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

It has been hypothesized that continuously releasing drug molecules into the tumor over an extended period of time may significantly improve the chemotherapeutic efficacy by overcoming physical transport limitations of conventional bolus drug treatment. In this paper, we present a generalized space- and time-dependent mathematical model of drug transport and drug-cell interactions to quantitatively formulate this hypothesis. Model parameters describe: perfusion and tissue architecture (blood volume fraction and blood vessel radius); diffusion penetration distance of drug (i.e., a function of tissue compactness and drug uptake rates by tumor cells); and cell death rates (as function of history of drug uptake). We performed preliminary testing and validation of the mathematical model using in vivo experiments with different drug delivery methods on a breast cancer mouse model. Experimental data demonstrated a 3-fold increase in response using nano-vectored drug vs. free drug delivery, in excellent quantitative agreement with the model predictions. Our model results implicate that therapeutically targeting blood volume fraction, e.g., through vascular normalization, would achieve a better outcome due to enhanced drug delivery.

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.     

Solid Lipid Nanoparticles (SLNs) for Intracellular Targeting Applications

Nanoparticle-based delivery vehicles have shown great promise for intracellular targeting applications, providing a mechanism to specifically alter cellular signaling and gene expression. In a previous investigation, the synthesis of ultra-small solid lipid nanoparticles (SLNs) for topical drug delivery and biomarker detection applications was demonstrated. SLNs are a well-studied example of a nanoparticle delivery system that has emerged as a promising drug delivery vehicle. In this study, SLNs were loaded with a fluorescent dye and used as a model to investigate particle-cell interactions. The phase inversion temperature (PIT) method was used for the synthesis of ultra-small populations of biocompatible nanoparticles. A 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenylphenyltetrazolium bromide (MTT) assay was utilized in order to establish appropriate dosing levels prior to the nanoparticle-cell interaction studies. Furthermore, primary human dermal fibroblasts and mouse dendritic cells were exposed to dye-loaded SLN over time and the interactions with respect to toxicity and particle uptake were characterized using fluorescence microscopy and flow cytometry. This study demonstrated that ultra-small SLNs, as a nanoparticle delivery system, are suitable for intracellular targeting of different cell types.     

Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules

While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600–800 hours, and this data is used to calibrate a family of cancer growth models, each consisting of a system of ordinary differential equations, derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval.     

Mesoscale Simulation and cryo-TEM of Nanoscale Drug Delivery Systems

This work sets out to study the effect of hydrophobic molecules on the morphology of aqueous solutions of amphiphilic block copolymer, which has potential drug delivery applications. The effect is studied both experimentally and by using simulations. Using cryogenic TEM observations, micelles can clearly be visualised and their core size measured. While pure polymer solutions form into spherical micelles with a narrow size distribution, addition of small amounts of hydrophobic drug molecules leads to distortions in shape, a wider size distribution, and larger average core diameter. Simulations are based on a mesoscale dynamic density functional method with Gaussian chain Hamiltonian and mean-field interactions, as implemented in the MesoDyn code. With parameters for the amphiphilic system established in earlier work, and mean-field interactions for the drug molecule derived from structure–property relationships, we obtain good agreement with the TEM observations for the effect of the hydrophobic molecules on the morphology. The simulations clearly show how increasing drug concentration leads to an increase in micelle size, a wider distribution and more elongated rather than spherical micelles.     

综述与专论: 适配体功能化外泌体在肿瘤靶向治疗中的应用

传统的肿瘤治疗方法因缺乏足够的靶向性而会产生严重的毒副作用。外泌体（exosome）是一种天然的纳米囊泡,参与细胞间的信息传递,并且作为药物递送载体具有出色的性能优势,包括低免疫原性、低毒性和能够穿越天然屏障等特点。然而以外泌体为载体的药物递送系统的靶向能力仍有不足。适配体（aptamer）是一类化学合成的单链核酸分子,具有分子质量小、易于修饰和免疫原性低等特点,可作为亲和性配体与靶向分子特异性结合。通过在外泌体表面修饰适配体,药物可以被精确递送到肿瘤细胞发生部位,从而实现对肿瘤的靶向治疗,提高肿瘤治疗效果,减少毒副作用。本篇综述将重点讨论适配体功能化外泌体药物靶向递送系统在各种肿瘤治疗方面的应用,并对其未来的挑战和机遇进行阐述。     

