Aim: Lysosomal pH changes are associated with drug resistance, cell growth and invasion of tumors, but effective and specific real-time monitoring of lysosomal pH compounds for cancer therapy is lacking. Materials & methods: Here, based on the covalent linkage of the anticancer drug palbociclib and fluorescent dye fluorescein isothiocyanate (FITC), we designed and developed a novel palbociclib-derived multifunctional molecule (Pal-FITC) for lysosomal targeting and diagnostic therapeutic integration. Results & discussion: Pal-FITC fluoresces is 20-fold stronger than that of FITC and shows a linear response in the pH range of 4.0-8.2 (R2 = 0.9901). Pal-FITC blocks cells in G1 phase via Cyclin D-CDK4/6-Rb. Conclusion: Our study provides new strategies for tumor-targeted imaging and personalized therapy.
Based on the covalent linkage of the anticancer drug and the fluorescent dye, we designed and developed a novel palbociclib-derived multifunctional molecule (Pal-FITC) for lysosomal targeting and diagnostic therapeutic integration. Pal-FITC responded linearly in the pH range of 4.0–8.2. In addition, Pal-FITC was able to effectively treat lung cancer without toxic side effects on normal cells. It has a significant cell cycle blocking phenomenon and blocks G1 phase cells via Cyclin D-CDK4/6-Rb. Our study provides a new strategy for tumor-targeted imaging and personalized therapy.

Anticancer drugs may affect the incidence of dementia by modulating the common pathophysiology between cancer and dementia. However, there is a paucity of research that focused on anticancer drugs with different mechanisms of action and their associations with subtypes of dementia. Therefore, we aimed to investigate the incidence of dementia according to various groups of anticancer drugs. From the Korea National Health Insurance Service database, our retrospective population-based cohort study enrolled 116,506 cancer patients aged 65 years and older who received anticancer drugs between January 1, 2008 and December 31, 2018. The hazard ratio was determined using Cox proportional hazards regression models, comparing each group of anticancer drugs to all other anticancer drugs, after adjusting for covariates. Antimetabolites (HR = 0.91; 95% CI 0.84-0.97) and molecular targeted therapies (HR = 0.60; 95% CI 0.49-0.74) were associated with a decreased incidence of dementia of the Alzheimer type (DAT), but not with vascular dementia. Among molecular targeted therapies, epidermal growth factor receptor inhibitors (HR = 0.60; 95% CI 0.46-0.79) and multikinase inhibitors (HR = 0.49; 95% CI 0.27-0.89) were associated with a low incidence of DAT only. Our findings highlight the potential for targeted repurposing of anticancer drugs to prevent dementia.

While drug therapy plays a crucial role in cancer treatment, many anticancer drugs, particularly cytotoxic and molecular-targeted drugs, cause severe side effects, which often limit the dosage of these drugs. Efforts have been made to alleviate these side effects by developing derivatives, analogues, and liposome formulations of existing anticancer drugs and by combining anticancer drugs with substances that reduce side effects. However, these approaches have not been sufficiently effective in reducing side effects. Molecular hydrogen (H2) has shown promise in this regard. It directly reduces reactive oxygen species, which have very strong oxidative capacity, and indirectly exerts antioxidant, anti-inflammatory, and anti-apoptotic effects by regulating gene expression. Its clinical application in various diseases has been expanded worldwide. Although H2 has been reported to reduce the side effects of anticancer drugs in animal studies and clinical trials, the underlying molecular mechanisms remain unclear. Our comprehensive literature review revealed that H2 protects against tissue injuries induced by cisplatin, oxaliplatin, doxorubicin, bleomycin, and gefitinib. The underlying mechanisms involve reductions in oxidative stress and inflammation. H2 itself exhibits anticancer activity. Therefore, the combination of H2 and anticancer drugs has the potential to reduce the side effects of anticancer drugs and enhance their anticancer activities. This is an exciting prospect for future cancer treatments.

The size of the drug particles is one of the essential factors for the proper absorption of the drug compared to the dose of the drug. When particle size is decreased, drug uptake into the body increases. Recent studies have revealed that the rapid expansion of supercritical solution with cosolvent plays a significant role in preparing micron and submicron particles. This paper examines the preparation of Erlotinib hydrochloride nanoparticles using a supercritical solution through the cosolvent method for the first time. An examination of the parameters of temperature (318-338 K), pressures (15-25 MPa) and nozzle diameter (300-700 μm) was investigated by Box-Behnken design, and their respective effects on particle size revealed that the nozzle diameter has a more significant impact on particle size than the other parameters. The smallest particles were produced at temperature 338 K, pressure 20 MPa, and nozzle diameter 700 μm. Besides, the ERL nanoparticles were characterized using SEM, DLS, XRD, FTIR, and DSC analyses. Finally, the results showed that the average size of the ERL particles decreased from 31.6 μm to 200-1100 nm.

