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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)

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applying computational toxicology and multicase mcase software to the fda mission
Applying Computational Toxicology and Multicase (MCASE) Softwareto 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)

1

slide2
MISSION of OTR Programsto 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

2

components of decision support
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

3

slide4

Decision Support Flow Chart

Test

Chemical

Computational

Toxicology

Evaluation

Consultant

Report

Test Chemical

& Congeners

Structure

Similarity

Search

ISIS/BASE

Computational

Chemical & Biological

Evaluation

4

ultimate goals of otr programs
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

4

architecture of a centralized client cder reviewer support service
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!

6

slide7

Integrated Knowledge Base for

Decision Support and Discovery

Pharm/Tox

Study

Summaries

Computational

Toxicology Data

MCASE-ES

Nonclinical Data

Toxicology Studies

FDA Substance

Inventory &

Pointer Index

E-Reviews

Freedom of

Information

Files

Clinical Data

*Trials, ADR &

AERS

Computational

Chemistry & Biology Data

Structural Similarity,

ADME & Bioavailability

*Clinical Post-Marketing Databases

Adverse Drug Reaction

Adverse Event Reporting Systems

7

slide8

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

+

SAR

Software

Toxicity Endpoint

(e.g. tumors)

Toxicity Response

(e.g. Carcinogenicity)

Toxicity Endpoint Dose

(e.g. mg/kg-bw/day)

Toxicity Dose

(e.g. MTD)

8

slide9

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

NONCARCINOGEN

FRAGMENTS

N ~ 500,000

Carcinogen Fragments

“MCASE Alerts”

N ~ 200

Identify:

Estimate Carcinogenic

Potential

  • Alerts & Carcinogenic potency
  • # Chemicals / Alert
  • QSAR / Fragment Modulators

9

s u c c e s s
S U C C E S S !

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 multiple computer automated structure evaluation

QSAR quantitative structure activity relationship

ES (human) expert system

CRADA cooperative research and development agreement

10

database differences between mcase mcase es
Database differences betweenMCASE & MCASE:ES

MCASE

NIEHS

Non-proprietary

Rats, Mice

Rodents

~ 300

~ 40,000

MCASE:ES

NIEHS, NCI, FDA/CDER

L. Gold, Literature

Non-proprietary & CDER

proprietary-derived

Male & Female Rats, &

Male & Female Mice

~ 1000 - 1100

~ 500,000

Data

Source

Data

Type

Module

Type

No. Chemicals

in Training Set

No. Fragments

Considered

11

logic differences between mcase mcase es
Logic differences betweenMCASE & MCASE:ES

Quantification of Carcinogenic PotencyMCASEMCASE-ES

Potent Carcinogens 40 - 79 CASE Units + +++

Weak Carcinogens 30 - 39 + +

Equivocal Findings 20 - 29 + -

Noncarcinogens 10 - 19 - -

Quantification of Structural Alerts

Potent Alert > 5 Chemicals/Alert NA +++

Alert 3 - 5 NA +

Inconclusive Alert 1 - 2 + -

Noncarcinogenic Fragments0 --

Module Response = (Potency) X (Alerts)

Positive > 150 Total CASE Units NA +

Inconclusive 100 - 150 NA (+)

Negative < 100 NA -

Quantification of Biological Potency

Positive Response 2-4 Carcin. Modules + +

Inconclusive 1 + -

Negative 0 - -

12

126 compound validation test of the fda mcase rodent carcinogenicity modules
126 Compound Validation Test of the FDAMCASE Rodent Carcinogenicity Modules

Reg.Toxicol.Pharmacol. 28:242-264 (1998)

13

slide14
RISK IDENTIFICATION:Advantages when MCASE-ES is optimized forHigh Specificity & High Predictive Value
  • 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

False Positives

False Negatives

14

risk management disadvantages when mcase es is optimized for high sensitivity
RISK MANAGEMENTDisadvantages when MCASE-ES is optimized forHigh Sensitivity
  • 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

False Positives

False Negatives

15

slide16

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

16

slide17

Quantification of Weight of Evidence

Reliable Prediction

Quantification of

Biological Significance

Confirmatory Evidence

from Related

Toxicological Endpoints

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 “

17

slide18

trans-gender/species Rodent Carcinogen

Expert

Prediction

Biological Significance

male rats -/+

female rats -/+

male mice -/+

female mice -/+

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

18

slide19

trans-species Mammalian Teratogen

Expert

Prediction

Biological Significance

rabbits -/+

rats -/+

mice -/+

other -/+

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

19

slide20

Human Immunotoxin

Expert

Prediction

Biological Significance

Adverse Affects

rash -/+

urticaria -/+

allergy/asthma -/+

anaphylaxis -/+

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

20

slide21

Human Liver Toxin

Expert

Prediction

Biological Significance

Test Subjects

adults -/+

males -/+

females -/+

elderly -/+

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

21

slide22

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)

Toxicological Potency

High 50 - 80

Medium 40 - 49

Low 30 - 39

Equivocal 20 - 29

Negative 10 - 19

+ is 150

- is <100

Structural Alerts

0 - 1 -

2 - 3 +?

>3 +++

22

slide23

Maximum-Recommended-Therapeutic-Dose

(MRD) and No-Effect-Level Dose in Humans

Chemical Toxicity

High (low MRDs)

Low (high MRDs)

Biological Significance

adults -/+

males -/+

females -/+

elderly -/+

Expert

Prediction

(mg/kg-bw/day)

Toxicological Potency

High 50 - 80

Medium 40 - 49

Low 30 - 39

Equivocal 20 - 29

Negative 10 - 19

+ is 150

- is <100

Structural Alerts

0 - 1 -

2 - 3 +?

>3 +++

23

multicase limitations
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

25

pre market applications for computational toxicology at cder
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)

26

pre market applications for computational toxicology at fda
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}

27

post market applications for computational toxicology at fda
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 ofcomponents of dietary and nutritional supplements, flavors, herbs, spices, herbal medicines, etc. }

28

otr computational toxicology and toxicology database programs 2002
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)

29

otr computational toxicology and toxicology database programs 200229
OTR Computational Toxicology and Toxicology Database Programs (2002)

Clinical Endpoint Projects

  • Neurotoxicity
  • Organ and organ system toxicities

ComputationalChemistry 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.})

30