A recent development of new therapeutic agents and novel drug targets for cancer treatment

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.

Network pharmacology, molecular simulation, and binding free energy calculation-based investigation of Neosetophomone B revealed key targets for the treatment of cancer

In the current study, Neosetophomone B (NSP-B) was investigated for its anti-cancerous potential using network pharmacology, quantum polarized ligand docking, molecular simulation, and binding free energy calculation. Using SwissTarget prediction, and Superpred, the molecular targets for NSP-B were predicted while cancer-associated genes were obtained from DisGeNet. Among the total predicted proteins, only 25 were reported to overlap with the disease-associated genes. A protein-protein interaction network was constructed by using Cytoscape and STRING databases. MCODE was used to detect the densely connected subnetworks which revealed three sub-clusters. Cytohubba predicted four targets, i.e., fibroblast growth factor , FGF20, FGF22, and FGF23 as hub genes. Molecular docking of NSP-B based on a quantum-polarized docking approach with FGF6, FGF20, FGF22, and FGF23 revealed stronger interactions with the key hotspot residues. Moreover, molecular simulation revealed a stable dynamic behavior, good structural packing, and residues' flexibility of each complex. Hydrogen bonding in each complex was also observed to be above the minimum. In addition, the binding free energy was calculated using the MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) and MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) approaches. The total binding free energy calculated using the MM/GBSA approach revealed values of -36.85 kcal/mol for the FGF6-NSP-B complex, -43.87 kcal/mol for the FGF20-NSP-B complex, and -37.42 kcal/mol for the FGF22-NSP-B complex, and -41.91 kcal/mol for the FGF23-NSP-B complex. The total binding free energy calculated using the MM/PBSA approach showed values of -30.05 kcal/mol for the FGF6-NSP-B complex, -39.62 kcal/mol for the FGF20-NSP-B complex, -34.89 kcal/mol for the FGF22-NSP-B complex, and -37.18 kcal/mol for the FGF23-NSP-B complex. These findings underscore the promising potential of NSP-B against FGF6, FGF20, FGF22, and FGF23, which are reported to be essential for cancer signaling. These results significantly bolster the potential of NSP-B as a promising candidate for cancer therapy.

Current status and prospects for improved targeted delivery approaches for cancer

Millions of individuals worldwide suffer from the complicated disease known as cancer. Though they frequently have serious side effects and can harm healthy cells, the current cancer treatments, such as radiation therapy and chemotherapy, are effective in many cases. Targeted drug delivery systems have emerged as a promising new paradigm in cancer treatment because they can deliver drugs directly to cancer cells with minimal harm to healthy cells. This review aims to give a broad overview of the state of targeted drug delivery systems for cancer treatment and investigate the technology's potential in the future. We'll go through the various kinds of targeted drug delivery systems, their drawbacks, the most recent developments, and possible future paths for further study and creation. This review aims to provide an overview of the current status of targeted drug delivery systems for cancer treatment, including the different types of targeted drug delivery systems, their limitations, recent advancements, and potential future directions for research and development. By examining the field's current state and exploring prospects, this review aims to highlight the potential of targeted drug delivery systems for improving cancer treatment and ultimately enhancing patient outcomes.

Synthetic lipo-polylysine with anti-cancer activity

Development of novel therapeutic agents that possess different anticancer mechanisms from the traditional antitumor drugs is highly attractive as no medication can cure all types of cancers. Herein, we report a rational design of antitumor lipo-polylysine polymers as synthetic mimics of biosynthetic lipopeptide surfactants featuring antimicrobial or cytotoxic activities for cancer therapy. The optimal polymer shows a wide range of anticancer activities against multiple cancer cells, including highly metastatic and drug-resistant ones, but low toxicity to normal cells. Mechanism studies show that the optimal polymer can interact with the membrane of cancer cells and induce cell necrosis by triggering cell membrane perforation, which is different from the therapeutic mechanisms of traditional anticancer drugs. In vivo studies imply that the optimal polymer efficiently inhibits tumor growth without causing obvious side effects on a C26 graft tumor model. Overall, the lipopeptide-mimicking lipo-polylysine with the advantages of easy synthesis and low cost provides a new anticancer strategy with high efficacy and biocompatibility.

Environmentally Friendly Strategies for Formulating Vegetable Oil-Based Nanoparticles for Anticancer Medicine

The development of green synthesized polymeric nanoparticles with anticancer studies has been an emerging field in academia and the pharmaceutical and chemical industries. Vegetable oils are potential substitutes for petroleum derivatives, as they present a clean and environmentally friendly alternative and are available in abundance at relatively low prices. Biomass-derived chemicals can be converted into monomers with a unique structure, generating materials with new properties for the synthesis of sustainable monomers and polymers. The production of bio-based polymeric nanoparticles is a promising application of green chemistry for biomedical uses. There is an increasing demand for biocompatible and biodegradable materials for specific applications in the biomedical area, such as cancer therapy. This is encouraging scientists to work on research toward designing polymers with enhanced properties and clean processes, containing oncology active pharmaceutical ingredients (APIs). The nanoencapsulation of these APIs in bio-based polymeric nanoparticles can control the release of the substances, increase bioavailability, reduce problems of volatility and degradation, reduce side effects, and increase treatment efficiency. This review discusses the use of green chemistry for bio-based nanoparticle production and its application in anticancer medicine. The use of castor oil for the production of renewable monomers and polymers is proposed as an ideal candidate for such applications, as well as more suitable methods for the production of bio-based nanoparticles and some oncology APIs available for anticancer application.

Emerging Pharmacotherapeutic Strategies to Overcome Undruggable Proteins in Cancer

Targeted therapies in cancer treatment can improve in vivo efficacy and reduce adverse effects by altering the tissue exposure of specific biomolecules. However, there are still large number of target proteins in cancer are still undruggable, owing to the following factors including (1) lack of ligand-binding pockets, (2) function based on protein-protein interactions (PPIs), (3) the highly specific conserved active sites among protein family members, and (4) the variability of tertiary docking structures. The current status of undruggable targets proteins such as KRAS, TP53, C-MYC, PTP, are carefully introduced in this review. Some novel techniques and drug designing strategies have been applicated for overcoming these undruggable proteins, and the most classic and well-known technology is proteolysis targeting chimeras (PROTACs). In this review, the novel drug development strategies including targeting protein degradation, targeting PPI, targeting intrinsically disordered regions, as well as targeting protein-DNA binding are described, and we also discuss the potential of these strategies for overcoming the undruggable targets. Besides, intelligence-assisted technologies like Alpha-Fold help us a lot to predict the protein structure, which is beneficial for drug development. The discovery of new targets and the development of drugs targeting them, especially those undruggable targets, remain a huge challenge. New drug development strategies, better extraction processes that do not disrupt protein-protein interactions, and more precise artificial intelligence technologies may provide significant assistance in overcoming these undruggable targets.