In Silico Methods as Translational Tools for Supporting the Safety Assessment of Natural

Health Products

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Kevin P. Cross, Ph.D.Leadscope, Inc.

1393 Dublin Road, Columbus, OH, USA

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Collaboration• Research conducted under an approved CRADA, and Research

Collaboration Agreement between U.S. FDA and Leadscope Inc. with:• Luis G. Valerio, Jr., Ph.D., Science and Research Staff, CDER

• Leadscope and the FDA have been collaborating on developing in silico predictive QSAR models for 7 years.

• Currently there are Research Collaboration Agreements with both FDA/CDER and CFSAN

• There are regulatory needs and active use of QSAR models for assessing the safety of food ingredients and drug impurities

• This talk will discuss how those research activities have evolved into translational tools for better assessing the safety of FDA regulated ingredients with attention to botanicals, dietary supplements, etc.

Disclaimer

The views expressed in this presentation are those of the author; this talk is not an official U.S. FDA presentation, guidance or policy statement.

Natural Health ProductSafety Assessment

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“A Perfect Storm?”• Globally chemically diverse compounds – compared to drugs• Many constituents per product – requiring analysis• Many unknown active constituents – requiring analysis• Data gaps in safety analysis – cost and incentive• Inconsistent and equivocal results (rodent, human)• Variation in dose - subspecies, part, extract, manufacturer, lot• Food matrix effects - dosage, absorption, toxicity• Quality Control and adulteration – non-GMP, synthetic additions• Metabolism-dependent toxicity – cytochrome P450 effects• Delayed onset of adverse symptoms – days to months• Co-administration – herb-herb, herb-drug interactions• Under-reporting of adverse events – manufacturers and doctors• Idiosyncratic effects – genetic basis, culture, immunology-mediated• “Leaky” Regulations – DSHEA versus the Delany Clause

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Natural Health ProductSafety Assessment

“A Perfect Storm?”• References:

• Jordan, S.A., Tox and App Pharm (2010) 243: 198-216• Teschke, R., et. al., Expert Opin Drg Saf, (2013), early online• National Health and Nutrition Examination Survey 1999–2012 (2013)• NIH NCC Roundtable Meeting on Dietary Supplement-Drug Interactions March 27, 2012 • Rietjens, I.M.C.M, Mol Nut Food Res (2010) 54(2): 175-185• Olsen, L.R., et. al., Chem Res Tox (2011) 24: 992-1002• Reddy, K.R., et. al., Aliment Pharm Ther (2013) 37: 3-17• Rietjens, I.M.C.M, Tox Letters (2008) 180: 131-136• Fourches, D., el. al., Chem Res Tox (2010) 23: 171-183• Stickel, F., et. al., Liver International (2011) 595-605• Estes, J.D., et. al., Arch Surg (2003) 138: 852-858• Valerio, L. G., et. al., Mol Nutr Food Res (2010) 53: 186-194• Clayton, N.P., et. al., Exp Tox Path (2007) 58: 223-236• Smith, D. A., et. al., Chem Res Tox (2006) 19: 1570-1579• Gurley, B.J., et. al., Clin Pharm Ther (2005) 77: 415-417• Denham, A., et. al., J. Alt. Complement Med (2002) 8: 237-263• Schiliter, B., et. al., Risk Asses of Phyto Foods Symp 2010, Wiley• Arvidson, K,.B., et. al., (2008) Tox Mech Methods 18: 229-242

Headlines of the Last Month• FDA Warns Consumers About Fraudulent Health Products, HealthDay

(3/7, Preidt)• FDA Issues Warning On Fake "Miracle Cures.“, The Hill (3/5, Wilson)• FDA Reviewers: Fish Hormone Use May Be Linked To Cancer Risk ,

MedPage Today (3/5, Gever)• Study: Approximately 30 Percent Of Chemotherapies Are Prescribed

Off-Label, Reuters (2/20, Seaman)• FDA Issues Warning On Fake Flu Therapies Sold Online, The

Wall Street Journal (2/16, A2, Rockoff)• Synthetic Marijuana Use Linked To Kidney Problems, The AP (2/14,

Stobbe)• FDA Seizes Supplements Containing Banned Ingredient, The AP

(2/15)• FDA Warns Company Over Unapproved Nasal Spray, USA Today

(1/31, Weise)

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Extracted from Drug Information Agency daily briefings Jan 31- Mar 7, 2013

Outline• Natural Health Products

• in silico methods including computational toxicology can serve to address data gaps during safety evaluations for regulatory and consumer product safety

• Why use in silico-based strategies to assess their toxicity?• In what specific areas can in silico-based strategies help?