Multiorgan microfluidic platform with breathable lung chamber for inhalation or intravenous drug screening and development

Efficient and economical delivery of pharmaceuticals to patients is critical for effective therapy. Here we describe a multiorgan (lung, liver, and breast cancer) microphysiological system (“Body-on-a-Chip”) designed to mimic both inhalation therapy and/or intravenous therapy using curcumin as a model drug. This system is “pumpless” and self-contained using a rocker platform for fluid (blood surrogate) bidirectional recirculation. Our lung chamber is constructed to maintain an air-liquid interface and contained a “breathable” component that was designed to mimic breathing by simulating gas exchange, contraction and expansion of the “lung” using a reciprocating pump. Three cell lines were used: A549 for the lung, HepG2 C3A for the liver, and MDA MB231 for breast cancer. All cell lines were maintained with high viability (>85%) in the device for at least 48 hr. Curcumin is used to treat breast cancer and this allowed us to compare inhalation delivery versus intravenous delivery of the drug in terms of effectiveness and potentially toxicity. Inhalation therapy could be potentially applied at home by the patient while intravenous therapy would need to be applied in a clinical setting. Inhalation therapy would be more economical and allow more frequent dosing with a potentially lower level of drug. For 24 hr exposure to 2.5 and 25 µM curcumin in the flow device the effect on lung and liver viability was small to insignificant, while there was a significant decrease in viability of the breast cancer (to 69% at 2.5 µM and 51% at 25 µM). Intravenous delivery also selectively decreased breast cancer viability (to 88% at 2.5 µM and 79% at 25 µM) but was less effective than inhalation therapy. The response in the static device controls was significantly reduced from that with recirculation demonstrating the effect of flow. These results demonstrate for the first time the feasibility of constructing a multiorgan microphysiological system with recirculating flow that incorporates a “breathable” lung module that maintains an air-liquid interface.     

Patho-biotechnology: using bad bugs to do good things

Pathogenic bacteria have evolved sophisticated strategies to overcome host defences, to interact with the immune system and to interfere with essential host systems. We coin the term 'patho-biotechnology' to describe the exploitation of these valuable traits in biotechnology, medicine and food. This approach shows promise for the development of novel vaccine and drug delivery systems, as well as for the design of more technologically robust and effective probiotic cultures with improved biotechnological and clinical applications. The genetic tractability of Listeria monocytogenes, the availability of the complete genome sequence of this intracellular pathogen, its ability to cope with stress, and its ability to traverse the gastrointestinal tract and induce a strong cellular immune response make L. monocytogenes an ideal model organism for demonstrating the patho-biotechnology concept.     

A model predicting delivery of saquinavir in nanoparticles to human monocyte/macrophage (Mo/Mac) cells

Modeling the influence of a technology such as nanoparticle systems on drug delivery is beneficial in rational formulation design. While there are many studies showing drug delivery enhancement by nanoparticles, the literature provides little guidance regarding when nanoparticles are useful for delivery of a given drug. A model was developed predicting intracellular drug concentration in cultured cells dosed with nanoparticles. The model considered drug release from nanoparticles as well as drug and nanoparticle uptake by the cells as the key system processes. Mathematical expressions for these key processes were determined using experiments in which each process occurred in isolation. In these experiments, intracellular delivery of saquinavir, a low solubility drug dosed as a formulation of poly(ethylene oxide)-modified poly(epsilon- caprolactone) (PEO-PCL) nanoparticles, was studied in THP-1 human monocyte/macrophage (Mo/Mac) cells. The model accurately predicted the enhancement in intracellular concentration when drug was administered in nanoparticles compared to aqueous solution. This simple model highlights the importance of relative kinetics of nanoparticle uptake and drug release in determining overall enhancement of intracellular drug concentration when dosing with nanoparticles.     

