Editorial: Modelling of intravascular drug delivery using nanocarriers
Bibliographic record
Abstract
Nanocarrier-based drug delivery systems have seen tremendous developments in recent years, enabled by rapid advances in both nano-scale technologies and in silico modelling tools. The articles published in this research topic capture a snapshot of this rapidly developing area, focusing on computational modelling methods that complement recent experimental approaches to studying nanocarrier intravascular drug delivery (NIVDD) systems. It has long been recognized that there is a need for the development of more standardized frameworks for in silico preclinical trials. Five papers are included in this special issue, and it is our hope that they can address this need by bringing together a broad range of recent approaches and highlighting their promises and limitations.The first paper by Buchete et al. is a comprehensive review of both main historical events resulting in the development of NIVDD systems and the broad range of multiscale physics-based approaches employed by different groups. The first section introduces nanoparticle (NP)-based drug delivery approaches, highlighting systematically ideas related to aspects such as NP types, approaches for NP loading with useful drugs and concepts related to the effective targeting and biophysical interactions (i.e., that ultimately modulate delivery) of drug-carrying NPs with their cellular targets. The second section illustrates the primarily molecular modelling-based approaches involved in microscale and mesoscale modelling, illustrating the corresponding molecular-level concepts and implications, from NP-functionalization and loading to form a NIVDD system to the NC's molecular interactions in the blood, including the formation of a protein corona, to the delivery stage when the desired targets (e.g., extracytosolic cellular membrane surfaces in the targets) are finally reached. Notably, multiscale modelling approaches of complex molecular processes, such as the complex NIVDD coverage by proteins while located in the blood, are also illustrated and discussed. The third and final section highlights the larger, macroscale aspects of functionalized NIVDD systems, covering physical properties such as the convection and diffusion of nanoparticles in biological media in general and blood vessels and capillaries, in particular. It is illustrated how models based on computational fluid dynamics (CFD) are needed and used to describe larger-scale processes such as particle-wall interactions in capillaries, related to triggering receptor-ligand reactions at vascular areas of interest. Ultimately, the CFD-based approaches can be both improved by the microscale molecular-level models presented in the second section and can provide a useful benchmark themselves (i.e., in a multiscale feedback loop) for achieving improved accuracy at both scales in a self-consistent manner.Another paper by Li et al. (Southeast University, Nanjing, China) presents recent developments in cancer treatment regarding the use of nanomaterials for iron homeostasis. Cells in highly vascularized tumor tissue exhibit a large increase in iron uptake, required for their pronounced growth, migration and possible invasion stages related to cancer proliferation. This paper illustrates the author's experience with a novel class of nanocarriers using iron chelating agents such as deferoxamine (DFO), deferasirox (DFX) and Dp44mT, which showed promise in being used in conjunction with different types of nanoparticles. This approach can advance the current NIVDD toolbox of systems that can be used to target selectively and efficiently cancer cells and tumors and impair their proliferation by modulating their access to iron.A third paper by Domenico Fuoco (Department of Chemical Engineering, Polytechnique Montréal, Montréal, QC, Canada) presents another interesting high-level view of using NPs as nanocarriers from a more user-facing perspective, providing not only a pragmatic summary of the recent concepts and key points involved in practical development of NIVDD systems, including pointers to different types of NP materials and nanotechnologies, but also to public and private-access databases and information sources that could be consulted for identifying recent developments in this field. Notably, the review highlights emerging biomedical aspects that need to be considered for improving the clinical outcomes of NIVDD systems, such as their "stealth effect" (i.e., the biophysical and biomolecular properties that can allow them to avoid recognition by the immune system and trigger their premature clearing). Other related aspects are also discussed, such as cellular or even molecular "Trojan-horse" strategies that may be used to improve both delivery efficiency and targeting specificity. Engineering, University of Cyprus, Nicosia, Cyprus) introduces a novel complex mathematical modelling approach to study and quantify the combined effects of both mechanotherapy and sonopermeation on tumor treatment. Once again, the accurate modelling of a cancer tumor's microenvironment (TME) can play a key role in modulating both its development and its response to various treatments (e.g., exposing the tumor to IV-delivered nanocarriers). This paper lays the foundations of developing an automatic and systematic modeling framework, including explicitly three principal states of nanocarrier-based drug delivery (i.e., (i) encapsulation/functionalization of the chemotherapeutic agent, (ii) free diffusion of the agent in the tumor interstitial space, and
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".