How to find the right drug for each patient? Advances and challenges in pharmacogenomics

Abstract

Cancer is a highly heterogeneous disease with complex underlying biology. For these reasons, effective cancer treatment is still a challenge. Nowadays, it is clear that a cancer therapy that fits all the cases cannot be found, and as a result the design of therapies tailored to the patient's molecular characteristics is needed. Pharmacogenomics aims to study the relationship between an individual's genotype and drug response. Scientists use different biological models, ranging from cell lines to mouse models, as proxies for patients for preclinical and translational studies. The rapid development of "-omics" technologies is increasing the amount of features that can be measured in these models, expanding the possibilities of finding predictive biomarkers of drug response. Finding these relationships requires diverse computational approaches ranging from machine learning to dynamic modeling. Despite major advances, we are still far from being able to precisely predict drug efficacy in cancer models, let alone directly on patients. We believe that the new experimental techniques and computational approaches covered in this review will bring us closer to this goal.

Figure 1

Timeline of developments towards personalized medicine. The figure shows schematically the timeline of development of cancer models and cancer therapies to address the challenges in personalized medicine. Dates are orientative as of when these developments started to be widespread.

Figure 2

Linking pharmacogenomics to pharmacokinetics for precision oncology. Pharmacogenomics can provide appropriate drug candidates to target at the molecular level the tumor. Integrating this pharmacodynamic (PD) information into a physiology-based pharmacokinetic (PBPK) model would allow us to precisely define the best drug dosage for individual patients.

Figure 3

From preclinical studies to the clinic. To bridge the formidable gap between in vitro pre-clinical data and patients, computational models can help to extrapolate from the former to the latter. To support this bridge, data from the different preclinical models can be integrated. The different models available have a tradeoff between their ease of use (and thus amount of data available) and medical relevance (defined by how close they are to a real patient).