Excipient Interaction Prediction: Application of the Purdue Ontology for Pharmaceutical Engineering - PowerPoint PPT Presentation

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Excipient Interaction Prediction: Application of the Purdue Ontology for Pharmaceutical Engineering

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A drug product includes the active pharmaceutical ingredient (API) and inactive' excipients ... Example: Cycloserine (Substance) has carbonyl, ether, amine Fragments ... – PowerPoint PPT presentation

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Title: Excipient Interaction Prediction: Application of the Purdue Ontology for Pharmaceutical Engineering


1
Excipient Interaction Prediction Application of
the Purdue Ontology for Pharmaceutical
Engineering (POPE)
  • L. Hailemariam, P. Suresh, P. Akkisetty, G.
    Joglekar,
  • S.-H. Hsu, A. Jain, K. R. Morris, G. V.
    Reklaitis, P. Basu,
  • V. Venkatasubramanian
  • School of Chemical Engineering, Purdue University
  • ESCAPE 18, Lyon, France
  • June, 2008

2
Drug Product Stability
  • A drug product includes the active pharmaceutical
    ingredient (API) and inactive excipients
  • Primary drug degradation reactions are
    hydrolysis, oxidation and photolysis
  • Affected by Temperature, pH, etc. (environmental
    conditions)
  • Reaction prediction systems
  • Computational Chemistry (Mechanistic Computation)
  • SPARTAN (Wavefunction) transition state geometry
  • Expert Systems MetabolExpert
  • Database Access
  • ROBIA (Reaction Outcomes By Informatics
    Analysis) rules
  • DELPHI (Degradant Expert Leading to
    PHarmaceutical Insight)
  • Little use of reaction environment information in
    reaction prediction
  • Challenge of integrating different forms of
    information

3
Information Structure
  • Ontology is the explicit description of domain
    concepts and relationships between concepts

has
Material
has
Property
Value
Water
Boiling point
100 0C
has
a
of
  • Ontology captures consensual knowledge, data
    representation and logic
  • Ontology Language Web Ontology Language (OWL)

4
Purdue Ontology for Pharmaceutical Engineering
  • Knowledge Representation
  • Guideline knowledge actions, decisions1
  • Mathematical knowledge math models (equations,
    assumptions, etc.)2
  • Information Model
  • Phase description (phase, composition)
  • Molecular structure description (fragments,
    connection)
  • Reactions (model, participants)
  • Material properties (density, flow, heat
    capacity)
  • Experiments (procedure, settings)
  • Unit operations (streams, interaction)
  • Equipment and Value
  • ? Superset Purdue Ontology for Pharmaceutical
    Engineering (POPE)

1Poster session 2Session 5.3, 1120, June 4 (Wed.)
5
POPE Overview
  • Information Modeling (POPE-Im)
  • Knowledge Modeling (POPE-Km)
  • Mathematical Modeling (POPE-Mm)

6
Applications
  • Decision Support for Product Development1
  • Decision steps modeled explicitly
  • Ontology instances accessed for property values
  • Mathematical Modeling of Unit Operations2
  • Input/output, assumptions, solution method
    described explicitly
  • Ontology instances accessed for property values
  • Reaction Prediction
  • Instance comparison, reasoning, database link

1Poster session 2Session 5.3, 1120, June 4 (Wed.)
7
Proposed Reaction Prediction System
  • ? An ontology-based approach proposed to assist
    reaction prediction
  • Ontology supports both data access and reasoning
  • Which reaction may occur and why
  • Link to relational database developed
  • Source Reaction databases MerckIndex,
    SigmaAldrich, Metasynthesis
  • CDK tools used to identify presence of common
    fragments

8
Reaction Prediction Application
Molecular Structure Information SMILES
string e.g. NC1ONC1O
Environmental Information text e.g. T25 C
Molecular Structure Ontology
Reaction Ontology
Deterministic Search Rule engine e.g. Substance
(Cycloserine) has_fragments(Cycloserine,
?a) participates_in_rxns(?a,?b)
has_temperature(?b,?c)
Score-based Search Molecular and environmental
similarity e.g. Tanimoto Score
  • Result
  • Test case 10 blockbuster drugs in accelerated
    testing
  • 50 of predicted reactions found in literature
  • 73 of known reactions predicted

