 cells into the mammary fat pad of BALB/c mouse significantly inhibits the growth of breast carcinoma as well as increases the survival time of tumor-bearing mice. These results altogether potentiate the anti-cancer efficacy of CuNPANP as a smart therapeutic nanomedicine for treating TNBC in near future after bio-safety evaluation in large animals.

Transport by circulating myeloid cells drives liposomal accumulation in inflamed synovium

The therapeutic potential of liposomes to deliver drugs into inflamed tissue is well documented. Liposomes are believed to largely transport drugs into inflamed joints by selective extravasation through endothelial gaps at the inflammatory sites, known as the enhanced permeation and retention effect. However, the potential of blood-circulating myeloid cells for the uptake and delivery of liposomes has been largely overlooked. Here we show that myeloid cells can transport liposomes to inflammatory sites in a collagen-induced arthritis model. It is shown that the selective depletion of the circulating myeloid cells reduces the accumulation of liposomes up to 50-60%, suggesting that myeloid-cell-mediated transport accounts for more than half of liposomal accumulation in inflamed regions. Although it is widely believed that PEGylation inhibits premature liposome clearance by the mononuclear phagocytic system, our data show that the long blood circulation times of PEGylated liposomes rather favours uptake by myeloid cells. This challenges the prevailing theory that synovial liposomal accumulation is primarily due to the enhanced permeation and retention effect and highlights the potential for other pathways of delivery in inflammatory diseases.

Radio Wave-Activated Chemotherapy-A Novel Nanoparticle Thermoresponsive Copolymer Drug Delivery Platform

Radio waves are highly penetrating, non-ionizing, and cause minimal damage to surrounding tissues. Radio wave control of drug release has been achieved using a novel thermoresponsive copolymer bound to a superparamagnetic iron oxide nanoparticle (SPION) core. A NIPAM-acrylamide-methacrolein copolymer underwent a coil-to-globular structure phase change upon reaching a critical temperature above the human body temperature but below hyperthermic temperatures. The copolymer was covalently bound to SPIONs which increase in temperature upon exposure to radio waves. This effect could be controlled by varying input energies and frequencies. For controlled drug release, proteins were bound via aldehyde groups on the copolymer and amine groups on the protein. The radio wave-induced heating of the complex thereby released the drug-bearing proteins. The fine-tuning of the radio wave exposure allowed multiple cycles of protein-drug release. The fluorescent tagging of the complex by FITC was also achieved in situ, allowing the tagging of the complex. The localization of the complex could also be achieved in vitro under a permanent magnetic field.

Evaluation of Anticancer Activity of Zhubech, a New 5-FU Analog Liposomal Formulation, against Pancreatic Cancer

Pancreatic cancer is projected to be the second leading cause of cancer-related death by 2030 in the US. The benefits of the most common systemic therapy for various pancreatic cancers have been masked by high drug toxicities, adverse reactions, and resistance. The use of nanocarriers such as liposomes to overcome these unwanted effects has become very popular. This study aims to formulate 1,3-bistertrahydrofuran-2yl-5FU (MFU)-loaded liposomal nanoparticles (Zhubech) and to evaluate itsstability, release kinetics, in vitro and in vivo anticancer activities, and biodistribution in different tissues. Particle size and zeta potential were determined using a particle size analyzer, while cellular uptake of rhodamine-entrapped liposomal nanoparticles (Rho-LnPs) was determined by confocal microscopy. Gadolinium hexanoate (Gd-Hex) was synthesized and entrapped into the liposomal nanoparticle (LnP) (Gd-Hex-LnP), as a model contrast agent, to evaluate gadolinium biodistribution and accumulation by LnPs in vivo using inductively coupled plasma mass spectrometry (ICP-MS). The mean hydrodynamic diameters of blank LnPs and Zhubech were 90.0 ± 0.65 nm and 124.9 ± 3.2 nm, respectively. The hydrodynamic diameter of Zhubech was found to be highly stable at 4 °C and 25 °C for 30 days in solution. In vitro drug release of MFU from Zhubech formulation exhibited the Higuchi model (R2 value = 0.95). Both Miapaca-2 and Panc-1 treated with Zhubech showed reduced viability, two- or four-fold lower than that of MFU-treated cells in 3D spheroid (IC50Zhubech = 3.4 ± 1.0 μM vs. IC50MFU = 6.8 ± 1.1 μM) and organoid (IC50Zhubech = 9.8 ± 1.4 μM vs. IC50MFU = 42.3 ± 1.0 μM) culture models. Confocal imaging confirmed a high uptake of rhodamine-entrapped LnP by Panc-1 cells in a time-dependent manner. Tumor-efficacy studies in a PDX bearing mouse model revealed a more than 9-fold decrease in mean tumor volumes in Zhubech-treated (108 ± 13.5 mm3) compared to 5-FU-treated (1107 ± 116.2 mm3) animals, respectively. This study demonstrates that Zhubech may be a potential candidate for delivering drugs for pancreatic cancer treatment.

Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy

This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pipelines, and approaches to increase precision. The protocol is highly predictive as early as the first restaging after treatment start and can be used with standard-of-care imaging measures. For complete details on the use and execution of this protocol, please refer to Butner et al. (2020)¹ and Butner et al. (2021).².

A Nanomedicine Structure-Activity Framework for Research, Development, and Regulation of Future Cancer Therapies

Despite their clinical success in drug delivery applications, the potential of theranostic nanomedicines is hampered by mechanistic uncertainty and a lack of science-informed regulatory guidance. Both the therapeutic efficacy and the toxicity of nanoformulations are tightly controlled by the complex interplay of the nanoparticle's physicochemical properties and the individual patient/tumor biology; however, it can be difficult to correlate such information with observed outcomes. Additionally, as nanomedicine research attempts to gradually move away from large-scale animal testing, the need for computer-assisted solutions for evaluation will increase. Such models will depend on a clear understanding of structure-activity relationships. This review provides a comprehensive overview of the field of cancer nanomedicine and provides a knowledge framework and foundational interaction maps that can facilitate future research, assessments, and regulation. By forming three complementary maps profiling nanobio interactions and pathways at different levels of biological complexity, a clear picture of a nanoparticle's journey through the body and the therapeutic and adverse consequences of each potential interaction are presented.

Dedifferentiation-mediated stem cell niche maintenance in early-stage ductal carcinoma in situ progression: insights from a multiscale modeling study

We present a multiscale agent-based model of ductal carcinoma in situ (DCIS) to study how key phenotypic and signaling pathways are involved in the early stages of disease progression. The model includes a phenotypic hierarchy, and key endocrine and paracrine signaling pathways, and simulates cancer ductal growth in a 3D lattice-free domain. In particular, by considering stochastic cell dedifferentiation plasticity, the model allows for study of how dedifferentiation to a more stem-like phenotype plays key roles in the maintenance of cancer stem cell populations and disease progression. Through extensive parameter perturbation studies, we have quantified and ranked how DCIS is sensitive to perturbations in several key mechanisms that are instrumental to early disease development. Our studies reveal that long-term maintenance of multipotent stem-like cell niches within the tumor are dependent on cell dedifferentiation plasticity, and that disease progression will become arrested due to dilution of the multipotent stem-like population in the absence of dedifferentiation. We have identified dedifferentiation rates necessary to maintain biologically relevant multipotent cell populations, and also explored quantitative relationships between dedifferentiation rates and disease progression rates, which may potentially help to optimize the efficacy of emerging anti-cancer stem cell therapeutics.

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

Purpose

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

Methods

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

Results

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

Conclusions

The present study highlights the translational potential of miRNA-22 nanotherapy for TNBC in combination with standard-of-care drugs. 

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-022-03176-3.

Nano to rescue: repository of nanocarriers for targeted drug delivery to curb breast cancer

Breast cancer is a heterogeneous disease with different intrinsic subtypes. The conventional treatment of surgical resection, chemotherapy, immunotherapy and radiotherapy has not shown significant improvement in the survival rate of breast cancer patients. The therapeutics used cause bystander toxicities deteriorating healthy tissues. The breakthroughs of nanotechnology have been a promising feat in selective targeting of tumor site thus increasing the therapeutic gain. By the application of nanoenabled carriers, nanomedicines ensure targeted delivery, stability, enhanced cellular uptake, biocompatibility and higher apoptotic efficacy. The present review focuses on breakthrough of nanoscale intervention in targeted drug delivery as novel class of therapeutics. Nanoenabled carriers like polymeric and metallic nanoparticles, dendrimers, quantum dots, liposomes, solid lipid nanoparticles, carbon nanotubes, drug-antibody conjugates and exosomes revolutionized the targeted therapeutic delivery approach. These nanoassemblies have shown additional effect of improving the solubility of drugs such as paclitaxel, reducing the dose and toxicity. The present review provides an insight on the different drug conjugates employed/investigated to curb breast cancer using nanocarrier mediated targeted drug delivery. However, identification of appropriate biomarkers to target, clearer insight of the biological processes, batch uniformity, reproducibility, nanomaterial toxicity and stabilities are the hurdles faced by nanodrugs. The potential of nano-therapeutics delivery necessitates the agglomerated efforts of research community to bridge the route of nanodrugs for scale-up, commercialization and clinical applications.

