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1.  Multiscale image-based modeling and simulation of gas flow and particle transport in the human lungs 
Improved understanding of structure and function relationships in the human lungs in individuals and sub-populations is fundamentally important to the future of pulmonary medicine. Image-based measures of the lungs can provide sensitive indicators of localized features, however to provide a better prediction of lung response to disease, treatment and environment, it is desirable to integrate quantifiable regional features from imaging with associated value-added high-level modeling. With this objective in mind, recent advances in computational fluid dynamics (CFD) of the bronchial airways - from a single bifurcation symmetric model to a multiscale image-based subject-specific lung model - will be reviewed. The interaction of CFD models with local parenchymal tissue expansion - assessed by image registration - allows new understanding of the interplay between environment, hot spots where inhaled aerosols could accumulate, and inflammation. To bridge ventilation function with image-derived central airway structure in CFD, an airway geometrical modeling method that spans from the model ‘entrance’ to the terminal bronchioles will be introduced. Finally, the effects of turbulent flows and CFD turbulence models on aerosol transport and deposition will be discussed.
CFD simulation of airflow and particle transport in the human lung has been pursued by a number of research groups, whose interest has been in studying flow physics and airways resistance, improving drug delivery, or investigating which populations are most susceptible to inhaled pollutants. The three most important factors that need to be considered in airway CFD studies are lung structure, regional lung function, and flow characteristics. Their correct treatment is important because the transport of therapeutic or pollutant particles is dependent on the characteristics of the flow by which they are transported; and the airflow in the lungs is dependent on the geometry of the airways and how ventilation is distributed to the peripheral tissue. The human airway structure spans more than 20 generations, beginning with the extra-thoracic airways (oral or nasal cavity, and through the pharynx and larynx to the trachea), then the conducting airways, the respiratory airways, and to the alveoli. The airways in individuals and sub-populations (by gender, age, ethnicity, and normal vs. diseased states) may exhibit different dimensions, branching patterns and angles, and thickness and rigidity. At the local level, one would like to capture detailed flow characteristics, e.g. local velocity profiles, shear stress, and pressure, for prediction of particle transport in an airway (lung structure) model that is specific to the geometry of an individual, to understand how inter-subject variation in airway geometry (normal or pathological) influences the transport and deposition of particles. In a systems biology – or multiscale modeling – approach, these local flow characteristics can be further integrated with epithelial cell models for the study of mechanotransduction. At the global (organ) level, one would like to match regional ventilation (lung function) that is specific to the individual, thus ensuring that the flow that transports inhaled particles is appropriately distributed throughout the lung model. Computational models that do not account for realistic distribution of ventilation are not capable of predicting realistic particle distribution or targeted drug deposition. Furthermore, the flow in the human lung can be transitional or turbulent in the upper and proximal airways, and becomes laminar in the distal airways. The flows in the laminar, transitional and turbulent regimes have different temporal and spatial scales. Therefore, modeling airway structure and predicting gas flow and particle transport at both local and global levels require image-guided multiscale modeling strategies.
In this article, we will review the aforementioned three key aspects of CFD studies of the human lungs: airway structure (conducting airways), lung function (regional ventilation and boundary conditions), and flow characteristics (modeling of turbulent flow and its effect on particle transport). For modeling airway structure, we will focus on the conducting airways, and review both symmetric vs. asymmetric airway models, idealized vs. CT-based airway models, and multiscale subject-specific airway models. Imposition of physiological subject-specific boundary conditions (BCs) in CFD is essential to match regional ventilation in individuals, which is also critical in studying preferential deposition of inhaled aerosols in sub-populations, e.g. normals vs. asthmatics that may exhibit different ventilation patterns. Subject-specific regional ventilation defines flow distributions and characteristics in airway segments and bifurcations, which subsequently determines the transport and deposition of aerosols in the entire lungs. Turbulence models are needed to capture the transient and turbulent nature of the gas flow in the human lungs. Thus, the advantages and disadvantages of different turbulence models as well as their effects on particle transport will be discussed. The ultimate goal of the development is to identify sensitive structural and functional variables in sub-populations of normal and diseased lungs for potential clinical applications.
PMCID: PMC3763693  PMID: 23843310
2.  Multiscale Kinetic Modeling of Liposomal Doxorubicin Delivery Quantifies the Role of Tumor and Drug-Specific Parameters in Local Delivery to Tumors 
Nanoparticle encapsulation has been used as a means to manipulate the pharmacokinetic (PK) and safety profile of drugs in oncology. Using pegylated liposomal doxorubicin (PLD) vs. conventional doxorubicin as a model system, we developed and experimentally validated a multiscale computational model of liposomal drug delivery. We demonstrated that, for varying tumor transport properties, there is a regimen where liposomal and conventional doxorubicin deliver identical amounts of doxorubicin to tumor cell nuclei. In mice, typical tumor properties consistently favor improved delivery via liposomes relative to free drug. However, in humans, we predict that some tumors will have properties wherein liposomal delivery delivers the identical amount of drug to its target relative to dosing with free drug. The ability to identify tumor types and/or individual patient tumors with high degree of liposome deposition may be critical for optimizing the success of nanoparticle and liposomal anticancer therapeutics.
PMCID: PMC3600732  PMID: 23835797
3.  Nanostructured materials for applications in drug delivery and tissue engineering* 
Research in the areas of drug delivery and tissue engineering has witnessed tremendous progress in recent years due to their unlimited potential to improve human health. Meanwhile, the development of nanotechnology provides opportunities to characterize, manipulate and organize matter systematically at the nanometer scale. Biomaterials with nano-scale organizations have been used as controlled release reservoirs for drug delivery and artificial matrices for tissue engineering. Drug-delivery systems can be synthesized with controlled composition, shape, size and morphology. Their surface properties can be manipulated to increase solubility, immunocompatibility and cellular uptake. The limitations of current drug delivery systems include suboptimal bioavailability, limited effective targeting and potential cytotoxicity. Promising and versatile nano-scale drug-delivery systems include nanoparticles, nanocapsules, nanotubes, nanogels and dendrimers. They can be used to deliver both small-molecule drugs and various classes of biomacromolecules, such as peptides, proteins, plasmid DNA and synthetic oligodeoxynucleotides. Whereas traditional tissue-engineering scaffolds were based on hydrolytically degradable macroporous materials, current approaches emphasize the control over cell behaviors and tissue formation by nano-scale topography that closely mimics the natural extracellular matrix (ECM). The understanding that the natural ECM is a multifunctional nanocomposite motivated researchers to develop nanofibrous scaffolds through electrospinning or self-assembly. Nanocomposites containing nanocrystals have been shown to elicit active bone growth. Drug delivery and tissue engineering are closely related fields. In fact, tissue engineering can be viewed as a special case of drug delivery where the goal is to accomplish controlled delivery of mammalian cells. Controlled release of therapeutic factors in turn will enhance the efficacy of tissue engineering. From a materials point of view, both the drug-delivery vehicles and tissue-engineering scaffolds need to be biocompatible and biodegradable. The biological functions of encapsulated drugs and cells can be dramatically enhanced by designing biomaterials with controlled organizations at the nanometer scale. This review summarizes the most recent development in utilizing nanostructured materials for applications in drug delivery and tissue engineering.
