epa summer 2013_portable pharmacokinetic parameter prediction tool
TRANSCRIPT
Portable Pharmacokinetic Parameter Prediction Tool
Emerald FengMentors: Chris Grulke, Rocky Goldsmith, Daniel Chang, Cecilia Tan, Mike Tornero
PBPK ModelingChemical health risk
assessmentsUsed to quantify
absorption, distribution, metabolism, and excretion (ADME)
Compartments are specific
Intrinsic and Extrinsic factors In relation to chemical
exposureIn silico vs in vitroDefinite use in
pharmacokinetics
PBPK ModelingModels vary based on complexityCompartment = theoretical value for a
chemicalConnections indicate how each parameter
calculates another
2 compartment 6 compartment Several Compartments
ParametersUsed 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 propertiesPhysiological, Chemical, Tissue
specificImportant!!!!!!!!!! in PBPK
modelingExamples: Absorption rate
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)
QSAR ModelingQuantitative
Structure-Activity Relationship
Relationship between chemical structure and biological activity
Similar structure indicates similar activity
Life-stage
gender
Survey from PBPK Modelers
Icons
Background/Our GoalMost experimentation is
done on real life organisms In silico models are not
favoredPBPK modeling doesn’t use
real organismsSaves lives and moneyCreate a mobile app that is
easily assessablePrevents loss of organisms’
lives
MethodsPrepare Spreadsheet
Initial Preparationhttp://dogwood.rtpnc
.epa.gov/Computer VersionGoal: transfer to
mobile app range
Methods
Weight Estimate
MethodsDatasets were first identified in the computer
toxicology book, curated, then modeled in MOEDatasets 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)
Calculations
Clearance_Oral DatasetIndex Compound Parent_SMILES
Observed CL(PO,man
)MLR
(Quadratic)AC-PLS
(Quadratic)MC-PLS (Tertiary
)
Simple Allometr
yMahmood Method
Ref_Num Reference
1 Meloxicams1c(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.co
m/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.co
m/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.co
m/doi/10.1002/jps.10510/pdf
4 Flunoxaprofen
Fc1ccc(cc1)-c1oc2c(n1)cc(cc2)C(C(O)
=O)C0.3790000
1 0.62 0.709 0.56 1.52 0.894 46http://
onlinelibrary.wiley.com/doi/10.1002/jps.10510/pdf
5 Fluconazole Fc1cc(F)ccc1C(O)(Cn1ncnc1)Cn1ncnc1
0.40000001 0.5 0.64 0.44 0.41 0.16 46
http://onlinelibrary.wiley.co
m/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 0.3846150.290323 0.25 0.1666670.144794 0.4552 0.427007 0.252
2 21 1 1 0.25999999 46.17 141.17 0.1 0.1538460.169355 0.125 0.0555560.0400490.2137350.171541 0.21
3 22 1 2 0.19 86.19 212.229 0.1333 0.2307690.177419 0.125 0.1111110.0292670.3945970.257887 0.28
3 33 2 3 3.6700001 63.33 285.274 0.1304 0.2307690.266129 0.25 0.1666670.5653110.2912860.3466470.273913
5 34 1 5 -1.124 81.65 306.276 0.2083 0.3846150.274194 0.125 0.277778 -0.17314 0.3740790.372167 0.4375
Sample group of Chemicals:
Different Descriptor Values:
Clearance_Oral Dataset Cont’dIndex Ran
k Compound Parent_SMILES a_acc a_count a_don b_rotN logP(o/w) TPSA Weight b_rotR Distance
1 59 Meloxicams1c(cnc1NC(=O)C
