Title: Excipient Interaction Prediction: Application of the Purdue Ontology for Pharmaceutical Engineering
1Excipient 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
2Drug 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
3Information 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)
4Purdue 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.)
5POPE Overview
- Information Modeling (POPE-Im)
- Knowledge Modeling (POPE-Km)
- Mathematical Modeling (POPE-Mm)
6Applications
- 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.)
7Proposed 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
8Reaction 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
9Reaction Score Table for Cycloserine
10Result
- 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
11Summary
- 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 13Score-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
14Purdue 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
15Purdue 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
16Purdue 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
17Purdue 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
18Property 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
19Purdue 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
20Unit Operation Ontology
- Previous Work Model.LA, ISO 1303, OntoCAPE
- Lack of integration with experiments
- Example boiling, drying
- Application unit operation modeling
21Equipment 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
22Value 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