Current Topics in Medicinal Chemistry - Volume 22, Issue 30, 2022

Biomarkers detection and quantification in biological fluids play a key role in the screening, diagnosing and treating several diseases. Recently, a large number of aptamers have been selected and applied for the sensing of different biomarkers. Combined with different transducers, aptamers provide simple and rapid tools that allow highly sensitive and selective detection. Cardiology requires an accurate assessment of cardiac biomarkers for a complete diagnosis of cardiovascular diseases. The analysis is generally performed by immunoassays using antibodies as biorecognition elements. This review paper focuses on using aptamers as a promising alternative for antibodies in cardiac biomarkers biosensing. First, the different aptamers specific to the most important cardiac biomarkers are Troponin I, the peptide of B-type natriuretic peptide and myoglobin. Then, in the second part, we overview the electrochemical aptasensors principle and characteristics reported in the literature in the last five years.

Polyhydroxy compounds are secondary metabolites that are ubiquitous in plants of higher genera. They possess therapeutic properties against a wide spectrum of diseases, including cancers, neurodegenerative disorders, atherosclerosis, as well as cardiovascular disease. The phytochemical flavonol (a type of flavonoid) kaempferol (KMP) (3,5,7-trihydroxy-2-(4-hydroxyphenyl)- 4Hchromen-4-one) is abundant in cruciferous vegetables, including broccoli, kale, spinach, and watercress, as well as in herbs like dill, chives, and tarragon. KMP is predominantly hydrophobic in nature due to its diphenylpropane structure (a characteristic feature of flavonoids). Recent findings have indicated the promise of applying KMP in disease prevention due to its potential antioxidant, antimutagenic, antifungal, and antiviral activities. In the literature, there is evidence that KMP exerts its anticancer effects by modulating critical elements in cellular signal transduction pathways linked to apoptosis, inflammation, angiogenesis, and metastasis in cancer cells without affecting the viability of normal cells. It has been shown that KMP triggers cancer cell death by several mechanisms, including cell cycle arrest, caspase activation, metabolic alteration, and impacting human telomerase reverse-transcriptase gene expression. This review is aimed at providing critical insights into the influence of KMP on the intracellular cascades that regulate metabolism and signaling in breast, ovarian, and cervical cancer cells.

Background: The artificial intelligence (AI)-assisted design of drug candidates with novel structures and desired properties has received significant attention in the recent past, so related areas of forward prediction that aim to discover chemical matters worth synthesizing and further experimental investigation. Objectives: The purpose behind developing AI-driven models is to explore the broader chemical space and suggest new drug candidate scaffolds with promising therapeutic value. Moreover, it is anticipated that such AI-based models may not only significantly reduce the cost and time but also decrease the attrition rate of drug candidates that fail to reach the desirable endpoints at the final stages of drug development. In an attempt to develop AI-based models for de novo drug design, numerous methods have been proposed by various study groups by applying machine learning and deep learning algorithms to chemical datasets. However, there are many challenges in obtaining accurate predictions, and real breakthroughs in de novo drug design are still scarce. Methods: In this review, we explore the recent trends in developing AI-based models for de novo drug design to assess the current status, challenges, and opportunities in the field. Conclusion: The consistently improved AI algorithms and the abundance of curated training chemical data indicate that AI-based de novo drug design should perform better than the current models. Improvements in the performance are warranted to obtain better outcomes in the form of potential drug candidates, which can perform well in in vivo conditions, especially in the case of more complex diseases.

As a major component of the stromal microenvironment of various solid tumors, the extracellular matrix (ECM) has attracted increasing attention in cancer-related studies. ECM in the tumor stroma not only provides an external barrier and framework for tumor cell adhesion and movement, but also acts as an active regulator that modulates the tumor microenvironment, including stromal immunity. Fibronectin (Fn), as a core component of the ECM, plays a key role in the assembly and remodeling of the ECM. Hence, understanding the role of Fn in the modulation of tumor stromal immunity is of great importance for cancer immunotherapy. Hence, in-depth studies on the underlying mechanisms of Fn in tumors are urgently needed to clarify the current understanding and issues and to identify new and specific targets for effective diagnosis and treatment purposes. In this review, we summarize the structure and role of Fn, its potent derivatives in tumor stromal immunity, and their biological effects and mechanisms in tumor development. In addition, we discuss the novel applications of Fn in tumor treatment. Therefore, this review can provide prospective insight into Fn immunotherapeutic applications in tumor treatment.

Pathogenic microorganisms, including bacteria, are becoming resistant to most existing drugs, which increases the failure of pharmacologic treatment. Therefore, new nanomaterials were studied to spearhead improvement against the same resistant pathogenic bacteria. This has increased the mortality in the world population, principally in under-developed countries. Moreover, recently there has been research to find new drug formulations to kill the most dangerous microorganisms, such as bacteria cells which should avoid the spread of disease. Therefore, lately, investigations have been focusing on nanomaterials because they can exhibit the capacity to show an antibacterial effect. These studies have been trying oriented in their ability to produce an improvement to get antibacterial damage against the same pathogenic bacteria resistance. However, there are many problems with the use of nanoparticles. One of them is understanding how they act against bacteria, "their mechanism(s) action" to induce reduction or even kill the bacterial strains. Therefore, it is essential to understand the specific mechanism(s) of each nanomaterial used to observe the interaction between bacteria cells and nanoparticles. In addition, since nanoparticles can be functionalized with different antibacterial drugs, it is necessary to consider and distinguish the antibacterial activity of the nanoparticles from the antibacterial activity of the drugs to avoid confusion about how the nanoparticles work. Knowledge of these differences can help better understand the applications of the primary nanoparticles (i.e., Ag, Au, CuO, ZnO, and TiO², among others) described in detail in this review which are toxic against various bacterial strains.

Background: Nanoparticles (NPs) have gained great importance during the last decades for developing new therapeutics with improved outcomes for biomedical applications due to their nanoscale size, surface properties, loading capacity, controlled drug release, and distribution. Among the carbon-based nanomaterials, one of the most biocompatible forms of graphene is graphene quantum dots (GQDs). GQDs are obtained by converting 2D graphene into zero-dimensional graphene nanosheets. Moreover, very few reports in the literature reported the pharmacokinetic studies proving the safety and effectiveness of GQDs for in vivo applications. Objectives: This study evaluated the pharmacokinetics of GQDs radiolabeled with ^99mTc, administered intravenously, in rodents (Wistar rats) in two conditions: short and long periods, to compare and understand the biological behavior. Methods: The graphene quantum dots were produced and characterized by RX diffractometry, Raman spectroscopy, and atomic force microscopy. The pharmacokinetic analysis was performed following the radiopharmacokinetics concepts, using radiolabeled graphene quantum dots with technetium 99 metastable (99mTc). The radiolabeling process of the graphene quantum dots with 99mTc was performed by the direct via. Results: The results indicate that the pharmacokinetic analyses with GQDs over a longer period were more accurate. Following a bicompartmental model, the long-time analysis considers each pharmacokinetic phase of drugs into the body. Furthermore, the data demonstrated that short-time analysis could lead to distortions in pharmacokinetic parameters, leading to misinterpretations. Conclusion: The evaluation of the pharmacokinetics of GQDs over long periods is more meaningful than the evaluation over short periods.