PMCID: PMC3017754  PMID: 17471764
Nanomaterials; biomaterials; drug delivery; tissue engineering; nanoparticles; nanocapsules; nanotubes; nanogels; dendrimers; nanofibril; network; hydrogel; electrospinning; self-assembly; nanocomposites
4.  Nanomedicines for cancer therapy: state-of-the-art and limitations to pre-clinical studies that hinder future developments 
The ability to efficiently deliver a drug or gene to a tumor site is dependent on a wide range of factors including circulation time, interactions with the mononuclear phagocyte system, extravasation from circulation at the tumor site, targeting strategy, release from the delivery vehicle, and uptake in cancer cells. Nanotechnology provides the possibility of creating delivery systems where the design constraints are decoupled, allowing new approaches for reducing the unwanted side effects of systemic delivery, increasing tumor accumulation, and improving efficacy. The physico-chemical properties of nanoparticle-based delivery platforms introduce additional complexity associated with pharmacokinetics, tumor accumulation, and biodistribution. To assess the impact of nanoparticle-based delivery systems, we first review the design strategies and pharmacokinetics of FDA-approved nanomedicines. Next we review nanomedicines under development, summarizing the range of nanoparticle platforms, strategies for targeting, and pharmacokinetics. We show how the lack of uniformity in preclinical trials prevents systematic comparison and hence limits advances in the field.
PMCID: PMC4142601  PMID: 25202689
drug delivery systems; nanoparticles; targeted therapy; pharmacokinetics; tumor accumulation
5.  Variational multiscale models for charge transport 
This work presents a few variational multiscale models for charge transport in complex physical, chemical and biological systems and engineering devices, such as fuel cells, solar cells, battery cells, nanofluidics, transistors and ion channels. An essential ingredient of the present models, introduced in an earlier paper (Bulletin of Mathematical Biology, 72, 1562-1622, 2010), is the use of differential geometry theory of surfaces as a natural means to geometrically separate the macroscopic domain from the microscopic domain, meanwhile, dynamically couple discrete and continuum descriptions. Our main strategy is to construct the total energy functional of a charge transport system to encompass the polar and nonpolar free energies of solvation, and chemical potential related energy. By using the Euler-Lagrange variation, coupled Laplace-Beltrami and Poisson-Nernst-Planck (LB-PNP) equations are derived. The solution of the LB-PNP equations leads to the minimization of the total free energy, and explicit profiles of electrostatic potential and densities of charge species. To further reduce the computational complexity, the Boltzmann distribution obtained from the Poisson-Boltzmann (PB) equation is utilized to represent the densities of certain charge species so as to avoid the computationally expensive solution of some Nernst-Planck (NP) equations. Consequently, the coupled Laplace-Beltrami and Poisson-Boltzmann-Nernst-Planck (LB-PBNP) equations are proposed for charge transport in heterogeneous systems. A major emphasis of the present formulation is the consistency between equilibrium LB-PB theory and non-equilibrium LB-PNP theory at equilibrium. Another major emphasis is the capability of the reduced LB-PBNP model to fully recover the prediction of the LB-PNP model at non-equilibrium settings. To account for the fluid impact on the charge transport, we derive coupled Laplace-Beltrami, Poisson-Nernst-Planck and Navier-Stokes equations from the variational principle for chemo-electro-fluid systems. A number of computational algorithms is developed to implement the proposed new variational multiscale models in an efficient manner. A set of ten protein molecules and a realistic ion channel, Gramicidin A, are employed to confirm the consistency and verify the capability. Extensive numerical experiment is designed to validate the proposed variational multiscale models. A good quantitative agreement between our model prediction and the experimental measurement of current-voltage curves is observed for the Gramicidin A channel transport. This paper also provides a brief review of the field.
PMCID: PMC3501390  PMID: 23172978
Variational multiscale models; Ion channels; Fuel cells; Nanofluidics; Electronic devices; Laplace-Beltrami equation; Poisson-Boltzmann equation; Nernst-Planck equation; Navier-Stokes equation
6.  Nanodrug Delivery Systems: A Promising Technology for Detection, Diagnosis, and Treatment of Cancer 
AAPS PharmSciTech  2014;15(3):709-721.
Nanotechnology has enabled the development of novel therapeutic and diagnostic strategies, such as advances in targeted drug delivery systems, versatile molecular imaging modalities, stimulus responsive components for fabrication, and potential theranostic agents in cancer therapy. Nanoparticle modifications such as conjugation with polyethylene glycol have been used to increase the duration of nanoparticles in blood circulation and reduce renal clearance rates. Such modifications to nanoparticle fabrication are the initial steps toward clinical translation of nanoparticles. Additionally, the development of targeted drug delivery systems has substantially contributed to the therapeutic efficacy of anti-cancer drugs and cancer gene therapies compared with nontargeted conventional delivery systems. Although multifunctional nanoparticles offer numerous advantages, their complex nature imparts challenges in reproducibility and concerns of toxicity. A thorough understanding of the biological behavior of nanoparticle systems is strongly warranted prior to testing such systems in a clinical setting. Translation of novel nanodrug delivery systems from the bench to the bedside will require a collective approach. The present review focuses on recent research efforts citing relevant examples of advanced nanodrug delivery and imaging systems developed for cancer therapy. Additionally, this review highlights the newest technologies such as microfluidics and biomimetics that can aid in the development and speedy translation of nanodrug delivery systems to the clinic.
PMCID: PMC4037475  PMID: 24550101
cancer therapy; liposome; nanodrug delivery systems; nanomedicine; polymer nanoparticles
7.  Multifunctional Nanocarriers for diagnostics, drug delivery and targeted treatment across blood-brain barrier: perspectives on tracking and neuroimaging 
Nanotechnology has brought a variety of new possibilities into biological discovery and clinical practice. In particular, nano-scaled carriers have revolutionalized drug delivery, allowing for therapeutic agents to be selectively targeted on an organ, tissue and cell specific level, also minimizing exposure of healthy tissue to drugs. In this review we discuss and analyze three issues, which are considered to be at the core of nano-scaled drug delivery systems, namely functionalization of nanocarriers, delivery to target organs and in vivo imaging. The latest developments on highly specific conjugation strategies that are used to attach biomolecules to the surface of nanoparticles (NP) are first reviewed. Besides drug carrying capabilities, the functionalization of nanocarriers also facilitate their transport to primary target organs. We highlight the leading advantage of nanocarriers, i.e. their ability to cross the blood-brain barrier (BBB), a tightly packed layer of endothelial cells surrounding the brain that prevents high-molecular weight molecules from entering the brain. The BBB has several transport molecules such as growth factors, insulin and transferrin that can potentially increase the efficiency and kinetics of brain-targeting nanocarriers. Potential treatments for common neurological disorders, such as stroke, tumours and Alzheimer's, are therefore a much sought-after application of nanomedicine. Likewise any other drug delivery system, a number of parameters need to be registered once functionalized NPs are administered, for instance their efficiency in organ-selective targeting, bioaccumulation and excretion. Finally, direct in vivo imaging of nanomaterials is an exciting recent field that can provide real-time tracking of those nanocarriers. We review a range of systems suitable for in vivo imaging and monitoring of drug delivery, with an emphasis on most recently introduced molecular imaging modalities based on optical and hybrid contrast, such as fluorescent protein tomography and multispectral optoacoustic tomography. Overall, great potential is foreseen for nanocarriers in medical diagnostics, therapeutics and molecular targeting. A proposed roadmap for ongoing and future research directions is therefore discussed in detail with emphasis on the development of novel approaches for functionalization, targeting and imaging of nano-based drug delivery systems, a cutting-edge technology poised to change the ways medicine is administered.
