What does not kill a tumour may make it stronger: In silico insights into chemotherapeutic drug resistance

A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.

Cryo-shocked tumor cells deliver CRISPR-Cas9 for lung cancer regression by synthetic lethality

Although CRISPR-mediated genome editing holds promise for cancer therapy, inadequate tumor targeting and potential off-target side effects hamper its outcomes. In this study, we present a strategy using cryo-shocked lung tumor cells as a CRISPR-Cas9 delivery system for cyclin-dependent kinase 4 (CDK4) gene editing, which initiates synthetic lethal in KRAS-mutant non-small cell lung cancer (NSCLC). By rapidly liquid nitrogen shocking, we effectively eliminate the pathogenicity of tumor cells while preserving their structure and surface receptor activity. This delivery system enables the loaded CRISPR-Cas9 to efficiently target to lung through the capture in pulmonary capillaries and interactions with endothelial cells. In a NSCLC-bearing mouse model, the drug accumulation is increased nearly fourfold in lung, and intratumoral CDK4 expression is substantially down-regulated compared to CRISPR-Cas9 lipofectamine nanoparticles administration. Furthermore, CRISPR-Cas9 editing-mediated CDK4 ablation triggers synthetic lethal in KRAS-mutant NSCLC and prolongs the survival of mice.

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

Nanoparticle-mediated cancer cell therapy: basic science to clinical applications

Every sixth person in the world dies due to cancer, making it the second leading severe cause of death after cardiovascular diseases. According to WHO, cancer claimed nearly 10 million deaths in 2020. The most common types of cancers reported have been breast (lung, colon and rectum, prostate cases), skin (non-melanoma) and stomach. In addition to surgery, the most widely used traditional types of anti-cancer treatment are radio- and chemotherapy. However, these do not distinguish between normal and malignant cells. Additional treatment methods have evolved over time for early detection and targeted therapy of cancer. However, each method has its limitations and the associated treatment costs are quite high with adverse effects on the quality of life of patients. Use of individual atoms or a cluster of atoms (nanoparticles) can cause a paradigm shift by virtue of providing point of sight sensing and diagnosis of cancer. Nanoparticles (1-100 nm in size) are 1000 times smaller in size than the human cell and endowed with safer relocation capability to attack mechanically and chemically at a precise location which is one avenue that can be used to destroy cancer cells precisely. This review summarises the extant understanding and the work done in this area to pave the way for physicians to accelerate the use of hybrid mode of treatments by leveraging the use of various nanoparticles.

Progresses, Challenges, and Prospects of CRISPR/Cas9 Gene-Editing in Glioma Studies

Glioma refers to a tumor that is derived from brain glial stem cells or progenitor cells and is the most common primary intracranial tumor. Due to its complex cellular components, as well as the aggressiveness and specificity of the pathogenic site of glioma, most patients with malignant glioma have poor prognoses following surgeries, radiotherapies, and chemotherapies. In recent years, an increasing amount of research has focused on the use of CRISPR/Cas9 gene-editing technology in the treatment of glioma. As an emerging gene-editing technology, CRISPR/Cas9 utilizes the expression of certain functional proteins to repair tissues or treat gene-deficient diseases and could be applied to immunotherapies through the expression of antigens, antibodies, or receptors. In addition, some research also utilized CRISPR/Cas9 to establish tumor models so as to study tumor pathogenesis and screen tumor prognostic targets. This paper mainly discusses the roles of CRISPR/Cas9 in the treatment of glioma patients, the exploration of the pathogenesis of neuroglioma, and the screening targets for clinical prognosis. This paper also raises the future research prospects of CRISPR/Cas9 in glioma, as well as the opportunities and challenges that it will face in clinical treatment in the future.

