Multiscale kinetic modeling of liposomal Doxorubicin delivery quantifies the role of tumor and drug-specific parameters in local delivery to tumors.

Nanoparticle encapsulation has been used as a means to manipulate the pharmacokinetic (PK) and safety profile of drugs in oncology. Using pegylated liposomal doxorubicin (PLD) vs. conventional doxorubicin as a model system, we developed and experimentally validated a multiscale computational model of liposomal drug delivery. We demonstrated that, for varying tumor transport properties, there is a regimen where liposomal and conventional doxorubicin deliver identical amounts of doxorubicin to tumor cell nuclei. In mice, typical tumor properties consistently favor improved delivery via liposomes relative to free drug. However, in humans, we predict that some tumors will have properties wherein liposomal delivery delivers the identical amount of drug to its target relative to dosing with free drug. The ability to identify tumor types and/or individual patient tumors with high degree of liposome deposition may be critical for optimizing the success of nanoparticle and liposomal anticancer therapeutics.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e15; doi:10.1038/psp.2012.16; advance online publication 21 November 2012.

Decreased internalisation of erbB1 mutants in lung cancer is linked with a mechanism conferring sensitivity to gefitinib.

A majority of gefitinib (IRESSA)-responsive tumours in non-small cell lung cancer have been found to carry mutations in ErbB1. Previously, it has been observed that internalisation-deficient ErbB1 receptors are strong drivers of oncogenesis. Using a computational model of ErbB1 trafficking and signalling, it is found that a deficiency in ErbB1 internalisation is sufficient to explain the observed signalling phenotype of these gefitinib-responsive ErbB1 mutants in lung cancer cell lines. Experimental tests confirm that gefitinib-sensitive cell lines with and without ErbB1 mutations exhibit markedly slower internalisation rates than gefitinib-insensitive cell lines. Moreover, the computational model demonstrates that reduced ErbB1 internalisation rates are mechanistically linked to upregulated AKT signalling. Experimentally it is confirmed that impaired internalisation of ErbB1 is associated with increased AKT activity, which can be blocked by gefitinib. On the basis of these experimental and computational results, it is surmised that gefitinib sensitivity is a marker of a reliance on AKT signalling for cell survival that may be brought about by impaired ErbB1 internalisation.

Computational modelling of ErbB family phosphorylation dynamics in response to transforming growth factor alpha and heregulin indicates spatial compartmentation of phosphatase activity.

Members of the ErbB receptor family are associated with several cancers and appear to be providing useful targets for pharmacological therapeutics for tumours of the lung and breast. Further improvements of these therapies may be guided by a quantitative, dynamic integrative systems understanding of the complexities of ErbB dimerisation, trafficking and activation, for it is these complexities that render difficult intuiting how perturbations such as drug intervention will affect ErbB signalling activities. Towards this goal, we have developed a computational model implementing commonly accepted principles governing ErbB receptor interaction, trafficking, phosphorylation and dephosphorylation. Using this model, we are able to investigate several hypotheses regarding the compartmental localisation of dephosphorylation. Model results applied to experimental data on ErbB 1, ErbB2 and ErbB3 phosphorylation in H292 human lung carcinoma cells support a hypothesis that key dephosphorylation activity for these receptors occurs largely in an intracellular, endosomal compartment rather than at the cell surface plasma membrane. Thus, the endocytic trafficking-related compartmentalisation of dephosphorylation may define a critical aspect of the ErbB signalling response to ligand.