# Patent

## Title
Method for predicting activation energy using atomic fingerprint descriptor or atomic descriptor

## Abstract

The present invention provides a method for constructing a database of atomic fingerprint descriptors. The invention provides a method for predicting activation energy using an atomic fingerprint descriptor and an atomic descriptor, the method comprising the steps of: (i) calculating the atomic fingerprint descriptor of a substrate; (ii) comparing the calculated atomic fingerprint descriptor with the constructed atomic fingerprint descriptor database to select an atomic position where cytochrome P450-mediated metabolism occurs; and (iii) predicting activation energy for the selected atomic position using an atomic descriptor. Also, the invention provides a method of predicting the activation energy of CYP450-mediated phase I metabolism using effective atomic descriptors. Specifically, the invention provides a method of predicting the activation energy either for cytochrome P450-mediated hydrogen abstraction or for tetrahedral intermediate formation in cytochrome P450-aromatic hydroxylation using equations including effective atomic descriptors.

## Background
CROSS-REFERENCES TO RELATED APPLICATIONS
This application is a continuation of Ser. No. 13/001,579, filed Jan. 6, 2011, which is the U.S. national phase application, pursuant to 35 U.S.C. §371, of PCT/KR2009/006660, filed Nov. 12, 2009, designating the United States, which claims priority to Korean Application No. 10-2008-0112389, filed Nov. 12, 2008, and Korean Application No. 10-2009-0108741, filed Nov. 11, 2009.


BACKGROUND OF THE DISCLOSURE
1. Technical Field
The present invention relates to a method for predicting the activation energy of phase I metabolism, mediated by CYP450 enzymes, using an effective atomic fingerprint descriptor or atomic descriptor.
2. Related Art
The prediction of absorption, distribution, metabolism and excretion (ADME) properties of drugs is a very important technique to shorten the drug development period and to enhance the probability of success of drug development. Among the drug's ADME properties, drug metabolism is a key determinant of metabolic stability, drug-drug interactions, and drug toxicity.
Metabolic reactions can be divided according to the reaction mechanism into two categories: aliphatic hydroxylation and aromatic hydroxylation. Also, they can be divided according to the type of reaction into the following categories: N-dealkylation, C-hydroxylation, N-oxidation, O-dealkylation and the like. In aliphatic hydroxylation, the iron (Fe) of compound I in the active site of CYP450 (cytochrome P450) is substituted with the hydrogen of the substrate, so that the substrate becomes a radical. Then, a hydroxyl group binds to the substrate to form a metabolite. In aromatic hydroxylation, the iron of compound I binds to the substrate to form a tetrahedral intermediate, and then becomes detached from the substrate while giving a hydroxyl group to the substrate, thereby forming a metabolite.
The metabolism of the compound may occur at most positions to which hydrogen is bound. The possibility of reaction at each position depends on how the compound binds well to CYP450 and how the reactivity at the bound position is high. To determine accessibility, a docking study on CYP450 can be carried out, followed by calculation of binding affinity.
Prediction of the metabolisms of external substances is important in the early stage of new drug development. Particularly, the reaction rate and regioselectivity of phase I metabolism are very important pharmacokinetic characteristics, through which the toxicity of metabolites can be predicted.
Such reaction rate and regioselectivity can be predicted from activation energy, but existing methods depend on time-consuming quantum mechanical calculations and difficult experiments. For example, K. R. Korzekwa et al. (J. Am. Chem. Soc. 1990, 112, 7042) reported a method of predicting the activation energy for hydrogen abstraction by quantum mechanical calculation, and T. S. Dowers et al. (Drug Metab. Dispos. 2004, 32, 328) reported a method of predicting the activation energy of aromatic hydroxylation by quantum mechanical calculation. However, such quantum mechanical methods perform calculations in various molecular states, and thus cannot determine accurate activation energy due to the complexity resulting from the conformational difference between these states.
Accordingly, the present inventors have developed a novel, fast and accurate model which can predict the activation energy of phase I metabolism on the basis of only the characteristics of an external substrate using an atomic fingerprint descriptor or an atomic descriptor, thereby completing the present invention.
SUMMARY OF THE DISCLOSURE
It is an object of the present invention to provide a method for constructing a database of atomic fingerprint descriptors.
Another object of the present invention is to provide a method for predicting activation energy using an atomic fingerprint descriptor and an atomic descriptor.
Still another object of the present invention is to provide a method for predicting activation energy using an atomic descriptor.
Still another object of the present invention is to provide a method of predicting i) a metabolite, ii) the relative rate of metabolism, iii) the regioselectivity of metabolism, iv) the inhibition of metabolism, v) a drug-drug interaction, and vi) the toxicity of a metabolite, through the activation energy predicted by said methods.
To achieve the above objects, the present invention provides a method for constructing a database of atomic fingerprint descriptors, the method comprising the steps of:
(i) calculating the atomic fingerprint descriptor of a substrate, which is represented by the following equation 1;
(ii) predicting activation energy for an atomic position using an atomic descriptor;
(iii) predicting cytochrome P450-mediated metabolism using the predicted activation energy; and
(iv) comparing the predicted metabolism with experimental metabolism and storing whether the metabolism occurs:

Xabc  [Equation 1]

wherein X is the chemical symbol of an atom; a is a bond indicator that indicates the number of atoms bonded; b is a ring indicator that indicates whether the atom is part of a ring; and c is an aromatic indicator that indicates whether the atom is an aromatic atom.

The metabolism in step (iii) is aliphatic hydroxylation or aromatic hydroxylation.
Also, the metabolism in step (iii) is N-dealkylation, C-hydroxylation, N-oxidation or O-dealkylation.
The present invention can be applied to all CYP 450 enzymes, and it is apparent that the present invention can be applied particularly to human CYP 450 enzymes. The cytochrome P450 enzymes according to the present invention include, but are not limited to, CYP2E1, CYP3A4, CYP2B6, CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1, and CYP2A6.
In another aspect, the present invention provides a method for predicting activation energy using an atomic fingerprint descriptor and an atomic descriptor, the method comprising the steps of:
(i) calculating the atomic fingerprint descriptor of a substrate, which is represented by the following equation 1;
(ii) comparing the calculated atomic fingerprint descriptor with the data, constructed by said method, to select an atomic position where cytochrome P450-mediated metabolism can occur; and
(iii) predicting activation energy for the selected atomic position using an atomic descriptor:

Xabc  [Equation 1]

wherein X is the chemical symbol of an atom; a is a bond indicator that indicates the number of atoms bonded; b is a ring indicator that indicates whether the atom is part of a ring; and c is an aromatic indicator that indicates whether the atom is an aromatic atom.

The metabolism in step (ii) is aliphatic hydroxylation or aromatic hydroxylation.
Also, the metabolism in step (ii) is N-dealkylation, C-hydroxylation, N-oxidation or O-dealkylation.
Examples of the cytochrome P450 enzyme include, but are not limited to, CYP2E1, CYP3A4, CYP2B6, CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1, and CYP2A6.
In step (iii), the activation energy for cytochrome P450-mediated hydrogen abstraction from a substrate of the following formula 1 can be predicted using the atomic descriptors [δhet], [max(δheavy)], [μC—H] and




  
    [
    
      
        ∑
        i
        
          R
          .
          C
          .
        
      
      ⁢
      
        α
        i
      
    
    ]
  
  :








wherein the circle together with Fe—O indicates an oxyferryl intermediate; [δhet] indicates the net atomic charge of a heteroatom in the alpha-position relative to the reaction center; [max(δheavy)] indicates the highest atomic charge in X1, X2 and X3 which are neither hydrogen nor helium; [μC—H] indicates the bond dipole of the carbon-hydrogen bond; and





  [
  
    
      ∑
      i
      
        R
        .
        C
        .
      
    
    ⁢
    
      α
      i
    
  
  ]




indicates the sum of the atomic polarizabilities of H, C, X1, X2 and X3.

According to the present invention, the atomic descriptors [δhet] and [max(δheavy)] can be calculated, and activation energy can be calculated according to the following equation 1-1:

EaHabs—(B)=25.94+1.88*[δhet]+1.03*[max(δheavy)]

wherein EaHabs—(B) indicates activation energy required for abstraction of hydrogen attached to a carbon atom having a heteroatom in the alpha-position relative to the reaction center.

Also, according to the present invention, the atomic descriptors [μC—H] and




  [
  
    
      ∑
      i
      
        R
        .
        C
        .
      
    
    ⁢
    
      α
      i
    
  
  ]




can be calculated, and activation energy can be calculated according to the following equation 1-2:





  
    
      
        
          E
          a
          
            Habs_
            ⁢
            
              (
              A
              )
            
          
        
        =
        
          28.50
          -
          
            2.22
            *
            
              [
              
                μ
                
                  C
                  -
                  H
                
              
              ]
            
          
          +
          
            1.12
            *
            
              [
              
                
                  ∑
                  i
                  
                    R
                    .
                    C
                    .
                  
                
                ⁢
                
                  α
                  i
                
              
              ]
            
          
        
      
    
    
      
        [
        
          Equation
          ⁢
          
            
          
          ⁢
          1
          ⁢
          
            -
          
          ⁢
          2
        
        ]
      
    
  




wherein EaHabs—(A) indicates activation energy required for abstraction of hydrogen attached to a carbon atom having no heteroatom in the alpha-position relative to the reaction center.

In step (iii), the activation energy for tetrahedral intermediate formation in cytochrome P450-mediated aromatic hydroxylation for a substrate of the following formula 2 can be predicted using the atomic descriptors [δH] and [mean(αalpha)]:




wherein the circle together with Fe—O indicates an oxyferryl intermediate; [δH] indicates the net atomic charge of the hydrogen of the substrate; and [mean(αalpha)] indicates the mean value of the polarizabilities of adjacent carbon atoms.

According to the present invention, the atomic descriptors [δH] and [mean(αalpha)] can be calculated, and activation energy can be calculated according to the following equations:

Eaaro—o,p=21.34−0.75*[δH]−1.24*[mean(αalpha)]  [Equation 2-1]

Eaarom=22.14−0.68*[δH]−0.83*[mean(αalpha)]  [Equation 2-2]

Eaaro—0,2,3=21.02−1.49*[δH]−0.92*[mean(αalpha)]  [Equation 2-3]

wherein Eaaro—o,p indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the ortho/para-position; Eaaro—m indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the meta-position; and Eaaro—0,2,3 indicates the activation energy for tetrahedral intermediate formation in a benzene having 0, 3 or 3 substituents.