A hybrid spatiotemporal model of PCa dynamics and insights into optimal therapeutic strategies

Using a hybrid cellular automaton with stochastic elements, we investigate the effectiveness of multiple drug therapies on prostate cancer (PCa) growth. The ability of Androgen Deprivation Therapy to reduce PCa growth represents a milestone in prostate cancer treatment, nonetheless most patients eventually become refractory and develop castration-resistant prostate cancer. In recent years, a "second generation" drug called enzalutamide has been used to treat advanced PCa, or patients already exposed to chemotherapy that stopped responding to it. However, tumour resistance to enzalutamide is not well understood, and in this context, preclinical models and in silico experiments (numerical simulations) are key to understanding the mechanisms of resistance and to assessing therapeutic settings that may delay or prevent the onset of resistance. In our mathematical system, we incorporate cell phenotype switching to model the development of increased drug resistance, and consider the effect of the micro-environment dynamics on necrosis and apoptosis of the tumour cells. The therapeutic strategies that we explore include using a single drug (enzalutamide), and drug combinations (enzalutamide and everolimus or cabazitaxel) with different treatment schedules. Our results highlight the effectiveness of alternating therapies, especially alternating enzalutamide and cabazitaxel over a year, and a comparison is made with data taken from TRAMP mice to verify our findings.

Morpholine substituted quinazoline derivatives as anticancer agents against MCF-7, A549 and SHSY-5Y cancer cell lines and mechanistic studies

A series of morpholine substituted quinazoline derivatives have been synthesized and evaluated for cytotoxic potential against A549, MCF-7 and SHSY-5Y cancer cell lines. These compounds were found to be non-toxic against HEK293 cells at 25 μM and hence display anticancer potential. In these series compounds, AK-3 and AK-10 displayed significant cytotoxic activity against all the three cell lines. AK-3 displayed IC50 values of 10.38 ± 0.27 μM, 6.44 ± 0.29 μM and 9.54 ± 0.15 μM against A549, MCF-7 and SHSY-5Y cancer cell lines. Similarly, AK-10 showed IC50 values of 8.55 ± 0.67 μM, 3.15 ± 0.23 μM and 3.36 ± 0.29 μM against A549, MCF-7 and SHSY-5Y, respectively. In the mechanistic studies, it was found that AK-3 and AK-10 inhibit the cell proliferation in the G1 phase of the cell cycle and the primary cause of death of the cells was found to be through apoptosis. Thus, morpholine based quinazoline derivatives have the potential to be developed as potent anticancer drug molecules.

Nanomedicine for brain cancer

CNS tumors remain among the deadliest forms of cancer, resisting conventional and new treatment approaches, with mortality rates staying practically unchanged over the past 30 years. One of the primary hurdles for treating these cancers is delivering drugs to the brain tumor site in therapeutic concentration, evading the blood-brain (tumor) barrier (BBB/BBTB). Supramolecular nanomedicines (NMs) are increasingly demonstrating noteworthy prospects for addressing these challenges utilizing their unique characteristics, such as improving the bioavailability of the payloadsviacontrolled pharmacokinetics and pharmacodynamics, BBB/BBTB crossing functions, superior distribution in the brain tumor site, and tumor-specific drug activation profiles. Here, we review NM-based brain tumor targeting approaches to demonstrate their applicability and translation potential from different perspectives. To this end, we provide a general overview of brain tumor and their treatments, the incidence of the BBB and BBTB, and their role on NM targeting, as well as the potential of NMs for promoting superior therapeutic effects. Additionally, we discuss critical issues of NMs and their clinical trials, aiming to bolster the potential clinical applications of NMs in treating these life-threatening diseases.

Optimal chemotherapy counteracts cancer adaptive resistance in a cell-based, spatially-extended, evolutionary model

Most aggressive cancers are incurable due to their fast evolution of drug resistance. We model cancer growth and adaptive response in a simplified cell-based (CB) setting, assuming a genetic resistance to two chemotherapeutic drugs. We show that optimal administration protocols can steer cells resistance and turned it into a weakness for the disease. Our work extends the population-based (PB) model proposed by Orlando et al. (Physical Biology, 2012), in which a homogeneous population of cancer cells evolves according to a fitness landscape. The landscape models three types of trade-offs, differing on whether the cells are more, less, or equal effective when generalizing resistance to two drugs as opposed to specializing to a single one. The CB framework allows us to include genetic heterogeneity, spatial competition, and drugs diffusion, as well as realistic administration protocols. By calibrating our model on Orlando et al.'s assumptions, we show that dynamical protocols that alternate the two drugs minimize the cancer size at the end of (or at mid-points during) treatment. These results significantly differ from those obtained with the homogeneous model---suggesting static protocols under the pro-generalizing and neutral allocation trade-offs---highlighting the important role of spatial and genetic heterogeneities. Our work is the first attempt to search for optimal treatments in a CB setting, a step forward toward realistic clinical applications.