Numerical Simulation of Enhancement of Superficial Tumor Laser Hyperthermia with Silicon Nanoparticles

Biodegradable and low-toxic silicon nanoparticles (SiNPs) have potential in different biomedical applications. Previous experimental studies revealed the efficiency of some types of SiNPs in tumor hyperthermia. To analyse the feasibility of employing SiNPs produced by the laser ablation of silicon nanowire arrays in water and ethanol as agents for laser tumor hyperthermia, we numerically simulated effects of heating a millimeter-size nodal basal-cell carcinoma with embedded nanoparticles by continuous-wave laser radiation at 633 nm. Based on scanning electron microscopy data for the synthesized SiNPs size distributions, we used Mie theory to calculate their optical properties and carried out Monte Carlo simulations of light absorption inside the tumor, with and without the embedded nanoparticles, followed by an evaluation of local temperature increase based on the bioheat transfer equation. Given the same mass concentration, SiNPs obtained by the laser ablation of silicon nanowires in ethanol (eSiNPs) are characterized by smaller absorption and scattering coefficients compared to those synthesized in water (wSiNPs). In contrast, wSiNPs embedded in the tumor provide a lower overall temperature increase than eSiNPs due to the effect of shielding the laser irradiation by the highly absorbing wSiNPs-containing region at the top of the tumor. Effective tumor hyperthermia (temperature increase above 42 °C) can be performed with eSiNPs at nanoparticle mass concentrations of 3 mg/mL and higher, provided that the neighboring healthy tissues remain underheated at the applied irradiation power. The use of a laser beam with the diameter fitting the size of the tumor allows to obtain a higher temperature contrast between the tumor and surrounding normal tissues compared to the case when the beam diameter exceeds the tumor size at the comparable power.

Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling

Background:

Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies.

Methods:

Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials.

Results:

The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology.

Conclusions:

These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis.

Funding:

We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

A Multiscale Model to Identify Limiting Factors in Nanoparticle-Based miRNA Delivery for Tumor Inhibition. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:4230-4233. [PMID: 34892157 PMCID: PMC8712117 DOI: 10.1109/embc46164.2021.9630862]

MicroRNA-based gene therapy for cancer treatment via nanoparticles (NPs) requires navigation of multiple physical and physiological barriers in order to efficiently deliver the miRNAs to the cancer cell cytoplasm. We here present a mathematical model to investigate the variability associated with tumor, NP, and miRNA characteristics, and identify the limiting factors in miRNA delivery to tumors. Through global parameter analysis, the miRNA release rate from NPs and NP degradability were found to have the most significant impact on cytosolic accumulation of miRNAs. These NP properties can be fine-tuned in order to optimize the delivery system for enhancing the efficacy of miRNA-based therapy.Clinical Relevance-Understanding the effect of nanoparticle, tumor, and miRNA characteristics in governing the efficacy of miRNA-based cancer therapy will support its clinical translation.

Targeting a cell surface vitamin D receptor on tumor-associated macrophages in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive tumor with limited treatment options and poor prognosis. We applied the in vivo phage display technology to isolate peptides homing to the immunosuppressive cellular microenvironment of TNBC as a strategy for non-malignant target discovery. We identified a cyclic peptide (CSSTRESAC) that specifically binds to a vitamin D receptor, protein disulfide-isomerase A3 (PDIA3) expressed on the cell surface of tumor-associated macrophages (TAM), and targets breast cancer in syngeneic TNBC, non-TNBC xenograft, and transgenic mouse models. Systemic administration of CSSTRESAC to TNBC-bearing mice shifted the cytokine profile toward an antitumor immune response and delayed tumor growth. Moreover, CSSTRESAC enabled ligand-directed theranostic delivery to tumors and a mathematical model confirmed our experimental findings. Finally, in silico analysis showed PDIA3-expressing TAM in TNBC patients. This work uncovers a functional interplay between a cell surface vitamin D receptor in TAM and antitumor immune response that could be therapeutically exploited.

Conversion of RNA Aptamer into Modified DNA Aptamers Provides for Prolonged Stability and Enhanced Antitumor Activity

Aptamers, synthetic single-strand oligonucleotides that are similar in function to antibodies, are promising as therapeutics because of their minimal side effects. However, the stability and bioavailability of the aptamers pose a challenge. We developed aptamers converted from RNA aptamer to modified DNA aptamers that target phospho-AXL with improved stability and bioavailability. On the basis of the comparative analysis of a library of 17 converted modified DNA aptamers, we selected aptamer candidates, GLB-G25 and GLB-A04, that exhibited the highest bioavailability, stability, and robust antitumor effect in in vitro experiments. Backbone modifications such as thiophosphate or dithiophosphate and a covalent modification of the 5'-end of the aptamer with polyethylene glycol optimized the pharmacokinetic properties, improved the stability of the aptamers in vivo by reducing nuclease hydrolysis and renal clearance, and achieved high and sustained inhibition of AXL at a very low dose. Treatment with these modified aptamers in ovarian cancer orthotopic mouse models significantly reduced tumor growth and the number of metastases. This effective silencing of the phospho-AXL target thus demonstrated that aptamer specificity and bioavailability can be improved by the chemical modification of existing aptamers for phospho-AXL. These results lay the foundation for the translation of these aptamer candidates and companion biomarkers to the clinic.

