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Emerald Feng
Mentors: Chris Grulke, Rocky Goldsmith, Daniel
Chang, Cecilia Tan, Mike Tornero
PBPK Modeling
 Chemical health risk
assessments
 Used to quantify
absorption, distribution,
metabolism, and excretion
(A...
PBPK Modeling
 Models vary based on complexity
 Compartment = theoretical value for a chemical
 Connections indicate ho...
Parameters
 Used to influence organ flows
and partitioning into
compartments: “factors”
related to uptake/circulation
and...
Molecular Descriptors to derive
ADME-specific parameters
 Chemical descriptors
used to predict
ADME models based
on known...
QSAR Modeling
 Quantitative
Structure-Activity
Relationship
 Relationship
between chemical
structure and
biological acti...
Life-stage
gender
Survey from
PBPK Modelers
Icons
Background/Our Goal
 Most experimentation is done on
real life organisms
 In silico models are not favored
 PBPK modeli...
Methods
 Prepare Spreadsheet
 Initial Preparation
 http://dogwood.rtpnc.ep
a.gov/
 Computer Version
 Goal: transfer t...
Methods
Weight Estimate
Methods
 Datasets were first identified in the computer
toxicology book, curated, then modeled in MOE
 Datasets used: Cl...
Calculations
Clearance_Oral Dataset
Index Compound Parent_SMILES
Observed
CL(PO,man)
MLR
(Quadratic)
AC-PLS
(Quadratic)
MC-PLS
(Tertiar...
Clearance_Oral Dataset Cont’d
Index Rank Compound Parent_SMILES a_acc a_count a_don b_rotN logP(o/w) TPSA Weight b_rotR Di...
Clearance_Oral Dataset Cont’d
descriptor test molecule value
1 a_acc 2
2 a_count 6
3 a_don 3
4 b_rotN 4.00
5 logP(o/w) 0.3...
Histograms
Decision Tree Classifier Process
Decision Trees
Hand drawn process from the
computerized version
Right: yes; Left: no
Total indicates misclassification rat...
Final Project
Dataset includes
671 chemicals
Distance Calculation
Entry
ID
rank SMILES Formula Name Weight logP(o/w) TPSA a_count a_acc a_don b_rotN Distance
1 307
OC[...
Input
descriptor test molecule value
1 Weight 179
2 logP(o/w) 2
3 TPSA 66
4 a_count 20.00
5 a_acc 1.00
6 a_don 0.00
7 b_ro...
Conversion to a Mobile Device
 SpreadsheetConverter
 Hid specific sheets
 Simplified the spreadsheet to fit into the sm...
URL and QR Code
http://goo.gl/UDR4U http://goo.gl/3X0pX
ADME by Analog App Physiology App
Snapshots from the Mobile App:
Snapshots from the Mobile App
Works Cited
"Assessment of chemicals - Organisation for Economic Co-operation and Development." Organisation for Economic ...
