A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy.

Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully translated into clinical cancer treatment. Here we developed a Quantitative Structure-Activity Relationships (QSAR) classification models using empirical molecular descriptors and fingerprints to predict the activity against the p53 protein, using the potency value with the active or inactive label, were developed. These models were built using in total 10,505 molecules that were extracted from the ChEMBL, ZINC and Reaxys® databases, and recent literature. Three machine learning (ML) techniques e.g., Random Forest, Support Vector Machine, Convolutional Neural Network were explored to build models for p53 inhibitor prediction. The performances of the models were successfully evaluated by internal and external validation. Moreover, based on the best in silico p53 model, a virtual screening campaign was carried out using 1443 FDA-approved drugs that were extracted from the ZINC database. A list of virtual screening hits was assented on base of some limits established in this approach, such as: (1) probability of being active against p53; (2) applicability domain; (3) prediction of the affinity between the p53, and ligands, through molecular docking. The most promising according to the limits established above was dihydroergocristine. This compound revealed cytotoxic activity against a p53-expressing CRC cell line with an IC50 of 56.8 µM. This study demonstrated that the computer-aided drug design approach can be used to identify previously unknown molecules for targeting p53 protein with anti-cancer activity and thus pave the way for the study of a therapeutic solution for CRC.

Bioequivalence study of alpha-dihydroergocryptine: utility of metabolite evaluation.

Metabolite assessment is an open question in bioequivalence studies. In situations of low absorption, high first-pass metabolism, and intrasubject variability, metabolites may reflect absorption more adequately than the parent drug, and their determination may help decision-making in bioequivalence issues. Treating alpha-dihydroergocryptine (DHECT) as a model, we used both unchanged DHECT and a pool of DHECT metabolites to evaluate the bioequivalence of 2 oral DHECT formulations (reference-R and test-T) in 12 subjects. DHECT and its metabolites were immunoassayed. There was no difference between the 2 formulations in terms of the AUC0-infinity (area under the curve) values determined from unchanged DHECT or DHECT with metabolites profiles: 572 +/- 490 pg/ml.h (R) and 442 +/- 276 pg/ml.h (T) for unchanged DHECT, and 7,141 +/- 2,936 pg/ml.h (R) and 6,941 +/- 1,462 pg/ml.h (R) for DHECT with metabolites. Confidence intervals were within the ranges 0.8-1.25 (AUC0-infinity) and 0.7-1.43 (Cmax) for DHECT with metabolites but not for unchanged DHECT. This study describes a particular case where only measurements on the basis of the metabolites can justify the assumption of bioequivalence.

Simultaneous isolation of Rutin and Esculin from plant material and drugs using solid-phase extraction.

Simultaneous isolation of Rutin and Esculin from pharmaceutical materials (plant--Flos hippocastani and drugs--Venescin, Venacorn) using solid-phase extraction (SPE) have been made. For this investigation the Bakerbond SPE columns with different unpolar and polar chemically bonded phases were used. On the basis of isolation investigation the influence of SPE packing materials on the selectivity change and recovery of both extracted substances were studied.