Development and Application of Computational Toxicology and Informatics Resources at the

FDA CDER Office ofPharmaceutical Science

Development and Application of Computational Toxicology and Informatics Resources at the

FDA CDER Office ofPharmaceutical Science

The Informatics and Computational Safety Analysis Staff (ICSAS)

Joseph F. Contrera, Ph.D.*Edwin J. Matthews, Ph.D.Naomi L. Kruhlak, Ph.D.

R. Daniel Benz, Ph.D.

Advisory Committee for Pharmaceutical Science (ACPS)Rockville, MD. October 19-20, 2004

The Informatics and Computational Safety Analysis

Staff (ICSAS)

The Informatics and Computational Safety Analysis

Staff (ICSAS)• Develops animal toxicology and clinical safety

databases and data transformation algorithms• Transforms data, developing human expert rules for

converting toxicological and clinical adverse effects data into a form suitable for computer modeling

• Evaluates and promotes the use of quantitative structure activity relationship (QSAR) and data mining software

• Leverages by working with the scientific community and software developers to create QSAR predictive toxicology software using mechanisms such as Material Transfer Agreements (MTAs) and Cooperative Research and Development Agreements (CRADAs)

• A Solution: “A new product development toolkit — containing powerful new scientific and technical methods such as animal or computer-based predictive models, biomarkers for safety and effectiveness, and new clinical evaluation techniques — is urgently needed to improve predictability and efficiency along the critical path from laboratory concept to commercial product.”

• The Problem: “Not enough applied scientific work has been done to create new tools to get fundamentally better answers about how the safety and effectiveness of new products can be demonstrated, in faster time frames, with more certainty, and at lower costs.”

FDA Critical Path InitiativeFDA Critical Path Initiative

ICSAS and the Critical Path Initiative

1. Develop and supply new databases and predictive toxicology software tools to the pharmaceutical and chemical industry to improve the lead candidate screening process

2. Develop better means to identify and eliminate compounds with potentially significant adverse properties early in the discovery and development process, thereby reducing the regulatory review burden for the FDA, CDER and other regulatory agencies

3. Facilitate the review process by making better use of accumulated toxicological and human clinical knowledge.

4. Reduce testing (and use of animals) by eliminating non-critical and redundant laboratory studies

5. Encourage the development of complementary software systems that can predict toxicity and adverse human effects through collaboration with software developers and the scientific community

Currently Used Applications for ICSAS Computational Toxicology Currently Used Applications for

ICSAS Computational Toxicology “where toxicology data are limited or lacking”’

• Lead Pharmaceutical Screening Lead Pharmaceutical Screening (Pharmaceutical Industry; (Pharmaceutical Industry; National Institute on Drug Abuse, NIH - - Drug Discovery Program for Medications Development for Addiction Treatment)

• Evaluating Contaminants and Degradants in New Drug ProductsEvaluating Contaminants and Degradants in New Drug Products and Generic Drugsand Generic Drugs

• Decision Support Information for Toxicology Issues Decision Support Information for Toxicology Issues Related to Drug Products in ONDCRelated to Drug Products in ONDC

• Food Contact SubstancesFood Contact Substances (CFSAN/OFAS - FDAMA, 1997)

• Environmental and Industrial Chemical Toxicity Environmental and Industrial Chemical Toxicity Screening (EPA)Screening (EPA)

• Hypothesis generation, identifying data gaps; prioritizing research

ProprietaryDatabases

Non-proprietaryDatabases

GuidancesDecision SupportR & DComputational Toxicology

APPLICATIONS

The FDA Information CycleThe FDA Information Cycle

Review ApprovalSubmission Post-Approval

Proprietaryclinical and

toxicology data

Non-proprietaryclinical and

toxicology data

ICSAS Leveraging Initiatives for Developing Informatic ResourcesICSAS Leveraging Initiatives for Developing Informatic Resources

Informatics (Database) CRADAs• MDL Information Systems / Reed Elsevier 2004 – 2008• Leadscope, Inc. (2005 – 2009)• LHASA Limited (2005 – 2009)

Objectives:• To construct endpoint specific, toxicity and adverse effect

databases that are suitable for data mining and QSAR modeling• To hasten the Agency review process• To identify non-proprietary data that can be shared with industry

and made publicly available through our CRADA partners• To investigate mechanisms of drug toxicity and develop human

expert rules to explain the toxicities

ComputationalPredictive Toxicology

ToxicologyDatabases

ClinicalDatabases

Chemical StructureSimilarity Searching(MDL ISIS™/Host)

Chemical Structure-Linked“Chemoinformatic” Knowledge Base

Chemical Structure-Linked“Chemoinformatic” Knowledge Base

ChemicalStructure-Based

Substance Inventory(“.mol”-file)

