Model For Glutamate Racemase inhibitors and Glutamate Racemase Antibacterial Agents

Abstract

Antibiotics that target the enzyme glutamate racemase are disclosed. Ligand based glutamate racemase inhibitors are developed using software to extract a pharmacophore model from a group of known glutamate racemase inhibitors. These compounds are assayed against S. pneumoniae and were shown to have antibacterial activity against the non-virulent strain R6 and against a multidrug resistant strain.

1 . A method of identifying antibacterial agents with enhanced pharmacokinetic properties comprising the steps of:
 a. Developing pharmacophore models based on known glutamate racemase inhibitors; and   b. Either
 i. Excluding models with more than one charged element; or 
 ii. Modifying elements in the pharmacophore models by replacing the negatively charged elements in the model with hydrogen-bond acceptor groups and the positively charged elements in the model with hydrogen-bond donor groups;
 To enhance pharmacokinetic properties of the models and obtain enhanced models; and 
 
   c. Identifying compounds by searching chemical databases for compounds comprising a structure closest to the enhanced models.   
     
     
         2 . (canceled) 
     
     
         3 . The method of  claim 1 , wherein the enhanced pharmacokinetic properties comprise lipophilicity and absorption properties. 
     
     
         4 . The method of  claim 3 , wherein the lipophilicity comprises increased lipophilicity relative to the known glutamate racemase inhibitors. 
     
     
         5 . The method of  claim 3 , wherein the absorption properties comprises increased membrane permeability relative to the known glutamate racemase inhibitors. 
     
     
         6 . The method of  claim 1 , wherein the step of developing the pharmacophore model comprises the steps of:
 a. Identifying known glutamate racemase inhibitors with biological activity and poor pharmacokinetic properties;   b. Identifying elements common to all the known glutamate racemase inhibitors; and   c. Developing models that contain about 3-6 common elements, preferably 5 elements.   
     
     
         7 . The method of  claim 6 , wherein the biological activity comprises antibacterial activity. 
     
     
         8 . The method of  claim 7 , wherein the biological activity is experimentally determined based on at least one of IC 50 , Ki, MIC value and any other experimental measure of biological activity. 
     
     
         9 . The method of  claim 1 , further comprising the step of selecting a remaining model with the highest (R 2 ) value and identifying compounds by searching chemical databases for compounds comprising a structure closest to the selected model. 
     
     
         10 . The method of  claim 1 , further comprising the steps of:
 a. Developing a quantitative structure-activity relationship (QSAR) model;   b. Selecting a QSAR model with the highest (R 2 ) value; and   c. Identifying compounds by searching chemical databases for compounds comprising a structure closest to the selected model.   
     
     
         11 . The method of  claim 10 , wherein the QSAR model predicts at least one of the IC 50 , Ki, MIC value and any other measure of biological activity of the compounds with an accuracy of at least about 70%, preferably 80%, more preferably 90%. 
     
     
         12 . The method of  claim 10 , further comprising the step of calculating the IC 50  value of the identified compounds. 
     
     
         13 . The method of  claim 10 , wherein the step of developing the QSAR model comprises the steps of:
 a. Identifying known glutamate racemase inhibitors with poor pharmacokinetic properties;   b. Classifying the known inhibitors into groups depending on their biological activity;   c. Creating a training set comprising about 25 inhibitors, wherein the training set comprises at least one known inhibitor from each group;   d. Creating a test set comprising the remaining known inhibitors;   e. Developing the QSAR model based on the training set;   f. Using the QSAR model to calculate at least one of the IC 50 , Ki, MIC value and any other measure of biological activity of the test set; and   g. Calculating the R 2  value by comparing the calculated IC 50 , Ki, MIC value and any other measure of biological activity of the test set with the known IC 50 , Ki, MIC value and any other measure of biological activity of the test set.   
     
     
         14 . The method of  claim 13 , wherein the step of classifying the known inhibitors by their biological activity comprises classifying the known inhibitors as highly active if they have an IC 50  value of less than 0.07, moderately active if they have an IC 50  value of 0.07-0.8, active if they have an IC 50  value of 0.8-10, slightly active if they have an IC 50  value of 10-100, and weakly active if they have an IC 50  of above 100. 
     
     
         15 . The method of  claim 1 , further comprising the steps of
 a. Calculating in silico pharmacokinetic properties of the identified inhibitors; and   b. Selecting the inhibitors comprising enhanced pharmacokinetic properties with respect to the known inhibitors.   
     
     
         16 . A pharmacophore model comprising the structure 
       
         
           
           
               
               
           
         
       
       wherein N9 represents a negative ionizable site, D7 a hydrogen bond donor site, A1 a hydrogen bond acceptor site and both R11 and R12 are aromatic ring sites. 
     
     
         17 . The model of  claim 16 , wherein the model comprises the ability to identify compounds with antibacterial activity. 
     
     
         18 . The model of  claim 17 , wherein the model comprises the ability to identify compounds with antibacterial activity against  Streptococcus pneumoniae.    
     
     
         19 . The model of  claim 17 , wherein the identified compound comprises the structure 
       
         
           
           
               
               
           
         
       
       wherein R comprises —CH3, —F, —Cl, and —Br. 
     
     
         20 . The model of  claim 19 , wherein the compound comprises 2-(2-(1H-indol-3-yl)ethylamino)-4-(4-fluorophenyl)-4-oxobutanoic acid. 
     
     
         21 . The model of  claim 19 , wherein the compound comprises 2-(2-(1H-indol-3-yl)ethylamino)-4-oxo-4-p-tolylbutanoic acid. 
     
     
         22 . The model of  claim 19 , wherein the compound comprises 2-(2-(1H-indol-3-yl)ethylamino)-4-(4-chlorophenyl)-4-oxobutanoic acid. 
     
     
         23 . The model of  claim 19 , wherein the compound comprises 2-(2-(1H-indol-3-yl)ethylamino)-4-(4-bromophenyl)-4-oxobutanoic acid. 
     
     
         24 . (canceled) 
     
     
         25 . (canceled) 
     
     
         26 . (canceled) 
     
     
         27 . (canceled) 
     
     
         28 . (canceled) 
     
     
         29 . A method of identifying antibacterial agents with enhanced pharmacokinetic properties comprising the steps of:
 a. Developing pharmacophore models based on known glutamate racemase inhibitors; and   b. Either
 i. Excluding models with more than one charged element; or 
 ii. Modifying elements in the pharmacophore models by replacing the negatively and positively charged elements in the model with neutral counterparts;
 To enhance pharmacokinetic properties of the models and obtain enhanced models; and 
 
   c. Identifying compounds by searching chemical databases for compounds comprising a structure closest to the enhanced models.   
     
     
         30 . The method of  claim 29 , wherein the neutral counterparts comprise a functional group that can interact with a polar group or charged group.