Current Topics in Medicinal Chemistry - Current Issue

The persistent drug resistance observed in lung cancer necessitates innovative strategies to improve therapeutic outcomes. This review investigates the potential of combining metformin (Met) and cisplatin (Cis) to overcome drug resistance and enhance treatment efficacy. Cis's limitations, including drug resistance and adverse effects, coupled with Met’s established safety profile, form the backdrop for this exploration.

Systematic literature searches across major databases identified relevant studies exploring the synergistic effects of Met and Cis in the context of drug-resistant lung cancer. Data extraction encompassed diverse facets, including treatment protocols, cellular responses, and mechanistic insights. The synthesis of these findings sheds light on the potential of this combination therapy to combat drug resistance.

Numerous in vitro and in vivo studies have demonstrated the ability of the Met + Cis combination to sensitize drug-resistant lung cancer cells. The co-treatment consistently showed enhanced inhibition of cell proliferation, elevated apoptosis rates, and attenuated migration and invasion capabilities compared to monotherapies. Mechanistically, Met’s modulatory effect on key pathways, such as AMPK-mTOR and ROS-mediated signaling, appears to underlie its ability to counter drug resistance.

The Met + Cis combination holds promise as an innovative strategy to counter drug resistance in lung cancer. By harnessing the synergistic effects of these agents, combination therapy offers a novel approach to enhance treatment efficacy and mitigate the challenges posed by drug-resistant lung cancer. Although further clinical validation is required, the Met + Cis synergy represents a promising avenue in the pursuit of improved lung cancer therapy outcomes.

Diabetes Mellitus (DM) is known to have an impact on the health of the male reproductive system. It is linked to low sperm quality, increased oxidative stress, and an increased generation of reactive oxygen species in the seminal fluid. Pomegranate extract has phenolic compounds and significant protective properties against oxidative stress, male sex hormone disruptions, and sperm abnormalities.

The current study aimed to evaluate the effectiveness of Pomegranate Peel Extract Nanoparticles (PPENPs) on male fertility in diabetic rats.

DM was induced in rats by intraperitoneal injection of streptozotocin (60 mg/kg). Twenty-four rats were divided into four groups, 6 rats in each group: control, DM, DM+empty NPs (60 mg/kg, orally), and DM+PPENPs (60 mg/kg, orally).

Administration of PPENPs increased the levels of insulin, FSH, LH, testosterone, catalase, glutathione reduced, and semen fructose. PPENPs also improved sperm quality, as seen by improvements in sperm morphology, motility, count, and the ability of metabolically active spermatozoa to convert blue resazurin dye to pink resorufin. However, PPENPs decreased levels of glucose, malonaldehyde, nitric oxide, and sperm abnormalities. Also, histological investigation of the PPENPs showed improvement in testis tissue architecture and increased the diameter size of seminiferous tubules and germinative layer thickness.

Our investigation proved that the treatment of PPENPs has a protective effect on the reproductive system of male diabetic rats, improving fertility parameters, healthy sperm profiles, and the antioxidant system.

As one of the common malignant tumors nowadays, liver cancer has more risk factors for its development and is characterized by a high recurrence rate, high mortality rate, and poor prognosis, which poses a great threat to people's health. The specific efficacy of traditional Chinese medicine is based on clinical practice, which is a high degree of generalization of the characteristics and scope of the clinical effects of prescription medicines and a special form of expression of the medical effects of the human body within the scope of traditional Chinese medicine. Because of its multi-ingredient, multi-target, and multi-pathway characteristics, it has a great advantage in the treatment of liver cancer. Still, at present, its specific molecular mechanism of action has not yet been clarified.

This study reviews the current status and characteristics of network pharmacology research in the treatment of liver cancer, aiming to provide new ideas and methods for traditional Chinese medicine treatment of the disease.

This study was searched on the Web of Science and PubMed using keywords, such as “traditional Chinese medicine”, “liver cancer,” and “network pharmacology.” The citation dates of the literature cited in this review are from 2000 to 2024.

The discovery of the key molecular mechanisms of traditional Chinese medicine in the treatment of liver cancer through the network pharmacology approach and the in-depth study of the related signaling pathways are conducive to a more in-depth exploration of traditional Chinese medicine.

