QSAR analysis of antibacterial activity of some 4-aminodiphenylsulfone derivatives

Dapsone is not a Pharmacodynamic Lead Compound for its Aryl Derivatives

BACKGROUND

The relatedness between the linear equations of thermodynamics and QSAR was studied thanks to the recently elucidated crystal structure complexes between sulfonamide pterin conjugates and dihydropteroate synthase (DHPS) together with a published set of thirty- six synthetic dapsone derivatives with their reported entropy-driven activity data. Only a few congeners were slightly better than dapsone.

OBJECTIVE

Our study aimed at demonstrating the applicability of thermodynamic QSAR and to shed light on the mechanistic aspects of sulfone binding to DHPS.

METHODS

To this end ligand docking to DHPS, quantum mechanical properties, 2D- and 3D-QSAR as well as Principle Component Analysis (PCA) were carried out.

RESULTS

The short aryl substituents of the docked pterin-sulfa conjugates were outward oriented into the solvent space without interacting with target residues which explains why binding enthalpy (ΔH) did not correlate with potency. PCA revealed how chemically informative descriptors are evenly loaded on the first three PCs (interpreted as ΔG, ΔH and ΔS), while chemically cryptic ones reflected higher dimensional (complex) loadings.

CONCLUSION

It is safe to utter that synthesis efforts to introduce short side chains for aryl derivatization of the dapsone scaffold have failed in the past. On theoretical grounds we provide computed evidence why dapsone is not a pharmacodynamic lead for drug profiling because enthalpic terms do not change significantly at the moment of ligand binding to target.

Folate biosynthesis pathway: mechanisms and insights into drug design for infectious diseases

Folate pathway is a key target for the development of new drugs against infectious diseases since the discovery of sulfa drugs and trimethoprim. The knowledge about this pathway has increased in the last years and the catalytic mechanism and structures of all enzymes of the pathway are fairly understood. In addition, differences among enzymes from prokaryotes and eukaryotes could be used for the design of specific inhibitors. In this review, we show a panorama of progress that has been achieved within the folate pathway obtained in the last years. We explored the structure and mechanism of enzymes, several genetic features, strategies, and approaches used in the design of new inhibitors that have been used as targets in pathogen chemotherapy.

3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy

Low range mass spectra (MS) characterization of serum proteome offers the best chance of discovering proteome-(early drug-induced cardiac toxicity) relationships, called here Pro-EDICToRs. However, due to the thousands of proteins involved, finding the single disease-related protein could be a hard task. The search for a model based on general MS patterns becomes a more realistic choice. In our previous work ( González-Díaz, H. , et al. Chem. Res. Toxicol. 2003, 16, 1318- 1327 ), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs). In this previous work, quantitative structure-toxicity relationship (QSTR) techniques allowed us to link 3D-MEDNEs with blood toxicological properties of drugs. In this second part, we extend 3D-MEDNEs to numerically encode biologically relevant information present in MS of the serum proteome for the first time. Using the same idea behind QSTR techniques, we can seek now by analogy a quantitative proteome-toxicity relationship (QPTR). The new QPTR models link MS 3D-MEDNEs with drug-induced toxicological properties from blood proteome information. We first generalized Randic's spiral graph and lattice networks of protein sequences to represent the MS of 62 serum proteome samples with more than 370 100 intensity ( I i ) signals with m/ z bandwidth above 700-12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCH-INSIDE. After that, we developed several QPTR models using different machine learning and MS representation algorithms to classify samples as control or positive Pro-EDICToRs samples. The best QPTR proposed showed accuracy values ranging from 83.8% to 87.1% and leave-one-out (LOO) predictive ability of 77.4-85.5%. This work demonstrated that the idea behind classic drug QSTR models may be extended to construct QPTRs with proteome MS data.