Targeted Drug Delivery in Cancer Tissues by Utilizing Big Data Analytics: Promising Approach of AI

Abstract

Cancer is considered as one of the fastest evolving, ever-changing complex disease. According to the WHO, cancer is the leading cause of death worldwide, accounting for nearly 10 million deaths in 2020. The global burden of cancer is continuously growing and creating tremendous emotional, physical, and financial strain on individuals, families, communities, and health systems. The fight against cancer has made huge progress over the last 30 years with a great improvement in the survival rate, but the general cure is still elusive. Targeting cancer depends on the proper understanding of cancer biology by applying different “omics” approaches like genomics, proteomics, and transcriptomics, which are considered as the predictive analytical tool for different cancers. Approaches for targeted delivery of therapeutics in cancer typically involve the systemic and localized administration of therapeutics or drug entrapped nanocarriers. Several steps are involved in designing a targeted 336drug delivery system, such as identifying suitable small molecular therapeutics and selecting different biomarkers, a ligand for targeting, optimization, and evaluation of the formulation. This is the era where most of the research activities are based on AI (artificial intelligence) or information technology, as they are less time-consuming and cost-effective. Despite such advancements, the challenges that oncologists face are managing huge data coming from different high throughput sources like computer-aided drug discovery (CADD), molecular biology, ADMET profiling, imaging, and pathological studies in vitro experiments, and statistical analysis. To mitigate this problem, “big data analytics” comes into the picture. “Big data analytics” plays an important role in integrating and interpreting the massive amount of data scattered around the world of cancer research. In a data-rich field like oncology, interpretation, storage, standardization, and sharing of data are also important, for example, different databases available in the public domain like Drug Bank which is a database composed of detailed information of different approved, investigational, and withdrawn drugs, The Cancer Genome Atlas (TCGA) which is a database that includes detailed information regarding the cancer patients, PubChem which is a chemical compound database, and Protein Data Bank which is crystal structure database for different proteins as well as ligands. In short “big data analytics” fuels cancer research by offering quality and precise data, thus intern accelerating the decision making, risk stratification, and prevention program. This book chapter deals with the emerging advances in “big data analytics” concerning targeted drug delivery toward cancer and its utility in the screening of drug molecules, selection of target, and ADMET profiling; further, the current challenges, as well as future applications of “big data analytics” in oncology, are also enlightened.