Recent Advances in Device Engineering and Computational Analysis for Characterization of Cell-Released Cancer Biomarkers

During cancer progression, tumors shed different biomarkers into the bloodstream, including circulating tumor cells (CTCs), extracellular vesicles (EVs), circulating cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA). The analysis of these biomarkers in the blood, known as 'liquid biopsy' (LB), is a promising approach for early cancer detection and treatment monitoring, and more recently, as a means for cancer therapy. Previous reviews have discussed the role of CTCs and ctDNA in cancer progression; however, ctDNA and EVs are rapidly evolving with technological advancements and computational analysis and are the subject of enormous recent studies in cancer biomarkers. In this review, first, we introduce these cell-released cancer biomarkers and briefly discuss their clinical significance in cancer diagnosis and treatment monitoring. Second, we present conventional and novel approaches for the isolation, profiling, and characterization of these markers. We then investigate the mathematical and in silico models that are developed to investigate the function of ctDNA and EVs in cancer progression. We convey our views on what is needed to pave the way to translate the emerging technologies and models into the clinic and make the case that optimized next-generation techniques and models are needed to precisely evaluate the clinical relevance of these LB markers.

MicroRNA and Alternative mRNA Splicing Events in Cancer Drug Response/Resistance: Potent Therapeutic Targets

Cancer is a multifaceted disease that involves several molecular mechanisms including changes in gene expression. Two important processes altered in cancer that lead to changes in gene expression include altered microRNA (miRNA) expression and aberrant splicing events. MiRNAs are short non-coding RNAs that play a central role in regulating RNA silencing and gene expression. Alternative splicing increases the diversity of the proteome by producing several different spliced mRNAs from a single gene for translation. MiRNA expression and alternative splicing events are rigorously regulated processes. Dysregulation of miRNA and splicing events promote carcinogenesis and drug resistance in cancers including breast, cervical, prostate, colorectal, ovarian and leukemia. Alternative splicing may change the target mRNA 3'UTR binding site. This alteration can affect the produced protein and may ultimately affect the drug affinity of target proteins, eventually leading to drug resistance. Drug resistance can be caused by intrinsic and extrinsic factors. The interplay between miRNA and alternative splicing is largely due to splicing resulting in altered 3'UTR targeted binding of miRNAs. This can result in the altered targeting of these isoforms and altered drug targets and drug resistance. Furthermore, the increasing prevalence of cancer drug resistance poses a substantial challenge in the management of the disease. Henceforth, molecular alterations have become highly attractive drug targets to reverse the aberrant effects of miRNAs and splicing events that promote malignancy and drug resistance. While the miRNA-mRNA splicing interplay in cancer drug resistance remains largely to be elucidated, this review focuses on miRNA and alternative mRNA splicing (AS) events in breast, cervical, prostate, colorectal and ovarian cancer, as well as leukemia, and the role these events play in drug resistance. MiRNA induced cancer drug resistance; alternative mRNA splicing (AS) in cancer drug resistance; the interplay between AS and miRNA in chemoresistance will be discussed. Despite this great potential, the interplay between aberrant splicing events and miRNA is understudied but holds great potential in deciphering miRNA-mediated drug resistance.

Quantifying ERK activity in response to inhibition of the BRAFV600E-MEK-ERK cascade using mathematical modelling

BACKGROUND

Simultaneous inhibition of multiple components of the BRAF-MEK-ERK cascade (vertical inhibition) has become a standard of care for treating BRAF-mutant melanoma. However, the molecular mechanism of how vertical inhibition synergistically suppresses intracellular ERK activity, and consequently cell proliferation, are yet to be fully elucidated.

