Integrating “Omics” Data for Quantitative and Systems Pharmacology in Translational Oncology - Bioanalysis Advanced Materials Methods and Devices - page 184

Fig. 12.1 Schematic diagram of integrating “omics” data with multi-scale quantitative systems
pharmacology models in cancer research. Cancer is the phenotypic end point of multiple genetic
aberrations and epigenetic modifications that have accumulated within its genome. These molec-
ular changes can be measured by high-throughput technologies such as microarray and next-
generation sequencing platforms. Using powerful computational algorithms, multiple “omics”
data will be integrated at the cellular level for QSP modeling. In QSP modeling, a “middle-out”
approach is used to evaluate the concentration-effect relationships of a particular drug. Using
mechanistic cellular models derived from “omis” data, detailed modeling can be performed to
predict the pharmacokinetics and pharmacodynamics of a drug within the “system.” Using these
computational approaches, translational biomarkers can be established to predict drug effects. The
ultimate goal of QSP is to mechanistically describe drug action of a “system” which should enable
better prediction of drug efficacy in patients
12.2 Molecular Levels
Cancer is the phenotypic end point of multiple genetic aberrations and epigenetic
modifications that have accumulated within its genome [ 10 ]. These genetic and
epigenetic alterations come together to form complex, dynamic, and plastic
networks that govern the “hallmarks of cancer” [ 33 , 34 ]. The most common genetic
alteration is the somatic mutation which is acquired in the origin of cancer devel-
opment, and this DNA sequence change is different from the normal genome [ 30 ].
This genetic event can be accumulated in the life of cancer and propagated to
different generations of cancer cells. Mutation can be classified into two classes,
“driver” and “passenger” mutations [ 32 ]. Driver mutations are the key mutation that
drive the development of cancer and provides survival advantage, whereas passen-
ger mutations are “by-stander” alterations that happen to be altered in the primary