Cyclodextrins (CDs) are cyclic oligosaccharides able to form noncovalent water-soluble complexes useful in many different applications for the solubilization, delivery, and greater bioavailability of hydrophobic drugs. The complexation of 5-fluorouracil (5-FU) with natural or synthetic cyclodextrins permits the solubilization of this poorly soluble anticancer drug. In this theoretical work, the complexes between β-CD and 5-FU are investigated using molecular mechanics (MM) and molecular dynamics (MD) simulations in water. The inclusion complexes are formed thanks to the favorable intermolecular interactions between β-CD and 5-FU. Both 1:1 and 1:2 β-CD/5-FU stoichiometries are investigated, providing insight into their interaction geometries and stability over time in water. In the 1:2 β-CD/5-FU complexes, the intermolecular interactions affect the drug\'s mobility, suggesting a two-step release mechanism: a fast release for the more exposed and hydrated drug molecule, with greater freedom of movement near the β-CD rims, and a slow one for the less-hydrated and well-encapsulated and confined drug. MD simulations study the intermolecular interactions between drugs and specific carriers at the atomistic level, suggesting a possible release mechanism and highlighting the role of the impact of the drug concentration on the kinetics process in water. A comparison with experimental data in the literature provides further insights.

Glucose transporters GLUT1 belong to the major facilitator superfamily and are essential to human glucose uptake. The overexpression of GLUT1 in tumor cells designates it as a pivotal target for glycoconjugate anticancer drugs. However, the interaction mechanism of glycoconjugate drugs with GLUT1 remains largely unknown. Here, we employed all-atom molecular dynamics simulations, coupled to steered and umbrella sampling techniques, to examine the thermodynamics governing the transport of glucose and two glycoconjugate drugs (i.e., 6-D-glucose-conjugated methane sulfonate and 6-D-glucose chlorambucil) by GLUT1. We characterized the specific interactions between GLUT1 and substrates at different transport stages, including substrate recognition, transport, and releasing, and identified the key residues involved in these procedures. Importantly, our results described, for the first time, the free energy profiles of GLUT1-transporting glycoconjugate drugs, and demonstrated that H160 and W388 served as important gates to regulate their transport via GLUT1. These findings provide novel atomic-scale insights for understanding the transport mechanism of GLUT1, facilitating the discovery and rational design of GLUT1-targeted anticancer drugs.

We experienced a case of bilateral corneal thinning during the oral taking of S-1, a combination anti-cancer drug of tegafur, gimeracil, and oteracil-potassium. A 69-year-old man was prescribed oral S-1 for the treatment of duodenal papilla adenocarcinoma and intraductal papillary mucinous neoplasm. However, he developed a decrease in visual acuity in both eyes after three cycles of S-1 oral taking, and ophthalmic examination revealed corneal thinning exceeding 100 µm and an increase in high-order irregularity of cornea in both eyes. After one month after discontinuation of S-1, his visual acuity and corneal thickness returned to its previous levels. Besides corneal ulcers and perforations, corneal thinning can be recognized as a potential corneal side effect necessitating monitoring during S-1 treatment.

Cryptophycins are microtubule-targeting agents (MTAs) that belong to the most potent antimitotic compounds known to date; however, their exact molecular mechanism of action remains unclear. Here, we present the 2.2 Å resolution X-ray crystal structure of a potent cryptophycin derivative bound to the αβ-tubulin heterodimer. The structure addresses conformational issues present in a previous 3.3 Å resolution cryo-electron microscopy structure of cryptophycin-52 bound to the maytansine site of β-tubulin. It further provides atomic details on interactions of cryptophycins, which had not been described previously, including ones that are in line with structure-activity relationship studies. Interestingly, we discovered a second cryptophycin-binding site that involves the T5-loop of β-tubulin, a critical secondary structure element involved in the exchange of the guanosine nucleotide and in the formation of longitudinal tubulin contacts in microtubules. Cryptophycins are the first natural ligands found to bind to this new \"βT5-loop site\" that bridges the maytansine and vinca sites. Our results offer unique avenues to rationally design novel MTAs with the capacity to modulate T5-loop dynamics and to simultaneously engage multiple β-tubulin binding sites.

A series of 20 novel gefitinib derivatives incorporating the 1,2,3-triazole moiety were designed and synthesized. The synthesized compounds were evaluated for their potential anticancer activity against EGFR wild-type human non-small cell lung cancer cells (NCI-H1299, A549) and human lung adenocarcinoma cells (NCI-H1437) as non-small cell lung cancer. In comparison to gefitinib, Initial biological assessments revealed that several compounds exhibited potent anti-proliferative activity against these cancer cell lines. Notably, compounds 7a and 7j demonstrated the most pronounced effects, with an IC50 value of 3.94 ± 0.17 µmol L-1 (NCI-H1299), 3.16 ± 0.11 µmol L-1 (A549), and 1.83 ± 0.13 µmol L-1 (NCI-H1437) for 7a, and an IC50 value of 3.84 ± 0.22 µmol L-1 (NCI-H1299), 3.86 ± 0.38 µmol L-1 (A549), and 1.69 ± 0.25 µmol L-1 (NCI-H1437) for 7j. These two compounds could inhibit the colony formation and migration ability of H1299 cells, and induce apoptosis in H1299 cells. Acute toxicity experiments on mice demonstrated that compound 7a exhibited low toxicity in mice. Based on these results, it is proposed that 7a and 7j could potentially be developed as novel drugs for the treatment of lung cancer.

BACKGROUND: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients\' subjective opinions (patients\' voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients\' narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients\' daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients.
OBJECTIVE: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients\' concerns at pharmacies was also assessed.
METHODS: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients\' concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs.
RESULTS: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients\' daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. \"Pain or numbness\" (n=57, 36.3%), \"fever\" (n=46, 29.3%), and \"nausea\" (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients\' daily lives.
CONCLUSIONS: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients\' subjective information recorded in pharmaceutical care records accumulated during pharmacists\' daily work.