A Structural Similarity Evaluation by SimScore in a Teratogenicity Information Sharing System



1 Introduction

Recent advances in laboratory automation and experimental techniques such as combinatorial chemistry and high-throughput screening resulted in the growing efficiency of the identification of novel drug candidates. However it became clear that currently one of the biggest challenges is the early determination of unfavorable ADMET properties. Since many failures due to toxicity have been recognized in the development stage [1, 2], the prediction of toxicity in silico has become desired before the screening assay in vitro [3, 4]. Because the teratogenic adverse events cannot be tested in the human body, the computer-aided screening for the reproductive and developmental toxicity is especially important to ensure the safety of drug candidates.
Expert systems such as DEREK [5], HazardExpert [6] and TOPKAT [7] have been currently available to predict the teratogenicity or reproductive toxicity of new chemical compounds. However, to obtain satisfactory results for the practical use, these systems have some limitations due to the following reasons. The knowledge necessary for prediction of teratogenic activity from chemical structures is not essentially adequate. The complicated and unknown molecular mechanism of teratogenicity makes it very difficult to properly identify the mode of action of molecules, and this fact makes the development of consistent prediction models a very challenging task [8 - 10]. Another obstacle in the way of proper statistical analyses is the insufficient biological and/or clinical data and the highly diverse nature of the compound sets.
In this paper, we briefly introduce a novel similarity algorithm and an expert system, SimScore for the predicting teratogenicity of a given compound by comparing its chemical information with that of each human teratogen categorized by the United States Food and Drug Administration (FDA). In SimScore, a molecular structure is divided into its skeletal and substituent parts and the similarity matching for each part is executed independently. The idea is that compounds with the same or similar skeleton show a similar biological activity, but their activity strengths depend on the variation of substituents.
We have been constructing a web-based drug safety information community system [11] to share the teratogenic information among community members [12]. SimScore is one of the subsystems and intended mainly for drug discovery researchers.

2 Database and similarity matching algorithm

2. 1 Molecule database of teratrogens (TeraMol DB)

The FDA used five categories to classify thousands of compounds with existing data (either human or animal) about teratogenic effects. These categories are as follows; A (controlled studies show no risk), B (no evidence of risk in humans), C (risk cannot be ruled out), D (positive evidence of risk), or X (contraindicated in pregnancy) [13]. Positive evidences of fetal abnormalities for the drugs belonging to the categories D and X have been confirmed by the epidemiological studies in pregnant women.
In this study, we have constructed the molecular database TeraMol DB, which contains the chemical structures of the drugs classified into the FDA categories D and X. In TeraMol DB, each chemical structure together with other information was stored as the MDL format [14]. The skeleton structure of each molecule in TeraMol DB was defined by the maximum common substructure among the structurally similar teratorogens. The substituent parts were defined as the rest of the whole structure. The skeleton structure was not necessarily defined as a unique chemical structure, but some changes of atom and bond types were allowed. This definition was somewhat arbitrary, but various alternative skeletal substructures were prepared and stored. Structural information for the skeletal and substituents structures was stored in an extended MDL format in TeraMol DB.

2. 2 Structure matching of skeletons

In SimScore, the atomic information in each molecule is expressed as an atom code array, which consists of the following eight atom codes; element, element group, hybridization type, ring, adjacent atom, hydrogen bonding, atomic charge and stereo codes. These atom codes are easily obtained from the molecular connection table in the MDL file. Based on the connection table and the above atomic codes, the structural similarity scores for the skeletal and substituent parts are calculated between a given molecule (Mol_G) and each molecule (Mol_T) in TeraMol DB. To compare the substructure of Mol_G, first, the atom codes (array A) and connection arrays (array B) of it in Mol_G are extracted. It is ensured that the size of the substructure is comparable to those of the skeletal substructure of Mol_T in the database. The arrays A and B are compared to those of the already defined skeletal structure of Mol_T. The extracted substructure in Mol_G and skeletal substructure in Mol_T are referred as SsG and SsT, respectively. The array A(i, k) indicates the k-th atom code of atom i (i=1, , , a number of the skeletal atoms in SsT). The array B(i, j) indicates a number of bonds from atom i to an atom with the j-th atomic number. If both of the arrays of SsG are identical with the corresponding ones of SsT, or they are subsets of SsT, then the skeletal similarity score is calculated, as defined in the next section. Otherwise the score is set to be zero. There are generally a huge number of ways to extract the arrays A and B from Mol_G. The above procedure is repeated until all of the possible substructures are extracted. The best match of the skeletal atoms between the two substructures is the one with the highest similarity score.

2. 3 Similarity score

The similarity scores of skeletal and substituents parts (noted as SkSS and BSS, respectively) between Mol_G and Mol_T are calculated independently and the total similarity score SSS is defined from SkSS and BSS.
SkSS is defined by eq. 1.

where Ss(k, i), explained below, is the similarity score between the i-th skeletal atom in SsT and its best matching atom i in SsG, k is the k-th atom code, ns is the number of skeletal atoms in SsT (i=1, 2, , , ns), and the summation is taken over all of the atom codes and matching atoms. For the element and element group codes, the Ss(k, i) value takes unity when the k-th atomic codes of atom i in SsG and SsT are the same and otherwise takes zero. For the hybridization, ring, adjacent, hydrogen bonding atom codes, their scores between 0 and 1 are assigned depending on their similarity.
BSS is defined by eq. 2.

where Sb(k, i) is the similarity score between the substituent atoms attached to the i-th skeletal atom in SsT and those to the i-th atom in SsG, k is the k-th atom code, nbi is the number of substituent atoms attached to the i-th atom in SsT, and the summation is taken over all of the atom codes and substituent atoms in SsT. The numbers of the k-th atom code in the substituent atoms attached to the i-th atom in SsT and SsG are stored in the two arrays VecT(k, i) and VecG(k, i), respectively. Then, the value of Sb(k, i) is calculated as the Tanimoto similarity coefficient [15] between VecT(k, i) and VecG(k, i).
Total similarity score SSS is defined by eq. 3 according to the Tanimoto similarity score.