Nanomaterials to Fight Cancer: An Overview on Their Multifunctional Exploitability

In recent years the worldwide research community has highlighted innumerable benefits of nanomaterials in cancer detection and therapy. Nevertheless, the development of cancer nanomedicines and other bionanotechnology requires a huge amount of considerations about the interactions of nanomaterials and biological systems, since long-term effects are not yet fully known. Open issues remain the determination of the nanoparticles distributions patterns and the internalization rate into the tumor while avoiding their accumulation in internal organs or other healthy tissues. The purpose of this work is to provide a standard overview of the most recent advances in nanomaterials to fight cancer and to collect trends and future directions to follow according to some critical aspects still present in this field. Complementary to the very recent review of Wolfram and Ferrari which discusses and classifies successful clinically-approved cancer nanodrugs as well as promising candidates in the pipeline, this work embraces part of their proposed classification system based on the exploitation of multifunctionality and extends the review to peer-reviewed journal articles published in the last 3 years identified through international databases.

Targeted Phage Display-based Pulmonary Vaccination in Mice and Non-human Primates

BACKGROUND

The extensive alveolar capillary network of the lungs is an attractive route for administration of several agents. One key functional attribute is the rapid onset of systemic action due to the absence of first-pass metabolism.

METHODS

Here we applied a combinatorial approach for ligand-directed pulmonary delivery as a unique route for systemic targeting in vaccination.

FINDINGS

We screened a phage display random peptide library in vivo to select, identify, and validate a ligand (CAKSMGDIVC) that specifically targets and is internalized through its receptor, α3β1 integrin, on the surface of cells lining the lung airways and alveoli and mediates CAKSMGDIVC-displaying phage binding and systemic delivery without compromising lung homeostasis. As a proof-of-concept, we show that the pulmonary delivery of targeted CAKSMGDIVC-displaying phage particles in mice and non-human primates elicit a systemic and specific humoral response.

CONCLUSIONS

This broad methodology blueprint represents a robust and versatile platform tool enabling new ligand-receptor discovery with many potential translational applications.

FUNDING

Cancer Center Support Grants to the University of Texas M.D. Anderson Cancer Center (CA016672), University of New Mexico Comprehensive Cancer Center (CA118100), Rutgers Cancer Institute of New Jersey (CA072720), research awards from the Gillson Longenbaugh Foundation, and National Institutes of Health (NIH) grant no. 1R01CA226537.

Biogenic synthesis and cytotoxic effects of silver nanoparticles mediated by white rot fungi

Silver nanoparticles (AgNPs) were successfully synthesized using silver nitrate via the biological route using the culture filtrate of Ganoderma enigmaticum as well as Trametes ljubarskyi white rot fungi materials at room temperature. The proposed synthetic technique was applied for the first time for AgNPs preparation via the biological route through a low-cost pathway, which considered as an adequate direction of preparation compared to the commercial methods. This study reports the in vitro cytotoxic effect of biologically synthesized AgNPs in disposing of the human lung cancer cell line (A549) and human breast cancer cell (MCF-7) by using a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. In addition, the viability of the tested cell lines was tested after treatment for 24 h in the presence of the prepared nanoparticles. The obtained results indicated the reduced viability of cancer cell lines with improving concentrations of AgNPs (40-120 μg/mL) at 24 h. Furthermore, at 120 μg/mL concentration, the fungal nanoparticles showed substantial cytotoxic effects toward the treated cells. Consequently, the results designated that the biologically synthesized silver nanoparticles have effective behavior for treating A549 and MCF-7 cancer cells from the laboratory experiment approach; however, additional studies are required to validate these results in vivo models as anticancer agents depending on their cytotoxic activity.