METHODS

We develop a mechanistic mathematical model that describes how the mutant BRAF inhibitor, dabrafenib, and the MEK inhibitor, trametinib, affect BRAFV600E-MEK-ERK signalling. The model is based on a system of chemical reactions that describes cascade signalling dynamics. Using mass action kinetics, the chemical reactions are re-expressed as ordinary differential equations that are parameterised by in vitro data and solved numerically to obtain the temporal evolution of cascade component concentrations.

RESULTS

The model provides a quantitative method to compute how dabrafenib and trametinib can be used in combination to synergistically inhibit ERK activity in BRAFV600E-mutant melanoma cells. The model elucidates molecular mechanisms of vertical inhibition of the BRAFV600E-MEK-ERK cascade and delineates how elevated BRAF concentrations generate drug resistance to dabrafenib and trametinib. The computational simulations further suggest that elevated ATP levels could be a factor in drug resistance to dabrafenib.

CONCLUSIONS

The model can be used to systematically motivate which dabrafenib-trametinib dose combinations, for treating BRAFV600E-mutated melanoma, warrant experimental investigation.

Targeting Cellular DNA Damage Responses in Cancer: An In Vitro-Calibrated Agent-Based Model Simulating Monolayer and Spheroid Treatment Responses to ATR-Inhibiting Drugs

We combine a systems pharmacology approach with an agent-based modelling approach to simulate LoVo cells subjected to AZD6738, an ATR (ataxia–telangiectasia-mutated and rad3-related kinase) inhibiting anti-cancer drug that can hinder tumour proliferation by targeting cellular DNA damage responses. The agent-based model used in this study is governed by a set of empirically observable rules. By adjusting only the rules when moving between monolayer and multi-cellular tumour spheroid simulations, whilst keeping the fundamental mathematical model and parameters intact, the agent-based model is first parameterised by monolayer in vitro data and is thereafter used to simulate treatment responses in in vitro tumour spheroids subjected to dynamic drug delivery. Spheroid simulations are subsequently compared to in vivo data from xenografts in mice. The spheroid simulations are able to capture the dynamics of in vivo tumour growth and regression for approximately 8 days post-tumour injection. Translating quantitative information between in vitro and in vivo research remains a scientifically and financially challenging step in preclinical drug development processes. However, well-developed in silico tools can be used to facilitate this in vitro to in vivo translation, and in this article, we exemplify how data-driven, agent-based models can be used to bridge the gap between in vitro and in vivo research. We further highlight how agent-based models, that are currently underutilised in pharmaceutical contexts, can be used in preclinical drug development.

Synthesis, Anti-Tumor Activity and Apoptosis-Inducing Effect of Novel Dimeric Keggin-Type Phosphotungstate

A dimeric Keggin-type phosphotungstate (ODA)₁₀[(PW₁₁FeO₃₉)₂O]·9H₂O (abbreviated as ODA₁₀[(PW₁₁Fe)₂], ODA = octadecyltrimethylammonium bromide) was synthesized and investigated comprehensively its antitumor activity on MCF-7 and A549 cells. The dimeric structure and amorphous morphology were characterized by FT-IR, UV-vis-DRS, SEM and XRD. The in vitro MTT assay of ODA₁₀[(PW₁₁Fe)₂] showed anticancer activity on MCF-7 and A549 cells in a dose- and time-dependent manner, and the IC₅₀ values for MCF-7 and A549 cells at 48 h were 5.83 μg/ml and 3.23 μg/ml, respectively. The images of the ODA₁₀[(PW₁₁Fe)₂]-treated cells observed by inverted biological microscope exhibited the characteristic morphology of apoptosis. Flow cytometric analysis showed cell apoptosis and cycle arrested at S phase induced by ODA₁₀[(PW₁₁Fe)₂]. The above results illuminated the main mechanism of the antitumor action of ODA₁₀[(PW₁₁Fe)₂] on MCF-7 and A549 cells, indicating that this dimeric phosphotungstate is a promising anticancer drug.