PMCID: PMC2847536  PMID: 20199661
8.  Evolving toward a human-cell based and multiscale approach to drug discovery for CNS disorders 
A disruptive approach to therapeutic discovery and development is required in order to significantly improve the success rate of drug discovery for central nervous system (CNS) disorders. In this review, we first assess the key factors contributing to the frequent clinical failures for novel drugs. Second, we discuss cancer translational research paradigms that addressed key issues in drug discovery and development and have resulted in delivering drugs with significantly improved outcomes for patients. Finally, we discuss two emerging technologies that could improve the success rate of CNS therapies: human induced pluripotent stem cell (hiPSC)-based studies and multiscale biology models. Coincident with advances in cellular technologies that enable the generation of hiPSCs directly from patient blood or skin cells, together with methods to differentiate these hiPSC lines into specific neural cell types relevant to neurological disease, it is also now possible to combine data from large-scale forward genetics and post-mortem global epigenetic and expression studies in order to generate novel predictive models. The application of systems biology approaches to account for the multiscale nature of different data types, from genetic to molecular and cellular to clinical, can lead to new insights into human diseases that are emergent properties of biological networks, not the result of changes to single genes. Such studies have demonstrated the heterogeneity in etiological pathways and the need for studies on model systems that are patient-derived and thereby recapitulate neurological disease pathways with higher fidelity. In the context of two common and presumably representative neurological diseases, the neurodegenerative disease Alzheimer’s Disease, and the psychiatric disorder schizophrenia, we propose the need for, and exemplify the impact of, a multiscale biology approach that can integrate panomic, clinical, imaging, and literature data in order to construct predictive disease network models that can (i) elucidate subtypes of syndromic diseases, (ii) provide insights into disease networks and targets and (iii) facilitate a novel drug screening strategy using patient-derived hiPSCs to discover novel therapeutics for CNS disorders.
PMCID: PMC4251289  PMID: 25520658
stem cell-based screening; systems biology and network biology; drug discovery screening; complex disease mechanism; high throughput biology
9.  Automatic validation of computational models using pseudo-3D spatio-temporal model checking 
BMC Systems Biology  2014;8(1):124.
Computational models play an increasingly important role in systems biology for generating predictions and in synthetic biology as executable prototypes/designs. For real life (clinical) applications there is a need to scale up and build more complex spatio-temporal multiscale models; these could enable investigating how changes at small scales reflect at large scales and viceversa. Results generated by computational models can be applied to real life applications only if the models have been validated first. Traditional in silico model checking techniques only capture how non-dimensional properties (e.g. concentrations) evolve over time and are suitable for small scale systems (e.g. metabolic pathways). The validation of larger scale systems (e.g. multicellular populations) additionally requires capturing how spatial patterns and their properties change over time, which are not considered by traditional non-spatial approaches.
We developed and implemented a methodology for the automatic validation of computational models with respect to both their spatial and temporal properties. Stochastic biological systems are represented by abstract models which assume a linear structure of time and a pseudo-3D representation of space (2D space plus a density measure). Time series data generated by such models is provided as input to parameterised image processing modules which automatically detect and analyse spatial patterns (e.g. cell) and clusters of such patterns (e.g. cellular population). For capturing how spatial and numeric properties change over time the Probabilistic Bounded Linear Spatial Temporal Logic is introduced. Given a collection of time series data and a formal spatio-temporal specification the model checker Mudi ( determines probabilistically if the formal specification holds for the computational model or not. Mudi is an approximate probabilistic model checking platform which enables users to choose between frequentist and Bayesian, estimate and statistical hypothesis testing based validation approaches. We illustrate the expressivity and efficiency of our approach based on two biological case studies namely phase variation patterning in bacterial colony growth and the chemotactic aggregation of cells.
The formal methodology implemented in Mudi enables the validation of computational models against spatio-temporal logic properties and is a precursor to the development and validation of more complex multidimensional and multiscale models.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-014-0124-0) contains supplementary material, which is available to authorized users.
PMCID: PMC4272535  PMID: 25440773
Stochastic spatial discrete event system (SSpDES); Probabilistic bounded linear spatial temporal logic (PBLSTL); Spatio-temporal; Multidimensional; Model checking; Mudi; Computational model; Model validation; Systems biology; Synthetic biology
10.  A Multiscale Approach to Modelling Drug Metabolism by Membrane-Bound Cytochrome P450 Enzymes 
PLoS Computational Biology  2014;10(7):e1003714.
Cytochrome P450 enzymes are found in all life forms. P450s play an important role in drug metabolism, and have potential uses as biocatalysts. Human P450s are membrane-bound proteins. However, the interactions between P450s and their membrane environment are not well-understood. To date, all P450 crystal structures have been obtained from engineered proteins, from which the transmembrane helix was absent. A significant number of computational studies have been performed on P450s, but the majority of these have been performed on the solubilised forms of P450s. Here we present a multiscale approach for modelling P450s, spanning from coarse-grained and atomistic molecular dynamics simulations to reaction modelling using hybrid quantum mechanics/molecular mechanics (QM/MM) methods. To our knowledge, this is the first application of such an integrated multiscale approach to modelling of a membrane-bound enzyme. We have applied this protocol to a key human P450 involved in drug metabolism: CYP3A4. A biologically realistic model of CYP3A4, complete with its transmembrane helix and a membrane, has been constructed and characterised. The dynamics of this complex have been studied, and the oxidation of the anticoagulant R-warfarin has been modelled in the active site. Calculations have also been performed on the soluble form of the enzyme in aqueous solution. Important differences are observed between the membrane and solution systems, most notably for the gating residues and channels that control access to the active site. The protocol that we describe here is applicable to other membrane-bound enzymes.
