Integrating Discovery, Development, and Commercial Data into

Data Mining

Jennifer Sloan

Data Mining Consultant

GlaxoSmithKline: US Pharma IT

15 September 2004

Data Mining Definition

Data Mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data that

may be used to make valid and accurate predictions.

Data Mining is a tool that allows us to

Identify problematic areas Control process variability Make concrete decisions on business needs Develop a model which can aid in future

business decisions

Commercial Data

Analyzing Multivariate Data

Managing Data Usage

Model Building

Multivariate Data Sets

Data are multivariate in nature

Large data sets containing multiple criteria within each observation

Comparing multiple vectors is nearly impossible without reducing to a single point

Here we view 5-dimensional information on one observation. Each point represents a prescriber and the color represents a Market Share increase or decrease. Overlapping distributions make this difficult to interpret and further analysis is required. Over 200K observations are represented in this graph.

The same observations are observed but now two-way interactions between the variables help us determine which variables are affecting market shifts and lead to constructing models which will predict prescriber behavior.

Drug Development

Drug Development Issues

Adverse Event Reporting System (AERS) Over 2 million AE reports and approximately 2000

drugs and biologics submitted to the FDA since 1968

Creates Extremely Complicated Matrix of Data Recently, Data Mining methods have helped

address this issue with the development of a method used to examine large databases for associations between drugs and AEs

Data Mining Algorithm

Multi-Item Gamma Poisson Shrinker (MGPS) Developed by William DuMochel (AT&T)

Through statistical modeling, this Empirical Bayesian method identifies higher-than-expected reporting relationships of drug-event combinations

Automated, web-based system with rapid drill-down capability

MGPS runs using all event terms and drugs in the AERS database and produces results for all drug-event combinations

MGPS: Significance

Handles Complex Stratification

(age, gender, year of report > 945 categories) Performs complex computations in minimal

amount of time: Much MORE EFFICIENT Real World Example:

Membership: PhRMA-FDAWorking Group

Chair: June Almenoff (GSK)

FDA Involvement

Involved PhRMA companies: Abbott, Allergan, AstraZeneca, Bristol-Myers Squibb, GlaxoSmithKline, Johnson & Johnson, Lilly, Merck, Novartis, Schering-Plough, Pfizer, Roche, Wyeth

Drug Discovery

SCAM—Statistical Classification of Activities of Molecules

Recursive partitioning customized for chemistry

Creates a structure activity relationship (SAR) mode7l

Handles large numbers of descriptors (> 1 million)

SCAM : Data Structure

1 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 11 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1

1 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 11 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 11 0 1 0 1 1 1 1 1 0 0 0 0 1 0 0 0 1 0 0 0 11 0 1 0 1 1 1 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1

1 0 1 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 11 0 1 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1

1 0 0 0 1 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 11 0 0 0 1 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 1

......

YY11

YY22

YY33

YY44

YYnn

ON S

HN

NO

ONH

......

BiologicalBiologicalActivitiesActivities

>100K>100K > 2 million> 2 million

SCAM’s Recursive Partitioning

n = 1614ave = 0.29sd = 0.73

n = 36ave = 2.60sd = 0.9

Signal 2.60 - 0.29t = = = 18.68

Noise 0.734 1 1 36 1614

+

FeaturerP = 2.03E-70aP = 1.30E-66

n = 1650Ave = 0.34SD = 0.81

SCAM Tree

Advantages of SCAM

Works for complex situations, mixtures and interactions.

Output is easy to understand and explain

High statistical power

Produces a valid answer

SCAM Drawbacks

Data greedy Only one view of the data Binary descriptors may be too “crude” Disposition of outliers is difficult Highly correlated variables may be obscured Higher order interactions may be masked

Concluding Remarks

Data Mining enables us to efficiently handle LARGE amounts of data

Data Mining allows us to perform analyses IN REAL TIME

Data Mining covers a wide array of topics in

drug industry and its benefits are plentiful

References

Almenoff, June S, et al. “Disproportionality Analysis Using Empirical Bayes Data Mining: A tool for the Evaluation of Drug Interactions in the Post-Marketing Setting.” Pharmacoepidemiology and Drug Safety,12, 517-521 (2003).

Donahue, Rafe. “An Overview of Data Mining in Drug Development and Marketing.” http://home.earthlink.net/~rafedonahue. May 2003.

Hawkins, D.M. and G.V. Kass, “Automatic Interaction Detection.” Topics in Applied Multivariate Analysis, ed. Hawkins, (1982).

Hawkins, D.M., S.S. Young and A. Rusinko. “Analysis of a Large Structure-Activity Data Set Using Recursive Partitioning.” QSAR, 16, 296-302 (1997).