Physiochemical property space distribution among human metabolites, drugs and toxins

<p>Abstract</p> <p>Background</p> <p>The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets.</p> <p>Results</p> <p>We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets.</p> <p>Conclusion</p> <p>The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.</p

Structural features governing the activity of lactoferricin-derived peptides that act in synergy with antibiotics against Pseudomonas aeruginosa in vitro and in vivo

Pseudomonas aeruginosa is naturally resistant to many antibiotics, and infections caused by this organism are a serious threat, especially to hospitalized patients. The intrinsic low permeability of P. aeruginosa to antibiotics results from the coordinated action of several mechanisms, such as the presence of restrictive porins and the expression of multidrug efflux pump systems. Our goal was to develop antimicrobial peptides with an improved bacterial membrane-permeabilizing ability, so that they enhance the antibacterial activity of antibiotics. We carried out a structure activity relationship analysis to investigate the parameters that govern the permeabilizing activity of short (8- to 12-amino-acid) lactoferricin-derived peptides. We used a new class of constitutional and sequence-dependent descriptors called PEDES (peptide descriptors from sequence) that allowed us to predict (Spearman's ρ = 0.74; P < 0.001) the permeabilizing activity of a new peptide generation. To study if peptide-mediated permeabilization could neutralize antibiotic resistance mechanisms, the most potent peptides were combined with antibiotics, and the antimicrobial activities of the combinations were determined on P. aeruginosa strains whose mechanisms of resistance to those antibiotics had been previously characterized. A subinhibitory concentration of compound P2-15 or P2-27 sensitized P. aeruginosa to most classes of antibiotics tested and counteracted several mechanisms of antibiotic resistance, including loss of the OprD porin and overexpression of several multidrug efflux pump systems. Using a mouse model of lethal infection, we demonstrated that whereas P2-15 and erythromycin were unable to protect mice when administered separately, concomitant administration of the compounds afforded long-lasting protection to one-third of the animals

Consensus rank orderings of molecular fingerprints illustrate the ‘most genuine’ similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates

We compare several molecular fingerprint encodings for marketed, small molecule drugs, and assess how their rank order varies with the fingerprint in terms of the Tanimoto similarity to the most similar endogenous human metabolite as taken from Recon2. For the great majority of drugs, the rank order varies very greatly depending on the encoding used, and also somewhat when the Tanimoto similarity (TS) is replaced by the Tversky similarity. However, for a subset of such drugs, amounting to some 10 % of the set and a Tanimoto similarity of ~0.8 or greater, the similarity coefficient is relatively robust to the encoding used. This leads to a metric that, while arbitrary, suggests that a Tanimoto similarity of 0.75-0.8 or greater genuinely does imply a considerable structural similarity of two molecules in the drug-endogenite space. Although comparatively few ( 0.75). This is referred to as the Take Your Pick Improved Cheminformatic Analytical Likeness or TYPICAL encoding, and on this basis some 66 % of drugs are within a TS of 0.75 to an endogenite. We next explicitly recognise that natural evolution will have selected for the ability to transport dietary substances, including plant, animal and microbial ‘secondary’ metabolites, that are of benefit to the host. These should also be explored in terms of their closeness to marketed drugs. We thus compared the TS of marketed drugs with the contents of various databases of natural products. When this is done, we find that some 80 % of marketed drugs are within a TS of 0.7 to a natural product, even using just the MACCS encoding. For patterned and TYPICAL encodings, 80 % and 98 % of drugs are within a TS of 0.8 to (an endogenite or) an exogenous natural product. This implies strongly that it is these exogeneous (dietary and medicinal) natural products that are more to be seen as the ‘natural’ substrates of drug transporters (as is recognised, for instance, for the solute carrier SLC22A4 and ergothioneine). This novel analysis casts an entirely different light on the kinds of natural molecules that are to be seen as most like marketed drugs, and hence potential transporter substrates, and further suggests that a renewed exploitation of natural products as drug scaffolds would be amply rewarded