Simulate true Cmax with ODE

This post outlines some considerations when simulating a true Cmax in your PK model using differential equations.

Cmax
ODE
Author

Kyle Baron

Published

November 14, 2024

It’s a common practice to find a true maximum concentration after a dose (Cmax) through simulation from a PK model written using differential equations. In this post, we’ll show this implementation and note some important points to consider when using this method for an infusion. My thanks to Félicien Le Louedec for discussion of the issue and inspiration to create the post.

1 Implementation

The true Cmax approach involves the following steps:

  1. Initialize a variable (like Cmax) to zero when starting on a new individual or with a subsequent dose within the same individual.
  2. Calculate concentration in the $DES block (CPode) .
  3. Update Cmax to the value of CPode calculated in $DES whenever CPode is greater than the current value of Cmax.

The code might look like this

$PLUGIN autodec

$SET outvars = c("CP", "Cmax")

$CMT @number 2

$PARAM CL = 1, V = 25, KA = 2.5

$PK
if(NEWIND <= 1 || EVID==1 || EVID==4) {
  Cmax = 0;   // Track Cmax in $DES
}

$DES
CPode = A2/V;

if(CPode > Cmax) Cmax = CPode;

dxdt_A1 = -KA * A1;
dxdt_A2 =  KA * A1 - (CL/V)*A2;

$ERROR
CP = A2/V;

$CAPTURE Cmax CP

I call this a “true” Cmax because the ODE solver will need to explore the shape of the concentration-time profile around Cmax in detail to understand when the concentration stops increasing and starts to decrease. And the solver will explore this part of the curve whether or not you ask for simulated output at the time of Cmax (Tmax) and you can get the true Cmax by logging the concentration at Tmax. If, instead, you simulated, say, every hour and then took the maximum CP over the dosing interval just from the output records, it will be an approximate Cmax since you likely didn’t ask for an output time exactly at Tmax.

2 Dose into extravascular compartment

After loading packages and our model, let’s calculate Cmax for an extravascular dose. We set the default output time step (delta) to 6 hours to reduce the resolution of the simulated output; this is pretty extreme, but I wanted to make the point that we’ll get poor approximation of Cmax if we only look at CP on output records. In other words, we have to use the ODE-derived Cmax to get a the true value back.

library(mrgsolve)
library(dplyr)

mod <- mread("true-cmax.mod", delta = 6)

We’ll put 1000 mg into the depot compartment as a single dose.

dose <- ev(amt = 1000, cmt = 1)

On simulation, we can check the maximum value of CP (calculated only at output records) and Cmax (calculated with the code in $DES).

out <- mrgsim(mod, dose)

plot(out, scales = "same")

And these are the results from the different approaches.

max(out$CP)   # Concentration from output records
[1] 31.97673
max(out$Cmax) # Max concentration from $DES
[1] 37.39886

We see that

  1. CP isn’t very accurate due to low-resolution output in the simulation
  2. Cmax is higher, reflecting the “true” Cmax

We can run this simulation with much smaller delta and with some more strict settings for the ODE solver.

out2 <- mrgsim(mod, dose, delta = 1e-3, rtol = 1e-12, hmax = 1e-3)

max(out2$CP)   # Concentration from output records
[1] 37.39889
max(out2$Cmax) # Max concentration from $DES
[1] 37.39889

Here, we see good agreement with the approximated Cmax and the true Cmax.

But we don’t need to go to these lengths; we can get a good approximation of Cmax from CP just by reducing delta to a reasonably small value.

out3 <- mrgsim(mod, dose, delta = 0.1)

approx <- max(out3$CP)

approx
[1] 37.39822
true_bolus <- max(out2$Cmax)

100 * (approx - true_bolus) / true_bolus
[1] -0.001783364

But note this is likely problem-dependent: a smaller delta value may be required for a good approximation in certain problems. But I find this approach works well for most situations.

3 Infusion dosing

Some care must be taken when utilizing the approach for doses that are administered by infusion into the central compartment. In this case, the true Cmax happens at the end of the infusion and mrgsolve creates a discontinuity in the simulation when the infusion is turned off. When the ODE solver runs into this discontinuity, it will “overshoot” the infusion end time and then get the right solution by interpolating back in time. This isn’t a bug in the solver; it’s a feature, enabling the ODE solver to more efficiently run through your simulation. The trouble is with the code we have in the model for tracking Cmax: the logged value will keep increasing during the overshoot period and we will have a higher than expected Cmax value at the end of the simulation. This part of the vignette will demonstrate this behavior and show you some ways to address it.

Let’s create a new event object infusing the dose into the central compartment.

dose <- ev(amt = 1000, rate = 149, cmt = 2)

We expect the infusion to end at 6.711409 hours

#' This infusion ends at 
end <- dose$amt / dose$rate
end
[1] 6.711409

and the “true” Cmax for the infusion is 35.08074

out <- mrgsim_df(mod, event = dose, end = -1, add = end, obsonly = TRUE)

select(out, time, CP)
      time       CP
1 6.711409 35.08074
true_infusion <- max(out$CP)

But what happens when we assess Cmax with our code in $DES? We see that the reported Cmax is higher than the true Cmax.

out <- mrgsim(mod, event = dose)

max(out$Cmax) # Max concentration from $DES
[1] 38.51896

This happens because the ODE solver overshoots the end of the infusion and corrects this overshoot later; but our Cmax logging code can’t deal with the overshoot and retains the Cmax value that was falsely inflated during that overshoot.

We can verify this is what is happening by reducing hmax, the maximum step size. When hmax is smaller, the ODE solver takes smaller steps, the overshoot won’t be as severe, and the simulated Cmax will be closer to the true value.

out <- mrgsim(mod, event = dose, hmax = 1e-3)

max(out$Cmax) # Max concentration from $DES
[1] 35.08344

Now, our Cmax is looking better. We can get even closer by lowering delta and rtol

out <- mrgsim(mod, event = dose, hmax = 1e-3, delta = 1e-4, rtol = 1e-12)

max(out$Cmax) # Max concentration from $DES
[1] 35.08085

Now, we’re getting much closer to the true Cmax value. Of course, there is a cost to this: all of these changes will cause the ODE solver to be less efficient and the simulation will take longer.

Here’s what happens with the reasonable approximation we used earlier.

out4 <- mrgsim(mod, event = dose, delta = 0.1)

max(out4$CP) # Concentration from output records
[1] 35.02874

The approximate Cmax is a little off the true, but still pretty reasonable.

approx <- max(out$CP)
100 * (approx - true_infusion) / true_infusion
[1] -0.1482349

3.1 Get Cmax from the end of the infusion

As an alternative, we can get Cmax as the predicted concentration exactly at the end of the infusion and it seems like this should be the gold standard for an infusion. It is possible to do this in mrgsolve right now, but I’m not going to publish it here since there is no public api for doing it. If you really need it, contact me and I’ll help you out. I’ll be working on public api for testing for end of infusion and update the vignette when that gets rolled out.