Extended Multitarget Pharmacology of Anticancer Drugs

Abstract

Multi-target pharmacology of small molecule cancer drugs significantly contributes to their mechanism of action, side effects, emergence of drug resistance, and opens ways to repurpose, combine or customize drug therapy. In most cases the set of targets affected at therapeutic concentrations is not fully characterized and/or the interaction efficacy values are not accurately quantified. We collected information about multiple targets for each cancer drug along with their experimental effective concentrations or binding activities from multiple sources. All multi-target activity values for each drug then were used to build two proximity network pharmacology maps of anti-cancer drugs and targets of those drugs, respectively. Together with the network map, we showed that the majority of the cancer drugs had substantial multi-target pharmacology based on our current knowledge. In addition, most of the cancer drugs simultaneously affect macromolecular targets from different classes and types. The target subset can further be accentuated and personalized by patient sample specific expression data. The network maps of cancer drugs and targets, as well as all quantified activity data were integrated into a freely available database, CancerDrugMap (http://ruben.ucsd.edu/dnet/maps/drugnet.html). The identified multi-target pharmacology of cancer drugs is essential for improving the efficacy of individually prescribed drugs and drug combinations and minimization of adverse effects.

Nilotinib, an approved leukemia drug, inhibits smoothened signaling in Hedgehog-dependent medulloblastoma

“…Vismodegib is a specific inhibitor of SMO, whereas Nilotinib is also capable of inhibiting multiple kinases and related pathways that may be important for cancer cell growth and maintenance[34]. The CNS-expression-weighted map of Nilotinib targets was generated[56] and is provided as S5 Fig. The proteomic and transcriptomic based analysis of DAOY cells (published by Higdon et al, 2017[57]) shows that in these cells the Hh pathway components are over-expressed along with several Nilotinib targets expressed in CNS, including DDR1/2, ABL1, MAPK8, MAPK14, NQO2, RAF1, SRC, CSF1, PGF, LYN etc.…”

Antineoplastic kinase inhibitors: a new class of potent anti-amoebic compounds Antineoplastic kinase inhibitors againstEntamoeba histolytica

“…A desired target profile of active drugs was generated using the "Multi-drug target finder" tool in the CancerDrugMap (http://ruben.ucsd.edu/dnet/maps/drug_find.html) (20). Drugs that were active (dasatinib, bosutinib, ibrutinib...) and inactive (nilotinib, imatinib...) in the E. histolytica proliferation assay were inputs, respectively.…”

“…Drugs that were active (dasatinib, bosutinib, ibrutinib...) and inactive (nilotinib, imatinib...) in the E. histolytica proliferation assay were inputs, respectively. Drug-target interaction activity data for the tool were collected from multiple sources as previously described, including ChEMBL, PubChem, and literature sources (20). Drug targets were ranked based on the drug-target activity data and using the following equations and assembled into the final anti-amoebic activity-associated profile.…”

“…Such maps could in turn be projected into the E. histolytica proteome and used to infer potential antiamoebic drug activity by identifying drugs with similar target profiles to known active compounds. We have previously published a computational tool capable of such mapping for antineoplastic drugs, including a large number of kinase inhibitors (20). We describe here the use of this tool to prioritize molecules for screening against E. histolytica trophozoites based on initial hits from a small primary screen.…”

“…In order to identify potent anti-amoebic candidates from the existing pool of AKI drugs, an approach was utilized where human protein target profiles were computationally generated for drugs active in the screen, followed by identification of additional drugs with matching or similar target profiles. To generate the target profiles for active drugs, a computational tool called CancerDrugMap (CDM) was utilized, which we have previously described, and which is available at: ruben.ucsd.edu/dnet/ (20). Using CDM, human targets of both the active and inactive compounds from the initial were compared.…”