DEMO
9
Reaction Score Table for Cycloserine
10
Result
  • Predicted reactions compared with open literature
  • 50 of predicted reactions found in lit. 73 of
    reported reactions predicted

O predicted, not reported in literature ?
predicted and reported x reported in literature
11
Summary
  • Presence of undesired reactions a major problem
    for pharmaceutical products
  • An ontology-based approach developed to assist
    information integration at multiple levels and
    reasoning for reaction prediction
  • The Purdue Ontology for Pharmaceutical
    Engineering (POPE) developed to describe
    materials, chemical structures, reactions,
    material properties and experiments
  • The system predicted most of the reported
    reactions for the test set
  • Future work includes incorporation of more
    chemistry descriptors and evaluation of multiple
    similarity measures

12
  • Appendix

13
Score-based reaction prediction
  • Considerable granularity in reaction prediction.
  • Secondary interactions (van-der-Waal ) affect
    reactivity
  • ? Suggest reactions, ranked by likelihood/similari
    ty to new environment
  • Rxn similarity mostly based on molecule
    similarity, Tanimoto Measure used

a, b of fragments of molecule 12 c number
of common fragments
  • Structural similarity (FragmentRing) common
    fragments, ring size coincidence
  • Environmental similarity Phase, Temperature, pH,
    RH, PSD range overlap
  • Structural Match Score Fragment Match
    ScoreRing Match Score
  • Total Match Score Structure Match
    ScoreEnvironment Match Score

14
Purdue Ontology for Material Entities (POME)
  • Previous Work Model.LA, ISO 10303, OntoCAPE
  • Example Substance H2O Phase system ice, steam
  • Application reaction prediction, formulation
    decision support system

11/14/2009
15
Purdue Ontology for Molecular Structures (POMS)
  • Previous Work ChEBI, Hsu et al (2006)
  • Constructed by looking at patterns in drug
    degradation reactions
  • 32 fragments, rings checkmol list (156) PubChem
    (880)
  • Example Cycloserine (Substance) has carbonyl,
    ether, amine Fragments
  • Application reaction prediction

16
Purdue Ontology for Reaction Expression (PORE)
  • Previous Work Hsu et al (LIPS)
  • Example hydrolysis, oxidation polymorphism,
    melting, condensation
  • Application reaction prediction, unit operation
    modeling

11/14/2009
17
Purdue Ontology for Material Properties (POMP)
  • Previous Work OntoCAPE, ISO10303, Model.LA
  • Little treatment of solid properties, lack of
    integration with experiments
  • Example angle of repose, powder density, heat
    capacity
  • Application formulation decision support system,
    unit operation modeling

11/14/2009
18
Property Hierarchy
  • Phase System Properties
  • Interphase Properties
  • Solid Properties
  • Particle Properties
  • Crystalline Properties
  • Powder Properties
  • Mechanical Properties
  • Micromeritics
  • Thermodynamic Properties
  • Energetics
  • Physical Chemistry
  • Substance Properties
  • Chemical Constants
  • Molecular Properties

19
Purdue Ontology for Description of Experiments
(PODE)
  • Previous Work EMB, GAML
  • Lack of integration with properties
  • Example Hausner Ratio measurement, HPLC
  • Application experiment analysis

11/14/2009
20
Unit Operation Ontology
  • Previous Work Model.LA, ISO 1303, OntoCAPE
  • Lack of integration with experiments
  • Example boiling, drying
  • Application unit operation modeling

21
Equipment Ontology
  • Previous Work CLiP, Sunagawa et al (2003), Lohse
    et al (2006)
  • Example Reactor has inlet port, outlet port has
    volume 1 m3
  • Application experiment analysis, unit operation
    modeling

22
Value Ontology
  • Previous Work EngMath, ISO 10303
  • Developed primarily for math modeling
  • Example Angle of Repose has Range 51,54
  • Application formulation decision support, unit
    operation modeling, reaction prediction,
    experiment analysis
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