Mechanistic Multiscale Pharmacokinetic Model for the Anticancer Drug 2',2'-difluorodeoxycytidine (Gemcitabine) in Pancreatic Cancer

Intracellular Gemcitabine Monophosphate Levels Predict Chemotherapy Efficacy in Gemcitabine-Treated Patients with Bladder Cancer

Gemcitabine monophosphate (dFdCMP), one of the intracellular forms of phosphorylated gemcitabine, determines its antitumor activity. A pharmaco-molecular model for determining relative gemcitabine monophosphate level based on the assessment of the activity of ENT1 and ENT2 channels as well as dCK and CDA enzymes in tumor tissue was developed. Relative gemcitabine monophosphate level is a more relevant predictive factor of gemcitabine resistance of bladder cancer as compared with the expression of individual markers related to dFdCMP formation.

A latent variable approach to account for correlated inputs in global sensitivity analysis

In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has proven an issue. Here we developed and evaluated a latent variable approach for dealing with correlated factors in GSA. An approach was developed that describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. The latent variable approach was applied to a set of algebraic models and a case from PBPK. Then, this method was compared to Sobol’s GSA assuming no correlations, Sobol’s GSA with groups and the Kucherenko approach. For the latent variable approach, GSA was performed with Sobol’s method. By using the latent variable approach, it is possible to devise a unique and easy interpretation of the sensitivity indices while maintaining the correlation between the factors. Compared methods either consider the parameters independent, group the dependent variables into one unique factor or present difficulties in the interpretation of the sensitivity indices. In situations where GSA is called upon to support model-informed decision-making, the latent variable approach offers a practical method, in terms of ease of implementation and interpretability, for applying GSA to models with correlated inputs that does not violate the independence assumption. Prerequisites and limitations of the approach are discussed.