Online structure-based screening of purchasable approved drugs and natural compounds: retrospective examples of drug repositioning on cancer targets

Abstract

Drug discovery is a long and difficult process that benefits from the integration of virtual screening methods in experimental screening campaigns such as to generate testable hypotheses, accelerate and/or reduce the cost of drug development. Current drug attrition rate is still a major issue in all therapeutic areas and especially in the field of cancer. Drug repositioning as well as the screening of natural compounds constitute promising approaches to accelerate and improve the success rate of drug discovery. We developed three compounds libraries of purchasable compounds: Drugs-lib, FOOD-lib and NP-lib that contain approved drugs, food constituents and natural products, respectively, that are optimized for structure-based virtual screening studies. The three compounds libraries are implemented in the MTiOpenScreen web server that allows users to perform structure-based virtual screening computations on their selected protein targets. The server outputs a list of 1,500 molecules with predicted binding scores that can then be processed further by the users and purchased for experimental validation. To illustrate the potential of our service for drug repositioning endeavours, we selected five recently published drugs that have been repositioned in vitro and/or in vivo on cancer targets. For each drug, we used the MTiOpenScreen service to screen the Drugs-lib collection against the corresponding anti-cancer target and we show that our protocol is able to rank these drugs within the top ranked compounds. This web server should assist the discovery of promising molecules that could benefit patients, with faster development times, and reduced costs and risk.

Keywords: cancer; docking; purchasable approved drugs; repositioning; virtual screening.

Figure 1. Schematic representation of the protocol used to generate the Drugs-lib, FOOD-lib and NP-lib

The number located close to the arrow indicates the number of compounds remaining in the database at each step. After the Download all stereoisomers available for sale in ZINC15 step, the number in brackets corresponds to the number of compounds when considering only one isomer in the library.

Figure 2

(A) Graphical representation of the problematic moieties identified using the of FAF-Drugs4 toxicophore-like filters for the Drugs-lib. (B) Heatmap representation of (1 - distance matrix) for the Drugs-lib. The distance matrix was computed using atom pair fingerprints and the dendogram was computed using PubChem's fingerprints. The closer the regions of the grid are to the color red, the less similar the pair of ligands.

Figure 3. Radar plot representation of the mean values of six descriptors computed with FAF-Drugs4

The Drugs-lib is shown in blue, the FOOD-lib in magenta and the NP-lib in brown. The plot involves the following molecular descriptors: molecular weight (MW), number of rotatable bonds (nrotB), topological polar surface area (tPSA), number of hydrogen bond acceptors (HBA) and donors (HBD) and octanol-water partition coefficient (logP).

Figure 4. Graphical representation of the retrospective virtual screening results for each example of repositioned drug - oncogenic target system: fluspirilene - CDK2, mebendazole – VEGFR2, raloxifene – IL6/GP130, sulindac – AKR1C3 and thalidomide – cereblon (each system is represented by one line)

For each line, each coloured square represents the rank obtained for the studied repositioned drug (i.e. fluspirilene, mebendazole, raloxifene, sulindac, thalidomide for the first, second, third, fourth and fifth lines respectively) in a given PDB structures. The gradient of colours scale from white for structures for which the repositioned drug was ranked in the first positions to blue for structures for which the repositioned drug was ranked close to the position 1500. Red square represents structure for which the repositioned drug was not identified as a hit (i.e. ranked over the 1500 position).

Figure 5. Venn diagram showing the overlapping between the 1500 best scored drugs resulting from the virtual screening with MTiOpenScreen of the Drugs-lib against the 5 cancer targets CDK2 (in light orange, PDB structure 4EK4), VEGFR2 (in green, PDB structure 2XIR), GP130 (in blue, PDB structure 1P9M), AKR1C3 (in yellow, PDB structure 4WDT) and cereblon (in pink, PDB structure 5FQD)