Structural Similarities between Some Common Fluorophores Used in Biology, Marketed Drugs, Endogenous Metabolites, and Natural Products

Abstract

It is known that at least some fluorophores can act as 'surrogate' substrates for solute carriers (SLCs) involved in pharmaceutical drug uptake, and this promiscuity is taken to reflect at least a certain structural similarity. As part of a comprehensive study seeking the 'natural' substrates of 'orphan' transporters that also serve to take up pharmaceutical drugs into cells, we have noted that many drugs bear structural similarities to natural products. A cursory inspection of common fluorophores indicates that they too are surprisingly 'drug-like', and they also enter at least some cells. Some are also known to be substrates of efflux transporters. Consequently, we sought to assess the structural similarity of common fluorophores to marketed drugs, endogenous mammalian metabolites, and natural products. We used a set of some 150 fluorophores along with standard fingerprinting methods and the Tanimoto similarity metric. Results: The great majority of fluorophores tested exhibited significant similarity (Tanimoto similarity > 0.75) to at least one drug, as judged via descriptor properties (especially their aromaticity, for identifiable reasons that we explain), by molecular fingerprints, by visual inspection, and via the "quantitative estimate of drug likeness" technique. It is concluded that this set of fluorophores does overlap with a significant part of both the drug space and natural products space. Consequently, fluorophores do indeed offer a much wider opportunity than had possibly been realised to be used as surrogate uptake molecules in the competitive or trans-stimulation assay of membrane transporter activities.

Keywords: Tanimoto distance; cheminformatics; drugs; fingerprinting; fluorophores; natural products; rdkit; similarity.

Figure 1

Principal components and t-SNE plots of the principal components of the variance in calculated properties of the molecules used. (A) The first two principal components of the variance in calculated properties of the four classes fluorophores, drugs, metabolites and natural products. Molecules are as in Supplementary Fluorophores SI.xlsx, with the drugs and metabolites those given in [45]. A sampling of 2000 natural products from our download [55] of UNPD was used. Descriptors were z-scores normalised and correlation filtered (threshold 0.98). (B) t-SNE plot of the data in (A), using the same colour-coding. (C). Plot of the first two principal components of the variance in the fluorophores alone. The excitation wavelength is encoded in the colour of the markers. The size of the symbol encodes the molecular weight, indicating that much of the first PC is due to this (plus any other covarying properties).

Figure 2

Ranked order of Tanimoto similarity for fluorophores vs. marketed drugs (green line/—), fluorophores vs. Recon2 metabolites (red line/—), and fluorophores vs. a 2000-member sampling of UNPD (blue line/—). Each fluorophore was encoded using the RDKit ‘Patterned’ encoding, then the Tanimoto similarity for it was calculated against each drug, metabolite or natural product sample. The highest value of TS for each fluorophore was recorded and those values ranked. Read from right to left.

Figure 3

Heat maps illustrating the Tanimoto similarities (using the RDKit patterned encoding) between our selected fluorophores and (A) Recon2 metabolites, (B) Drugs, and (C) a subset of 2000 natural products from UNPD.

Figure 4

Observable structural similarities between selected fluorophores and drugs. The chosen molecules are (A) fluorescein, (B) dapoxyl (both fluorophores) and (C) nitisinone (a drug). Data are annotated and/or zoomed from those in Figure 1B.

Figure 5

Distribution of quantitative estimate of drug-likeness (QED) values in different classes of molecule. (A). Cumulative distributions for the four classes. (B). Relationship between QED and aromaticity for the four classes as encoded by the fraction of C atoms exhibiting sp³ bonding. QED values were calculated using the RDKit Python code as described in Methods and plotted in (A) using ggplot2 and in (B) using Spotfire. (C). Density distribution of fraction of C atoms with sp³ bonding. (D). Histogram of distributions of numbers of aromatic rings in the four given classes.

Figure 6

UMAP projection into two dimensions of the four classes of molecules, annotated by the type of molecular structure in the various clusters.