Advances in Nanoparticles as Anticancer Drug Delivery Vector: Need of this Century

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

AIM

Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.

METHODS

A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace.

RESULTS

A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.

CONCLUSIONS

Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.

A critical review on nanoplastics and its future perspectives in the marine environment

Nanoplastics (plastic particles smaller than 1 μm) are the least-known type of marine litter. Nanoplastics (NPs) have attracted much interest in recent years because of their prevalence in the environment and the potential harm they can cause to living organisms. This article focuses on understanding NPs and their fate in the marine environment. Sources of NPs have been identified, including accidental release from products or through nano-fragmentation of larger plastic debris. As NPs have a high surface area, they may retain harmful compounds. The presence of harmful additives in NPs poses unique practical challenges for studies on their toxicity. In this review, several methods specifically adapted for the physical and chemical characterization of NPs have been discussed. Furthermore, the review provides an overview of the translocation and absorption of NPs into organisms, along with an evaluation of the release of potential toxins from NPs. Further, we have provided an overview about the existing methods suggested for the possible degradation of these NPs. We conclude that the hazards of NPs are plausible but unknown, necessitating a thorough examination of NPs' sources, fate, and effects to better mitigate and spread awareness about this emerging contaminant.

Preparation and application of pH-responsive drug delivery systems

Microenvironment-responsive drug delivery systems (DDSs) can achieve targeted drug delivery, reduce drug side effects and improve drug efficacies. Among them, pH-responsive DDSs have gained popularity since the pH in the diseased tissues such as cancer, bacterial infection and inflammation differs from a physiological pH of 7.4 and this difference could be harnessed for DDSs to release encapsulated drugs specifically to these diseased tissues. A variety of synthetic approaches have been developed to prepare pH-sensitive DDSs, including introduction of a variety of pH-sensitive chemical bonds or protonated/deprotonated chemical groups. A myriad of nano DDSs have been explored to be pH-responsive, including liposomes, micelles, hydrogels, dendritic macromolecules and organic-inorganic hybrid nanoparticles, and micron level microspheres. The prodrugs from drug-loaded pH-sensitive nano DDSs have been applied in research on anticancer therapy and diagnosis of cancer, inflammation, antibacterial infection, and neurological diseases. We have systematically summarized synthesis strategies of pH-stimulating DDSs, illustrated commonly used and recently developed nanocarriers for these DDSs and covered their potential in different biomedical applications, which may spark new ideas for the development and application of pH-sensitive nano DDSs.

Mesoporous Bioactive Glasses in Cancer Diagnosis and Therapy: Stimuli-Responsive, Toxicity, Immunogenicity, and Clinical Translation

Cancer is one of the top life-threatening dangers to the human survival, accounting for over 10 million deaths per year. Bioactive glasses have developed dramatically since their discovery 50 years ago, with applications that include therapeutics as well as diagnostics. A new system within the bioactive glass family, mesoporous bioactive glasses (MBGs), has evolved into a multifunctional platform, thanks to MBGs easy-to-functionalize nature and tailorable textural properties-surface area, pore size, and pore volume. Although MBGs have yet to meet their potential in tumor treatment and imaging in practice, recently research has shed light on the distinguished MBGs capabilities as promising theranostic systems for cancer imaging and therapy. This review presents research progress in the field of MBG applications in cancer diagnosis and therapy, including synthesis of MBGs, mechanistic overview of MBGs application in tumor diagnosis and drug monitoring, applications of MBGs in cancer therapy ( particularly, targeted delivery and stimuli-responsive nanoplatforms), and immunological profile of MBG-based nanodevices in reference to the development of novel cancer therapeutics.

Vaginal drug delivery approaches for localized management of cervical cancer

Cervical cancer or cervical intraepithelial neoplasia (CIN) remain a major public health problem among women globally. Traditional methods such as surgery are often associated with possible complications which may impact future pregnancies and childbirth especially for young female patients. Vagina with a high contact surface is a suitable route for the local and systemic delivery of drugs but its abundant mucus in continuous exchange presents a barrier for the popularization of conventional vaginal formulations including suppositories, gel, patch, creams and so on. So the development of new pharmaceutical forms based on nanotechnology became appealing owing to its several advantages such as mucosa penetration, bioadhesion, controlled drug release, and decreased adverse effects. This review provided an overview of the development of topical treatment of cervical cancer or CIN through vaginal drug delivery ranging from conventional vaginal formulations to new nanocarriers to the newly developed phototherapy and gene therapy, analyzing the problems faced by current methods used, and advising the developing trend in future. The methods of establishing preclinical animal model are also discussed.

Application progress of RVG peptides to facilitate the delivery of therapeutic agents into the central nervous system

The incidence of central nervous system (CNS) diseases is increasing with the aging population. However, it remains challenging to deliver drugs into the CNS because of the existence of a blood-brain barrier (BBB). Notably, rabies virus glycoprotein (RVG) peptides have been developed as delivery ligands for CNS diseases. So far, massive RVG peptide modified carriers have been reported, such as liposomes, micelles, polymers, exosomes, dendrimers, and proteins. Moreover, these drug delivery systems can encapsulate almost all small molecules and macromolecule drugs, including siRNA, microRNAs, DNA, proteins, and other nanoparticles, to treat various CNS diseases with efficient and safe drugs. In this review, targeted delivery systems with RVG peptide modified carriers possessing favorable biocompatibility and delivery efficiency are summarized.

Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models

Nanoparticles (NPs) decorated with coating agents (polymers, gels, proteins, etc.) form Nanoparticle Drug Delivery Systems (DDNS), which are of high interest in nanotechnology and biomaterials science. There have been increasing reports of experimental data sets of biological activity, toxicity, and delivery properties of DDNS. However, these data sets are still dispersed and not as large as the datasets of DDNS components (NP and drugs). This has prompted researchers to train Machine Learning (ML) algorithms that are able to design new DDNS based on the properties of their components. However, most ML models reported up to date predictions of the specific activities of NP or drugs over a determined target or cell line. In this paper, we combine Perturbation Theory and Machine Learning (PTML algorithm) to train a model that is able to predict the best components (NP, coating agent, and drug) for DDNS design. In so doing, we downloaded a dataset of >30 000 preclinical assays of drugs from ChEMBL. We also downloaded an NP data set formed by preclinical assays of coated Metal Oxide Nanoparticles (MONPs) from public sources. Both the drugs and NP datasets of preclinical assays cover multiple conditions of assays that can be listed as two arrays, namely, cjdrug and cjNP. The cjdrug array includes >504 biological activity parameters (c0drug), >340 target proteins (c1drug), >650 types of cells (c2drug), >120 assay organisms (c3drug), and >60 assay strains (c4drug). On the other hand, the cjNP array includes 3 biological activity parameters (c0NP), 40 types of proteins (c1NP), 10 shapes of nanoparticles (c2NP), 6 assay media (c3NP), and 12 coating agents (c4NP). After downloading, we pre-processed both the data sets by separate calculation PT operators that are able to account for changes (perturbations) in the drug, coating agents, and NP chemical structure and/or physicochemical properties as well as for the assay conditions. Next, we carry out an information fusion process to form a final dataset of above 500 000 DDNS (drug + MONP pairs). We also trained other linear and non-linear PTML models using R studio scripts for comparative purposes. To the best of our knowledge, this is the first multi-label PTML model that is useful for the selection of drugs, coating agents, and metal or metal-oxide nanoparticles to be assembled in order to design new DDNS with optimal activity/toxicity profiles.