Medicinal Research Reviews最新文献

In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.

Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.

Leishmaniasis is a group of neglected tropical diseases caused by at least 20 species of Leishmania protozoa, which are spread by the bite of infected sandflies. There are three main forms of the disease: cutaneous leishmaniasis (CL, the most common), visceral leishmaniasis (VL, also known as kala-azar, the most serious), and mucocutaneous leishmaniasis. One billion people live in areas endemic to leishmaniasis, with an annual estimation of 30,000 new cases of VL and more than 1 million of CL. New treatments for leishmaniasis are an urgent need, as the existing ones are inefficient, toxic, and/or expensive. We have revised the experimental structure-based drug design (SBDD) efforts applied to the discovery of new drugs against leishmaniasis. We have grouped the explored targets according to the metabolic pathways they belong to, and the key achieved advances are highlighted and evaluated. In most cases, SBDD studies follow high-throughput screening campaigns and are secondary to pharmacokinetic optimization, due to the majoritarian belief that there are few validated targets for SBDD in leishmaniasis. However, some SBDD strategies have significantly contributed to new drug candidates against leishmaniasis and a bigger number holds promise for future development.

The burgeoning prodrug strategy offers a promising avenue toward improving the efficacy and specificity of cytotoxic drugs. Elevated intracellular levels of glutathione (GSH) have been regarded as a hallmark of tumor cells and characteristic feature of the tumor microenvironment. Considering the pivotal involvement of elevated GSH in the tumorigenic process, a diverse repertoire of GSH-triggered prodrugs has been developed for cancer therapy, facilitating the attenuation of deleterious side effects associated with conventional chemotherapeutic agents and/or the attainment of more efficacious therapeutic outcomes. These prodrug formulations encompass a spectrum of architectures, spanning from small molecules to polymer-based and organic–inorganic nanomaterial constructs. Although the GSH-triggered prodrugs have been gaining increasing interests, a comprehensive review of the advancements made in the field is still lacking. To fill the existing lacuna, this review undertakes a retrospective analysis of noteworthy research endeavors, based on a categorization of these molecules by their diverse recognition units (i.e., disulfides, diselenides, Michael acceptors, and sulfonamides/sulfonates). This review also focuses on explaining the distinct benefits of employing various chemical architecture strategies in the design of these prodrug agents. Furthermore, we highlight the potential for synergistic functionality by incorporating multiple-targeting conjugates, theranostic entities, and combinational treatment modalities, all of which rely on the GSH-triggering. Overall, an extensive overview of the emerging field is presented in this review, highlighting the obstacles and opportunities that lie ahead. Our overarching goal is to furnish methodological guidance for the development of more efficacious GSH-triggered prodrugs in the future. By assessing the pros and cons of current GSH-triggered prodrugs, we expect that this review will be a handful reference for prodrug design, and would provide a guidance for improving the properties of prodrugs and discovering novel trigger scaffolds for constructing GSH-triggered prodrugs.

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as “big-data” in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

Ischemic stroke (IS) poses a significant threat to global human health and life. In recent decades, we have witnessed unprecedented progresses against IS, including thrombolysis, thrombectomy, and a few medicines that can assist in reopening the blocked brain vessels or serve as standalone treatments for patients who are not eligible for thrombolysis/thrombectomy therapies. However, the narrow time windows of thrombolysis/thrombectomy, coupled with the risk of hemorrhagic transformation, as well as the lack of highly effective and safe medications, continue to present big challenges in the acute treatment and long-term recovery of IS. In the past 3 years, several excellent articles have reviewed pathophysiology of IS and therapeutic medicines for the treatment of IS based on the pathophysiology. Regretfully, there is no comprehensive overview to summarize all categories of anti-IS drugs/agents designed and synthesized based on molecular mechanisms of IS pathophysiology. From medicinal chemistry view of point, this article reviews a multitude of anti-IS drugs/agents, including small molecule compounds, natural products, peptides, and others, which have been developed based on the molecular mechanism of IS pathophysiology, such as excitotoxicity, oxidative/nitrosative stresses, cell death pathways, and neuroinflammation, and so forth. In addition, several emerging medicines and strategies, including nanomedicines, stem cell therapy and noncoding RNAs, which recently appeared for the treatment of IS, are shortly introduced. Finally, the perspectives on the associated challenges and future directions of anti-IS drugs/agents are briefly provided to move the field forward.