A model for the intrinsic limit of cancer therapy: Duality of treatment-induced cell death and treatment-induced stemness

Intratumor cellular heterogeneity and non-genetic cell plasticity in tumors pose a recently recognized challenge to cancer treatment. Because of the dispersion of initial cell states within a clonal tumor cell population, a perturbation imparted by a cytocidal drug only kills a fraction of cells. Due to dynamic instability of cellular states the cells not killed are pushed by the treatment into a variety of functional states, including a “stem-like state” that confers resistance to treatment and regenerative capacity. This immanent stress-induced stemness competes against cell death in response to the same perturbation and may explain the near-inevitable recurrence after any treatment. This double-edged-sword mechanism of treatment complements the selection of preexisting resistant cells in explaining post-treatment progression. Unlike selection, the induction of a resistant state has not been systematically analyzed as an immanent cause of relapse. Here, we present a generic elementary model and analytical examination of this intrinsic limitation to therapy. We show how the relative proclivity towards cell death versus transition into a stem-like state, as a function of drug dose, establishes either a window of opportunity for containing tumors or the inevitability of progression following therapy. The model considers measurable cell behaviors independent of specific molecular pathways and provides a new theoretical framework for optimizing therapy dosing and scheduling as cancer treatment paradigms move from “maximal tolerated dose,” which may promote therapy induced-stemness, to repeated “minimally effective doses” (as in adaptive therapies), which contain the tumor and avoid therapy-induced progression.     

Understanding co-loading of doxorubicin and camptothecin on graphene and folic acid-conjugated graphene for targeting drug delivery: classical MD simulation and DFT calculation

Abstract

The surface modification ability is one of the remarkable characters of graphene (G) nanosheet. Based on this strategy, G surface is modified with folic acid (FA) to improve the targeting delivery of chemotherapy agents. The dual delivery strategy for the transport of doxorubicin (DOX) and camptothecin (CPT) by using G and folic acid functionalized G nanocarriers is examined. The density functional theory (DFT) and molecular dynamics (MD) simulation are employed to gain a deep insight into the nature of the drug and the carrier interactions. The obtained results indicate that the drug molecules spontaneously move toward the carriers and form stable complexes. In the graphene-based systems, the drug molecules form strong π-π interactions with the carrier surface. It is found that the FA functionalization of G (FA-G) not only improves targeting effect but also reinforces drug-carrier interaction. Furthermore, the MD and DFT results show that interaction of DOX molecules with G and FA-G is stronger than CPT. We believe that the results obtained from this study can be helpful to improve the drug effectiveness in cancer treatment.

Communicated by Ramaswamy H. Sarma     

Drug delivery patterns for different stenting techniques in coronary bifurcations: a comparative computational study

The treatment of coronary bifurcation lesions represents a challenge for the interventional cardiologists due to the lower rate of procedural success and the higher risk of restenosis. The advent of drug-eluting stents (DES) has dramatically reduced restenosis and consequently the request for re-intervention. The aim of the present work is to provide further insight about the effectiveness of DES by means of a computational study that combines virtual stent implantation, fluid dynamics and drug release for different stenting protocols currently used in the treatment of a coronary artery bifurcation. An explicit dynamic finite element model is developed in order to obtain realistic configurations of the implanted devices used to perform fluid dynamics analysis by means of a previously developed finite element method coupling the blood flow and the intramural plasma filtration in rigid arteries. To efficiently model the drug release, a multiscale strategy is adopted, ranging from lumped parameter model accounting for drug release to fully 3-D models for drug transport to the artery. Differences in drug delivery to the artery are evaluated with respect to local drug dosage. This model allowed to compare alternative stenting configurations (namely the Provisional Side Branch, the Culotte and the Inverted Culotte techniques), thus suggesting guidelines in the treatment of coronary bifurcation lesions and addressing clinical issues such as the effectiveness of drug delivery to lesions in the side branch, as well as the influence of incomplete strut apposition and overlapping stents.     

Dynamics of microbial competition,commensalism, and cooperation and its implications for coculture and microbiome engineering

