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University of Toronto L L University of Toronto C C F F E E - - PowerPoint PPT Presentation

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University of Toronto L L University of Toronto C C F F E E Laboratory for Applied Surface Thermodynamics Laboratory of Colloid and Formulation Engineering predicting lung mechanics from dynamic surface tension evaluations of lung

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predicting lung mechanics from dynamic surface tension evaluations of lung surfactants

Edgar Acosta, Z. Policova, S. Saad , A. W. Neumann.

February 22, 2012 Workshop on Surfactant Driven Thin Film Flows to be held at the Fields Institute Laboratory for Applied Surface Thermodynamics Laboratory of Colloid and Formulation Engineering

L C F E L C F E

University of Toronto University of Toronto

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Upon compression (exhalation) the lung surfactants produce a near zero surface tension that reduce the pressure difference between the smaller alveoli and the airways Laplace Pressure: ΔP ~ γ/R (R, radius of the alveolus)

Lung Surfactants and Lung Physiology

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The Engineering Approach

In vivo

(c)

5 15 25 35

S u r f a c e t e n s i

  • n

, m J / m 2

0.8 1.0

Relative Area Ar

Humid air

In vitro Surfactant and lung mechanics Surfactant chemistry and additives

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Composition of lung surfactants

  • Phospholipids ~ 85-90%
  • Mainly phostphatidyl cholines (zwitterionic), and particularly dipalmitoyl

phosphatidyl cholines (DPPC) to give solid-like properties.

  • Phosphatidyl glycerols (anionic) that impart appropriate dynamic

folding/unfolding properties to the surfactant film

  • Neutral Lipids ~ 1-5% (cholesterol)
  • Proteins ~ 5-10%
  • Surfactant Proteins A and D => anionic, hydrophilic
  • Surfactant Proteins B and C => cationic, hydrophobic
  • Surfactant Protein B is essential
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Wilhelmy Balance

Surfactant Evaluation => Compression isotherms

Surface pressure = surface tension of the pure liquid (γ0)- surface tension (γ) “Ideal gas” “condensed” “solid” “film collapse” Molecular area = 1/surface concentration=1/Γ

Elasticity ε=dγ/dln(A) = -dπ/dln(A)

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Evaluation of Surfactant Dynamics

Captive Bubble Pendant Drop Constrain Sessile Drop

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0.7 0.8 0.9 1 0.5 1 1.5 2 normalized time, t/cycle period Relative area A/Ao 5 10 15 20 25 30 35 0.5 1 1.5 2 Normalized time, t/cycle period Surface tension, mJ/m^2 3 s/cycle 10 s/cycle

Dynamic Evaluation => adsorption and relaxation effects

Adsorption Adsorption Relaxation Adsorption and relaxation effects depend on: Compression dynamics Environment Surfactant composition

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Compression Relaxation Model

γeq Equilibrium surface tension γmin,c Minimum surface tension at collapse ka, kr First order adsorption and relaxation constants εc, εe Elasticity during compression and expansion

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Compression Relaxation Model

Formulation εc, mJ/m2 εe, mJ/m2 ka, s-

1

kr, s-1 γmin, mJ/m2 γeq, mJ/m2 BLES 120 130 2.5 0.0 2 22 BLES-albumin 72 78 1.5 2.5 20 25 Formulation εc, mJ/m2 εe, mJ/m2 ka, s-

1

kr, s-1 γmin, mJ/m2 γeq, mJ/m2

Parameters for specific scenarios Typical fit of CRM model

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CRM parameters: γeq, γmin,c, ka, kr , εc, εe γ =f(A, t) Prokop et al. (1999) A =f(V, γ) Smith et al. (1986) P =Ptissue(V) +Pcapillary (γ) Ventilator waveform: V=f(t)

CRM - Pressure-Volume Model

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Tissue contribution to lung pressure

25 50 75 100 5 10 15 20 % Total lung capacity (TLC) Recoil pressure - tissue, cm H2O Simon et al, 2010 (mice) Smith and Stamenovic, 1987 (rabbits) Dixon et al, 2009 (rats)