CRISPR-Cas9 genome editing using targeted lipid nanoparticles for cancer therapy

Harnessing CRISPR-Cas9 technology for cancer therapeutics has been hampered by low editing efficiency in tumors and potential toxicity of existing delivery systems. Here, we describe a safe and efficient lipid nanoparticle (LNP) for the delivery of Cas9 mRNA and sgRNAs that use a novel amino-ionizable lipid. A single intracerebral injection of CRISPR-LNPs against PLK1 (sgPLK1-cLNPs) into aggressive orthotopic glioblastoma enabled up to ~70% gene editing in vivo, which caused tumor cell apoptosis, inhibited tumor growth by 50%, and improved survival by 30%. To reach disseminated tumors, cLNPs were also engineered for antibody-targeted delivery. Intraperitoneal injections of EGFR-targeted sgPLK1-cLNPs caused their selective uptake into disseminated ovarian tumors, enabled up to ~80% gene editing in vivo, inhibited tumor growth, and increased survival by 80%. The ability to disrupt gene expression in vivo in tumors opens new avenues for cancer treatment and research and potential applications for targeted gene editing of noncancerous tissues.

Effect of feedback regulation on stem cell fractions in tissues and tumors: Understanding chemoresistance in cancer

While resistance mutations are often implicated in the failure of cancer therapy, lack of response also occurs without such mutants. In bladder cancer mouse xenografts, repeated chemotherapy cycles have resulted in cancer stem cell (CSC) enrichment, and consequent loss of therapy response due to the reduced susceptibility of CSCs to drugs. A particular feedback loop present in the xenografts has been shown to promote CSC enrichment in this system. Yet, many other regulatory loops might also be operational and might promote CSC enrichment. Their identification is central to improving therapy response. Here, we perform a comprehensive mathematical analysis to define what types of regulatory feedback loops can and cannot contribute to CSC enrichment, providing guidance to the experimental identification of feedback molecules. We derive a formula that reveals whether or not the cell population experiences CSC enrichment over time, based on the properties of the feedback. We find that negative feedback on the CSC division rate or positive feedback on differentiated cell death rate can lead to CSC enrichment. Further, the feedback mediators that achieve CSC enrichment can be secreted by either CSCs or by more differentiated cells. The extent of enrichment is determined by the CSC death rate, the CSC self-renewal probability, and by feedback strength. Defining these general characteristics of feedback loops can guide the experimental screening for and identification of feedback mediators that can promote CSC enrichment in bladder cancer and potentially other tumors. This can help understand and overcome the phenomenon of CSC-based therapy resistance.

Combining hypoxia-activated prodrugs and radiotherapy in silico: Impact of treatment scheduling and the intra-tumoural oxygen landscape

Hypoxia-activated prodrugs (HAPs) present a conceptually elegant approach to not only overcome, but better yet, exploit intra-tumoural hypoxia. Despite being successful in vitro and in vivo, HAPs are yet to achieve successful results in clinical settings. It has been hypothesised that this lack of clinical success can, in part, be explained by the insufficiently stringent clinical screening selection of determining which tumours are suitable for HAP treatments. Taking a mathematical modelling approach, we investigate how tumour properties and HAP-radiation scheduling influence treatment outcomes in simulated tumours. The following key results are demonstrated in silico: (i) HAP and ionising radiation (IR) monotherapies may attack tumours in dissimilar, and complementary, ways. (ii) HAP-IR scheduling may impact treatment efficacy. (iii) HAPs may function as IR treatment intensifiers. (iv) The spatio-temporal intra-tumoural oxygen landscape may impact HAP efficacy. Our in silico framework is based on an on-lattice, hybrid, multiscale cellular automaton spanning three spatial dimensions. The mathematical model for tumour spheroid growth is parameterised by multicellular tumour spheroid (MCTS) data.

An in-silico study of cancer cell survival and spatial distribution within a 3D microenvironment

3D cell cultures are in-vitro models representing a significant improvement with respect to traditional monolayers. Their diffusion and applicability, however, are hampered by the complexity of 3D systems, that add new physical variables for experimental analyses. In order to account for these additional features and improve the study of 3D cultures, we here present SALSA (ScAffoLd SimulAtor), a general purpose computational tool that can simulate the behavior of a population of cells cultured in a 3D scaffold. This software allows for the complete customization of both the polymeric template structure and the cell population behavior and characteristics. In the following the technical description of SALSA will be presented, together with its validation and an example of how it could be used to optimize the experimental analysis of two breast cancer cell lines cultured in collagen scaffolds. This work contributes to the growing field of integrated in-silico/in-vitro analysis of biological systems, which have great potential for the study of complex cell population behaviours and could lead to improve and facilitate the effectiveness and diffusion of 3D cell culture models.

