Bioequivalence and Bioavailability Forum

Helmut
★★★

Vienna, Austria,
2012-03-29 20:52
(4753 d 15:11 ago)

Posting: # 8347
Views: 15,959

PK simulation [🇷 for BE/BA]

Dear all,

I tried to implement PK model I (one-compartment open, lag-time):^*

parameter distribution mean CV(%) truncation Volume of distribution log-normal 1 10 elimination rate constant log-normal 0.17375 20 absorption rate constant log-normal 1.39 20 bioavailability uniform 0.5 — 0.4–0.6 lag time normal 1 50 0–2

LLOQ 1% of theoretical C_max, analytical error: multiplicative component (log-normal, assay error 10%), additive component (log-normal, LLOQ × assay error).

My clumsy code (requires package truncnorm for the truncated normal distribution):

#set.seed(29081957)# uncomment this line to compare results only

require(truncnorm)

n      <- 16       # no of sims

t      <- c(0,0.25,0.5,0.75,1,1.25,1.5,2,2.5,3,3.5,4,5,6,9,12,16,24)

D      <- 500

# Index ".d" denotes default values, ".c" CV

F.d    <- 0.5      # fraction absorbed (BA)

F.c    <- 0.1      # ±0.1, uniform

V.d    <- 1        # volume of distribution

V.c    <- 0.1      # CV 10%, lognormal

k01.d  <- 1.39     # absorption rate constant (Wagner: ka)

k01.c  <- 0.2      # CV 20%, lognormal

k10.d  <- 0.17375  # elimination rate constant (Wagner: K)

k10.c  <- 0.2      # CV 20%, lognormal

tlag.d <- 1

tlag.c <- 0.5      # CV 50%, truncated normal

tlag.l <- 0        # lower truncation 0

tlag.u <- 2        # upper truncation 2

AErr   <- 0.1      # analytical error CV 10%, lognormal

curve(F.d*D/V.d*k01.d/(k01.d-k10.d)*(exp(-k10.d*(x-tlag.d))

  -exp(-k01.d*(x-tlag.d))), from=tlag.d, to=24, n=(24-tlag.d)*5+1,

  type="l", lwd=2, col="red", xlim=c(0,24), ylim=c(0,350),

  xlab="time", ylab="concentration", add=FALSE)

tmax.th<- log(k01.d/k10.d)/(k01.d-k10.d)+tlag.d

Cmax.th<- F.d*D*k01.d/(V.d*(k01.d-k10.d))*(exp(-k10.d*(tmax.th-tlag.d))

  -exp(-k01.d*(tmax.th-tlag.d)))

LLOQ   <- 0.01*Cmax.th # 1% of theoretical Cmax

abline(v=0)

abline(h=0)

abline(h=LLOQ, lty=3)

for (i in 1:n){

  F      <- runif(n=1, min=F.d-F.c, max=F.d+F.c)

  V      <- rlnorm(n=1, meanlog=log(V.d)-0.5*log(V.c^2+1),

              sdlog=sqrt(log(V.c^2+1)))

  k01    <- rlnorm(n=1, meanlog=log(k01.d)-0.5*log(k01.c^2+1),

              sdlog=sqrt(log(k01.c^2+1)))

  k10    <- rlnorm(n=1, meanlog=log(k10.d)-0.5*log(k10.c^2+1),

              sdlog=sqrt(log(k10.c^2+1)))

  tlag   <- rtruncnorm(n=1, a=tlag.l, b=tlag.u, mean=tlag.d, sd=tlag.c)

  # C    <- F*D/V*k01/(k01-k10)*(exp(-k10*(t-tlag))-exp(-k01*(t-tlag)))

  # explicit (compute macro-constants, Wagner’s notation)

  # John G. Wagner, Pharmacokinetics for the Pharmaceutical Scientist, pp 4–5, 1993)

  ka     <- k01

  K      <- k10

  C0     <- F*D/V  # fictive iv concentration at t=0

  B1     <- C0*ka/(ka-K)*exp(K*tlag)

  B2     <- C0*ka/(ka-K)*exp(ka*tlag)

  C      <- B1*exp(-K*t)-B2*exp(-ka*t)

  for (j in 1:length(t)){ # analytical error (multiplicative 10% C)

    if (j==1)

      AErr1 <- rlnorm(n=1, meanlog=0, sdlog=sqrt(log(C[j]*AErr^2+1)))

    else

      AErr1 <- c(AErr1, rlnorm(n=1, meanlog=0, sdlog=sqrt(log(C[j]*AErr^2+1))))

  }

  AErr2  <- rep(AErr*LLOQ, length(t)) # analytical error (constant 10% LLOQ)