In another aspect, the present invention provides a method for predicting a metabolite using the activated energy predicted by said method. Herein, an atomic position having the lowest activation energy can be predicted as a position where metabolism occurs.
In still another aspect, the present invention provides a method of predicting a drug-drug interaction through the activation energy predicted by said method.
As used herein, the term “drug-drug interaction” refers to the effects that occur when two or more drugs are used at the same time. Such effects include changes in the kinetics of drug absorption by the intestinal tract, changes in the rate of detoxification and elimination of the drug by the liver or other organs, new or enhanced side effects and changes in the drug's activity. CYP2C9 which is a CYP isoform is one of the major enzymes that are involved in the phase I metabolism of drugs. The inhibition of this enzyme can result in an undesirable drug-drug interaction or drug toxicity [see Lin, J. H.; Lu, A. Y. H., Inhibition and induction of cytochrome P450 and the clinical implications. Clin. Pharmacokinet. 1998, 35 (5), 361-390]. Namely, if the activation energy of a substrate is relatively high, metabolism can be inhibited to result in the inhibition of CYP450 enzymes, thus causing an undesirable drug-drug interaction.
Also, metabolites, obtained by oxidation or reduction of substrates by cytochromes, can cause toxicities such as chemical carcinogenesis or mutagenesis, and for this reason, it is very important to predict metabolites, including substrate specificity for cytochromes (Vermeulen N P E, Donne-Op den Kelder G, Commandeur J N M. Molecular mechanisms of toxicology and drug design, in Trend in Drug Research, Proc. 7th Noordwijkerhout-Camerino Symp., Claassen, V., Ed., Elsevier Science Publishers, Amsterdam, 1990, 253).
The present invention provides a method of predicting a metabolite of a CYP450 enzyme by predicting binding possibility using an atomic-type fingerprint descriptor, which includes the type of atom and the surrounding bond order, and by predicting reactivity using an atomic descriptor. The method of the present invention solves a time-consuming problem in predicting accessibility using the three-dimensional structure of a CYP450 enzyme and does not require any quantum mechanical calculation or experiment.
The atomic fingerprint descriptor for predicting the possibility of binding of a cytochrome P450 enzyme to a substrate can be expressed as follows:




The atomic fingerprint descriptor consists of: the element symbol of an atom; a bond order indicating the number of atoms bonded; a ring indicator that indicates whether the atom is part of a ring; and an aromatic indicator that indicates whether the atom is one included in an aromatic group. This expression method intuitively and simply expresses the type of atom and the surrounding bonding environment. However, the atomic fingerprint descriptor has its own information, but does not have the surrounding bonded atoms, and for this reason, the surrounding environment is reflected by writing the surrounding bonded atomic fingerprint descriptors therewith. The larger the connectivity, the more the information of the surrounding environment is included. However, if atomic fingerprint descriptors become excessively large, over-fitting can occur. In the present invention, when the information of atoms connected directly to the atomic fingerprint descriptor was used, the most efficient calculation results were shown.
If atomic fingerprint descriptors for all the atomic positions of a substrate are the same as the atomic fingerprint descriptions of the metabolism of the substrate used in a training set, it is determined to be “on”, and if not so, it is determined to be “off”. Then, since the positions where metabolic reactions can occur were determined, the prediction of reactivity is performed by calculating activation energy, and the relative order of priority is determined.
Prediction of the reactivity of cytochrome P450 enzymes with the substrates was carried out using the calculation methods described in Korean Patent Application No. 10-2008-0112389 (entitled “Method for predicting activation energy using effective atomic descriptors).
Finally, the prediction of metabolic reactions of cytochrome P450 enzymes with the substrates is performed through the prediction of binding possibility and the prediction of reactivity, and the activation energies of individual positions are calculated using reactivity prediction models. The activation energies are arranged in the order of lower to higher energy, and three positions having lower activation energies are determined to be positions at which metabolic reactions can occur. The analysis of the results is carried out by determining whether the two positions selected as described include an experimentally known metabolic position.
To achieve another object, the present invention provides a method of predicting the activation energy for CYP450-mediated hydrogen abstraction according to an equation including an effective atomic descriptor. This method of the present invention is fast and accurate and does not require any quantum mechanical calculation or experiment.
Hydrogen abstraction by a cytochrome P450 enzyme may be shown in the following reaction scheme 1:




wherein the circle together with Fe—O indicates an oxyferryl intermediate.

The present invention provides a method of predicting the activation energy for cytochrome P450-mediated hydrogen abstraction from a substrate of the following formula 1 using the atomic descriptors [δhet], [max(δheavy)], [μC—H] and




  
    [
    
      
        ∑
        i
        
          R
          .
          C
          .
        
      
      ⁢
      
        α
        i
      
    
    ]
  
  :








wherein the circle together with Fe—O indicates an oxyferryl intermediate; [δhet] indicates the net atomic charge of a heteroatom in the alpha-position relative to the reaction center; [max(δheavy)] indicates the highest atomic charge in X1, X2 and X3 which are neither hydrogen nor helium; [μC—H] indicates the bond dipole of the carbon-hydrogen bond; and





  [
  
    
      ∑
      i
      
        R
        .
        C
        .
      
    
    ⁢
    
      α
      i
    
  
  ]




indicates the sum of the atomic polarizabilities of the atoms H, C, X1, X2 and X3.

The present invention can be applied to all CYP 450 enzymes, and it is apparent that the present invention can be applied particularly to human CYP 450 enzymes. The cytochrome P450 enzymes according to the present invention include, but are not limited to, CYP2E1, CYP3A4, CYP2B6, CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1, and CYP2A6.
In the method of predicting the activation energy, any C—H bond to a target molecule can be recognized as a position where metabolism can occur in the target molecule. If the C atom of the C—H bond of the target molecule is aliphatic carbon, it can be determined to be a position where hydrogen abstraction can occur.
In hydrogen abstraction by the CYP450 enzyme, the type of atom can be determined depending on whether a heteroatom is present or not in the alpha-position with respect to the reaction center (C—H where actual metabolism occurs).
If there is a heteroatom in the alpha-position relative to the reaction center, the atomic descriptors [δhet] and [max(δheavy)] can be calculated, and the activation energy for hydrogen abstraction can be predicted according to the following equation 1-1:

EaHabs—(B)=25.94+1.88*[δhet]+1.03*[max(δheavy)]  [Equation 1-1]

wherein EaHabs—(B) indicates activation energy required for abstraction of hydrogen attached to a carbon atom having a heteroatom in the alpha-position relative to the reaction center.

If there is no heteroatom in the alpha-position relative to the reaction center, the atomic descriptors can be calculated, and the activation energy for hydrogen abstraction can be predicted according to the following equation [1-2]:




  
    
      
        
          E
          a
          
            Habs_
            ⁢
            
              (
              A
              )
            
          
        
        =
        
          28.50
          -
          
            2.22
            *
            
              [
              
                μ
                
                  C
                  -
                  H
                
              
              ]
            
          
          +
          
            1.12
            *
            
              [
              
                
                  ∑
                  i
                  
                    R
                    .
                    C
                    .
                  
                
                ⁢
                
                  α
                  i
                
              
              ]
            
          
        
      
    
    
      
        [
        
          Equation
          ⁢
          
            
          
          ⁢
          1
          ⁢
          
            -
          
          ⁢
          2
        
        ]
      
    
  




wherein EaHabs—(A) indicates activation energy required for abstraction of hydrogen attached to a carbon atom having no heteroatom in the alpha-position relative to the reaction center.

To achieve another object, the present invention provides a method of predicting the activation energy for CYP450-mediated aromatic hydroxylation according to an equation including an effective atomic descriptor. The method of the present invention is fast and accurate and does not require any quantum mechanical calculation or experiment.
The tetrahedral intermediate formation reaction in cytochrome P450-mediated aromatic hydroxylation may be shown in the following reaction scheme 2:




wherein the circle together with Fe—O indicates an oxyferryl intermediate.

The present invention provides a method of predicting the activation energy for tetrahedral intermediate formation in cytochrome P450-mediated aromatic hydroxylation for a substrate of the following formula 2 using the atomic descriptors [δH] and [mean(αalpha)]:




wherein the circle together with Fe—O indicates an oxyferryl intermediate; [δH] indicates the net atomic charge of the hydrogen; and [mean(αalpha)] indicates the mean value of the polarizabilities of adjacent carbon atoms.

The present invention may be applied to all CYP 450 enzymes, and it is apparent that the present invention can be applied particularly to human CYP 450 enzymes. The cytochrome P450 enzymes according to the present invention include, but are not limited to, CYP2E1, CYP3A4, CYP2B6, CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1, and CYP2A6.
In the method of predicting the activation energy for tetrahedral intermediate formation, any C—H bond to a target molecule can be determined to be a position where metabolism can occur in the target molecule. Also, if the C atom of the C—H bond of the target molecule is aromatic carbon, it can be determined to be a metabolic position where aromatic hydroxylation can occur.
According to the present invention, the atomic descriptors [(δH] and [mean(αalpha)] can be calculated, and the activation energy can be predicted according to the following equations:

Eaaro—o,p=21.34−0.75*[δH]−1.24*[mean(αalpha)]  [Equation 2-1]

Eaarom=22.14−0.68*[δH]−0.83*[mean(αalpha)]  [Equation 2-2]

Eaaro—0,2,3=21.02−1.49*[δH]−0.92*[mean(αalpha)]  [Equation 2-3]

wherein Eaaro—o,p indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the ortho/para-position; Eaaro—m indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the meta-position; and Eaaro—m indicates the activation energy for tetrahedral intermediate formation in a benzene having 0, 2 or 3 substituents.

In another aspect, the present invention provides a method of predicting the relative rate of metabolism (k) according to the following Arrhenius equation 2 using the activation energy predicted by said method:

k=Ae−Ea/RT  [Equation 2]

wherein k is a reaction rate constant, A is a frequency factor, Ea is activation energy, R is a gas constant, and T is absolute temperature.

The reason why the above equation 2 was designed is because of the atomic fraction f=e−Ea/RT exceeding activation energy. Namely, because only a molecule exceeding activation energy can cause a reaction, the reaction rate constant is determined by the ratio exceeding activation energy.
In another aspect, the present invention provides a method of predicting metabolic regioselectivity using the activation energy predicted by said method.
More specifically, the present invention provides a method of predicting the relative rate of metabolism according to the Arrhenius equation using the activation energy predicted by said method and predicting metabolic regioselectivity according to the following reaction scheme 3 and equation 3 using the predicted relative rate of metabolism:








  
    
      
        
          
            P
            1
          
          
            P
            2
          
        
        =
        
          
            
              [
              
                ES
                1
              
              ]
            
            
              [
              
                ES
                2
              
              ]
            
          
          ⁢
          
            
              k
              5
            
            
              k
              6
            
          
        
      
    
    
      
        [
        
          Equation
          ⁢
          
            
          
          ⁢
          3
        
        ]
      
    
  




wherein P indicates the relative probability of formation of any metabolite of all possible metabolites of a substrate, E is an enzyme, S is a substrate, ES is an enzyme-substrate complex, [ES] is the concentration of the enzyme-substrate complex, and k is a reaction rate constant.

Namely, once the reaction rate of each atom in one molecule is determined according to the Arrhenius equation, the regioselectivity in the molecule can be determined, because metabolism occurs as the reaction rate decreases. [see Higgins, L.; Korzekwa, K. R.; Rao, S.; Shou, M.; Jones, J. P., An assessment of the reaction energetics for cytochrome P450-mediated reactions. Arch. Biochem. Biophys. 2001, 385, 220-230].
In still another aspect, the present invention provides a method of predicting the inhibition of metabolism using the activation energy predicted by said method. For example, it can be predicted that, if a substrate has relatively high activation energy, the substrate will not be metabolized, and thus will remain in the active site of CYP450 enzymes.
In still another aspect, the present invention provides a method of predicting a drug-drug interaction using the activation energy predicted by said method.
As used herein, the term “atomic fingerprint descriptor” refers to a value defined to express the type of atom and the surrounding bonding environment. It consists of the element symbol of an atom, a bond order indicating the number of atoms bonded, a ring indicator that indicates whether the atom is part of a ring, and an aromatic indicator that indicates whether the atom is one included in an aromatic group.
As used herein, the term “atomic descriptor” refers to a value defined to reflect the properties of an atom itself and the bonding environment of the atom. Examples of atomic descriptors that are used in the present invention include, but are not limited to, [δhet], [max(δheavy)], [μC—H],




  
    
      
        
          [
          
            
              ∑
              i
              
                R
                .
                C
                .
              
            
            ⁢
            
              α
              i
            
          
          ]
        
        ,
      
    
    
      
        
      
    
  




[δH], [mean(αalpha)], etc.




