Target-Based Small Molecule Drug Discovery for Colorectal Cancer: A Review of Molecular Pathways and In Silico Studies

Abstract

Colorectal cancer is one of the most prevalent cancer types. Although there have been breakthroughs in its treatments, a better understanding of the molecular mechanisms and genetic involvement in colorectal cancer will have a substantial role in producing novel and targeted treatments with better safety profiles. In this review, the main molecular pathways and driver genes that are responsible for initiating and propagating the cascade of signaling molecules reaching carcinoma and the aggressive metastatic stages of colorectal cancer were presented. Protein kinases involved in colorectal cancer, as much as other cancers, have seen much focus and committed efforts due to their crucial role in subsidizing, inhibiting, or changing the disease course. Moreover, notable improvements in colorectal cancer treatments with in silico studies and the enhanced selectivity on specific macromolecular targets were discussed. Besides, the selective multi-target agents have been made easier by employing in silico methods in molecular de novo synthesis or target identification and drug repurposing.

The digital transformation in pharmacy: embracing online platforms and the cosmeceutical paradigm shift

“…A wide array of ML techniques, ranging from supervised to unsupervised learning, are employed to discern chemical attributes that are indicative of potential therapeutic efficacy against a spectrum of cancer targets. This methodology is crucial in identifying novel compounds that could be effective in cancer treatment, leveraging the rich and complex data available in oncological research [ 164 ].…”

Support Vector Machine-Based Prediction Models for Drug Repurposing and Designing Novel Drugs for Colorectal Cancer

“…This emphasizes the clinical importance of augmenting the currently available drug arsenal to enhance the therapeutic methodologies for CRC. Implementing machine learning-based in silico methods, e.g., Quantitative Structure Activity Relationship (QSAR) models, is an attractive approach to bypass the time- and cost-exhaustive traditional drug discovery process . The in silico methods can be used to screen large chemical libraries to predict novel drugs for CRC and boost drug discovery and development .…”

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review

“…With the aid of in-silico tools, the number of chemical candidates to be tested in-vitro or in-vivo are greatly reduced, the success rate of clinical trials is also increased, leading to the optimization of resources and enhanced cost-effectiveness throughout the trajectory of drug discovery and development [ 9 ]. The advantages of CADD are also evident in discovering novel drug to tackle allosteric cancer targets or management of tumours that formed through complicated pathways [ 10 ].…”