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Predicting Chemical Absorption from Complex Chemical Mixtures Jim E. Riviere, DVM, PhD, DSc ( hon ) Center for Chemical Toxicology Research and Pharmacokinetics North Carolina State University. Work supported by NIH OH-07555, OH-03669; AFOSR FA 9550-04-1-0376, Novartis.

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Predicting Chemical Absorption from Complex Chemical MixturesJim E. Riviere, DVM, PhD, DSc (hon)Center for Chemical Toxicology Research and Pharmacokinetics North Carolina State University

Work supported by NIH OH-07555, OH-03669; AFOSR FA 9550-04-1-0376, Novartis

how do drugs and chemicals interact with skin
How do drugs and chemicals interact with skin?

Skin is often touted as being the body’s primary barrier to absorption of toxic chemicals


Skin is often a preferred route of drug administration (e.g. transdermal nicotine, scopolamine patches)

  • Biological Functions of Skin include:
    • Physical Barrier: stratum corneum
    • Thermoregulation: hair, sweat glands, blood flow shunts
    • Mechanical support: collagen, water
    • Neurosensory reception:
    • Immunological response: keratinocytes, Langerhans Cells
anatomical considerations
Anatomical Considerations
  • Primary barrier to drug absorption is the stratum corneum
  • Composed of dead keratinocytes embedded in a lipid matrix, through which most drugs are absorbed.
  • Lipid matrix excreted by cells in lower layer
  • Basal layer contains viable keratinocytes which migrate to surface and are ultimately shed.

Skin: PORTAL of entry and TARGET for toxicity


IL-8, TNF, Others


Diffusion is the primary driving force moving chemicals across the stratum corneum. Rate of transfer is dependent upon Fick’s Law where:
  • D = Diffusivity, d = Distance
  • Pc = Partition Coefficient,
  • SA=Surface Area, C = Concentration
  • Rate constant: permeability coefficient Kp dCH/dt = (DPc Sa / d) CH

KP=D Pc / d

Pharmacokinetics: dX/dt / Sa = (Kp) (X)

  • Rate of drug movement is proportional to the dose (X).
importance of lipid biochemistry biophysics
Importance of Lipid Biochemistry/ Biophysics
  • Removal of stratum corneum increases absorption
  • Intercellular lipids are the primary pathway for drug absorption
  • Consist primarily of ceramides, sterols, and other neutral lipids
  • Exist in a liquid-crystalline matrix, the fluidity of which is related to permeability of hydrophilic drugs
  • Temperature, hydration and chemical penetration enhancers increase fluidity and permeability
Major thrust of research is on the dermal absorption of drugs and chemicals across skin.

-Developed a suite of novel in silico, in vitro and in vivo animal models- Current focus is on predicting mixture interactions based on physical chemical properties, and studying dermal toxicity and absorption of nanomaterial across skin

- Need is for design of transdermal and topical pharmaceutics and for risk assessment after human chemical exposure to environmental and occupational chemicals

suite of research models
Suite of Research Models
  • In silico:
    • Mathematical Models
  • In Vitro:
    • Membrane Coated Fiber Array
    • Silastic Diffusion Cells
    • Porcine and human skin diffusion cells
    • Keratinocyte cell culture (Human and Porcine)
    • Isolated Perfused Porcine Skin Flap (IPPSF)
  • In Vivo: Pigs

Porcine skin is very similar to human skin :

  • Structure
  • Function
  • Lipid composition

Human abdominal skin, 150X

Pig abdominal skin, 150X

two novel approaches to predict dermal absorption of complex 2 components mixtures
Two Novel Approaches to Predict Dermal Absorption of Complex (> 2 Components)Mixtures
  • Mixture factor (MF) incorporated into a QSAR/QSPR model
    • Diffusion cell experiments
    • Isolated Perfused Porcine Skin Flap (IPPSF)
  • Membrane Coated Fiber (MCF) array
  • Present data as “Proof of Concept”
  • Time permits focus on highlights and results only.
qspr of dermal absorption has a long history including work by potts hansch and abraham
QSPR of dermal absorption has a long history including work by Potts, Hansch, and Abraham
  • Attempt to correlate permeability through skin (Kp) to log Koct/water or physical chemical properties
  • Extension of solvation energy (LFER) relationships
  • First widespread use in skin was by Potts and Guy

