Selection of safe artemisinin derivatives using a machine learning-based cardiotoxicity platform and in vitro and in vivo validation

Biological Response Following the Systemic Injection of PEG-PAMAM-Rhodamine Conjugates in Zebrafish

Numerous therapeutic and diagnostic approaches used within a clinical setting depend on the administration of compounds via systemic delivery. Biomaterials at the nanometer scale, as dendrimers, act as delivery systems by improving cargo bioavailability, circulation time, and the targeting of specific tissues. Although evaluating the efficacy of pharmacological agents based on nanobiomaterials is crucial, conducting toxicological assessments of biomaterials is essential for advancing clinical translation. Here, a zebrafish larvae model was explored to assess the biocompatibility of poly(amido amine) (PAMAM), one of the most exploited dendrimers for drug delivery. We report the impact of a systemic injection of polyethylene glycol (PEG)-modified G4 PAMAM conjugated with rhodamine (Rho) as a mimetic drug (PEG-PAMAM-Rho) on survival, animal development, inflammation, and neurotoxicity. A concentration- and time-dependent effect was observed on mortality, developmental morphology, and innate immune system activation (macrophages). Significant effects in toxicological indicators were reported in the highest tested concentration (50 mg/mL PEG-PAMAM-Rho) as early as 48 h post-injection. Additionally, a lower concentration of PEG-PAMAM-Rho (5 mg/mL) was found to be safe and subsequently tested for neurotoxicity through behavioral assays. In accordance, no significative signs of toxicity were detected. In conclusion, the dose response of the animal was assessed, and the safe dosage for future use in theragnostics was defined. Additionally, new methodologies were established that can be adapted to further studies in toxicology using other nanosystems for systemic delivery.

The cardiac glycoside ZINC253504760 induces parthanatos-type cell death and G2/M arrest via downregulation of MEK1/2 phosphorylation in leukemia cells

Overcoming multidrug resistance (MDR) represents a major obstacle in cancer chemotherapy. Cardiac glycosides (CGs) are efficient in the treatment of heart failure and recently emerged in a new role in the treatment of cancer. ZINC253504760, a synthetic cardenolide that is structurally similar to well-known GCs, digitoxin and digoxin, has not been investigated yet. This study aims to investigate the cytotoxicity of ZINC253504760 on MDR cell lines and its molecular mode of action for cancer treatment. Four drug-resistant cell lines (P-glycoprotein-, ABCB5-, and EGFR-overexpressing cells, and TP53-knockout cells) did not show cross-resistance to ZINC253504760 except BCRP-overexpressing cells. Transcriptomic profiling indicated that cell death and survival as well as cell cycle (G2/M damage) were the top cellular functions affected by ZINC253504760 in CCRF-CEM cells, while CDK1 was linked with the downregulation of MEK and ERK. With flow cytometry, ZINC253504760 induced G2/M phase arrest. Interestingly, ZINC253504760 induced a novel state-of-the-art mode of cell death (parthanatos) through PARP and PAR overexpression as shown by western blotting, apoptosis-inducing factor (AIF) translocation by immunofluorescence, DNA damage by comet assay, and mitochondrial membrane potential collapse by flow cytometry. These results were ROS-independent. Furthermore, ZINC253504760 is an ATP-competitive MEK inhibitor evidenced by its interaction with the MEK phosphorylation site as shown by molecular docking in silico and binding to recombinant MEK by microscale thermophoresis in vitro. To the best of our knowledge, this is the first time to describe a cardenolide that induces parthanatos in leukemia cells, which may help to improve efforts to overcome drug resistance in cancer. A cardiac glycoside compound ZINC253504760 displayed cytotoxicity against different multidrug-resistant cell lines. ZINC253504760 exhibited cytotoxicity in CCRF-CEM leukemia cells by predominantly inducing a new mode of cell death (parthanatos). ZINC253504760 downregulated MEK1/2 phosphorylation and further affected ERK activation, which induced G2/M phase arrest.

Mechanism of Artemisia annua L. in the treatment of acute myocardial infarction: network pharmacology, molecular docking and in vivo validation

This study was to evaluate the potential mechanism of action of Artemisia annua L. (A. annua) in the treatment of acute myocardial infarction (AMI) using network pharmacology, molecular docking and in vivo experiments. 22 active chemical compounds and 193 drug targets of A. annua were screened using the Traditional Chinese Medicine System Pharmacological (TCMSP) database. 3876 disease targets were also collected. Then 158 intersection targets between AMI and A. annua were obtained using R 4.2.0 software. String database was used to construct the protein-protein interaction (PPI) network and 6 core targets (MAPK1, TP53, HSP90AA1, RELA, AKT1, and MYC) were screened. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the R package. GO enrichment results were mainly related to cell responses to chemical stress and cell membrane microregions. KEGG pathways were mainly involved in lipids, atherosclerosis and fluid shear stress. In addition, molecular docking between A. annua active compounds and core targets showed high binding activity. As for in vivo validation, A. annua extract showed significant effects on improving post-infarction ventricular function, delaying ventricular remodeling, and reducing myocardial fibrosis and apoptosis. This study has revealed the potential components and molecular mechanisms of A. annua in the treatment of AMI. Our work also showed that A. annua has great effect on reducing myocardial fibrosis and scar area after infarction.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

An artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline

Although anti-cancer therapy-induced cardiotoxicity is known, until now it lacks a reliable risk predictive model of the subsequent cardiotoxicity in breast cancer patients receiving anthracycline therapy. An artificial intelligence (AI) with a machine learning approach has yet to be applied in cardio-oncology. Herein, we aimed to establish a predictive model for differentiating patients at a high risk of developing cardiotoxicity, including cancer therapy-related cardiac dysfunction (CTRCD) and symptomatic heart failure with reduced ejection fraction. This prospective single-center study enrolled patients with newly diagnosed breast cancer who were preparing for anthracycline therapy from 2014 to 2018. We randomized the patients into a 70%/30% split group for ML model training and testing. We used 15 variables, including clinical, chemotherapy, and echocardiographic parameters, to construct a random forest model to predict CTRCD and heart failure with a reduced ejection fraction (HFrEF) during the 3-year follow-up period (median, 30 months). Comparisons of the predictive accuracies among the random forest, logistic regression, support-vector clustering (SVC), LightGBM, K-nearest neighbor (KNN), and multilayer perceptron (MLP) models were also performed. Notably, predicting CTRCD using the MLP model showed the best accuracy compared with the logistic regression, random forest, SVC, LightGBM, and KNN models. The areas under the curves (AUC) of MLP achieved 0.66 with the sensitivity and specificity as 0.86 and 0.53, respectively. Notably, among the features, the use of trastuzumab, hypertension, and anthracycline dose were the major determinants for the development of CTRCD in the logistic regression. Similarly, MLP, logistic regression, and SVM also showed higher AUCs for predicting the development of HFrEF. We also validated the AI prediction model with an additional set of patients developing HFrEF, and MLP presented an AUC of 0.81. Collectively, an AI prediction model is promising for facilitating physicians to predict CTRCD and HFrEF in breast cancer patients receiving anthracycline therapy. Further studies are warranted to evaluate its impact in clinical practice.