  C      <- C+AErr1+AErr2 # add analytical error

  C[C<LLOQ] <- NA  # assign NAs to Cs below LLOQ

  if (i==1) P <- log(C) else P <- P+log(C) # Prod. for geom. means

  if (i==1) S <- C else S <- S+C           # Sum  for arithm. means

  lines(t, C, type="l") # spaghetti plots

  }

GM       <- exp(P/n)  # geometric means

AM       <- S/n       # arithmetic means

points(t, GM, pch=16, col="blue")

lines(t, GM, type="l", col="blue", lwd=2)

points(t, AM, pch=16, col="#12DD00")

lines(t, AM, type="l", col="#12DD00", lwd=2)

legend("topright",

  c("theoretical", "geometric means", "arithmetic means"),

  pch=c(3,16,16),

  col=c("red","blue","#12DD00"))

A run of the set random seed below:

[image]

Is this correct? I think that I screwed up the analytical error. Original text:

Analytical assay errors were generated from log-normal distributions with no bias, a CV of 10%, plus a constant term equal to the product of the assay CV and the limit of quantification, LQ.

Shouldn’t I rather use a normal distribution instead (AErr1 <- rnorm(n=1, mean=0, sd=abs(C[j]*AErr)))? …which would give:

[image]

Csizmadia F, Endrényi L. Model-Independent Estimation of Lag Times with First-Order Absorption and Disposition. J Pharm Sci. 1998;87(5):608–12. doi:10.1021/js9703333

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes

d_labes
★★★

Berlin, Germany,
2012-03-30 17:38
(4752 d 18:24 ago)

@ Helmut
Posting: # 8356
Views: 13,461

PK simulation

Post reply

Dear Helmut,

❝ Is this correct? I think that I screwed up the analytical error. Original text:

Analytical assay errors were generated from log-normal distributions with no bias, a CV of 10%, plus a constant term equal to the product of the assay CV and the limit of quantification, LQ.

❝ Shouldn’t I rather use a normal distribution instead (AErr1 <- rnorm(n=1, mean=0, sd=abs(C[j]*AErr)))? …

I'm not quite sure If I really understand what you attempt here.

But your implementation of the analytical error via log-normal distribution seems correct for me.

What I absolutely don't understand is the "... constant term ...". What is it good for :confused:

. This is only a shift in the concentration levels constant over the whole curve and also for all simulated profiles the same, if I understand. But nothing like a random term as errors usually are deemed for.

BTW: Why do you think you have screwed up something? Because the scatter in the simulated data is too smooth compared to real data :smoke:

—
Regards,

Detlew

Helmut
★★★

Vienna, Austria,
2012-03-30 17:56
(4752 d 18:06 ago)

@ d_labes
Posting: # 8357
Views: 13,475

PK simulation

Post reply

Dear Detlew!

❝ ❝ Is this correct? I think that I screwed up the analytical error. Original text:

Analytical assay errors were generated from log-normal distributions with no bias, a CV of 10%, plus a constant term equal to the product of the assay CV and the limit of quantification, LQ.

❝ ❝ Shouldn’t I rather use a normal distribution instead (AErr1 <- rnorm(n=1, mean=0, sd=abs(C[j]*AErr)))? …

❝ I'm not quite sure If I really understand what you attempt here.

Well, reproduce the sims of the paper…

❝ But your implementation of the analytical error via log-normal distribution seems correct for me.

Really? Im not sure about meanlog=0 since dlnorm(1)==dnorm(0); shouldn’t I rather use meanlog=1?^*

❝ What I absolutely don't understand is the "... constant term ...". What is it good for :confused: . This is only a shift in the concentration levels constant over the whole curve and also for all simulated profiles the same, if I understand. But nothing like a random term as errors usually are deemed for.

Me too. I don't get the idea as well. Maybe it’s time to ask László.

❝ BTW: Why do you think you have screwed up something? Because the scatter in the simulated data is too smooth compared to real data :smoke: .

Exactly. Also I don’t get the point why I should go with a log-normal here (noise is not necessarily positive). Analytical error is normal, IMHO.

No big difference:

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes

d_labes
★★★

Berlin, Germany,
2012-03-31 16:40
(4751 d 19:22 ago)

@ Helmut
Posting: # 8363
Views: 13,510

PK simulation - log-normal

Post reply

Dear Helmut!

❝ ❝ But your implementation of the analytical error via log-normal distribution seems correct for me.

❝

❝ Really? Im not sure about meanlog=0 since dlnorm(1)==dnorm(0); shouldn’t I rather use meanlog=1?^*

If it comes to the log-normal I prefer always to work in the log domain, i.e. simulate the errors via normal distribution add it to the log-transformed theoretical value and than transform it back. The syntax of *lnorm() functions (which values to use as meanlog, meansd) is too complicated for my mind :cool:

. Eventually this helps to figure out.

