2.2.2 TGI-OS Modeling
The TGI-OS model is a disease progression model. It is a useful tool in
oncology to delineate E-R relationships in the presence of confounding
factors. The model is composed of two parts: a TGI model that describes
tumor dynamics, and a multivariate survival model that incorporates a
TGI metric as a covariate on OS. The TGI metric serves as a marker of
disease status. TGI-OS modeling mitigates confounding in the E-R
analysis by directly evaluating the treatment effect on TGI then
separately accounting for the effect of prognostic factors on OS. By
mitigating the confounding effects of prognostic factors on the
relationship between treatment effect and OS, the TGI-OS model can avoid
a false positive E-R relationship.24-26 The TGI model
structure is typically a simple biexponential model (Equation
3).27
\(f\left(t\right)=exp\ \left(-d\times t\right)\ +exp\ \left(g\times t\right)\ -1\)(Equation 3 ) 27
where f(t) is tumor size at time t , d is the decay
rate constant, and g is the growth rate constant.
In multiple cancer types, the OS is correlated with the tumor dynamics
such that the probability of survival decreases with the increase in
tumor growth rate (g in Equation 3).27-37 Other
key determinants for survival are baseline prognostic factors specific
for the cancer type. Drug exposure is evaluated as a covariate in the
multivariate survival model.24, 25, 29, 32 If it is
not significant this suggests a flat E-R relationship. For
exposure-driven TGI models drug exposure is not evaluated as a covariate
in the multivariate survival model. OS can be simulated for exposure
quartiles with normalized prognostic factors to evaluate the presence of
an E-R relationship. This approach can remove the confounding effects of
imbalanced prognostic factors in different exposure quartiles. TGI-OS
modeling has successfully evaluated E-R relationships for atezolizumab
in multiple indications, and its role in E-R analysis has been
increasingly accepted by regulatory agencies.25, 28,
29, 32
While TGI-OS modeling allows for the direct separation of treatment
effect and disease effects, several limitations must be considered.
Non-exposure driven TGI models while simpler and more flexible than
exposure-driven models require assumptions and empirical descriptions of
tumor shrinkage and growth. Model building for both exposure and
non-exposure driven models requires one or more post-treatment
assessments for tumor size, and this may not be feasible in some
patients. The incorporation of multiple tumor size assessments in the
model, however, makes tumor dynamics a patient-specific explanatory
variable and informative predictor of survival. With the TGI-OS model,
it is also difficult to account for the potential appearance of new
lesions. Zecchin et al . developed a pharmacometric model to
incorporate the effect of new lesions on OS in metastatic ovarian
cancer, but additional examples and uses of this approach are currently
limited.38, 39 Because the TGI-OS model predicts OS
based on tumor dynamics it is more suitable for use in advanced
malignancies, where tumor size is typically measured over time.