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.

Drug screening model meets cancer organoid technology

“…So, it was necessary and urgent to establish effective and stable preclinical tumor drug screening models, which could faithfully recapitulate the morphological and molecular characteristics of various human tumors. In the era of precision oncology, scientists and oncologists were committed to finding drug screening models that would be more effective, realistic, time-saving and labor-saving to study the response of tumor patients to drugs [ 2 , 3 ]. Recently, more and more preclinical drug screening models had been emerging, Such as commercial 2D cell lines, patient-derived xenograft (PDX), primary patient-derived 2D cell lines, and organoid etc .…”

Assessment of modelling strategies for drug response prediction in cell lines and xenografts

“…predictor variables), incomplete omics characterization, and the static nature of molecular data. Molecular data in such studies are usually acquired only before drug treatment 7 . Another important problem is the consistency of pharmacogenomics associations derived from different datasets.…”

COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics

“…None of these drugs are novel. Rather, they exemplify a ‘drug repurposing/repositioning’ approach ( 26 , 34 , 50–52 ). Recently, numerous COVID19-specific web pages and chemical libraries have been created by different research organizations (CAS, IUPHAR ( 53 , 54 ), ChEMBL, OpenData Portal ( https://opendata.ncats.nih.gov/covid19/index.html ), etc.)…”