A novel in-silico approach for QSAR Studies of Anabolic and Androgenic Activities in the 17β-hydroxy-5α-androstane Steroid Family

Abstract

Predictive Quantitative Structure-Activity Relationship (QSAR) models of anabolic and androgenic activities for the 17b-hydroxy-5a-androstane steroid family were obtained by means of multi-linear regression using quantum and physicochemical molecular descriptors and a genetic algorithm for the selection of the best set of descriptors. The model allows the identification, selection and future design of new steroid molecules with increased anabolic activity. Molecular descriptors included in reported models allow the structural interpretation of the biological process, evidencing the main role of the shape of molecules, hydrophobicity and electronic properties. The model for the anabolic/ androgenic ratio (expressed by the weight of the levator ani muscle and ventral prostate in mice) predicts that: a) 2-cyano-17-a-methyl-17-b-acetoxy-5a-androst-2-ene is the most potent anabolic steroid in the group and b) the testosterone-3-cyclopentenyl-enoleter is the less potent one. The approach described in this paper is an alternative for the discovery and optimization of leading anabolic compounds.

Applying pattern recognition methods plus quantum and physico‐chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids

“…The dataset of steroids, studied more extensively with different methods by several research groups, is the steroid benchmark with the corresponding globulin affinity. [15][16][17] Recently, we reported QSAR models for congeneric series of AASs: 17-hydroxy-5-androstane, 18 4,5-dihydrotestosterone 19 and testosterone 20 steroid families.…”

Univariate marginal distribution algorithm dynamics for a class of parametric functions with unitation constraints

“…There are several problems that can be approached as the optimization of a function with unitation constraints. Examples include the search of graph substructures [29], feature subset selection constraining the size of the feature subsets [27], and QSAR studies in chemistry [1]. This type of function has also been used to investigate the effects of adding general constraints on the generation of probabilistic dependencies in EDAs [33].…”

“…Results have also been reported on minimum population size bounds and the number of generations to convergence [23,24] as well as dynamics analyse of some EDAs for certain classes of functions [7,14,18,40]. Recently, work on the mathematical modeling of EDAs has been divided into two classes: (1) analysis based on the theory of Markov models, and (2) analysis based on dynamical systems theory. The work presented in this paper falls into this second class.…”

Biotransformation of methyltestosterone by the filamentous fungus Mucor racemosus

“…In this study, it was demonstrated that a 5-day incubation of M. racemosus in the presence of methyltestosterone under appropriate conditions results in the formation of metabolites 2 to 4. Some of these modifications may be of pharmaceutical importance [15]. For example, 7D-hydroxymethyltestosterone (2) shows antagonist properties on androgen receptors [22].…”

“…Methyltestosterone (1), an important anabolic steroid derived from testosterone, is used for therapeutic purposes such as weight gain after surgery, treatment of trauma, birth control, regulation of inflammation, and treatment of other diseases [15]. Although some fungi have been used to transform methyltestosterone, the ability of M. racemosus to transform this compound has not been evaluated.…”