Pharm/ToxStudy

Summaries

e-Reviews;Freedom ofInformation

Files

ClinicalStudy

Summaries

AdverseEvent

ReportingSystems

• Dose Related Endpoints (e.g., MTD, MRDD, LD50)

ToxicityDoseData

ChemicalStructure

Data

SARSoftware

ToxicityDose

Predictions+ +

Trans-formedToxicity

Data

ChemicalStructure

Data

SARSoftware

ToxicityResponse

Predictions

+ +

• Toxicologic Endpoints (e.g., Carcinogenicity, Mutagenicity)

Computational PredictiveComputational Predictive ToxicologyToxicology

Computational PredictiveComputational Predictive ToxicologyToxicology

ICSAS Evaluated Predictive Toxicology Software

ICSAS Evaluated Predictive Toxicology Software

Statistical Correlative In Silico Programs• MCASE(-ES) / MC4PC MultiCASE, Inc. CRADA*• MDL-QSAR MDL Information Systems, Inc. CRADA• ClassPharmer Bioreason, Inc. MTA• Leadscope Enterprise Leadscope, Inc. MTA• BioEpisteme Prous Science MTA• *CRADA = Cooperative Research and Development Agreement• MTA = Material Transfer Agreement

Human Expert Rule-Based In Silico Programs• DEREK for Windows LHASA, Limited MTA

• ONCOLOGIC LogiChem, Inc. & EPA

In Vivo and In Vitro Toxicity Endpoints

ICSAS Animal EffectsDiscovery System

ICSAS Animal EffectsDiscovery System

• Carcinogenicity in Rodents (male and female, rats and mice) M,Q

• Teratogenicity in Mammals (rabbits, rats, mice) M,Q

• Mutagenicity in Salmonella t. (TA100, TA1535, TA1537, TA98) M

• Genetic Toxicity (chromosome aberrations)

• Genetic Toxicity (mouse micronucleus; mouse lymphoma)

• Reproductive Toxicity (male & female rats)

• Behavioral Toxicity (rats)

• Acute Toxicity (rats, mice, rabbits)

Other Chemical Toxicity Endpoints

• 90-Day Organ Toxicity (rats, mice, rabbits, dogs)

Organ System Adverse Endpoints

FDA / CDER/ ICSAS Human Effects Discovery System

FDA / CDER/ ICSAS Human Effects Discovery System

Modeling the Maximum Recommended Daily Dose (MRDD)Estimating the Safe Starting Dose in Phase I Clinical Trials No-effect-level (NOEL) of Chemicals in Humans

Dose Related Endpoints

• Hepatic Effects in Humans• Cardiac Effects in Humans• Renal / Bladder Effects in Humans• Immunological Effects in Humans

Problems

• Industry and Agency archives contain critical positive control, toxic chemical data that are necessary for training QSAR models

• Identity of proprietary substances in Agency and Industry archives are confidential and legally protected

Proprietary DataProprietary Data

Technical Solutions for Sharing Data

Technical Solutions for Sharing Data

• Sharing study results linked to molecular attributes that do not disclose the name or molecular structure of proprietary compounds

• Data linked to MDL-QSAR E-state descriptors or MULTICASE molecular fragments can supply useful molecular information that cannot be used to unambiguously reconstruct the molecular structure of a proprietary compound

• MCASE / MC4PC and MDL-QSAR provide acceptable solutions

74 MethylthiouracilMDL QSAR Descriptors

74 MethylthiouracilMDL QSAR Descriptors

SsCH3 SdsCH SdssC SssNH SdO SdS SsCH3_acnt SdsCH_acnt SdssC_acnt

1.78278005 1.45416999 1 5.18490982 10.56890011 4.675930023 1 1 3

SssNH_acnt SdO_acnt SdS_acnt x0 x1 x2 xp3 xp4 xp5

2 1 1 6.85337019 4.181540012 4.022620201 2.414210081 2.349339962 0.985598981

xp6 xc3 xpc4 xch6 xv0 xv1 xv2 xvp3 xvp4

0.69692302 0.86602497 1 0.0680414 5.710340023 2.893850088 2.178970098 1.099159956 0.852613986

xvp5 xvp6 xvc3 xvpc4 xvch6 SHssNH SHother Hmax Gmax

0.29146501 0.170004 0.356355995 0.35635599 0.018042199 3.579040051 1.934990048 1.833960056 10.56890011

Hmin Gmin Hmaxpos SHBint2 SHBint2_Acnt SHBint4_Acnt k0 k1 k2

0.61274999 -0.15625 1.833960056 19.3829994 2 2 8.588179588 7.11111021 2.722219944

k3 ka1 ka2 ka3 fw nvx nelem nrings ncirc

2 6.53876019 2.344990015 1.67136002 142.1809998 9 5 1 1

phia knotp numHBa numHBd SHHBd SHBa Qs Qsv Qv

1.70370996 -0.133975 4 2 3.579040051 20.42970085 1.930330038 0.65535903 0.965165973