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CRM-PV algorithm

(P)t = 0.027γt + a/(b-Vt) At = 0.0324Vt + 1.734 - 0.707γt

0.366

Vt+δt from ventilation function γa = γt At +δt= 0.0324Vt+δt + 1.734 - 0.707γa

0.366

t A A dt dA

t t t t

δ

δ −

=      

+

( )

a eq t t

k dt dA A dt d

γ γ ε γ − +       =      

If (dA/dt)t<0, ε= εc , else ε= εe If γa < γeq , k=kr, else k=ka γn = γt +(dγ/dt)t δt If γn < γmin , γt+δt = γmin, else γt+δt = γn γt+δt = γa ? γa = γ t+δt Save γt ,Vt, (P)t , At no t > tmax ? t= t+δt yes no yes

End

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CRM – PV –rabbit model

CRM-PV prediction –BLES Bachofen et al. (1987) 25 50 75 100 10 20 30 40 % Total lung capacity (TLC) Expansion Compression Surface tension, mJ/m2 40 60 80 100 10 20 30 % Total lung capacity (TLC) Expansion Compression Pressure, cm H2O Expansion Compression 1 2 3 40 60 80 100 Lung area, m2 % Total lung capacity (TLC)

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CRM – PV –mice model

40 60 80 100 15 30

% T

  • t

a l l u n g c a p a c i t y ( T L C )

Pressure, cm H2O Experimental CRM-PV

40 60 80 100 10 20 30 % Total lung capacity (TLC) Pressure, cm H2O 0.25 0.5 0.75

  • 5

5 15 25 Volume, ml Pressure, cm H2O CRM-PV BLES CRM-PV BLES - ALBUMIN Experiments of Allen and Bates CONTROL HCl injury model of ARDS

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10 20 30 40 50 60 70 1 2 3 4

E l a s t a n c e , c m H 2 O / m l

Lung volumes of air ventilated

CRM – PV, dynamic properties

CRM-PV prediction of lung elastance (ΔP/ ΔV) – left – and experimental values –right - using variable ventilation *** low minimum surface tension is not always important *** Fast surfactant adsorption is essential

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Conclusions

1 – In vitro – in vivo correlations are closer to reality => integrated approach to design surfactant therapies 2 – Much to be learned of the physics of surfactant membranes at the molecular scale 3 – A combination of strategies: surfactant additives, method of ventilation may be used in alternative therapies 4 – Need to introduce flow-driven pressure drop 5 – Need to incorporate surfactant spreading

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Acknowledgements

  • Canada Institute for Health Research (CIHR)
  • BLES Biochemicals (London, Ontario)
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air water

Surfactant membrane conformations

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Compression Relaxation Model

% Area reduction (compression) Elasticity slightly improves with surfactant concentration

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Compression Relaxation Model

% Area reduction (compression) Relaxation constant is not a function of surfactant concentration

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Compression Relaxation Model

% Area reduction (compression) Adsorption constant tends to increase with surfactant concentration

BLES 2 mg/ml BLES 27 mg/ml

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Cationic Surfactant Additives

Reasoning: Cationic additives can be use to induce flocculation and larger, more active, surfactant aggregates SP-B, a cationic protein, is essential to life The anionic headgroup of phosphatidyl glycerols seems to easily hydrate, weakening the surfactant film

+ + + + + + + + Anchors Air Liquid

NH3

+

Chitosan

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Effect of Chitosan on BLES

Addition of chitosan, up to a certain ratio, induce larger aggregates to form, also improving the surface activity

Optimal molar ratio

  • f number
  • f cationic

groups in polymer to anionic groups in lipids

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50 100 150 200 0.5 1 1.5 2 2.5 0.5 1 1.5 2 2.5 0.5 1 1.5 2 2.5 Elasticity, mJ/m2 n+/n- binding ratio Film collapse

Effect of Chitosan on BLES

Cationic surfactant additives can improve the elasticity of exogenous surfactant and reduce the relaxation constant

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Cationic additives may be the answer to ARDS

20 40 60 80 100 120 140 160 2 4 6 8 10 Lung surfactant (BLES) concentration, mg/L Serum threshold dosage, μl/ml Optimized BLES + Chitosan BLES only

550 μl/ml serum simulates the high protein content in the lungs of ARDS patients. Even a high exogenous surfactant concentration ~ 27 mg/ml BLES would not work

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26 2mg/ml BLES +additive 6 12 18 24 250 500 750 Serum content, μl/ml Minimum surface tension , mJ/m2 0.10 mg/ml Polymxyin B BLES only Complete lung surfactant 0.20 mg/ml Polylysine 50kDa Physiologically active formulations

Effect of cationic peptides

NH3+ NH3+ NH3+ NH3+ NH3+

Polymyxin B