Re: cyst size modeling

Nele, You give quite a lot of information about your problem but no details of what kind of cysts (origin, size, composition) or what biological process you think might be involved in making them get smaller or what pharmacological process might modify the natural history. Without this kind of information it is really hard to propose anything that is not simply empirical e.g. exponential natural history ('baseline') plus effect compartment for drug treatment. You say the objective is to "find the dose that would result in cyst eradication if the baseline effect would not be there". The best dose is obviously a dose of zero because the cysts will be eradicated anyway without any risk of adverse effects <grin>. So I suspect you are also interested in eradicating the cysts more quickly. There does seem to be a more practical problem however. With only baseline and 4 week observations it is impossible to distinguish a natural history model from the time course of drug effect. One additional measurement at 2 weeks for the highest dose might give you a small clue about the time course of drug effect but it will be confounded with the exposure response model. I think you will agree that the design of the experiment is pretty bad for learning anything useful about the time course of response (with or without treatment). Can you persuade your experimental pharmacologists to work a bit harder? Best wishes, Nick [EMAIL PROTECTED] wrote: > Dear nmusers, > > I would like to seek your advice on a PKPD modeling problem to maybe get some further ideas on how to address this problem. A pharmacological experiment in rats was performed, where cysts are placed by surgery. 3 weeks later, the size of all cysts is assessed (this time point is defined as zero). In addition, I know the cyst size without treatment 2 weeks and 4 weeks after. Overall, the size remains the same for the first two weeks, but decreases afterwards. This is not unusual, as it is also known that the cysts will all eventually go away after a while, even without treatment. So I know that I would need a baseline model of some sort in order to derive an additional compound effect. The compound was tested in 3 different doses. Cyst size after treatment with the two lower doses was only assessed after 4 weeks of treatment, whereas for the highest dose the size is available after 2 and 4 weeks of treatment. Thus, I have some information about effect over time (i.e. time zero, after 2 weeks and after 4 weeks). My question is: What kind of baseline model would be a good choice? Does anybody have experience with cyst modeling and can share what would be pharmacologically plausible? What should be the natural course of cyst shrinkage (linear, turnover)? Moreover I am wondering how to best describe the influence of the drug. I know that it will not directly kill the cysts, so I thought about including an effect compartment. I have written a Berkeley Madonna code to play around with the model. What are your thoughts on a model like this (code below)? Would the available data be able to support the parameters? The model is intended to find the dose that would result in cyst eradication if the baseline effect would not be there. > > Thank you and best regards > Nele > > PS: If you send any answers, I would be very grateful if you could also send a copy to [EMAIL PROTECTED] > > Thank you! > ________________________________________________________________________________ > > Berkeley Madonna code (parameters were chosen arbitrarily as no analysis has been performed yet): > > METHOD RK4 > > STARTTIME = 0 > STOPTIME=696 > DT = 0.2 > > INIT (gut) = X > INIT (central) = 0 > INIT(effect)=0 > INIT(PD)=1 > > D/DT(gut)=-KA*gut+DOSING > > D/DT(central)=KA*gut -K20*central D/DT(effect)= KEO*central-KEO*effect > > D/DT(PD) = KIN - KOUT*FAC*PD- KOUT*effect*PD > > cyst=100*PD ;100 as real cyst size at the start of the experiment > > Ccentral=central/V2*1000 > > CL=0.387 > V2=1.83 > KA=0.724 > KEO=0.01 > KIN=0.0001 > KOUT=KIN > FAC=IF TIME < 336 THEN 0 ELSE 10 > > X=0.1 > K20=CL/V2 > > ;--------MULTIPLE DOSING-------- > DOSING=PULSE(DOS,START,INTERVAL) > DOS= IF TIME < 100000 THEN X ELSE 0 > START= 24 > INTERVAL=24 > ________________________________________________________________________________ > Dr. Nele Plock > Bayer Schering Pharma AG > Drug Metabolism & Pharmacokinetics > Development Pharmacokinetics > Scientific Expert Development Pharmacokinetics > D- 13342 Berlin > > Phone : +49-30-468 15146 > Fax: +49-30-468 95146 > [EMAIL PROTECTED] > http://www.bayerscheringpharma.de > > Vorstand: Arthur J. Higgins, Vorsitzender | Werner Baumann, Andreas Busch, Ulrich Köstlin, Kemal Malik, Gunnar Riemann > > Vorsitzender des Aufsichtsrats: Werner Wenning > > Sitz der Gesellschaft: Berlin | Eintragung: Amtsgericht Charlottenburg 93 HRB 283 -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand [EMAIL PROTECTED] tel:+64(9)373-7599x86730 fax:+64(9)373-7090 http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford