Post on 02-Jan-2016
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)