PBPK Model for Lead: Uncertainties and Parameter Estimation

1. PBPK Model for Lead: Uncertainties and Parameter Estimation by Sangam Uma Reddy Thesis Supervisor Dr. Mukesh Sharma

2. Overview • Introduction • Objective of the study • Literature review • Methodology • Results and discussion • Conclusions

3. Lead • Versatile heavy metal • Extensively used • Cheap, useful, easy to mine, physical properties - ubiquitous in air, food, water and soil • Cumulative Neurotoxin, no known biological function • one of most hazardous substances (ATSDR)

4. Usage of Lead • Batteries • Pigments • Rolled/Extrusions • Ammunitions • Cable Sheathing • Petrol Additives Source: ILZSG, 1997

5. Effects of Lead • Damage Central Nervous System • Causes reduction in IQ and attention span • Affects mental and physical development • Reading and learning disabilities, hyperactivity and other behavioral problems • Impairs formation of Hemoglobin, thus Anemia • Irreversible brain damage • Even death at higher concentration

6. However… Lead continues to be in environment after several years of unleaded gasoline (Morisawa et al. 2001) – Why? • After phase out of lead from gasoline: • Immediate drop in air • Exposure continues: • Food • Water • Soil • Air ???

7. Objective of the study “To estimate the parameter values (KELI and KEKI) of PBPK model using the observed blood and urine lead levels”

8. Source: Tripathi et al., 1997 Exposure Mechanism Routes of humans exposure Absorption - 50% in children - 10% in adults • Ingestion Absorption - 50% in children and adults • Respiration • Dermal Absorption - Insignificant

9. Distribution • Blood • Soft tissue • Bone Liver, Kidneys, Brain and Muscle • 95% of the Pb body burden in bones (O’Flaherty, 1993) Excretion • Urine • Bile • Sweat • Nails • Hair

10. Blood lead level… Lead health effects are many indicated by blood lead levels (PbB) PbB – an important biomarker Acceptable levels of PbB – 10 g/dL Source: CDC, 1991

11. PBPK Model • PBPK - Physiologically Based Pharmacokinetic Model • mathematical description of uptake and disposition of substances to quantitatively describe relationship among critical biological process • Requires chemical substance-specific physicochemical parameters and species-specific physiological and biological parameters • Numerical estimates of parameters are incorporated with set of differential and algebraic equations that describes the pharmacokinetic process

12. PBPK model for a chemical substance • Model Representation • Model Parameterization • Model Simulation • Model Validation Source: Krishna and Anderson, 1994

13. Inhalation Exhalation Venous Blood ALU Arterial Blood Q Q Lungs Cven Cart Cven,RA QRA Rapidly Perfused Tissues QSL Cven,SL Slowly Perfused Tissues Cven,BO QBO Bone Cven,KI QKI Kidney KEKI Cven,LI QLI Liver AGI KELI Gastrointestinal Tract PBPK Model for Lead Source: Morisawa et al., 2001

14. PBPK Model for Lead Liver Kidney Rapid Perfused Tissues Slow Perfused Tissues Bone

15. PBPK Model for Lead Partitioning between tissue and plasma Conc. in venous blood of each organ

16. Model Parameters • Absorption through Inhalation Exposure (ALU) 30 – 50% (adults) • Absorption through Gastrointestinal Tract (AGI) 8 -11% (adults) 40 – 50% (children) • Metabolic Constants (KELI and KEKI) 30% (liver) 70% (kidney)

17. Uncertainty and Variability in PBPK models • Model errors and data gaps • Uncertainty in extrapolating animal data to the case of humans (especially metabolic parameters) • Measurement errors and analytical uncertainties • Uncertainty in exposure levels and parameter values • Inter-or-intra species variability in kinetics may be due to differences in: • Physiology (body weight, %body fat, Organ sizes, shapes) • Variation (e.g. genetic) in metabolism and biochemistry • Co-exposure to other chemicals • Disease states

18. Selection of Sampling location Food Items Collection of Samples Analysis on AAS Laboratory Analysis Sample Processing Digestion Interpretation of Data Methodology of the Study Blood samples Urine samples Water Samples Air samples Filtration Parameter Estimation and Risk characterization

19. Sampling Location

20. Sample Collection • Air Sample Collection • Food Sample Collection • Blood Sample Collection • Urine Sample Collection

21. Collection of Food Samples Collected using Market Basket method

22. Collection of Food Samples No. of Food Samples Collected for Study

23. Food Sample Collection Duplicate Diet Survey

24. Blood Sample collection

25. Sample Analysis • Air Sample Analysis • Food Sample Analysis • Filter Paper Conditioning • Sample Extraction • Instrumentation and Analysis • Sample Processing • Sample Extraction • Instrumentation and Analysis

26. Sample Analysis • Blood Sample Analysis • Urine Sample Analysis • Sample Extraction • Instrumentation and Analysis • Sample Extraction • Instrumentation and Analysis

27. Sample Extraction Extraction of Pb Microwave Digestion System (Ethos Ez Labsatation, Milestone, Italy)

28. Sample Analysis Sample Analysis: GFAAS (GBC Avanta Sigma) Calibration Working Standards Wavelength: 283.3 nm Volume injected: 20 L Graphite Furnace Program MDL: 0.8 ppb Recovery Food Samples: 94-95% Blood Samples: 89%