BRIEF DESCRIPTION OF THE DRAWINGS
The above and other objects, features and advantages of the present invention will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawing, in which:
FIG. 1 is a flowchart showing a method of constructing a database of atomic fingerprint descriptors according to the present invention;
FIG. 2 is a flowchart showing a method of predicting activation energy using an atomic fingerprint descriptor and an atomic descriptor according to the present invention and predicting i) a metabolite, ii) the relative rate of metabolism, iii) the regioselectivity of metabolism, iv) the inhibition of metabolism, v) a drug-drug interaction, and vi) the toxicity of a metabolite;
FIG. 3 is a flowchart showing a method of predicting activation energy using atomic descriptors according to the present invention;
FIG. 4 shows the correlation between the quantum-mechanically calculated activation energy (QM Ea) for CYP450-mediated hydrogen abstraction and the activation energy (Predicted Ea) predicted according to the present invention; and
FIG. 5 shows the correlation between the quantum-mechanically calculated activation energy (QM Ea) for CYP450-mediated aromatic hydroxylation and the activation energy (Predicted Ea) predicted according to the present invention.



DETAILED DESCRIPTION OF THE DISCLOSURE
Hereinafter, the elements and technical features of the present invention will be described in further detail with reference to examples. It is to be understood, however, that these examples are for illustrative purposes only and are not to be construed to limit the scope of the present invention. All literature cited herein is incorporated by reference.
EXAMPLES
Example 1
Construction of Database of Atomic Fingerprint Descriptors
As shown in FIG. 1, the present inventors constructed a database of atomic fingerprint descriptors through a training method comprising the following steps (see FIG. 1):
(i) calculating the atomic fingerprint descriptor of a substrate, which is represented by the following equation 1;
(ii) predicting activation energy for an atomic position using an atomic descriptor;
(iii) predicting cytochrome P450-mediated metabolism using the predicted activation energy; and
(iv) comparing the predicted metabolism with experimental metabolism and storing whether the predicted metabolism occurs:

Xabc  [Equation 1]

wherein X is the chemical symbol of an atom; a is a bond order that indicates the number of atoms bonded; b is a ring indicator that indicates whether the atom is part of a ring; and c is an aromatic indicator that indicates whether the atom is an aromatic atom.

Using the above-constructed database of atomic fingerprint descriptors, the possibility of reaction of the atomic fingerprint descriptor of a given substrate with each CYP450 isoform was analyzed.







TABLE 1







Results of analysis for possibility of reaction of a given


substrate using constructed atomic fingerprint descriptor


database












NO.
Atomic fingerprint descriptors
CYP1A2
CYP2C9
CYP2D6
CYP3A4















1
C400C400H100H100H100
1
1
−1
1


2
C400C361C400H100H100
1
1
−1
1


3
C361C361C361H100
1
1
1
1


4
C400C361H100H100H100
1
1
1
1


5
C460C460H100H100N360
−1
1
1
1


6
C361C361H100N261
−1
−1
−1
1


7
C460C460C460C460H100
−1
−1
−1
−1


8
C460C361C460C460H100
−1
−1
−1
−1


9
C400C460H100H100H100
−1
−1
−1
−1


10
C400C400C400H100H100
1
1
1
1


11
C460C360C460H100H100
1
1
1
1


12
C360C360C460H100
1
0
−1
1


13
C300C360H100O100
0
0
0
1


14
C400C400C400H100N300
1
1
1
1


15
C400H100H100H100O200
1
1
1
1


16
C400C400H100H100N300
1
1
1
1


17
C400C351C400H100H100
−1
1
−1
−1


18
C361C351C361H100
1
−1
1
1


19
C351C351H100N351
−1
−1
−1
−1


20
C400H100H100H100N300
1
1
1
1


21
C460C400C460C460H100
0
−1
−1
−1


22
C460C460C460H100H100
−1
−1
1
1


23
C460C360C460C460H100
0
−1
0
−1









In Table 1 above, “1” indicates that, in a training set, there is a case in which a reaction occurred in a site having the relevant atomic fingerprint descriptor. “−1” indicates that, in a training set, there is no case in which a reaction occurred in a site having the relevant atomic fingerprint descriptor. “0” indicates that an atom having the relevant atomic fingerprint descriptor does not exist in a training set.
Example 2
Prediction of Metabolite of 2-methoxyamphetamine Using the Prediction Method of the Present Invention
As shown in FIG. 2, the present inventors predicted activation energy using a method comprising the following steps (see FIG. 2):
(i) calculating the atomic fingerprint descriptor of a substrate, which is represented by the following formula 1;
(ii) comparing the calculated atomic fingerprint descriptor with the data, constructed by the method of Example 1, to select an atomic position where cytochrome P450-mediated metabolism can occur; and
(iii) predicting activation energy for the selected atomic position using an atomic descriptor:

Xabc  [Equation 1]

wherein X is the chemical symbol of an atom; a is a bond order that indicates the number of atoms bonded; b is a ring indicator that indicates whether the atom is part of a ring; and c is an aromatic indicator that indicates whether the atom is an aromatic atom.

After predicting the activation energy of 2-methoxyamphetamine using the above method, the metabolite of 2-methoxyamphetamine was predicted. 2-methoxyamphetamine has a chemical structure of the following formula 3:




First, the positions of carbon atoms having hydrogen at positions 1, 2, 3, 6, 7, 8, 9 and 10 were examined.
Then, the atomic fingerprint descriptors of positions 1, 2, 3, 6, 7, 8, 9 and 10 were calculated and compared with the atomic fingerprint descriptor database constructed in Example 10, thereby selecting an atomic position where metabolism may occur (see Table 1).







TABLE 2







Selection of atomic positions having the possibility of


metabolism through the comparison of atomic fingerprint


descriptors










Atomic

Results of
Possibility of


position
Atomic fingerprint descriptor
comparison
metabolism













Atom 1
C400C400H100H100H100
−1
Impossible


Atom 2
C400C400C400H100N300
1
Possible


Atom 3
C400C361C400H100H100
−1
Impossible


Atom 6
C361C361C361H100
1
Possible


Atom 7
C361C361C361H100
1
Possible


Atom 8
C361C361C361H100
1
Possible


Atom 9
C361C361C361H100
1
Possible


Atom 10
C400H100H100H100O200
1
Possible









Then, the activation energies of the atomic positions having the possibility of metabolism were calculated.







TABLE 3







Calculation of activation energies of atomic positions


having the possibility of metabolism (see Example 6)










Atomic position
Activation energy






Atom 2
22.93



Atom 6
25.60



Atom 7
27.42



Atom 8
27.25



Atom 9
27.30



Atom 10
22.22









Then, atomic position 10 having the lowest activation energy was predicted as a position where a reaction occurs. Also, the following metabolite (formula 4) where O-dealkylation occurred at position 10 was predicted in the following manner.




Example 3
Prediction of Metabolite Using Only Reactivity Prediction Model
A metabolite was predicted only with a reactivity prediction model without considering the binding possibility of a substrate. When analysis was carried out using a method of selecting two positions having the highest possibility, a predictability of about 62-70% was generally shown.







TABLE 4







Results of metabolite prediction carried out using only


reactivity prediction model











Na
Ncb
Nc/N(%)















CYP1A2
144
101
70.1



CYP2C9
119
83
69.7



CYP2D6
146
91
62.3



CYP3A4
196
128
65.3





aNumber of substrates used in training;


bNumber of substrates that accurately reproduced an experimentally known metabolism.






Example 4
Prediction of Metabolite Using Accessibility Prediction Model and Reactivity Prediction Model
In order to add the possibility of binding of various CYP450 enzymes to substrates, atomic fingerprint descriptors were used. A total of 185 atomic fingerprint descriptors were used, and the possibility of metabolism by each CYP450 isoform was analyzed. Using a combination of an accessibility prediction model and a reactivity prediction model, two positions having the highest possibility and experimentally known metabolic positions were comparatively analyzed, and the results of the analysis are shown in Table 5 below.










TABLE 5






Na
Ncb
Nc/N(%)




















CYP1A2
144
112
77.8



CYP2C9
119
92
77.3



CYP2D6
146
102
69.9



CYP3A4
196
145
74.0





aNumber of substrates used in training;


bNumber of substrates that accurately reproduced an experimentally known metabolism.






Generally, a predictability of 70-78% was shown, and the predictability was more than 5% higher than that of Example 3 in which only the reactivity prediction model was used.
For reference, the substrates used in the metabolite prediction training according to each cytochrome P450 isoform in Tables 4 and 5 above are shown in the following Tables.







TABLE 6







Substrates used in training for prediction


of metabolites with CYP1A2 (144 cases)









