Application of clinical trial simulation to compare proof-of-concept study designs for drugs with a slow onset of effect; an example in Alzheimer's disease
Catherine
Lloyd
Bioengineering Institute, University of Auckland
Model Status
This CellML model runs in both PCEnv and COR to replicate the published results (as confirmed by the original model author). The units have been checked and they are consistent.
Model Structure
ABSTRACT: OBJECTIVE: Clinical trial simulation (CTS) was used to select a robust design to test the hypothesis that a new treatment was effective for Alzheimer's disease (AD). Typically, a parallel group, placebo controlled, 12-week trial in 200-400 AD patients would be used to establish drug effect relative to placebo (i.e., Ho: Drug Effect = 0). We evaluated if a crossover design would allow smaller and shorter duration trials. MATERIALS AND METHODS: A family of plausible drug and disease models describing the time course of the AD assessment scale (ADAS-Cog) was developed based on Phase I data and litreature reports of other treatments for AD. The models included pharmacokinetic, pharmacodynamic, disease progression, and placebo components. Eight alternative trial designs were explored via simulation. One hundred replicates of each combination of drug and disease model and trial design were simulated. A 'positive trial' reflecting drug activity was declared considering both a dose trend test (p less than 0.05) and pair-wise comparisons to placebo (p less than 0.025). RESULTS: A 4 x 4 Latin Square design was predicted to have at least 80% power to detect activity across a range of drug and disease models. The trial design was subsequently implemented and the trial was completed. Based on the results of the actual trial, a conclusive decision about further development was taken. The crossover design provided enhanced power over a parallel group design due to the lower residual variability. CONCLUSION: CTS aided the decision to use a more efficient proof of concept trial design, leading to savings of up to US 4 M dollars in direct costs and a firm decision 8-12 months earlier than a 12-week parallel group trial.
The original paper reference is cited below:
Application of clinical trial simulation to compare proof-of-concept study designs for drugs with a slow onset of effect; an example in Alzheimer's disease, Peter Lockwood, Wayne Ewy, David Hermann and Nick Holford, 2006, Pharmaceutical Research, 23, (9), 2050-2059. PubMed ID: 16906456
Lawson
James
j.lawson@auckland@auckland.ac.nz
The University of Auckland
Auckland Bioengineering Institute
2009-12-07
keyword
PKPD
pharmacokinetic pharmacodynamic model
16906456
Lockwood
P
Ewy
W
Hermann
D
Holford
N
Application of clinical trial simulation to compare proof-of-concept study designs for drugs with a slow onset of effect; an example in Alzheimer's disease
2006-09
Pharmaceutical Research
23
2050
2059
Note that the epsilon in this component is a random variable with a mean of 0 and a standard deviation sigma= 4 ADASC units. However in the CellML we have to represent this as a constant value - at least for now - because as yet there is no way of representing a random variable. Possible solutions to this problem are currently being discussed and will probably involve SED-ML.
$S=\mathrm{S0}+\mathrm{alpha}\mathrm{time}+\mathrm{ADAS\_Cog\_p}+\mathrm{PD\_CeA}+\mathrm{epsilon}$
$\mathrm{ADAS\_Cog\_p}=\frac{\mathrm{beta\_P}\mathrm{Keq\_p}}{\mathrm{Keq\_p}-\mathrm{Kel\_p}}(e^{-\mathrm{Kel\_p}\mathrm{time}}-e^{-\mathrm{Keq\_p}\mathrm{time}})\mathrm{Keq\_p}=\frac{\ln 2}{\mathrm{t\_half\_eq\_p}}\mathrm{Kel\_p}=\frac{\ln 2}{\mathrm{t\_half\_el\_p}}$
$\mathrm{CL}=2268.0e^{-0.0135(\mathrm{age}-40)}\mathrm{smk}$
This component has been included to match the published paper. However in the CellML model it is a bit redundant because population statistics are not considered - at least not yet.
$\mathrm{Sv}=e^{-0.00145\mathrm{time}}$
$\mathrm{PD\_CeA}=\mathrm{beta\_a}\mathrm{CeA}$
$\frac{d \mathrm{CC}}{d \mathrm{time}}=\frac{\mathrm{k\_ab}\mathrm{A\_in}-\mathrm{CL}\mathrm{CC}+\mathrm{CL\_ic}(\mathrm{CC}-\mathrm{PC})}{\mathrm{Vc}}\frac{d \mathrm{PC}}{d \mathrm{time}}=\frac{\mathrm{CL\_ic}(\mathrm{CC}-\mathrm{PC})}{\mathrm{Vp}}\frac{d \mathrm{A\_in}}{d \mathrm{time}}=-115.44\mathrm{A\_in}$