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Meteor Reasoning Methodologies

Features & Benefits

Features

  • Meteor Nexus version 3.0.0 has been improved to contain three different methodologies for assessing the likelihood of observed metabolites. These are:
    • Absolute/Relative Reasoning: This is the original Meteor Nexus prediction methodology. Ideal for those who wish to have the maximum predictive coverage of biotransformations, to spot obscure metabolites not necessarily identified experimentally.
    • Static Scoring (Occurrence Ratio): This methodology uses a pre-computed score for how predictive a biotransformation is, based on experimental data, and orders the predicted metabolites according to this score.
    • Site of Metabolism Scoring (SOM) (with Molecular Mass Variance): This methodology builds on the Static Scoring methodology by tailoring the score using experimental data for compounds that match the same biotransformation, have similar molecular weights and are chemically similar around the site of metabolism to the query compound. In addition:
      • The site of metabolism is highlighted.
      • The similar compounds which were used to modify the score are displayed.
    • Toxicity information is provided in the Meteor Nexus tree*: Select single or multiple metabolites and have toxicity information for those metabolites displayed directly in the Meteor tree:
      • Derek and Sarah Nexus predictions.
      • Information on toxicity data held within Vitic Nexus
    • Both the Static Scoring and Site of Metabolism Scoring rank metabolites in order of their scores. Users can then choose to see:
      • A certain number of the highest scoring metabolites.
      • Only metabolites that have a score within a certain threshold of the metabolite with the highest score.

Benefits

  • Reduction of perceived false positives:
    • The Static Scoring and Site of Metabolism Scoring methodologies have been developed to reduce the perceived “false positives” in Meteor Nexus, in that they both have the ability to reduce the appearance of experimentally unobserved metabolites.
  • Rapid identification of potentially toxic metabolites:
    • Users can choose to include toxicity predictions from Derek and Sarah Nexus and information on toxicity data held within Vitic Nexus directly within the Meteor output.*

 

*Relevant licenses are required for Derek, Sarah and Vitic Nexus.

Absolute

Absolute/Relative Reasoning

Using Nexus in the Lhasa Knowledge Suite, you submit your structures to Meteor Nexus. The structures are compared to the certified Lhasa knowledge base, or your own knowledge base. There is a tool in Nexus within the Lhasa Knowledge Suite that enables you to convert your own knowledge base into a format that is suitable for use with Meteor Nexus.

Meteor Nexus then predicts the first generation metabolites by using:

  • Lipophilicity
  • The presence or absence of structural features
  • The relative likelihood of competing transformations

Each metabolite is analysed to predict further metabolism and its level of likelihood is shown. The results are displayed in an interactive tree graphic along with supporting data, such as the reasoning behind each prediction, biotransformation details and metabolite path. The prediction is generated by applying expert knowledge rules in metabolism to the data returned from the knowledge base.

 

A detailed workflow, describing this methodology, is available for members here

Metabolism Data 

Lhasa Limited Metabolism Data

Lhasa Limited has developed a metabolism database (Lhasa Limited Metabolism Data 1.0.0) which is comprised of parent compounds, their experimentally seen metabolites, and the biotransformation reactions which occur. This data has been manually curated by Lhasa scientists from a variety of sources (including, but not limited to, Drug Metabolism and Disposition, Xenobiotica, Biochemical Pharmacology, Journal of Medicinal Chemistry, Journal of Pharmacology and Experimental Therapeutics) and is comprised of more than 17,000 reactions for more than 2,000 parent compounds. 

This curated metabolism data has been used to develop two new prediction methodologies that have now been implemented within Meteor Nexus version 3.0.0; Static Scoring and Site of Metabolism Scoring.

Static

Static Scoring

The metabolism database was used to assess how often a Meteor Nexus biotransformation actually occurs versus how often a biotransformation could occur resulting in the generation of an occurrence ratio for each Meteor Nexus biotransformation (Figure 2). This occurrence ratio is then multiplied by 1,000 to give a Static Score. Thus all biotransformations in Meteor Nexus have been given a pre-defined Static Score which is displayed to users in the prediction. 