Log Kp = 0.71 log Ko/w – 0.0061 MW – 6.3

Log Kp = 0.03MV – 1.72H – 3.92H – 4.8

Log Kp = c + rR + s + a + b + vV

  • Specific model selected is not crucial as is approach for incorporating mixture effect into model.
experimental design
Experimental Design
  • Dependent variable is log Kp.
  • After fitting LFER model to data independent of mixture, a Mixture Factor (MF) is calculated based on a physical chemical property of the mixture components {refractive index, polarizability, log (1/Henry Constant)} weighted by their percent composition in the mixture.
  • Selected by examining relationship between MF and LFER residues through improvement in R2, Q2 and F.
  • Will present flow-through diffusion cell study of 12 compounds dosed in 24 mixtures for a total of 288 treatment combinations (balanced full-factorial experimental design).

Log Kp = c + mMF + rR + s + a + b + vV

R = Excess molar refraction;  = Dipolarity/Polarizability;

 = H-Bond Acidity;  = H-Bond Basicity;

V = McGowan molecular volume

  • Penetrants:

Substituted phenols

(Nonylphenol, Pentachlorophenol, Phenol, - Nitrophenol)


(Chlorpyrifos, Ethylparathion, Fenthion, Methylparathion)

Triazine Herbicides

(Atrazine, Propazine, Simazine, Triazine)

  • Mixture Components:

Ethanol, Water, Propylene glycol

Methylnicotinate, Sodium lauryl sulfate

summary of diffusion cell data
Summary of Diffusion Cell Data

Improvement of Log Kp Predictability (R2) using Physical Chemical Properties of Mixture

Pred vs Obs Residuals

No MF 0.58 0

Refractive Index 0.80 0.53

Polarizability 0.76 0.43

log (1/ Henry Constant) 0.77 0.45

  • Analysis shows clear improvement of Log Kp when MF included.
  • Principal Component analyses clustered MF descriptors into three “descriptor classes,” with those above representing class members with strongest associations.
applied mf to two other qsprs
Applied MF to two other QSPRs
  • Also conducted on the following models as an example of general applicability of this approach
  • Potts and Guy (1992)

log kp = i + mMF + a log Poct + bMW:

  • Hostynek and Magee(1997)

log kp = i + mMF + bMR + cHBA + dHBD

Had used indicator variable for vehicles

isolated perfused porcine skin flap ippsf
Isolated Perfused Porcine Skin Flap (IPPSF)
  • Isolated system with control over physiological parameters and perfusate composition
  • Intact microcirculation with viable epidermis and dermis
  • Large dosing area with predictable extrapolation to in vivo
  • Allows for simultaneous assessment of absorption, skin disposition, pharmacokinetics and irritation
  • Humane alternative animal model
ippsf studies
IPPSF Studies
  • Studies very similar to those described for diffusion cells, however experiments are more costly and time consuming.
  • Used Area Under the Curve (AUC) of flux profile rather than Kp.
  • Analysis A: used subset of same compound and mixture set as diffusion cell studies (10 compounds in 50 treatment combinations) (Full Factorial experimental design).
  • Analysis B: added 11 other compounds and mixtures for a total of 21 chemicals in 114 treatment combinations.

Analysis A

(10 chemicals

50 trts)

 No Mixture Factor

Best Mixture Factor 


Analysis B

(21 chemicals

114 trts)

 No Mixture Factor

Best Mixture Factor 

summary of mf approach to date
Summary of MF Approach to Date
  • Approach appears robust across different experimental models and QSAR approaches
  • Need to pick molecular descriptors that reflect mechanism of action (Abraham model works best of those studied)
  • IPPSF expected to be less predicted as multiple points of interaction now present.
  • MF can be a function based on mechanism using mixed-effect modeling techniques.
  • Would descriptors in the QSAR model be different knowing a MF would be used?
  • As data set expands, chemical inference space will be richer and multiple component MF would be statistically identifiable
membrane coated fiber array
Membrane Coated Fiber Array

