Using Data under Drug Development Yoshitaka Yano Kyoto Pharm. - - PowerPoint PPT Presentation

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Prediction of Human Pharmacokinetic Profiles Using Data under Drug Development Yoshitaka Yano Kyoto Pharm. Univ., Japan - WCoP 2012, Seoul, Korea - 1 Purpose of PK Prediction During drug development, we would like to.. determine FTIH

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Prediction of Human Pharmacokinetic Profiles Using Data under Drug Development

Yoshitaka Yano Kyoto Pharm. Univ., Japan

  • WCoP 2012, Seoul, Korea -

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SLIDE 2

Purpose of PK Prediction

  • During drug development, we would like to..

– determine FTIH dose regimens, – by predicting PK (and hopefully PD) parameters in human, – by predicting PK (and hopefully PD) in profiles human.

  • Several prediction strategies have been proposed

– Parameters : Regression of chemical / biological Information – Profiles : Model-based approach

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SLIDE 3

Prediction of PK Profiles

  • Available data for a new compound:

– Chemical properties of the compound

  • Log P, Molecular weight etc.

– PK data (PK parameters) in animals

  • CL, Vss, Cmax, Tmax, etc.
  • Css-MRT Method to predict PK profile
  • Normalized Curve:

y = Conc. / Css, x = time / MRT

where Css = Dose / Vss = Dose / (AUC / MRT)

3

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SLIDE 4

Topics

  • Review of Css-MRT Method for PK Prediction
  • Prediction of Oral PK Profiles
  • Prediction of Tissue Distribution Profiles
  • Prediction of Pediatric PK from Adults’ PK

4

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SLIDE 5

Css-MRT Method (for i.v. prediction)

i.v. PK Profiles

  • f Animals
  • Calc. Vss, CL

and Css, MRT Model Fitting (e.g. exponentials) Normalized Profile Predicted Vss, CL .. and Css, MRT Back- Normalization Any Prediction Methods

  • e.g. QSAR-based Wajima-Method

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Wajima, Yano et al., J.P.S. 93: 1890- (2004). Wajima, Fukumura et al., J.P.S. 91: 2489- (2002). Wajima, Fukumura et al., J. Pharm. Pharmacol. 55: 939- (2003). Log (CLman) = function( log (CLrat), log(CLdog), MW, etc.) Log (Vssman) = function( log (Vssrat), log(Vssdog), MW, etc.)

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SLIDE 6

Examples of Css-MRT Method

  • In Css-MRT method, predictability of PK profiles

depends on accuracy of PK parameter Prediction.

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a: CL overestimated, b: CL underestimated, c: acceptable CL, Vss estimation, d: CL underestimated, Vss overestimated

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SLIDE 7

Topics

  • Review of Css-MRT Method
  • Prediction of Oral PK Profiles
  • Prediction of Tissue Distribution Profiles
  • Prediction of Pediatric PK from Adults’ PK

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SLIDE 8
  • Function for IV PK profiles predicted by Css-MRT

method can be a core module for prediction system.

  • Under linear PK assumption, convolution theory can

be applied.

Convolution Theory for Predicting Human PK Profiles

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Input Function Weighting Function Output Function Absorption process Intravenous PK Profile Oral PK Profile Intravenous PK Profile Tissue Distribution Process Tissue PK Profile

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Cmax, Tmax are More Frequently Available

  • For PO prediction, F (bioavailability) and Ka required

– Some approaches are reported;

  • Predicted F (BA) by regression, averaged Ka in animals.

– Futa et. al., Biopharm. Drug Disp. 29: 455- (2008) – Review by Vuppugalla et al., J.P.S. 100: 4111- (2011)

  • Tmax, Cmax

– F, Ka of drugs are not necessarily reported. – Tmax, Cmax are reported more frequently.

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Cmax Tmax Slope = Cmax / Tmax Ka

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Prediction of Human Cmax by Regression

  • Same Concept as Wajima’s Regression

– Use dose normalized Cmax (and Tmax) in rat and dog.

  • ln(Cmax/Dose|man) = function {ln(Cmax/Dose|rat), ln(Cmax/Dose|dog), MW,

cLogP, etc.}

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Correlation Plot for ln(Cmax/Dose|man)

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SLIDE 11

Prediction of Human Cmax and Tmax

  • Cmax: Prediction using regression results

– ln(Cmax/Dose|man) = 2.2 + 0.62 * ln(Cmax/Dose|dog) + ..