Microbial consortium is a complex adaptive system with higher‐order dynamic characteristics that are not present by individual members. To accurately predict the social interactions, we formulate a set of unstructured kinetic models to quantitatively capture the dynamic interactions of multiple microbial species. By introducing an interaction coefficient, we analytically derived the steady‐state solutions for the interacting species and the substrate‐depleting profile in the chemostat. We analyzed the stability of the possible coexisting states defined by competition, parasitism, amensalism, commensalism, and cooperation. Our model predicts that only parasitism, commensalism, and cooperation could lead to stable coexisting states. We also determined the optimal social interaction criteria of microbial coculture when sequential metabolic reactions are compartmentalized into two distinct species. Coupled with Luedeking–Piret and Michaelis–Menten equations, accumulation of metabolic intermediates in one species and formation of end‐product in another species could be derived and assessed. We discovered that parasitism consortia disfavor the bioconversion of intermediate to final product; and commensalism consortia could efficiently convert metabolic intermediates to final product and maintain metabolic homeostasis with a broad range of operational conditions (i.e., dilution rates); whereas cooperative consortia leads to highly nonlinear pattern of precursor accumulation and end‐product formation. The underlying dynamics and emergent properties of microbial consortia may provide critical knowledge for us to understand ecological coexisting states, engineer efficient bioconversion process, deliver effective gut therapeutics as well as elucidate probiotic‐pathogen or tumor‐host interactions in general.     

Impact of tumor-specific targeting and dosing schedule on tumor growth inhibition after intravenous administration of siRNA-containing nanoparticles

This study addresses issues of relevance for siRNA nanoparticle delivery by investigating the functional impact of tumor-specific targeting and dosing schedule. The investigations are performed using an experimental system involving a syngeneic mouse cancer model and a theoretical system based on our previously described mathematical model of siRNA delivery and function. A/J mice bearing subcutaneous Neuro2A tumors approximately 100 mm(3) in size were treated by intravenous injection with siRNA-containing nanoparticles formed with cyclodextrin-containing polycations (CDP). Three consecutive daily doses of transferrin (Tf)-targeted nanoparticles carrying 2.5 mg/kg of two different siRNA sequences targeting ribonucleotide reductase subunit M2 (RRM2) slowed tumor growth, whereas non-targeted nanoparticles were significantly less effective when given at the same dose. Furthermore, administration of the three doses on consecutive days or every 3 days did not lead to statistically significant differences in tumor growth delay. Mathematical model calculations of siRNA-mediated target protein knockdown and tumor growth inhibition are used to elucidate possible mechanisms to explain the observed effects and to provide guidelines for designing more effective siRNA-based treatment regimens regardless of delivery methodology and tumor type.     

An integrated comprehensive workbench for inferring genetic networks: voyagene

We propose an integrated, comprehensive network-inferring system for genetic interactions, named VoyaGene, which can analyze experimentally observed expression profiles by using and combining the following five independent inferring models: Clustering, Threshold-Test, Bayesian, multi-level digraph and S-system models. Since VoyaGene also has effective tools for visualizing the inferred results, researchers may evaluate the combination of appropriate inferring models, and can construct a genetic network to an accuracy that is beyond the reach of a single inferring model. Through the use of VoyaGene, the present study demonstrates the effectiveness of combining different inferring models.     

Small interfering RNA delivery and gene silencing using polymeric nanocarrier systems

The effectiveness of a drug is determined by the ability to migrate through the body and reach target sites in therapeutically relevant levels. Nanocarriers for delivery of bioactive agents are being developed at iNANO to maximise drug payload at target sites. The inclusion of “biological triggers” into the nanocarrier design is used for modulation of cellular nucleic acid trafficking and increased target interaction. Polymers were used to formulate nanocarriers in the size range 30–250 nm containing small interfering RNAs (siRNAs) for gene silencing applications. PAGE analysis showed the structural integrity of the siRNA was maintained during particle formation. In systems composed of bioresponsive polymers, nanocarrier disassembly and siRNA release under cellular conditions were shown, using Atomic Force Microscopy. The time course for siRNA uptake into NIH cells was visualised using confocal microscopy. In addition, siRNA localisation within cells could be modulated by the composition of the polymer used. The ability of the nanocarrier system to mediate gene expression was investigated in a cell line stably expressing enhanced green fluorescent protein (eGFP). Furthermore, the various delivery systems were tested in a mouse model stably expressing the eGFP protein using both nasal and intravenous delivery routes. The systems described in this work demonstrate an ability to transport siRNA into cells whilst maintaining siRNA functionality; essential properties for nanocarrier-based RNA interference strategies.     

