Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study

Interactions between biological systems and nanomaterials have become an important area of study due to the application of nanomaterials in medicine. In particular, the application of nanomaterials for cancer diagnosis or treatment presents a challenging opportunity due to the complex biology of this disease spanning multiple time and spatial scales. A system-level analysis would benefit from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated parameters driving this system and a patient's overall response. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, "inheritance" of NPs by daughter cells at cell division, cell pharmacodynamic response to the intracellular drug, and overall drug effect on tumor dynamics. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on the tumor dynamics. In particular, through the exploration of NP "inheritance" at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dosage of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by "heritable" NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.

Identification of Idiopathic Pulmonary Fibrosis and Prediction of Disease Severity via Machine Learning Analysis of Comprehensive Metabolic Panel and Complete Blood Count Data

BACKGROUND

Diagnosis of idiopathic pulmonary fibrosis (IPF) typically relies on high-resolution computed tomography imaging (HRCT) or histopathology, while monitoring disease severity is done via frequent pulmonary function testing (PFT). More reliable and convenient methods of diagnosing fibrotic interstitial lung disease (ILD) type and monitoring severity would allow for early identification and enhance current therapeutic interventions. This study tested the hypothesis that a machine learning (ML) ensemble analysis of comprehensive metabolic panel (CMP) and complete blood count (CBC) data can accurately distinguish IPF from connective tissue disease ILD (CTD-ILD) and predict disease severity as seen with PFT.

METHODS

Outpatient data with diagnosis of IPF or CTD-ILD (n = 103 visits by 53 patients) were analyzed via ML methodology to evaluate (1) IPF vs CTD-ILD diagnosis; (2) %predicted Diffusing Capacity of Lung for Carbon Monoxide (DLCO) moderate or mild vs severe; (3) %predicted Forced Vital Capacity (FVC) moderate or mild vs severe; and (4) %predicted FVC mild vs moderate or severe.

RESULTS

ML methodology identified IPF from CTD-ILD with AUCTEST = 0.893, while PFT was classified as DLCO moderate or mild vs severe with AUCTEST = 0.749, FVC moderate or mild vs severe with AUCTEST = 0.741, and FVC mild vs moderate or severe with AUCTEST = 0.739. Key features included albumin, alanine transaminase, %lymphocytes, hemoglobin, %eosinophils, white blood cell count, %monocytes, and %neutrophils.

CONCLUSION

Analysis of CMP and CBC data via proposed ML methodology offers the potential to distinguish IPF from CTD-ILD and predict severity on associated PFT with accuracy that meets or exceeds current clinical practice.

Rising role of 3D-printing in delivery of therapeutics for infectious disease

Modern drug delivery to tackle infectious disease has drawn close to personalizing medicine for specific patient populations. Challenges include antibiotic-resistant infections, healthcare associated infections, and customizing treatments for local patient populations. Recently, 3D-printing has become a facilitator for the development of personalized pharmaceutic drug delivery systems. With a variety of manufacturing techniques, 3D-printing offers advantages in drug delivery development for controlled, fine-tuned release and platforms for different routes of administration. This review summarizes 3D-printing techniques in pharmaceutics and drug delivery focusing on treating infectious diseases, and discusses the influence of 3D-printing design considerations on drug delivery platforms targeting these diseases. Additionally, applications of 3D-printing in infectious diseases are summarized, with the goal to provide insight into how future delivery innovations may benefit from 3D-printing to address the global challenges in infectious disease.

Disease diagnosis and severity classification in pulmonary fibrosis using carbonyl volatile organic compounds in exhaled breath

BACKGROUND

Pathophysiological conditions underlying pulmonary fibrosis remain poorly understood. Exhaled breath volatile organic compounds (VOCs) have shown promise for lung disease diagnosis and classification. In particular, carbonyls are a byproduct of oxidative stress, associated with fibrosis in the lungs. To explore the potential of exhaled carbonyl VOCs to reflect underlying pathophysiological conditions in pulmonary fibrosis, this proof-of-concept study tested the hypothesis that volatile and low abundance carbonyl compounds could be linked to diagnosis and associated disease severity.

METHODS

Exhaled breath samples were collected from outpatients with a diagnosis of Idiopathic Pulmonary Fibrosis (IPF) or Connective Tissue related Interstitial Lung Disease (CTD-ILD) with stable lung function for 3 months before enrollment, as measured by pulmonary function testing (PFT) DLCO (%), FVC (%) and FEV1 (%). A novel microreactor was used to capture carbonyl compounds in the breath as direct output products. A machine learning workflow was implemented with the captured carbonyl compounds as input features for classification of diagnosis and disease severity based on PFT (DLCO and FVC normal/mild vs. moderate/severe; FEV1 normal/mild/moderate vs. moderately severe/severe).

RESULTS

The proposed approach classified diagnosis with AUROC=0.877 ± 0.047 in the validation subsets. The AUROC was 0.820 ± 0.064, 0.898 ± 0.040, and 0.873 ± 0.051 for disease severity based on DLCO, FEV1, and FVC measurements, respectively. Eleven key carbonyl VOCs were identified with the potential to differentiate diagnosis and to classify severity.

CONCLUSIONS

Exhaled breath carbonyl compounds can be linked to pulmonary function and fibrotic ILD diagnosis, moving towards improved pathophysiological understanding of pulmonary fibrosis.

Anti-tumor effect of pH-sensitive drug-loaded nanoparticles optimized via an integrated computational/experimental approach

The acidic pH of tumor tissue has been used to trigger drug release from nanoparticles. However, dynamic interactions between tumor pH and vascularity present challenges to optimize therapy to particular microenvironment conditions. Despite recent development of pH-sensitive nanomaterials that can accurately quantify drug release from nanoparticles, tailoring release to maximize tumor response remains elusive. This study hypothesizes that a computational modeling-based platform that simulates the heterogeneously vascularized tumor microenvironment can enable evaluation of the complex intra-tumoral dynamics involving nanoparticle transport and pH-dependent drug release, and predict optimal nanoparticle parameters to maximize the response. To this end, SPNCD nanoparticles comprising superparamagnetic cores of iron oxide (Fe3O4) and a poly(lactide-co-glycolide acid) shell loaded with doxorubicin (DOX) were fabricated. Drug release was measured in vitro as a function of pH. A 2D model of vascularized tumor growth was calibrated to experimental data and used to evaluate SPNCD effect as a function of drug release rate and tissue vascular heterogeneity. Simulations show that pH-dependent drug release from SPNCD delays tumor regrowth more than DOX alone across all levels of vascular heterogeneity, and that SPNCD significantly inhibit tumor radius over time compared to systemic DOX. The minimum tumor radius forecast by the model was comparable to previous in vivo SPNCD inhibition data. Sensitivity analyses of the SPNCD pH-dependent drug release rate indicate that slower rates are more inhibitory than faster rates. We conclude that an integrated computational and experimental approach enables tailoring drug release by pH-responsive nanomaterials to maximize the tumor response.

A clinical marker-based modeling framework to preoperatively predict lymph node and vascular space involvement in endometrial cancer patients

INTRODUCTION

Endometrial cancer (EC) has high mortality at advanced stages. Poor prognostic factors include grade 3 tumors, deep myometrial invasion, lymph node metastasis (LNM), and lymphovascular space invasion (LVSI). Preoperative knowledge of patients at higher risk of lymph node involvement, when such involvement is not suspected, would benefit surgery planning and patient prognosis. This study implements an ensemble machine learning approach that evaluates Cancer Antigen 125 (CA125) along with histologic type, preoperative grade, and age to predict LVSI, LNM and stage in EC patients.

METHODS

A retrospective chart review spanning January 2000 to January 2015 at a regional hospital was performed. Women 18 years or older with a diagnosis of EC and preoperative or within one-week CA125 measurement were included (n = 842). An ensemble machine learning approach was implemented based on a stacked generalization technique to evaluate CA125 in combination with histologic type, preoperative grade, and age as predictors, and LVSI, LNM and disease stage as outcomes.