Author Summary
A significant amount of information about how enzymes and other proteins function has been obtained from computer simulations. Often, the size of the system that is required to provide a sufficiently realistic model places limitations on both the timescale of the simulation, and the level of detail that can be studied. Computational approaches that utilise more than one type of method (so-called ‘multiscale methods’) allow the size of system, and timescale of study, to be increased. Membrane-bound proteins, such as cytochrome P450 enzymes (P450s), are an example of where multiscale simulations can be used. P450s are important in drug metabolism, and are known to be involved in adverse drug reactions. Access of substrate to the active site of these enzymes through the membrane is not well-understood. In the present work, a simulation pipeline is presented that leads through the construction and refinement of a realistic protein:membrane system by molecular dynamics simulations to reaction modelling. An important drug-metabolising P450, CYP3A4, is used as an example, together with the anticoagulant drug R-warfarin. Our investigations reveal that a membrane-bound model is required to fully capture the gating mechanisms and substrate ingress/egress channels. The simulation protocol described here is transferrable to other membrane-bound proteins.
PMCID: PMC4102395  PMID: 25033460
11.  Analysis of multiple compound–protein interactions reveals novel bioactive molecules 
The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases.
We have demonstrated that machine learning of multiple compound–protein interactions is useful for efficient ligand screening and for assessing drug polypharmacology.This approach successfully identified novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein-coupled receptors and protein kinases.These bioactive compounds were not detected by existing computational ligand-screening methods in comparative studies.The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. Perturbations of biological systems by chemical probes provide broader applications not only for analysis of complex systems but also for intentional manipulations of these systems. Nevertheless, the lack of well-characterized chemical modulators has limited their use. Recently, chemical genomics has emerged as a promising area of research applicable to the exploration of novel bioactive molecules, and researchers are currently striving toward the identification of all possible ligands for all target protein families (Wang et al, 2009). Chemical genomics studies have shown that patterns of compound–protein interactions (CPIs) are too diverse to be understood as simple one-to-one events. There is an urgent need to develop appropriate data mining methods for characterizing and visualizing the full complexity of interactions between chemical space and biological systems. However, no existing screening approach has so far succeeded in identifying novel bioactive compounds using multiple interactions among compounds and target proteins.
High-throughput screening (HTS) and computational screening have greatly aided in the identification of early lead compounds for drug discovery. However, the large number of assays required for HTS to identify drugs that target multiple proteins render this process very costly and time-consuming. Therefore, interest in using in silico strategies for screening has increased. The most common computational approaches, ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS; Oprea and Matter, 2004; Muegge and Oloff, 2006; McInnes, 2007; Figure 1A), have been used for practical drug development. LBVS aims to identify molecules that are very similar to known active molecules and generally has difficulty identifying compounds with novel structural scaffolds that differ from reference molecules. The other popular strategy, SBVS, is constrained by the number of three-dimensional crystallographic structures available. To circumvent these limitations, we have shown that a new computational screening strategy, chemical genomics-based virtual screening (CGBVS), has the potential to identify novel, scaffold-hopping compounds and assess their polypharmacology by using a machine-learning method to recognize conserved molecular patterns in comprehensive CPI data sets.
The CGBVS strategy used in this study was made up of five steps: CPI data collection, descriptor calculation, representation of interaction vectors, predictive model construction using training data sets, and predictions from test data (Figure 1A). Importantly, step 1, the construction of a data set of chemical structures and protein sequences for known CPIs, did not require the three-dimensional protein structures needed for SBVS. In step 2, compound structures and protein sequences were converted into numerical descriptors. These descriptors were used to construct chemical or biological spaces in which decreasing distance between vectors corresponded to increasing similarity of compound structures or protein sequences. In step 3, we represented multiple CPI patterns by concatenating these chemical and protein descriptors. Using these interaction vectors, we could quantify the similarity of molecular interactions for compound–protein pairs, despite the fact that the ligand and protein similarity maps differed substantially. In step 4, concatenated vectors for CPI pairs (positive samples) and non-interacting pairs (negative samples) were input into an established machine-learning method. In the final step, the classifier constructed using training sets was applied to test data.
To evaluate the predictive value of CGBVS, we first compared its performance with that of LBVS by fivefold cross-validation. CGBVS performed with considerably higher accuracy (91.9%) than did LBVS (84.4%; Figure 1B). We next compared CGBVS and SBVS in a retrospective virtual screening based on the human β2-adrenergic receptor (ADRB2). Figure 1C shows that CGBVS provided higher hit rates than did SBVS. These results suggest that CGBVS is more successful than conventional approaches for prediction of CPIs.
We then evaluated the ability of the CGBVS method to predict the polypharmacology of ADRB2 by attempting to identify novel ADRB2 ligands from a group of G-protein-coupled receptor (GPCR) ligands. We ranked the prediction scores for the interactions of 826 reported GPCR ligands with ADRB2 and then analyzed the 50 highest-ranked compounds in greater detail. Of 21 commercially available compounds, 11 showed ADRB2-binding activity and were not previously reported to be ADRB2 ligands. These compounds included ligands not only for aminergic receptors but also for neuropeptide Y-type 1 receptors (NPY1R), which have low protein homology to ADRB2. Most ligands we identified were not detected by LBVS and SBVS, which suggests that only CGBVS could identify this unexpected cross-reaction for a ligand developed as a target to a peptidergic receptor.
The true value of CGBVS in drug discovery must be tested by assessing whether this method can identify scaffold-hopping lead compounds from a set of compounds that is structurally more diverse. To assess this ability, we analyzed 11 500 commercially available compounds to predict compounds likely to bind to two GPCRs and two protein kinases. Functional assays revealed that nine ADRB2 ligands, three NPY1R ligands, five epidermal growth factor receptor (EGFR) inhibitors, and two cyclin-dependent kinase 2 (CDK2) inhibitors were concentrated in the top-ranked compounds (hit rate=30, 15, 25, and 10%, respectively). We also evaluated the extent of scaffold hopping achieved in the identification of these novel ligands. One ADRB2 ligand, two NPY1R ligands, and one CDK2 inhibitor exhibited scaffold hopping (Figure 4), indicating that CGBVS can use this characteristic to rationally predict novel lead compounds, a crucial and very difficult step in drug discovery. This feature of CGBVS is critically different from existing predictive methods, such as LBVS, which depend on similarities between test and reference ligands, and focus on a single protein or highly homologous proteins. In particular, CGBVS is useful for targets with undefined ligands because this method can use CPIs with target proteins that exhibit lower levels of homology.
In summary, we have demonstrated that data mining of multiple CPIs is of great practical value for exploration of chemical space. As a predictive model, CGBVS could provide an important step in the discovery of such multi-target drugs by identifying the group of proteins targeted by a particular ligand, leading to innovation in pharmaceutical research.
The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.
PMCID: PMC3094066  PMID: 21364574
chemical genomics; data mining; drug discovery; ligand screening; systems chemical biology
12.  Carrier-Based Drug Delivery System for Treatment of Acne 
The Scientific World Journal  2014;2014:276260.
