Mathematical and computational modeling in biology at multiple scales

Investigation of pH-dependent Paclitaxel delivery mechanism employing Chitosan-Eudragit bioresponsive nanocarriers: a molecular dynamics simulation

Before embarking on any experimental research endeavor, it is advisable to do a mathematical computation and thoroughly examine the methodology. Despite the use of polymeric nanocarriers, the regulation of bioavailability and drug release at the disease site remains insufficient. Several effective methods have been devised to address this issue, including the creation of polymeric nanocarriers that can react to stimuli such as redox potential, temperature, pH, and light. The present study has been utilized all-atom molecular dynamics (AA-MD) and coarse-grained molecular dynamics (CG-MD) methods and illustrated the drug release mechanism, which is influenced by pH, for Chitosan-Eudragit bioresponsive nanocarriers. The aim of current work is to study the molecular mechanism and atomistic interactions of PAX delivery using a Chitosan-Eudragit carrier. The ability of Eudragit polymers to dissolve in various organic solvents employed in the process of solvent evaporation is a crucial benefit in enhancing the solubility of pharmaceuticals. This study investigated the use of Chitosan-Eudragit nanocarriers for delivering an anti-tumor drug, namely Paclitaxel (PAX). Upon analyzing several significant factors affecting the stability of the drug and nanocarrier, it has been shown that the level of stability is more significant in the neutral state than the acidic state. Furthermore, the system exhibits higher stability in the neutral state. The used Chitosan-Eudragit nanocarriers exhibit a stable structure under alkaline conditions, but undergo deformation and release their payloads under acidic conditions. It was demonstrated that the in silico analysis of anti-tumor drugs and carriers' integration could be quantified and validated by experimental results (from previous works) at an acceptable level.

Demystifying neuroblastoma malignancy through fractal dimension, entropy, and lacunarity

PURPOSE

Neuroblastoma is a pediatric solid tumor with a prognosis associated with histology and age of the patient, which are the parameters of the well-established current classification (Shimada classification). Despite the development of new treatment options, the prognosis of high-risk neuroblastoma patients is still poor. Therefore, there is a continuous need to stratify the children suffering from this tumor. A mathematical and computational approach is proposed to enable automatic and precise cancer diagnosis on the histological slide.

METHODS

We targeted the complexity of neuroblastoma by calculating its image entropy (S), fractal dimension (FD), and lacunarity (λ) in a combined mathematical code. First, we tested the proposed method for patient-derived glioma images. It allowed distinguishing between normal brain tissue, grade II, and grade III glioma, which harbor different outcomes.

RESULTS

In neuroblastoma, our analysis of image's FD, S, and λ combined with a machine learning algorithm automatically predicted tumor malignancy with a receiver operating characteristic curve of 0.82. FD, S, and λ distinguish between neuroblastoma and ganglioneuroma, but they only partially differentiate between the normal samples and the other classes. Ganglioneuroma, the most differentiated form, and poorly-differentiated neuroblastoma display different values of FD, S, and λ.

CONCLUSIONS

FD, S, and λ of imaging recognize groups in neuroblastic tumors. We suggest that future studies including these features may challenge the current Shimada classification of neuroblastoma with categories of favorable and unfavorable histology. It is expected that this methodology could trigger multicenter studies and potentially find practical use in the clinical setting of children's hospitals worldwide.

Development of Optimal Virtual Screening Strategies to Identify Novel Toll-Like Receptor Ligands Using the DockBox Suite

Toll-like receptors (TLRs) represent attractive targets for developing modulators for the treatment of many pathologies, including inflammation, cancer, and autoimmune diseases. Here, we describe a protocol based on the DockBox package that enables to set up and perform structure-based virtual screening in order to increase the chance of identifying novel TLR ligands from chemical libraries.

Understanding Calmodulin Variants Affecting Calcium-Dependent Inactivation of L-Type Calcium Channels through Whole-Cell Simulation of the Cardiac Ventricular Myocyte