RESULTS

The ensemble approach predicted LNM and LVSI in EC patients with AUROCTEST of 0.857 and 0.750, respectively, and predicted disease stage with AUROCTEST of 0.665. The approach achieved AUROCTEST for LVSI and LNM of 0.750 and 0.643 for grade 1 patients, and of 0.689 and 0.952 for grade 2 patients, respectively.

CONCLUSION

An ensemble machine learning approach offers the potential to preoperatively predict LVSI, LNM and stage in EC patients with adequate accuracy based on CA125, histologic type, preoperative grade, and age.

Mesh and layered electrospun fiber architectures as vehicles for Lactobacillus acidophilus and Lactobacillus crispatus intended for vaginal delivery

Bacterial vaginosis (BV) is a recurrent condition that affects millions of women worldwide. The use of probiotics is a promising alternative or an adjunct to traditional antibiotics for BV prevention and treatment. However, current administration regimens often require daily administration, thus contributing to low user adherence and recurrence. Here, electrospun fibers were designed to separately incorporate and sustain two lactic acid producing model organisms, Lactobacillus crispatus (L. crispatus) and Lactobacillus acidophilus (L. acidophilus). Fibers were made of polyethylene oxide and polylactic-co-glycolic acid in two different architectures, one with distinct layers and the other with co-spun components. Degradation of mesh and layered fibers was evaluated via mass loss and scanning electron microscopy. The results show that after 48 h and 6 days, cultures of mesh and layered fibers yielded as much as 108 and 109 CFU probiotic/mg fiber in total, respectively, with corresponding daily recovery on the order of 108 CFU/(mg·day). In addition, cultures of the fibers yielded lactic acid and caused a significant reduction in pH, indicating a high level of metabolic activity. The formulations did not affect vaginal keratinocyte viability or cell membrane integrity in vitro. Finally, mesh and layered probiotic fiber dosage forms demonstrated inhibition of Gardnerella, one of the most prevalent and abundant bacteria associated with BV, respectively resulting in 8- and 6.5-log decreases in Gardnerella viability in vitro after 24 h. This study provides initial proof of concept that mesh and layered electrospun fiber architectures developed as dissolving films may offer a viable alternative to daily probiotic administration.

Rapid-dissolving electrospun nanofibers for intra-vaginal antibiotic or probiotic delivery

The emergence of probiotics as an alternative and adjunct to antibiotic treatment for microbiological disturbances of the female genitourinary system requires innovative delivery platforms for vaginal applications. This study developed a new, rapid-dissolving form using electrospun polyethylene oxide (PEO) fibers for delivery of antibiotic metronidazole or probiotic Lactobacillus acidophilus, and performed evaluation in vitro and in vivo. Fibers did not generate overt pathophysiology or encourage Gardnerella growth in a mouse vaginal colonization model, inducing no alterations in vaginal mucosa at 24 hr post-administration. PEO-fibers incorporating metronidazole (100 µg MET/mg polymer) effectively prevented and treated Gardnerella infections (∼3- and 2.5-log reduction, respectively, 24 hr post treatment) when administered vaginally. Incorporation of live Lactobacillus acidophilus (107 CFU/mL) demonstrated viable probiotic delivery in vitro by PEO and polyvinyl alcohol (PVA) fibers to inhibit Gardnerella (108 CFU/mL) in bacterial co-cultures (9.9- and 7.0-log reduction, respectively, 24 hr post-inoculation), and in the presence of vaginal epithelial cells (6.9- and 8.0-log reduction, respectively, 16 hr post-inoculation). Administration of Lactobacillus acidophilus in PEO-fibers achieved vaginal colonization in mice similar to colonization observed with free Lactobacillus. acidophilus. These experiments provide proof-of-concept for rapid-dissolving electrospun fibers as a successful platform for intra-vaginal antibiotic or probiotic delivery.

Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach

Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.

Computational Modeling of Probiotic Recovery from 3D-Bioprinted Scaffolds for Localized Vaginal Application

Lactobacilli, play a beneficial role in the female reproductive tract (FRT), regulating pH via lactic acid metabolism to help maintain a healthy environment. Bacterial vaginosis (BV) is characterized by a dysregulated flora in which anaerobes such as Gardnerella vaginalis (Gardnerella) create a less acidic environment. Current treatment focuses on antibiotic administration, including metronidazole, clindamycin, or tinidazole; however, lack of patient compliance as well as antibiotic resistance may contribute to 50% recurrence within a year. Recently, locally administered probiotics such as Lactobacillus crispatus (L. crispatus) have been evaluated as a prophylactic against recurrence. To mitigate the lack of patient compliance, sustained probiotic delivery has been proposed via 3D-bioprinted delivery vehicles. Successful delivery depends on a variety of vehicle fabrication parameters influencing timing and rate of probiotic recovery; detailed evaluation of these parameters would benefit from computational modeling complementary to experimental evaluation. This study implements a novel simulation platform to evaluate sustained delivery of probiotics from 3D-bioprinted scaffolds, taking into consideration bacterial lactic acid production and associated pH changes. The results show that the timing and rate of probiotic recovery can be realistically simulated based on fabrication parameters that affect scaffold degradation and probiotic survival. Longer term, the proposed approach could help personalize localized probiotic delivery to the FRT to advance women's health.

Development and Characterization of Lactobacillus rhamnosus-Containing Bioprints for Application to Catheter-Associated Urinary Tract Infections

Catheter-associated urinary tract infections (CAUTI) are a significant healthcare burden affecting millions of patients annually. CAUTI are characterized by infection of the bladder and pathogen colonization of the catheter surface, making them especially difficult to treat. Various catheter modifications have been employed to reduce pathogen colonization, including infusion of antibiotics and antimicrobial compounds, altering the surface architecture of the catheter, or coating it with nonpathogenic bacteria. Lactobacilli probiotics offer promise for a "bacterial interference" approach because they not only compete for adhesion to the catheter surface but also produce and secrete antimicrobial compounds effective against uropathogens. Three-dimensional (3D) bioprinting has enabled fabrication of well-defined, cell-laden architectures with tailored release of active agents, thereby offering a novel means for sustained probiotic delivery. Silicone has shown to be a promising biomaterial for catheter applications due to mechanical strength, biocompatibility, and its ability to mitigate encrustation on the catheter. Additionally, silicone, as a bioink, provides an optimum matrix for bioprinting lactobacilli. This study formulates and characterizes novel 3D-bioprinted Lactobacillus rhamnosus (L. rhamnosus)-containing silicone scaffolds for future urinary tract catheterization applications. Weight-to-weight (w/w) ratio of silicone/L. rhamnosus was bioprinted and cured with relative catheter dimensions in diameter. Scaffolds were analyzed in vitro for mechanical integrity, recovery of L. rhamnosus, antimicrobial production, and antibacterial effect against uropathogenic Escherichia coli, the leading cause of CAUTI. The results show that L. rhamnosus-containing scaffolds are capable of sustained recovery of live bacteria over 14 days, with sustained production of lactic acid and hydrogen peroxide. Through the use of 3D bioprinting, this study presents a potential alternative strategy to incorporate probiotics into urinary catheters, with the ultimate goal of preventing and treating CAUTI.

Metabolomic differentiation of tumor core versus edge in glioma

OBJECTIVE

Gliomas exhibit high intratumor and interpatient heterogeneity. Recently, it has been shown that the microenvironment and phenotype differ significantly between the glioma core (inner) and edge (infiltrating) regions. This proof-of-concept study differentiates metabolic signatures associated with these regions, with the potential for prognosis and targeted therapy that could improve surgical outcomes.

METHODS

Paired glioma core and infiltrating edge samples were obtained from 27 patients after craniotomy. Liquid-liquid metabolite extraction was performed on the samples and metabolomic data were obtained via 2D liquid chromatography-mass spectrometry/mass spectrometry. To gauge the potential of metabolomics to identify clinically relevant predictors of survival from tumor core versus edge tissues, a boosted generalized linear machine learning model was used to predict metabolomic profiles associated with O6-methylguanine DNA methyltransferase (MGMT) promoter methylation.