Approximately 95% of the population suffers at some point in their lifetime from acne vulgaris. Acne is a multifactorial disease of the pilosebaceous unit. This inflammatory skin disorder is most common in adolescents but also affects neonates, prepubescent children, and adults. Topical conventional systems are associated with various side effects. Novel drug delivery systems have been used to reduce the side effect of drugs commonly used in the topical treatment of acne. Topical treatment of acne with active pharmaceutical ingredients (API) makes direct contact with the target site before entering the systemic circulation which reduces the systemic side effect of the parenteral or oral administration of drug. The objective of the present review is to discuss the conventional delivery systems available for acne, their drawbacks, and limitations. The advantages, disadvantages, and outcome of using various carrier-based delivery systems like liposomes, niosomes, solid lipid nanoparticles, and so forth, are explained. This paper emphasizes approaches to overcome the drawbacks and limitations associated with the conventional system and the advances and application that are poised to further enhance the efficacy of topical acne formulations, offering the possibility of simplified dosing regimen that may improve treatment outcomes using novel delivery system.
PMCID: PMC3934386  PMID: 24688376
13.  Nanotechnology 
Executive Summary
Due to continuing advances in the development of structures, devices, and systems with a length of about 1 to 100 nanometres (nm) (1 nm is one billionth of a metre), the Medical Advisory Secretariat conducted a horizon scanning appraisal of nanotechnologies as new and emerging technologies, including an assessment of the possibly disruptive impact of future nanotechnologies.
The National Cancer Institute (NCI) in the United States proclaimed a 2015 challenge goal of eliminating suffering and death from cancer. To help meet this goal, the NCI is engaged in a concerted effort to introduce nanotechnology “to radically change the way we diagnose, treat and prevent cancer.” It is the NCI’s position that “melding nanotechnology and cancer research and development efforts will have a profound, disruptive effect on how we diagnose, treat, and prevent cancer.”
Thus, this appraisal sought to determine the systemic effects of nanotechnologies that target, image and deliver drugs, for example, with respect to health human resources, training, and new specialties; and to assess the current status of these nanotechnologies and their projected timeline to clinical utilization.
Clinical Need: Target Population and Condition
Cancer is a heterogeneous set of many malignant diseases. In each sex, 3 sites account for over one-half of all cancers. In women, these are the breast (28%), colorectum (13%) and lungs (12%). In men, these are the prostate (28%), lungs (15%), and the colorectum (13%).
It is estimated that 246,000 people in Ontario (2% of the population) have been diagnosed with cancer within the past 10 years and are still alive. Most were diagnosed with cancer of the breast (21%), prostate (20%), or colon or rectum (13%).
The number of new cancer cases diagnosed each year in Ontario is expected to increase from about 53,000 in 2001 to 80,000 in 2015. This represents more than a 50% increase in new cases over this period. An aging population, population growth, and rising cancer risk are thought to be the main factors that will contribute to the projected increase in the number of new cases.
The Technology Being Reviewed - Medical Advisory Secretariat Definition of Nanotechnology
First-Generation Nanotechnologies
Early application of nanotechnology-enabled products involved drug reformulation to deliver some otherwise toxic drugs (e.g., antifungal and anticancer agents) in a safer and more effective manner.
Examples of first-generation nanodevices include the following:
albumin bound nanoparticles;
gadolinium chelate for magnetic resonance imaging (MRI);
iron oxide particles for MRI;
silver nanoparticles (antibacterial wound dressing); and
nanoparticulate dental restoratives.
First-generation nanodevices have been in use for several years; therefore, they are not the focus of this report.
Second-Generation Nanotechnologies
Second-generation nanotechnologies are more sophisticated than first- generation nanotechnologies, due to novel molecular engineering that enables the devices to target, image, deliver a therapeutic agent, and monitor therapeutic efficacy in real time. Details and examples of second-generation nanodevices are discussed in the following sections of this report.
Review Strategy
The questions asked were as follows:
What is the status of these multifunctional nanotechnologies? That is, what is the projected timeline to clinical utilization?
What are the systemic effects of multifunctional nanodevices with integrated applications that target, image, and deliver drugs? That is, what are the implications of the emergence of nanotechnology on health human resources training, new specialties, etc.?
The Medical Advisory Secretariat used its usual search techniques to conduct the literature review by searching relevant databases. Outcomes of interest were improved imaging, improved sensitivity or specificity, improved response rates to therapeutic agents, and decreased toxicity.
The search yielded 1 health technology assessment on nanotechnology by The Centre for Technology Assessment TA-Swiss and, in the grey literature, a technology review by RAND. These, in addition to data from the National Cancer Institute (United States) formed the basis for the conclusions of the review.
With respect to the question as to how soon until nanotechnology is used in patient care, overall, the use of second-generation nanodevices, (e.g., quantum dots [QDs]), nanoshells, dendrimers) that can potentially target, image, and deliver drugs; and image cell response to therapy in real time are still in the preclinical benchwork stage.
Table 1 summarizes the projected timelines to clinical utilization.
Summary of Timelines to Clinical Use*
NCI refers to National Cancer Institute; QD, quantum dot.
Medical Advisory Secretariat Estimated Timeline for Ontario
Upon synthesizing the estimated timelines from the NCI, the Swiss technology assessment and the RAND reports (Figure 1), it appears that:
the clinical use of separate imaging and therapeutic nanodevices is estimated to start occurring around 2010;
the clinical use of combined imaging and therapeutic nanodevices is estimated to start occurring around 2020;
changes in the way disease is diagnosed, treated and monitored are anticipated; and
the full (and realistic) extent of these changes within the next 10 to 20 years is uncertain.
Medical Advisory Secretariat Estimated Timeline for the Clinical Use of Second-Generation Nanodevices in Ontario
With respect to the question on potential systemic effects of second-generation nanodevices (i.e., the implications of the emergence of these nanodevices on health human resources training, new specialties etc.), Table 2 summarizes the findings from the review.
Potential Systemic Effects Caused by Second Generation Nanodevices*
MRI indicates magnetic resonance imaging; PSA, prostate-specific antigen; QD, quantum dot.
Uncertainties Not Addressed in the Literature
The United States National Nanotechnology Initiative (NNI) funds a variety of research in the economic, ethical, legal, and cultural implications of the use of nanotechnology, as well as the implications for science, education and quality of life.
There are many uncertainties that are sparsely or not addressed at all in the literature regarding second generation nanodevices. These include the following:
long-term stability and toxicology of nanodevices;
cost-effectiveness of nanodevices;
refinement of specific targeting;
effects on hospitals, physician/nurse training, creation/removal of specialties; and
that pertaining to the question, where does disease begin if therapy is applied before the symptoms have appeared?
