The code will work as written (if you add ENDIF after TVVM = THETA (6),
and define all parameters CL, V1, Q, V2, KINT, ...) but mechanistically,
this is not a good idea to have two models for two dose levels. You may
want to try QSS model with non-constant Rtot (MM model is usually good
when Rtot is low, while QSS is good when Rtot has accumulation, so may
be this is why you see MM model at low doses and QSS at high doses).
Also, what is measured, is it free or total concentration? This part of
the code was not shown, and it depends on the assay (for QSS part of the
model).
Thanks
Leonid
On 4/23/2019 8:34 AM, Niurys.CS wrote:
Dear All,
I'm working on the population pharmacokinetics of a mAb, in this study
4 dose levels (50, 100, 200 and 400 mg) were evaluated. I tested
different models, but none of them fit well; that's why I decided to
find for each dose level the best model. I found the two lower dose
levels fitted to Michaelis Menten + CL linear model and the two higher
dose levels fitted to QSS Rtot model.
I think if I use this code, I'll find the best model for my data, so I
appreaciate your suggestions:
$PK
TVCL= THETA(1)
TVV1= THETA(2)
TVQ = THETA(3)
TVV2 = THETA (4)
TVKM = 0
TVVM = 0
IF(DOS.LT.200) THEN
TVKM = THETA (5)
TVVM = THETA (6)
TVKSS = 0
TVKINT = 0
TVKDEG = 0
TVRMAX = 0
IF(DOS.GT.100) THEN
TVKSS = THETA (7)
TVKKINT = THETA(8)
TVRMAX = THETA(9)
ENDIF
K = CL/V1
K12 = Q/V1
K21 = Q/V2
S1 = V1
;----------------------------------
$DES
CONC=0.5*(A(1)/V1-RMAX-KSS)+0.5*SQRT((A(1)/V1-RMAX-KSS)**2+4*KSS*A(1)/V1)
DADT(1) =
-(K+K12)*CONC*V1+K21*A(2)-KINT*RMAX*CONC*V1/(KSS+CONC)-VM*CONC*V1/(KM+CONC)
DADT(2) = K12*CONC*V1-K21*A(2)
Thank you,
Regards,
Niurys de Castro
