Data Mining in the Pharmaceutical Industry

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Data Mining in the Pharmaceutical Industry

• By Jerry Swartz

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Introduction

• Since I am a remote student, if there are
  questions, feel free to e-mail jswartz_at_ligand.com

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Pharmaceutical Development

• Four Stages of Drug Development
• Research finds new drugs
• Development tests and predicts drug behavior
• Clinical trials test the drug in humans
• Commercialization takes drug and sells it to
  likely consumers (doctors and patients)
• Ill show an example for the Research,
  Development, and Clinical Trials stages

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Research Stage

• Huge user of data mining tools and techniques
• Scientists run experiments to determine activity
  of potential drugs
• Uses high speed screening to test tens, hundreds,
  or thousands of drugs very quickly this
  generates microarray data

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Research Stage

• Bioinformatics is a general term for the
  information processing activities on data
  generated in Research Stage, especially
  microarray data
• General goal is to find activity on relevant
  genes or to find drug compounds that have
  desirable characteristics (whatever those may be)

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Research Stage

• Data mining techniques used
• Clustering
• Classification
• Neural networks

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Research Stage Example 1

• Goal Determine compounds with similar activity
• Why Compounds with similar activity may behave
  similarly
• When
• Have known compound and are looking for something
  better
• Dont have known compound but have desired
  activity and want to find compound that exhibits
  this activity

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Research Stage Example 1

• Sample data

Structure\Activity Alpha Beta Delta Gamma
CO2 0.07 0.88 0.62 0.09
H2O 0.80 0.54 0.32 0.79
H2O2 0.34 0.91 0.44 0.40
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Research Stage Example 1

• Cluster compounds that have similar activity
• We like behavior of H2O and want to see what
  compounds have similar activity
• Example derived from Application of
  Nearest-Neighbor and Cluster Analyses in
  Pharmaceutical Lead Discovery
• Clustering takes place based on similar activity
  using Euclidean distance.

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Research Stage Example 1

• For simplicity, distance in example is simply
  difference between Beta and Delta values, not
  Euclidean
• Distances

CO2 H2O H2O2
CO2 0.00 0.65 0.16
H2O 0.65 0.00 0.49
H2O2 0.16 0.49 0.00
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Research Stage Example 1

• Dendrogram

0.49
0.16
0.00
H2O2
CO2
H2O
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Research Stage Example 1

• Conclusion
• H2O2 and CO2 are most alike but,
• H2O2 behaves more like H2O than CO2 behaves like
  H2O

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Research Stage Example 1

• Variations
• Example clustering performed on activity
• Clustering could have been performed on structure
  (i.e. find chemically similar compounds)
• Clustering could have been performed on both
  structure and activity (called SAR Structure
  Activity Relationship, see next slide)

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Research Stage Example 1
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Development Stage

• Company thinks drug might have some benefit
• Undergoes testing in animals, human tissue to
  observe effect maybe limited human tests
• Determine how much drug to consume for desired
  effect
• How dangerous is drug?

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Development Stage

• Data mining techniques used
• Classification
• Neural networks

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Development Stage Example 2

• Goal Predict if treatment will aid patients
• Why If drug will not aid patients, what purpose
  does drug serve?
• When
• Have data supporting use of drug
• Have training data that shows effects of drug
  (positive or negative)
• Want to be able to predict which patients will
  benefit

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Development Stage Example 2

• Will treatment help sickle cell anemia patients?
• We have information like gender, body weight,
  disease state, etc.
• Feed these into neural network and predict
  whether patient will benefit from drug.
• Example derived from Prediction of Sickle Cell
  Anemia Patients Response to Hydroxyurea
  Treatment Using ARTMAP Network

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Development Stage Example 2

• Uses ARTMAP network which is similar to neural
  network
• Instead of activation function, uses choice
  function which compares two values
• Basically matches input to template and
  generates output
• If input is similar enough to template it
  generates the corresponding output

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Development Stage Example 2

• Imagine training data has one of two
  classifications (Yes and No)
• Network is trained for the Yes classifications
  and a snapshot is taken of the neural network.
• Network then trained for the No classifications
  and another snapshot is taken.
• Output is Yes or No, depending on whether the
  inputs are more similar to the Yes or the No
  training data.

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Development Stage Example 2

• ARTMAP

Imagine array of weights, one for each template
Template closest to input chosen.
Weight
Height
Patient Benefits?
Gender
Blood Pressure
Path of least resistance chosen for output.
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Clinical Trials Stage

• Company tests drugs in actual patients on larger
  scale
• Must keep track of data about patient progress
• Government wants to protect health of citizens,
  many rules govern clinical trials
• In USA, Food and Drug Administration oversees
  trials.

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Clinical Trials Stage

• Data mining techniques used
• Neural networks

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Clinical Trials Stage

• Data is collected by pharmaceutical company but
  undergoes statistical analysis to determine
  success of trial
• Data reported to FDA inspected closely. Too many
  negative reactions might indicate drug is too
  dangerous these are adverse events
• Adverse event might be medicine causing
  drowsiness
• Data mining performed by FDA, not as much by
  pharmaceutical companies

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Clinical Trials Stage Example 3

• Goal Detect when too many adverse events occur
  or detect link between drug and adverse event
• Why Too many adverse events linked to a drug
  might indicate drug is too dangerous or health of
  patient is at risk
• When
• As adverse events are reported to FDA
• Or when link is suspected

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Clinical Trials Stage Example 3

• Is a drug causing too many adverse events?
• We have number of reports of adverse events
  pertaining to drugs.
• Feed these into neural network and let network
  lead us to what is too many.
• Example derived from Data mining in the US
  Vaccine Adverse Event Reporting System (VAERS)
  early detection of intussusception and other
  events after rotavirus vaccination

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Clinical Trials Stage Example 3

• Sample data cells contain number of reports
  linking drug and adverse event

Adverse Event\Drug Tylenol Motrin Rotovirus
Coughing 1 2 1
Fever 4 5 2
Intussusception 1 3 5
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Clinical Trials Stage Example 3

• Uses Bayesian neural network
• Prior probability is probability that any report
  contains reference to adverse event
• Posterior probability is probability that report
  has link between drug and adverse event
• Determines strength of link between adverse
  event and drug (called Information Component or
  IC)
• More complicated than appears patient may
  consume multiple drugs which one caused adverse
  event?

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Clinical Trials Stage Example 3

• Bayesian Neural Network

Adverse Event
Strength of link between adverse event and drug
Drug
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Clinical Trials Stage Example 3

• Could be solved using Bayes Theorem and
  correlation techniques
• Number of possible drug/adverse event
  combinations is very, very, large
• Training data is from FDA, WHO databases
• Neural network hides statistical complexity
• Unfortunately details of NN like activation
  function and hidden nodes are unknown

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Data Mining Benefits

• Research Stage instead of trial and error, data
  mining can help find drugs that have desirable
  activity
• Development Stage data mining can help predict
  who will benefit from drug
• Clinical Trials Stage data mining protects
  patients and helps regulate drug testing
• Commercialization Stage data mining can
  optimize use of sales resources like manpower,
  advertising