Blackboard to Bedside: A Mathematical Modeling Bottom-Up Approach Toward Personalized Cancer Treatments

Cancers present with high variability across patients and tumors; thus, cancer care, in terms of disease prevention, detection, and control, can highly benefit from a personalized approach. For a comprehensive personalized oncology practice, this personalization should ideally consider data gathered from various information levels, which range from the macroscale population level down to the microscale tumor level, without omission of the central patient level. Appropriate data mined from each of these levels can significantly contribute in devising personalized treatment plans tailored to the individual patient and tumor. Mathematical models of solid tumors, combined with patient-specific tumor profiles, present a unique opportunity to personalize cancer treatments after detection using a bottom-up approach. Here, we discuss how information harvested from mathematical models and from corresponding in silico experiments can be implemented in preclinical and clinical applications. To conceptually illustrate the power of these models, one such model is presented, and various pertinent tumor and treatment scenarios are demonstrated in silico. The presented model, specifically a multiscale, hybrid cellular automaton, has been fully validated in vitro using multiple cell-line-specific data. We discuss various insights provided by this model and other models like it and their role in designing predictive tools that are both patient, and tumor specific. After refinement and parametrization with appropriate data, such in silico tools have the potential to be used in a clinical setting to aid in treatment protocols and decision making.

Pure abscopal effect of radiotherapy in a salivary gland carcinoma: Case report, literature review, and a search for new approaches

We report the case of an 84-year-old woman with poorly differentiated non-small cell carcinoma of the right parotid who presented with headache, was found to have a primary right parotid gland cancer as well as metastatic disease, and underwent palliative radiotherapy to the primary site. The patient received no chemotherapy or immunotherapy, but both the primary site and several non-irradiated foci in the lungs regressed or completely resolved. The patient remained free of disease for about one year before progression. The case is a rare instance of abscopal regression of metastatic disease in the absence of pharmacologic immunomodulation. A literature review surveys the history of the abscopal effect of radiation therapy, attempts to understand the mechanisms of its successes and failures, and points to new approaches that can inform and improve the outcomes of radioimmunotherapy.

Structure-Activity Relationship Study of Novel 6-Aryl-2-benzoyl-pyridines as Tubulin Polymerization Inhibitors with Potent Antiproliferative Properties

We recently reported the crystal structure of tubulin in complex with a colchicine binding site inhibitor (CBSI), ABI-231, having 2-aryl-4-benzoyl-imidazole (ABI). Based on this and additional crystal structures, here we report the structure-activity relationship study of a novel series of pyridine analogues of ABI-231, with compound 4v being the most potent one (average IC₅₀ ∼ 1.8 nM) against a panel of cancer cell lines. We determined the crystal structures of another potent CBSI ABI-274 and 4v in complex with tubulin and confirmed their direct binding at the colchicine site. 4v inhibited tubulin polymerization, strongly suppressed A375 melanoma tumor growth, induced tumor necrosis, disrupted tumor angiogenesis, and led to tumor cell apoptosis in vivo. Collectively, these studies suggest that 4v represents a promising new generation of tubulin inhibitors.

