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