Substrate












1
1-ethylpyrene


2
1-methylpyrene


3
2,3,7-trichlorooxanthrene


4
(5S)-5-(3-hydroxyphenyl)-5-phenylimidazolidine-2,4-dione


5
(5S)-5-(4-hydroxyphenyl)-5-phenylimidazolidine-2,4-dione


6
7-ethoxy-4-(trifluoromethyl)-2H-chromen-2-one


7
7-ethoxycoumarin


8
7-ethoxyresorufin


9
7-methoxyresorufin


10
1-[(2S)-4-(5-benzylthiophen-2-yl)but-3-yn-2-yl]urea


11
aflatoxin-b1


12
all-trans-retinol


13
almotriptan


14
Ametryne


15
amitriptyline


16
amodiaquine


17
Antipyrine


18
Apigenin


19
atomoxetine


20
Atrazine


21
Azelastine


22
7-(benzyloxy)-4-(trifluoromethyl)-2H-chromen-2-one


23
Biochainin-a


24
bropirimine


25
Bufuralol


26
Bunitrolol


27
bupivacaine


28
Capsaicin


29
carbamazepine


30
Carbaryl


31
Carbofuran


32
Carvedilol


33
7-ethoxy-2-oxo-2H-chromene-3-carbonitrile


34
Celecoxib


35
chloroquine


36
chlorpromazine


37
chlorpropamide


38
Cilostazol


39
Cisapride


40
clomipramine


41
clozapine


42
2-chloro-3-(pyridin-3-yl)-5,6,7,8-tetrahydroindolizine-1-



carboxamide


43
curcumin


44
cyclobenzaprine


45
dacarbazine


46
dimethyl 7,7′-dimethoxy-4,4′-bi-1,3-benzodioxole-5,5′-



dicarboxylate


47
deprenyl


48
dextromethorphan


49
dibenzo-a-h-anthracene


50
diclofenac


51
dihydrodiol


52
dimethoxyisoflavone


53
dimethyloxoxanthene


54
dimmamc


55
domperidone


56
doxepin


57
eletriptan


58
ellipticine


59
estradiol-methyl-ether


60
estrone


61
etoricoxib


62
fenproporex


63
fluoxetine


64
flurbiprofen


65
formononetin


66
N-[2-(5-methoxy-1H-indol-3-yl)ethyl]-N-(propan-2-



yl)propan-2-amine


67
galangin


68
genistein


69
2-[(R)-{[5-(cyclopropylmethoxy)pyridin-3-



yl]methyl}sulfinyl]-5-fluoro-1H-benzimidazole


70
harmaline


71
harmine


72
hesperetin


73
imipramine


74
kaempferide


75
kaempferol


76
N-carbamimidoyl-4-cyano-1-benzothiophene-2-carboxamide


77
levobupivacaine


78
lidocaine


79
loratadine


80
4-(aminomethyl)-7-methoxy-2H-chromen-2-one


81
maprotiline


82
(2R)-1-(1,3-benzodioxol-5-yl)-N-ethylpropan-2-amine


83
(2R)-1-(1,3-benzodioxol-5-yl)-N-methylpropan-2-amine


84
3,8-dimethyl-3H-imidazo[4,5-f]quinoxalin-2-amine


85
melatonin


86
mephenytoin


87
methoxychlor


88
methoxychlor-mono-oh


89
methyleugenol


90
metoclopramide


91
mexiletine


92
mianserin


93
mirtazapine


94
(2S)-1-(4-methylphenyl)-2-(pyrrolidin-1-yl)propan-1-one


95
1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine


96
n-nitrosodiamylamine


97
naproxen


98
naringenin


99
nefiracetam


100
nn-dimethyl-m-toluamide


101
4-[methyl(nitroso)amino]-1-(pyridin-3-yl)butan-1-one


102
nordiazepam


103
nortriptyline


104
ochratoxin-a


105
olanzapine


106
olopatadine


107
(3S)-3-[3-(methylsulfonyl)phenyl]-1-propylpiperidine


108
oxycodone


109
perazine


110
perphenazine


111
phenytoin


112
1-methyl-6-phenyl-1H-imidazo[4,5-b]pyridin-2-amine


113
pimobendan


114
progesterone


115
propafenone


116
propanolol


117
prunetin


118
pyrazoloacridine


119
quinacrine


120
ropinirole


121
ropivacaine


122
rosiglitazone


123
safrole


124
sertraline


125
sildenafil


126
stilbene


127
(3Z)-3-[(3,5-dimethyl-1H-pyrrol-2-yl)methylidene]-1,3-



dihydro-2H-indol-2-one


128
tacrine


129
tamarixetin


130
tangeretin


131
tauromustine


132
terbinafine


133
terbuthylazine


134
testosterone


135
theobromine


136
theophylline


137
tolperisone


138
N-(2,6-dichlorobenzoyl)-4-(2,6-dimethoxy-phenyl)-L-



phenylalanine


139
trans-retinoic-acid


140
warfarin


141
zileuton


142
zolmitriptan


143
zolpidem


144
zotepine
















TABLE 7







Substrates used in training for prediction of metabolites


with CYP2C9 (119 cases)









Substrate











1
2n-propylquinoline


2
(5S)-5-(3-hydroxyphenyl)-



5-phenylimidazolidine-



2,4-dione


3
(5S)-5-(4-hydroxyphenyl)-



5-phenylimidazolidine-



2,4-dione


4
5-hydroxytryptamine


5
2-(trans-4-tert-



butylcyclohexyl)-3-



hydroxynaphthalene-1,4-



dione


6
7-ethoxy-4-



(trifluoromethyl)-2H-



chromen-2-one


7
7-ethoxycoumarin


8
7-ethoxyresorufin


9
9-cis-retinoic-acid


10
1-[(2S)-4-(5-



benzylthiophen-2-yl)but-3-



yn-2-yl]urea


11
aceclofenac


12
Ametryne


13
amitriptyline


14
Antipyrine


15
atomoxetine


16
7-(benzyloxy)-4-



(trifluoromethyl)-2H-



chromen-2-one


17
Biochainin-a


18
Bufuralol


19
Capsaicin


20
carbamazepine


21
Carvedilol


22
7-ethoxy-2-oxo-2H-



chromene-3-carbonitrile


23
Celecoxib


24
chlorpropamide


25
Cisapride


26
clomipramine


27
Clozapine


28
2-chloro-3-(pyridin-3-yl)-



5,6,7,8-



tetrahydroindolizine-1-



carboxamide


29
N,4-dimethyl-N-(1-phenyl-



1H-pyrazol-5-



yl)benzenesulfonamide


30
2-[(3S,4R)-3-benzyl-4-



hydroxy-3,4-dihydro-2H-



chromen-7-yl]-4-



(trifluoromethyl)benzoic



acid


31
cyclophosphamide


32
dimethyl 7,7′-dimethoxy-



4,4′-bi-1,3-benzodioxole-



5,5′-dicarboxylate


33
Deprenyl


34
dexloxiglumide


35
dextromethorphan


36
Diazepam


37
dibenzo-a-h-anthracene


38
Diclofenac


39
Diltiazem


40
disopyramide


41
doxepin


42
eletriptan


43
ellipticine


44
estradiol


45
estradiol-methyl-ether


46
estrone


47
etodolac


48
etoperidone


49
Fluoxetine


50
flurbiprofen


51
fluvastatin


52
N-[2-(5-methoxy-1H-indol-



3-yl)ethyl]-N-(propan-2-



yl)propan-2-amine


53
galangin


54
2-[(R)-{[5-



(cyclopropylmethoxy)pyridin-



3-yl]methyl}sulfinyl]-5-



fluoro-1H-benzimidazole


55
harmaline


56
harmine


57
hydromorphone


58
ibuprofen


59
ifosfamide


60
imipramine


61
indomethacin


62
kaempferide


63
ketamine


64
(1S,4S)-(6-dimethylamino-



4,4-diphenyl-heptan-3-



yl)acetate


65
lansoprazole


66
lidocaine


67
loratadine


68
lomoxicam


69
losartan


70
luciferin


71
[(4E)-7-chloro-4-



[(sulfooxy)imino]-3,4-



dihydroquinolin-1(2H)-



yl](2-



methylphenyl)methanone


72
mefenamic-acid


73
melatonin


74
meloxicam


75
mephenytoin


76
methadone


77
methoxychlor-mono-oh


78
methyleugenol


79
mianserin


80
midazolam


81
mirtazapine


82
4-{[(5S)-2,4-dioxo-1,3-thiazolidin-



5-yl]methyl}-2-methox-N-[4-



(trifluoromethyl)benzyl]benzamide


83
(2S)-1-(4-methylphenyl)-2-



(pyrrolidin-1-yl)propan-1-one


84
n-nitrosodiamylamine


85
naproxen


86
nevirapine


87
ochratoxin-a


88
omeprazole


89
oxybutynin


90
oxycodone


91
perazine


92
perphenazine


93
phenacetin


94
phencyclidine


95
phenprocoumon


96
phenytoin


97
piroxicam


98
progesterone


99
rosiglitazone


100
(5Z)-7-[(1S,2R,3R,4R)-3-



benzenesulfonamidobicyclo[2.2.1]



heptan-2-yl]hept-5-enoic acid


101
sertraline


102
sildenafil


103
7-chloro-N-({5-



[(dimethylamino)methyl]cyclopenta-



1,4-dien-1-yl}methyl)quinolin-4-



amine


104
tamarixetin


105
tauromustine


106
temazepam


107
terbinafine


108
testosterone


109
theophylline


110
tolbutamide


111
torasemide


112
N-(2,6-dichlorobenzoyl)-4-(2,6-



dimethoxy-phenyl)-L-



phenylalanine


113
trans-retinoic-acid


114
valdecoxib


115
valsartan


116
venlafaxine


117
vivid-red


118
warfarin


119
zolpidem
















TABLE 8







Substrates used in training for prediction


of metabolites with CYP2D6 (146 cases)









Substrate












1
2-(piperazin-1-yl)pyrimidine


2
2-methoxyamphetamine


3
4-methoxyamphetamine


4
2-(5-methoxy-1H-indol-3-yl)-N,N-dimethylethanamine


5
5-methoxytryptamine


6
5-methoxytryptamine


7
7-ethoxycoumarin


8
all-trans-retinol


9
all-trans-retinol


10
amitriptyline


11
amodiaquine


12
aripiprazole


13
atomoxetine


14
atrazine


15
azelastine


16
biochainin-a


17
bisoprolol


18
N-({4-[(5-bromopyrimidin-2-yl)oxy]-3-



methylphenyl}carbamoyl)-2-(dimethylamino)benzamide


19
brofaromine


20
bunitrolol


21
bupivacaine


22
capsaicin


23
carbamazepcapsaicinine


24
carbamazepcapsaicinine


25
carbofuran


26
Carvedilol


27
7-ethoxy-2-oxo-2H-chromene-3-carbonitrile


28
celecoxib


29
celecoxib


30
chlorpromazine


31
chlorpropamide


32
cibenzoline


33
cilostazol


34
cisapride


35
citalopram


36
clomipramine


37
clozapine


38
codeine


39
curcumin


40
cyclophosphamide


41
delavirdine


42
deprenyl


43
dextromethorphan


44
diclofenac


45
dihydrocodeine


46
diltiazem


47
dimmamc


48
domperidone


49
doxepin


50
2-(hydroxymethyl)-4-[5-(4-methoxyphenyl)-3-



(trifluoromethyl)-1H-pyrazol-1-yl]benzenesulfonamide


51
eletriptan


52
ellipticine


53
estradiol


54
estrone


55
etoperidone


56
etoricoxib


57
eugenol


58
fenproporex


59
fluoxetine


60
fluvastatin


61
N-[2-(5-methoxy-1H-indol-3-yl)ethyl]-N-(propan-2-



yl)propan-2-amine


62
galantamine


63
gefitinib


64
genistein


65
granisetron


66
harmaline


67
harmine


68
hydrocodone


69
hydromorphone


70
ibogaine


71
iloperidone


72
imipramine


73
cilostazol


74
(1S,4S)-(6-dimethylamino-4,4-diphenyl-heptan-3-yl)acetate


75
lidocaine


76
loratadine


77
4-(aminomethyl)-7-methoxy-2H-chromen-2-one


78
maprotiline


79
(2R)-1-(1,3-benzodioxol-5-yl)-N-ethylpropan-2-amine


80
(2R)-1-(1,3-benzodioxol-5-yl)-N-methylpropan-2-amine


81
melatonin


82
mequitazine


83
meta-chlorophenylpiperazine


84
methadone


85
methadone


86
methoxychlor-mono-oh


87
methoxyphenamine


88
methyleugenol


89
metoclopramide


90
metoprolol


91
mexiletine


92
mianserin


93
minaprine


94
mirtazapine


95
(2S)-1-(4-methoxyphenyl)-2-(pyrrolidin-1-yl)propan-1-one


96
(2S)-1-(4-methylphenyl)-2-(pyrrolidin-1-yl)propan-1-one


97
1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine


98
n-nitrosodiamylamine


99
nevirapine


100
4-[methyl(nitroso)amino]-1-(pyridin-3-yl)butan-1-one


101
nortriptyline


102
5-(diethylamino)-2-methylpent-3-yn-2-yl(2S)-2-cyclohexyl-



2-hydroxy-2-phenylacetate


103
olanzapine


104
omeprazole


105
ondansetron


106
(3S)-3-[3-(methylsulfonyl)phenyl]-1-propylpiperidine


107
oxybutynin


108
oxycodone


109
perazine


110
perphenazine


111
phenacetin


112
phencyclidine


113
phenformin


114
phenytoin


115
pinoline


116
(2R)-1-(4-methoxyphenyl)-N-methylpropan-2-amine


117
procainamide


118
progesterone


119
promethazine


120
propafenone


121
propanolol


122
3-(2-chlorophenyl)-N-[(1S)-1-(3-



methoxyphenyl)ethyl]propan-1-amine


123
reduced-dolasetron


124
ropivacaine


125
sertraline


126
sildenafil


127
sparteine


128
spirosulfonamide


129
7-chloro-N-({5-[(dimethylamino)methyl]cyclopenta-1,4-



dien-1-yl}methyl)quinolin-4-amine


130
stilbene


131
stilbene


132
tangeretin


133
tauromustine


134
tegaserod


135
testosterone


136
theophylline


137
tolperisone


138
tramadol


139
traxoprodil


140
tropisetron


141
valdecoxib


142
venlafaxine


143
warfarin


144
yohimbine


145
zolpidem


146
zotepine
















TABLE 9







Substrates used in training for prediction


of metabolites with CYP3A4 (196 cases)