• What in silico predictive models are in use at FDA?• Leadscope technology• Genetic toxicity and carcinogenicity models• Clinical cardiovascular adverse effects models

• Case examples

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At a Glance – Regulatory Framework in the U.S.

• In October 1994, the Dietary Supplement Health and Education Act (DSHEA) was signed into law by President Clinton. Before this time, dietary supplements were subject to the same regulatory requirements as were other foods. This new law, which amended the Federal Food, Drug, and Cosmetic Act, created a new regulatory framework for the safety and labeling of dietary supplements.

• Under DSHEA, a firm is responsible for determining that the dietary supplements it manufactures or distributes are safe and that any representations or claims made about them are substantiated by adequate evidence to show that they are not false or misleading. This means that dietary supplements do not need approval from FDA before they are marketed. Except in the case of a However, for new dietary ingredients now marketed before Oct 1994, pre-market review for safety data and other information is required by law. a firm does not have to provide FDA with the evidence it relies on to substantiate safety or effectiveness before or after it markets its products.

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N

O

O H

C OO

H

H3C

H2C

C

HO CH2OH

O

H3C

H3C OH

HO

CH3

O

C

O

N

O

O CH3

H3C

CH3

CH3

HOOC

H

HO

OH3C

O

O

O

O

O

HO

HO

N

OO

H

HO

O

O

O

O

OH

O

O

HH

S

HO

O

NH

O OO

OCH3

H3CO^N

O

OO

O

H3C

CH3

CH3

COOH

H

OH

HOO

O

O

OH

OH

OH

O

O

OO

HO

H

H

OO

O

NH2

NH

OH

O

H2N

NH

OH

O

O

OHOH

O

General Problem- Data Gaps• Botanicals are complex mixtures of chemicals, which

often contain ‘active’ constituents that have little or no toxicological data

• Chemicals of natural origin with vast structural diversity (e.g. phytochemicals) are being discovered at an accelerating pace

• Present a data-poor situation in assessment of toxic potential

• Prime example of a need for efficient prioritization and toxicity screening methods

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Why In Silico?Safety

Evaluation with Data

Gaps

Better Decisions

In Vitro Data

Equivocal Animal Study

in Silico Models

In Vitro Data

Equivocal Animal Study

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In what specific areas can in silico-based strategies help?

Predictive

Modeling

• Genetic toxicity (ICH M7)

• Human Adverse Effects (Clinical QT)

Screening

studies

• Black cohosh (FDA/OWH project)

Toxicity Data

Mining

• Read-across (FDA Indirect Food Additives)

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FDA Critical Path Initiative(March 2004)

• Identifies need to address drug development issues• Reduce drug product attrition and improve chances

for success• Emphasizes industry – FDA interactions• Use science to create new tools to get better

answers for demonstrating safety and effectiveness, faster time frames, more certainty, and lower costs• Product development toolkit*

New scientific and technical methods Animal or computer-based predictive models Biomarkers for safety and effectiveness New clinical evaluation techniques

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*Valerio, L.G., Human Gen (2011) 5(3) 200-207

FDA-related Guidance Involving QSAR

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• The ICH M7 Guideline on Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk.• Status: Step 2. Draft released for public comment.• Allows submission of QSAR AMES prediction results in place of

genotoxic testing for certain types of impurities under a threshold level.

• Two (Q)SAR prediction methodologies that complement each other should be applied. One methodology should be expert rule-based and the second methodology should be statistical-based.

• (Q)SAR models utilizing these prediction methodologies should follow the validation principles set forth by the Organization for Economic Co-operation and Development (OECD).

• The outcome of any computer system-based analysis should be reviewed with the use of expert knowledge

http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Multidisciplinary/M7/M7_Step_2.pdfValerio L.G. Jr, Cross K.P., Toxicol Appl Pharmacol. (2012) 260(3):209-21

Leadscope Model Suites Developed Under Collaboration with the FDA

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Non-clinical Effects Models

Human Clinical andAdverse Effects Models

Carcinogenicity (v2) Clinical Cardiological*Genetic Toxicity (v3)1 Cardiological Adverse EventsReproductive Toxicity Hepatobiliary Adverse Events2

Developmental Toxicity Urinary Tract Adverse Events

Behavioral ToxicityMax Recommended Daily Dose*

Phospholipidosis* Max Tolerated Dose**not yet commercially available

1Stavitskaya, L., Development of Improved Salmonella Mutagenicity QSAR Models Using Structural Fingerprints of Known Toxicophores2Zhu, X., and Kruhlak ,N. L., Construction of a Human Hepatotoxicity Database for QSAR Modeling

SOT 2013 Posters FDA CDER Division of Drug Safety Research:

Clinical QT Model Development

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• Only FDA clinical data adhering to ICH E14 clinical standards on evaluating proarrythmic risk was gathered*

• All data reviewed by FDA/CDER’s QT Interdisciplinary Review Team

• Clinical long QT and high-risk Torsade de Pointes QSAR models were built using the Leadscope Predictive Data Miner1

• QSAR models were built independent of molecular mechanism to avoid limitations with existing tests (e.g. hERG)

• Training sets are small (187) so coverage is currently limited• QSAR models are being tested at the FDA to support safety

decision-making – they are not required during the review process

• Funded by the FDA Critical Path Initiative*ICH Industry Guidance E14 Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic Drugs, October 2005, U.S. FDA

1Valerio et al, Expert Opin. Drug Metab. Toxicol, March 2013 accepted

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Cardiac Safety Translational Tools

Predictive Performance Characteristics (1% CV)

Model Type 

%Sensitivity

%Specificity

%Positive Predictivi

ty

%Negative

Predictivity

%Concordance

Cohen’s Kappa

General Screening QT Model 

85 83 84 84 84 0.679

In-House QT Model 162 

87 88 85 90 88 0.749

High-Risk TdP:  

82 90 80 91 88 0.720

FDA Critical Path ProjectDevelopment of Clinical based QT Prolongation and Torsade de Pointes Models

Valerio et al, Expert Opin. Drug Metab. Toxicol, March 2013 accepted

External Validation Test of Predictive Performance of the Clinical QT Prolongation

Model

External Validation of the In-House QT Prolongation ModelPredictive Performance Characteristics

#Positive Test Compounds %Sensitivity %Coverage

26 75 62

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Computational Assessment of Liver Toxicity for Botanicals Used

in Menopausal Symptom Relief

Wang, Y., et. al., Reg Tox Pharma (2011) 59: 111-124

Computational Assessment of Liver Toxicity for Botanicals Used

in Menopausal Symptom Relief1. Botanical constituents collected from public

databases for black cohosh, chasteberry, hops, and red clover*

2. Constituent compounds present in relevant parts of plant extract identified

3. Hepatotoxicity predictions made using five (Q)SAR software applications

4. Developed a “positive” prediction list5. Identification of structural features contributing

to “positive” predictions*Betz et al. Black Cohosh. Considerations of Safety and Benefit. Nutrition Today, (2009 ) 44(4), 155-162.

Identification of Constituents and Prediction of Liver Toxicity in Botanicals

Botanicals# Total

Constituents Identified

# Predicted Liver

Positives*Black Cohosh 103 37

Red Clover 94 17Chasteberry 153 22

Hops 227 14Total 577 90

Wang, Y., et. al., Reg Tox Pharma (2011) 59: (111-124)

Structural features (applied model features and contributions)

Calculated properties (LogP, molecular weight, polar surface area, number of rotational bonds, etc.)

Analysis of a (Q)SAR Prediction

Model features used to make this prediction

Calculated Properties

Structure with model features highlighted

Feature contribution to predicted positive probability

Example: 3-methoxy-7-O-beta-D-glucoside quercetin (Red Clover)Endpoint: Enzyme Release

Identifying Structural Features Contributing to Positive Predictions

summarizes atom contribution in the model

predicted probability

positive and negative contributions

positive contributions

mitigating contributions

percentage contribution from structural features and properties

*Analysis using Leadscope prediction Explain functionality

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Computational Assessment of Carcinogenicity and Liver Toxicity

of Kava

Major Constituents of Kava

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Lactones

Alkaloids

Flavokavains

Olsen, L. R., Grillo, M. P., Skonberg, C., Chem. Res. Toxicol 2011, 24, 992-102

Read-Across Data for Kava

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(flavokavain C)

O

OO

OH

OH

NIEHS 2-Year Rodent Carcinogenicity Study of Dietary Herbal Medicines

(published December 2012)

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Herb2-Year Carc Mouse

2-Year Carc Rat

Aloe vera positive positiveGinkgo biloba positive negativeGinseng negative negativeGoldenseal positive positiveKava positive negativeMilk thistle negative negativeTumeric oleoresin negative negative