Mesenchymal stem cells (MSCs) are one of the few stem cell types used in clinical practice as therapeutic agents for immunomodulation and ischemic tissue repair, due to their unique paracrine capacity, multiple differentiation potential, active components in exosomes, and effective mitochondria donation. At present, MSCs derived from tissues such as bone marrow and umbilical cord are widely applied in preclinical and clinical studies. Nevertheless, there remain challenges to the maintenance of consistently good quality MSCs derived from different donors or tissues, directly impacting their application as advanced therapy products. In this review, we discuss the promises, problems, and prospects associated with translation of MSC research into a pharmaceutical product. We review the hurdles encountered in translation of MSCs and MSC–exosomes from the research bench to an advanced therapy product compliant with good manufacturing practice (GMP). These difficulties include how to set up GMP-compliant protocols, what factors affect raw material selection, cell expansion to product formulation, establishment of quality control (QC) parameters, and quality assurance to comply with GMP standards. To avoid human error and reduce the risk of contamination, an automatic, closed system that allows real-time monitoring of QC should be considered. We also highlight potential advantages of pluripotent stem cells as an alternative source for MSC and exosomes generation and manufacture.

Fe-based nanostructures have possessed promising properties that make it suitable for chiral sensing and imaging applications owing to their ultra-small size, non-toxicity, biocompatibility, excellent photostability, tunable fluorescence, and water solubility. This review summarizes the recent research progress in the field of Fe-based nanostructures and places special emphases on their applications in chiral sensing and imaging. The synthetic strategies to prepare the targeted Fe-based structures were also introduced. The chiral sensing and imaging applications of the nanostructures are discussed in details.

Autoimmune diseases are characterized by the immune system's attack on one's own tissues which are highly diverse and diseases differ in severity, causing damage in virtually all human systems including connective tissue (e.g., rheumatoid arthritis), neurological system (e.g., multiple sclerosis) and digestive system (e.g., inflammatory bowel disease). Historically, treatments normally include pain-killing medication, anti-inflammatory drugs, corticosteroids, and immunosuppressant drugs. However, given the above characteristics, treatment of autoimmune diseases has always been a challenge. Artemisinin is a natural sesquiterpene lactone initially extracted and separated from Chinese medicine Artemisia annua L., which has a long history of curing malaria. Artemisinin's derivatives such as artesunate, dihydroartemisinin, artemether, artemisitene, and so forth, are a family of artemisinins with antimalarial activity. Over the past decades, accumulating evidence have indicated the promising therapeutic potential of artemisinins in autoimmune diseases. Herein, we systematically summarized the research regarding the immunoregulatory properties of artemisinins including artemisinin and its derivatives, discussing their potential therapeutic viability toward major autoimmune diseases and the underlying mechanisms. This review will provide new directions for basic research and clinical translational medicine of artemisinins.

Lysine-specific demethylase 1 (LSD1) is a flavin adenine dinucleotide (FAD) dependent monoamine oxidase (MAO) that erases the mono-, and dimethylation of histone 3 lysine 4 (H3K4), resulting in the suppression of target gene transcriptions. Besides, it can also demethylate some nonhistone substrates to regulate their biological functions. As reported, LSD1 is widely upregulated and plays a key role in several kinds of cancers, pharmacological or genetic ablation of LSD1 in cancer cells suppresses cell aggressiveness by several distinct mechanisms. Therefore, numerous LSD1 inhibitors, including covalent and noncovalent, have been developed and several of them have entered clinical trials. Herein, we systemically reviewed and discussed the biological function of LSD1 in tumors, lymphocytes as well as LSD1-targeting inhibitors in clinical trials, hoping to benefit the field of LSD1 and its inhibitors.