A subset of broad specificity SLC transporter families, as well as ABC transporters such as ABCB1 (P-glycoprotein) and ABCG2 (BCRP), are well studied for their influence in pharmacokinetics and pharmacodynamics and are regularly screened for in drug development. Emerging evidence suggests, however, that other transporter families may also play roles in determining intracellular drug concentrations, and significant debate continues within the field as to the proportion of drugs that enter via the lipid bilayer versus those that require transporters.
Heterogeneity of transporters
With support from CRUK and the MRC, my lab aims to understand the roles of transporters in drug uptake within tumours, and how this intersects with the role that they play in cancer metabolism.
To do this, we are using a range of systems-level approaches, in combination with cell and patient-derived models. We want to establish whether we can use the transporter expression profile of a patient’s tumour to help us to predict which drugs they are likely to respond to.
As well as inter-patient differences, it is also important to consider intra-tumour heterogeneity when it comes to transporters. The specifics of the local metabolic environment of tumour cells – be that proximity to blood vessels or the local density of stromal or immune cells – will not only influence their transporter expression but will also determine the local levels of potentially competing transporter substrates within the interstitial fluid.
By understanding how these transporters contribute to the tumours’ metabolism, we hope to gain knowledge that could provide us with rational approaches to maximise transporter expression, minimise competing substrates and therefore improve drug permeability in tumours. Equally, if providing a means for drug entry into cells, it is possible that down regulation of a transporter could be a contributing factor to drug resistance.
Increasing public availability of well-annotated RNA sequencing data that includes both pre- and post- treatment sampling of tumours will allow us to further investigate that, as well as a host of other non-genetic mechanisms of resistance.
Personalisation of patient therapeutic plans is beginning to change the way we treat cancer and improving outcomes. Going forwards, as the non-genetic as well as genetic factors of a patient’s disease start to be considered, we will be able to push this personalisation further.
We hope that being able to predict drug permeability will become part of a toolkit to enable the drugs with the best chance of working to be chosen for each patient.