Human nerve growth factor delivery to the retina: Quantitative methodology and mathematical modeling in preclinical settings.

Nerve growth factor (NGF) plays a critical neuroprotective role in retinal health, supporting neuronal survival and regeneration. Recombinant human NGF (rhNGF) holds promise for treating retinal degenerative diseases such as glaucoma, retinitis pigmentosa, and optic neuropathies. However, efficient retinal delivery of rhNGF remains a major challenge due to anatomical barriers and rapid clearance from conventional routes.

Prediction of silica nanoparticle biodistribution using a calibrated physiologically based model: Unbound fraction and elimination rate constants for the kidneys and phagocytosis identified as major determinants.

Objectives: This study aimed to develop a minimal physiologically based pharmacokinetic (mPBPK) model to predict the biodistribution of silica nanoparticles (SiNPs) and evaluate how variations in surface charge, size, porosity, and geometry influence their systemic disposition.

Materials: The mPBPK model was calibrated using in vivo pharmacokinetic data from mice administered aminated, mesoporous, and rod-shaped SiNPs. Human data were collected from clinical trial data from Cornell dots.

Rational Design of Immunogenic Nanoparticles as a Platform for Enhanced Ovarian Cancer Immunotherapy in Mice.

Ovarian cancer immunotherapy remains a challenge based on the "cold" tumor microenvironment. Herein we present a rational design to create immunogenic nanoparticles as a multi-agent platform that promotes immune response in a mouse model of ovarian cancer. The hybrid lipid-silica nanosystem is capable of co-loading four types of cargo molecules including a model antigen, nucleic acid-based adjuvant Cytosine-p-linked to Guanine (CpG, TLR3/9 agonist), lipid-based adjuvant (MPLA, TLR4 agonist) integrated into the lipid coat, and optionally a small molecule drug, such as the chemotherapeutic agent oxaliplatin, a well-established treatment for ovarian cancer.

Microneedle patch-based transnasal delivery of naloxone.

This study evaluates dissolving microneedle (MN) patches for naloxone (NAL) delivery via the transnasal route, addressing limitations seen with transdermal application of same and the limitations of conventional NAL intranasal sprays, which often require frequent redosing, particularly for long-acting opioids like fentanyl. MN patches composed of polyvinylpyrrolidone (PVP) and PVP/Chitosan were tested on porcine nasal mucosa. PVP patches achieved significantly higher 1-h cumulative permeation (7295.

Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning.

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both and , leveraging physicochemical properties and experimental conditions. A curated cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships.

Image-guided targeting of mitochondrial metabolism sensitizes pediatric malignant rhabdoid tumors to low-dose radiotherapy.

Tumor hypoxia leads to radioresistance and markedly worse clinical outcomes for pediatric malignant rhabdoid tumors (MRTs). Our transcriptomics and bioenergetic profiling data reveal that mitochondrial oxidative phosphorylation is a metabolic vulnerability of MRT and can be exploited to overcome consumptive hypoxia by repurposing an FDA-approved antimalarial drug, atovaquone (AVO). We then establish the utility of oxygen-enhanced-multispectral optoacoustic tomography, a label-free, ionizing radiation-free imaging modality, to visualize and quantify spatiotemporal changes in tumor hypoxia in response to AVO.

Development of a Multiscale Mechanistic Model for Predicting Tumor Response to Anti-miR-155.

In this paper, we present a two-compartmental multiscale mechanistic model for investigating anti-miR-155 monotherapy for non-small cell lung cancer (NSCLC). The model was first quantified using in vivo data and subsequently extrapolated to human-scale for evaluating its translational potential in patients. Using the human-scale model, we explored the impact of dosing schedules on tumor response.

Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-informed Machine Learning.

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine-learning (ML) framework that predicts NP toxicity both and , leveraging physicochemical properties and experimental conditions. A curated cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships.

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).

A Minimal PBPK Model Describes the Differential Disposition of Silica Nanoparticles In Vivo.

Nanoparticles (NPs) have emerged as promising candidates for drug delivery due to their tunable physical and chemical properties. Among these, silica nanoparticles (SiNPs) are particularly valued for their biocompatibility and adaptability in applications like drug delivery and medical imaging. However, predicting SiNP biodistribution and clearance remains a significant challenge.

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.

Iontophoresis-coupled rapidly dissolving polymeric microneedle patch of naloxone for its enhanced transdermal delivery.

The transdermal delivery of naloxone for opioid overdose emergency purposes is a challenge due to its poor rate of diffusion through the layers of skin. This results in delayed delivery of an insufficient amount of the drug within minimal time as is desired to save lives. The ability of dissolving polymeric microneedles to shorten the lag time significantly has been explored and shown to have prospects in terms of the transdermal delivery of naloxone.

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.