Insights from a multiscale framework on metabolic rate variation driving glioblastoma multiforme growth and invasion.

Non-physiological levels of oxygen and nutrients within the tumors result in heterogeneous cell populations that exhibit distinct necrotic, hypoxic, and proliferative zones. Among these zonal cellular properties, metabolic rates strongly affect the overall growth and invasion of tumors. Here, we report on a hybrid discrete-continuum (HDC) mathematical framework that uses metabolic data from a biomimetic two-dimensional (2D) in-vitro cancer model to predict three-dimensional (3D) behaviour of in-vitro human glioblastoma (hGB).

Gradient-induced instability in tumour spheroids unveils the impact of microenvironmental nutrient changes.

Tumours often display invasive behaviours that induce fingering, branching and fragmentation processes. The phenomenon, known as diffusional instability, is driven by differential cell proliferation, migration, and death due to the presence of metabolite and catabolite concentration gradients. An understanding of the intricate dynamics of this spatially heterogeneous process plays a key role in the investigation of tumour growth and invasion.

Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy.

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data.

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer.

Background: Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. Therapeutic targeting of miR-155 through its antagonist, anti-miR-155, has proven challenging due to its dual molecular effects.

Clinical Validation of Mathematically Derived Early Tumor Dynamics for Solid Tumors in Response to Durvalumab.

Purpose: Early prediction of response to immunotherapy may help guide patient management by identifying resistance to treatment and allowing adaptation of therapies. This analysis evaluated a mathematical model of response to immunotherapy that provides patient-specific prediction of outcome using the initial change in tumor size/burden from baseline to the first follow-up visit on standard imaging scans.

Methods: We applied the model to 600 patients with advanced solid tumors who received durvalumab in Study 1108, a phase I/II trial, and compared outcome prediction performance versus size-based criteria with RECIST version 1.

Hybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy.

We present a study where predictive mechanistic modeling is used in combination with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) therapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models (but may not be directly measurable in the clinic) and easily measurable quantities or characteristics (that are not always readily incorporated into predictive mechanistic models). The mechanistic model we have applied here can predict tumor response from CT or MRI imaging based on key mechanisms underlying checkpoint inhibitor therapy, and in the present work, its parameters were combined with readily-available clinical measures from 93 patients into a hybrid training set for a deep learning time-to-event predictive model.

Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer.

Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. We developed a multiscale mechanistic model, calibrated with data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs.

Mathematical modeling of cancer immunotherapy for personalized clinical translation.

Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives.

Mechanistic modeling of anti-microRNA-155 therapy combinations in lung cancer.

To improve treatment outcomes in non-small cell lung cancer (NSCLC), it is crucial to identify treatment strategies with the potential to exhibit drug synergism. This can lower the required effective dose, reducing exposure to drugs and associated toxicities, while improving treatment efficacy. In previous studies, drugs targeting the microRNA-155 or PD-L1 have been promising in restraining NSCLC tumor growth.

Mechanistic modeling for optimal design of dissolvable microneedle-based patches for transdermal drug delivery.

Polymeric microneedle (MN)-based patches are an efficient, non-invasive, and painless means of drug delivery through the skin to systemic circulation. The design of these MN-based patches can be customized for various drug delivery applications, particularly modified release of drugs. In this study, we developed a mathematical model of drug delivery via MN-based patches to study the effect of patch design properties on drug delivery kinetics and systemic pharmacokinetics (PK).

Hybrid Cellular Automata Modeling Reveals the Effects of Glucose Gradients on Tumour Spheroid Growth.

Purpose: In recent years, mathematical models have become instrumental in cancer research, offering insights into tumor growth dynamics, and guiding the development of pharmacological strategies. These models, encompassing diverse biological and physical processes, are increasingly used in clinical settings, showing remarkable predictive precision for individual patient outcomes and therapeutic responses.

Methods: Motivated by these advancements, our study introduces an innovative in silico model for simulating tumor growth and invasiveness.

Exploring Cell Migration Mechanisms in Cancer: From Wound Healing Assays to Cellular Automata Models.

Purpose: Cell migration is a critical driver of metastatic tumor spread, contributing significantly to cancer-related mortality. Yet, our understanding of the underlying mechanisms remains incomplete.

Methods: In this study, a wound healing assay was employed to investigate cancer cell migratory behavior, with the aim of utilizing migration as a biomarker for invasiveness.