Network pharmacology research plays a key role in the treatment and prevention of liver cancer and deserves deeper exploration in the future.

Oral squamous cell carcinoma (OSCC) is a prevalent malignant condition. This study aimed to investigate the role of mTORC1 signaling and develop a prognostic model for OSCC.

The single-sample gene set enrichment analysis (ssGSEA) algorithm was utilized to calculate the Z-Score of Hallmarks in OSCC, followed by univariate Cox regression analysis to identify processes associated with prognosis. Weighted gene co-expression network analysis (WGCNA) was performed using transcriptomic data from the cancer genome atlas (TCGA) cohort to identify genes correlated with mTORC1 signaling. A six-gene prognostic model was constructed using multifactorial Cox regression analysis and validated using an external dataset.

The study uncovered a strong linkage between mTORC1, glycolysis, hypoxia, and the prognosis of OSCC. mTORC1 signaling emerged as the most significant risk factor, negatively impacting patient survival. Additionally, a six-gene prognostic risk score model was developed which provided a quantitative measure of patients' survival probabilities. Interestingly, within the context of these findings, TP53 gene mutations were predominantly observed in the high-risk group, potentially underlining the genetic complexity of this patient subgroup. Additionally, differential immune cell infiltration and an integrated nomogram were also reported.

This study highlights the importance of mTORC1 signaling in OSCC prognosis and presents a robust prognostic model for predicting patient outcomes.

Hyperpigmentation disorders are caused by excess production of the pigment melanin, catalyzed by the enzyme tyrosinase. Novel tyrosinase inhibitors are needed as therapeutic agents to treat these conditions.

To discover new inhibitors, we performed a virtual screening of the ZINC20 library containing 1.4 billion compounds. An initial filter for drug-likeness, ADMET properties, and synthetic accessibility reduced the library to 10,217 hits. Quantitative structure-activity relationship (QSAR) modeling of this subset predicted nanomolar inhibitory potency for several chemical scaffolds. Comparative molecular docking studies and rigorous binding energy calculations further prioritized four cysteine-containing dipeptide compounds based on predicted strong binding affinity and mode to tyrosinase.

Microsecond-long molecular dynamics simulations provided additional atomistic insights into the stability of inhibitor-enzyme binding interactions. This integrated computational workflow effectively sampled an extremely large chemical space to discover four novel tyrosinase inhibitors with half-maximal inhibitory concentration values below 10 nM.

Overall, this demonstrates the power of virtual screening and multi-faceted computational techniques to accelerate the discovery of potent bioactive ligands from massive compound libraries by efficiently sampling chemical space.

The TLR4 (Toll-like receptor 4)/MD2 (Myeloid differentiation protein-2) is a crucial target for developing novel anti-inflammatory drugs. Nevertheless, current inhibitors often have significant adverse effects, necessitating the discovery of safer alternatives.

The investigation aims to identify novel TLR4/MD2 inhibitors with potential anti-inflammatory activity using machine learning and virtual screening technology.

A machine-learning model was created using the MACCS (Molecular ACCess Systems) key fingerprint. Subsequently, virtual screening and molecular docking were used to evaluate candidate compounds' binding free energy to the TLR4/MD2 complex. Furthermore, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction was used to assess the druggable properties of compounds. The most promising compound, T19093, was considered for molecular dynamic simulation. Finally, the anti-inflammatory efficacy of T19093 was further validated using LPS-treated THP-1 cells.

T19093, a polyphenolic compound isolated from the Gnaphalium plant genus, showed strong binding to key residues of the TLR4/MD2 complex, with a docking score of -11.29 kcal/mol. Furthermore, ADMET predicted that T19093 has good pharmacokinetic properties and balanced physicochemical properties. Moreover, molecular dynamics simulation confirmed stable binding between T19093 and TLR4/MD2 complex. Finally, it was found that T19093 alleviated LPS-induced inflammatory response by inhibiting the activation of TLR4/MD2 downstream signaling pathways and disrupting the TLR4/MD2 interaction.

T19093 was discovered as a potential novel TLR4/MD2 inhibitor using machine learning and virtual screening techniques and showed potent anti-inflammatory activity, which could provide a new therapeutic alternative for the treatment of inflammation-related diseases.