Transcript No Slide Title

Applying Computational Toxicology and
Multicase (MCASE) Software
to the FDA Mission
Edwin J. Matthews, Ph.D., Director
Computational Toxicology Program
Computational Toxicology Consultant Service
Joseph F. Contrera, Ph.D., Director
Regulatory Research and Analysis Staff (RRAS)
Disclaimer: This is not an official guidance or policy statement of
the U.S. Food and Drug Administration (FDA) and Center for
Drug Evaluation and Research (CDER)
FDA/CDER/Office of Testing and Research (OTR)
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MISSION of OTR Programs
to provide decision support to strengthen
scientific basis of regulatory decisions
Objectives are to provide:
 A Reviewer Support Service
 A Source of Scientific Information
 An Institutional Memory
 A Resource for Information Applications
 A Vehicle for Regulatory and Applied Research
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Components of Decision Support
 A Knowledge Base of Clinical & Non-clinical Studies
- ORACLE toxicology database tables connected to a chemical
structure key field and ISIS/BASE search engine
 Computational Toxicology
- toxicity estimates based upon MCASE-ES software and quantification of
toxicity (biologic potency), structural alert representation, and biological
significance (trans-specie potency)
 Computational Chemistry & Biology
- estimates of chemical structural similarity, ADME, and bioavailability
using MCASE-ES, QSBR, ISIS/BASE, and other software
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Decision Support Flow Chart
Test
Chemical
Structure
Similarity
Search
ISIS/BASE
Computational
Toxicology
Evaluation
Test Chemical
& Congeners
Consultant
Report
Computational
Chemical & Biological
Evaluation
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Ultimate Goals of OTR Programs
 A new IND Therapeutic MOL-structure file(s) is entered in the
Center’s Substance Inventory
 Structurally Similarity Chemicals are Identified
 Computational Toxicology Analyses are Performed
 Computational Chemical & Biological Analyses are Conducted
 Data is made available to Center Scientists at the time the IND
is Assigned & Reviewed via CDER-net
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ARCHITECTURE of a Centralized Client
(CDER Reviewer) Support Service
 Consistent Decision Support
- using standardized study & endpoint evaluation criteria
 Easy Access
- using web-base service (CDER-net) and
simple on screen request forms
 Rapid Response (2-3 weeks)
 Limited Requirements
- requires only chemical structures
- requires NO new software to learn!
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Integrated Knowledge Base for
Decision Support and Discovery
Pharm/Tox
Study
Summaries
Computational
Toxicology Data
MCASE-ES
FDA Substance
Inventory &
Pointer Index
Computational
Chemistry & Biology Data
Structural Similarity,
ADME & Bioavailability
Nonclinical Data
Toxicology Studies
Clinical Data
*Trials, ADR &
AERS
E-Reviews
Freedom of
Information
Files
*Clinical Post-Marketing Databases
Adverse Drug Reaction
Adverse Event Reporting Systems
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Computational Toxicology
The application of computer technology and information processing
(informatics) to analyze, model, and predict toxicological activity
based upon chemical structure activity relationships (SAR)
Chemical
Structure
Data
Toxicity Endpoint
(e.g. tumors)
Toxicity Response
(e.g. Carcinogenicity)
Toxicity Endpoint
Dose
(e.g. mg/kg-bw/day)
Toxicity Dose
(e.g. MTD)
+
SAR
Software
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CDER-MULTICASE Rodent Carcinogenicity
“Structure Activity Alerts”


Reduce molecule’s SMILEs code to 2-10 atom fragments
Compares fragments of active & inactive molecules ( N ~ 1000)
Fragments not
represented in
control data set
Estimate
Carcinogenic
Potential
NONCARCINOGEN
FRAGMENTS
N ~ 500,000
Carcinogen Fragments
“MCASE Alerts”
N ~ 200
Identify:

Alerts & Carcinogenic
potency
 # Chemicals / Alert
 QSAR / Fragment Modulators
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SUCCESS!
New: FDA MCASE:ES
Software developed under FDA and
Multicase, Inc. CRADA (1997-2002)
Old: MCASE / CASETOX / CASE
Software developed at Case Western
Reserve University (~1985-1997)
MCASE
QSAR
ES
CRADA
multiple computer automated structure evaluation
quantitative structure activity relationship
(human) expert system
cooperative research and development agreement
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Database differences between
MCASE & MCASE:ES
MCASE
MCASE:ES
Data
Source
NIEHS
NIEHS, NCI, FDA/CDER
L. Gold, Literature
Data
Type
Non-proprietary
Non-proprietary & CDER
proprietary-derived
Module
Type
Rats, Mice
Rodents
Male & Female Rats, &
Male & Female Mice
No. Chemicals
in Training Set
~ 300
~ 1000 - 1100
No. Fragments
Considered
~ 40,000
~ 500,000
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Logic differences betweenMCASE & MCASE:ES
Quantification of Carcinogenic Potency
Potent Carcinogens40 - 79 CASE Units
Weak Carcinogens
30 - 39
Equivocal Findings
20 - 29
Noncarcinogens
10 - 19
MCASE
+
MCASE-ES
+++
+
+
-
+
-
Quantification of Structural Alerts
Potent Alert
Alert
Inconclusive Alert
Noncarcinogenic Fragments
> 5 Chemicals/Alert
3 - 5
1 - 2
0
NA
NA
+
-
+++
+
-
Module Response = (Potency) X (Alerts)
Positive
Inconclusive
Negative
> 150 Total CASE Units
100 - 150
NA
< 100
NA
+
(+)
NA
-
+
+
-
+
-
Quantification of Biological Potency
Positive Response
Inconclusive
Negative
2-4 Carcin. Modules
1
0
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126 Compound Validation Test of the FDA
MCASE Rodent Carcinogenicity Modules
100%
90%
80%
70%
60%
50%
Old MCASE
40%
OTR MCASE
30%
20%
10%
0%
Coverage
Specificity
Sensitivity
Predictivity
Reg.Toxicol.Pharmacol. 28:242-264 (1998)
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RISK IDENTIFICATION:
Advantages when MCASE-ES is optimized for
High Specificity & High Predictive Value
False Positives
False Negatives
 MCASE-ES false negatives are correctable
{enhancement of data set improves software sensitivity}
 MCASE-ES predictions often reflect known
mechanisms and are defensible
{studies from knowledge base support conclusions}
 MCASE-ES predictions provide new insights
 Program is optimal for lead chemical selection and is
possible alternative for In Vitro/In Vivo studies
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RISK MANAGEMENT
Disadvantages when MCASE-ES is optimized for
High Sensitivity
False Positives
False Negatives
 MCASE-ES false positives are not correctable
{model is flawed; whimsical predictions of chemical toxicity}
 MCASE-ES predictions are not defensible and usually
do not reflect known mechanisms
{increased probability of controversy; knowledge base studies do
not support conclusions}
 Predictions do not provide insights to unknown
 Program is not useful for lead chemical selection or as
a possible substitute for animal studies
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Supportive Citations
 MULTICASE SOFTWARE: A new highly specific method for
predicting the carcinogenic potential of pharmaceuticals in rodents
using enhanced MCASE QSAR-ES software. Edwin Matthews
and Joseph Contrera (1998) Reg.Toxicol.Pharmacol. 28:242-264
 CASE SOFTWARE: CASE-SAR Analysis of polycyclic aromatic
hydrocarbon carcinogenicity. Ann Richard and Yin-tak Woo.
(1990) Mutat.Res. 242:285-303.
 TOXICOLOGIC POTENCY: Stratification of carcinogenicity
bioassay results to reflect relative human hazard. Raymond
Tennant. Mutat.Res. 286:111-118.
 VALIDATION CRITERIA: Describing the validity of carcinogen
screening tests. J.A. Cooper, R. Saracci, & P. Cole (1979) Br. J.
Cancer 39:87-89
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Quantification of Weight of Evidence
Quantification of
Biological Significance
Confirmatory Evidence
from Related
Toxicological Endpoints
Reliable Prediction
Quantification of Alerts
+ is >3 chemicals/alert
+? is 2 - 3 “
- is 0 - 2 “
+ is 150
- is < 100
Quantification of
Toxicological Potency
log-normalized scale:
+ is 30 - 80 CASE Units
+? is 20 - 29 “
- is 10 - 19 “
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trans-gender/species Rodent Carcinogen
Biological Significance
male rats -/+
female rats -/+
male mice -/+
female mice -/+
Expert
Prediction
Structural Alerts
0-1 2-3 +?
>3
+++
+ is 150
- is < 100
Carcinogenic Potential
tSp/ms 70-79 tGe/ms 50-69
tSp/ss 40-49 tGe/ss 30-39
ss/ss 20-29 noncar. 10-19
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trans-species Mammalian Teratogen
Biological Significance
rabbits -/+
rats
-/+
mice -/+
other -/+
Expert
Prediction
Structural Alerts
0-1 2-3 +?
>3 +++
+ is 150
- is <100
Teratogenic Potential
ms defects 50-80
ss defects 30-49
equivocal 20-29
nonteratogen. 10-19
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Human Immunotoxin
Biological Significance
Adverse Affects
rash
-/+
urticaria
-/+
allergy/asthma -/+
anaphylaxis -/+
Expert
Prediction
Structural Alerts
0-1 2-3 +?