Innate Immunity Plays a Key Role in Controlling Viral Load in COVID-19: Mechanistic Insights from a Whole-Body Infection Dynamics Model

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pathogen of immense public health concern. Efforts to control the disease have only proven mildly successful, and the disease will likely continue to cause excessive fatalities until effective preventative measures (such as a vaccine) are developed. To develop disease management strategies, a better understanding of SARS-CoV-2 pathogenesis and population susceptibility to infection are needed. To this end, mathematical modeling can provide a robust in silico tool to understand COVID-19 pathophysiology and the in vivo dynamics of SARS-CoV-2. Guided by ACE2-tropism (ACE2 receptor dependency for infection) of the virus and by incorporating cellular-scale viral dynamics and innate and adaptive immune responses, we have developed a multiscale mechanistic model for simulating the time-dependent evolution of viral load distribution in susceptible organs of the body (respiratory tract, gut, liver, spleen, heart, kidneys, and brain). Following parameter quantification with in vivo and clinical data, we used the model to simulate viral load progression in a virtual patient with varying degrees of compromised immune status. Further, we ranked model parameters through sensitivity analysis for their significance in governing clearance of viral load to understand the effects of physiological factors and underlying conditions on viral load dynamics. Antiviral drug therapy, interferon therapy, and their combination were simulated to study the effects on viral load kinetics of SARS-CoV-2. The model revealed the dominant role of innate immunity (specifically interferons and resident macrophages) in controlling viral load, and the importance of timing when initiating therapy after infection.

A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases

Simple Summary

It is known that drug transport barriers in the tumor determine drug concentration at the tumor site, causing disparity from the systemic (plasma) drug concentration. However, current clinical standard of care still bases dosage and treatment optimization on the systemic concentration of drugs. Here, we present a proof of concept observational cohort study to accurately estimate drug concentration at the tumor site from mathematical modeling using biologic, clinical, and imaging/perfusion data, and correlate it with outcome in colorectal cancer liver metastases. We demonstrate that drug concentration at the tumor site, not in systemic circulation, can be used as a credible biomarker for predicting chemotherapy outcome, and thus our mathematical modeling approach can be applied prospectively in the clinic to personalize treatment design to optimize outcome.

Abstract

Chemotherapy remains a primary treatment for metastatic cancer, with tumor response being the benchmark outcome marker. However, therapeutic response in cancer is unpredictable due to heterogeneity in drug delivery from systemic circulation to solid tumors. In this proof-of-concept study, we evaluated chemotherapy concentration at the tumor-site and its association with therapy response by applying a mathematical model. By using pre-treatment imaging, clinical and biologic variables, and chemotherapy regimen to inform the model, we estimated tumor-site chemotherapy concentration in patients with colorectal cancer liver metastases, who received treatment prior to surgical hepatic resection with curative-intent. The differential response to therapy in resected specimens, measured with the gold-standard Tumor Regression Grade (TRG; from 1, complete response to 5, no response) was examined, relative to the model predicted systemic and tumor-site chemotherapy concentrations. We found that the average calculated plasma concentration of the cytotoxic drug was essentially equivalent across patients exhibiting different TRGs, while the estimated tumor-site chemotherapeutic concentration (eTSCC) showed a quadratic decline from TRG = 1 to TRG = 5 (p < 0.001). The eTSCC was significantly lower than the observed plasma concentration and dropped by a factor of ~5 between patients with complete response (TRG = 1) and those with no response (TRG = 5), while the plasma concentration remained stable across TRG groups. TRG variations were driven and predicted by differences in tumor perfusion and eTSCC. If confirmed in carefully planned prospective studies, these findings will form the basis of a paradigm shift in the care of patients with potentially curable colorectal cancer and liver metastases.

ncRNA therapy with miRNA-22-3p suppresses the growth of triple-negative breast cancer

Deregulation of noncoding RNAs, including microRNAs (miRs), is implicated in the pathogenesis of many human cancers, including breast cancer. Through extensive analysis of The Cancer Genome Atlas, we found that expression of miR-22-3p is markedly lower in triple-negative breast cancer (TNBC) than in normal breast tissue. The restoration of miR-22-3p expression led to significant inhibition of TNBC cell proliferation, colony formation, migration, and invasion. We demonstrated that miR-22-3p reduces eukaryotic elongation factor 2 kinase (eEF2K) expression by directly binding to the 3' untranslated region of eEF2K mRNA. Inhibition of EF2K expression recapitulated the effects of miR-22-3p on TNBC cell proliferation, motility, invasion, and suppression of phosphatidylinositol 3-kinase/Akt and Src signaling. Systemic administration of miR-22-3p in single-lipid nanoparticles significantly suppressed tumor growth in orthotopic MDA-MB-231 and MDA-MB-436 TNBC models. Evaluation of the tumor response, following miR-22-3p therapy in these models using a novel mathematical model factoring in various in vivo parameters, demonstrated that the therapy is highly effective against TNBC. These findings suggest that miR-22-3p functions as a tumor suppressor by targeting clinically significant oncogenic pathways and that miR-22-3p loss contributes to TNBC growth and progression. The restoration of miR-22-3p expression is a potential novel noncoding RNA-based therapy for TNBC.

