A side-effect free method for identifying cancer drug targets

Abstract

Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.

Common cancer biomarkers of breast and ovarian types identified through artificial intelligence

“…In an attempt to consolidate PIK3CA as an important biomarker in our study, we found it to be mutated and amplified in all four cancers we utilized (Samuels & Waldman, 2010). Moreover, there are several approved inhibitors for PIK3CA as a cancer target, which is supported by its high eigenvector value (0.09) and its placement in the 20th core by Ashraf et al (2018). Nevertheless, in the same study, CREB1 is classified as a kinless non-hub node (R4) whereas PIK3CA is a connector non-hub node (R3), bearing less potential as compared to CREB1.…”

In silico Identification of the Indispensable Quorum Sensing Proteins of Multidrug Resistant Proteus mirabilis

“…Furthermore, the very concept of Eigenvector centrality, which reflects the important proteins' connectivity with other such important proteins in terms of their function, finalize the indispensable factor. This method of utilizing the k -core, functional module and centrality measure, like that of Eigenvector, has been used to analyze large networks to reveal the important proteins, albeit, in a complete different scenario (Ashraf et al, 2018 ). Utilizing this method, referred to as KFC, we found the three topmost indispensable factors for P. mirabilis are gltB, PMI3678 , and rcsC (Supplementary Data 5 ).…”

Precision medicine and future of cancer treatment

“…Thus, the protein products of these genes, if targeted indiscriminately through different drugs, could then be affected. In this context, a detailed holistic approach has been attempted for identifying novel cancer genes and proteins which upon drug targeting, would give rise to side effects (14). However, to cure/treat complex diseases like cancer, it is essential that we identify and target their root causes.…”

“…In fact, utilization of several such molecular networks with proteins, drugs, and/or genes have enabled the researchers in the last decade to aid into identify potential targets and pathways through network topological analysis and other graph theoretical measures (61). One such work, reported by Ashraf et al, is a recent description where different centrality measures have been utilized to compare and contrast among them and identify targets without potential side-effects (14). Essentially, they have shown the effectiveness of eigenvector centrality (C) along with other network parameters like k-core (K) and functional modularity (F) to bring forth the KFC criterion as crucial to determine side-effects of marketed drugs reflected in Drugbank (14,56).…”

“…One such work, reported by Ashraf et al, is a recent description where different centrality measures have been utilized to compare and contrast among them and identify targets without potential side-effects (14). Essentially, they have shown the effectiveness of eigenvector centrality (C) along with other network parameters like k-core (K) and functional modularity (F) to bring forth the KFC criterion as crucial to determine side-effects of marketed drugs reflected in Drugbank (14,56). Table 2 records several such gene-drug interactions as found from different specific tumor types.…”