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. Here, we show that an endothelial lung-homing peptide (CGSPGWVRC) interacts with C16-ceramide, a bioactive sphingolipid that mediates several biological functions. Upon binding to cell surfaces, CGSPGWVRC triggers ceramide-rich platform formation, activates acid sphingomyelinase and ceramide production, without the associated downstream apoptotic signaling. We also show that the lung selectivity of CGSPGWVRC homing peptide is dependent on ceramide production in vivo. Finally, we demonstrate two potential applications for this lipid vascular targeting system: i) as a bioinorganic hydrogel for pulmonary imaging and ii) as a ligand-directed lung immunization tool against COVID-19. Thus, C16-ceramide is a unique example of a lipid-based receptor system in the lung vascular endothelium targeted in vivo by circulating ligands such as CGSPGWVRC.

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. METHODS: We developed a multiscale mechanistic model, calibrated with in vivo 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. RESULTS: Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimens to prevent antagonistic effects. CONCLUSIONS: This work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

Genetic and Structural Analysis of SARS-CoV-2 Spike Protein for Universal Epitope Selection.

Evaluation of immunogenic epitopes for universal vaccine development in the face of ongoing SARS-CoV-2 evolution remains a challenge. Herein, we investigate the genetic and structural conservation of an immunogenically relevant epitope (C662-C671) of spike (S) protein across SARS-CoV-2 variants to determine its potential utility as a broad-spectrum vaccine candidate against coronavirus diseases. Comparative sequence analysis, structural assessment, and molecular dynamics simulations of C662-C671 epitope were performed. Mathematical tools were employed to determine its mutational cost. We found that the amino acid sequence of C662-C671 epitope is entirely conserved across the observed major variants of SARS-CoV-2 in addition to SARS-CoV. Its conformation and accessibility are predicted to be conserved, even in the highly mutated Omicron variant. Costly mutational rate in the context of energy expenditure in genome replication and translation can explain this strict conservation. These observations may herald an approach to developing vaccine candidates for universal protection against emergent variants of coronavirus.

Amphibian regeneration and mammalian cancer: Similarities and contrasts from an evolutionary biology perspective: Comparing the regenerative potential of mammalian embryos and urodeles to develop effective strategies against human cancer.

Here we review and discuss the link between regeneration capacity and tumor suppression comparing mammals (embryos versus adults) with highly regenerative vertebrates. Similar to mammal embryo morphogenesis, in amphibians (essentially newts and salamanders) the reparative process relies on a precise molecular and cellular machinery capable of sensing abnormal signals and actively reprograming or eliminating them. As the embryo's evil twin, tumor also retains common functional attributes. The immune system plays a pivotal role in maintaining a physiological balance to provide surveillance against tumor initiation or to support its initiation and progression. We speculate that susceptibility to cancer development in adult mammals may be determined by the loss of an advanced regenerative capability during evolution and believe that gaining mechanistic insights into how regenerative capacity linked to tumor suppression is postnatally lost in mammals might illuminate an as yet unrecognized route to cancer treatment.

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. Computed tomography (CT) imaging performed in vivo of C57BL/6 mice harboring Lewis lung carcinoma (LLC) tumors on their flank and gross visualization of excised tumors consistently revealed that intratumorally administered citrate-GNPs accumulate in particle clusters in central areas of the tumor, while GNPs functionalized with thiolated phosphothioethanol (PTE-GNPs) and thiolated polyethylene glycol (PEG-GNPs) regularly accumulate in the tumor periphery. Further, PEG functionalization resulted in larger tumoral surface coverage than PTE, reaching beyond the outer zone of the tumor mass and into the surrounding stroma. To understand the dissimilarities in spatiotemporal evolution across the different GNP surface chemistries, we modeled their intratumoral transport with reaction-diffusion equations. Our results suggest that GNP surface passivation affects nanoparticle reactivity with the tumor microenvironment, leading to differential transport behavior across tumor zones. The present study provides a mechanistic understanding of the factors affecting spatiotemporal distribution of nanoparticles in the tumor. Our proof of concept of zonal delivery within the tumor may prove useful for directing anticancer therapies to regions of biomarker overexpression.