Effects of Doxorubicin Delivery by Nitrogen-Doped Graphene Quantum Dots on Cancer Cell Growth: Experimental Study and Mathematical Modeling

With 18 million new cases diagnosed each year worldwide, cancer strongly impacts both science and society. Current models of cancer cell growth and therapeutic efficacy in vitro are time-dependent and often do not consider the E_max value (the maximum reduction in the growth rate), leading to inconsistencies in the obtained IC₅₀ (concentration of the drug at half maximum effect). In this work, we introduce a new dual experimental/modeling approach to model HeLa and MCF-7 cancer cell growth and assess the efficacy of doxorubicin chemotherapeutics, whether alone or delivered by novel nitrogen-doped graphene quantum dots (N-GQDs). These biocompatible/biodegradable nanoparticles were used for the first time in this work for the delivery and fluorescence tracking of doxorubicin, ultimately decreasing its IC₅₀ by over 1.5 and allowing for the use of up to 10 times lower doses of the drug to achieve the same therapeutic effect. Based on the experimental in vitro studies with nanomaterial-delivered chemotherapy, we also developed a method of cancer cell growth modeling that (1) includes an E_max value, which is often not characterized, and (2), most importantly, is measurement time-independent. This will allow for the more consistent assessment of the efficiency of anti-cancer drugs and nanomaterial-delivered formulations, as well as efficacy improvements of nanomaterial delivery.

A mathematical model for the quantification of a patient's sensitivity to checkpoint inhibitors and long-term tumour burden

A large proportion of patients with cancer are unresponsive to treatment with immune checkpoint blockade and other immunotherapies. Here, we report a mathematical model of the time-course of tumour responses to immune-checkpoint inhibitors. The model takes into account intrinsic tumour-growth rates, the rates of immune activation and of tumour–immune-cell interactions, and the efficacy of immune-mediated tumour killing. For 124 patients, four cancer types and two immunotherapy agents, the model reliably described the immune responses and final tumour burden across all different cancers and drug combinations examined. In validation cohorts from four clinical trials of checkpoint inhibitors (with a total of 177 patients), the model accurately stratified the patients according to reduced or increased long-term tumour burden. We also provide model-derived quantitative measures of treatment sensitivity for specific drug–cancer combinations. The model can be used to predict responses to therapy and to quantify specific drug–cancer sensitivities in individual patients.

Investigating the Effect of Aging on the Pharmacokinetics and Tumor Delivery of Nanomaterials using Mathematical Modeling

The application of nanomedicine for diagnosis and treatment of cancer has immense potential, but has witnessed only limited clinical success, in part due to insufficient understanding of the role of nanomaterial properties and physiological variables in governing nanoparticle (NP) pharmacology. Here, we present a multiscale mathematical model to examine the effects of physiological changes associated with patient age on the pharmacokinetics and tumor delivery efficiency of NPs. We show that physiological changes due to aging prolong the residence of NPs in the systemic circulation, thereby improving passive accumulation of NPs in tumors.Clinical Relevance - Understanding the effect of inter-individual variability on the pharmacological behavior of nanomaterials will improve their clinical translatability.

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.

Image-guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies

While plasma concentration kinetics has traditionally been the predictor of drug pharmacological effects, it can occasionally fail to represent kinetics at the site of action, particularly for solid tumors. This is especially true in the case of delivery of therapeutic macromolecules (drug-loaded nanomaterials or monoclonal antibodies), which can experience challenges to effective delivery due to particle size-dependent diffusion barriers at the target site. As a result, disparity between therapeutic plasma kinetics and kinetics at the site of action may exist, highlighting the importance of target site concentration kinetics in determining the pharmacodynamic effects of macromolecular therapeutic agents. Assessment of concentration kinetics at the target site has been facilitated by non-invasive in vivo imaging modalities. This allows for visualization and quantification of the whole-body disposition behavior of therapeutics that is essential for a comprehensive understanding of their pharmacokinetics and pharmacodynamics. Quantitative non-invasive imaging can also help guide the development and parameterization of mathematical models for descriptive and predictive purposes. Here, we present a review of the application of state-of-the-art imaging modalities for quantitative pharmacological evaluation of therapeutic nanoparticles and monoclonal antibodies, with a focus on their integration with mathematical models, and identify challenges and opportunities. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > in vivo Nanodiagnostics and Imaging Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery

Towards clinical translation of cancer nanomedicine, it is important to systematically investigate the various parameters related to nanoparticle (NP) physicochemical properties, tumor characteristics, and inter-individual variability that affect the tumor delivery efficiency of therapeutic nanomaterials. Comprehensive investigation of these parameters using traditional experimental approaches is impractical due to the vast parameter space; mathematical models provide a more tractable approach to navigate through such a multidimensional space. To this end, we have developed a predictive mathematical model of whole-body NP pharmacokinetics and their tumor delivery in vivo, and have conducted local and global sensitivity analyses to identify the factors that result in low tumor delivery efficiency and high off-target accumulation of NPs. Our analyses reveal that NP degradation rate, tumor blood viscosity, NP size, tumor vascular fraction, and tumor vascular porosity are the key parameters in governing NP kinetics in the tumor interstitium. The impact of these parameters on tumor delivery efficiency of NPs is discussed, and optimal values for maximizing NP delivery are presented.

