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Food Animal Residue Food Animal Residue Avoidance DatabankAvoidance Databank
∑ NCSUUCDUF
UVPC.FARAD
∑ NCSUUCDUF
∑ NCSUUCDUF
UVPC.FARAD
∑ NCSUUCDUF
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FARAD is a consortium for identifying, gathering, extracting, analyzing, generating, and extending residue avoidance information to ensure that animal derived foods will be free of illegal chemical & drug residues and safe for consumers
North Carolina State University - Raleigh
University of California – Davis
University of Florida – Gainesville
FARAD is aFARAD is a consortium for identifying, consortium for identifying, gathering, extracting, analyzing, generating, gathering, extracting, analyzing, generating, and extending residue avoidance information and extending residue avoidance information to ensure that animal derived foods will be to ensure that animal derived foods will be free of illegal chemical & drug residues and free of illegal chemical & drug residues and safe for consumerssafe for consumers
North Carolina State University North Carolina State University -- Raleigh Raleigh
University of California University of California –– DavisDavis
University of Florida University of Florida –– GainesvilleGainesville
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.FARAD’s Collaborations.FARAD’s Collaborations.FARAD’s CollaborationsUSDA
FDACABI
USPADDS
NRSP-7VADDS
gFARAD
USDAUSDAFDAFDA
CABICABIUSPUSP
ADDSADDSNRSPNRSP--77
VADDSVADDSgFARADgFARAD
Taiwan Taiwan CanadaCanadaFranceFranceChinaChinaUKUK⎨
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Functions of FARAD include:Functions of FARAD include:•• ResearchResearch
Data extraction and analysis, interspecies Data extraction and analysis, interspecies comparisons, algorithms comparisons, algorithms ⇒⇒ Withdrawal timeWithdrawal time
•• ServiceServiceExtraExtra--label use advice (hotlabel use advice (hot--line), compendia, line), compendia, website, VetGRAM, CABI Compendiumwebsite, VetGRAM, CABI Compendium
•• ExtensionExtensionBooks, journal articles, FARAD DigestsBooks, journal articles, FARAD Digests
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Pharmacokinetic and Pharmacokinetic and Computer Computer AlgorithmsAlgorithms
Expert System Expert System InteGRAMInteGRAM
FARAD CONCEPT:FARAD CONCEPT:Evolution from Hotline to DatabankEvolution from Hotline to Databank
CentralizedCentralizedDataBankDataBank-- RegulatoryRegulatory-- LiteratureLiterature
TraditionalTraditionalHot LineHot Line
Old Old FARADFARAD
-- WWW.FARAD.ORGWWW.FARAD.ORG-- WWW.WWW.gFARADgFARAD.ORG.ORG
FARAD started out as the source of approved animal drugs in USFARAD started out as the source of approved animal drugs in US28 years of operation
1-888-USFARAD
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Primary Task and WorkPrimary Task and Work--Load of FARADLoad of FARAD::
How do you establish withdrawal intervals How do you establish withdrawal intervals when there is:when there is:
1.) Extralabel Drug Use 1.) Extralabel Drug Use –– higher higher dosedose than labelthan label
–– different different route route than labelthan label
–– different different speciesspecies than labelthan label
–– different different disease indicationdisease indication than label than label 2.) Minor Species2.) Minor Species 3.) Environmental Contaminants3.) Environmental Contaminants
1010--0909Accidental/unavoidable (feed and water)Accidental/unavoidable (feed and water)
a. Natural contaminantsa. Natural contaminants
b. Humanb. Human--made contaminantsmade contaminants
c. Biotoxins (Botulism)c. Biotoxins (Botulism)
IntentionalIntentionala. Drugs a. Drugs
b. Pesticidesb. Pesticides
c. Agroc. Agro--terrorismterrorism
Natural DisastersNatural Disasters
a. Hurricanes, Floodsa. Hurricanes, Floods
b. Fires (Flame Retardants)b. Fires (Flame Retardants)
1010--0909ExtraExtra--label Drug Use: World after AMDUCAlabel Drug Use: World after AMDUCA
ELDU becomes a tool to administer higher antimicrobial drug doseELDU becomes a tool to administer higher antimicrobial drug doses to s to minimize development of resistant bacteria minimize development of resistant bacteria –– Prudent Drug UsePrudent Drug Use
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Knust Knust et al. Proc. AABP, Sept. 2007et al. Proc. AABP, Sept. 2007
How do US veterinarians determine How do US veterinarians determine witholdingwitholding time?time?
