Using Spotfire DecisionSite to Realize the Full Value of High-Throughput Screening ADME Data

Using Spotfire DecisionSite to Realize the Full Value of High-Throughput Screening ADME Data

Eric MilgramPfizer Global Research & Development – La Jolla

Spotfire Users’ Group Meeting

Wednesday October 15, 2003

San Francisco, California

Eric MilgramPfizer Global Research & Development – La Jolla

Spotfire Users’ Group Meeting

Wednesday October 15, 2003

San Francisco, California

Challenge faced by Pharmaceutical IndustryChallenge faced by Pharmaceutical Industry

Reduce Attrition

Increase Productivity

No growth

Budgetary Pressure

Reduce Attrition

Increase Productivity

No growth

Budgetary Pressure $0

$5

$10

$15

$20

$25

Source: PhRMA annual survey, 2000

Cost and Number of NDAs per year

Why Do Candidates Fail?

Drug Discovery Today 2:436, 1997

198 NCEs

Pharmacokinetics39%

30%

11%

10%

5%5%

Lack of Efficacy

Animal Toxicity

Adverse effectsin man

MiscellaneousCommercial Reasons

The Fate of a Medication after AdministrationADME

• Absorption

• The movement of drugs into the bloodstream or lymphatic system from the site of administration

• Distribution

• The distribution of absorbed drugs from the absorption site to all areas of the body

• Metabolism

• The biotransformation of drugs to more polar forms (hydrolysis, oxidation, conjugation, etc.)

• Excretion

• The elimination of “unwanted” substances

Drug Metabolism in Drug Discovery

• Early assessment is critical, since the duration of action is dependent on structural modifications induced by in vivo metabolizing systems.

• Early knowledge of metabolic products permits metabolism guided structure modification schemes, such as modification of metabolic “soft spots” to achieve prolonged drug action.

• Identify pharmacologically or toxicologically active metabolites.

Physicochemical & Biochemical In- Vitro Assays

• Solution Properties• Solubility

• Log D

• Protein Binding

• pKa

• Absorption• PAMPA

• Caco-2, MDCK

• Pgp transport

• IAM

• Metabolism• Metabolic stability

• Liver microsomes, S-9, hepatocytes

• Metabolic profile

• CYP450 enzyme inhibition

• Safety Assessment• Cell viability

• Mutagenesis (Ames)

• Glutathione level

• Dofetilide binding

Predictive of In-Vivo Absorption, Distribution, Metabolism, Excretion

Pritchard, et al., “Making Better Drugs: Decision Gates in Non-Clinical Drug Development”Nature Reviews: Drug Discovery, 2003, vol 2(7), pp. 542-553. (http://www.nature.com/reviews/)

Advances in Laboratory Robotics and Instrumentation Have Been SwiftAdvances in Laboratory Robotics and Instrumentation Have Been Swift

Difficulties Resulting from HTSDifficulties Resulting from HTS

The rate at which we can collect data far exceeds our capacity to transform this data into information that can be used most effectively to drive important business decisions

Relevance of data?

Number of data dimensions?

What do we do when two different dimensions are in conflict?

Unmasking subtleties (ie “data-mining”)

The rate at which we can collect data far exceeds our capacity to transform this data into information that can be used most effectively to drive important business decisions

Relevance of data?

Number of data dimensions?

What do we do when two different dimensions are in conflict?

Unmasking subtleties (ie “data-mining”)

Visualization is a Powerful Tool For Data AnalysisVisualization is a Powerful Tool For Data Analysis

Spotfire can be used to find trends related to how samples are formatted on plates.Spotfire can be used to find trends related to how samples are formatted on plates.