RESULTS

A panel of 66 (of 168) metabolites was found to significantly differ between glioma core and edge regions (p ≤ 0.05). Top metabolites with significantly different relative abundances included DL-alanine, creatine, cystathionine, nicotinamide, and D-pantothenic acid. Significant metabolic pathways identified by quantitative enrichment analysis included glycerophospholipid metabolism; butanoate metabolism; cysteine and methionine metabolism; glycine, serine, alanine, and threonine metabolism; purine metabolism; nicotinate and nicotinamide metabolism; and pantothenate and coenzyme A biosynthesis. The machine learning model using 4 key metabolites each within core and edge tissue specimens predicted MGMT promoter methylation status, with AUROCEdge = 0.960 and AUROCCore = 0.941. Top metabolites associated with MGMT status in the core samples included hydroxyhexanoycarnitine, spermine, succinic anhydride, and pantothenic acid, and in the edge samples metabolites included 5-cytidine monophosphate, pantothenic acid, itaconic acid, and uridine.

CONCLUSIONS

Key metabolic differences are identified between core and edge tissue in glioma and, furthermore, demonstrate the potential for machine learning to provide insight into potential prognostic and therapeutic targets.

Release Kinetics of Metronidazole from 3D Printed Silicone Scaffolds for Sustained Application to the Female Reproductive Tract

Sustained vaginal administration of antibiotics or probiotics has been proposed to improve treatment efficacy for bacterial vaginosis. 3D printing has shown promise for development of systems for local agent delivery. In contrast to oral ingestion, agent release kinetics can be fine-tuned by the 3D printing of specialized scaffold designs tailored for particular treatments while enhancing dosage effectiveness via localized sustained release. It has been challenging to establish scaffold properties as a function of fabrication parameters to obtain sustained release. In particular, the relationships between scaffold curing conditions, compressive strength, and drug release kinetics remain poorly understood. This study evaluates 3D printed scaffold formulation and feasibility to sustain the release of metronidazole, a commonly used antibiotic for BV. Cylindrical silicone scaffolds were printed and cured using three different conditions relevant to potential future incorporation of temperature-sensitive labile biologics. Compressive strength and drug release were monitored for 14d in simulated vaginal fluid to assess long-term effects of fabrication conditions on mechanical integrity and release kinetics. Scaffolds were mechanically evaluated to determine compressive and tensile strength, and elastic modulus. Release profiles were fitted to previous kinetic models to differentiate potential release mechanisms. The Higuchi, Korsmeyer-Peppas, and Peppas-Sahlin models best described the release, indicating similarity to release from insoluble or polymeric matrices. This study shows the feasibility of 3D printed silicone scaffolds to provide sustained metronidazole release over 14d, with compressive strength and drug release kinetics tuned by the fabrication parameters.

Lactobacillus crispatus-loaded electrospun fibers yield viable and metabolically active bacteria that kill Gardnerella in vitro

Bacterial vaginosis (BV) is a common condition that affects one-third of women worldwide. BV is characterized by low levels of healthy lactobacilli and an overgrowth of common anaerobes such as Gardnerella. Antibiotics for BV are administered orally or vaginally; however, approximately half of those treated will experience recurrence within 6 months. Lactobacillus crispatus present at high levels has been associated with positive health outcomes. To address the high recurrence rates following BV treatment, beneficial bacteria have been considered as an alternative or adjunct modality. This study aimed to establish proof-of-concept for a new long-acting delivery vehicle for L. crispatus. Here, it is shown that polyethylene oxide (PEO) fibers loaded with L. crispatus can be electrospun with poly(lactic-co-glycolic acid) (PLGA) fibers (ratio 1:1), and that this construct later releases L. crispatus as metabolically viable bacteria capable of lactic acid production and anti-Gardnerella activity. Probiotic-containing fibers were serially cultured in MRS (deMan, Rogosa, Sharpe) broth with daily media replacement and found to yield viable L. crispatus for at least 7 days. Lactic acid levels and corresponding pH values generally corresponded with levels of L. crispatus cultured from the fibers and strongly support the conclusion that fibers yield viable L. crispatus that is metabolically active. Cultures of L. crispatus-loaded fibers limited the growth of Gardnerella in a dilution-dependent manner during in vitro assays in the presence of cultured vaginal epithelial cells, demonstrating bactericidal potential. Exposure of VK2/E6E7 cells to L. crispatus-loaded fibers resulted in minimal loss of viability relative to untreated cells. Altogether, these data provide proof-of-concept for electrospun fibers as a candidate delivery vehicle for application of vaginal probiotics in a long-acting form.

Formulation and Characterization of Pressure-Assisted Microsyringe 3D-Printed Scaffolds for Controlled Intravaginal Antibiotic Release

Bacterial vaginosis (BV) is a highly recurrent vaginal condition linked with many health complications. Topical antibiotic treatments for BV are challenged with drug solubility in vaginal fluid, lack of convenience and user adherence to daily treatment protocols, among other factors. 3D-printed scaffolds can provide sustained antibiotic delivery to the female reproductive tract (FRT). Silicone vehicles have been shown to provide structural stability, flexibility, and biocompatibility, with favorable drug release kinetics. This study formulates and characterizes novel metronidazole-containing 3D-printed silicone scaffolds for eventual application to the FRT. Scaffolds were evaluated for degradation, swelling, compression, and metronidazole release in simulated vaginal fluid (SVF). Scaffolds retained high structural integrity and sustained release. Minimal mass loss (<6%) and swelling (<2%) were observed after 14 days in SVF, relative to initial post-cure measurements. Scaffolds cured for 24 hr (50°C) demonstrated elastic behavior under 20% compression and 4.0 N load. Scaffolds cured for 4 hr (50°C), followed by 72 hr (4°C), demonstrated the highest, sustained, metronidazole release (4.0 and 27.0 µg/mg) after 24 hr and 14 days, respectively. Based upon daily release profiles, it was observed that the 24 hr timepoint had the greatest metronidazole release of 4.08 μg/mg for scaffolds cured at 4 hr at 50°C followed by 72 hr at 4°C. For all curing conditions, release of metronidazole after 1 and 7 days showed >4.0-log reduction in Gardnerella concentration. Negligible cytotoxicity was observed in treated keratinocytes comparable to untreated cells, This study shows that pressure-assisted microsyringe 3D-printed silicone scaffolds may provide a versatile vehicle for sustained metronidazole delivery to the FRT.

Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study

BACKGROUND

The application of machine learning (ML) to analyze clinical data with the goal to predict patient outcomes has garnered increasing attention. Ensemble learning has been used in conjunction with ML to improve predictive performance. Although stacked generalization (stacking), a type of heterogeneous ensemble of ML models, has emerged in clinical data analysis, it remains unclear how to define the best model combinations for strong predictive performance. This study develops a methodology to evaluate the performance of "base" learner models and their optimized combination using "meta" learner models in stacked ensembles to accurately assess performance in the context of clinical outcomes.

METHODS

De-identified COVID-19 data was obtained from the University of Louisville Hospital, where a retrospective chart review was performed from March 2020 to November 2021. Three differently-sized subsets using features from the overall dataset were chosen to train and evaluate ensemble classification performance. The number of base learners chosen from several algorithm families coupled with a complementary meta learner was varied from a minimum of 2 to a maximum of 8. Predictive performance of these combinations was evaluated in terms of mortality and severe cardiac event outcomes using area-under-the-receiver-operating-characteristic (AUROC), F1, balanced accuracy, and kappa.

RESULTS

The results highlight the potential to accurately predict clinical outcomes, such as severe cardiac events with COVID-19, from routinely acquired in-hospital patient data. Meta learners Generalized Linear Model (GLM), Multi-Layer Perceptron (MLP), and Partial Least Squares (PLS) had the highest AUROC for both outcomes, while K-Nearest Neighbors (KNN) had the lowest. Performance trended lower in the training set as the number of features increased, and exhibited less variance in both training and validation across all feature subsets as the number of base learners increased.