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other vets calculatefrom data
FARAD university/extension
other
Res
pons
es
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Submission Methods StatisticsSubmission Methods StatisticsJanuaryJanuary--October 2008October 2008
Web29%
G-Mail21%
Phone50%
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Submission Statistics (5 yrs)Submission Statistics (5 yrs)Animal CategoriesAnimal Categories
Cattle (Beef)15%
Cattle (Dairy)34%
Fish0%General Info
7%
Goats11%
Other10%
Poultry6%
Sheep5%
Swine12%
(Rabbits & Cervids)
1010--0909Submission Statistics (5 yrs)Submission Statistics (5 yrs)
Drug ClassificationDrug Classification
Antimicrobials/Antimicrobials/AntifungalsAntifungals; 44%; 44%
AntiparasiticsAntiparasitics; 3%; 3%Anesthetics/Analgesics; 8%Anesthetics/Analgesics; 8%
TxTx Drugs/Agents; 18%Drugs/Agents; 18%
Other; 9%Other; 9%
NSAIDsNSAIDs; 5%; 5%
Hormones; 2%Hormones; 2%Environmental Environmental Contaminants; 7%Contaminants; 7%
CorticosteroidsCorticosteroids/Steroids; 4%/Steroids; 4%
Withdrawal Time Withdrawal Time DeterminationDetermination
The BUSINESS of FARADThe BUSINESS of FARAD
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WDI
International RegulatoryData (Tol, MRL, PARs)
PK DataApproved Uses
Modeling and Interspecies Extrapolation-EWE
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FARAD Web AccessFARAD Web Access
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FARAD Web AccessFARAD Web Access
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FARAD Web AccessFARAD Web Access
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FARAD Web AccessFARAD Web Access
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How Does FARAD Work? How Does FARAD Work? Question SubmissionQuestion Submission
Toll Free HotlineToll Free HotlineEE--MailMailWebWeb--Based SubmissionBased Submission
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How Does FARAD Work?How Does FARAD Work?WebWeb--Based SubmissionBased Submission
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How Does FARAD Work?How Does FARAD Work?WebWeb--Based SubmissionBased Submission
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How Does FARAD Work?How Does FARAD Work?WebWeb--Based SubmissionBased Submission
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How Does FARAD Work?How Does FARAD Work?WebWeb--Based SubmissionBased Submission
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How Does FARAD Work?How Does FARAD Work?WebWeb--Based SubmissionBased Submission
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Answering Submissions:Answering Submissions:FarmCallFarmCall DatabaseDatabase
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Answering Submissions:Answering Submissions:Bibliographic DatabaseBibliographic Database
1010--0909Answering Submissions:Answering Submissions:
Pharmacokinetic DatabasePharmacokinetic Database
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Current Statistics: Current Statistics: Kinetic DatabaseKinetic Database
Animal GroupAvian10%
Bovine23%
Caprine4%Equine
4%Fish/Shellfish8%
Ovine8%
Rodents19%
Swine8%
Other16%
Total of 45,900 kinetic recordsTotal of 45,900 kinetic records
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FARAD PK CompilationFARAD PK CompilationTabulation of FARAD Comparative Tabulation of FARAD Comparative
and Veterinary Pharmacokinetic Dataand Veterinary Pharmacokinetic DataCraigmillCraigmill AL, Riviere JE, Webb AIAL, Riviere JE, Webb AI
Blackwell Press, Ames, IA, 2006 (1935 pgs)Blackwell Press, Ames, IA, 2006 (1935 pgs)
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INTEGRAM DATA FILE STRUCTURE
TRADES KINETICS FIELD DATA *
Public Domain:US (literature) TRIMS
Fore ign1 Regulatory Data:* RVISFore ign2 NADA Validation
Minor SpeciesClinical Trials
Industry IPPIA
ALLOMETRY ADI TOLERANCE/MRL
Completed U.S. FDA,Allometric EPA, ATSDR U.S. Tol. or PATsStudies CODEX U.S. SC or PARs
Mean PK CVMP MRLsby Species
Extralabel Analytical
Trades from Previous lyCalcuated WDIs
Screening Test* = Access Restricted to Core FARAD Sites Regulatory Assays
DATABASES FOR COMPLETE FARAD IMPLEMENTATIONDATABASES FOR COMPLETE FARAD IMPLEMENTATION
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Strategies for Extrapolating ExtraStrategies for Extrapolating Extra--Label Withdrawal IntervalsLabel Withdrawal Intervals
Historical experienceHistorical experienceExtrapolate from approved product Extrapolate from approved product