Mutations in the calcium-sensing protein calmodulin (CaM) have been linked to two cardiac arrhythmia diseases, Long QT Syndrome 14 (LQT14) and Catecholaminergic Polymorphic Ventricular Tachycardia Type 4 (CPVT4), with varying degrees of severity. Functional characterization of the CaM mutants most strongly associated with LQT14 show a clear disruption of the calcium-dependent inactivation (CDI) of the L-Type calcium channel (LCC). CPVT4 mutants on the other hand are associated with changes in their affinity to the ryanodine receptor. In clinical studies, some variants have been associated with both CPVT4 and LQT15. This study uses simulations in a model for excitation-contraction coupling in the rat ventricular myocytes to understand how LQT14 variant might give the functional phenotype similar to CPVT4. Changing the CaM-dependent transition rate by a factor of 0.75 corresponding to the D96V variant and by a factor of 0.90 corresponding to the F142L or N98S variants, in a physiologically based stochastic model of the LCC prolonger, the action potential duration changed by a small amount in a cardiac myocyte but did not disrupt CICR at 1, 2, and 4 Hz. Under beta-adrenergic simulation abnormal excitation-contraction coupling was observed above 2 Hz pacing for the mutant CaM. The same conditions applied under beta-adrenergic stimulation led to the rapid onset of arrhythmia in the mutant CaM simulations. Simulations with the LQT14 mutations under the conditions of rapid pacing with beta-adrenergic stimulation drives the cardiac myocyte toward an arrhythmic state known as Ca2+ overload. These simulations provide a mechanistic link to a disease state for LQT14-associated mutations in CaM to yield a CPVT4 phenotype. The results show that small changes to the CaM-regulated inactivation of LCC promote arrhythmia and underscore the significance of CDI in proper heart function.

Novel pH-responsive nanohybrid for simultaneous delivery of doxorubicin and paclitaxel: an in-silico insight

BACKGROUND

The distribution of drugs could not be controlled in the conventional delivery systems. This has led to the developing of a specific nanoparticle-based delivery system, called smart drug delivery systems. In cancer therapy, innovative biocompatible nanocarriers have received much attention for various ranges of anti-cancer drugs. In this work, the effect of an interesting and novel copolymer named "dimethyl acrylamide-trimethyl chitosan" was investigated on delivery of paclitaxel and doxorubicin applying carboxylated fullerene nanohybrid. The current study was run via molecular dynamics simulation and quantum calculations based on the acidic pH differences between cancerous microenvironment and normal tissues. Furthermore, hydrogen bonds, radius of gyration, and nanoparticle interaction energies were studied here. Stimulatingly, a simultaneous pH and temperature-responsive system were proposed for paclitaxel and doxorubicin for a co-polymer. A pH-responsive and thermal responsive copolymer were utilized based on trimethyl chitosan and dimethyl acrylamide, respectively. In such a dualistic approach, co-polymer makes an excellent system to possess two simultaneous properties in one bio-polymer.

RESULTS

The simulation results proposed dramatic and indisputable effects of the copolymer in the release of drugs in cancerous tissues, as well as increased biocompatibility and drug uptake in healthy tissues. Repeated simulations of a similar article performed for the validation test. The results are very close to those of the reference paper.

CONCLUSIONS

Overall, conjugated modified fullerene and dimethyl acrylamide-trimethyl chitosan (DMAA-TMC) as nanohybrid can be an appropriate proposition for drug loading, drug delivery, and drug release on dual responsive smart drug delivery system.

Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations

Background

A thorough understanding of the ecological and evolutionary mechanisms that drive the phenotypic evolution of neoplastic cells is a timely and key challenge for the cancer research community. In this respect, mathematical modelling can complement experimental cancer research by offering alternative means of understanding the results of in vitro and in vivo experiments, and by allowing for a quick and easy exploration of a variety of biological scenarios through in silico studies.

Results

To elucidate the roles of phenotypic plasticity and selection pressures in tumour relapse, we present here a phenotype-structured model of evolutionary dynamics in a cancer cell population which is exposed to the action of a cytotoxic drug. The analytical tractability of our model allows us to investigate how the phenotype distribution, the level of phenotypic heterogeneity, and the size of the cell population are shaped by the strength of natural selection, the rate of random epimutations, the intensity of the competition for limited resources between cells, and the drug dose in use.

Conclusions

Our analytical results clarify the conditions for the successful adaptation of cancer cells faced with environmental changes. Furthermore, the results of our analyses demonstrate that the same cell population exposed to different concentrations of the same cytotoxic drug can take different evolutionary trajectories, which culminate in the selection of phenotypic variants characterised by different levels of drug tolerance. This suggests that the response of cancer cells to cytotoxic agents is more complex than a simple binary outcome, i.e., extinction of sensitive cells and selection of highly resistant cells. Also, our mathematical results formalise the idea that the use of cytotoxic agents at high doses can act as a double-edged sword by promoting the outgrowth of drug resistant cellular clones. Overall, our theoretical work offers a formal basis for the development of anti-cancer therapeutic protocols that go beyond the ‘maximum-tolerated-dose paradigm’, as they may be more effective than traditional protocols at keeping the size of cancer cell populations under control while avoiding the expansion of drug tolerant clones.

Reviewers

This article was reviewed by Angela Pisco, Sébastien Benzekry and Heiko Enderling.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-016-0143-4) contains supplementary material, which is available to authorized users.