Data mining methodologies for pharmacovigilance

1

ABDELFATTAH AL ZAQQA
SCHOOL OF COMPUTER SCIENCE
PRINCESS SUMAYA UNIVERSITY FOR TECHNOLOGY

Abdelfattah Al Zaqqa, PSUT-Amman-Jordan

Data Mining
Methodologies for
Pharmacovigilance

Agenda


Introduction



Examples



Some facts of ADRs and drugs.



Pharmacovigilance



Phv methodologies



Data mining



Computational methodology-Pre-Marketing



Computational methodology-Post Marketing



Future perspectives


2

Introduction



Medicines: is the applied science or
practice of the diagnosis, treatment, and
prevention of disease.



Most medicines have both good and bad
effects.



Bad effects called Adverse Drug Reactions
(ADRs) , it differs from side effects.



Side effects whether therapeutic or
adverse

3


ADRs cause over 700,000
emergency department visits
each year in the United States

Example of ADRs and side effects
• Desired and undesired effects of an aspirin therapy
reduce your headache or
fever
reduce the ability of your
blood to clot

× bleeding of intestine


4

5

Facts
New drug may takes 10 years and
billions of dollars.



Drug interactions may also increase
the risk of ADRs

ADRs may cause over 100,000
deaths among hospitalized
patients each year.



ADRs is the fourth largest cause of
death in US
136 $ billion annual cost in US from
ADRs.

ADRs may led to withdrawals drug.










6

Pharmacovigilance (PhV)


Pharmacovigilance (PhV) is the science that
concerns with the
detection, assessment, understanding and
prevention of ADRs



Pharmacovigilance (PhV)=drug safety
surveillance



Surveillance for premarketing (i.e. Data from
preclinical & clinical trials) and post-marketing
(i.e. throughout a drug’s market life)




Phv trend to link the Preclinical
human safety with information
from post marketing.

Phv methodologies



7

Phv historically relied on biological
experiments or manual review of case
report
In vitro Safety Pharmacology
Profiling (SPP) is one of the
fundamental method for preclinical;
by testing compounds with
biochemical and cellular assays.
SPP still not efficient (cost and time)


Computational methodologies for PhV



Vast quantities and complexity of
data to be analyzed



Computational methods at both premarketing and post-marketing stages
are more efficient in time and cost (i.e.
can accurately detect ADRs in a
timely fashion)



SPP still not efficient (cost and time)


8



Datasets are available



EMA and NCA are example of
specialized companies that maintain
and develop database of ADRs

What is Data mining ?!


Data mining  the process of extracting previously unknown, valid and
actionable information from large information sources or databases



So what we will need to do this process?!


project goals: detection and prevention of ADRs



dataset acquisition: Available



data cleaning and preprocessing: organize the raw data obtained



data mining: extract useful information



data interpretation: Analysis of data



utilization: the act of using


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Computational methodology-Pre-Marketing



Most of existing research devoted to develop computational methods.



These research can be categorized into
I.

protein target-based.

II.

chemical structure-based approaches.

III.

integrative approach.


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Computational methodology-Pre-Marketing-Protein
target-based



Drugs typically work by activating or inhibiting the function of
a protein, which in turn results in therapeutic benefits to a
patient.



drugs with similar in vitro protein binding profiles tend to similar
side-effects, Fliri et al.



Fukuzaki et al, proposed a method to predict ADRs using subpathways “cooperative pathways” (pathways that function
together).



They developed an algorithm called CoopeRativE Pathway
Enumerator (CREPE) to select combinations of sub-pathways



it depends on the availability of gene-expression data
observed under identical conditions.


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CoopeRativE Pathway Enumerator
(CREPE)

V vertex, I itemset (activation conditions)

12

Computational methodology-PreMarketing-Protein target-based


More recently, Brouwers et al proposed that the side
effect similarity of drugs could be attributed to their target
proteins being close in a molecular network.



They proposed a pathway neighborhood measure to
assess the closest distance of drug pairs according to their
target proteins in the human protein protein interaction
network and found network neighborhoods to only
account for 5.8% of the side-effect similarities compared
to 64% by shared drug targets.