CONCLUSION

This study offers a methodology to robustly evaluate ensemble ML performance when analyzing clinical data.

Fabrication and characterization of bioprints with Lactobacillus crispatus for vaginal application

Bacterial vaginosis (BV) is characterized by low levels of lactobacilli and overgrowth of potential pathogens in the female genital tract. Current antibiotic treatments often fail to treat BV in a sustained manner, and > 50% of women experience recurrence within 6 months post-treatment. Recently, lactobacilli have shown promise for acting as probiotics by offering health benefits in BV. However, as with other active agents, probiotics often require intensive administration schedules incurring difficult user adherence. Three-dimensional (3D)-bioprinting enables fabrication of well-defined architectures with tunable release of active agents, including live mammalian cells, offering the potential for long-acting probiotic delivery. One promising bioink, gelatin alginate has been previously shown to provide structural stability, host compatibility, viable probiotic incorporation, and cellular nutrient diffusion. This study formulates and characterizes 3D-bioprinted Lactobacillus crispatus-containing gelatin alginate scaffolds for gynecologic applications. Different weight to volume (w/v) ratios of gelatin alginate were bioprinted to determine formulations with highest printing resolution, and different crosslinking reagents were evaluated for effect on scaffold integrity via mass loss and swelling measurements. Post-print viability, sustained-release, and vaginal keratinocyte cytotoxicity assays were conducted. A 10:2 (w/v) gelatin alginate formulation was selected based on line continuity and resolution, while degradation and swelling experiments demonstrated greatest structural stability with dual genipin and calcium crosslinking, showing minimal mass loss and swelling over 28 days. 3D-bioprinted L. crispatus-containing scaffolds demonstrated sustained release and proliferation of live bacteria over 28 days, without impacting viability of vaginal epithelial cells. This study provides in vitro evidence for 3D-bioprinted scaffolds as a novel strategy to sustain probiotic delivery with the ultimate goal of restoring vaginal lactobacilli following microbiological disturbances.

Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling

The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems. To address this need, this study employs a multiscale modeling approach to evaluate first-line chemotherapy response of individual patient tumors based on metabolomic analysis of tumor core biopsies obtained during routine clinical evaluation. Model parameters were calculated for a patient cohort as a function of these metabolomic profiles, previously obtained from high-resolution 2DLC-MS/MS analysis. Evaluation metrics were defined to classify patients as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) following first-line chemotherapy. Response was simulated for each patient and compared to actual response. The results show that patient classifications were significantly separated from each other, and also when grouped as DC vs. PD and as CR/PR vs. SD/PD, by fraction of initial tumor radius metric at 6 days post simulated bolus drug injection. This study shows that patient first-line chemotherapy response can in principle be evaluated from multiscale modeling integrated with tumor tissue metabolomic data, offering a first step towards individualized lung cancer treatment prognosis.

Evaluation of innate and adaptive immune system interactions in the tumor microenvironment via a 3D continuum model

Immune cells in the tumor microenvironment (TME) are known to affect tumor growth, vascularization, and extracellular matrix (ECM) deposition. Marked interest in system-scale analysis of immune species interactions within the TME has encouraged progress in modeling tumor-immune interactions in silico. Due to the computational cost of simulating these intricate interactions, models have typically been constrained to representing a limited number of immune species. To expand the capability for system-scale analysis, this study develops a three-dimensional continuum mixture model of tumor-immune interactions to simulate multiple immune species in the TME. Building upon a recent distributed computing implementation that enables efficient solution of such mixture models, major immune species including monocytes, macrophages, natural killer cells, dendritic cells, neutrophils, myeloid-derived suppressor cells (MDSC), cytotoxic, helper, regulatory T-cells, and effector and regulatory B-cells and their interactions are represented in this novel implementation. Immune species extravasate from blood vasculature, undergo chemotaxis toward regions of high chemokine concentration, and influence the TME in proportion to locally defined levels of stimulation. The immune species contribute to the production of angiogenic and tumor growth factors, promotion of myofibroblast deposition of ECM, upregulation of angiogenesis, and elimination of living and dead tumor species. The results show that this modeling approach offers the capability for quantitative insight into the modulation of tumor growth by diverse immune-tumor interactions and immune-driven TME effects. In particular, MDSC-mediated effects on tumor-associated immune species' activation levels, volume fraction, and influence on the TME are explored. Longer term, linking of the model parameters to particular patient tumor information could simulate cancer-specific immune responses and move toward a more comprehensive evaluation of immunotherapeutic strategies.

Multiple breast cancer liver metastases response to macrophage‐delivered nanotherapy evaluated via a 3D continuum model

Breast cancer liver metastases (BCLM) are usually unresectable and difficult to treat with systemic chemotherapy. A major reason for chemotherapy failure is that BCLM are typically small, avascular nodules, with poor transport and fast washout of therapeutics from surrounding capillaries. We have previously shown that nanoalbumin-bound paclitaxel (nab-PTX) encapsulated in porous silicon multistage nanovectors (MSV) is preferentially taken up by tumour-associated macrophages (TAM) in the BCLM microenvironment. The TAM alter therapeutic transport characteristics and retain it in the tumour vicinity, increasing cytotoxicity. Computational modeling has shown that therapeutic regimens could be designed to eliminate single lesions. To evaluate clinically-relevant scenarios, this study develops a modeling framework to evaluate MSV-nab-PTX therapy targeting multiple BCLM. An experimental model of BCLM, splenic injection of breast cancer 4 T1 cells was established in BALB/C mice. Livers were analyzed histologically to determine size and density of BCLM. The data were used to calibrate a 3D continuum mixture model solved via distributed computing to enable simulation of multiple BCLM. Overall tumour burden was analyzed as a function of metastases number and potential therapeutic regimens. The computational model enables realistic 3D representation of metastatic tumour burden in the liver, with the capability to evaluate BCLM growth and therapy response for hundreds of lesions. With the given parameter set, the model projects that repeated MSV-nab-PTX treatment in intervals <7 days would control the tumour burden. We conclude that nanotherapy targeting TAM associated with BCLM may be evaluated and fine-tuned via 3D computational modeling that realistically simulates multiple metastases.

Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data

INTRODUCTION

While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value.

OBJECTIVES

Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies.

METHODS

Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility.

RESULTS

Overall survival (OS) is predicted in external validation cohort with AUROC_TEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROC_TEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased.

CONCLUSION

This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.

Prediction of lung cancer immunotherapy response via machine learning analysis of immune cell lineage and surface markers

BACKGROUND: Although advances have been made in cancer immunotherapy, patient benefits remain elusive. For non-small cell lung cancer (NSCLC), monoclonal antibodies targeting programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1) have shown survival benefit compared to chemotherapy. Personalization of treatment would be facilitated by a priori identification of patients likely to benefit. OBJECTIVE: This pilot study applied a suite of machine learning methods to analyze mass cytometry data of immune cell lineage and surface markers from blood samples of a small cohort (n= 13) treated with Pembrolizumab, Atezolizumab, Durvalumab, or Nivolumab as monotherapy. METHODS: Four different comparisons were evaluated between data collected at an initial visit (baseline), after 12-weeks of immunotherapy, and from healthy (control) samples: healthy vs patients at baseline, Responders vs Non-Responders at baseline, Healthy vs 12-week Responders, and Responders vs Non-Responders at 12-weeks. The algorithms Random Forest, Partial Least Squares Discriminant Analysis, Multi-Layer Perceptron, and Elastic Net were applied to find features differentiating between these groups and provide for the capability to predict outcomes. RESULTS: Particular combinations and proportions of immune cell lineage and surface markers were sufficient to accurately discriminate between the groups without overfitting the data. In particular, markers associated with the B-cell phenotype were identified as key features. CONCLUSIONS: This study illustrates a comprehensive machine learning analysis of circulating immune cell characteristics of NSCLC patients with the potential to predict response to immunotherapy. Upon further evaluation in a larger cohort, the proposed methodology could help guide personalized treatment selection in clinical practice.

Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival

INTRODUCTION

Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive.

OBJECTIVES

This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS.

METHODS

Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events.

RESULTS

Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates).

CONCLUSION

Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.

Bioengineered Models to Study Microenvironmental Regulation of Glioblastoma Metabolism

Despite extensive research and aggressive therapies, glioblastoma (GBM) remains a central nervous system malignancy with poor prognosis. The varied histopathology of GBM suggests a landscape of differing microenvironments and clonal expansions, which may influence metabolism, driving tumor progression. Indeed, GBM metabolic plasticity in response to differing nutrient supply within these microenvironments has emerged as a key driver of aggressiveness. Additionally, emergent biophysical and biochemical interactions in the tumor microenvironment (TME) are offering new perspectives on GBM metabolism. Perivascular and hypoxic niches exert crucial roles in tumor maintenance and progression, facilitating metabolic relationships between stromal and tumor cells. Alterations in extracellular matrix and its biophysical characteristics, such as rigidity and topography, regulate GBM metabolism through mechanotransductive mechanisms. This review highlights insights gained from deployment of bioengineering models, including engineered cell culture and mathematical models, to study the microenvironmental regulation of GBM metabolism. Bioengineered approaches building upon histopathology measurements may uncover potential therapeutic strategies that target both TME-dependent mechanotransductive and biomolecular drivers of metabolism to tackle this challenging disease. Longer term, a concerted effort integrating in vitro and in silico models predictive of patient therapy response may offer a powerful advance toward tailoring of treatment to patient-specific GBM characteristics.

Simulation of 3D centimeter-scale continuum tumor growth at sub-millimeter resolution via distributed computing

Simulation of cm-scale tumor growth has generally been constrained by the computational cost to numerically solve the associated equations, with models limited to representing mm-scale or smaller tumors. While the work has proven useful to the study of small tumors and micro-metastases, a biologically-relevant simulation of cm-scale masses as would be typically detected and treated in patients has remained an elusive goal. This study presents a distributed computing (parallelized) implementation of a mixture model of tumor growth to simulate 3D cm-scale vascularized tissue at sub-mm resolution. The numerical solving scheme utilizes a two-stage parallelization framework. The solution is written for GPU computation using the CUDA framework, which handles all Multigrid-related computations. Message Passing Interface (MPI) handles distribution of information across multiple processes, freeing the program from RAM and the processing limitations found on single systems. On each system, Nvidia's CUDA library allows for fast processing of model data using GPU-bound computing on fewer systems. The results show that a combined MPI-CUDA implementation enables the continuum modeling of cm-scale tumors at reasonable computational cost. Further work to calibrate model parameters to particular tumor conditions could enable simulation of patient-specific tumors for clinical application.

Modeling of tumor response to macrophage and T lymphocyte interactions in the liver metastatic microenvironment

The dynamic interactions between macrophages and T-lymphocytes in the tumor microenvironment exert both antagonistic and synergistic functions affecting tumor growth. Extensive experimental effort has been expended to investigate immunotherapeutic strategies targeting macrophage polarization as well as T-cell activation with the goal to promote tumor cell killing and cancer elimination. However, these interactions remain poorly understood, and cancer immunotherapeutic strategies are often disappointing. The complex system encompassing innate and adaptive immune cell activity in response to tumor growth could benefit from a systems perspective built upon mathematical modeling. This study develops a modeling system to help evaluate the effects of macrophage and T-lymphocyte interactions on tumor growth. The system enables simulating the combined cytotoxic and tumor-promoting interactions of these two immune cell populations in a vascularized organ microenvironment, such as in liver metastases. A hypothetical immunotherapeutic strategy is simulated to increase the number of tumor-suppressive (M1-phenotype) vs. tumor-promoting (M2-phenotype) macrophages to gauge their effects on CD8+ T-cells and CD4+ T-helper cells, which in turn affect the macrophage functions. The results highlight the dynamic interactions between macrophages and T-lymphocytes in the tumor microenvironment and show that with the chosen set of parameter values, the overall cytotoxic effect from macrophages and T-lymphocytes obtained by driving the M1:M2 ratio higher could saturate and fail to achieve tumor regression. Further expansion of this modeling platform to include additional tumor-immune cell interactions, coupled with parameters representing particular tumor characteristics, could enable systematic evaluation of immunotherapeutic strategies tailored to patient-tumor specific conditions, including metastatic disease.

Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data

OBJECTIVES

Despite extensive effort, the search for clinically-relevant metabolite biomarkers for early detection, disease monitoring, and outcome prediction in lung cancer remains unfulfilled. Although biofluid evaluation has been explored, the complexity inherent in metabolite data and the dynamic discrepancy between metabolites in biofluids vs. tumor tissue have prevented conclusive results. This proof-of-concept study explored models predictive of staging and chemotherapy response based on metabolomic analysis of fresh, patient-derived non-small cell lung cancer (NSCLC) core biopsies.

MATERIALS AND METHODS

Samples (n = 36) were evaluated with high-resolution 2DLC-MS/MS and ¹³C-glucose enrichment, and the data were comprehensively analyzed with machine learning techniques. Patients were categorized as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) in terms of first-line chemotherapy. Four major types of learning methods (partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), artificial neural networks, and random forests (RF)) were applied to differentiate between positive (DC and CR/PR) and poor (PD and SD/PD) responses, and between stage I/II/III and stage IV disease. Models were trained with forward feature selection based on variable importance and tested on validation subsets.

RESULTS

The models predicted patient classifications in the validation subsets with AUC (95 % CI): DC vs. PD (SVM), 0.970(0.961-0.979); CR/PR vs. SD/PD (PLS-DA), 0.880(0.865-0.895); stage I/II/III vs. IV (SVM), 0.902(0.880-0.924). Highest performing model was SVM for DC vs. PD (balanced accuracy = 0.92; kappa = 0.74).

CONCLUSION

This study illustrates a comprehensive evaluation of patient tumor-specific metabolic profiles, with the potential to identify disease stage and predict response to first-line chemotherapy.

Discrepancies in metabolomic biomarker identification from patient-derived lung cancer revealed by combined variation in data pre-treatment and imputation methods

INTRODUCTION

The identification of metabolomic biomarkers predictive of cancer patient response to therapy and of disease stage has been pursued as a "holy grail" of modern oncology, relying on the metabolic dysfunction that characterizes cancer progression. In spite of the evaluation of many candidate biomarkers, however, determination of a consistent set with practical clinical utility has proven elusive.

OBJECTIVE

In this study, we systematically examine the combined role of data pre-treatment and imputation methods on the performance of multivariate data analysis methods and their identification of potential biomarkers.

METHODS

Uniquely, we are able to systematically evaluate both unsupervised and supervised methods with a metabolomic data set obtained from patient-derived lung cancer core biopsies with true missing values. Eight pre-treatment methods, ten imputation methods, and two data analysis methods were applied in combination.

RESULTS

The combined choice of pre-treatment and imputation methods is critical in the definition of candidate biomarkers, with deficient or inappropriate selection of these methods leading to inconsistent results, and with important biomarkers either being overlooked or reported as a false positive. The log transformation appeared to normalize the original tumor data most effectively, but the performance of the imputation applied after the transformation was highly dependent on the characteristics of the data set.

CONCLUSION

The combined choice of pre-treatment and imputation methods may need careful evaluation prior to metabolomic data analysis of human tumors, in order to enable consistent identification of potential biomarkers predictive of response to therapy and of disease stage.

Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach

Colorectal cancer and other cancers often metastasize to the liver in later stages of the disease, contributing significantly to patient death. While the biomechanical properties of the liver parenchyma (normal liver tissue) are known to affect tumor cell behavior in primary and metastatic tumors, the role of these properties in driving or inhibiting metastatic inception remains poorly understood, as are the longer-term multicellular dynamics. This study adopts a multi-model approach to study the dynamics of tumor-parenchyma biomechanical interactions during metastatic seeding and growth. We employ a detailed poroviscoelastic model of a liver lobule to study how micrometastases disrupt flow and pressure on short time scales. Results from short-time simulations in detailed single hepatic lobules motivate constitutive relations and biological hypotheses for a minimal agent-based model of metastatic growth in centimeter-scale tissue over months-long time scales. After a parameter space investigation, we find that the balance of basic tumor-parenchyma biomechanical interactions on shorter time scales (adhesion, repulsion, and elastic tissue deformation over minutes) and longer time scales (plastic tissue relaxation over hours) can explain a broad range of behaviors of micrometastases, without the need for complex molecular-scale signaling. These interactions may arrest the growth of micrometastases in a dormant state and prevent newly arriving cancer cells from establishing successful metastatic foci. Moreover, the simulations indicate ways in which dormant tumors could "reawaken" after changes in parenchymal tissue mechanical properties, as may arise during aging or following acute liver illness or injury. We conclude that the proposed modeling approach yields insight into the role of tumor-parenchyma biomechanics in promoting liver metastatic growth, and advances the longer term goal of identifying conditions to clinically arrest and reverse the course of late-stage cancer.

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.

Multi-objective optimization of tumor response to drug release from vasculature-bound nanoparticles

The pharmacokinetics of nanoparticle-borne drugs targeting tumors depends critically on nanoparticle design. Empirical approaches to evaluate such designs in order to maximize treatment efficacy are time- and cost-intensive. We have recently proposed the use of computational modeling of nanoparticle-mediated drug delivery targeting tumor vasculature coupled with numerical optimization to pursue optimal nanoparticle targeting and tumor uptake. Here, we build upon these studies to evaluate the effect of tumor size on optimal nanoparticle design by considering a cohort of heterogeneously-sized tumor lesions, as would be clinically expected. The results indicate that smaller nanoparticles yield higher tumor targeting and lesion regression for larger-sized tumors. We then augment the nanoparticle design optimization problem by considering drug diffusivity, which yields a two-fold tumor size decrease compared to optimizing nanoparticles without this consideration. We quantify the tradeoff between tumor targeting and size decrease using bi-objective optimization, and generate five Pareto-optimal nanoparticle designs. The results provide a spectrum of treatment outcomes - considering tumor targeting vs. antitumor effect - with the goal to enable therapy customization based on clinical need. This approach could be extended to other nanoparticle-based cancer therapies, and support the development of personalized nanomedicine in the longer term.

Nonlinear response to cancer nanotherapy due to macrophage interactions revealed by mathematical modeling and evaluated in a murine model via CRISPR-modulated macrophage polarization

Tumor-associated macrophages (TAMs) have been shown to both aid and hinder tumor growth, with patient outcomes potentially hinging on the proportion of M1, pro-inflammatory/growth-inhibiting, to M2, growth-supporting, phenotypes. Strategies to stimulate tumor regression by promoting polarization to M1 are a novel approach that harnesses the immune system to enhance therapeutic outcomes, including chemotherapy. We recently found that nanotherapy with mesoporous particles loaded with albumin-bound paclitaxel (MSV-nab-PTX) promotes macrophage polarization towards M1 in breast cancer liver metastases (BCLM). However, it remains unclear to what extent tumor regression can be maximized based on modulation of the macrophage phenotype, especially for poorly perfused tumors such as BCLM. Here, for the first time, a CRISPR system is employed to permanently modulate macrophage polarization in a controlled in vitro setting. This enables the design of 3D co-culture experiments mimicking the BCLM hypovascularized environment with various ratios of polarized macrophages. We implement a mathematical framework to evaluate nanoparticle-mediated chemotherapy in conjunction with TAM polarization. The response is predicted to be not linearly dependent on the M1:M2 ratio. To investigate this phenomenon, the response is simulated via the model for a variety of M1:M2 ratios. The modeling indicates that polarization to an all-M1 population may be less effective than a combination of both M1 and M2. Experimental results with the CRISPR system confirm this model-driven hypothesis. Altogether, this study indicates that response to nanoparticle-mediated chemotherapy targeting poorly perfused tumors may benefit from a fine-tuned M1:M2 ratio that maintains both phenotypes in the tumor microenvironment during treatment.

Modeling of Combination Chemotherapy and Immunotherapy for Lung Cancer

Cancer has traditionally been studied from a basic science perspective, focusing on the underlying biology, physiology, and biochemistry. Engineering has supplemented this effort via the development of technology, e.g., microscopy. In recent times, engineering and the physical sciences have positioned themselves as approaches on par with the traditional basic sciences to tackle the study of cancer. Mathematical modeling and computational simulation have become key elements of this engineering-focused effort, evaluating the growth of tumors and their response to therapy as problems that could benefit from a systems analysis perspective. Building upon previous work in this field, here is developed a modeling framework to help evaluate the response of tumors to the combination of chemotherapy and immunotherapy, focusing on non-small cell lung cancer (NSCLC). With system parameters set with patient tumor-specific parameters, the longer term goal of this work is to advance personalized cancer treatment.

Computational/experimental evaluation of liver metastasis post hepatic injury: interactions with macrophages and transitional ECM

The complex interactions between subclinical changes to hepatic extracellular matrix (ECM) in response to injury and tumor-associated macrophage microenvironmental cues facilitating metastatic cell seeding remain poorly understood. This study implements a combined computational modeling and experimental approach to evaluate tumor growth following hepatic injury, focusing on ECM remodeling and interactions with local macrophages. Experiments were performed to determine ECM density and macrophage-associated cytokine levels. Effects of ECM remodeling along with macrophage polarization on tumor growth were evaluated via computational modeling. For primary or metastatic cells in co-culture with macrophages, TNF-α levels were 5× higher with M1 vs. M2 macrophages. Metastatic cell co-culture exhibited 10× higher TNF-α induction than with primary tumor cells. Although TGFβ1 induction was similar between both co-cultures, levels were slightly higher with primary cells in the presence of M1. Simulated metastatic tumors exhibited decreased growth compared to primary tumors, due to high local M1-induced cytotoxicity, even in a highly vascularized microenvironment. Experimental analysis combined with computational modeling may provide insight into interactions between ECM remodeling, macrophage polarization, and liver tumor growth.

Mathematical modeling of tumor-immune cell interactions

The anti-tumor activity of the immune system is increasingly recognized as critical for the mounting of a prolonged and effective response to cancer growth and invasion, and for preventing recurrence following resection or treatment. As the knowledge of tumor-immune cell interactions has advanced, experimental investigation has been complemented by mathematical modeling with the goal to quantify and predict these interactions. This succinct review offers an overview of recent tumor-immune continuum modeling approaches, highlighting spatial models. The focus is on work published in the past decade, incorporating one or more immune cell types and evaluating immune cell effects on tumor progression. Due to their relevance to cancer, the following immune cells and their combinations are described: macrophages, Cytotoxic T Lymphocytes, Natural Killer cells, dendritic cells, T regulatory cells, and CD4+ T helper cells. Although important insight has been gained from a mathematical modeling perspective, the development of models incorporating patient-specific data remains an important goal yet to be realized for potential clinical benefit.

Lung Cancer Survival Prediction via Machine Learning Regression, Classification, and Statistical Techniques

A regression model is developed to predict survival time in months for lung cancer patients. It was previously shown that predictive models perform accurately for short survival times of less than 6 months; however, model accuracy is reduced when attempting to predict longer survival times. This study employs an approach for which regression models are used in combination with a classification model to predict survival time. A set of de-identified lung cancer patient data was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The models use a subset of factors selected by ANOVA. Model accuracy is measured by a confusion matrix for classification and by Root Mean Square Error (RMSE) for regression. Random Forests are used for classification, while general Linear Regression, Gradient Boosted Machines (GBM), and Random Forests are used for regression. The regression results show that RF had the best performance for survival times ≤6 and >24 months (RMSE 10.52 and 20.51, respectively), while GBM performed best for 7-24 months (RMSE 15.65). Comparison plots of the results further indicate that the regression models perform better for shorter survival times than the RMSE values are able to reflect.

