Abstract
Despite numerous efforts, the therapeutic advancement for neuroblastoma and other cancer treatments is still ongoing due to multiple challenges, such as the increasing prevalence of cancers and therapy resistance development in tumors. To overcome such obstacles, drug combinations are one of the promising applications. However, identifying and implementing effective drug combinations are critical for achieving favorable treatment outcomes. Given the enormous possibilities of combinations, a rational approach is required to predict the impact of drug combinations. Thus, CRISPR-Cas-based and other approaches, such as high-throughput pharmacological and genetic screening approaches, have been used to identify possible drug combinations. In particular, the CRISPR-Cas system (Clustered Regularly Interspaced Short Palindromic Repeats) is a powerful tool that enables us to efficiently identify possible drug combinations that can improve treatment outcomes by reducing the total search space. In this review, we discuss the rational approaches to identifying, examining, and predicting drug combinations and their impact.