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EPA Summer 2013_Portable Pharmacokinetic Parameter Prediction Tool

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EPA Summer 2013_Portable Pharmacokinetic Parameter Prediction Tool

  1. 1. Emerald Feng Mentors: Chris Grulke, Rocky Goldsmith, Daniel Chang, Cecilia Tan, Mike Tornero
  2. 2. PBPK Modeling  Chemical health risk assessments  Used to quantify absorption, distribution, metabolism, and excretion (ADME)  Compartments are specific  Intrinsic and Extrinsic factors  In relation to chemical exposure  In silico vs in vitro  Definite use in pharmacokinetics
  3. 3. PBPK Modeling  Models vary based on complexity  Compartment = theoretical value for a chemical  Connections indicate how each parameter calculates another 2 compartment 6 compartment Several Compartments
  4. 4. Parameters  Used to influence organ flows and partitioning into compartments: “factors” related to uptake/circulation and elimination (or ADME)  Contains descriptors, such as molecular weight or surface area( MW or TPSA)  Derived from different chemical properties  Physiological, Chemical, Tissue specific  Important!!!!!!!!!! in PBPK modeling  Examples: Absorption rate
  5. 5. Molecular Descriptors to derive ADME-specific parameters  Chemical descriptors used to predict ADME models based on known value of a (ADME) response variable  Chemical structure and biological activity  Calculate descriptors for chemicals in a database  Using Molecular Operating Environment (MOE)
  6. 6. QSAR Modeling  Quantitative Structure-Activity Relationship  Relationship between chemical structure and biological activity  Similar structure indicates similar activity
  7. 7. Life-stage gender Survey from PBPK Modelers
  8. 8. Icons
  9. 9. Background/Our Goal  Most experimentation is done on real life organisms  In silico models are not favored  PBPK modeling doesn’t use real organisms  Saves lives and money  Create a mobile app that is easily assessable  Prevents loss of organisms’ lives
  10. 10. Methods  Prepare Spreadsheet  Initial Preparation  http://dogwood.rtpnc.ep a.gov/  Computer Version  Goal: transfer to mobile app range
  11. 11. Methods Weight Estimate
  12. 12. Methods  Datasets were first identified in the computer toxicology book, curated, then modeled in MOE  Datasets used: Clearance_Oral, Human Clearance, Hepatic Clearance, Human Intestinal Absorption, Human Oral Intestinal Absorption  Descriptors: Hba hydrogen bond acceptor count (a_acc), Hbd hydrogen bond donor count (a_don), molecular weight (MW), octanol water partition coefficient (logP), topological polar surface area (TPSA), fraction of rotatable bonds (b_rotR), Number of atoms (a_count)
  13. 13. Calculations