Design Optimization of Tumor Vasculature-Bound Nanoparticles

Nanotherapy may constitute a promising approach to target tumors with anticancer drugs while minimizing systemic toxicity. Computational modeling can enable rapid evaluation of nanoparticle (NP) designs and numerical optimization. Here, an optimization study was performed using an existing tumor model to find NP size and ligand density that maximize tumoral NP accumulation while minimizing tumor size. Optimal NP avidity lies at lower bound of feasible values, suggesting reduced ligand density to prolong NP circulation. For the given set of tumor parameters, optimal NP diameters were 288 nm to maximize NP accumulation and 334 nm to minimize tumor diameter, leading to uniform NP distribution and adequate drug load. Results further show higher dependence of NP biodistribution on the NP design than on tumor morphological parameters. A parametric study with respect to drug potency was performed. The lower the potency of the drug, the bigger the difference is between the maximizer of NP accumulation and the minimizer of tumor size, indicating the existence of a specific drug potency that minimizes the differential between the two optimal solutions. This study shows the feasibility of applying optimization to NP designs to achieve efficacious cancer nanotherapy, and offers a first step towards a quantitative tool to support clinical decision making.

Evaluation of Drug-Loaded Gold Nanoparticle Cytotoxicity as a Function of Tumor Vasculature-Induced Tissue Heterogeneity

The inherent heterogeneity of tumor tissue presents a major challenge to nanoparticle-mediated drug delivery. This heterogeneity spans from the molecular (genomic, proteomic, metabolomic) to the cellular (cell types, adhesion, migration) and to the tissue (vasculature, extra-cellular matrix) scales. In particular, tumor vasculature forms abnormally, inducing proliferative, hypoxic, and necrotic tumor tissue regions. As the vasculature is the main conduit for nanotherapy transport into tumors, vasculature-induced tissue heterogeneity can cause local inadequate delivery and concentration, leading to subpar response. Further, hypoxic tissue, although viable, would be immune to the effects of cell-cycle specific drugs. In order to enable a more systematic evaluation of such effects, here we employ computational modeling to study the therapeutic response as a function of vasculature-induced tumor tissue heterogeneity. Using data with three-layered gold nanoparticles loaded with cisplatin, nanotherapy is simulated interacting with different levels of tissue heterogeneity, and the treatment response is measured in terms of tumor regression. The results quantify the influence that varying levels of tumor vascular density coupled with the drug strength have on nanoparticle uptake and washout, and the associated tissue response. The drug strength affects the proportion of proliferating, hypoxic, and necrotic tissue fractions, which in turn dynamically affect and are affected by the vascular density. Higher drug strengths may be able to achieve stronger tumor regression but only if the intra-tumoral vascular density is above a certain threshold that affords sufficient transport. This study establishes an initial step towards a more systematic methodology to assess the effect of vasculature-induced tumor tissue heterogeneity on the response to nanotherapy.

Modeling of nanoparticle transport through the female reproductive tract for the treatment of infectious diseases

The secreted mucus layer in the vaginal epithelium presents a formidable barrier to the transport of active agents for the prevention and treatment of female reproductive tract (FRT) infections. Nanoparticle-mediated drug delivery has been proposed to help facilitate the transport and release of active agents through the cervicovaginal mucus (CVM) and underlying mucosa. However, both nanoparticles (NPs) and free active agents face a variety of challenges, often requiring the administration of high localized doses to circumvent leakage and poor penetration to targeted intravaginal tissue compartments. To address these challenges, "stealth" NP modifications have been investigated, due to their favorable mucus-penetrating properties, resulting in improved intravaginal active agent retention and transport. A number of other NP characteristics including size, surface modification type, ligand density, and co-modification, as well as the complexity of the FRT tissue are involved in obtaining adequate tissue penetration and, if needed, cell internalization. Studies that systematically investigate variations of these characteristics have yet to be conducted, with the goal to obtain a better understanding of what properties most impact prophylactic and therapeutic benefit. To complement the progress made with experimental evaluation of active agent transport in in vitro and in vivo, mathematical modeling has recently been applied to analyze the transport performance of agents and delivery vehicles in the FRT. Here, we build upon this work to simulate NP transport through mucus gel, epithelial, and stromal compartments, with the goal to provide a platform that can systematically evaluate transport based on NP and tissue characteristics. Model parameters such as PEG density and NP release (decay) rate from mucus gel into the epithelium, are set from previous in vitro and in vivo experimental work that assessed the transport of poly(lactic-co-glycolic acid (PLGA) NPs. The modeling results show that while unmodified and 2% PEG-modified NPs were retained in mucus for ∼1-4 h, dependent upon decay constant values, and traverse to the epithelium, no NP penetration was observed in the stroma. In contrast, NPs modified with 3% PEG, exhibited prolonged retention in each compartment, remaining for ∼4-6 h. Moreover, a significant concentration of NPs is observed in the stroma, indicating a transition in transport behavior. For NPs modified with 5, 8, or 25% PEG, steady retention profiles were noted, which gradually decline over 24 h. To supplement this modeling study and to develop a more representative experimental system that may be useful in future work, we report on the feasibility of constructing single and multicellular layered (MCL) culture systems to represent the epithelial and stromal tissue of the FRT. We anticipate that a combined mathematical/experimental approach may longer term enable prediction and customization of patient tissue-specific approaches to attain effective NP-mediated drug delivery and release for the treatment of infectious disease.

Simulation of Multispecies Desmoplastic Cancer Growth via a Fully Adaptive Non-linear Full Multigrid Algorithm

A fully adaptive non-linear full multigrid (FMG) algorithm is implemented to computationally simulate a model of multispecies desmoplastic tumor growth in three spatial dimensions. The algorithm solves a thermodynamic mixture model employing a diffuse interface approach with Cahn-Hilliard-type fourth-order equations that are coupled, non-linear, and numerically stiff. The tumor model includes extracellular matrix (ECM) as a major component with elastic energy contribution in its chemical potential term. Blood and lymphatic vasculatures are simulated via continuum representations. The model employs advection-reaction-diffusion partial differential equations (PDEs) for the cell, ECM, and vascular components, and reaction-diffusion PDEs for the elements diffusing from the vessels. This study provides the details of the numerical solution obtained by applying the fully adaptive non-linear FMG algorithm with finite difference method to solve this complex system of PDEs. The results indicate that this type of computational model can simulate the extracellular matrix-rich desmoplastic tumor microenvironment typical of fibrotic tumors, such as pancreatic adenocarcinoma.

A review of metabolism-associated biomarkers in lung cancer diagnosis and treatment

INTRODUCTION

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection has proven essential to extend survival. Genomic and proteomic advances have provided impetus to the effort dedicated to detect and diagnose the disease at an earlier stage. Recently, the study of metabolites associated with tumor formation and progression has inaugurated the era of cancer metabolomics to aid in this effort.

OBJECTIVES

This review summarizes recent work regarding novel metabolites with the potential to serve as biomarkers for early lung tumor detection, evaluation of disease progression, and prediction of patient outcomes.

METHOD

We compare the metabolite profiling of cancer patients with that of healthy individuals, and the metabolites identified in tissue and biofluid samples and their usefulness as lung cancer biomarkers. We discuss metabolite alterations in tumor versus paired non-tumor lung tissues, as well as metabolite alterations in different stages of lung cancers and their usefulness as indicators of disease progression and overall survival. We evaluate metabolite dysregulation in different types of lung cancers, and those associated with lung cancer versus other lung diseases. We also examine metabolite differences between lung cancer patients and smokers/risk-factor individuals.

