Title: Data Mining in the Pharmaceutical Industry
1Data Mining in the Pharmaceutical Industry
2Introduction
- Since I am a remote student, if there are
questions, feel free to e-mail jswartz_at_ligand.com
3Pharmaceutical 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
4Research 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
5Research 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)
6Research Stage
- Data mining techniques used
- Clustering
- Classification
- Neural networks
7Research 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
8Research Stage Example 1
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
9Research 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.
10Research 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
11Research Stage Example 1
0.49
0.16
0.00
H2O2
CO2
H2O
12Research Stage Example 1
- Conclusion
- H2O2 and CO2 are most alike but,
- H2O2 behaves more like H2O than CO2 behaves like
H2O
13Research 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)
14Research Stage Example 1
15Development 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?
16Development Stage
- Data mining techniques used
- Classification
- Neural networks
17Development 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
18Development 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
19Development 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
20Development 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.
21Development Stage Example 2
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.
22Clinical 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.
23Clinical Trials Stage
- Data mining techniques used
- Neural networks
24Clinical 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
25Clinical 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
26Clinical 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
27Clinical 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
28Clinical 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?
29Clinical Trials Stage Example 3
Adverse Event
Strength of link between adverse event and drug
Drug
30Clinical 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
31Data 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