Development of a Physiologically-Based Mathematical Model for Quantifying Nanoparticle Distribution in Tumors

Nanomedicine holds promise for the treatment of cancer, as it enables tumor-targeted drug delivery. However, reports on translation of most nanomedicine strategies to the clinic so far have been less than satisfactory, in part due to insufficient understanding of the effects of nanoparticle (NP) physiochemical properties and physiological variables on their pharmacological behavior. In this paper, we present a multiscale mathematical model to examine the efficacy of NP delivery to solid tumors; as a case example, we apply the model to a clinically detectable primary pancreatic ductal adenocarcinoma (PDAC) to assess tissue-scale spatiotemporal distribution profiles of NPs. We integrate NP systemic disposition kinetics with NP-cell interactions in PDAC abstractly described as a two-dimensional structure, which is then parameterized with human physiological data obtained from published literature. Through model analysis of delivery efficiency, we verify the multiscale approach by showing that NP concentration kinetics of interest in various compartments predicted by the whole-body scale model were in agreement with those obtained from the tissue-scale model. We also found that more NPs were trapped in the outer well-perfused tumor region than the inner semi-necrotic domain. Further development of the model may provide a useful tool for optimal NP design and physiological interventions.

Mathematical Modeling to Address Challenges in Pancreatic Cancer

Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents, thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidisciplinary approach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis, and treatment. To this end, the application of quantitative tools can help improve the understanding of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.

Size-Optimized Ultrasmall Porous Silica Nanoparticles Depict Vasculature-Based Differential Targeting in Triple Negative Breast Cancer

Rapid sequestration and prolonged retention of intravenously injected nanoparticles by the liver and spleen (reticuloendothelial system (RES)) presents a major barrier to effective delivery to the target site and hampers clinical translation of nanomedicine. Inspired by biological macromolecular drugs, synthesis of ultrasmall (diameter ≈12-15 nm) porous silica nanoparticles (UPSNs), capable of prolonged plasma half-life, attenuated RES sequestration, and accelerated hepatobiliary clearance, is reported. The study further investigates the effect of tumor vascularization on uptake and retention of UPSNs in two mouse models of triple negative breast cancer with distinctly different microenvironments. A semimechanistic mathematical model is developed to gain mechanistic insights into the interactions between the UPSNs and the biological entities of interest, specifically the RES. Despite similar systemic pharmacokinetic profiles, UPSNs demonstrate strikingly different tumor responses in the two models. Histopathology confirms the differences in vasculature and stromal status of the two models, and corresponding differences in the microscopic distribution of UPSNs within the tumors. The studies demonstrate the successful application of multidisciplinary and complementary approaches, based on laboratory experimentation and mathematical modeling, to concurrently design optimized nanomaterials, and investigate their complex biological interactions, in order to drive innovation and translation.

Mathematical modeling in cancer nanomedicine: a review

Cancer continues to be among the leading healthcare problems worldwide, and efforts continue not just to find better drugs, but also better drug delivery methods. The need for delivering cytotoxic agents selectively to cancerous cells, for improved safety and efficacy, has triggered the application of nanotechnology in medicine. This effort has provided drug delivery systems that can potentially revolutionize cancer treatment. Nanocarriers, due to their capacity for targeted drug delivery, can shift the balance of cytotoxicity from healthy to cancerous cells. The field of cancer nanomedicine has made significant progress, but challenges remain that impede its clinical translation. Several biophysical barriers to the transport of nanocarriers to the tumor exist, and a much deeper understanding of nano-bio interactions is necessary to change the status quo. Mathematical modeling has been instrumental in improving our understanding of the physicochemical and physiological underpinnings of nanomaterial behavior in biological systems. Here, we present a comprehensive review of literature on mathematical modeling works that have been and are being employed towards a better understanding of nano-bio interactions for improved tumor delivery efficacy.

Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies

In clinical breast cancer intervention, selection of the optimal treatment protocol based on predictive biomarkers remains an elusive goal. Here, we present a modeling tool to predict the likelihood of breast cancer response to neoadjuvant chemotherapy using patient specific tumor vasculature biomarkers. A semi-automated analysis was implemented and performed on 3990 histological images from 48 patients, with 10-208 images analyzed for each patient. We applied a histology-based model to resected primary breast cancer tumors (n = 30), and then evaluated a cohort of patients (n = 18) undergoing neoadjuvant chemotherapy, collecting pre- and post-treatment pathology specimens and MRI data. We found that core biopsy samples can be used with acceptable accuracy (r = 0.76) to determine histological parameters representative of the whole tissue region. Analysis of model histology parameters obtained from tumor vasculature measurements, specifically diffusion distance divided by radius of drug source (L/rb) and blood volume fraction (BVF), provides a statistically significant separation of patients obtaining a pathologic complete response (pCR) from those that do not (Student's t-test; P < 0.05). With this model, it is feasible to evaluate primary breast tumor vasculature biomarkers in a patient specific manner, thereby allowing a precision approach to breast cancer treatment.

Dynamic Targeting in Cancer Treatment

With the advent of personalized medicine, design and development of anti-cancer drugs that are specifically targeted to individual or sets of genes or proteins has been an active research area in both academia and industry. The underlying motivation for this approach is to interfere with several pathological crosstalk pathways in order to inhibit or at the very least control the proliferation of cancer cells. However, after initially conferring beneficial effects, if sub-lethal, these artificial perturbations in cell function pathways can inadvertently activate drug-induced up- and down-regulation of feedback loops, resulting in dynamic changes over time in the molecular network structure and potentially causing drug resistance as seen in clinics. Hence, the targets or their combined signatures should also change in accordance with the evolution of the network (reflected by changes to the structure and/or functional output of the network) over the course of treatment. This suggests the need for a "dynamic targeting" strategy aimed at optimizing tumor control by interfering with different molecular targets, at varying stages. Understanding the dynamic changes of this complex network under various perturbed conditions due to drug treatment is extremely challenging under experimental conditions let alone in clinical settings. However, mathematical modeling can facilitate studying these effects at the network level and beyond, and also accelerate comparison of the impact of different dosage regimens and therapeutic modalities prior to sizeable investment in risky and expensive clinical trials. A dynamic targeting strategy based on the use of mathematical modeling can be a new, exciting research avenue in the discovery and development of therapeutic drugs.

Three dimensional coupled reaction–diffusion modeling of calcium and inositol 1,4,5-trisphosphate dynamics in cardiomyocytes

Nanoparticles have shown great promise in improving cancer treatment efficacy by changing the intracellular calcium level through activation of intracellular mechanisms. One of the mechanisms of the killing of the cancerous cell by a nanoparticle is through elevation of the intracellular calcium level. Evidence accumulated over the past decade indicates a pivotal role for the IP₃ receptor mediated Ca²⁺ release in the regulation of the cytosolic and the nuclear Ca²⁺ signals. There have been various studies done suggesting the role of IP₃ receptors (IP₃R) and IP₃ production and degradation in cardiomyocytes. In the present work, we have proposed a three-dimensional unsteady-state mathematical model to describe the mechanism of cardiomyocytes which focuses on evaluation of various parameters that affect these coupled dynamics and elevate the cytosolic calcium concentration which can be helpful to search for novel therapies to cure these malignancies by targeting the complex calcium signaling process in cardiomyocytes. Our study suggests that there are other factors involved in this signaling which can increase the calcium level, which can help in finding treatment for cancer. The cytosolic calcium level may be controlled by IP₃ signaling, leak, source influx of calcium (σ) and maximum production of IP₃ (V_P). We believe that the proposed model suggests new insight into finding treatment for cancer in cardiomyocytes through elevation of the cytosolic Ca²⁺ concentration by various parameters like leak, σ, V_P and especially by other complex cell signaling dynamics, namely IP₃ dynamics.

We propose a three-dimensional unsteady-state mathematical model to describe the mechanism of cardiomyocytes.

Establishing the effects of mesoporous silica nanoparticle properties on in vivo disposition using imaging-based pharmacokinetics

The progress of nanoparticle (NP)-based drug delivery has been hindered by an inability to establish structure-activity relationships in vivo. Here, using stable, monosized, radiolabeled, mesoporous silica nanoparticles (MSNs), we apply an integrated SPECT/CT imaging and mathematical modeling approach to understand the combined effects of MSN size, surface chemistry and routes of administration on biodistribution and clearance kinetics in healthy rats. We show that increased particle size from ~32- to ~142-nm results in a monotonic decrease in systemic bioavailability, irrespective of route of administration, with corresponding accumulation in liver and spleen. Cationic MSNs with surface exposed amines (PEI) have reduced circulation, compared to MSNs of identical size and charge but with shielded amines (QA), due to rapid sequestration into liver and spleen. However, QA show greater total excretion than PEI and their size-matched neutral counterparts (TMS). Overall, we provide important predictive functional correlations to support the rational design of nanomedicines.