RESULT

Although an extensive list of metabolites has been evaluated to distinguish between these cases, refinement of methods is further required for adequate patient diagnosis.

CONCLUSION

We conclude that with technological advancement, metabolomics may be able to replace more invasive and costly diagnostic procedures while also providing the means to more effectively tailor treatment to patient-specific tumors.

Nanoparticle-mediated drug delivery to treat infections in the female reproductive tract: evaluation of experimental systems and the potential for mathematical modeling

A variety of drug-delivery platforms have been employed to deliver therapeutic agents across cervicovaginal mucus (CVM) and the vaginal mucosa, offering the capability to increase the longevity and retention of active agents to treat infections of the female reproductive tract (FRT). Nanoparticles (NPs) have been shown to improve retention, diffusion, and cell-specific targeting via specific surface modifications, relative to other delivery platforms. In particular, polymeric NPs represent a promising option that has shown improved distribution through the CVM. These NPs are typically fabricated from nontoxic, non-inflammatory, US Food and Drug Administration-approved polymers that improve biocompatibility. This review summarizes recent experimental studies that have evaluated NP transport in the FRT, and highlights research areas that more thoroughly and efficiently inform polymeric NP design, including mathematical modeling. An overview of the in vitro, ex vivo, and in vivo NP studies conducted to date – whereby transport parameters are determined, extrapolated, and validated – is presented first. The impact of different NP design features on transport through the FRT is summarized, and gaps that exist due to the limitations of iterative experimentation alone are identified. The potential of mathematical modeling to complement the characterization and evaluation of diffusion and transport of delivery vehicles and active agents through the CVM and mucosa is discussed. Lastly, potential advancements combining experimental and mathematical knowledge are suggested to inform next-generation NP designs, such that infections in the FRT may be more effectively treated.

Pharmacokinetic/pharmacodynamic modeling of combination-chemotherapy for lung cancer

Chemotherapy for non-small cell lung cancer (NSCLC) typically involves a doublet regimen for a number of cycles. For any particular patient, a course of treatment is usually chosen from a large number of combinational protocols with drugs in concomitant or sequential administration. In spite of newer drugs and protocols, half of patients with early disease will live less than five years and 95% of those with advanced disease survive for less than one year. Here, we apply mathematical modeling to simulate tumor response to multiple drug regimens, with the capability to assess maximum tolerated dose (MTD) as well as metronomic drug administration. We couple pharmacokinetic-pharmacodynamic intracellular multi-compartment models with a model of vascularized tumor growth, setting input parameters from in vitro data, and using the models to project potential response in vivo. This represents an initial step towards the development of a comprehensive virtual system to evaluate tumor response to combinatorial drug regimens, with the goal to more efficiently identify optimal course of treatment with patient tumor-specific data. We evaluate cisplatin and gemcitabine with clinically-relevant dosages, and simulate four treatment NSCLC scenarios combining MTD and metronomic therapy. This work thus establishes a framework for systematic evaluation of tumor response to combination chemotherapy. The results with the chosen parameter set indicate that although a metronomic regimen may provide advantage over MTD, the combination of these regimens may not necessarily offer improved response. Future model evaluation of chemotherapy possibilities may help to assess their potential value to obtain sustained NSCLC regression for particular patients, with the ultimate goal of optimizing multiple-drug chemotherapy regimens in clinical practice.

Computational Modeling of Antiviral Drug Diffusion from Poly(lactic- co-glycolic-acid) Fibers and Multicompartment Pharmacokinetics for Application to the Female Reproductive Tract

The need for more versatile technologies to deliver antiviral agents to the female reproductive tract (FRT) has spurred the development of on-demand and sustained-release platforms. Electrospun fibers (EFs), in particular, have recently been applied to FRT delivery, resulting in an alternative dosage form with the potential to provide protection and therapeutic effect against a variety of infection types. However, a multitude of fabrication parameters, as well as the resulting complexities of solvent-drug, drug-polymer, and solvent-polymer interactions, are known to significantly impact the loading and release of incorporated agents. Numerous processing parameters, in addition to their combined interactions, can hinder the iterative development of fiber formulations to achieve optimal release for particular durations, doses, and polymer-drug types. The experimental effort to design and develop EFs could benefit from mathematical analysis and computational simulation that predictively evaluate combinations of parameters to meet product design needs. Here, existing modeling efforts are leveraged to develop a simulation platform that correlates and predicts the delivery of relevant small molecule antivirals from EFs that have been recently applied to target sexually transmitted infections (STIs). A pair of mathematical models is coupled to simulate the release of two structurally similar small molecule antiretroviral reverse transcriptase inhibitors, Tenofovir (TFV) and Tenofovir disoproxil fumarate (TDF), from poly(lactic- co-glycolic acid) (PLGA) EFs, and to evaluate how changes in the system parameters affect the distribution of encapsulated agent in a three-compartment model of the vaginal epithelium. The results indicate that factors such as fiber diameter, mesh thickness, antiviral diffusivity, and fiber geometry can be simulated to create an accurate model that distinguishes the different release patterns of TFV and TDF from EFs, and that enables detailed evaluation of the associated pharmacokinetics. This simulation platform offers a basis with which to further study EF parameters and their effect on antiviral release and pharmacokinetics in the FRT.

Mathematical modeling of tumor-associated macrophage interactions with the cancer microenvironment

Background

Immuno-oncotherapy has emerged as a promising means to target cancer. In particular, therapeutic manipulation of tumor-associated macrophages holds promise due to their various and sometimes opposing roles in tumor progression. It is established that M1-type macrophages suppress tumor progression while M2-types support it. Recently, Tie2-expressing macrophages (TEM) have been identified as a distinct sub-population influencing tumor angiogenesis and vascular remodeling as well as monocyte differentiation.

Methods

This study develops a modeling framework to evaluate macrophage interactions with the tumor microenvironment, enabling assessment of how these interactions may affect tumor progression. M1, M2, and Tie2 expressing variants are integrated into a model of tumor growth representing a metastatic lesion in a highly vascularized organ, such as the liver. Behaviors simulated include M1 release of nitric oxide (NO), M2 release of growth-promoting factors, and TEM facilitation of angiogenesis via Angiopoietin-2 and promotion of monocyte differentiation into M2 via IL-10.

Results

The results show that M2 presence leads to larger tumor growth regardless of TEM effects, implying that immunotherapeutic strategies that lead to TEM ablation may fail to restrain growth when the M2 represents a sizeable population. As TEM pro-tumor effects are less pronounced and on a longer time scale than M1-driven tumor inhibition, a more nuanced approach to influence monocyte differentiation taking into account the tumor state (e.g., under chemotherapy) may be desirable.

Conclusions

The results highlight the dynamic interaction of macrophages within a growing tumor, and, further, establish the initial feasibility of a mathematical framework that could longer term help to optimize cancer immunotherapy.

Model of vascular desmoplastic multispecies tumor growth

We present a three-dimensional nonlinear tumor growth model composed of heterogeneous cell types in a multicomponent-multispecies system, including viable, dead, healthy host, and extra-cellular matrix (ECM) tissue species. The model includes the capability for abnormal ECM dynamics noted in tumor development, as exemplified by pancreatic ductal adenocarcinoma, including dense desmoplasia typically characterized by a significant increase of interstitial connective tissue. An elastic energy is implemented to provide elasticity to the connective tissue. Cancer-associated fibroblasts (myofibroblasts) are modeled as key contributors to this ECM remodeling. The tumor growth is driven by growth factors released by these stromal cells as well as by oxygen and glucose provided by blood vasculature which along with lymphatics are stimulated to proliferate in and around the tumor based on pro-angiogenic factors released by hypoxic tissue regions. Cellular metabolic processes are simulated, including respiration and glycolysis with lactate fermentation. The bicarbonate buffering system is included for cellular pH regulation. This model system may be of use to simulate the complex interactions between tumor and stromal cells as well as the associated ECM and vascular remodeling that typically characterize malignant cancers notorious for poor therapeutic response.