RESUMO
Colorectal cancer (CRC) is a malignant tumor initiating from the mucosa of the colorectum. According to the 2020 statistics from the World Health Organization, there are 10.0% CRC cases among all 19.3 million new cancers, followed by lung and breast cancer, and 9.4% CRC cases among all 9.9 million cancer deaths, ranking second. The population of CRC patients in China is large, and its incidence and mortality continue to increase each year. Despite the continuous development of surgical methods, chemotherapy, radiotherapy, targeted therapy and immunotherapy, the overall survival of CRC patients remains low. Past research has suggested that c-myc plays a pivotal role in the development of CRC. A higher expression level of c-Myc is a negative prognostic marker in CRC. However, there are few drugs targeting c-myc directly. Therefore, we focused on discovering the mechanism of c-myc in CRC to provide a reference for a better therapy choice for patients.
Assuntos
Neoplasias Colorretais , Proteínas Proto-Oncogênicas c-myc , China , Neoplasias Colorretais/genética , Regulação Neoplásica da Expressão Gênica , Humanos , Proteínas Proto-Oncogênicas c-myc/genética , Proteínas Proto-Oncogênicas c-myc/metabolismoRESUMO
Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, "big data" approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.