=1N(S(=O)(=O)c2c(cccc2)C=
1O)C)C
-0.2307
7-
0.27419 0-
0.05556
0.163278
-0.4410
8-
0.42458 3.948 4.014935
2 57 Ethosuximide
O=C1NC(=O)CC1(CC)C 0 -
0.15323 0.125 0.055556
0.268022
-0.1996
2-
0.16911 3.99 4.012801
3 45 ZonisamideS(=O)(=O)
(N)Cc1noc2c1cccc2
-0.0769
2-
0.16129 0.125 0 0.278805
-0.3804
8-
0.25546 3.92 3.962538
4 47 Flunoxaprofen
Fc1ccc(cc1)-c1oc2c(n1)cc(cc2)
C(C(O)=O)C
-0.0769
2-0.25 0
-0.0555
6-
0.25724-
0.27717
-0.34422
3.926087
3.968267
5 29 FluconazoleFc1cc(F)ccc1C(O)(Cn1ncnc1)Cn1ncn
c1
-0.2307
7-
0.25806 0.125-
0.16667
0.481208
-0.3599
6-
0.36974 3.7625 3.84935A_acc = aA_count = bA_don = cB_rotN = d
logP(o/w) = eTPSA = fWeight = gb_rotR = h
The equation:Descriptor Coefficients
Clearance_Oral Dataset Cont’ddescriptor 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 Fu1 Ranitidine 10.402 Nizatidine 12.803 Recainam 10.704 Felbramate 0.705 Tamsulosin 0.52
top 3 mean/sd 11.30 1.31top 5 mean/sd 7.02 5.93
Histograms
Decision Tree Classifier Process
Decision TreesHand drawn process from the computerized versionRight: yes; Left: noTotal indicates misclassification rateExample:
Final ProjectDataset includes 671 chemicals
Distance CalculationEntry
ID rank SMILES Formula Name Weight logP(o/w) TPSA a_count a_acc a_don b_rotN Distance
1 307OC[C@H]1C[C@@H](n2c3nc(nc(NC4CC4)
c3nc2)N)C=C1C14H18N6
O Abacavir -0.05897 0.058892-
0.04989
-0.08444-
0.10714
-0.1363
6-0.01639 0.21658
6
2 372O(C)c1cc2c([nH+]c(N3CCc4cc(OC)c(OC)cc
4C3)cc2N)cc1OCC22H25N3
O4 Abanoquil -0.11956 -0.08726-
0.01955
-0.15556-
0.10714
0 -0.03279 0.242987
3 655
O1[C@H](C)[C@@H]([NH2+]
[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
-0.3760
1-0.30222
-0.6071
4
-0.5909
1-0.16393 1.11212
4
4 412O(C[C@@H]
(O)C[NH2+]C(C)C)c1ccc(NC(=O)CCC)cc1C
(=O)C
C18H28N2O4 Acebutolol -0.08708 -0.00778
-0.0363
3-0.14667
-0.1071
4
-0.0909
1-0.13115 0.25965
1
5 227O=C(NCC[NH+]
(CC)CC)c1ccc(NC(=O)C)cc1
C15H23N3O2
Acecainide (N-acetylprocaina
mide)-0.05459 0.027862 0.0053
34 -0.10667-
0.03571
-0.0909
1-0.09836 0.18541
1
Inputdescriptor test molecule value
1 Weight 1792 logP(o/w) 23 TPSA 664 a_count 20.005 a_acc 1.006 a_don 0.007 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 CIQPlog
SQPlogH
ERGQPPCac
oQPlogB
BQPPMD
CKQPlogK
p #metab QPlogKhsa
HumanOralAbsorption
PercentHumanOralAbsorption
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 71.371.10 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 0.000.71 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 47.435.90 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 92.540.38 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
Conversion to a Mobile DeviceSpreadsheetConverter
Hid specific sheetsSimplified the spreadsheet to fit into the
smaller areaConverted spreadsheets to URL compatibleCreated 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
URL and QR Codehttp://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 Co-operation and Development. OECD, n.d. Web. 12 July 2013. <http://www.oecd.org/env/ehs/risk-assessment/introductiontoquantitativestructureactivityrelationships.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