NIR Light-Degradable Antimony Nanoparticle-Based Drug-Delivery Nanosystem for Synergistic Chemo-Photothermal Therapy in Vitro

A novel drug-delivery nanosystem based on near-infrared (NIR) light-degradable antimony nanoparticles (AMNP) have been developed for synergistic chemo-phototherapy in vitro. The monodispersed AMNP were synthesized by using a simple and cost-effective method. Positively charged doxorubicin hydrochloride (DOX) was loaded onto the negatively charged surface of AMNP via electrostatic interaction and finally modified by polyacrylic acid (PAA) to enhance biocompatibility. Under NIR (808 nm) laser irradiation of the AMNP-DOX-PAA nanosystem, not only was high photothermal conversion efficiency of AMNP achieved but also pH-dependent DOX release was enhanced due to laser-induced hyperthermia. As a consequence, almost all of the HeLa cells (around 97%) were killed because of the combined effects of chemotherapy and photothermal therapy. More interestingly, AMNP showed very fast (about 10 min) laser-induced degradation that may help to minimize long-term toxicity after therapy by using same-wavelength NIR laser irradiation (808 nm). Computational total energy calculations and molecular dynamics simulations based on density functional theory (DFT) suggest that the NIR laser irradiation induces a photothermally activated reaction on the surface of AMNP in water, which can lead to surface degradation via the formation of Sb-H bonds first and then Sb-OH bonds upon further increase of temperature. This work demonstrates a simple platform that has potential applications for synergistic and highly effective chemo-photothermal therapy based on photodegradable nanoparticles.

A multiscale model to design therapeutic strategies that overcome drug resistance to tyrosine kinase inhibitors in multiple myeloma

Drug resistance (DR) is a phenomenon characterized by the tolerance of a disease to pharmaceutical treatment. In cancer patients, DR is one of the main challenges that limit the therapeutic potential of the existing treatments. Therefore, overcoming DR by restoring the sensitivity of cancer cells would be greatly beneficial. In this context, mathematical modeling can be used to provide novel therapeutic strategies that maximize the efficiency of anti-cancer agents and potentially overcome DR. In this paper, we present a new multiscale model devoted to the interaction of potential treatments with multiple myeloma (MM) development. In this model, MM cells are represented as individual objects that move, divide, and die by apoptosis. The fate of each cell depends on intracellular and extracellular regulation, as well as the administered treatment. The model is used to explore the combined effects of a tyrosine-kinase inhibitor (TKI) with a pentose phosphate pathway (PPP) inhibitor. We use numerical simulations to tailor effective and safe treatment regimens that may eradicate the MM tumors. The model suggests that an interval for the daily dose of the PPP inhibitor can maximize the responsiveness of MM cells to the treatment with TKIs. Then, it demonstrates that the combination of high-dose pulsatile TKI treatment with high-dose daily PPP inhibitor therapy can potentially eradicate the tumor.The predictions of numerical simulations using such a model can be considered as testable hypotheses in future pre-clinical experiments and clinical studies.

Redox-Responsive Disulfide Bond-Bridged mPEG-PBLA Prodrug Micelles for Enhanced Paclitaxel Biosafety and Antitumor Efficacy

The toxicity and side effects of traditional chemotherapeutic drugs are the main causes of chemotherapy failure. To improve the specificity and selectivity of chemotherapeutic drugs for tumor cells, a novel redox-sensitive polymer prodrug, polyethylene glycol-poly (β-benzyl-L-aspartate) (PEG-PBLA)-SS-paclitaxel (PPSP), was designed and synthesized in this study. The PPSP micelle was manufactured via high-speed dispersion stirring and dialysis. The particle size and zeta potential of this prodrug micelle were 63.77 ± 0.91 nm and −25.8 ± 3.24 mV, respectively. The micelles were uniformly distributed and presented a spherical morphology under a transmission electron microscope. In the tumor physiological environment, the particle size of the PPSP micelles and the release rate of paclitaxel (PTX) were significantly increased compared with those of mPEG-PBLA-CC-PTX (PPCP) micelles, reflecting the excellent redox-sensitive activity of the PPSP micelles. The inhibitory effect of PPSP on HepG2, MCF-7 and HL-7702 cell proliferation was investigated with MTT assays, and the results demonstrated that PPSP is superior to PTX with respect to the inhibition of two tumor cell types at different experimental concentration. Simultaneously PPSP has lower toxicity against HL-7702 cells then PTX and PPCP. Moreover, the blank micelle from mPEG-PBLA showed no obvious toxicity to the two tumor cells at different experimental concentrations. In summary, the redox-sensitive PPSP micelle significantly improved the biosafety and the anti-tumor activity of PTX.

