Discovering Anti-Cancer Drugs via Computational Methods

Advancing a new drug to market requires substantial investments in time as well as financial resources. The traditional drug development process takes about 12 years on average and costs 2.7 billion dollars. Over the past three to four decades, the use of computational methods in the process of drug discovery has steadily increased and computations have become an integral part of discovery research. The employment of computer-aided drug discovery (CADD) techniques by top pharmaceutical companies and other research groups became essential for the preclinical stage of drug discovery to expedite the drug development process in a more cost-efficient way and to minimize failures in the final stage.

Recently, Dr. Cui’s group at the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences provided an overview of different subjects of the computational-method-aided new drug discovery process with a focus on anticancer drugs, which was published on the Frontiers in Pharmacology.

Since the first drug, Viracept, which was fully driven by its’ target structure, was successfully developed, “computational methods have served as an essential tool in drug discovery projects and have been a cornerstone for new drug development approaches.” The advance in computational power, including massively parallel computing on graphical processing units (GPUs) and the continuous advances in artificial intelligence (AI) tools has also promoted the translation of fundamental research into practical applications in the drug discovery field.

“There are several drugs, including anticancer drugs, whose corresponding target proteins (both primary and non-target) remain yet unidentified or unknown. Furthermore, some attractive and potentially effective cancer targets remain outside of the scope of pharmacological regulation. ” To solve this problem, a wide range of drug target interactive web servers that provides a series of drug-target databases (DrugBank, TTD, MATADOR, SuperTarget, etc) and prediction tools (SEA, Pharmamapper, Chemmapper, Tide, etc) has been established and various computational approaches can be used to study potential interactions between proteins and drugs.

Capoten (captopril), an ACE (angiotensin-converting enzyme) inhibitor, was one of the first successful examples of using structural information to optimize drug designs in the 1980s. Since then, structure-based drug development started to serve as a novel and powerful algorithm and technique to promote faster, cheaper, and more effective drug development. Structure-based drug discovery approaches include molecular docking and structure-based pharmacophore mapping. Ligand-based drug discovery approaches include similarity searching, ligand-based pharmacophore mapping, and QSAR modeling.

Computational drug design has successfully promoted the discovery of several new anticancer drugs, including Gefitinib, Erlotinib, Sorafenib, Lapatinib, Abiraterone, and Crizotinib, which has become a milestone in this area. “Since computational methods could cover almost all stages of the drug discovery pipeline, the applications of computational methods in anticancer drug discoveries have shown great advantages in terms of the required investment, resources, and time.” Combined with experimental validations, the useful predictions provided by computational models could further speed up the anti-cancer drug development.