(Half(Half--Life multipliers, Effective Residue halfLife multipliers, Effective Residue half--life)life)Match available tissue pharmacokinetic dataMatch available tissue pharmacokinetic dataInternational drug approvalsInternational drug approvalsPharmacokinetic modelsPharmacokinetic modelsInterInter--species extrapolationsspecies extrapolationsEWE, PARS, Population and Physiological Based EWE, PARS, Population and Physiological Based PharmacokineticsPharmacokinetics
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1010--0909Tolerance/MRL based on parent drug and metaboliteTolerance/MRL based on parent drug and metabolite
Strategies for Extrapolating Strategies for Extrapolating ExtraExtra--Label Withdrawal Label Withdrawal
Intervals Intervals Extrapolate from approved product Extrapolate from approved product
(WDT=3(WDT=3--5 T5 T1/2 1/2 s)s)Available tissue pharmacokinetic dataAvailable tissue pharmacokinetic data–– WDT =WDT = lnln (C(C00 / TOL) / K/ TOL) / K
Historical experienceHistorical experienceForeign drug approvalsForeign drug approvalsPharmacokinetic modelsPharmacokinetic modelsInterInter--species extrapolationsspecies extrapolations
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Pharmacokinetic parameters in literature based on Mean (50th percentile)
Regulatory Tolerance/MRL based on 99th percentile
The BIG Problem in WDT Extrapolations from PK DataThe BIG Problem in WDT Extrapolations from PK Data
1010--0909Estimated WithdrawalEstimated Withdrawal--Interval Interval Estimator (EWE) algorithmEstimator (EWE) algorithm
US Patent 6,066,091US Patent 6,066,091
1010--0909Comparison of PK versus HLM MethodsComparison of PK versus HLM Methods
Figure 3
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Oxytetracycline (Muscle)
Oxytetracycline (Liver)
Oxytetracycline (Kidney)
Danofloxacin (Liver)Tilmicosin (Liver)
Florfenicol (Liver)
Erythromycin (Kidney)
Erythromycin (Liver)
Calculated using ETH with HLM 5 Calculated using actual tissue half-lives
Gehring Gehring et al., JFP 67:558, 2004et al., JFP 67:558, 2004
1010--0909Develop Algorithms for Assessing Develop Algorithms for Assessing
Published Kinetic DataPublished Kinetic Data
Citations
Sim
ilari
ty
1757649811101221413161531
61.09
74.06
87.03
100.00
Intramuscular ampmicillin trihydrate
Gehring Gehring et al., AJVR 66:110, 2005et al., AJVR 66:110, 2005
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KINETIC VARIABILITYKINETIC VARIABILITY
We Treat Herds / Flocks but Sample IndividualsWe Treat Herds / Flocks but Sample IndividualsVariance is as Important as the Mean !Variance is as Important as the Mean !
Population PK is a tool to address thisPopulation PK is a tool to address this
1010--0909Dispersion or variance Dispersion or variance around the mean may around the mean may often be the most often be the most important parameter in important parameter in predicting an predicting an individual’s response to individual’s response to drugdrug
Population analysis is a Population analysis is a tool that correlates this tool that correlates this variance to some variance to some clinically measurable clinically measurable parameter.parameter.
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Requires Use of Pharmaco-Statistical Approaches
Healthy Disease I Disease 2
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Relationship between mean and frequency distribution
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Consideration of Variance in Consideration of Variance in Pharmacokinetic ModelsPharmacokinetic Models
Define basic equation to include statistical error components:
Ct = [A1e -λ1t] + μ t+ εt
μ t = unexplained variation in C due to lack of model fit
εt = intraindividual random errorIf multiple individuals, also introduce
interindividual variation
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Classic Approach to Model PopulationsClassic Approach to Model PopulationsNaive Pooled Data– Ignore that samples came from different individuals– Analyze all data at each time point– Often done in lab animal studies
Standard Two-Stage– Conduct PK studies in individuals– Analyze each individual– Summarize parameters across individuals using classical
statistics (means, SD, etc)– Requires intensive sampling for each individual– Pools all errors and exaggerates inter-individual errors– Study group may not be representative of population
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Population PharmacokineticsPopulation Pharmacokinetics
Mixed-Effect Modeling (MEM)“Traditional” pharmacokinetics does not allow for variability to be easily integrated into an analysisRequires less samples per individual but more individualsSeparates the individual in the analysisPopular software WinNonMix®
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[k = Cl/Vd]
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Data Set Modeled Data Set Modeled with No Covariates.with No Covariates.