A second approach to predict mixtures

  • Empirical - experimental based model
  • Based on GC/MS SPME fibers
  • Validated for prediction of chemical absorption across porcine skin
  • Holds promise to identify mechanisms of chemical mixture interactions
  • Overview approach and proof of concept that dermal absorpiton can be predicted

Conceptual Basis of MCF to Predict Membrane Interactions

  • Log Kp = c + rR + s + a + b + vV
  • The interactions reflected in the strength coefficients are what is modeled using the MCF array
  • LFERs can be defined for KS in skin, and different fibers
  • PDMS, PA, CarboWax (PEG)
  • LogKS = f(logKMCF1+ logKMCF2+ logKMCF3)
  • Not dependent upon literature data
membrane coated fiber mcf technique
How It Works

A membrane is coated on an inert fiber, MCF.

MCFs are immersed into the donor solution, chemicals are partitioned into the membrane.

MCF is directly injected into GC/MS for quantitative/ qualitative analyses.

Membrane-Coated Fiber (MCF) Technique
permeation study of a chemical mixture with mcf technique
Permeation Study of a Chemical Mixture with MCF Technique

GC/MS Spectra Acquired with the MCF for 30 diverse organic probe compounds

experimental data
Experimental Data

no = Maximum permeation amount

a = Shape function


Correlation of Skin Permeability from aqueous vehicle with partition coefficients of individual MCF

PDMS, PA, CarboWax

expand this system to capture the nature of mixture interactions across solvents using mcf
Expand this system to capture the nature of mixture interactions across solvents using MCF

Log Kp = c + rR + s + a + b + vV

MCF vs Skin predicted:

ax  Descriptors

mf versus mcf
Applicable to any existing QSPR model

Does not require laboratory studies

Assumes linearity and independence of all interactions

MF could be stated as a function of properties

Laboratory based

No need to know chemical descriptors

Applicable to very complex mixtures and formulations

Does not assume linearity or independence

MF versus MCF

Both approaches currently limited by chemical space defining QSPR or MCF calibration

relevant mf and mcf manuscripts
Relevant MF and MCF Manuscripts


Riviere JE, Brooks JD: Predicting skin permeability from complex chemical mixtures. Toxicology and Applied Pharmacology 208: 99-110, 2005.

Riviere JE, Brooks JD: Prediction of dermal absorption from complex chemical mixtures: Incorporation of vehicle effects and interactions into a QSPR framework. SAR and QSAR in Environmental Research 18: 31-44, 2007


Xia XR, Baynes RE, Monteiro-Riviere NA, Leidy RB, Shea D, Riviere JE. A novel in vitro technique for studying percutaneous permeation with a membrane coated fiber and gas chromatography / mass spectrometry. Pharmaceutical Research. 20: 275-282, 2003.

Xia XR, Baynes RE, Monteiro-Riviere NA, Riviere JE. Determination of the partition coefficient and absorption kinetic parameters of chemicals in a lipophilic membrane/water system by using a membrane coated fiber technique. European Journal of Pharmaceutical Sciences 24: 15-23, 2005.

Xia XR, Baynes RE, Monteiro-Riviere NA, Riviere JE: An experimental based approach for predicting skin permeability of chemicals and drugs using a membrane coated fiber array. Toxicology and Applied Pharmacology 221: 320-328, 2007.

Riviere JR, Baynes RE, Xia XR: Membrane Coated Fiber (MCF) array approach for predicting skin permeability of chemical mixtures from different vehicles. Toxicological Sciences

99: 153-161, 2007.

Xia XR, Baynes RE, Monteiro-Riviere NA, Riviere JE. A system coefficient approach for quantitative assessment of the solvent effects on membrane absorption f

rom chemical mixtures. SAR and QSAR in Environmental Research.18: 579-593, 2007.

Baynes RE, Xia XR, Imram M, Riviere JE: Quantification of chemical mixture interactions that modulate dermal absorption using a multiple membrane coated fiber array. Chemical Research in Toxicology. 21: 591-599, 2008.