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

PURPOSE: Downregulation of miRNA-22 in triple-negative breast cancer (TNBC) is associated with upregulation of eukaryotic elongation 2 factor kinase (eEF2K) protein, which regulates tumor growth, chemoresistance, and tumor immunosurveillance. Moreover, exogenous administration of miRNA-22, loaded in nanoparticles to prevent degradation and improve tumor delivery (termed miRNA-22 nanotherapy), to suppress eEF2K production has shown potential as an investigational therapeutic agent in vivo. METHODS: To evaluate the translational potential of miRNA-22 nanotherapy, we developed a multiscale mechanistic model, calibrated to published in vivo data and extrapolated to the human scale, to describe and quantify the pharmacokinetics and pharmacodynamics of miRNA-22 in virtual patient populations. RESULTS: Our analysis revealed the dose-response relationship, suggested optimal treatment frequency for miRNA-22 nanotherapy, and highlighted key determinants of therapy response, from which combination with immune checkpoint inhibitors was identified as a candidate strategy for improving treatment outcomes. More importantly, drug synergy was identified between miRNA-22 and standard-of-care drugs against TNBC, providing a basis for rational therapeutic combinations for improved response CONCLUSIONS: The present study highlights the translational potential of miRNA-22 nanotherapy for TNBC in combination with standard-of-care drugs.

Novel intranasal treatment for anxiety disorders using amiloride, an acid-sensing ion channel antagonist: Pharmacokinetic modeling and simulation.

OBJECTIVE: To develop a physiologically based pharmacokinetic (PBPK) model for amiloride, an acid-sensing ion channel (ASIC) antagonist, and to simulate its pharmacokinetics in plasma and the central nervous system following intranasal administration in a virtual human population. MATERIALS AND METHODS: We first developed a PBPK model of amiloride after oral administration and optimized the model using data from five clinical studies. Next, we added a nasal compartment to the amiloride oral PBPK model and parameterized using data from previous clinical studies. We simulated amiloride's pharmacokinetics in plasma, brain, and cerebrospinal fluid (CSF) after intranasal administration of amiloride at various doses in a virtual human population. RESULTS: The target amiloride concentration in the central nervous system required for maximal ASIC inhibition was achieved with a 75-mg intranasal amiloride dose. However, this finding is based on simulations performed using a mathematical model and needs to be further validated with appropriate clinical data. CONCLUSION: The nasal PBPK model of amiloride could be used to design future clinical studies and allow for successful clinical translation of intranasal amiloride formulation.

A modeling platform for the lymphatic system.

We present a physiologically-based pharmacokinetic modeling platform capable of simulating the biodistribution of different therapeutic agents, including cells, their interactions within the immune system, redistribution across lymphoid compartments, and infiltration into tumor tissues. This transport-based platform comprises a distinctive implementation of a tumor compartment with spatial heterogeneity which enables the modeling of tumors of different size, necrotic state, and agent infiltration capacity. We provide three validating and three exploratory examples that illustrate the capabilities of the proposed approach. The results show that the model can recapitulate immune cell balance across different compartments, respond to antigen stimulation, simulate immune vaccine effects, and immune cell infiltration to tumors. Based on the results, the model can be used to study problems pertinent to current immunotherapies and has the potential to assist medical techniques that rely on the transport of biological species.

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

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

Mathematical modeling in cancer nanomedicine: a review.

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

Integrated nanotechnology platform for tumor-targeted multimodal imaging and therapeutic cargo release.

A major challenge of targeted molecular imaging and drug delivery in cancer is establishing a functional combination of ligand-directed cargo with a triggered release system. Here we develop a hydrogel-based nanotechnology platform that integrates tumor targeting, photon-to-heat conversion, and triggered drug delivery within a single nanostructure to enable multimodal imaging and controlled release of therapeutic cargo. In proof-of-concept experiments, we show a broad range of ligand peptide-based applications with phage particles, heat-sensitive liposomes, or mesoporous silica nanoparticles that self-assemble into a hydrogel for tumor-targeted drug delivery. Because nanoparticles pack densely within the nanocarrier, their surface plasmon resonance shifts to near-infrared, thereby enabling a laser-mediated photothermal mechanism of cargo release. We demonstrate both noninvasive imaging and targeted drug delivery in preclinical mouse models of breast and prostate cancer. Finally, we applied mathematical modeling to predict and confirm tumor targeting and drug delivery. These results are meaningful steps toward the design and initial translation of an enabling nanotechnology platform with potential for broad clinical applications.

Theory and Experimental Validation of a Spatio-temporal Model of Chemotherapy Transport to Enhance Tumor Cell Kill.

AUTHOR SUMMARY: Cancer treatment efficacy can be significantly enhanced through the elution of drug from nano-carriers that can temporarily stay in the tumor vasculature. Here we present a relatively simple yet powerful mathematical model that accounts for both spatial and temporal heterogeneities of drug dosing to help explain, examine, and prove this concept. We find that the delivery of systemic chemotherapy through a certain form of nano-carriers would have enhanced tumor kill by a factor of 2 to 4 over the standard therapy that the patients actually received. We also find that targeting blood volume fraction (a parameter of the model) through vascular normalization can achieve more effective drug delivery and tumor kill. More importantly, this model only requires a limited number of parameters which can all be readily assessed from standard clinical diagnostic measurements (e.g., histopathology and CT). This addresses an important challenge in current translational research and justifies further development of the model towards clinical translation.

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. In this study, we developed an in vitro tumour invasion assay to investigate cell invasiveness in tumour spheroids under a chemotactic stimulus. Our method, employing tumour spheroids seeded in a 3D collagen gel within a microfluidic chemotaxis chamber, focuses on the role of diffusive gradients. Using Time-Lapse Microscopy, the dynamic evolution of tumour spheroids was monitored in real-time, providing a comprehensive view of the morphological changes and cell migration patterns under different chemotactic conditions. Specifically, we explored the impact of fetal bovine serum (FBS) gradients on the behaviour of CT26 mouse colon carcinoma cells and compared the effects of varying FBS concentrations to two isotropic control conditions. Furthermore, a finite element in silico model was developed to quantify the diffusive flow of nutrients in the chemotaxis chamber and obtain a detailed understanding of tumour dynamics. Our findings reveal that the presence of a chemotactic gradient significantly influences tumour invasiveness, with higher concentrations of nutrients associated with increased cancer growth and cell migration.

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. To address this, we developed a minimal physiologically-based pharmacokinetic (mPBPK) model to simulate the systemic disposition of SiNPs, calibrated using in vivo PK data from mice. The model assesses how variations in surface charge, size, porosity, and geometry influence SiNP biodistribution across key organs, including the kidneys, lungs, liver, and spleen. A global sensitivity analysis identified the most influential parameters, with the unbound fraction and elimination rate constants for the kidneys and MPS emerging as critical determinants of SiNP clearance. Non-compartmental analysis (NCA) further revealed that aminated SiNPs exhibit high accumulation in the liver, spleen, and kidneys, while mesoporous SiNPs primarily accumulate in the lungs. Rod-shaped SiNPs showed faster clearance compared to spherical NPs. The mPBPK model was extrapolated to predict SiNP behavior in humans, yielding strong predictive accuracy with Pearson correlation coefficients of 0.98 for mice and 0.92 for humans. This model provides a robust framework for predicting the pharmacokinetics of diverse SiNPs, offering valuable insights for optimizing NP-based drug delivery systems and guiding the translation of these therapies from preclinical models to human applications.

