Mathematical modeling in cancer nanomedicine: a review

Dogra, Prashant; Butner, Joseph D.; Chuang, Yao-Li; Caserta, Sergio; Goel, Shreya; Brinker, C. Jeffrey; Cristini, Vittorio; Wang, Zhihui

doi:10.1007/s10544-019-0380-2

Cited by 130 publications

(92 citation statements)

References 142 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Model Parameterization : Compartment and sub‐compartment volumes, plasma flow rates, and excretion rates were known a priori from published literature (Table S7, Supporting Information) . Due to extreme disparity in the literature, the lymph flow rates were assumed to be a fraction of plasma flow rates,

L_{normali} = \frac{Q_{normali}}{f}

, where f > 1, and was estimated by nonlinear regression of the model to the experimental data.…”

Section: Methodsmentioning

confidence: 99%

“…Nanoparticles (NPs) have been often termed as “magic bullets” for their ability to carry out targeted drug delivery, and the past two decades have seen an unprecedented boom in research activities in the area of nanomedicine, specifically in oncology, resulting in a tremendous volume of work published each year . Unfortunately, the pace of translation has failed to keep up with the rapid pace of innovation, and only a handful of nanotechnologies have made it to the clinic or clinical trials . A recently published meta‐analysis concluded that less than 0.7% of intravenously injected (i.v.)…”

Section: Introductionmentioning

confidence: 99%

“…These results were subsequently modeled using a multicompartment, semimechanistic mathematical model to provide a description of UPSN disposition kinetics in vivo. Compared to empirical PK modeling approaches that solely model plasma concentration kinetics of NPs, multicompartment, whole‐body scale models are physiologically more realistic, and provide a thorough understanding of systemic distribution and clearance of NPs, including kinetics in tumor . Thus, mathematical models that incorporate the relevant physiological information can more reliably predict the pharmacological effects of NPs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Goel

Ferreira

Dogra

et al. 2019

Small

Self Cite

View full text Add to dashboard Cite

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

show abstract

L_{normali} = \frac{Q_{normali}}{f}

, where f > 1, and was estimated by nonlinear regression of the model to the experimental data.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Goel

Ferreira

Dogra

et al. 2019

Small

Self Cite

View full text Add to dashboard Cite

show abstract

“…The complexity of cancer development is manifested in at least three scales that can be distinguished and described by mathematical models, namely microscale, mesoscale, and macroscale. Wang et al conducted a number of studies on how to use multiscale models for the identification and combination therapy of drug targets [105][106][107][108][109][110][111]. This method is based on quantification of relationship between intracellular epidermal growth factor receptor (EGFR) signaling kinetics, lung cancer extracellular epidermal growth factor (EGF) stimulation and multicellular growth.…”

Section: Cancer Modeling and Network Biologymentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

View full text Add to dashboard Cite

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure-and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.Molecules 2020, 25, 1375 2 of 17 Biomolecular Simulations in Drug Screening and DesignThe Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt, and Arieh Warshel for their pioneering contributions in the development of multiscale models for complex biochemical systems, recognizing the important role that theory and computational methods play as a direct and necessary complement to experiments [12]. Further, applications of biomolecular simulations in identifying drug binding sites and elucidating the molecular mechanisms of diseases have been subject to rapid developments [13][14][15].Depending on the biological problems to be studied, multiscale models will be used in biomolecular simulations. The combination of quantum mechanics (QM) and molecular mechanics (MM) (QM/MM) can be used to study the electronic properties [16], simulate chemical reactions (e.g., the enzyme catalysis mechanism [17]), and calculate spectra [18] in a single simulation, which can be used to elucidate the action mechanism of certain drugs. Another widely used biomolecular method is molecular dynamics (MD) simulation [19][20][21][22][23][24], which applies empirical molecular mechanics (MM) force fields and is based on classical Newtonian mechanics. According to different accuracy requirements, all-atom (AA), united-atom (UA), and coarse-grained (CG) MD simulations as well as explicit/implicit solvent models, which allow simulations of temporal and spatial scales, can be used to facilitate the drug discovery [25,26]. Generally, MD simulations have been used for the identification of potential drug binding sites on target proteins, the calculation of binding free energy between target proteins and drug molecules, the action mechanism of drug molecules, etc. [27,28]. However, it is worth mentioning that MD simulations are also limited to time and length scales. Currently, AAMD simulations can explore time-dependent phenomena of large systems such as viral capsids in atomic detail as long as microseconds or even milliseconds [21,29,30]. Therefore, different types of ...

show abstract

“…The nanoparticles have potential to be used as an alternative medication since they offer great benefits for targeted drug delivery directly to cancerous cells and neoplasms and enhance the therapeutic efficacy (Loukanov et al 2018a). The na-several biological barriers involved in cancer, readily biodegradable or suitable for easy excretion from the patient (Dogra et al 2019). Exceptions of this rule are some agents applied in cell cultures or used for microscopy of biopsy samples.…”

Section: Introductionmentioning

confidence: 99%

Nanomaterials for cancer medication: from individual nanoparticles toward nanomachines and nanorobots

Loukanov¹,

Nikolova²,

Filipov³

et al. 2019

PHAR

View full text Add to dashboard Cite

The nanomaterials for cancer medication are already reality providing a wide range of new tools and possibilities, from earlier diagnostics and improved imaging to better, more efficient, and more targeted anticancer therapies. The purpose of this critical review is to focus on the current use of clinically approved nanoparticles for cancer theranostic, nanovaccines and delivery platforms for gene therapy. These include inorganic, metal and polymer nanoparticles, nanocrystals and varieties of drug delivery nanosystems (micelles, liposomes, microcapsules and etc.). The recent progress in cancer nanomedicine enables to combine the benefits of individual nanoparticles with biomolecules into a multifunction nanomachines and even highly advanced nanorobots for targeted therapies. Nowadays clinical trials with advanced anticancer nanomachines provide potential for more accurately and effective identification and destruction of the cancer cells present in the human body.

show abstract

Mathematical modeling in cancer nanomedicine: a review

Cited by 130 publications

References 142 publications

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

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

A Review on Applications of Computational Methods in Drug Screening and Design

Nanomaterials for cancer medication: from individual nanoparticles toward nanomachines and nanorobots

Contact Info

Product

Resources

About