Plot of observed versus Plot of observed versus predicted concentrationspredicted concentrations
Plot of residuals versus Plot of residuals versus body weight as a body weight as a covariate indicating covariate indicating overprediction in large overprediction in large and underpredictionand underprediction in small animals
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Data Set Modeled with Data Set Modeled with Body Weight as a Body Weight as a
CovariateCovariate
CC--T profile showing T profile showing improved prediction and improved prediction and reduced variability.reduced variability.
Residual plot now shows Residual plot now shows homogeneous scatter homogeneous scatter across body weights. across body weights.
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PBPK modelsPBPK models
Mathematical descriptions of actual anatomical Mathematical descriptions of actual anatomical structures and physiological processesstructures and physiological processes
Varies in complexity and scopeVaries in complexity and scopeSimplified fullSimplified full--body modelsbody modelsHighly complex, comprehensive modelsHighly complex, comprehensive modelsModels of specific structures and processesModels of specific structures and processes
1010--0909Sulfamethazine Sulfamethazine PBPK ModelPBPK Model
Burr et al., AJVR 66:1686, 2005; AAC 50:2344, 2006Burr et al., AJVR 66:1686, 2005; AAC 50:2344, 2006
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SulfamethazineSulfamethazine Water Contamination Water Contamination in Pig Production Unitin Pig Production Unit
0.15 mg/kg followed by continuous 0.15 mg/kg followed by continuous lower exposure at 0.059 mg/kg lower exposure at 0.059 mg/kg matched observed exposure. matched observed exposure.
Used SMZ PBPK model to Used SMZ PBPK model to estimate dose required to match estimate dose required to match observed plasma levels in observed plasma levels in untreated pen mates.untreated pen mates.
Mason, Baynes, Mason, Baynes, BuurBuur, Riviere, Almond. , Riviere, Almond. J. Food Prot.J. Food Prot. 71:58471:584--589, 2008589, 2008
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BuurBuur, Baynes, Riviere. , Baynes, Riviere. Reg. Reg. ToxTox. . PharmacolPharmacol.. 51:32451:324--331, 2008.331, 2008.
y = 0.724x - 0.7562R2 = 0.8873
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IVDose
Urine
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LiverKst Ka
Rat Plasma
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Time (h)
Mel
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e C
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Melamine PBPK ModelMelamine PBPK Model
ObsObs vs vs PredPred Swine PlasmaSwine Plasma
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Estimated WDT of 19Estimated WDT of 19--21 hrs after single dose oral exposure to 321 hrs after single dose oral exposure to 3--5 5 mg/kg melamine in feed. WDT of 20mg/kg melamine in feed. WDT of 20--21 hrs estimated after 21 hrs estimated after repeated dosing.repeated dosing.
Single Exposure
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elam
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This nicely illustrates use of PBPK model simulations in contextThis nicely illustrates use of PBPK model simulations in context of of FARAD task of estimating withdrawal times when complete studiesFARAD task of estimating withdrawal times when complete studiescannot be performed. cannot be performed.
1010--0909ConclusionsConclusions
FARAD is a “portal” for residue avoidance FARAD is a “portal” for residue avoidance information and a resource for exchange of such information and a resource for exchange of such information globallyinformation globallyIt is an ever evolving, scienceIt is an ever evolving, science--based program whose based program whose goal is to provide a chemical residue free food goal is to provide a chemical residue free food supplysupplyAreas of science expertise are in applying novel Areas of science expertise are in applying novel pharmacokinetic approaches to food animal therapypharmacokinetic approaches to food animal therapyExperience in tabulating and integrating animal Experience in tabulating and integrating animal drug databasesdrug databasesApproaches allow optimal antimicrobial drug Approaches allow optimal antimicrobial drug regimens to be employed that minimize the selection regimens to be employed that minimize the selection of resistant bacteria thus improving public healthof resistant bacteria thus improving public health
