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Drug resistance is a hot topic issue in cancer research and therapy. Although cancer therapy including radiotherapy and anti-cancer drugs can kill malignant cells within the tumor, cancer cells can develop a wide range of mechanisms to resist the toxic effects of anti-cancer agents. Cancer cells may provide some mechanisms to resist oxidative stress and escape from apoptosis and attack by the immune system. Furthermore, cancer cells may resist senescence, pyroptosis, ferroptosis, necroptosis, and autophagic cell death by modulating several critical genes. The development of these mechanisms leads to resistance to anti-cancer drugs and also radiotherapy. Resistance to therapy can increase mortality and reduce survival following cancer therapy. Thus, overcoming mechanisms of resistance to cell death in malignant cells can facilitate tumor elimination and increase the efficiency of anti-cancer therapy. Natural-derived molecules are intriguing agents that may be suggested to be used as an adjuvant in combination with other anticancer drugs or radiotherapy to sensitize cancer cells to therapy with at least side effects. This paper aims to review the potential of triptolide for inducing various types of cell death in cancer cells. We review the induction or resistance to different cell death mechanisms such as apoptosis, autophagic cell death, senescence, pyroptosis, ferroptosis, and necrosis following the administration of triptolide. We also review the safety and future perspectives for triptolide and its derivatives in experimental and human studies. The anticancer potential of triptolide and its derivatives may make them effective adjuvants for enhancing tumor suppression in combination with anticancer therapy., (© 2023 International Union of Biochemistry and Molecular Biology.)

We studied the impact of dopants Al, Ga and In atoms on the dacarbazine (DAC) drug delivery performance of a BC3 nanosheet (BCNS) using the density functionals τ-HCTHhyb, M06-2X and B3LYP. It was found that the pristine BCNS is not a good choice for this drug delivery. Doping of the Al, Ga and In atoms into the BCNS surface raised the energy of adsorption of DAC from −6.7 to −26.4, −27.8 and −32.1 kcal / mol, respectively. According to the analysis of the partial density of states, the dopant atoms considerably contributed to the generation of virtual orbitals of doped BCNS. This showed that the dopants are more suitable for nucleophilic attack than the B atoms. Finally, the adsorption performance and capacity of the DAC are increased by dopants, making the nanosheet more favourable for drug delivery. A drug release mechanism was introduced in cancerous tissues, showing substantial protonation of the DAC in cancerous cells with low pH, leading to the separation of DAC from the sheet surface. The reaction mechanism of DAC with the BCNS changed from covalent bonding in the natural environment to H-bonding in the acidic environment of the cancer tissues. [ABSTRACT FROM AUTHOR]

[Display omitted] • 5-FU adsorption on MgO nanotube is evaluated using DFT study and molecular docking simulation. • Adsorption process of 5-FU on MgO nanotube surface is chemisorption. • MgO nanotube is electronically sensitive to 5-FU drug. • Molecular docking results illustrated that 5-FU/MgO complexes have a good docking score with HER2. We evaluated the adsorption process and anti-cancer activity of functionalized magnesium oxide nanotube (MgONT) adsorbed with 5-Fluorouracil (5-FU) in vacuum and solution (water and toluene) phases via density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations. 5-FU is chemisorbed preferentially by oxygen and hydrogen atoms to Mg and O atoms at the end tube (state III) with adsorption energies of approximately −2.01 eV (vacuum), −1.72 eV (water), and −1.84 eV (toluene) by the Perdew-Burke-Ernzerhof (PBE) functional and 6-311+G** standard basis set. TDDFT methods illustrated that the maximum optical absorption by the CAM-B3LYP method is in the UV region with an energy of 4.57 eV and a maximum absorption at 271.36 nm. Analysis of molecular docking illustrated that the interaction of 5-FU, as antineoplastic agent, with Mg and O atoms of the nanotube via its O- and H-atoms can enhanced inhibitory effects against Human Epidermal growth factor Receptor 2 (HER2) and Epidermal growth factor receptor (EGFR). The results illustrated that 5-FU/MgO complex could be useful for the development of novel anti-cancer agents to treat breast cancer patients. [ABSTRACT FROM AUTHOR]

• Solubility enhancement through advanced artificial intelligence model. • Applying different modeless of machine learning such as Random Forest, Extremely Random Trees, and Gradient Boosting Trees. • The GBRT has shown the less error rate and can be used as the best model. Over the last ten years, the application of novel mathematical models of Machine Learning employed to model the solubility of drugs especially anticancer drugs, in supercritical carbon dioxide (ScCO 2) system has gained remarkable popularity. In this research, three distinct ensemble models have been employed on the data as a novel method for busulfan as anticancer drug for the first time, based on decision trees, including Random Forest (RF), Gradient Boosting Trees (GBRT), and Extremely Randomized Tree (ERT) to predict the solubility of busulfan as an anticancer drug. The dataset has two input parameters, T = Temperature and P = Pressure, and Y = Solubility is the single output. After implementing and tuning these ensemble models' hyper parameters, the performance has been assessed through several metrics. All three models show R-squared score of more than 0.9, but in terms of RMSE, the error rates are 1.80E-04, 1.72E-04, and 1.03E-04 for RF, ERT, and GBRT models, respectively. Also, MAPE metrics 4.51E-01, 4.87E-01, and 3.62E-01 errors had found for RF, ERT, and GBRT models, respectively. GBRT has been selected as the best model due to the less rate of RMSE and MAPE. An analysis has also been performed to find the optimal amount of solubility, which can be considered the (x1 = 38.3, x2 = 333.1, Y = 1.36E-03) vector. [ABSTRACT FROM AUTHOR]