A minimal PKPD model to evaluate synergic effects of combined non small cell lung cancer therapies

Pros, cons & Future potential

The proposed PKPD model is minimal and simple which provides a good overview of essential features necessary to be introduced, such as effect sites, isobole surface models of interaction, degree of synergy among drugs and patient response to treatment (i.e., drug resistance). The model is directly scalable to diagnosed/treated NSCLC patients.

The toxicity and risk of Radiation Pneumonitis (RP) affecting the healthy tissue in proximity of tumor volume voxel during RT treatment are not included in this model. Tissues with heterogeneous density have non-homogeneous drug distribution, hence drug trapping occurs. This can be modelled in PK compartmental formulation by adding a memory term, able to characterize long tail dynamics of drug release. The challenge to accommodate this in our model is the multi-scale pathway.

When available, the numerical values introduced in the model parameters were assumed from literature. However, several parameters were not available, so the study introduced variability to assess their effect. The results suggest this is a fairly sensible model to be further calibrated on experimental data.

Such a minimal PKPD model once calibrated for the patient at hand, hence addressing the personalised medicine concept, can be used for evaluating various combination therapies. The model is successfully used to predict RP incidence in NSCLC treated with SBRT in patients. In view of time, cost and patient-related risks associated with unique emergency situations (e.g., pandemic outbreak), their relevance in regulatory decision-making on dose modification becomes crucial.