  • Tmax:

– Low correlation with animals – Tmax, (and also Cmax) depends on sampling design – Possible ways of prediction

  • Regression based
  • Same as those in dog
  • Arbitrarily choose
  • …

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FILT and Convolution Theory

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           

                    

  

t 5 . n j a F Im 1 F F t e t f s F L t f

n n 1 n n a 1

f1(s) f2(s)

  • Transfer function

– Laplace transform of weighting function

  • Convolution: f1(s) * f2(s)
  • FILT (Fast Inverse Laplace Transform) algorithm

– for model-based convolution / deconvolution – FORTRAN program available

  • FILT algorithm: Hosono, Radio Sci., 16: 1015- (1981).
  • Application of FILT to PK Analysis: Yano, Yamaoka et al.,
  • Chem. Pharm. Bull., 37: 1035- (1989).
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Modeling of Oral Absorption Process

  • Convolution - Transfer function in Laplace domain (fa(s), fp(s))
  • fa(s) : Laplace transform of fa(t) - for absorption process
  • fp(s) : Laplace transform of fp(t) - predicted or observed iv plasma profile
  • Modeling of fa(s); e.g. Mono-exponential

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Absorption, fa(s) Plasma(IV), fp(s)

         

                                              s B s A k s k F s s C s dt t dC s B s A k s k F s C , s B s A s f , k s k F s f

app , a app , a p p app , a app , a p p app , a app , a a

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Simulation of Plasma Profile after Oral Dose

  • Estimation of F and Ka,app

– IV profile assumed to be predicted by Css-MRT method – Simultaneous fitting of an example data

(Tmax, Cmax) = (2.0, 2.35), (Tmax, dCp(t)/dt|t=Tmax) = (2.0, 0)

– Estimated F = 0.472, ka,app = 0.562

  • M&S with FILT

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Predicted PO profile (solid curve).

     

                                     s B s A k s k F s s C s dt t dC s B s A k s k F s C

app , a app , a p p app , a app , a p

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Topics

  • Review of Css-MRT Method for PK Prediction
  • Prediction of Oral PK Profiles
  • Prediction of Tissue Distribution Profiles
  • Prediction of Pediatric PK from Adults’ PK

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Tissue Distribution Parameters for Convolution

  • Convolution - Transfer function in Laplace domain (fp(s), ft(s))
  • fp(s): Laplace transform of fp(t) - (predicted or observed) plasma iv profile
  • ft(s): Laplace transform of ft(t) - function for tissue distribution kinetics
  • Modeling of ft(s); e.g. Mono-exponential
  • Kp = AUCtissue / AUCplasma, MTT = MRTtissue – MRTplasma

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Plasma, fp(s) Tissue, ft(s)

     

1 1 p t p 1 1 p t

MTT s MTT K s B s A s C , s B s A s f , MTT s MTT K s f

   

                         

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SLIDE 17

Simulations of Tissue PK Profiles by FILT

  • Modeling for Ft(s): e.g. mono-exponential;

– Kp = 2, MTT = 0.1 (rapid equilibrium), 1.0, 5.0 (slow eq.)

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Bottom panels are the weighting functions.

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SLIDE 18

Topics

  • Review of Css-MRT Method for PK Prediction
  • Prediction of Oral PK Profiles
  • Prediction of Tissue Distribution Profiles
  • Prediction of Pediatric PK from Adults’ PK

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SLIDE 19

Pediatric PK Prediction using Adults’ PK Data

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Plasma PK Profiles in Adults and Pediatrics for a Series of Drugs

  • Calc. Mean of Vss and CL for each drug

Allometric Regression for Vss and CL vs. Body Weight Relationships Using Mixed-Effect Modeling

Shimamura, Wajima, Yano, J.P.S. 96: 3125- (2007).

       

 

j , b j j , a j jk jk j j jk

b b a log a log BW log b a log CL log           

CL (L/hr) BW (kg)

for j-th drug, k-th subject

Example in 15 beta-lactam antibiotics Vss (L) BW (kg) b = 0.670 (Vss) b = 0.565 (CL)

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Pediatric PK Prediction using Adults’ PK Data

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Allometric Regression for Vss and CL vs. Body Weight Relationships Using Mixed-Effect Modeling Empirical Bayes Estimation of aj and bj using Adults Vss and CL for New Drug Adults PK of New Drug Estimate Individual Vss and CL for a Pediatric Patient using BW, and Prediction by Css-MRT Method

Predicted Conc. Observed Conc. Time (hr) Conc. Details were presented at, “International symposium in drug development -Modeling & Simulation in Drug Development and Clinical Applications”, Yonsei University in Seoul, 15-16 November, 2006.

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Summary (1)

  • Today’s presentation shows only the ideas of new

types of modeling and simulations focused on the use of FILT.

  • These methods are practical because the data are all

available during routine experiments for drug development.

  • Predictability (reliability) of the methods should be

more precisely and systematically evaluated.

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Summary (2)

  • Intravenous PK profiles predicted by Css-MRT

method can be a core module of the integrated prediction system.

  • Any methods for prediction of PK parameters can be

combined (as a module) with the Css-MRT method. – Flexible Integrated prediction system.

  • Prediction is never perfect, results should be given

with predictive (credible) region.

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Acknowledgements

  • Collaborators

– Toshihiro Wajima Ph.D., Shionogi & Co. Ltd., Japan – Kenji Shimamura, Shionogi & Co. Ltd., Japan – Shunsuke Kawabe, Kyoto Pharm. Univ., Japan

  • Special Friend

– Atsunori Kaibara Ph.D., Astellas, Japan.

  • This work was supported in part by a Grant-in-Aid for

Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan (22590153).

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