Ceramide as an endothelial cell surface receptor and a lung-specific lipid vascular target for circulating ligands.

The vascular endothelium from individual organs is functionally specialized, and it displays a unique set of accessible molecular targets. These serve as endothelial cell receptors to affinity ligands. To date, all identified vascular receptors have been proteins.

Prospective Subunit Nanovaccine against Infection─Cubosome Lipid Nanocarriers of Cord Factor, Trehalose 6,6' Dimycolate.

An improved vaccine is urgently needed to replace the now more than 100-year-old Bacillus Calmette-Guérin (BCG) vaccine against tuberculosis (TB) disease, which represents a significant burden on global public health. Mycolic acid, or cord factor trehalose 6,6' dimycolate (TDM), a lipid component abundant in the cell wall of the pathogen (MTB), has been shown to have strong immunostimulatory activity but remains underexplored due to its high toxicity and poor solubility. Herein, we employed a novel strategy to encapsulate TDM within a cubosome lipid nanocarrier as a potential subunit nanovaccine candidate against TB.

A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection.

While the development of different vaccines slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections has continued to fuel the COVID-19 pandemic. To secure at least partial protection in the majority of the population through 1 dose of a COVID-19 vaccine, delayed administration of boosters has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may induce breakthrough infections due to intermittent lapses in protection.

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.

A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection.

While the development of different vaccines has slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections continues to fuel the pandemic. As a strategy to secure at least partial protection, with a single dose of a given COVID-19 vaccine to maximum possible fraction of the population, administration of subsequent doses (or boosters) has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may jeopardize the attainment of herd immunity due to intermittent lapses in protection.

Zonal Intratumoral Delivery of Nanoparticles Guided by Surface Functionalization.

Delivery of small molecules and anticancer agents to malignant cells or specific regions within a tumor is limited by penetration depth and poor spatial drug distribution, hindering anticancer efficacy. Herein, we demonstrate control over gold nanoparticle (GNP) penetration and spatial distribution across solid tumors by administering GNPs with different surface chemistries at a constant injection rate via syringe pump. A key finding in this study is the discovery of different zone-specific accumulation patterns of intratumorally injected nanoparticles dependent on surface functionalization.

Immune-checkpoint inhibitor therapy response evaluation using oncophysics-based mathematical models.

The field of oncology has transformed with the advent of immunotherapies. The standard of care for multiple cancers now includes novel drugs that target key checkpoints that function to modulate immune responses, enabling the patient's immune system to elicit an effective anti-tumor response. While these immune-based approaches can have dramatic effects in terms of significantly reducing tumor burden and prolonging survival for patients, the therapeutic approach remains active only in a minority of patients and is often not durable.

Localization of drug biodistribution in a 3D-bioengineered subcutaneous neovascularized microenvironment.

Local immunomodulation has shown the potential to control the immune response in a site-specific manner for wound healing, cancer, allergy, and cell transplantation, thus abrogating adverse effects associated with systemic administration of immunotherapeutics. Localized immunomodulation requires confining the biodistribution of immunotherapeutics on-site for maximal immune control and minimal systemic drug exposure. To this end, we developed a 3D-printed subcutaneous implant termed 'NICHE', consisting of a bioengineered vascularized microenvironment enabled by sustained drug delivery on-site.

Mechanistic modeling-guided optimization of microneedle-based skin patch for rapid transdermal delivery of naloxone for opioid overdose treatment.

Naloxone, an FDA-approved opioid inhibitor, used to reverse opioid overdose complications has up till date faced challenges associated with its delivery. Limitations include the use of invasive delivery forms and the need for frequent redosing due to its short half-life. The goal of the current study was to design a transdermal rapidly dissolving polymeric microneedle (MN) patch with delivery and pharmacokinetic properties comparable to that seen with the commercially available NAL products, eliminating their delivery limitations.

Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer.

Purpose: The benefit of radiation therapy for pancreatic ductal adenocarcinoma (PDAC) remains unclear. We hypothesized that a new mechanistic mathematical model of chemotherapy and radiation response could predict clinical outcomes a priori, using a previously described baseline measurement of perfusion from computed tomography scans, normalized area under the enhancement curve (nAUC).

Methods And Materials: We simplified an existing mass transport model that predicted cancer cell death by replacing previously unknown variables with averaged direct measurements from randomly selected pathologic sections of untreated PDAC.

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.