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Computational methodology-PreMarketing-Protein target-based


Pouliot et al. applied logistic regression (LR) models.



To identify potential ADRs manifesting in 19 specific
system organ classes (SOCs), as defined by the Medical
Dictionary for Regulatory Activities ,across 485
compounds in 508 BioAssays in the PubChem database.



The models were evaluated using leave-one-out-crossvalidation. The mean AUCs (area under the receiver
operating characteristic curve) ranged from 0.60 to 0.92
across different SOCs.


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Chemical Structure-based Approachpremarketing


It attempts to link ADRs to their chemical structure.



Bender et al, explore the correlation but the positive predictive was quit
low under 0.5. but at least he proved the concept.



Hammann et al, employed decision tree to determine the
chemical, physical, and structural properties of compounds that
predispose them to causing ADRs



Hammann focused on ADRs in centerla nervous system (CNS),liver, and
kidney.



Hammann decision tree model positive predictive accuracies ranging
from 78.9% to 90.2%.


15

Chemical Structure-based Approachpremarketing


Pauwels et al. developed a sparse canonical correlation analysis (SCCA)
method to predict high-dimensional side-effect profiles of drug molecules
based on the chemical structures.



They predict 1385 side effects in the SIDER DB from chemical structures of
888 approved drugs.



16

Pauwels et al best resulting AUC(area under curve) was between 0.6088
and 0.8932
• SCCA examines the
relationships of many
variables of diﬀerent
types simultaneously


Integrative Approach- premarketing


Huang et al. proposed a new computational framework to predict ADRs
by integrating systems biology data that include protein targets, proteinprotein interaction network, gene ontology (GO) annotation ,and
reported side effects. They predict heart-related ADRs (i.e. cardio
toxicity), which resulted in the highest AUC of 0.771.



Recently, Liu et al. investigated the use of phenotypic
information, together with chemical and biological properties of drugs, to
predict ADRs. using five machine learning algorithms: LR, Naïve Bayes
(NB), KNearest Neighbor (KNN), Random Forest (RF), and SVM.


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Integrative Approach


integration of chemical, biological, and phenotypic properties
outperforms the chemical structured-based method (from 0.9054 to 0.9524
with SVM) and has the potential to detect clinically important ADRs at
both preclinical and post-market phases for drug surveillance.


18

Post Marketing



many ADRs may still be missed
because the clinical trials are often
small, short, and biased by excluding
patients with comorbid diseases.



do not mirror actual clinical use
situations for diverse populations




(e.g. inpatient)
thus it is important to continue the
surveillance postmarket.


19

Computational methodology-Post
marketing-Data sources


Spontaneous reporting systems (SRSs) is the
core data-collection system for postmarketing drug surveillance since 1960. US
FDA and the VigiBase maintain such as these
report.



World Health Organization (WHO) manage
these SRSs.


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Post marketing-Spontaneous Reports



Disproportionality Analysis (DPA) involves frequency analyses of 2x2
contingency tables to quantify the degree to which a drug and ADR cooccurs “disproportionally” compared with what would be expected if
there were no association
ADR

No ADR

Total

Drug

a

b

N=a+b

No Drug

c

d

c+d

Total

M=a+c

B+d

T=a+b+c+d


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

Many approaches are applied the straightforward method is the
calculation of frequentist metrics
Association Measures

Definition

Relative Reporting Ratio (RRR)

(t * a) / (m * n)

Proportional Reporting Ratio (PRR)

(a * (t – n)) / (c * n)

Reporting Odds Ratio (ROR)

(a * d) / (c * b)

•

Definitions of the frequentist measures of association


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

Other algorithms were also developed but they are more complex, such
as gamma-Poisson shrinker (GPS) and the multi-item gamma-Poisson
shrinker (MGPS)



DPA methods are effective in detecting single Drug-ADR associations


23

Data Mining Algorithms



DPA methods are effective in detecting single Drug-ADR associations



Data mining for multi-item ADR associations.



