Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays

Abstract

QSAR (quantitative structure–activity relationship) studies of tyrosinase inhibitors employing Dragon descriptors and linear discriminant analysis (LDA) are presented here. A data set of 653 compounds, 245 with tyrosinase inhibitory activity and 408 having other clinical uses were used. The active data set was processed by k-means cluster analysis in order to design training and prediction series. Seven LDA-based QSAR models were obtained. The discriminant functions applied showed a globally good classification of 99.79% for the best model $\text{Class}=-96.067+1.988\times {10}^2\mathbf{X0Av}+91.907\mathbf{BIC3}+6.853\mathbf{CIC1}$ in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. This external prediction set had an accuracy of 99.44%. After that, the developed models were used in ligand-based virtual screening of tyrosinase inhibitors from the literature and never considered in either training or predicting series. In this case, all screened chemicals were correctly classified by the LDA-based QSAR models. As a final point, these fitted models were used in the screening of new bipiperidine series as new tyrosinase inhibitors. These methods are an adequate alternative to the process of selection/identification of new bioactive compounds. The biosilico assays and in vitro results of inhibitory activity on mushroom tyrosinase showed good correspondence. It is important to stand out that compound BP4 (IC₅₀ = 1.72 μM) showed higher activity in the inhibition against the enzyme than reference compound kojic acid (IC₅₀ = 16.67 μM) and l-mimosine (IC₅₀ = 3.68 μM). These results support the role of biosilico algorithm for the identification of new tyrosinase inhibitor compounds.

Introduction

Tyrosinase (EC. 1.14.18.1) is a copper-containing enzyme widely distributed in nature including fungi, higher plants, and animals. This enzyme catalyzes two key reactions in the melanin biosynthesis pathway, the hydroxylation of monophenol to o-diphenol (monophenolase activity) and conversion of an o-diphenol to the corresponding o-quinone (diphenolase activity), involving reactive oxygen species (ROS) [1], [2]. Quinones are highly reactive compounds and can polymerize spontaneously to form high-molecular weight compounds or brown pigments (melanins), or react with amino acids and proteins that enhance the brown color produced [3].

Alterations in melanin synthesis occur in many disease states like hyperpigmentation, melasma and age spots [4]. Melanin pigments are also found in mammalian brain and tyrosinase may play a role in neuromelanin formation in the human brain. This mixed-function oxidase could be central to dopamine neurotoxicity and may contribute to the neurodegeneration associated with Parkinson's disease [5]. Melanoma specific anticarcinogenic activity is also known to be linked with tyrosinase activity [6].

The standard topical treatments for hyperpigmentation disorders include tyrosinase inhibitors, some compounds with the inhibitory activity are used in medicine, but the majority of them do not satisfy all requirements of clinical efficacy, or adverse effects are observed [4], [7]. As result of these clinical behaviors and other side effects, there has been a constant search for new herbal or synthesized compounds with anti-tyrosinase activity [8], [9], [10]. In this sense, one of our group's researches has been focused on finding new potent tyrosinase inhibitors through ‘trial-and-error’ techniques [11], [12].

By other way, the in silico techniques have proven their usefulness in the pharmaceutical research for the selection/identification and/or design/optimization of new chemical entities (NCE), to transform early stage drug discovery, particularly in terms of time- and cost-savings [13]. QSAR approaches report a high incidence of the use of different molecular descriptors for the in silico drug screening [14], [15], [16], [17].

The congeneric data set used in SAR and QSAR studies of tyrosinase inhibitors [11], [12], [18], [19], [20] do not provide enough tools for drug development, this kind of data can only be applied to structural lead optimization. Therefore, database of heterogeneous compounds may be a successful tool in QSAR research of tyrosinase inhibitors and the discovery of novel lead compounds with different structural features and more effective activity [21], [22], [23].

In the present paper, we used the Dragon descriptors, extensively applied to describe biological activities [24], [25] and linear discriminant analysis (LDA) strategy to find classification functions that allow to discriminate tyrosinase inhibitor compounds from inactive ones. As a final point, the in silico selection (identification), isolation, and in vitro assays of a new series of compounds were carried out to show the applicability of Dragon descriptors in the biosilico drug discovery processes.