Substrate












1
1-ethylpyrene


2
1-methylpyrene


3
2n-propylquinoline


4
(5S)-5-(3-hydroxyphenyl)-5-phenylimidazolidine-2,4-dione


5
(5S)-5-(4-hydroxyphenyl)-5-phenylimidazolidine-2,4-dione


6
5-methylchrysene


7
1-ethoxycoumarin


8
7-methoxyresorufin


9
1-[(2S)-4-(5-benzylthiophen-2-yl)but-3-yn-2-yl]urea


10
1-[(2S)-4-(5-benzylthiophen-2-yl)but-3-yn-2-yl]urea


11
acetochlor


12
adinazolam


13
aflatoxin-b1


14
alachlor


15
alfentanil


16
all-trans-retinol


17
almotriptan


18
dextromethorphan


19
ambroxol


20
ametryne


21
amitriptyline


22
amodiaquine


23
androstenedione


24
apigenin


25
aripiprazole


26
atomoxetine


27
atrazine


28
azelastine


29
7-(benzyloxy)-4-(trifluoromethyl)-2H-chromen-2-one


30
bisoprolol


31
N-({4-[(5-bromopyrimidin-2-yl)oxy]-3-



methylphenyl}carbamoyl)-2-(dimethylamino)benzamide


32
brotizolam


33
budesonide


34
bufuralol


35
bupivacaine


36
bupropion


37
capsaicin


38
carbamazepine


39
carbaryl


40
carbofuran


41
carvedilol


42
celecoxib


43
cerivastatin


44
chloroquine


45
chlorpropamide


46
cibenzoline


47
cisapride


48
citalopram


49
clobazam


50
clomipramine


51
clozapine


52
2-chloro-3-(pyridin-3-yl)-5,6,7,8-tetrahydroindolizine-



1-carboxamide


53
cocaine


54
codeine


55
colchicine


56
(3S)-3-(6-methoxypyridin-3-yl)-3-{2-oxo-3-[3-(5,6,7,8-



tetrahydro-1,8-naphthyridin-2-yl)propyl]imidazolidin-



1-yl}propanoic acid


57
2-[(3S,4R)-3-benzyl-4-hydroxy-3,4-dihydro-2H-chromen-7-



yl]-4-(trifluoromethyl)benzoic acid


58
diethyl({[(2R,4S,7S)-1]-ethyl-6-methyl-6,11-



diazatetracyclo[7.6.1.0{circumflex over ( )}{2,7}.0{circumflex over ( )}{12,16}]hexadeca-



1(15),9,12(16),13-tetraen-4-yl]sulfamoyl})amine


59
cyclobenzaprine


60
cyclophosphamide


61
dimethyl 7,7′-dimethoxy-4,4′-bi-1,3-benzodioxole-5,5′-



dicarboxylate


62
delavirdine


63
deoxycholic-acid


64
deprenyl


65
deramciclane


66
dexamethasone


67
dexloxiglumide


68
dextromethorphan


69
dextropropoxyphene


70
(3S,8R,9S,10R,13S,14S)-3-hydroxy-10,13-dimethyl-



1,2,3,4,7,8,9,11,12,14,15,16-



dodecahydrocyclopenta[a]phenanthren-17-one


71
diazepam


72
dibenzo-a-h-anthracene


73
diclofenac


74
dihydrocodeine


75
dihydrodiol


76
diltiazem


77
disopyramide


78
domperidone


79
doxepin


80
ecabapide


81
eletriptan


82
ellipticine


83
eplerenone


84
estazolam


85
estradiol


86
estrone


87
etoperidone


88
etoricoxib


89
felodipine


90
fenproporex


91
fentanyl


92
finasteride


93
flucloxacillin


94
fluoxetine


95
fluvastatin


96
N-[2-(5-methoxy-1H-indol-3-yl)ethyl]-N-(propan-2-



yl)propan-2-amine


97
gepirone


98
granisetron


99
2-[(R)-{[5-(cyclopropylmethoxy)pyridin-3-



yl]methyl}sulfinyl]-5-fluoro-1H-benzimidazole


100
hydrocodone


101
hydromorphone


102
ibogaine


103
ifosfamide


104
iloperidone


105
imipramine


106
ketamine


107
ketobemidone


108
N-carbamimidoyl-4-cyano-1-benzothiophene-2-carboxamide


109
(1S,4S)-(6-dimethylamino-4,4-diphenyl-heptan-3-yl)acetate


110
laquinimod


111
levobupivacaine


112
lidocaine


113
lisofylline


114
ropinirole


115
loratadine


116
losartan


117
lovastatin


118
[(4E)-7-chloro-4-[(sulfooxy)imino]-3,4-dihydroquinolin-



1(2H)-yl](2-methylphenyl)methanone


119
(2R)-1-(1,3-benzodioxol-5-yl)-N-ethylpropan-2-amine


120
(2R)-1-(1,3-benzodioxol-5-yl)-N-methylpropan-2-amine


121
melatonin


122
meloxicam


123
1-(4-methoxyphenyl)piperazine


124
methadone


125
methoxychlor


126
methoxychlor-mono-oh


127
metoclopramide


128
mianserin


129
midazolam


130
mirtazapine


131
4-{[(5S)-2,4-dioxo-1,3-thiazolidin-5-yl]methyl}-2-



methoxy-N-[4-(trifluoromethyl)benzyl]benzamide


132
(2S)-1-(4-methylphenyl)-2-(pyrrolidin-1-yl)propan-1-one


133
1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine


134
mycophenolic-acid


135
n-nitrosodiamylamine


136
naringenin


137
nefiracetam


138
nn-dimethyl-m-toluamide


139
4-[methyl(nitroso)amino]-1-(pyridin-3-yl)butan-1-one


140
diethyl{4-[(4-bromo-2-



cyanophenyl)carbamoyl]benzyl}phosphonate


141
nordiazepam


142
nortriptyline


143
5-(diethylamino)-2-methylpent-3-yn-2-yl(2S)-2-cyclohexyl-



2-hydroxy-2-phenylacetate


144
ochratoxin-a


145
olanzapine


146
olopatadine


147
(1R,2R,10R,11S,14R,15R)-14-ethynyl-14-hydroxy-15-methyl-



17-methylidenetetracyclo[8.7.0.0{circumflex over ( )}{2,7}.0{circumflex over ( )}{11,15}]hepta



deca-6,12-dien-5-one


148
(3S)-3-[3-(methylsulfonyl)phenyl]-1-propylpiperidine


149
oxybutynin


150
oxycodone


151
perazine


152
perphenazine


153
phenacetin


154
phencyclidine


155
phenprocoumon


156
pimobendan


157
pradefovir


158
progesterone


159
propafenone


160
pyrazoloacridine


161
quinacrine


162
rebamipide


163
reboxetine


164
ropinirole


165
ropivacaine


166
roquinimex


167
safrole


168
safrole


169
salmeterol


170
senecionine


171
seratrodast


172
seratrodast


173
seratrodast


174
7-chloro-N-({5-[(dimethylamino)methyl]cyclopenta-1,4-



dien-1-yl}methyl)quinolin-4-amine


175
tamarixetin


176
tamsulosin


177
tangeretin


178
tauromustine


179
temazepam


180
terbinafine


181
terbuthylazine


182
testosterone


183
theophylline


184
tramadol


185
trans-retinoic-acid


186
trazodone


187
triazolam


188
trofosfamide


189
tropisetron


190
valdecoxib


191


192


193
yohimbine


194
zaleplon


195
zolpidem


196
zotepine









Example 5
Comparison of Existing Metabolic Prediction Model with Prediction Model of the Present Invention
The present invention is an improved model compared to an existing metabolic prediction model.
The existing QSAR model (Sheridan R P, Korzekwa K R, Torres R A, Walker M J. J. Med. Chem. (2007) 50; 3173) and the present invention select two highly possible positions, and the MetaSite program (Cruciani G, Carosati E, Boeck B D, Ethirajulu K, Mackie C, Howe T, Vianello R. J. Med. Chem. (2005)48; 6970) selects three highly possible positions. Thus, these cannot be directly compared with each other, but as can be seen in Table 3 below, the present invention shows improved predictability.







TABLE 10







Comparison of existing metabolic prediction model and


inventive prediction model












3A4
2D6
2C9
1A2
















QSAR modela
84%
70%
67%
—



MetaSiteb
72%
86%
86%
75%



Inventiona
74%
70%
77%
78%





aselection of two highly possible positions


bselection of three highly possible positions






Example 6
Prediction of Activation Energy Using Atomic Descriptors
6-1. Prediction of Activation Energy for Hydrogen Abstraction Using Atomic Descriptors
Hydrogen abstraction by a cytochrome P450 enzyme may be shown in the following reaction scheme 1:




wherein the cycle together with Fe—O indicates an oxyferryl intermediate.

In the present invention, the activation energy for cytochrome P450-mediated hydrogen abstraction from a substrate of the following formula 1 was predicted using the atomic descriptors [δhet], [max(δheavy)], [μC—H] and




  
    [
    
      
        ∑
        i
        
          R
          .
          C
          .
        
      
      ⁢
      
        α
        i
      
    
    ]
  
  :








wherein the circle together with Fe—O indicates an oxyferryl intermediate;





  
    
      
        
          
            E
            a
            
              Habs_
              ⁢
              
                (
                B
                )
              
            
          
          =
          
            25.94
            +
            
              1.88
              *
              
                [
                
                  δ
                  het
                
                ]
              
            
            +
            
              1.03
              *
              
                [
                
                  max
                  ⁡
                  
                    (
                    
                      δ
                      heavy
                    
                    )
                  
                
                ]
              
            
          
        
        ;
      
    
    
      
        [
        
          Equation
          ⁢
          
            
          
          ⁢
          1
          ⁢
          
            -
          
          ⁢
          1
        
        ]
      
    
  
  
    
      
        
          E
          a
          
            Habs_
            ⁢
            
              (
              A
              )
            
          
        
        =
        
          28.50
          -
          
            2.22
            *
            
              [
              
                μ
                
                  C
                  -
                  H
                
              
              ]
            
          
          +
          
            1.12
            *
            
              [
              
                
                  ∑
                  i
                  
                    R
                    .
                    C
                    .
                  
                
                ⁢
                
                  α
                  i
                
              
              ]
            
          
        
      
    
    
      
        [
        
          Equation
          ⁢
          
            
          
          ⁢
          1
          ⁢
          
            -
          
          ⁢
          2
        
        ]
      
    
  




wherein EaHabs—(B) indicates activation energy required for hydrogen attached to a carbon atom having a heteroatom (an atom other than carbon) in the alpha-position relative to the reaction center; EaHabs—(A) indicates activation energy required for hydrogen attached to a carbon atom having no heteroatom (an atom other than carbon) in the alpha-position relative to the reaction center; and [δhet] indicates the net atomic charge of a heteroatom (an atom other than carbon) in the alpha-position relative to the reaction center; [max(δheavy)] indicates the highest atomic charge in X1, X2 and X3 which are neither hydrogen nor helium; [μC—H] indicates the bond dipole of the carbon-hydrogen bond; and





  [
  
    
      ∑
      i
      
        R
        .
        C
        .
      
    
    ⁢
    
      α
      i
    
  
  ]




indicates the sum of the atomic polarizabilities of the atoms H, C, X1, X2 and X3.