Dunnick, J.K., Nyska, A., Tox Pathology, 2012, 00: 1-13

Kava Rodent Carcinogenicity QSAR Prediction Results

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CompoundCompound Type

Predicted Carc Mouse

Predicted Carc Rat

desmethoxyyangonin lactone not in domain not in domaindihydrokavain lactone not in domain not in domaindihydromethysticin lactone positive (.57) negativekavain lactone negative negativemethysticin lactone not in domain not in domainyangonin lactone positive (.97) positive (.52)awaine alkaloid negative negativeepoxy-pipermethystine alkaloid not in domain not in domainpipermethystine alkaloid negative negativeflavokavain A flavokavain positive (.92) negativeflavokavain B flavokavain negative negativeflavokavain C flavokavain positive (.78) negative

Kava Adverse Events Reporting Liver QSAR Prediction Results

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CompoundPredicted Bile Duct

Predicted Enzyme Release

Predicted Gall Bladder

Predicted Jaundice

Predicted Liver Acute

desmethoxyyangonin

not in domain not in domain

not in domain

not in domain not in domain

dihydrokawainnot in domain not in domain

not in domain

not in domain not in domain

dihydromethysticinnot in domain negative

not in domain negative negative

kavainnot in domain not in domain

not in domain

not in domain not in domain

awainenot in domain not in domain

not in domain

not in domain not in domain

methysticinnot in domain not in domain

not in domain

not in domain not in domain

epoxy-pipermethystine

not in domain not in domain

not in domain

not in domain not in domain

pipermethystinenot in domain negative

not in domain negative negative

yangonin negative negative negative negative negativeflavokavain A negative negative negative negative negativeflavokavain B negative negative negative negative negativeflavokavain C negative negative negative negative negative

Conclusions

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1. In silico methods can be applied to natural health products to support safety analysis by:

• data mining existing databases• using QSAR predictions to fill data gaps• screening large numbers of constituents without requiring

synthesis• prioritizing constituents for further safety analysis• helping to understand chemical toxicity and mechanisms

2. In silico methods are being used in a regulatory setting to:

• analyze drug impurities in drug applications containing QSAR results

• support review of drug safety analysis (FDA Div Drug Safety Research)

• support safety assessments of food ingredients and indirect additives

The Future

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In silico methods will continue to provide better support for safety assessment of Natural Health Products:

• by providing supporting chemical and mechanistic evidence for predictions to help define toxicity pathways1

• supporting a broader set of diverse compound predictions

• through integration with other data (in vitro) in decision support2

• by continually collecting more data for model revisions• by supporting creation of chemical categories of toxicity

by common mechanism and class31Collins, F.S., et. al., Science (2008) 319: 906-9072Committee on Toxicity Testing and Assessment of Environment Agents, National Research Council 20073Rietjens, I.M.C.M, Tox Letters (2008) 180: 131-136

Thank You!

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kcross@leadscope.com

Predictive Feature Analysis Across Several Constituents

Example: Red Clover constituents

Who has the responsibility for ensuring that a dietary

supplement is safe?• By law (DSHEA), the manufacturer is responsible for ensuring that

its dietary supplement products are safe before they are marketed. Unlike drug products that must be proven safe and effective for their intended use before marketing, there are no provisions in the law for FDA to "approve" dietary supplements for safety or effectiveness before they reach the consumer. Under DSHEA, once the product is marketed, FDA has the responsibility for showing that a dietary supplement is "unsafe," before it can take action to restrict the product's use or removal from the marketplace. However, manufacturers and distributors of dietary supplements must record, investigate and forward to FDA any reports they receive of serious adverse events associated with the use of their products that are reported to them directly. FDA is able to evaluate these reports and any other adverse event information reported directly to us by healthcare providers or consumers to identify early signals that a product may present safety risks to consumers.

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When must a manufacturer or distributor notify FDA about a

dietary supplement it intends to market in the U.S.?

• The Dietary Supplement Health and Education Act (DSHEA) requires that a manufacturer or distributor notify FDA if it intends to market a dietary supplement in the U.S. that contains a "new dietary ingredient." The manufacturer (and distributor) must demonstrate to FDA why the ingredient is reasonably expected to be safe for use in a dietary supplement, unless it has been recognized as a food substance and is present in the food supply.

• There is no authoritative list of dietary ingredients that were marketed before October 15, 1994. Therefore, manufacturers and distributors are responsible for determining if a dietary ingredient is "new", and if it is not, for documenting that the dietary supplements its sells, containing the dietary ingredient, were marketed before October 15, 1994. 34