PMCID: PMC3379172  PMID: 23074489
14.  Convective diffusion of nanoparticles from the epithelial barrier towards regional lymph nodes 
Drug delivery using nanoparticles as drug carriers has recently attracted the attention of many investigators. Targeted delivery of nanoparticles to lymph nodes is especially important to prevent cancer metastasis or infection, and to diagnose disease stage. However, systemic injection of nanoparticles often results in organ toxicity because they reach and accumulate in all the lymph nodes in the body. An attractive strategy would be to deliver the drug-loaded nanoparticles to a subset of draining lymph nodes corresponding to a specific site or organ to minimize systemic toxicity. In this respect, mucosal delivery of nanoparticles to regional draining lymph nodes of a selected site creates a new opportunity to accomplish this task with minimal toxicity. One example is the delivery of nanoparticles from the vaginal lumen to draining lymph nodes to prevent the transmission of HIV in women. Other known examples include mucosal delivery of vaccines to induce immunity. In all cases, molecular and particle transport by means of diffusion and convective diffusion play a major role. The corresponding transport processes have common inherent regularities and are addressed in this review. Here we use nanoparticles delivery from the vaginal lumen to lymph nodes as an example to address the many aspects of associated transport processes. In this case, nanoparticles penetrate the epithelial barrier and move through the interstitium (tissue) to the initial lymphatics until they finally reach the lymph nodes.
Since the movement of interstitial liquid near the epithelial barrier is retarded, nanoparticles transport was found to take place through special foci present in the epithelium. Immediately after nanoparticles emerge from the foci, they move through the interstitium due to diffusion affected by convection (convective diffusion). Specifically, the convective transport of nanoparticles occurs due to their convection together with interstitial fluid through the interstitium towards the initial lymph capillaries. Afterwards, nanoparticles move together with the lymph flow along the initial lymph capillaries and then enter the afferent lymphatics and ultimately reach the lymph node. As the liquid moves through the interstitium towards the initial lymph capillaries due to the axial movement of lymph along the lymphatics, the theory for coupling between lymph flow and concomitant flow through the interstitium is developed to describe this general case.
The developed theory is applied to interpret the large uptake of Qdots by lymph nodes during inflammation, which is induced by pre-treating mouse vagina with the surfactant Nonoxynol-9 prior to instilling the Qdots. Inflammation is viewed here to cause broadening of the pores within the interstitium with the concomitant formation of transport channels which function as conduits to transport the nanoparticles to the initial lymph capillaries. We introduced the term “effective channels” to denote those channels which interconnect with foci present in the epithelial barrier and which function to transport nanoparticles to initial lymph capillaries. The time of transport towards the lymph node, predicated by the theory, increases rapidly with increasing the distance y0 between the epithelial barrier and the initial lymph capillaries. Transport time is only a few hours, when y0 is small, about some R (where R is the initial lymph capillary radius), due to the predomination of a rather rapid convection in this case. This transport time to lymph nodes may be tens of hours (or longer) when y0 is essentially larger and the slow diffusion controls the transport rate in a zone not far from the epithelial barrier, where convection is weak at large y0. Accounting for transport by diffusion only, which is mainly considered in many relevant publications, is not sufficient to explain our nanoparticles uptake kinetics because the possibility of fast transport due to convection is overlooked. Our systematic investigations have revealed that the information about the main transport conditions, namely, y0 and the pore broadening up to the dimension of the interstitial transport channels, is necessary to create the quantitative model of enhanced transport during inflammation with the use of the proposed model as a prerequisite.
The modeling for convective diffusion of nanoparticles from the epithelial barrier to the lymph node has been mainly accomplished here, while the diffusion only scenario is accounted for in other studies. This first modeling is a semi-quantitative one. A more rigorous mathematical approach is almost impossible at this stage because the transport properties of the model are introduced here for the first time. These properties include: discovery of foci in the epithelium, formation of transport channels, definition of channels interconnecting with foci (effective foci and channels), generation of flow in the interstitium towards the initial lymph capillaries due to axial flow within afferent lymphatics, deformation of this flow due to hydrodynamic impermeability of the squamous layer with the formation of the hydrodynamic stagnation zone near the epithelial barrier, predomination of slow diffusion transport within the above zone, and predomination of fast convection of nanoparticles near the initial lymph capillaries.
PMCID: PMC3804055  PMID: 23859221
15.  Systems Biology of Coagulation 
Accurate computer simulation of blood function can inform drug target selection, patient-specific dosing, clinical trial design, biomedical device design, as well as the scoring of patient-specific disease risk and severity. These large-scale simulations rely on hundreds of independently measured physical parameters and kinetic rate constants. However, the models can be validated against large scale, patient-specific laboratory measurements. By validation with high dimensional data, modelling becomes a powerful tool to predict clinically complex scenarios. Currently, it is possible to accurately predict the clotting rate of plasma or blood in a tube as it is activated with a dose of tissue factor, even as numerous coagulation factors are altered by exogenous attenuation or potentiation. Similarly, the dynamics of platelet activation, as indicated by calcium mobilisation or inside-out signalling, can now be numerically simulated with accuracy in cases where platelets are exposed to combinations of agonists. Multiscale models have emerged to combine platelet function and coagulation kinetics into complete physics-based descriptions of thrombosis under flow. Blood flow controls platelet fluxes, delivery and removal of coagulation factors, adhesive bonding, and von Willebrand factor conformation. The field of Blood Systems Biology has now reached a stage that anticipates the inclusion of contact, complement, and fibrinolytic pathways along with models of neutrophil and endothelial activation. Along with “-omics” data sets, such advanced models seek to predict the multifactorial range of healthy responses and diverse bleeding and clotting scenarios, ultimately to understand and improve patient outcomes.
PMCID: PMC3713523  PMID: 23809126
16.  Materials innovation for co-delivery of diverse therapeutic cargos 
RSC advances  2013;3(47):24794-24811.
Co-delivery is a rapidly growing sector of drug delivery that aspires to enhance therapeutic efficacy through controlled delivery of diverse therapeutic cargoes with synergistic activities. It requires the design of carriers capable of simultaneously transporting to and releasing multiple therapeutics at a disease site. Co-delivery has arisen from the emerging trend of combination therapy, where treatment with two or more therapeutics at the same time can succeed where single therapeutics fail. However, conventional combination therapy offers little control over achieving an optimized therapeutic ratio at the target site. Co-delivery via inclusion of multiple therapeutic cargos within the same carrier addresses this issue by not only ensuring delivery of both therapeutics to the same cell, but also offering a platform for control of the delivery process, from loading to release. Co-delivery systems have been formulated using a number of carriers previously developed for single-therapeutic delivery. Liposomes, polymeric micelles, PLGA nanoparticles, and dendrimers have all been adapted for co-delivery. Much of the effort focuses on dealing with drugs having dissimilar properties, increasing loading efficiencies, and controlling loading and release ratios. In this review, we highlight the innovations in carrier designs and formulations to deliver combination cargoes of drug/drug, drug/siRNA, and drug/pDNA toward disease therapy. With rapid advances in mechanistic understanding of interrelating molecular pathways and development of molecular medicine, the future of co-delivery will become increasingly promising and prominent.
