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Breast cancer remains the most commonly diagnosed cancer among women worldwide, with approximately 2.3 million new cases reported in 2022. In the United States alone, an estimated 310,720 new cases of female breast cancer are expected in 2024. HER2-positive breast cancer, characterized by the overexpression of the human epidermal growth factor receptor 2 (HER2), accounts for about 20 % of all breast cancer cases. The development of anti-HER2 therapies has significantly improved survival rates for patients with HER2-positive breast cancer. In this study, we employed in silico methods to evaluate the potential of natural alkaloids, Mitragynine and 7-Hydroxymitragynine, as HER2 inhibitors. Molecular docking revealed binding energies of -7.56 kcal/mol and -8.77 kcal/mol, respectively, with key interactions involving residues such as Leu726, Val734, Ala751, Lys753, Thr798, and Asp863. Molecular dynamics simulations demonstrated the stability of all three complexes, including Mitragynine, 7-Hydroxymitragynine, and Native (SYR127063), over the simulation period. Mitragynine exhibited stronger interaction stability, supported by a higher hydrogen bond occupancy of 39.19 %, compared to 4.32 % for 7-Hydroxymitragynine, while Native (SYR127063) displayed the highest occupancy at 49.66 %. MM-PBSA analysis further validated these findings, with Native (SYR127063) exhibiting the most favorable total binding energy of -163.448 ± 17.288 kJ/mol, followed by Mitragynine at -112.33 ± 22.41 kJ/mol, and 7-Hydroxymitragynine at -103.56 ± 15.61 kJ/mol. ADMET, physicochemical properties, and drug-likeness evaluations indicated that all compounds satisfy Lipinski, Ghose, Veber, Egan, and Muegge rules, confirming their suitability as lead-like molecules. Based on these findings, Mitragynine and 7-Hydroxymitragynine are promising candidates for HER2-targeted breast cancer therapy, with further experimental validation recommended to confirm their clinical potential.
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http://dx.doi.org/10.1016/j.crstbi.2025.100171 | DOI Listing |
EBioMedicine
September 2025
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China. Electronic address:
JMIR Cancer
September 2025
Cancer Patients Europe, Rue de l'Industrie 24, Brussels, 1000, Belgium.
Background: Breast cancer is the most common cancer among women and a leading cause of mortality in Europe. Early detection through screening reduces mortality, yet participation in mammography-based programs remains suboptimal due to discomfort, radiation exposure, and accessibility issues. Thermography, particularly when driven by artificial intelligence (AI), is being explored as a noninvasive, radiation-free alternative.
View Article and Find Full Text PDFEpidemiol Serv Saude
September 2025
Universidade Estadual do Norte do Paraná, Programa de Pós-Graduação em Enfermagem em Atenção Primária à Saúde Bandeirantes, PR, Brazil.
Objectives: To analyze the temporal trend and identify spatial clusters of breast cancer mortality in Paraná state between 2012 and 2021.
Methods: This was a time series study, with spatial analysis of breast cancer mortality rates in the 399 municipalities of Paraná. Data were selected from the Mortality Information System.
Cien Saude Colet
August 2025
Faculdade de Medicina da Universidade Federal de Pelotas. Pelotas RS Brasil.
The objective of this study was to analyze the characteristics of avoidable mortality in the population aged five to 69 years living in the city of Pelotas/RS, comparing it with the rest of the state of Rio Grande do Sul, from 2000 to 2021. An ecological study was conducted analyzing avoidable mortality coefficients according to sex and age, from 2000 to 2021. The data source was the Mortality Information System, and the trend analysis was performed using Prais-Winsten regression, with standardization of coefficients.
View Article and Find Full Text PDFCien Saude Colet
August 2025
Programa de Pós-Graduação em Nutrição e Saúde, Universidade Estadual do Ceará. R. Betel 1958, Itaperi. 60714-230 Fortaleza CE Brasil.
This study aimed to evaluate mortality due to female breast cancer attributable to overweight and obesity and to estimate the number of preventable deaths with a reduction in the Body Mass Index in Brazil. An ecological study was carried out with investigation of information on overweight, obesity, sociodemographic characteristics based on a national survey carried out in 2013-14; breast cancer mortality rate in 2019 using the Online Atlas of Mortality and Relative Risk Meta-Analyses. The Potential Impact Fraction analysis was carried out, considering the following counterfactual scenarios related to the reduction in BMI: Scenario A - population contingent of women that make up the prevalence of overweight and obesity now composes the prevalence of eutrophy; Scenario B - population contingent of women that make up the prevalence of overweight starts to make up the prevalence of eutrophy; Scenario C - population contingent of women that make up the prevalence of obesity becomes part of the prevalence of overweight.
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