Indiana University-Purdue University Indianapolis (IUPUI) / Drug-Drug Interaction (DDI) is one of the major causes of adverse drug reaction (ADR) and
has been demonstrated to threat public health. It causes an estimated 195,000
hospitalizations and 74,000 emergency room visits each year in the USA alone. Current
DDI research aims to investigate different scopes of drug interactions: molecular level of
pharmacogenetics interaction (PG), pharmacokinetics interaction (PK), and clinical
pharmacodynamics consequences (PD). All three types of experiments are important, but
they are playing different roles for DDI research. As diverse disciplines and varied studies
are involved, interaction evidence is often not available cross all three types of evidence,
which create knowledge gaps and these gaps hinder both DDI and pharmacogenetics
research.
In this dissertation, we proposed to distinguish the three types of DDI evidence (in vitro
PK, in vivo PK, and clinical PD studies) and identify all knowledge gaps in experimental
evidence for them. This is a collective intelligence effort, whereby a text mining tool will
be developed for the large-scale mining and analysis of drug-interaction information such
that it can be applied to retrieve, categorize, and extract the information of DDI from
published literature available on PubMed. To this end, three tasks will be done in this
research work: First, the needed lexica, ontology, and corpora for distinguishing three
different types of studies were prepared. Despite the lexica prepared in this work, a
comprehensive dictionary for drug metabolites or reaction, which is critical to in vitro PK study, is still lacking in pubic databases. Thus, second, a name entity recognition tool will
be proposed to identify drug metabolites and reaction in free text. Third, text mining tools
for retrieving DDI articles and extracting DDI evidence are developed. In this work, the
knowledge gaps cross all three types of DDI evidence can be identified and the gaps
between knowledge of molecular mechanisms underlying DDI and their clinical
consequences can be closed with the result of DDI prediction using the retrieved drug
gene interaction information such that we can exemplify how the tools and methods can
advance DDI pharmacogenetics research. / 2 years
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/14594 |
| Date | 15 August 2017 |
| Creators | Wu, Heng-Yi |
| Contributors | Jones, Josette, Li, Lang, Palakal, Mathew, Wu, Huanmei |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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