Harpaz et al identified 1167 multi-item ADR associations Using a set of
162,744 reports submitted to the FDA in 2008, 67% were validated by a
domain expert



Tatonetti et al applied the bi clustering algorithm to identify drug groups
that share a common set of ADRs in SRS data.



They discovered ADRs between drugs that couldn’t be discovered using
DPA method.(e.g pravastatin and paroxetine had effect on blood
glucose)


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Post marketing -Electronic Medical
Records


Electronic Medical Records :is a computerized medical record created in
an organization that delivers care, EMRs contain not only detailed patient
information but also copious longitudinal clinical data.



EMR databases consist of data in two types formats:
(1) structured (e.g., laboratory data)


Several groups have employed computational methods on

structured or coded data in EMRs to identify specific ADR signals
(2) unstructured (narrative clinical notes).


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Structured & unstructured


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Records-structured data


Yoon et al, demonstrated laboratory abnormality to be a valuable source
for PhV by examining the odds ratio of laboratory abnormalities between a
drug-exposed and a matched unexposed group using 10 years of EMR
data.



Evaluation of their algorithm on 470 randomly selected drug-andabnormal-lab-event pairs produced a positive predictive value of 0.837
and negative predictive value of 0.659.


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Records-Unstructured Data


natural language processing (NLP) technique is required to extract the
needed information from unstructured data.



Wang et al first employed NLP techniques to extract drug-ADR



Link


28

Non-conventional Data Sources-Post
marketing
1.

Biomedical Literature



Shetty and Dalal retrieved articles (published between
1949 and2009), for prioritizing drug-ADR associations.



DPA was applied to identify statistically significant pairs
from the thousands of pairs in the remaining articles.



Evaluation showed that the method identified true
associations with 0.41 and 0.71 inprecision and
recall, respectively.


29

Non-conventional Data Sources
2. Health Forums


Data posted by users on health-related
websites may also contain valuable drug
safety information



mine drug-and-ADR from health –related
websites (e.g. DailyStrength
(http://www.dailystrength.org/))



System evaluation was conducted on a
manually annotated set of 3600 user
posts corresponding to 6 drugs. The
system was shown to achieve 0.78 in
precision and 0.70 in recall.


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Non-conventional Data Sources



Chee et al, aggregated individuals’
opinions and review of drugs and used
NLP technique to group drugs.



Some drugs were withdrawn from
based on these messages.


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Future perspectives





This presentation provides a general
overview of the current computational
methodologies applied for PhV. basic
concepts and highlight some
representative work
it is desirable to incorporate various
data sources into one framework to
understand ADRs.




Data mining algorithms are applicable
and useful to detect drugs
interactions.



EMR for ADR prediction is not readily
accessible for data mining, more
sophisticated studies and NLP
techniques is needed.



cause-and-effect relationships is an
intrinsically hard problem in data
mining and need to be further
investigated for the PhV application.

Useful links



https://www.mediguard.org



http://www.jmedicalcasereports.com



http://wwwstat.stanford.edu/~tibs/Correlate/



http://www.iom.edu/



http://www.smartlogic.com/



http://blogs.sas.com/content/jmp/201
2/06/04/disproportionality-analysis-iscoming-in-jmp-clinical-4-0/


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References


Oxford English Dictionary definition of "medicine“



Source: The Importance of Pharmacovigilance, WHO 2002



Budnitz, D.S., Pollock, D.A., Weidenbach, K.N.,Mendelsohn, A.B., Schroeder, T.J.
and Annest, J.L. National surveillance of emergency department visits for
outpatient adverse drug events. JAMA, 296, 15 (Oct 18 2006), 1858-1866.



Hopkins, A.L. Network pharmacology: the next paradigm in drug discovery.
Nat Chem Biol, 4, 11 (Nov 2008), 682-690.



Helma C, Gottmann E, Kramer S. Knowledge discovery and data mining in
toxicology. Stat Meth Med Res. 2000;9:329–58.



http://articles.mercola.com/sites/articles/archive/2012/02/11/leading-causesof-death-cost-for-us-economy.aspx



Mutsumi Fukuzaki, Mio Seki,Side Effect Prediction using Cooperative Pathways


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Thank you!


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