SSS varies in the range between 0 and 1 and its score of unity represents the perfect similarity between Mol_G and Mol_T.
The details for the matching algorithm and similarity scores will be reported elsewhere.

2. 4 Development environments

SimScore was developed on Windows XP. Its front-end for molecule input, similarity calculations, and other subprograms were coded in Java 1.5, Visual C++ 6.0 and Visual Studio NET2005, and Fortran77, respectively. The schematic overview of the SimScore system is depicted in Figure 1. Multithreading technique is used in the search engine for achieving an efficient structural matching search.

Figure 1. Architecture of SimScore

2. 5 Graphic interface

By the use of an interface screen as shown in Figure 2, a query molecular structure is directly drawn there and also a MDL moll file is loadable. The results of SimScore are visualized in the window lists of matched structures in TeraMol DB and their similarity scores as shown in Figure 3.

Figure 2. Screen shot of input window in SimScore

Figure 3. Screen shots of result windows in SimScore. (a) Structures of matched drugs. (b) Similarity scores.

3 Validation of algorithm

Clobazam is a benzodiazepine agent that is used orally as an anticonvulsant, and it was approved by Japanese Ministry of Health, Labor and Welfare in 2000. While benzodiazepine compounds have generally two nitrogen atoms at the 1, 4-positions (Figure 4) in the heterocyclic ring, clobazam is the first benzodiazepine in which the nitrogen atoms are in the 1, 5-positions in the heterocyclic ring. The fetal risk of this drug has not been evaluated by the FDA risk classification system. For critical validation of SimScore, the teratogenic possibility of clobazam was searched using SimScore. As a result, twelve teratogenic drugs in TeraMol DB exhibited nice matching with clobazam and their structures are listed in Table 1 with their similarity scores and FDA pregnancy category codes. The total similarity score of diazepam was computed to be 0.982, which was the highest score among the matched drugs. When the structural differences among clobazam and matched ones were compared, it was confirmed that the similarity scores reflected well their chemical differences, as shown in Figure 4.

Table 1. Results of SimScore for clobazam
moleculeCAS No.SkSSaBSSbSSScFDA Pregnancy Categoryd
a SkSS is the skeletal similarity score. b BSS is the substituent similarity score.c SSS is the total similarity score. d The FDA pregnancy category codes.

Figure 4. Structures of clobazam and matched drugs in the database. Characters in parentheses are the FDA pregnancy category codes.

4 Conclusive Remarks

The prediction of drug teratogenicity induced in the human body has been one of the most serious difficulties in the drug discovery process. The algorithms in the available teratogenicity prediction systems can be classified into two classes. The softwares belonging to one class are using knowledge-based approaches, such as DEREK [5] and HazardExpert, which predict various toxic activities of a test compound [6]. The software of the other class utilizes statistical methods, like TOPKAT [7] which utilizes some quantitative structure-activity relationships (QSAR) models. There is also their hybrid type of system such as MCASE [16]. However, the reproductive and developmental toxicology is a very complicated phenomenon because many different and usually unknown mechanisms are involved. Therefore, it is not easy to identify "structural alerts" as substructures responsible for the toxicity. In fact, in DEREK only nine structural alerts are prepared for the reproductive toxicity endpoints [8]. The developmental toxicity prediction model in TOPKAT is based on data of rat studies [7, 17]. However, the chemicals, which have positive teratogenicity in laboratory animals, do not always induce the same result in humans [18]. Enhancement of prediction accuracy for reproductive and developmental toxicity, especially in humans, has been one of the most important issues.
In the present study, SimScore was developed as a new type of knowledge-based system. The structural similarity search in SimScore utilizes chemical information in the whole structure of a given compound to overcome problems arising from the inconsistent fact data and high chemical diversity of possible teratogens. SimScore allows us to quantitatively predict the teratogenic possibility of a given compound by the structural similarity comparison between it and each human teratogen stored in the knowledge database. Furthermore, the other difference of SimScore, compared to other systems is that it is linked to another knowledge-database system which contains the documentary information such as drug-package inserts, scientific/clinical literatures and physicochemical properties of human teratogens, and so on. SimScore works as a part of our drug safety information community system on the web, as shown in Figure 5. Thus, SimScore guides us in the evaluation of the possible risk of human teratogenicity of a chemical from the comprehensive knowledge of chemistry and clinical fact data. SimScore will be applicable to the evaluation of other specific toxicities and activities of a candidate compound, if the corresponding database is prepared instead of the database of teratogens. Thus, SimScore could be a potentially useful tool in pharmaceutical R&D and drug therapy. The details of the algorithm used in SimScore will be reported elsewhere.

Figure 5. SimScore and teratogenicity information sharing systems

This research was supported by the Research Institute of Science and Technology for Society, Japan Science and Technology Agency, and Grants-in-Aid for Scientific Research (No. 17590126) from the Ministry of Education, Culture, Sports, Science and Technology, Japan. We wish to thank Mr. Makoto Tani, Drs. A.Ammar Ghaibeh and Hiroki Gotoh in Saila Systems for coding the programs. We also thank Dr. Zsolt Lepp of the University of Tokushima for his linguistic suggestions.


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