  14. 14. Clearance_Oral Dataset Index Compound Parent_SMILES Observed CL(PO,man) MLR (Quadratic) AC-PLS (Quadratic) MC-PLS (Tertiary) Simple Allometry Mahmood Method Ref_Num Reference 1 Meloxicam s1c(cnc1NC(=O)C=1N(S(=O)(= O)c2c(cccc2)C=1O)C)C 0.15000001 0.21 0.275 0.15 0.112 0.044 46 http://onlinelibrary.wiley. com/doi/10.1002/jps.10510 /pdf 2 Ethosuximide O=C1NC(=O)CC1(CC)C 0.152 0.55 0.903 0.58 0.183 0.183 46 http://onlinelibrary.wiley. com/doi/10.1002/jps.10510 /pdf 3 Zonisamide S(=O)(=O)(N)Cc1noc2c1cccc2 0.33000001 0.45 0.473 0.23 0.307 0.204 46 http://onlinelibrary.wiley. com/doi/10.1002/jps.10510 /pdf 4 Flunoxaprofen Fc1ccc(cc1)- c1oc2c(n1)cc(cc2)C(C(O)=O)C 0.37900001 0.62 0.709 0.56 1.52 0.894 46 http://onlinelibrary.wiley. com/doi/10.1002/jps.10510 /pdf 5 Fluconazole Fc1cc(F)ccc1C(O)(Cn1ncnc1)C n1ncnc1 0.40000001 0.5 0.64 0.44 0.41 0.16 46 http://onlinelibrary.wiley. com/doi/10.1002/jps.10510 /pdf Calculated Normalized a_acc a_count a_don b_rotN logP(o/w) TPSA Weight b_rotR a_acc a_count a_don b_rotR logP(o/w) TPSA Weight b_rotR 5 36 2 3 0.94 99.6 351.407 0.12 0.384615 0.290323 0.25 0.166667 0.144794 0.4552 0.427007 0.252 2 21 1 1 0.25999999 46.17 141.17 0.1 0.153846 0.169355 0.125 0.055556 0.040049 0.213735 0.171541 0.21 3 22 1 2 0.19 86.19 212.229 0.1333 0.230769 0.177419 0.125 0.111111 0.029267 0.394597 0.257887 0.28 3 33 2 3 3.6700001 63.33 285.274 0.1304 0.230769 0.266129 0.25 0.166667 0.565311 0.291286 0.346647 0.273913 5 34 1 5 -1.124 81.65 306.276 0.2083 0.384615 0.274194 0.125 0.277778 -0.17314 0.374079 0.372167 0.4375 Sample group of Chemicals: Different Descriptor Values:
  15. 15. Clearance_Oral Dataset Cont’d Index Rank Compound Parent_SMILES a_acc a_count a_don b_rotN logP(o/w) TPSA Weight b_rotR Distance 1 59 Meloxicam s1c(cnc1NC(=O)C=1N( S(=O)(=O)c2c(cccc2)C =1O)C)C -0.23077 -0.27419 0 -0.05556 0.163278 - 0.44108 -0.42458 3.948 4.014935 2 57 Ethosuximide O=C1NC(=O)CC1(CC) C 0 -0.15323 0.125 0.055556 0.268022 - 0.19962 -0.16911 3.99 4.012801 3 45 Zonisamide S(=O)(=O)(N)Cc1noc2 c1cccc2 -0.07692 -0.16129 0.125 0 0.278805 - 0.38048 -0.25546 3.92 3.962538 4 47 Flunoxaprofen Fc1ccc(cc1)- c1oc2c(n1)cc(cc2)C(C( O)=O)C -0.07692 -0.25 0 -0.05556 -0.25724 -0.27717 -0.34422 3.926087 3.968267 5 29 Fluconazole Fc1cc(F)ccc1C(O)(Cn1n cnc1)Cn1ncnc1 -0.23077 -0.25806 0.125 -0.16667 0.481208 - 0.35996 -0.36974 3.7625 3.84935 A_acc = a A_count = b A_don = c B_rotN = d logP(o/w) = e TPSA = f Weight = g b_rotR = h The equation: Descriptor Coefficients
  16. 16. Clearance_Oral Dataset Cont’d descriptor test molecule value 1 a_acc 2 2 a_count 6 3 a_don 3 4 b_rotN 4.00 5 logP(o/w) 0.39 6 TPSA 300.00 7 Weight 3.00 8 b_rotR 0.41 Fu model (0=>90,1:(gt30,lt90),2:(lt30)) 3-class 0 molecule similar Fu 1 Ranitidine 10.40 2 Nizatidine 12.80 3 Recainam 10.70 4 Felbramate 0.70 5 Tamsulosin 0.52 top 3 mean/sd 11.30 1.31 top 5 mean/sd 7.02 5.93
  17. 17. Histograms
  18. 18. Decision Tree Classifier Process
  19. 19. Decision Trees Hand drawn process from the computerized version Right: yes; Left: no Total indicates misclassification rate Example:
  20. 20. Final Project Dataset includes 671 chemicals
  21. 21. Distance Calculation Entry ID rank SMILES Formula Name Weight logP(o/w) TPSA a_count a_acc a_don b_rotN Distance 1 307 OC[C@H]1C[C@@H](n2c 3nc(nc(NC4CC4)c3nc2)N )C=C1 C14H18N6O Abacavir -0.05897 0.058892 -0.04989 -0.08444 -0.10714 -0.13636 -0.01639 0.216586 2 372 O(C)c1cc2c([nH+]c(N3CC c4cc(OC)c(OC)cc4C3)cc2 N)cc1OC C22H25N3O4 Abanoquil -0.11956 -0.08726 -0.01955 -0.15556 -0.10714 0 -0.03279 0.242987 3 655 O1[C@H](C)[C@@H]([N H2+][C@H]2C=C(CO)[C @@H](O)[C@H](O)[C@ H]2O)[C@H](O)[C@@H] (O)[C@H]1O[C@H]1[C@ H](O)[C@@H](O)[C@H] (O[C@@H]1CO)O[C@H]( [C@H](O)CO)[C@H](O)[ C@@H](O)C=O C25H43NO18 Acarbose -0.25718 0.439646 -0.37601 -0.30222 -0.60714 - 0.59091 -0.16393 1.112124 4 412 O(C[C@@H](O)C[NH2+] C(C)C)c1ccc(NC(=O)CCC )cc1C(=O)C C18H28N2O4 Acebutolol -0.08708 -0.00778 -0.03633 -0.14667 -0.10714 - 0.09091 -0.13115 0.259651 5 227 O=C(NCC[NH+](CC)CC) c1ccc(NC(=O)C)cc1 C15H23N3O2 Acecainide (N- acetylprocainami de) -0.05459 0.027862 0.005334 -0.10667 -0.03571 - 0.09091 -0.09836 0.185411
