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    Conclusions This paper is the first meta-analysis about the association between SRD5A2 V89L polymorphism and hypospadias risk. The results of this meta-analysis have revealed that V89L polymorphism definitely increases the risk of hypospadias, and the C allele is a genetic risk factor for hypospadias occurrence. However, the results should be interpreted with caution due to heterogeneity existing. Therefore, studies with larger cases, and well-designed multicenter and more ethnic groups should be performed to re-evaluate the association.
    Conflicts of interest
    Ethical standard
    Introduction The 5-α-reductase (5AR) enzyme is bound to the nuclear membrane and converts the endogenous hormone, testosterone (T), to the more potent androgen dihydrotestosterone (DHT), with the involvement of the cofactor NADPH [1]. The enzyme is involved in many conditions and diseases with elevated DHT levels, including benign prostatic hyperplasia, prostate cancer, hirsutism, acne and male patterned baldness [2]. Inhibitors of 5AR are thus of therapeutic interest, [3] particularly for prostate cancer. This type of cancer is the most common non-cutaneous cancer among men in most western countries and the second most deadly cancer [4]. There are two isozymes of 5AR, namely types I and II. Type I is prevalent in hair follicles and subcutaneous glands of the skin while type II is prevalent in the prostate, genital skin, seminal vesicles and tcs products australia [5]. The occurrence of 5AR has also been noted in the liver and central nervous system [6]. The isoforms have optimal activities at different pH ranges and there are also differences in the enzyme between humans, rats and dogs [7]. By designing different isoform selective inhibitors the likelihood of unwanted side effects should be decreased. For example a selective type 1 inhibitor could treat acne without significantly affecting testosterone metabolism in the prostate, thus allowing sexual function to continue as normal. There is no crystal structure for 5AR, thus the use of ligand-based pharmacophore design is pertinent for inhibitor development. Various steroidal [8] and non-steroidal [9–16] inhibitors have been synthesized and tested against 5AR. Of these, only Finasteride (PROSCAR®) (1) (Fig. 1), a 4-azasteroid, has been used clinically as a type II-selective inhibitor for benign prostatic hyperplasia [8]. However, Finasteride is slow acting [8] and produces side effects affecting sexual function [17] which may be associated with its steroidal structure. Therefore there is a clear need for new non-steroidal inhibitors as therapeutics for diseases involving 5AR for both isozymes. As part of a program to develop such non-steroidal inhibitors we have investigated ligand-based pharmacophores for human 5AR types I and II, and rat type II enzyme, to inform the design process for new selective inhibitors and to gain a greater understanding of the structure–5AR inhibitory activity relationships between species. Recently, a pharmacophore for human 5AR type II inhibitors was published but included steroidal structures, [16] and in the present paper a comparison of pharmacophores is presented together with the first pharmacophore (preliminary) for non-steroidal inhibitors of human 5AR type I, and for inhibitors of rat type II enzymes.
    Results and discussion CATALYST® generates hypotheses by assigning chemical features to the training set molecules, then arranging the features so that the molecules map with a ranking which correlates with their activity. The hypothesis generation places a greater importance on the molecules contained in the most active group of the training set. CATALYST® produces 10 hypotheses (sometimes 9) which are arranged in a hierarchical manner according to the CATALYST® cost analysis, which takes into account many factors including the size of the training set. The most influential parameter contributing to this cost analysis is the correlation between the hypothesis estimated activity values and the real activity values. The correlation value can lie between zero and one, with one being a perfect correlation. CATALYST® cost analysis also takes into account a configurational parameter which describes the complexity of the problem (this parameter should be under 17, otherwise not all possibilities for patterns will be searched exhaustively). CATALYST® cost analysis also employs the principle of Occam’s razor [24] whereby a hypothesis should be as simple as possible. This cost analysis allows one to assess the validity of the produced hypotheses. This involves the use of three cost parameters: