Drug delivery in a tumour cord model: a computational simulation

In silico study about the influence of electroporation parameters on the cellular internalization, spatial uniformity, and cytotoxic effects of chemotherapeutic drugs using the Method of Fundamental Solutions

Reversible electroporation is a suitable technique to aid the internalization of medicaments in cancer tissues without inducing permanent cellular damage, allowing the enhancement of cytotoxic effects without incurring in electric-driven necrotic or apoptotic processes by the presence of non-reversible aqueous pores. An adequate selection of electroporation parameters acquires relevance to reach these goals and avoid opposite effects. This work applies the Method of Fundamental Solutions (MFS) for drug transport simulations in electroporated cancer tissues, using a continuum tumor cord approach and considering both electro-permeabilization and vasoconstriction effects. The MFS algorithm is validated with published results, obtaining satisfactory accuracy and convergence. Then, MFS simulations are executed to study the influence of electric field magnitude [Formula: see text], number of electroporation treatments [Formula: see text], and electroporation time [Formula: see text] on three assessment parameters of electrochemotherapy: the internationalization efficacy accounting for the ability of the therapy to introduce moles into viable cells, cell-kill capacity indicating the faculty to reduce the survival fraction of cancer cells, and distribution uniformity specifying the competence to supply drug homogeneously through the whole tissue domain. According to numerical results, when [Formula: see text] is the reversibility threshold, a positive influence on the first two parameters is only possible once specific values of [Formula: see text] and [Formula: see text] have been exceeded; when [Formula: see text] is just the irreversibility threshold, any combination of [Formula: see text] and [Formula: see text] is beneficial. On the other hand, the drug distribution uniformity is always adversely affected by the application of electric pulses, being this more noticeable as [Formula: see text], [Formula: see text], and [Formula: see text] increases.

Influence of electric pulse characteristics on the cellular internalization of chemotherapeutic drugs and cell survival fraction in electroporated and vasoconstricted cancer tissues using boundary element techniques

Electroporation has emerged as a suitable technique to induce the pore formation in the cell membrane of cancer tissues, facilitating the cellular internalization of chemotherapeutic drugs. An adequate selection of the electric pulse characteristics is crucial to guarantee the efficiency of this technique, minimizing the adverse effects. In the present work, the dual reciprocity boundary element method (DR-BEM) is applied for the simulation of drug transport in the extracellular and intracellular space of cancer tissues subjected to the application of controlled electric pulses, using a continuum tumour cord approach, and considering both the electro-permeabilization and vasoconstriction phenomena. The developed DR-BEM algorithm is validated with numerical and experimental results previously published, obtaining a satisfactory accuracy and convergence. Using the DR-BEM code, a study about the influence of the magnitude of electric field (E) and pulse spacing (dpulses) on the time behavior and spatial distribution of the internalized drug, as well as on the cell survival fraction, is carried out. In general, the change of drug concentration, drug exposure and cell survival fraction with the parameters E and dpulses is ruled by two important factors: the balance between the electro-permeabilization and vasoconstriction phenomena, and the relative importance of the sources of cell death (electric pulses and drug cytotoxicity); these two factors, in turn, significantly depend on the reversible and irreversible thresholds considered for the electric field.

Nanoconstructs for theranostic application in cancer: Challenges and strategies to enhance the delivery

Nanoconstructs are made up of nanoparticles and ligands, which can deliver the loaded cargo at the desired site of action. Various nanoparticulate platforms have been utilized for the preparation of nanoconstructs, which may serve both diagnostic as well as therapeutic purposes. Nanoconstructs are mostly used to overcome the limitations of cancer therapies, such as toxicity, nonspecific distribution of the drug, and uncontrolled release rate. The strategies employed during the design of nanoconstructs help improve the efficiency and specificity of loaded theranostic agents and make them a successful approach for cancer therapy. Nanoconstructs are designed with a sole purpose of targeting the requisite site, overcoming the barriers which hinders its right placement for desired benefit. Therefore, instead of classifying modes for delivery of nanoconstructs as actively or passively targeted systems, they are suitably classified as autonomous and nonautonomous types. At large, nanoconstructs offer numerous benefits, however they suffer from multiple challenges, too. Hence, to overcome such challenges computational modelling methods and artificial intelligence/machine learning processes are being explored. The current review provides an overview on attributes and applications offered by nanoconstructs as theranostic agent in cancer.