6-2. Prediction of Activation Energy for Tetrahedral Intermediate Formation in Aromatic Hydroxylation Using Atomic Descriptors
Tetrahedral intermediate formation reaction in cytochrome P450-mediated aromatic hydroxylation may be shown in the following reaction scheme 2:




wherein the circle together with O—Fe indicates an oxyferryl intermediate.

In the present invention, the activation energy for tetrahedral intermediate formation in cytochrome P450-mediated aromatic hydroxylation of a substrate of the following formula 2 was predicted using the atomic descriptors [δH] and [mean(αalpha)]:




wherein the circle together with Fe—O indicates an oxyferryl intermediate;

Eaaro—o,p=21.34−0.75*[δH]−1.24*[mean(αalpha)]  [Equation 2-1]

Eaarom=22.14−0.68*[δH]−0.83*[mean(αalpha)]  [Equation 2-2]

Eaaro—0,2,3=21.02−1.49*[δH]−0.92*[mean(αalpha)]  [Equation 2-3]

wherein Eaaro—o,p indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the ortho/para-position; Eaaro—m indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the meta-position; Eaaro—0,2,3 indicates the activation energy for tetrahedral intermediate formation in a benzene having 0, 2 or 3 substituents; [δH] indicates the net atomic charge of the hydrogen; and [mean(αalpha)] indicates the mean value of the polarizabilities of adjacent carbon atoms.

Example 7
Development of Model for Predicting Activation Energy for Hydrogen Abstraction
The activation energy for hydrogen abstraction is a good measure for predicting the regioselectivity of aliphatic hydroxylation and dehydroxylation in phase I metabolism.




wherein the circle together with Fe—O indicates an oxyferryl intermediate.

In order to model the above reaction, the activation energies of 431 cases of 119 molecules were calculated using the AM1 (Austin Model 1) molecular orbital method.
Herein, the term “cases” refers to the number of atoms. For example, if there are 3 molecules having 3, 4 and 7 atoms, respectively, there will be 14 cases of 3 molecules. The AM1 method is a semi-empirical method for quantum calculation of the electronic structures of molecules in computational chemistry and is a generalization of the modified neglect of differential diatomic overlap approximation (Dewar, M. J. S. et al., Journal of the American Chemical Society, 1985, 107, 3902).
The list of organic molecules calculated is shown in Table 11 below.







TABLE 11





Organic molecules used in training and verification for


hydrogen abstraction (119 organic molecules)


List of organic molecules
















(3-amino-propyl)-dimethyl-amine
1-chloro-4-methyl-pentane


(3-bromo-propyl)-dimethyl-amine
1-chloro-butane


(3-chloro-propyl)-dimethyl-amine
1-chloro-heptane


(3-fluoro-propyl)-dimethyl-amine
1-chloro-hexane


(3-iodo-propyl)-dimethyl-amine
1-chloromethyl-3-methyl-benzene


1,2,3-trimethylbenzene
1-chloromethyl-4-methyl-benzene


1,2,4-trimethylbenzene
1-chloro-octane


1,2-difluoro-3-methyl-butane
1-chloro-pentane


1-bromo-2-methyl-benzene
1-chloro-propane


1-bromo-3-methyl-benzene
1-ethoxy-3-fluoro-benzene


1-bromo-4-methyl-benzene
1-ethyl-4-methylbenzene


1-bromo-4-methyl-pentane
1-fluoro-2,4-dimethyl-pentane


1-bromo-heptane
1-fluoro-2-methyl-benzene


1-bromo-hexane
1-fluoro-2-methyl-octane


1-bromo-octane
1-fluoro-3-methyl-benzene


1-bromo-pentane
1-fluoro-4-methyl-butane


1-bromo-propane
1-fluoro-4-methyl-benzene


1-chloro-2-methylbenzene
1-fluoro-4-methyl-heptane


1-chloro-3-methylbenzene
1-fluoro-4-methyl-pentane


1-chloro-4-methylbenzene
1-fluoro-butane


1,2,3-trimethylbenzene
Fluoro-benzene


1,2,4-trimethylbenzene
Iodo-benzene


1-ethyl-4-methylbenzene
mesitylene


1-methyl-2-propylbenzene
methoxybenzene


1-o-tolylpropan-1-one
m-xylene


2,4-difluoro-1-methylbenzene
n,4-dimethylbenzenamine


2-fluoro-phenylamine
o-xylene


2-methylanisol
phenol


3-fluoro-4-methylbenzeneamine
propylbenzene


3-fluoro-phenylamine
p-toluidine


4-ethoxy-aniline
p-xylene


4-ethoxy-phenol



4-fluoro-phenylamine



aniline



benzene



benzenethiol



chloro-benzene



cyanobenzene



ethoxybenzene



ethylbenzene









Such information was used to train and evaluate the empirical equations. These cases include methyl, primary, secondary and tertiary carbon atoms, etc., in various chemical environments.
The present inventors divided these cases into two types depending on whether electrically negative atoms (i.e. heteroatoms) exist around the breaking carbon-hydrogen bond.
Equations modeled with atomic descriptors through the correlation between effective atomic descriptors and quantum-mechanically calculated Ea for hydrogen abstraction are shown in Tables 12 and 13 below.







TABLE 12







Correlation between effective atomic descriptors and quantum-


mechanically calculated Ea for hydrogen abstraction (the case of


having no heteroatom in the alpha-position)










Training set











Atomic descriptor
Ra
RMSEb
Equation





μC-H
0.88
0.63
EaHabs_(A) = 28.50-1.19*[μC-H]


 






  
    ∑
    i
    
      R
      .
      C
      .
    
  
  ⁢
  
    α
    i
  




0.67
1.00




  
    E
    a
    
      Habs
      ⁢
      
        
      
      ⁢
      _
      ⁢
      
        
      
      ⁢
      
        (
        A
        )
      
    
  
  =
  
    28.50
    -
    
      
        0.90
        *
      
      ⁡
      
        [
        
          
            ∑
            i
            
              R
              .
              C
              .
            
          
          ⁢
          
            α
            i
          
        
        ]
      
    
  









Ra: correlation coefficient;


RMSEb: root mean squared error.













TABLE 13







Correlation between effective atomic descriptors and quantum-


mechanically calculated Ea for hydrogen abstraction (the case of


having a heteroatom in the alpha-position)










Training set













Atomic descriptor
Ra
RMSEb
Equation






δhet
0.82
1.51
EaHabs—(B) = 25.94 +






2.14 * [δhet]



max(δheavy)
0.57
2.16
EaHabs—(B) = 25.94 +






1.51 * [max(δheavy)]





Ra: correlation coefficient;


RMSEb: root mean squared error.






The present inventors performed the training processes shown in Tables 12 and 13 above, thereby allowing linear equations to predict activation energy in various chemical environments using two normalized effective atomic descriptors suited to each case (equations 1-1 and 1-2 below).
Among these effective atomic descriptors, [δhet], [max(δheavy)] and [μC—H] indicate the degree of weakness of the carbon-hydrogen bond, and




  [
  
    
      ∑
      i
      
        R
        .
        C
        .
      
    
    ⁢
    
      α
      i
    
  
  ]




indicates the stability of transition states. In the present invention, all transition states were verified through the analysis of frequencies.

FIG. 3 is a flowchart showing a method of predicting activation energy using the model of the present invention.
Specifically, the model for predicting the activation energy for CYP450-mediated hydrogen abstraction, developed in the present invention, comprises the following steps:
i) examining the metabolic position of a target molecule;
ii) determining the reaction type of the target molecule;
iii) determining the atomic type depending on whether there is a heteroatom in the alpha-position relative to the reaction center of hydrogen abstraction;
iv) if there is a heteroatom in the alpha-position, calculating the atomic descriptors [δhet] and [max(δheavy)], and if there is no heteroatom in the alpha-position, calculating the atomic descriptors [μC—H] and




  
    [
    
      
        ∑
        i
        
          R
          .
          C
          .
        
      
      ⁢
      
        α
        i
      
    
    ]
  
  ;




v) normalizing the atomic descriptors; and
vi) predicting activation energy according to the following equations:




wherein the circle together with O—Fe indicates an oxyferryl intermediate;





  
    
      
        
          
            E
            a
            
              Habs_
              ⁢
              
                (
                B
                )
              
            
          
          =
          
            25.94
            +
            
              1.88
              *
              
                [
                
                  δ
                  net
                
                ]
              
            
            +
            
              1.03
              *
              
                [
                
                  max
                  ⁡
                  
                    (
                    
                      δ
                      heavy
                    
                    )
                  
                
                ]
              
            
          
        
        ⁢
        
          

        
        ⁢
        
          R
          =
          0.91
        
        ,
        
          RMSE
          =
          1.14
        
        ,
        
          n
          =
          62
        
        ,
        
          
            
              P
              ⁢
              
                
              
              ⁢
              value
            
            <
            0.0001
          
          ;
        
      
    
    
      
        [
        
          Equation
          ⁢
          
            
          
          ⁢
          1
          ⁢
          
            -
          
          ⁢
          1
        
        ]
      
    
  
  
    
      
        
          
            E
            a
            
              Habs_
              ⁢
              
                (
                A
                )
              
            
          
          =
          
            28.50
            -
            
              2.22
              *
              
                [
                
                  μ
                  
                    C
                    -
                    H
                  
                
                ]
              
            
            +
            
              1.12
              *
              
                [
                
                  
                    ∑
                    i
                    
                      R
                      .
                      C
                      .
                    
                  
                  ⁢
                  
                    α
                    i
                  
                
                ]
              
            
          
        
        ⁢
        
          

        
        ⁢
        
          R
          =
          0.95
        
        ,
        
          RMSE
          =
          0.43
        
        ,
        
          n
          =
          224
        
        ,
        
          
            P
            ⁢
            
              
            
            ⁢
            value
          
          <
          0.0001
        
      
    
    
      
        [
        
          Equation
          ⁢
          
            
          
          ⁢
          1
          ⁢
          
            -
          
          ⁢
          2
        
        ]
      
    
  




wherein EaHabs—(B) indicates the activation energy required for abstraction of hydrogen attached to a carbon atom having a heteroatom (an atom other than carbon) in the alpha-position relative to the reaction center; EaHabs—(A) indicates activation energy required for abstraction of hydrogen attached to a carbon atom having no heteroatom (an atom other than carbon) in the alpha-position relative to the reaction center; [δhet] indicates the net atomic charge of a heteroatom (an atom other than carbon) in the alpha-position relative to the reaction center; [max(δheavy)] indicates the highest atomic charge in X1, X2 and X3 which are neither hydrogen nor helium; [μC—H] indicates the bond dipole of the carbon-hydrogen bond; and





  [
  
    
      ∑
      i
      
        R
        .
        C
        .
      
    
    ⁢
    
      α
      i
    
  
  ]




indicates the sum of the atomic polarizabilities of the atoms H, C, X1, X2 and X3.