Elucidating the Signal Responses of Multi-Parametric Surface Plasmon Resonance Living Cell Sensing: A Comparison between Optical Modeling and Drug–MDCKII Cell Interaction Measurements

In vitro cell-based assays are widely used during the drug discovery and development process to test the biological activity of new drugs. Most of the commonly used cell-based assays, however, lack the ability to measure in real-time or under dynamic conditions (e.g. constant flow). In this study a multi-parameter surface plasmon resonance approach in combination with living cell sensing has been utilized for monitoring drug-cell interactions in real-time, under constant flow and without labels. The multi-parameter surface plasmon resonance approach, i.e. surface plasmon resonance angle versus intensity plots, provided fully specific signal patterns for various cell behaviors when stimulating cells with drugs that use para- and transcellular absorption routes. Simulated full surface plasmon resonance angular spectra of cell monolayers were compared with actual surface plasmon resonance measurements performed with MDCKII cell monolayers in order to better understand the origin of the surface plasmon resonance signal responses during drug stimulation of cells. The comparison of the simulated and measured surface plasmon resonance responses allowed to better understand and provide plausible explanations for the type of cellular changes, e.g. morphological or mass redistribution in cells, that were induced in the MDCKII cell monolayers during drug stimulation, and consequently to differentiate between the type and modes of drug actions. The multi-parameter surface plasmon resonance approach presented in this study lays the foundation for developing new types of cell-based tools for life science research, which should contribute to an improved mechanistic understanding of the type and contribution of different drug transport routes on drug absorption.     

Modeling the influence of cyclodextrins on oral absorption of low‐solubility drugs: I. Model development

The ability to quantitatively predict the influence of a solubilization technology on oral absorption would be highly beneficial in rational selection of drug delivery technology and formulation design. Cyclodextrins (CDs) are cyclic oligosaccharides which form inclusion complexes with a large variety of compounds including drugs. There are many studies in the literature showing that complexation between CD and drug enhances oral bioavailability and some demonstrating failure of CD in bioavailability enhancement, but relatively little guidance regarding when CD can be used to enhance bioavailability. A model was developed based upon mass transport expressions for drug dissolution and absorption and a pseudo‐equilibrium assumption for the complexation reaction with CD. The model considers neutral compound delivered as a physical mixture with CD in both immediate release (IR) and controlled release (CR) formulations. Simulation results demonstrated that cyclodextrins can enhance, have no effect, or hurt drug absorption when delivered as a physical mixture with drug. The predicted influence depends on interacting parameter values, including solubility, drug absorption constant, binding constant, CD:drug molar ratio, dose, and assumed volume of the intestinal lumen. In general, the predicted positive influence of dosing as a physical mixture with CD was minimal, alluding to the significance of dosing as a preformed complex. The model developed enabled examination of which physical and chemical properties result in oral absorption enhancement for neutral drug administered as a physical mixture with CD, demonstrating the utility of modeling the influence of a drug delivery agent (e.g., CD) on absorption for rational dosage form design. Biotechnol. Bioeng. 2010; 105: 409–420. © 2009 Wiley Periodicals, Inc.     

Frustration and Direct-Coupling Analyses to Predict Formation and Function of Adeno-Associated Virus

Adeno-associated virus (AAV) is a promising gene therapy vector because of its efficient gene delivery and relatively mild immunogenicity. To improve delivery target specificity, researchers use combinatorial and rational library design strategies to generate novel AAV capsid variants. These approaches frequently propose high proportions of nonforming or noninfective capsid protein sequences that reduce the effective depth of synthesized vector DNA libraries, thereby raising the discovery cost of novel vectors. We evaluated two computational techniques for their ability to estimate the impact of residue mutations on AAV capsid protein-protein interactions and thus predict changes in vector fitness, reasoning that these approaches might inform the design of functionally enriched AAV libraries and accelerate therapeutic candidate identification. The Frustratometer computes an energy function derived from the energy landscape theory of protein folding. Direct-coupling analysis (DCA) is a statistical framework that captures residue coevolution within proteins. We applied the Frustratometer to select candidate protein residues predicted to favor assembled or disassembled capsid states, then predicted mutation effects at these sites using the Frustratometer and DCA. Capsid mutants were experimentally assessed for changes in virus formation, stability, and transduction ability. The Frustratometer-based metric showed a counterintuitive correlation with viral stability, whereas a DCA-derived metric was highly correlated with virus transduction ability in the small population of residues studied. Our results suggest that coevolutionary models may be able to elucidate complex capsid residue-residue interaction networks essential for viral function, but further study is needed to understand the relationship between protein energy simulations and viral capsid metastability.