❝ ❝ What I absolutely don't understand is the "... constant term ...". What is it good for :confused: . This is only a shift in the concentration levels constant over the whole curve and also for all simulated profiles the same, if I understand. But nothing like a random term as errors usually are deemed for.

❝

❝ Me too. I don't get the idea as well. Maybe it’s time to ask László.

That seems a very good idea. Which László ever :-D

❝ ❝ BTW: Why do you think you have screwed up something? Because the scatter in the simulated data is too smooth compared to real data :smoke: .

❝

❝ Exactly. Also I don’t get the point why I should go with a log-normal here (noise is not necessarily positive). Analytical error is normal, IMHO.

(Emphasis by me) This is a very good question, able to battle on over long evenings at beer with smoking heads :smoke:

.

Regarding positive noise via log-normal you mix up somefink here, I think. The log-normal lends to positive or negative errors in the log-domain which back-transformed gives values greater or lower than the theoretical one in a multiplicative fashion.

The log-normal lends to variances (in the original domain) which are proportional to the values itself (higher variability at higher values). Maybe this is not the correct behavior since in bioanalysis it is often so that the errors are biggest at the lowest concentration. But here you are the expert :-D

—
Regards,

Detlew

Helmut
★★★

Vienna, Austria,
2012-03-30 20:46
(4752 d 15:16 ago)

@ d_labes
Posting: # 8358
Views: 13,988

Why all this fuzz?

Post reply

Dear Detlew!

❝ I'm not quite sure If I really understand what you attempt here.

In more detail now. The authors explored different methods for estimating t_lag. Clearly the one used in standard PK software (the last timepoint before the first measured concentration) performed worst – especially if k_a is high and few timepoints are available. We discussed that already (#1872, #4850). Csizmadia and Endrényi studied the methods in terms of RSME and bias; I’m also interested in setting up simulations of BE studies (formulations different in F, k_a, t_lag, or combinations). In other words: If we use a ‘bad’ method – is the BE outcome substantially affected? Some ideas:

Reproduce the paper’s results for comparability.
Explore t_lag as x of the last time point where C≤LLOQ and the first >LLOQ.
Think about DR formulations. Is a shift in t_lag unbiased reflected in t_max? Would be nice because (already) frequent sampling around C_max would make additional frequent sampling around the expected t_lag unnecessary.

In my experience one- and two-compartment models with a lag-time cover the vast majority of IR and DR formulations. Even if in all subjects concentrations were measured at the first sampling time point PK models with a lag-time generally result in better fits (especially of the absorption phase and of C_max). Once I have a suitable setup I could try to answer questions only a few people dare to ask, like:

AUC_t if t_last,R ≠ t_last,T (the wonderful missing value story). Explore the bias if the true ratio ≠ 1.
AUC₇₂ or AUC_τ: Inter-/extrapolate to nominal time? Extrapolate a missing value?
Missing values generally…
C_last or estimated C_min at τ?¹
Explore different algorithms in selecting the terminal phase in multicompartmental models.

BTW, Csizmadia and Endrényi refer in their models’ parameters to an earlier (and much quoted) publication by Bois et al (1994).² Interesting that there normal distributed data were assumed (why?) and a truncation of ±3 SD was used (though with the same CVs). Does anybody know an R package supporting the truncated log-normal?

The analytical error was already defined in a strange way in Bois’ paper:

Analytical assay errors were generated from truncated normal distributions with no bias (mean zero), a CV of 10%, truncation at ±3 CV, plus a fixed term equal to the product of the assay CV and the limit of quantification, LQ.

Also interesting that they also showed a large positive bias and terrible CV of AUC_∞, especially for two-compartment models. Of the extrapolation methods using the estimated C_last instead of the measured C_last performed slightly better. AUC_t performed best by far. The study was sponsored by the FDA – still requiring AUC_∞… :not really:

Paixão P, Gouveia LF, Morais JGA. An alternative single dose parameter to avoid the need for steady-state studies on oral extended-release drug products. Eur J Pharm Biopharm. 2012;80(2):410–7. doi:10.1016/j.ejpb.2011.11.001
Bois FY, Tozer TN, Hauck WW, Chen M-L, Patnaik R, Williams RL. Bioequivalence: Performance of Several Measures of Extent of Absorption. Pharm Res. 1994;11(5):715–22. doi:10.1023/A:1018932430733

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes

jag009
★★★

NJ,
2012-03-30 22:34
(4752 d 13:28 ago)

@ Helmut
Posting: # 8359
Views: 13,497

PK simulation

Post reply

Hi Helmut,

❝ V <- rlnorm(n=1, meanlog=log(V.d)-0.5*log(V.c^2+1),

❝ sdlog=sqrt(log(V.c^2+1)))

❝ k01 <- rlnorm(n=1, meanlog=log(k01.d)-0.5*log(k01.c^2+1),

❝ sdlog=sqrt(log(k01.c^2+1)))

Sorry for the interruption and excuse me for my novice question... How did you arrive at the meanlog equation? I looked the rlnorm syntax from R manual and the mean equation different.

