The phosphoinositide 3-kinase (PI3K) signaling pathway is well-known for its important role in cancer growth, proliferation and migration. The activation of PI3K pathway is always connected with endocrine resistance and poor prognosis in cancers. Alpelisib, a selective inhibitor of PI3K, has been demonstrated to be effective in combination with endocrine therapy in HR+ PIK3CA-mutated advanced breast cancer in preclinical and clinical trials. Recently, the synergistic effects of alpelisib combined with targeted agents have been widely reported in PIK3CA-mutated cancer cells, such as breast, head and neck squamous cell carcinoma (HNSCC), cervical, liver, pancreatic and lung cancer. However, previous reviews mainly focused on the pharmacological activities of alpelisib in breast cancer. The synergistic therapeutic potential of alpelisib in other cancers has not yet been well reviewed. In this review, an extensive study of related literatures (published until December 20, 2022) regarding the anti-cancer functions and synergistic effects of alpelisib was carried out through the databases. Useful information was extracted. We summarized the preclinical and clinical studies of alpelisib in combination with targeted anti-cancer agents in cancer treatment (excluding breast cancer). The combinations of alpelisib and other targeted agents significantly improved the therapeutic efficacy both in preclinical and clinical studies. Unfortunately, synergistic therapies still could not effectively avoid the possible toxicities and adverse events during treatment. Finally, some prospects for the combination studies in cancer treatment were provided in the paper. Taken together, this review provided valuable information for alpelisib in preclinical and clinical applications.

Fast nondominated sorting genetic algorithm II (NSGA-II) is a classical method for multiobjective optimization problems and has exhibited outstanding performance in many practical engineering problems. However, the tournament selection strategy used for the reproduction in NSGA-II may generate a large amount of repetitive individuals, resulting in the decrease of population diversity. To alleviate this issue, Lévy distribution, which is famous for excellent search ability in the cuckoo search algorithm, is incorporated into NSGA-II. To verify the proposed algorithm, this paper employs three different test sets, including ZDT, DTLZ, and MaF test suits. Experimental results demonstrate that the proposed algorithm is more promising compared with the state-of-the-art algorithms. Parameter sensitivity analysis further confirms the robustness of the proposed algorithm. In addition, a two-objective network topology optimization model is then used to further verify the proposed algorithm. The practical comparison results demonstrate that the proposed algorithm is more effective in dealing with practical engineering optimization problems.