PMCID: PMC4012692  PMID: 24818000
17.  Translational Bioinformatics Approaches to Drug Development 
Advances in Wound Care  2013;2(9):470-489.
A majority of therapeutic interventions occur late in the pathological process, when treatment outcome can be less predictable and effective, highlighting the need for new precise and preventive therapeutic development strategies that consider genomic and environmental context. Translational bioinformatics is well positioned to contribute to the many challenges inherent in bridging this gap between our current reactive methods of healthcare delivery and the intent of precision medicine, particularly in the areas of drug development, which forms the focus of this review.
Recent Advances
A variety of powerful informatics methods for organizing and leveraging the vast wealth of available molecular measurements available for a broad range of disease contexts have recently emerged. These include methods for data driven disease classification, drug repositioning, identification of disease biomarkers, and the creation of disease network models, each with significant impacts on drug development approaches.
Critical Issues
An important bottleneck in the application of bioinformatics methods in translational research is the lack of investigators who are versed in both biomedical domains and informatics. Efforts to nurture both sets of competencies within individuals and to increase interfield visibility will help to accelerate the adoption and increased application of bioinformatics in translational research.
Future Directions
It is possible to construct predictive, multiscale network models of disease by integrating genotype, gene expression, clinical traits, and other multiscale measures using causal network inference methods. This can enable the identification of the “key drivers” of pathology, which may represent novel therapeutic targets or biomarker candidates that play a more direct role in the etiology of disease.
PMCID: PMC3817001  PMID: 24527359
18.  Nanomedicines for Back of the Eye Drug Delivery, Gene Delivery, and Imaging 
Treatment and management of diseases of the posterior segment of the eye such as diabetic retinopathy, retinoblastoma, retinitis pigmentosa, and choroidal neovascularization is a challenging task due to the anatomy and physiology of ocular barriers. For instance, traditional routes of drug delivery for therapeutic treatment are hindered by poor intraocular penetration and/or rapid ocular elimination. One possible approach to improve ocular therapy is to employ nanotechnology. Nanomedicines, products of nanotechnology, having at least one dimension in the nanoscale include nanoparticles, micelles, nanotubes, and dendrimers, with and without targeting ligands, are making a significant impact in the fields of ocular drug delivery, gene delivery, and imaging, the focus of this review. Key applications of nanotechnology discussed in this review include a) bioadhesive nanomedicines; b) functionalized nanomedicines that enhance target recognition and/or cell entry; c) nanomedicines capable of controlled release of the payload; d) nanomedicines capable of enhancing gene transfection and duration of transfection; f) nanomedicines responsive to stimuli including light, heat, ultrasound, electrical signals, pH, and oxidative stress; g) diversely sized and colored nanoparticles for imaging, and h) nanowires for retinal prostheses. Additionally, nanofabricated delivery systems including implants, films, microparticles, and nanoparticles are described. Although the above nanomedicines may be administered by various routes including topical, intravitreal, intravenous, transscleral, suprachoroidal, and subretinal routes, each nanomedicine should be tailored for the disease, drug, and site of administration. In addition to the nature of materials used in nanomedicine design, depending on the site of nanomedicine administration, clearance and toxicity are expected to differ.
PMCID: PMC3926814  PMID: 23603534
Nanotechnology; Drug delivery; Gene delivery; Imaging; Controlled release; Stimuli responsive delivery
19.  Nanotechnology-based drug delivery systems for treatment of oral cancer: a review 
Oral cancer (oral cavity and oropharynx) is a common and aggressive cancer that invades local tissue, can cause metastasis, and has a high mortality rate. Conventional treatment strategies, such as surgery and chemoradiotherapy, have improved over the past few decades; however, they remain far from optimal. Currently, cancer research is focused on improving cancer diagnosis and treatment methods (oral cavity and oropharynx) nanotechnology, which involves the design, characterization, production, and application of nanoscale drug delivery systems. In medicine, nanotechnologies, such as polymeric nanoparticles, solid lipid nanoparticles, nanostructured lipid carriers, gold nanoparticles, hydrogels, cyclodextrin complexes, and liquid crystals, are promising tools for diagnostic probes and therapeutic devices. The objective of this study is to present a systematic review of nanotechnology-based drug delivery systems for oral cancers.
PMCID: PMC4134022  PMID: 25143724
targeted delivery; oral squamous cell carcinoma; oral cancer treatment
20.  Differential Geometry Based Multiscale Models 
Bulletin of mathematical biology  2010;72(6):1562-1622.
Large chemical and biological systems such as fuel cells, ion channels, molecular motors, and viruses are of great importance to the scientific community and public health. Typically, these complex systems in conjunction with their aquatic environment pose a fabulous challenge to theoretical description, simulation, and prediction. In this work, we propose a differential geometry based multiscale paradigm to model complex macromolecular systems, and to put macroscopic and microscopic descriptions on an equal footing. In our approach, the differential geometry theory of surfaces and geometric measure theory are employed as a natural means to couple the macroscopic continuum mechanical description of the aquatic environment with the microscopic discrete atom-istic description of the macromolecule. Multiscale free energy functionals, or multiscale action functionals are constructed as a unified framework to derive the governing equations for the dynamics of different scales and different descriptions. Two types of aqueous macromolecular complexes, ones that are near equilibrium and others that are far from equilibrium, are considered in our formulations. We show that generalized Navier–Stokes equations for the fluid dynamics, generalized Poisson equations or generalized Poisson–Boltzmann equations for electrostatic interactions, and Newton's equation for the molecular dynamics can be derived by the least action principle. These equations are coupled through the continuum-discrete interface whose dynamics is governed by potential driven geometric flows. Comparison is given to classical descriptions of the fluid and electrostatic interactions without geometric flow based micro-macro interfaces. The detailed balance of forces is emphasized in the present work. We further extend the proposed multiscale paradigm to micro-macro analysis of electrohydrodynamics, electrophoresis, fuel cells, and ion channels. We derive generalized Poisson–Nernst–Planck equations that are coupled to generalized Navier–Stokes equations for fluid dynamics, Newton's equation for molecular dynamics, and potential and surface driving geometric flows for the micro-macro interface. For excessively large aqueous macromolecular complexes in chemistry and biology, we further develop differential geometry based multiscale fluid-electro-elastic models to replace the expensive molecular dynamics description with an alternative elasticity formulation.
PMCID: PMC2914853  PMID: 20169418
Variational principle; Multiscale; Geometric flows; Solvation analysis; Electrostatic analysis; Implicit solvent models; Molecular dynamics; Elasticity; Navier–Stokes equation; Poisson–Boltzmann equation; Nernst–Planck equation
21.  Improvement of different vaccine delivery systems for cancer therapy 
Molecular Cancer  2011;10:3.