In equations 1-1 and 1-2 above, R: correlation coefficient; RMSE: root mean squared error; n: the number of atoms used in training; and P value: the significance of the correlation coefficient.
In step i), any C—H bond to the target molecule can be regarded as a position where metabolism can occur in the target molecule.
In step ii), if the carbon in any C—H bond to the target molecule is aliphatic carbon, it can be regarded as a position where H abstraction from the target molecule can occur.
In step iii), if there is a heteroatom in the alpha-position relative to the reaction center (C—H where actual metabolism occurs), equation 1-1 is used, and if there is no heteroatom in the alpha-position, equation 1-2 is used.
In step v), the term “normalization” refers to normalizing the mean of the values of atomic descriptors to zero (0) and the standard deviation to 1, from a statistical viewpoint. Namely, before prediction, normalization is carried out using the mean and, standard deviation of the values of the atomic descriptors used in the training of the prediction model of the present invention.
As shown in FIG. 4, the activation energy predicted using the model of the present invention showed a high correlation with the quantum-mechanically calculated activation energy. 386 cases of 430 cases are within chemical accuracy (1 kcal per mol). Some inconsistent cases are attributable to interactions other than carbon-hydrogen-oxygen interactions during quantum mechanical calculation. Activation energies of various molecules in a gaseous state were calculated using Gaussian 03 [revision C.02, M. J. Frisch et al., Pittsburgh, Pa., USA, 2003].
Example 8
Verification of Activation Energy Predicted by Model for Predicting Activation Energy for Hydrogen Abstraction
Activation energies for hydrogen abstraction from the following four molecules, predicted using the prediction model of Example 7, were verified by comparison with experimental values:















TABLE 14








Metabolic






rate






induced





Predicted
from





activation
activation
Experimental


Molecule
#[a]
energy[b]
energy[c]
metabolic rate[d]



















Hexane
1
26.89
4.1
4.5



2
28.20
46.6
49



3
28.16
49.3
46.5


Octane
1
29.69
8.2
2.5



2
28.21
91.8
97.5


Ethylbenzene
1
30.32
0.1
0.2



2
25.73
99.9
99.8


1-chloromathyl-4-
1
27.51
12.5
16.0


methyl-benzene
2
26.31
87.5
84.0





[a]# indicates the atomic number of each molecule in formula 2;


[b]activation energy predicted by the method of the present invention;


[c]metabolic rate induced by introducing the predicted activation energy [b] into the Arrhenius equation; and


[d]in vitro experimental metabolic rate.






The experimental metabolic rates of the molecules shown in Table 14 above are already known in the art. Specifically, the experimental metabolic rate of hexane can be found in the literature [Ken-ichirou MOROHASHI, Hiroyuki SADANO, Yoshiie OKADA, Tsuneo OMURA. Position Specificity in n-Hexane Hydroxylation by two forms of Cytochrome P450 in Rat liver Microsomes. J. Biochem. 1983, 93, 413-419]; the experimental metabolic rate of octane in the literature [Jeffrey P. Jones, Allan E. Rettie, William F. Trager. Intrinsic Isotope Effects Suggest That the Reaction Coordinate Symmetry for the Cytochrome P-450 Catalyzed Hydroxylation of Octane Is Isozyme Independent. J. Med. Chem. 1990, 33, 1242-1246]; the experimental metabolic rate of ethylbenzene can be found in the literature [Ronald E. White, John P. Miller, Leonard V. Favreau, Apares Bhattacharyya. Stereochemical Dynamics of Aliphatic Hydroxylation by Cytochrome P-450. J. AM. Chem. Soc. 1986, 108, 6024-6031]; and the experimental metabolic rate of 1-chloromethyl-4-methyl-benzene can be found in the literature [LeeAnn Higgins, Kenneth R. Korzekwa, Streedhara Rao, Magong Shou, and Jeffrey P. Jones. An Assessment of the Reaction Energetics for Cytochrome P450-Mediated Reactions. Arch. Biochem. Biophys. 2001, 385, 220-230].
As can be seen in Table 14 above, when the metabolic rates[c] (induced by substituting into the Arrhenius equation the activation energies for hydrogen abstraction from the four molecules, hexane, octane, ethylbenzene and 1-chloromethyl-4-methyl-benzene, predicted according to the present invention) were compared with the experimental metabolic rates[d], these metabolic rates showed similar tendencies. This suggests that the experimental metabolic rates can be predicted through the activation energies predicted according to the present invention.
Example 9
Development of Model for Predicting the Activation Energy for Tetrahedral Intermediate Formation in Aromatic Hydroxylation
The present inventors modeled tetrahedral intermediate formation serving as a good measure of the regioselectivity of aromatic hydroxylation in phase I metabolism.




wherein the circle together with Fe—O indicates an oxyferryl intermediate.

To model the above reaction, the activation energies of 85 cases of 31 benzene molecules in various chemical environments were calculated using the AM1 (Austin Model 1) molecular orbital method.
Herein, the term “cases” refers to the number of atoms. For example, if there are 3 molecules having 3, 4 and 7 atoms, respectively, there will be 14 cases of 3 molecules. The AM1 method is a semi-empirical method for quantum calculation of the electronic structures of molecules in computational chemistry and is a generalization of the modified neglect of differential diatomic overlap approximation (Dewar, M. J. S. et al., Journal of the American Chemical Society, 1985, 107, 3902).
The list of organic molecules calculated is shown in Table 15 below.







TABLE 15





Organic molecules used in training and verification for


tetrahedral intermediate formation (31 organic molecules)


List of organic molecules


















1,2,3-trimethylbenzene
Fluoro-benzene



1,2,4-trimethylbenzene
Iodo-benzene



1-ethyl-4-methylbenzene
mesitylene



1-methyl-2-propylbenzene
methoxybenzene



1-o-tolylpropan-1-one
m-xylene



2,4-difluoro-1-methylbenzene
n,4-dimethylbenzenamine



2-fluoro-phenylamine
o-xylene



2-methylanisol
phenol



3-fluoro-4-methylbenzenamine
propylbenzene



3-fluoro-phenylamine
p-toluidine



4-ethoxyaniline
p-xylene



4-ethoxy-phenol




4-fluoro-phenylamine




aniline




benzene




benzenethiol




chloro-benzene




cyanobenzene




ethoxybenzene




ethylbenzene









Such information was used to train and evaluate the empirical equations. These cases were divided into three types: i) having one substituent in the ortho/para position; ii) having one substituent in the meta-position; and iii) having 0, 2 or 3 substituents.
Equations modeled with atomic descriptors through the correlation between effective atomic descriptors and quantum-mechanically calculated Ea for aromatic hydroxylation are shown in Tables 16, 17 and 18 below.







TABLE 16







Correlation between effective atomic descriptors and quantum-


mechanically calculated Ea for aromatic hydroxylation (the case of


having a substituent in the ortho-position)










Training set













Atomic descriptor
Ra
RMSEb
Equation






δH
0.08
1.31
Eaaro—o,p = 14.67 +






63.33 * [δH]



αalpha
0.57
1.07
Eaaro—o,p = 61.60 −






26.53 * [αalpha]





Ra: correlation coefficient;


RMSEb: root mean squared error.













TABLE 17







Correlation between effective atomic descriptors and quantum-


mechanically calculated Ea for aromatic hydroxylation (the case of


having a substituent in the meta-position)










Training set













Atomic descriptor
Ra
RMSEb
Equation






δH
0.03
0.56
Eaaro—m = −12.61 +






333.87 * [δH]



αalpha
0.50
0.49
Eaaro—m = 132.75 −






72.54 * [αalpha]





Ra: correlation coefficient;


RMSEb: root mean squared error.













TABLE 18







Correlation between effective atomic descriptors and quantum-


mechanically calculated Ea for aromatic hydroxylation (the case of


having 0, 2 or 3 substituents)










Training set













Atomic descriptor
Ra
RMSEb
Equation






δH
0.69
0.95
Eaaro—0.2,3 = 70.00 −






465.88 * [δH]



αalpha
0.05
1.31
Eaaro—0.2,3 = 17.65 +






2.21 * [αalpha]





Ra: correlation coefficient;


RMSEb: root mean squared error.






The present inventors performed the training processes shown in Tables 16 to 18 above, thereby allowing linear equations to predict activation energy in various chemical environments using two normalized effective atomic descriptors suited to each case (equations 2-1, 2-2 and 2-3 below).
Among effective atomic descriptors which are used in the equations for predicting the activation energy for tetrahedral intermediate formation, [δH] determines the proximity between oxygenating species and substrate, and [mean (αalpha)] is related to the stability of transition states. In the present invention, para-nitrosophenoxy radical (PNR) was used as oxygenating species, and all transition states were verified through the analysis of frequencies.
FIG. 3 shows a flowchart showing a method of predicting activation energy using the model used in the present invention.
Specifically, the model for predicting the activation energy for tetrahedral intermediate formation in CYP450-mediated aromatic hydroxylation, developed in the present invention, comprises the following steps:
i) examining the metabolic position of a target molecule;
ii) determining the reaction type of the target molecule;
iii) if the reaction type in step ii) is determined to be aromatic hydroxylation, calculating the atomic descriptors [δH] and [mean(αalpha)];
iv) normalizing the atomic descriptors; and
v) predicting activation energy according to the following equations:




wherein the circle together with Fe—O indicates an oxyferryl intermediate;

Eaaro—o,p=21.34−0.75*[δH]−1.24*[mean(αalpha)]

R=0.71, RMSE=0.95, n=16, P value=0.009;  [Equation 2-1]

Eaarom=22.14−0.68*[δH]−0.83*[mean(αalpha)]

R=0.88, RMSE=0.30, n=8, P value=0.026;  [Equation 2-2]

Eaaro—0,2,3=21.02−1.49*[δH]−0.92*[mean(αalpha)]

R=0.87, RMSE=0.65, n=33, P<0.0001  [Equation 2-3]

wherein Eaaro—o,p indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the ortho/para position; Eaaro—m indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the meta-position; Eaaro—0,2,3 indicates the activation energy for tetrahedral intermediate formation in a benzene having 0, 2 or 3 substituents; [δH] indicates the net atomic charge of the hydrogen; and [mean(αalpha)] indicates the mean of the polarizabilities of adjacent carbon atoms.

In equations 2-1, 2-2 and 2-3 above, R: correlation coefficient; RMSE: root mean squared error; n: the number of atoms used in training; and P value: the significance of the correlation coefficient.
As shown in FIG. 5, the activation energy predicted using the model of the present invention showed a high correlation with the quantum-mechanically calculated activation energy. 70 cases of 85 cases are within chemical accuracy (1 kcal per mol). Some inconsistent cases occurred because the model did not consider the ortho-, meta- and para-effects when modeling the benzene molecule having 0, 2 or 3 substituents. Activation energies of various molecules in a gaseous state were calculated using Gaussian 03 [revision C.02, M. J. Frisch et al., Pittsburgh, Pa., USA, 2003].
Example 10
Verification of Activation Energy Predicted by Model for Predicting Activation Energy for Tetrahedral Intermediate Formation in Aromatic Hydroxylation
The activation energies for tetrahedral intermediate formation for the following two molecules, predicted by the prediction model of Example 9, were verified by comparison with experimental values.















TABLE 19








Metabolic






rate






induced





Predicted
from





activation
activation
Experimental


Molecule
#[a]
energy[b]
energy[c]
metabolic rate[d]



















Methoxybenzene
2
21.79
30.8
15-24



3
22.41
11.1
1-3



4
21.40
58.1
62-75


Chlorobenzene
2
22.81
8.0
17-19



3
22.58
11.6
5-9



4
21.39
80.4
71-79





[a]# indicates the atomic number of each molecule in formula 4;


[b]activation energy predicted by the method of the present invention;


[c]metabolic rate induced by introducing the predicted activation energy [b] into the Arrhenius equation; and


[d]in vitro experimental metabolic rate.






