Interpretation of Urinary Excretion Data from Plutonium Wound Cases Treated with DTPA D. Poudel, L. Bertelli, J.A. Klumpp, T.L. Waters Radiation Protection Division, Los Alamos National Laboratory HPS Annual Meeting 2017 Raleigh, NC LA-UR-17-24026

Introduction Behavior of chelate (Pu- DTPA) dominates that of Pu; Systemic models no longer valid Several empirical methods; no consensus model Different methods discussed

Data Several 239Pu wound cases Chelated at least once Not confounded by significantly larger previous intakes Excluded cases without proper recordings of the times and number of chelation treatments

Data Case 1: Left index finger, working with Pu metals 29 treatments including one on 590d post intake Case 2: Deposition of a metallic fragment on the right thumb 4 treatments within the first week of intake No fixed protocol for collecting bioassay after DTPA treatment then Samples collected around and after 100d after the last chelation Case 1 followed for ~3y; Case 2 for ~7y

Biokinetic Models NCRP 156 Wound Model Leggett’s Pu Systemic Model ICRP 100 HATM

Biokinetic Models

Maximum Likelihood Analysis Maximum likelihood method (IDEAS Guidelines)

Maximum Likelihood Analysis

Evaluation of Affected Data Empirical urinary excretion model (Hall et. al. 1978) Compact description of the method (La Bone 2002)

Evaluation of Affected Data

Evaluation of Affected Data Parameter Value Source Enhancement factor 1 to 130 Norwood 1962, Anderson et al. 1970, Jech et al. 1972, Jolly et al. 1972, Parker 1973, Schofield and Lynn 1973, Hall et al. 1978, Piechowski et al. 1989, La Bone et al. 1994a and b, Bailey et al. 2003, James et al. 2008, Bertelli et al. 2010, Davesne et al. 2016 h1 0.1 to 1d Majority of chelate is excreted within a day (Stather et al. 1983; Durbin et al. 1987) h2 1 to 10d DTPA effect may last from a few weeks up to 100d post chelation (Schofield et al. 1973; Jech et al. 1972; Davesne et al. 2016)

Evaluation of Affected Data

Bayesian Analysis Bayesian MCMC Internal Dosimetry Code (LANL) Equal probability is assigned to several biokinetic models (priors) Analysis of data 100d post last chelation Predominance of “LANL Soluble” form

Comparison of Results MLA resulted in intakes approximately twice as large as Bayesian analysis Differences in model assumptions

Discussion – Hall’s Method Hall’s method is largely empirical Information on E and the behavior of chelate E of 12.5 and 20.5 Allows to calculate the “actual intakes” had there been no chelation Requires no “waiting time” (e.g., 100d after last chelation) Determine efficacy of chelation

Discussion – Intracellular Action of DTPA? Significant urinary enhancement when tc = 590d Near-negligible amount of Pu in the blood or ECF Lends support to the hypothesis that DTPA may decorporate Pu in intracellular sites as well (Fritsch et al. 2010; Gremy et al. 2016) Liver known to be a site of DTPA influence (e.g., Roedler et al. 1989; Breitenstin and Palmer 1989) Observation important for compartmental modeling approaches; several models have recognized (e.g., Fritsch et al. 2007; Breusted et al. 2009; Konzen and Brey 2010) From bone surfaces and bone marrow? (e.g., James and Taylor 1971; James et al. 2007)

Discussion – Simpler Method? Divide by the enhancement factor and use IRFs obtained from standard biokinetic model E of 1 to 130 in the literature; which one to use? E has a significant impact on the estimation of intake and dose; and possibly the actions taken after the intake Large uncertainty, and should be treated as such E of 50 would have underestimated the intake for both of the cases

Conclusions Many facilities discard the data during the treatment period; and use the data “unaffected” by chelation Maximum likelihood analysis Use of proper biokinetic model Bayesian analysis Facility-specific models may be more appropriate than generic models

Conclusions Pu behavior well understood; that of Pu-DTPA still largely empirical Consensus approach needed Literature review shows that the HP community is moving towards compartmental modeling of Pu-DTPA (e.g., Bailey et al. 2003, Fritsch et al. 2007 and 2010, James et al. 2008, Breustedt et al. 2009 and 2010, Konzen and Brey 2015)