Predictive Simulations in Preclinical Oncology to Guide the Translation of Biologics

Preclinical in vivo studies form the cornerstone of drug development and translation, bridging in vitro experiments with first-in-human trials. However, despite the utility of animal models, translation from the bench to bedside remains difficult, particularly for biologics and agents with unique mechanisms of action. The limitations of these animal models may advance agents that are ineffective in the clinic, or worse, screen out compounds that would be successful drugs. One reason for such failure is that animal models often allow clinically intolerable doses, which can undermine translation from otherwise promising efficacy studies. Other times, tolerability makes it challenging to identify the necessary dose range for clinical testing. With the ability to predict pharmacokinetic and pharmacodynamic responses, mechanistic simulations can help advance candidates from in vitro to in vivo and clinical studies. Here, we use basic insights into drug disposition to analyze the dosing of antibody drug conjugates (ADC) and checkpoint inhibitor dosing (PD-1 and PD-L1) in the clinic. The results demonstrate how simulations can identify the most promising clinical compounds rather than the most effective in vitro and preclinical in vivo agents. Likewise, the importance of quantifying absolute target expression and antibody internalization is critical to accurately scale dosing. These predictive models are capable of simulating clinical scenarios and providing results that can be validated and updated along the entire development pipeline starting in drug discovery. Combined with experimental approaches, simulations can guide the selection of compounds at early stages that are predicted to have the highest efficacy in the clinic.

A one-stop microfluidic-based lung cancer organoid culture platform for testing drug sensitivity

Microfluidic devices as translational research tools provide a potential alternative to animal experiments due to their ability to mimic physiological parameters. Several approaches that can be used to predict the efficacy or toxicity of anticancer drugs are available. In general, standard cell culture systems have the advantages of being relatively cost-effective, having high-throughput capability, and providing convenience. However, these models are inadequate to accurately recapitulate the complex organ-level physiological and pharmacological responses. Here, we present a one-stop microfluidic device enabling both 3-dimensional (3D) lung cancer organoid culturing and drug sensitivity tests directly on a microphysiological system (MPS). Our platform reproducibly yields 3D lung cancer organoids in a size-controllable manner and demonstrates for the first time the production of lung cancer organoids from patients with small-cell lung cancer. Lung cancer organoids derived from primary small-cell lung cancer tumors can rapidly proliferate and exhibit disease-specific characteristics in our MPS. Cisplatin and etoposide, the standard regimen for lung cancer, showed increased apoptosis induction in a concentration-dependent manner, but the organoids contained chemo-resistant cells in the core. We envision that this system may provide important information to guide therapeutic approaches at the preclinical level.

Pore pressure evolution and mass loss of broken gangue during the seepage

Broken gangue consists of different particles, and it has more complicated seepage characteristics than intact rock sample. Using the self-designed instrument, the permeability, mass loss and pore pressure of crushed gangue during the seepage are tested. The result shows that permeability parameter k of crushed rock has a polynomial relationship with effective stress σ' in inverse proportion, and permeability parameter β of crushed gangue has power exponent relationship with effective stress σ' increasing in direct proportion. The particle size of 8.0-10.0 mm has a good support effect. The inner pressure of crushed rock is mostly linear distribution along the tube wall. After the seepage, mass loss of broken gangue mainly increases with large particle size out of proportion.

Computational modelling of drug delivery to solid tumour: Understanding the interplay between chemotherapeutics and biological system for optimised delivery systems

Drug delivery to solid tumour involves multiple physiological, biochemical and biophysical processes taking place across a wide range of length and time scales. The therapeutic efficacy of anticancer drugs is influenced by the complex interplays among the intrinsic properties of tumours, biophysical aspects of drug transport and cellular uptake. Mathematical and computational modelling allows for a well-controlled study on the individual and combined effects of a wide range of parameters on drug transport and therapeutic efficacy, which would not be possible or economically viable through experimental means. A wide spectrum of mathematical models has been developed for the simulation of drug transport and delivery in solid tumours, including PK/PD-based compartmental models, microscopic and macroscopic transport models, and molecular dynamics drug loading and release models. These models have been used as a tool to identify the limiting factors and for optimal design of efficient drug delivery systems. This article gives an overview of the currently available computational models for drug transport in solid tumours, together with their applications to novel drug delivery systems, such as nanoparticle-mediated drug delivery and convection-enhanced delivery.

Drug delivery in a tumour cord model: a computational simulation

The tumour vasculature and microenvironment is complex and heterogeneous, contributing to reduced delivery of cancer drugs to the tumour. We have developed an in silico model of drug transport in a tumour cord to explore the effect of different drug regimes over a 72 h period and how changes in pharmacokinetic parameters affect tumour exposure to the cytotoxic drug doxorubicin. We used the model to describe the radial and axial distribution of drug in the tumour cord as a function of changes in the transport rate across the cell membrane, blood vessel and intercellular permeability, flow rate, and the binding and unbinding ratio of drug within the cancer cells. We explored how changes in these parameters may affect cellular exposure to drug. The model demonstrates the extent to which distance from the supplying vessel influences drug levels and the effect of dosing schedule in relation to saturation of drug-binding sites. It also shows the likely impact on drug distribution of the aberrant vasculature seen within tumours. The model can be adapted for other drugs and extended to include other parameters. The analysis confirms that computational models can play a role in understanding novel cancer therapies to optimize drug administration and delivery.