  22. 22. Input descriptor test molecule value 1 Weight 179 2 logP(o/w) 2 3 TPSA 66 4 a_count 20.00 5 a_acc 1.00 6 a_don 0.00 7 b_rotN 3.00 Fu model (0=>90,1:(gt30,lt90),2:(lt30)) 3-class 0 molrank similar Fu 1 Acetylsalicylic Acid 0.68 2 Pyridostigmine 1.00 3 Gabapentin 0.97 4 Mexiletine 0.36 5 Tranexamic acid 0.00 top 3 mean/sd 0.88 0.18 top 5 mean/sd 0.60 0.42 EXPT VDss (L/kg) EXPT CL (mL/min/ kg) EXPT fu EXPT MRT (h) EXPT t1/2 (h) QPlogS CIQPlogS QPlogHE RG QPPCaco QPlogBB QPPMDC K QPlogKp #metab QPlogKhs a HumanO ralAbsorp tion PercentH umanOra lAbsorpti on 0.22 12.00 0.68 0.30 0.26 -1.67 -1.58 -1.23 124.94 -0.57 66.44 -3.33 0.00 -0.77 3.00 71.37 1.10 9.60 1.00 1.80 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.71 1.70 0.97 7.00 5.30 -0.82 -0.37 -1.60 31.96 -0.33 16.84 -5.71 3.00 -0.66 2.00 47.43 5.90 8.30 0.36 12.00 9.90 -1.31 -1.31 -4.49 916.00 0.35 497.79 -3.59 5.00 -0.08 3.00 92.54 0.38 2.40 0.00 2.60 2.30 -0.82 -0.14 -1.68 22.39 -0.41 11.46 -6.11 3.00 -0.69 2.00 43.63
  23. 23. Conversion to a Mobile Device  SpreadsheetConverter  Hid specific sheets  Simplified the spreadsheet to fit into the smaller area  Converted spreadsheets to URL compatible  Created a tiny.url for the newly made webpage  QR code then calculated for the specific URL  End-user of package is now able to view
  24. 24. URL and QR Code http://goo.gl/UDR4U http://goo.gl/3X0pX ADME by Analog App Physiology App
  25. 25. Snapshots from the Mobile App:
  26. 26. Snapshots from the Mobile App
  27. 27. Works Cited "Assessment of chemicals - Organisation for Economic Co-operation and Development." Organisation for Economic Co- operation and Development. OECD, n.d. Web. 12 July 2013. <http://www.oecd.org/env/ehs/risk-assessment/intro ductiontoquantitativestructureactivityrelationships.htm>. MacDonald, Alex J., and Neil Parrott. "MODELLING AND SIMULATION OF PHARMACOKINETIC AND PHARMACODYNAMIC SYSTEMS - APPROACHES IN DRUG DISCOVERY." Beilstein-Institut. Beilstein-Institut Workshop, 22 July 2005. Web. 16 July 2013. <www.beilstein- institut.de/bozen2004/proceedings/MacDonald/MacDonald.htm>. U.S. Environmental Protection Agency, Office of Research and Development. (2008). Uncertainty and variability in physiologically based pharmacokinetic models: Key issues and case studies (EPA/600/R-08/090). Washington, DC: National Center for Environment Assessment. Zhao, P. Food and Drug Administration, Center for Drug Evaluation and Research. (2011). Applications of physiologically based pharmacokinetic (pbpk) modeling and simulation during regulatory review (21191381). Retrieved from Office of Clinical Pharmacology website: http://www.ncbi.nlm.nih.gov/pubmed/21191381

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