Cancer vaccines are the promising tools in the hands of the clinical oncologist. Many tumor-associated antigens are excellent targets for immune therapy and vaccine design. Optimally designed cancer vaccines should combine the best tumor antigens with the most effective immunotherapy agents and/or delivery strategies to achieve positive clinical results. Various vaccine delivery systems such as different routes of immunization and physical/chemical delivery methods have been used in cancer therapy with the goal to induce immunity against tumor-associated antigens. Two basic delivery approaches including physical delivery to achieve higher levels of antigen production and formulation with microparticles to target antigen-presenting cells (APCs) have demonstrated to be effective in animal models. New developments in vaccine delivery systems will improve the efficiency of clinical trials in the near future. Among them, nanoparticles (NPs) such as dendrimers, polymeric NPs, metallic NPs, magnetic NPs and quantum dots have emerged as effective vaccine adjuvants for infectious diseases and cancer therapy. Furthermore, cell-penetrating peptides (CPP) have been known as attractive carrier having applications in drug delivery, gene transfer and DNA vaccination. This review will focus on the utilization of different vaccine delivery systems for prevention or treatment of cancer. We will discuss their clinical applications and the future prospects for cancer vaccine development.
PMCID: PMC3024302  PMID: 21211062
22.  Nanotechnology-based drug delivery systems 
Nanoparticles hold tremendous potential as an effective drug delivery system. In this review we discussed recent developments in nanotechnology for drug delivery. To overcome the problems of gene and drug delivery, nanotechnology has gained interest in recent years. Nanosystems with different compositions and biological properties have been extensively investigated for drug and gene delivery applications. To achieve efficient drug delivery it is important to understand the interactions of nanomaterials with the biological environment, targeting cell-surface receptors, drug release, multiple drug administration, stability of therapeutic agents and molecular mechanisms of cell signalling involved in pathobiology of the disease under consideration. Several anti-cancer drugs including paclitaxel, doxorubicin, 5-fluorouracil and dexamethasone have been successfully formulated using nanomaterials. Quantom dots, chitosan, Polylactic/glycolic acid (PLGA) and PLGA-based nanoparticles have also been used for in vitro RNAi delivery. Brain cancer is one of the most difficult malignancies to detect and treat mainly because of the difficulty in getting imaging and therapeutic agents past the blood-brain barrier and into the brain. Anti-cancer drugs such as loperamide and doxorubicin bound to nanomaterials have been shown to cross the intact blood-brain barrier and released at therapeutic concentrations in the brain. The use of nanomaterials including peptide-based nanotubes to target the vascular endothelial growth factor (VEGF) receptor and cell adhesion molecules like integrins, cadherins and selectins, is a new approach to control disease progression.
PMCID: PMC2222591  PMID: 18053152
23.  A review of combined experimental and computational procedures for assessing biopolymer structure–process–property relationships 
Biomaterials  2012;33(33):8240-8255.
Tailored biomaterials with tunable functional properties are desirable for many applications ranging from drug delivery to regenerative medicine. To improve the predictability of biopolymer materials functionality, multiple design parameters need to be considered, along with appropriate models. In this article we review the state of the art of synthesis and processing related to the design of biopolymers, with an emphasis on the integration of bottom-up computational modeling in the design process. We consider three prominent examples of well-studied biopolymer materials – elastin, silk, and collagen – and assess their hierarchical structure, intriguing functional properties and categorize existing approaches to study these materials. We find that an integrated design approach in which both experiments and computational modeling are used has rarely been applied for these materials due to difficulties in relating insights gained on different length- and time-scales. In this context, multiscale engineering offers a powerful means to accelerate the biomaterials design process for the development of tailored materials that suit the needs posed by the various applications. The combined use of experimental and computational tools has a very broad applicability not only in the field of biopolymers, but can be exploited to tailor the properties of other polymers and composite materials in general.
PMCID: PMC3787124  PMID: 22938765
Biopolymers; Material design; Elastin; Collagen; Silk; Integration
24.  Nanoparticles for oral delivery: Targeted nanoparticles with peptidic ligands for oral protein delivery 
Advanced drug delivery reviews  2012;65(6):822-832.
As the field of biotechnology has advanced, oral protein delivery has also made significant progress. Oral delivery is the most common method of drug administration with high levels of patient acceptance. Despite the preference of oral delivery, administration of therapeutic proteins has been extremely difficult. Increasing the bioavailability of oral protein drugs to the therapeutically acceptable level is still a challenging goal. Poor membrane permeability, high molecular weight, and enzymatic degradation of protein drugs have remained unsolved issues. Among diverse strategies, nanotechnology has provided a glimpse of hope in oral delivery of protein drugs. Nanoparticles have advantages, such as small size, high surface area, and modification using functional groups for high capacity or selectivity. Nanoparticles with peptidic ligands are especially worthy of notice because they can be used for specific targeting in the gastrointestinal (GI) tract. This article reviews the transport mechanism of the GI tract, barriers to protein absorption, current status and limitations of nanotechnology for oral protein delivery system.
PMCID: PMC3574626  PMID: 23123292
Oral protein delivery; Nanoparticles; Peptidic ligands; GI tract; Protein absorption
25.  Quantum dots as a platform for nanoparticle drug delivery vehicle design 
Advanced drug delivery reviews  2012;65(5):703-718.
Nanoparticle-based drug delivery (NDD) has emerged as a promising approach to improving upon the efficacy of existing drugs and enabling the development of new therapies. Proof-of-concept studies have demonstrated the potential for NDD systems to simultaneously achieve reduced drug toxicity, improved bio-availability, increased circulation times, controlled drug release, and targeting. However, clinical translation of NDD vehicles with the goal of treating particularly challenging diseases, such as cancer, will require a thorough understanding of how nanoparticle properties influence their fate in biological systems, especially in vivo. Consequently, a model system for systematic evaluation of all stages of NDD with high sensitivity, high resolution, and low cost is highly desirable. In theory, this system should maintain the properties and behavior of the original NDD vehicle, while providing mechanisms for monitoring intracellular and systemic nanocarrier distribution, degradation, drug release, and clearance. For such a model system, quantum dots (QDots) offer great potential. QDots feature small size and versatile surface chemistry, allowing their incorporation within virtually any NDD vehicle with minimal effect on overall characteristics, and offer superb optical properties for real-time monitoring of NDD vehicle transport and drug release at both cellular and systemic levels. Though the direct use of QDots for drug delivery remains questionable due to their potential long-term toxicity, the QDot core can be easily replaced with other organic drug carriers or more biocompatible inorganic contrast agents (such as gold and magnetic nanoparticles) by their similar size and surface properties, facilitating translation of well characterized NDD vehicles to the clinic, maintaining NDD imaging capabilities, and potentially providing additional therapeutic functionalities such as photothermal therapy and magneto-transfection. In this review we outline unique features that make QDots an ideal platform for nanocarrier design and discuss how this model has been applied to study NDD vehicle behavior for diverse drug delivery applications.
PMCID: PMC3541463  PMID: 23000745
Nanoparticle; Drug delivery vehicle; Nanocarrier; Quantum dot; Fluorescence imaging; Traceable drug delivery; Nanomedicine

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