When a user processes a compound through Meteor Nexus using the Static Scoring methodology, any biotransformations that fire will be displayed in the order of their Static Score (Figure 1). Users can control how many metabolites they are presented with by modifying the processing constraints associated with the methodology:

  • Top N threshold - the top N biotransformations are displayed and N can be determined by the user i.e. the user can request to see the N biotransformations with the highest Static Score
  • Relative threshold - the biotransformations that are displayed are at or above a percentage of the maximum score i.e. the user can request to see only biotransformations that have a Static Score within a defined percentage of the biotransformation with the highest static score.

It must be noted that Static Scores do not represent quantitative information.

 

Figure 1: A screenshot from Nexus v2.1 showing Rotigotine within the Meteor prediction tree. The Biotransformations are ranked in the table according to their score.

 

 

Figure 2: The Occurrence Ratio.

 A detailed workflow, describing this methodology, is available for members here

SOM

Site of Metabolism (SOM) Scoring (with Molecular Mass Variance)

The Site of Metabolism (SOM) Scoring with molecular mass variance is now the default setting in Meteor Nexus

The SOM methodology builds on the Static Scoring methodology by tailoring the score using experimental data for compounds (from the metabolism database) that match the same biotransformation, have similar molecular weights and are chemically similar around the site of metabolism. This is done by:

  1. Generating the atomic fingerprint of the site of metabolism of the query compound (the atomic fingerprint is generated to a depth of 8 bonds). (Figure 2)
  2. Identifying the examples in the metabolism database that match the same biotransformation as the query compound.
  3. Eliminating the examples that do not have a molecular weight within a particular percentage of the query compound (user configurable, but the default setting is 70%). (Figure 3)
  4. Generating the atomic fingerprint of the site of metabolism of each example compound. (Figure 2)
  5. Ordering the examples in terms of the similarities of their atomic fingerprints, of their sites of metabolism, to the query compound using the Tanimoto coefficient. (Figure 2)
  6. The most similar example compounds (user configurable, but the default is 8) are then used to modify the Static Score, either upwards or downwards depending on whether or not the biotransformation has been seen experimentally for them, to generate the Site of Metabolism Score.

When a user processes a compound through Meteor Nexus using the Site of Metabolism Scoring methodology, any biotransformations that fire will be displayed in the order of their Site of Metabolism Score (Figure 1).  Users can control how many metabolites they are presented with by modifying the processing constraints associated with the methodology:

  • Top N threshold - the top N biotransformations are displayed and N can be determined by the user i.e. the user can request to see the N biotransformations with the highest Static Score
  • Relative threshold - the biotransformations that are displayed are at or above a percentage of the maximum score i.e. the user can request to see only biotransformations that have a Static Score within a defined percentage of the biotransformation with the highest static score.

It must be noted that Site of Metabolism Scores do not represent quantitative information.

Figure 1: A screenshot from Nexus v2.1 of Rotigotine in the Meteor tree, the biotransformations are ordered in the table in terms of their score. Notice the differences compared to the Static Scores above. 

Take, for example, the case of caffeine, which can perform biotransformation 242 at two sites of metabolism (Figure 2). 

  • The sites of metabolism that biotransformation 242 can occur at are calculated for caffeine.
  • The atom fingerprint for each of these sites of metabolism is then determined to a depth of 8 bonds.

The diagram below shows how this is done for methyl group number 2 of caffeine. The product shown is known as the major metabolite1, which Meteor predicts as having a higher score.  

1B. Testa et al. Biochemistry of Redox Reactions (Metabolism of Drugs and Other Xenobiotics), Academic Press, 1994

Figure 2: Finding the Site of Metabolism Score for Caffeine.

Figure 3: Cyclosporine G has a large molecular weight and numerous sites of metabolism. Many example compounds will have matching sites of metabolism, but those that do not have a molecular weight within a particular percentage of the query compound are eliminated.

 

 A detailed workflow, describing this methodology, is available for members here

 

 

 

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