>3 +++
+ is 150
- is <100
Toxicological Potency
Cumulative Costart
Term(s) & Signal Score(s)
High 50-80 Equiv. 20-29
Med. 40-49 Neg. 10-19
Low 30-39
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Human Liver Toxin
Biological Significance
Test Subjects
adults -/+
males -/+
females -/+
elderly -/+
Expert
Prediction
Structural Alerts
0-1 2-3 +?
>3
+++
+ is 150
- is <100
Toxicological Potency
Cumulative Costart
Term(s) Signal Score(s)
High 50-80 Equiv. 20-29
Med. 40-49 Neg. 10-19
Low 30-39
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Maximum-Tolerated-Dose (MTD) in Rats/Mice
Chemical Toxicity
High (low MTDs)
Low (high MTDs)
Biological Significance
male rats
-/+
female rats -/+
male mice -/+
female mice -/+
Expert
Prediction
(mg/kg-bw/day)
Structural Alerts
0-1
2-3
+?
>3
+++
+ is 150
- is <100
Toxicological Potency
High
50 - 80
Medium 40 - 49
Low
30 - 39
Equivocal 20 - 29
Negative 10 - 19
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Maximum-Recommended-Therapeutic-Dose
(MRD) and No-Effect-Level Dose in Humans
Biological Significance
adults -/+
males -/+
females -/+
elderly -/+
Chemical Toxicity
High (low MRDs)
Low (high MRDs)
Expert
Prediction
(mg/kg-bw/day)
Structural Alerts
0-1
2-3
+?
>3
+++
+ is 150
- is <100
Toxicological Potency
High
50 - 80
Medium 40 - 49
Low
30 - 39
Equivocal 20 - 29
Negative 10 - 19
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Multicase Limitations
 Non-organics (salts, metals)
 Polymers (fibers, proteins, polysaccharides; however, <5000 mw
substructures OK)
 Organometallics (- metal OK)
 Certain Organic Chemicals
Mixtures, but individual components OK
2 or more unknown fragments, but <2 OK
small molecules 1-7 atoms, excluding H
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Pre-Market Applications for
Computational Toxicology at CDER
“when toxicology data is limited or absent!”
 Potential Hazard(s) of Contaminants and Degradents
in IND and NDA Therapeutics
 Potential Hazard(s) of Excipients, Additives, and New
Contaminants in Generic Therapeutics
 Toxicological Profile of Newly Submitted Therapeutics
Integrated knowledge base(OTR Programs); Proposed to support
entry of women of child bearing potential into phase I clinical trials
(FDA/Office of Women’s Health)
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Pre-Market Applications for
Computational Toxicology at FDA
“ when toxicology data is limited or absent!”
 Potential Hazard(s) of Food Contact Substances
{CFSAN/OPA (FDAMA, 1997; Dr. Cheeseman); FDA/Office of the
Commissioner, Office of Science)}
 Potential Hazard(s) of Lead Pharmaceuticals
{IAG with National Institute for Drug Abuse, NIH: Drug Discovery
Program for Medications Development for Addiction Treatment }
 Potential Hazard(s) of Non-pharmaceutical Substances
with Pharmacologic Properties
{e.g., EPA, RTP, NC; ATSDR, Atlanta, GA}
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Post-Market Applications for
Computational Toxicology at FDA
“when required toxicology data is limited or absent”
 Potential Hazard(s) of Active Ingredients of Cosmetics
{CFSAN/OCAC (Dr. Milstein); Offices of Commissioner, Science, &
Women’s Health}
 Potential Hazards of Therapeutics in Humans
{Model data in CDER’s Adverse Drug Reaction (ADR) and Adverse
Event Reporting System (AERS) databases; Dr. Szarfman,
{CDER/OB/QMRS ; Drs. Hanig,Weaver (OTR}
 Potential Hazards of Mixtures of Concern to FDA
{Evaluation of components of dietary and nutritional supplements,
flavors, herbs, spices, herbal medicines, etc. }
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OTR Computational Toxicology and
Toxicology Database Programs (2002)
Non-clinical Endpoint Projects
 Behavioral toxicity (rats)
 Reproductive toxicity (male & female rats)
 Genetic Toxicity {Salmonella t. Mutagenicity (Multicase, Inc.);
Chromosome aberrations; Mouse micronucleus; Mouse lymphoma, Cell
transformation (BALB/c-3T3 & SHE)}
 90-Day Organ Toxicity (rats, mice, rabbits, dogs)
 Acute Toxicity (rats, mice, rabbits)
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OTR Computational Toxicology and
Toxicology Database Programs (2002)
Clinical Endpoint Projects
 Neurotoxicity
 Organ and organ system toxicities
Computational Chemistry Projects
 Metabolism
{MTA with MDL (Elsevier) to add FDA/CDER drug metabolism
data to ISIS/BASE:Metabolite}
 ADME and Bioavailability
{Dr. Saiakhov, Multicase, Inc.; Dr. Yu, OTR; MTA with
Camitro Corporation, Inc.})
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