Author: Lang Li, PhD on March 15, 2017 
Fueled by the pan-cancer genomics landscape and druggable targets discovered in The Cancer Genome Atlas (TCGA), and great successes of clinically actionable pharmacogenetic variants, precision medicine is one of the driving forces for biomedical research. It is becoming a reality to benefit patient care. Pharmacometrics and systems pharmacology are critical for precision medicine research, and are making a number of key contributions. The impact can be broadly categorized into the following areas: omics data, drug data, and clinical data integration; target selection, drug selection, dose selection, and precision medicine clinical implementation.
Although omics, drug, and clinical data are three major data types that need to be integrated in the precision medicine research, the data integration scheme depends on its own purpose. In the precision medicine clinical implementations, the drug target biomarkers shall have validated function. Clinical data, including both efficacy and adverse drug events, shall reflect their clinical definitions. If the data are integrated for the precision medicine research, the integration scheme needs to be more comprehensive. For example, both genomic variant location annotations and functional annotations shall be collected, and all preclinical and animal studies of drug potency shall be included.
One of the major findings from TCGA is that many cancer types share the same somatic mutations. Many of them are the drug targets. Therefore, in current precision medicine clinics, off-label drug usage is becoming a routine practice. However, limited evidence is available to support the off-label drug usage. Because of the newly available cancer cell line drug screening data and omics data, systems pharmacology models are able to predict drug response based on omics profiles, and translate these evidences to the clinical setting. Considering multiple potential drug targets revealed in the genomics profiles from the same patient, more sophisticated systems pharmacology models are needed to integrate pathway data for prioritizing the target selections. In addition, pharmacometrics are highly valuable to address the dose selection for drug combinations.
The ultimate goal of the precision medicine is to improve patient care. Even with well-integrated omics, drug, and clinical data, there are some challenges in precision medicine clinical implementation. First, genetic effects on drug response are not yet fully integrated with the other risk factors to assist physicians to make clinical decisions. Second, the overwhelmingly rich genomics data and clinical annotations become an enormous barrier for a physician to digest the information and communicate their decisions with patients. It calls for a user-friendly interface to visualize the integrated omics, drug, and phenotype data. This is where the pharmacokinetics, pharmacodynamics, and disease progression models can make a major contribution to integrate all the risk factors to predict both efficacy and side effects at the same time.
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