Hybrid modeling frameworks of tumor development and treatment

Tumors are complex multicellular heterogeneous systems comprised of components that interact with and modify one another. Tumor development depends on multiple factors: intrinsic, such as genetic mutations, altered signaling pathways, or variable receptor expression; and extrinsic, such as differences in nutrient supply, crosstalk with stromal or immune cells, or variable composition of the surrounding extracellular matrix. Tumors are also characterized by high cellular heterogeneity and dynamically changing tumor microenvironments. The complexity increases when this multiscale, multicomponent system is perturbed by anticancer treatments. Modeling such complex systems and predicting how tumors will respond to therapies require mathematical models that can handle various types of information and combine diverse theoretical methods on multiple temporal and spatial scales, that is, hybrid models. In this update, we discuss the progress that has been achieved during the last 10 years in the area of the hybrid modeling of tumors. The classical definition of hybrid models refers to the coupling of discrete descriptions of cells with continuous descriptions of microenvironmental factors. To reflect on the direction that the modeling field has taken, we propose extending the definition of hybrid models to include of coupling two or more different mathematical frameworks. Thus, in addition to discussing recent advances in discrete/continuous modeling, we also discuss how these two mathematical descriptions can be coupled with theoretical frameworks of optimal control, optimization, fluid dynamics, game theory, and machine learning. All these methods will be illustrated with applications to tumor development and various anticancer treatments.

This article is characterized under:

Analytical and Computational Methods > Computational Methods

Translational, Genomic, and Systems Medicine > Therapeutic Methods

Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models

Approaches to combat hypoxia in cancer therapy and the potential for in silico models in their evaluation

AIM

The negative impact of tumour hypoxia on cancer treatment outcome has been long-known, yet there has been little success combating it. This paper investigates the potential role of in silico modelling to help test emerging hypoxia-targeting treatments in cancer therapy.

METHODS

A Medline search was undertaken on the current landscape of in silico models that simulate cancer therapy and evaluate their ability to test hypoxia-targeting treatments. Techniques and treatments to combat tumour hypoxia and their current challenges are also presented.

RESULTS

Hypoxia-targeting treatments include tumour reoxygenation, hypoxic cell radiosensitization with nitroimidazoles, hypoxia-activated prodrugs and molecular targeting. Their main challenges are toxicity and not achieving adequate delivery to hypoxic regions of the tumour. There is promising research toward combining two or more of these techniques. Different types of in silico therapy models have been developed ranging from temporal to spatial and from stochastic to deterministic models. Numerous models have compared the effectiveness of different radiotherapy fractionation schedules for controlling hypoxic tumours. Similarly, models could help identify and optimize new treatments for overcoming hypoxia that utilize novel hypoxia-targeting technology.

CONCLUSION

Current therapy models should attempt to incorporate more sophisticated modelling of tumour angiogenesis/vasculature and vessel perfusion in order to become more useful for testing hypoxia-targeting treatments, which typically rely upon the tumour vasculature for delivery of additional oxygen, (pro)drugs and nanoparticles.

Interplay of Darwinian Selection, Lamarckian Induction and Microvesicle Transfer on Drug Resistance in Cancer

Development of drug resistance in cancer has major implications for patients’ outcome. It is related to processes involved in the decrease of drug efficacy, which are strongly influenced by intratumor heterogeneity and changes in the microenvironment. Heterogeneity arises, to a large extent, from genetic mutations analogously to Darwinian evolution, when selection of tumor cells results from the adaptation to the microenvironment, but could also emerge as a consequence of epigenetic mutations driven by stochastic events. An important exogenous source of alterations is the action of chemotherapeutic agents, which not only affects the signalling pathways but also the interactions among cells. In this work we provide experimental evidence from in vitro assays and put forward a mathematical kinetic transport model to describe the dynamics displayed by a system of non-small-cell lung carcinoma cells (NCI-H460) which, depending on the effect of a chemotherapeutic agent (doxorubicin), exhibits a complex interplay between Darwinian selection, Lamarckian induction and the nonlocal transfer of extracellular microvesicles. The role played by all of these processes to multidrug resistance in cancer is elucidated and quantified.