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. This is an option that offers critical advantages to the ongoing opioid crisis, including ease of distribution and easy administration, with little to no need for intervention by clinicians. Nonetheless, this approach by itself needs augmentation to meet pharmacokinetic delivery attributes desired for a viable clinical alternative to existing market dosage forms. In this study, we report the success of an optimized iontophoresis-coupled naloxone loaded dissolving microneedle patch which had facilitated a 12- fold increase in average cumulative permeation and a 6-fold increase in drug flux over a conventional dissolving microneedle patch within 60 min of application (p < 0.05). This translates to a 30 % decrease in dose requirement in a mechanistically predicted microneedle patch established to be able to achieve the desired early plasma concentration time profile needed in an opioid overdose emergency. Applying a predictive mathematical model, we describe an iontophoresis-coupled microneedle patch design capable of meeting the desired pharmacokinetic profile for a viable naloxone delivery form through skin.

Deep learning-derived optimal aviation strategies to control pandemics.

The COVID-19 pandemic affected countries across the globe, demanding drastic public health policies to mitigate the spread of infection, which led to economic crises as a collateral damage. In this work, we investigate the impact of human mobility, described via international commercial flights, on COVID-19 infection dynamics on a global scale. We developed a graph neural network (GNN)-based framework called Dynamic Weighted GraphSAGE (DWSAGE), which operates over spatiotemporal graphs and is well-suited for dynamically changing flight information updated daily. This architecture is designed to be structurally sensitive, capable of learning the relationships between edge features and node features. To gain insights into the influence of air traffic on infection spread, we conducted local sensitivity analysis on our model through perturbation experiments. Our analyses identified Western Europe, the Middle East, and North America as leading regions in fueling the pandemic due to the high volume of air traffic originating or transiting through these areas. We used these observations to propose air traffic reduction strategies that can significantly impact controlling the pandemic with minimal disruption to human mobility. Our work provides a robust deep learning-based tool to study global pandemics and is of key relevance to policymakers for making informed decisions regarding air traffic restrictions during future outbreaks.

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. Analysis revealed that training an artificial neural network with both mechanistic modeling-derived and clinical measures achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when only mechanistic model-derived values or only clinical data were used. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in neural network decision making, and in increasing prediction accuracy, further supporting the advantage of our hybrid approach. We anticipate that many existing mechanistic models may be hybridized with deep learning methods in a similar manner to improve predictive accuracy through addition of additional data that may not be readily implemented in mechanistic descriptions.

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. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.

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 in vivo 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. Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimen to prevent antagonistic effects. Thus, this work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.

Measurable imaging-based changes in enhancement of intrahepatic cholangiocarcinoma after radiotherapy reflect physical mechanisms of response.

Background: Although escalated doses of radiation therapy (RT) for intrahepatic cholangiocarcinoma (iCCA) are associated with durable local control (LC) and prolonged survival, uncertainties persist regarding personalized RT based on biological factors. Compounding this knowledge gap, the assessment of RT response using traditional size-based criteria via computed tomography (CT) imaging correlates poorly with outcomes. We hypothesized that quantitative measures of enhancement would more accurately predict clinical outcomes than size-based assessment alone and developed a model to optimize RT. Methods: Pre-RT and post-RT CT scans of 154 patients with iCCA were analyzed retrospectively for measurements of tumor dimensions (for RECIST) and viable tumor volume using quantitative European Association for Study of Liver (qEASL) measurements. Binary classification and survival analyses were performed to evaluate the ability of qEASL to predict treatment outcomes, and mathematical modeling was performed to identify the mechanistic determinants of treatment outcomes and to predict optimal RT protocols. Results: Multivariable analysis accounting for traditional prognostic covariates revealed that percentage change in viable volume following RT was significantly associated with OS, outperforming stratification by RECIST. Binary classification identified ≥33% decrease in viable volume to optimally correspond to response to RT. The model-derived, patient-specific tumor enhancement growth rate emerged as the dominant mechanistic determinant of treatment outcome and yielded high accuracy of patient stratification (80.5%), strongly correlating with the qEASL-based classifier. Conclusion: Following RT for iCCA, changes in viable volume outperformed radiographic size-based assessment using RECIST for OS prediction. CT-derived tumor-specific mathematical parameters may help optimize RT for resistant tumors.