29. Cereals Fruits Pulses Leafy Vegetables Milk Water Monte Carlo Simulation Risk Characterization Examine Probability Distribution of lead levels of Food Items and Quantity of Food Consumed Non-Leafy Vegetables

30. PBPK Model Exposure through Air Risk PbB 10g/dL Risk Characterization Quantity of Food Consumed Pb Levels in Food Items Dietary Lead Intake

31. Results and Discussion Average lead levels in food items • Conclusions: • Pb concentrations in food items in Kanpur city are high compared to other cities. • High in leafy vegetables. • Concentration in food items from rural area is somewhat less to urban samples. 2Sharma et al. (2005); 3-ATSDR (1999); 4-Tripathi et al. (1997); 5-Zhang et al. (1998); 6-Ysart et al.(1999); 7-Urieta et al. (1996); 8**-Cuadrado et al. (1995), only data of Madrid are taken. 9*-Krishnamurti and Vishwanathan (1991), only data of Uttar Pradesh are taken.

32. Probability Distribution Plots – Pb Levels in Food Items • Conclusion: • Except for non leafy vegetables and pulses Pb levels in all food items are normally distributed at 95% confidence.

33. Probability Distribution Plots – Food Intake Conclusion: Food intake for all food items are normally distributed at 95% confidence

34. Lead intake • Lead intake through Inhalation • Rural – 0.28 µg/m3 • Urban – 0.66 µg/m3 (Maloo, 2003) • Lead intake through Ingestion Diet consumption pattern a Source: Planning Commission, India (2002) b Source: Survey Conducted at Pratap Pur Hari

35. Pictorial Depiction of Probability exposure assessment = x + Exposure I2 C2 I1 C1 Exposure using Planning Commission data x x = + I1 C1 I2 C2 Exposure Exposure using Field data I = Food intake C = Concentration of Pb in food items

36. Comparison of Dietary Intake Values obtained using Field data and Planning Commission data • Conclusion: • To address the variability/uncertainty actual measurements of dietary Intake should be taken rather than going by fixed food consumption pattern

37. Blood and Urine Pb Levels Mean=8.34 SD=1.94 Mean=8.37 SD=2.02

38. Comparison of Present Study PbB Levels with Other Studies • Conclusion: • PbB observed in present study are comparable to that of Deonar Study • Study by Seth (2000) shows higher levels as data reported is for year 1996 when leaded gasoline was used Source a : R. N. Khandekar et at, (1987) Source b: Seth (2000)

39. PbB and PbU Levels • Conclusions: • High correlation between PbB and PbU was observed in present study, Study by Gross (1979) and Shimbo et al. (1999). • If PbB levels are high, kidney enhances its performance in terms of getting toxic metals out of system Numbers in the parentheses show standard deviation a. P < 0.05 b. P < 0.01

40. Relationship of Urinary Lead Excretion Rate and PbB Present Study Gross, 1979 Conclusions: • Trend is comparable with that of Gross, 1979 • KEKI may be variable from one person to another

41. Validation of PBPK Model Morisawa et al. (2001) examined for reliability of PBPK model by comparing simulated results with experimental data Same exercise performed in present study on same data using Mathematica program for confidence on model performance Experiment I (Rabinowitz et al. (1976))

42. Validation of PBPK Model Conclusion: The output from Mathematica program matches with experimental data Dots represent experimental data Solid line output by Mathematica program

43. Performance of PBPK Model • Conclusion: • In no case PbB level for an individual was outside the model computed range of his/her interval estimate of PbB.

44. Parameters (KELI, KEKI) Estimation Steady state PBPK Model ……(1) ……….(2) Cart= Cven, RA ……………………………………………(3) Cart= Cven, SL ……………………………………………(4) Cart= Cven, BO ……………………………………………(5)

45. Cont…… ………………………………………………….(6) …………….(7) Cart = Cven ……………………………………………………(8) ……..(9) Let us take, MUout = KEKICKIVKI (mass/day) MUout = Mass of lead excreted in urine (mass/day)

46. Cont…… MUout = PbU x Urine discharge From eq. (2), …………………………………….(10) Recall Cart = Measured PbB from subjects, Cven,KI for all subjects calculated from eq (10) Rewriting eq (9) to obtain Cven,LI ……(11)

47. Cont…… RHS of eq (11) is known Corresponding CPi for Liver and kidney Calculate Cven,LI and Cven,KI CPi Ci Concentrationof Pb in organ/tissue is known Recall, MUout = KEKICKIVKI…………………………………(12) In eq (12) all variables known, KEKI can be estimated In eq (1) all variables known, KELI can be estimated

48. KELI Mean = 0.16 SD = 0.03

49. KEKI Mean = 0.66 SD = 0.11 • Conclusion: • Metabolic parameters (KELI and KEKI) show substantial variation and one should take these parameters as random variables in model to fully reflect the uncertainties caused due to variability in KELI and KEKI

50. Lead in air, blood and urine (Azar et al., 1975) • Conclusions: • Average value of clearance is close to the value reported in other studies through renal clearance. • This study additionally provides information on associated uncertainties in renal clearance.