Application of Toxicology Databases in Drug Development(Estimating potential toxicity)

Joseph F. Contrera, Ph.D.

Director, Regulatory Research and Analysis

FDA Center for Drug Evaluation and Research (CDER), Office of Testing and Research

Contrerajf@cder.fda.gov

Combinatorial ChemistryHigh Through-Put Screening

The Human Genome

The Rapidly Increasing Number and Diversity of Potential New Products

The Limitations of Current Toxicology Screening Methods

Increasing Demands on Regulatory Processes

THE REVOLUTION IN PHARMACEUTICAL DEVELOPMENT

• For lead selection of the products of high through-put technology

• To more efficiently assess the potential hazard of substances especially when limited experimental evidence is available

• As a rational basis for decisions on the nature and degree of testing

• Reduce animal testing

The Need for Rapid and Effective Screening Methods to Identify and Prioritize Potential Toxicity

Toxicology Studies: Promise

• There are 6 major categories of toxicology studies: genotoxicity, acute toxicity, chronic toxicity, reproductive and developmental toxicity and carcinogenicity

• The design of studies in these categories is relatively standardized to meet regulatory requirements

• Post-GLP (Good Lab Practices;1978) studies and reviews are a potentially rich resource of good quality toxicology data

Information Applications Toxicology Databases

• Regulatory decision support

• Retrospective analysis

• Product development

• Guidance development; improving and updating regulatory standards

• Identifying relationships between animal toxicology and human adverse events

CDER Toxicology Databases Contributed to International Conference on Harmonization

(ICH) Guidances for Pharmaceuticals

• ICH S1B: Testing for Carcinogenicity of Pharmaceuticals

• ICH S1C: Dose Selection for Carcinogenicity Studies of Pharmaceuticals

• ICH S1CR: Use of Limit Dose in Dose Selection for Carcinogenicity Studies

• ICH S4;S4B: Duration of Chronic Toxicity Testing in Animals

Information Applications

Computational Toxicology; SAR; E-Tox

• Structure activity analysis (SAR) and predictive modeling for regulatory decision support

• Lead selection in drug development

• Estimating and prioritizing potential hazard when data is limited

• Hypothesis generation, identifying data gaps; prioritizing research

Computational Toxicology; E-Tox The application of computer technology

to analyze, model and predict toxicological activity

E-ADMEThe application of computer technology to

analyze, model and predict absorption, distribution, metabolism and excretion

Current Database Needs and Issues

• Critical need for uniform compound identification; problems with multiple drug names, codes, CAS numbers for same active ingredient

• Better search and retrieval capability within and across databases

• Chemical structure similarity search and clustering capability

• Data entry, quality and compatibility issues • Proprietary issues; Data sharing

Major FDA/CDER Carcinogenicity Database Fields

• Drug name• *Molfile digital

chemical structure• 2D structure• Administrative code

(NDA, IND number)• Clinical indication(s)• Pharmacological or

chemical class

• Species, strain• Sex• Route• Doses• Duration of dosing• Tumor site, type• Tumor incidence

Using Chemical Structure (Molfile) as a Key Field to Link Databases and

Expand Search CapabilitiesMolfile

“core”structurefingerprintKey Field

Structural SimilaritySearching, Cluster

Analysis(ISIS-Base)

SAR/E-ToxMCASE structural

alerts

CompoundNames

CompoundStructure

ComputationalToxicology

E-Tox

ToxicologyData Bases

Clinical *ADR

AERS

Chemical StructureSimilarity Searching

(MDL Isis-Base)

ChemicalStructure Based

Substance Inventory(MOLFILE)

FDA CDER TOXICOLOGY KNOWLEDGE BASE For Decision Support and Discovery

Pharm/Tox Study

Summaries

E-ReviewsFreedom ofInformation

Files

*Clinical Post-Marketing Adverse Drug Reaction Adverse Event Reporting Systems Databases

A Knowledge Base is the Combination of Databases and Computational

Methods to Discover Meaningful Relationships

The CDER Toxicology Knowledge Base is a Prototype for an FDA

Knowledge Base

Estimating Potential Toxicity

MolecularDescriptors

BiologicalDescriptors

E-Tox/SAR Modeling

Weight of Evidence Factors

Major Structure-Activity (SAR) Based Predictive Models

• Expert Rule Based Methods• Prior expert knowledge and mechanistic

hypotheses required• Derek; Oncologic

• Statistical/Correlative Methods• Little prior knowledge required. Computer

generated patterns and relationships from a statistical analysis of a data set

• MCASE; Topkat

Representative Molecular Descriptors

• 2D molecular structure based clustering

• 2D molecular substructure clustering; molecular fragmentation

• 3D rigid and flexible molecular configuration clustering

• Physical chemical parameters, eg. Log P; homolumo constants; electrotopographic properties

Modeling Biological DescriptorsMajor Sources of Error

• Inadequate size of control data set• Inadequate representation of molecular

diversity (coverage)• Over simplification, poor use of biological data • Unbalanced representation of biological

activity• Inadequate validation of predictive models due

to lack of studies not included in the control data set

The Representation of Molecular Diversity The Size and Diversity of Control Data Set

• Coverage: The FDA rodent carcinogenicity data base contains more than 1000 compounds that include both pharmaceuticals and non-pharmaceuticals

• Balanced representation: Approximately equal number of positive and negative studies in the FDA carcinogenicity database

• Validation: Availability of a large pool of new studies improves the validation process

The Representation of Biological ActivityTwo Year Rodent Carcinogenicity Studies

• Male and female dose groups • Male and female untreated control groups• 50+ animals/sex/group (400+ total)• 40+ organ/tissue pathology analyses/animal• Relatively high spontaneous age related

background tumor rate• Relatively high probability of some treatment

related findings• Sensitivity/Specificity Issues

The Representation of Biological ActivityModeling Rodent Carcinogenicity Studies

• Four Study Cells• Male and Female Rats• Male and Female Mice• Each study cell can be considered an

independent study • More than one positive study cell is

necessary to corroborate a positive finding

The Representation of Biological ActivityWeight of Evidence and Data Quality

• Separate evaluation/modeling of male and female rat and mouse study results (4 study cells)

• More positive cells=greater potency and confidence

• A biologically relevant molecular descriptor is one that is linked to positive findings in at least two study cells

• The greater the number of compounds containing a molecular descriptor associated with carcinogenicity in the database, the greater the degree of confidence in the finding

Assignment of Carcinogenic PotencyCompounds that induce trans-species tumors present the

highest degree of risk because they adversely alter mechanisms that are conserved across species.

Tennant, Mutat. Res. (1993) 286, 111-118.

TUMOR FINDINGS POTENCY( log units)

Trans-species, multiple site (++++Potent)

70-79

Single/trans-gender, multiple site(+++Potent)

50-69

Trans-species single site(++Potent)

40-49

Trans-gender, single site(+Weak)

30-39

Single gender, single site(Equivocal)

20-29

No findings 10-19

Review Approval

THE FDA-CDER INFORMATION CYCLE

NDA ReviewsNon-proprietary

Drug R & D

Submission

IND ReviewsProprietary Data

NonproprietaryDatabases

ProprietaryDatabases

ApplicationsR&DDecision SupportGuidancesE-ToxInstitutional Memory

Primary Science

Labs/Patients

Secondary Science eR&D

in-silicocomputers

PrimaryScience

SecondaryScience

Exp. ScienceeR&D

computers

ConfirmatoryScience

Labs/Patients

Now Transition Future

From Pharma 2005: An Industrial Revolution in R&D Pricewaterhouse Coopers