Thanks

John

Hi Helmut,
Never mind.. It's Friday here. Wasn't thinking straight... :sleeping:

Edit: Copypasted from follow-up post. You can edit your posts for 24 hours. [Helmut]
P.S.: See Martin’s post.

jag009
★★★

NJ,
2012-04-04 19:03
(4747 d 16:59 ago)

(edited on 2012-04-04 22:05)
@ Helmut
Posting: # 8383
Views: 13,370

PK simulation

Post reply

Hi Helmut,

I have a question on R, excuse me for being a novice.. I just started using it :)

❝ I tried to implement PK model I (one-compartment open, lag-time):

❝ parameter distribution mean CV(%) truncation

❝ Volume of distribution log-normal 1 10

...

❝ V.d <- 1 # volume of distribution

❝ V.c <- 0.1 # CV 10%, lognormal

...

❝ V <- rlnorm(n=1, meanlog=log(V.d)-0.5*log(V.c^2+1),

In your program, is the mean of V from a log-normal distribution? If so, can't you just specify rlnorm statment as rlnorm(n=1, meanlog=V.d, ...) instead of rlnorm(n=1, meanlog=log(v.d)-0.5...)?

The reason I asked is because of the following I saw from an article on "PK Modeling and Simulation of Mixed Pellets" by Watanalumlerd et at. The author listed a table:

Parameter                Distribution        Mean+/-SD

...

lag time of emptying     Log-Normal          0.75+/-0.22

He wrote "A lognormal distribution was chosen ... because time cannot be negative"

In R, Does this mean the rlnorm statement will be:

rlnorm (n=1, mean=0.75, sdlog=0.22)?

Thanks

John

d_labes ★★★ Berlin, Germany, 2012-04-05 11:15 (4747 d 00:48 ago) @ jag009 Posting: # 8384 Views: 13,436	R function rlnorm() Post reply
	Dear John, try `?rlnorm` or `help("rlnorm")` in the R console. Or (as Helmut has already pointed to) see this post from Martin. — Regards, Detlew

Helmut
★★★

Vienna, Austria,
2012-04-05 16:27
(4746 d 19:36 ago)

@ jag009
Posting: # 8385
Views: 13,573

Lognormal vs. truncated normal

Post reply

Hi John!

❝ […] I saw from an article on "PK Modeling and Simulation of Mixed Pellets" by Watanalumlerd et at. The author listed a table:

❝ Parameter Distribution Mean+/-SD

❝ lag time of emptying Log-Normal 0.75+/-0.22

❝ He wrote "A lognormal distribution was chosen ... because time cannot be negative"

The complete quote:^*

Gastric emptying time, lag time of emptying, and their variability (standard deviation) were obtained from the literature […]. A lognormal distribution was chosen for all time parameters because time cannot be negative.

I don’t have the referred literature but I have strong doubts that the sample sizes in these scintigraphy studies were large enough to distinguish between normal and lognormal distributions. I would suspect that reported arithmetic means ± SD were (erroneously) used by Watanalumlerd et al. BTW, they employed a nice software named Crystal Ball. ;-)

Time parameters in PK are a battleground. I wouldn’t opt for lognormal in simulations “because time cannot be negative” – unphysiological high values are still possible. I would rather go with a truncated normal (like in the paper of Csizmadia and Endrényi). The reasoning for truncated AUC₇₂ is also based on the fact that a GI transit time of larger than 72 hours was never observed in any study.

Watanalumlerd P, Christensen JM, Ayres JW. Pharmacokinetic Modeling and Simulation of Gastrointestinal Transit Effects on Plasma Concentrations of Drugs from Mixed Immediate-Release and Enteric-Coated Pellet Formulations. Pharma Dev Technol. 2007;12:193–202. doi:10.1080/10837450701212750

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes

jag009 ★★★ NJ, 2012-04-05 19:52 (4746 d 16:10 ago) @ Helmut Posting: # 8389 Views: 13,374	Lognormal vs. truncated normal Post reply
	Thanks Helmut and D_labes

PK simulation [🇷 for BE/BA]

PK simulation

PK simulation

PK simulation - log-normal

Why all this fuzz?

PK simulation

PK simulation

R function rlnorm()

Lognormal vs. truncated normal

Lognormal vs. truncated normal