Scatter Plot

WELL_COLUMN

PLATE_NUMBER - 1

PLATE_NUMBER - 6

PLATE_NUMBER - 11

PLATE_NUMBER - 16

PLATE_NUMBER - 2

PLATE_NUMBER - 7

PLATE_NUMBER - 12

PLATE_NUMBER - 23

PLATE_NUMBER - 3

PLATE_NUMBER - 8

PLATE_NUMBER - 13

PLATE_NUMBER - 24

PLATE_NUMBER - 4

PLATE_NUMBER - 9

PLATE_NUMBER - 14

PLATE_NUMBER - 5

PLATE_NUMBER - 10

PLATE_NUMBER - 15

2 4 6 8 10 12 2 4 6 8 10 12 2 4 6 8 10 12 2 4 6 8 10 12 2 4 6 8 10 12

A

B

CD

EF

G

H

A

B

CD

EF

G

H

A

B

CD

EF

G

H

A

B

CD

EF

G

H

17-Apr-2003 3:59:09 PM 20-Apr-2003

Internal Standard Signal vs Acquisition DateIS

Pea

k A

rea

Acquisition Date

Dirty MS Source

Liquid HandlingError Inconsistent Sample

Handling

Figure 3. Spotfire plot of variation in the internal standard for a series of Caco-2 assay analyses.

17-Apr-2003 3:59:09 PM 20-Apr-2003

Internal Standard Signal vs Acquisition DateIS

Pea

k A

rea

Acquisition Date

Dirty MS Source

Liquid HandlingError Inconsistent Sample

Handling

17-Apr-2003 3:59:09 PM 20-Apr-2003

Internal Standard Signal vs Acquisition DateIS

Pea

k A

rea

Acquisition Date

17-Apr-2003 3:59:09 PM 20-Apr-2003

Internal Standard Signal vs Acquisition DateIS

Pea

k A

rea

Acquisition Date

17-Apr-2003 3:59:09 PM 20-Apr-2003

Internal Standard Signal vs Acquisition DateIS

Pea

k A

rea

Acquisition Date

Dirty MS Source

Liquid HandlingError Inconsistent Sample

Handling

Figure 3. Spotfire plot of variation in the internal standard for a series of Caco-2 assay analyses.

How do we use Spotfire DecisionSite to Allocate Resources Efficiently?How do we use Spotfire DecisionSite to Allocate Resources Efficiently?

Quality Control and Quality Assurance

Results Analysis and Trending

Quality Control and Quality Assurance

Results Analysis and Trending

When combined with chemometrics techniques, such as principal components analysis (PCA), Spotfire enables viewing of interesting trends in large, multidimensional data sets.

When combined with chemometrics techniques, such as principal components analysis (PCA), Spotfire enables viewing of interesting trends in large, multidimensional data sets.

PCA (2 components) For LJ -EDT Data (human/rat liver/microsomes and caco-2 AB/BA)

PCA 1-125 -100 -75 -50 -25 0 25 50 75

-100

-80

-60

-40

-20

0

20

40

60STABLE HLM

STABLE RHEP

STABLE RLM

UNSTABLE RHEP

UNSTABLE RLM

UNSTABLE HLM

Spotfire enables viewing of trends that would be difficult to spot otherwiseSpotfire enables viewing of trends that would be difficult to spot otherwise

IS Vals vs Ret Time

RET_TIME_MIN

ASSAY_TYPE ASSAY_TYPE

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

0

20000

40000

60000

80000

100000

120000

140000

SummarySummary

Spotfire DecisionSite enables powerful interrogation of large data sets

Ability to generate quickly new views of the same dataset is essential in a high-throughput discovery environment

Sometimes, weaknesses in experiment design are uncovered

Having a collection of “standard” visualizations greatly facilitates QA

Integration of chemometrics tools (e.g. clustering, PCA, etc) enables researchers to “gain a deeper understanding of their data” (DataInformation)

Spotfire DecisionSite enables powerful interrogation of large data sets

Ability to generate quickly new views of the same dataset is essential in a high-throughput discovery environment

Sometimes, weaknesses in experiment design are uncovered

Having a collection of “standard” visualizations greatly facilitates QA

Integration of chemometrics tools (e.g. clustering, PCA, etc) enables researchers to “gain a deeper understanding of their data” (DataInformation)