ABSQ ABSQon Dipole MaxHp MaxNeg MaxQp Ovality Polarizability SpcPolarizability

2.0053401 0.82385498 3.54124999 0.17860299 -0.416218996 0.190889001 1.201370001 2.322000027 0.0304779

Surface Volume

104.411003 76.1864014

(S = E-state descriptors)

Kier, L.B. and L.H. Hall. Molecular Structure Description: The Electrotopological State, Academic Press

Estimate Animal NOAEL mg/kg/day

Convert NOAEL to Human Equivalent Dose (HED) (mg/kg/day)

Select Most AppropriateSpecies Based on Species Sensitivity; ADME

Estimate Maximum Recommended Starting Dose (MRSD)

Human MRDDQSAR Model

Predicted MRDD mg/kg/day

Add Uncertainty-Safety Factor(s)

Add Uncertainty-Safety Factor(s)

Selecting the Maximum Starting Dose in Clinical Trials

Multiple Dose Toxicity Studiesin Rodents and Non-rodents

Present Method Near Future

• No need for interspecies uncertainty factors

• Increased accuracy, sensitivity and specificity over animal models (identifies chemical adverse effects not detected in animal studies)

• Batch processing(prioritization of large test chemical data sets)

• No animal test data are required (3Rs: Reduce, Refine, Replace)

• Reduced cost

Benefits of Using QSAR Modeling of the Benefits of Using QSAR Modeling of the MRDD To Estimate the Safe Starting MRDD To Estimate the Safe Starting

Dose in Phase I Clinical TrialsDose in Phase I Clinical Trials

Benefits of Using QSAR Modeling of the Benefits of Using QSAR Modeling of the MRDD To Estimate the Safe Starting MRDD To Estimate the Safe Starting

Dose in Phase I Clinical TrialsDose in Phase I Clinical Trials

Future Application? • Two year rat and mouse carcinogenicity studies are the most

costly and controversial non-clinical regulatory testing requirement. The results can have a major impact on the approvability and marketing of a drug product.

• Is carcinogenicity testing necessary for all new drugs?

• Can computational methods eventually replace carcinogenicity studies for compounds that are highly represented in the carcinogenicity database?

• With increased experience and confidence with predictive software, it may be possible to reduce or eliminate carcinogenicity testing for compounds that have molecular structures that are highly represented in the carcinogenicity database.

• This would reduce unnecessary testing and free resources for testing compounds that are truly new molecular entities and are poorly represented in the carcinogenicity database.

Challenges for the Regulatory Acceptance of In Silico Testing

Challenges for the Regulatory Acceptance of In Silico Testing

• Regulatory scientists and managers willing to consider and use new approaches

• Need for an objective appraisal of the limitations of current testing methods

• Accurate, validated in silico software• Standardization• Experience, training• Databases: data sharing with adequate protection of

proprietary information

Pharma 2005: An Industrial Revolution in R&D - PricewaterhouseCoopers

Pharma 2005: An Industrial Revolution in R&D - PricewaterhouseCoopers

Now

Primary Science:Labs/Patients

Secondary Science:e-R&D / Computers

Future

Experimental Science:

e-R&D / Computers

Confirmatory Science:

Labs/Patients

Transition

PrimaryScience

SecondaryScience

Science

PrimaryScience

SecondaryScience

ReferencesReferences

ICSAS website: www.fda.gov/cder/offices/ops_io/default.htm

Contrera, J. F., L. H. Hall, L. B. Kier, P. MacLaughlin, (2005) QSAR Modeling of Carcinogenic Risk Using Discriminant Analysis and Topological Molecular Descriptors, Regulatory Toxicology and Pharmacology, In press.

Contrera, J. F., E. J. Matthews and R. D. Benz, (2003). Predicting the Carcinogenic Potential of Pharmaceuticals in Rodents Using Molecular Structural Similarity and E-State Indices. Regulatory Toxicology and Pharmacology, 38(3):243-259.

ReferencesReferencesReferencesReferences

Contrera, J. F., E. J. Matthews, N. L. Kruhlak and R.D.Benz, (2004). Estimating Maximum Recommended Daily Dose (MRDD) and No Effect Level (NOEL) Based on QSAR Modeling of Human Data. Regulatory Toxicology and Pharmacology, In press.

Matthews, E. J., N. L. Kruhlak, R. D. Benz, and J. F. Contrera (2004). Assessment of the Health Effects of Chemicals in Humans: I. QSAR Estimation of the Maximum Recommended Therapeutic Dose (MRTD) and No Effect Level (NOEL) of Organic Chemicals Based on Clinical Trial Data. Current Drug Discovery Technologies, 1:61-76.

Matthews, E. J. and Contrera, J. F. (1998). A new highly specific method predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASE QSAR-ES software. Regulatory Toxicology and Pharmacology 28:242 – 264.