The experimental metabolic rates of the molecules shown in Table 19 above are already known in the art. Specifically, the experimental metabolic rate of methoxybenzene can be found in the literature [Robert P. Hanzlik, Kerstin Hogberg, Charles M. Judson. Microsomal hydroxylation of specifically deuterated monosubstituted benzenes. Evidence for direct aromatic hydroxylation. Biochemistry. 1984, 23, 3048-3055]; and the chlorobenzene can be found in the literature [H. G. Selander, D. M. Jerina, J. W. Daly. Metabolism of Chlorobenzene with Hepatic Microsomes and Solubilized Cytochrome P-450 Systems. Arch. Biochem. Biophys. 1975, 168, 309-321].
As can be seen in Table 19 above, when the metabolic rates (induced by substituting into the Arrhenius equation the activation energies for hydrogen abstraction from the two molecules, methoxybenzene and chorobenzene, predicted according to the present invention) were compared with the experimental metabolic rates[d], these metabolic rates showed similar tendencies. This suggests that the experimental metabolic rates can be predicted through the activation energies predicted according to the present invention.
As described above, the method of the present invention can rapidly predict activation energy for phase I metabolites at a practical level without having to perform a docking experiment between any additional CYP450 and the substrate, or a quantum mechanical calculation, thereby making it easier to develop new drugs using a computer. Also, the present invention may propose a strategy for increasing the bioavailability of drugs through the avoidance of metabolites based on the possibility of drug metabolism. Furthermore, the method of the present invention proposes new empirical approaches which can also be easily applied to activation energies for various chemical reactions, and makes it possible to explain physical and chemical factors that determine activation energy. In addition, through the prediction of activation energy according to the present invention, it is possible to predict i) metabolic products, ii) the relative rate of metabolism, iii) metabolic regioselectivity, iv) metabolic inhibition, v) drug-drug interactions, and vi) the toxicity of a metabolite.
The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative, and not restrictive. The scope of the invention is, therefore, indicated by the appended claims, rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within the scope of the present invention.

## Claims

1. A method for constructing a database of atomic fingerprint descriptors by a processor for predicting a reaction rate for a cytochrome P450-mediated reaction on a substrate, the method comprising the steps of:
(i) providing an atomic fingerprint descriptor of a substrate, which is obtained by characterizing one or more atomic positions of the substrate according to the following Equation 1:


Xabc  [Equation 1]


wherein X is the chemical symbol of an atom; a is a bond indicator noting the number of atoms bonded; b is a ring indicator noting whether the atom is part of a ring; and c is an aromatic indicator noting whether the atom is an aromatic atom;
(ii) calculating an activation energy for the one or more atomic positions of step (i) based on two or more atomic descriptors which valuate atomic interactions at said atomic position,
wherein the two or more atomic descriptors are selected from the group consisting of [δhet], [max(δheavy)], [μC—H],





  
    [
    
      
        ∑
        i
        
          R
          .
          C
          .
        
      
      ⁢
      
        α
        i
      
    
    ]
  
  ,




[δH], and [mean (αalpha)];
(iii) predicting a reaction rate for a cytochrome P450-mediated reaction by introducing the calculated activation energy obtained in step (ii) into an Arrhenius equation;
(iv) performing a statistical correlation which relates the predicted reaction rate of step (iii) with an experimental reaction rate; and
(v) storing the predicted reaction rate, the experimental reaction rate, and the statistical correlation for each atomic fingerprint descriptor of the substrate, into a database of atomic fingerprint descriptors for predicting reaction rates for cytochrome P450-mediated reactions of a substrate.

2. The method of claim 1, wherein the cytochrome P450-mediated reaction on a substrate is aliphatic hydroxylation or aromatic hydroxylation.

3. The method of claim 1, wherein the cytochrome P450-mediated reaction on a substrate is N-dealkylation, C-hydroxylation, N-oxidation, or O-dealkylation.

4. The method of claim 1, wherein an enzyme catalyzing the cytochrome P450-mediated reaction is selected from the group consisting of CYP2E1, CYP3A4, CYP2B6, CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1, and CYP2A6.

5. The method of claim 1, wherein the cytochrome P450-mediated reaction is a cytochrome P450-mediated hydrogen abstraction on a substrate of the following Formula 1:



wherein the circle together with Fe—O indicates an oxyferryl intermediate;
wherein the two or more atomic descriptors are [δhet], [max(δheavy)], [μC—H], and





  
    [
    
      
        ∑
        i
        
          R
          .
          C
          .
        
      
      ⁢
      
        α
        i
      
    
    ]
  
  ;




and
wherein [δhet] indicates the net atomic charge of a heteroatom in the alpha-position relative to the reaction center, [max(δheavy)], indicates the highest atomic charge in X1, X2 and X3 which are neither hydrogen nor helium, [μC—H] indicates the bond dipole of the carbon-hydrogen bond, and





  [
  
    
      ∑
      i
      
        R
        .
        C
        .
      
    
    ⁢
    
      α
      i
    
  
  ]




indicates the sum of the atomic polarizabilities of H, C, X1, X2 and X3.

6. The method of claim 5, wherein the activation energy is calculated in step (ii) according to the following equation:


EaHabs—(B)=25.94+1.88*[δhet]+1.03*[max(δheavy)]

wherein EaHabs—(B) indicates activation energy required for abstraction of hydrogen attached to a carbon atom having a heteroatom in the alpha-position relative to the reaction center.

7. The method of claim 5, wherein the activation energy is calculated in step (ii) according to the following equation:




  
    E
    a
    
      Habs_
      ⁢
      
        (
        A
        )
      
    
  
  =
  
    28.50
    -
    
      2.22
      *
      
        [
        
          μ
          
            C
            -
            H
          
        
        ]
      
    
    +
    
      1.12
      *
      
        [
        
          
            ∑
            i
            
              R
              .
              C
              .
            
          
          ⁢
          
            α
            i
          
        
        ]
      
    
  



wherein EaHabs—(A) indicates activation energy required for abstraction of hydrogen attached to a carbon atom having no heteroatom in the alpha-position relative to the reaction center.

8. The method of claim 1, wherein the cytochrome P450-mediated reaction is formation of a tetrahedral intermediate which occurs via a cytochrome P450-mediated aromatic hydroxylation on a substrate of the following Formula 2:



wherein the circle together with Fe—O indicates an oxyferryl intermediate;
wherein the two or more atomic descriptors are [δH] and [mean(αalpha)]; and
wherein [δH] indicates the net atomic charge of the hydrogen of the substrate and [mean(αalpha)] indicates the mean value of the polarizabilities of adjacent carbon atoms.

9. The method of claim 8, wherein the activation energy is calculated in step (ii) according to the following equation:


Eaaro—o,p=21.34−0.75*[δH]−1.24*[mean(αalpha)]

wherein Eaaro—o,p indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the ortho/para-position.

10. The method of claim 8, wherein the activation energy is calculated in step (ii) according to the following equation:


Eaaro—m=22.14−0.68*[δH]−0.83*[mean(αalpha)]

wherein Eaaro—m indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the meta-position.

11. The method of claim 8, wherein the activation energy is calculated in step (ii) according to the following equation:


Eaaro—0,2,3=21.02−1.49*[δH]−0.92*[mean(αalpha)]

wherein Eaaro—0,2,3 indicates the activation energy for tetrahedral intermediate formation in a benzene having 0, 2, or 3 substituents.

12. A method for a processor to calculate an activation energy for a cytochrome P450-mediated hydrogen abstraction on a substrate of the following Formula 1:



wherein the circle together with Fe—O indicates an oxyferryl intermediate; and
wherein the atomic position comprises two or more atomic descriptors selected from the group consisting of [δhet], [max(δheavy)], [μC—H], and





  
    [
    
      
        ∑
        i
        
          R
          .
          C
          .
        
      
      ⁢
      
        α
        i
      
    
    ]
  
  ;




and
wherein [δhet] indicates the net atomic charge of a heteroatom in the alpha-position relative to the reaction center, [max(δheavy)] indicates the highest atomic charge in X1, X2 and X3 which are neither hydrogen nor helium, [μC—H] indicates the bond dipole of the carbon-hydrogen bond, and





  [
  
    
      ∑
      i
      
        R
        .
        C
        .
      
    
    ⁢
    
      α
      i
    
  
  ]




indicates the sum of the atomic polarizabilities of the atoms H, C, X1, X2 and X3;
the method further comprise making a comparison of the calculated activation energy to a database of atomic fingerprint descriptors;
wherein the comparison provides a prediction regarding one or more of metabolic products, relative rate of metabolism, metabolic regioselectivity, metabolic inhibition, drug-drug interactions, and toxicity of a metabolite.

13. The method of claim 12, wherein the cytochrome P450-mediated hydrogen abstraction is catalyzed by a cytochrome P450 enzyme selected from the group consisting of CYP2E1, CYP3A4, CYP2B6, CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1, and CYP2A6.

14. The method of claim 12, wherein hydrogen abstraction can occur if a C atom of a C—H bond in the substrate is an aliphatic carbon.

15. The method of claim 12, wherein the two or more atomic descriptors are [δhet] and [max(δheavy)] when there is a heteroatom in the alpha-position relative to the reaction center and wherein the activation energy is calculated according to the following equation:


EaHabs—(B)=25.94+1.88*[δhet]+1.03*[max(δheavy)].

16. The method of claim 12, wherein the two or more atomic descriptors are [μC—H] and




  [
  
    
      ∑
      i
      
        R
        .
        C
        .
      
    
    ⁢
    
      α
      i
    
  
  ]




when there is no heteroatom in the alpha-position relative to the reaction center and wherein the activation energy is calculated according to the following equation:




  
    E
    a
    
      Habs_
      ⁢
      
        (
        A
        )
      
    
  
  =
  
    28.50
    -
    
      2.22
      *
      
        [
        
          μ
          
            C
            -
            H
          
        
        ]
      
    
    +
    
      1.12
      *
      
        
          [
          
            
              ∑
              i
              
                R
                .
                C
                .
              
            
            ⁢
            
              α
              i
            
          
          ]
        
        .

17. A method for a processor to calculate an activation energy for formation of a tetrahedral intermediate by a cytochrome P450-mediated aromatic hydroxylation at an atomic position in a substrate of the following Formula 2:



wherein the circle together with Fe—O indicates an oxyferryl intermediate;
wherein the atomic position comprises atomic descriptors [δH] and [mean(αalpha)]; and
wherein [δH] indicates the net atomic charge of the hydrogen of the substrate and [mean(αalpha)] indicates the mean values of polarizabilities of adjacent carbon atoms;
the method further comprises making a comparison of the calculated activation energy to a database of atomic fingerprint descriptors;
wherein the comparison provides a prediction regarding one or more of metabolic products, relative rate of metabolism, metabolic regioselectivity, metabolic inhibition, drug-drug interactions, and toxicity of a metabolite.

18. The method of claim 17, wherein the cytochrome P450-mediated aromatic hydroxylation is catalyzed by a cytochrome P450 enzyme selected from the group consisting of CYP2E1, CYP3A4, CYP2B6, CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1, and CYP2A6.

19. The method of claim 17, wherein aromatic hydroxylation can occur if a C atom of a C—H bond in the substrate is an aliphatic carbon.

20. The method of claim 17, wherein the atomic descriptors [δH] and [mean(αalpha)] are determined.

21. The method of claim 20, wherein the activation energy is calculated according to the following equation:


Eaaro—o,p=21.34−0.75*[δH]−1.24*[mean(αalpha)]

wherein Eaaro—o,p indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the ortho/para-position.

22. The method of claim 20, wherein the activation energy is calculated according to the following equation:


Eaaro—m=22.14−0.68*[δH]−0.83*[mean(αalpha)]

wherein Eaaro—m indicates the activation energy for tetrahedral intermediate formation in a benzene having one substituent in the meta-position.

23. The method of claim 20, wherein the activation energy is calculated according to the following equation:


Eaaro—0,2,3=21.02−1.49*[δH]−0.92*[mean(αalpha)]

wherein Eaaro—0,2,3 indicates the activation energy for tetrahedral intermediate formation in a benzene having 0, 2, or 3 substituents.