153Another important stage in drug discovery is lead selection that can be based
equally upon biomarkers of toxicity or biomarkers of effi cacy. Use of mRNA tran-
script profi ling technology coupled to database search, enables creation of expres-
sion pharmacogenomic profi les of drug response for many classes of drugs in target
tissues. These response profi les can be analyzed to uncover biomarkers that correlate
with toxicity or effi cacy. These biomarkers can help triage hepatotoxicity and car-
diotoxicity among other response profi les and reduce the cost of drug development.
Target selection in the future should be genetics-based rather than the currently
popular target validation. Use of genetic evidence-based methods of target selection
should reduce the testing of too many hypotheses that are eventually proven wrong.
Reducing attrition and improving a product’s return on investment measure success
in discovery. As molecules pass through the development pipelines, choices made
in 2015 will undoubtedly play a role in the outcomes in 2020.
Most disease susceptibility genes are not drug targets by themselves. At fi rst,
knowledge of the gene has to be translated into an understanding of the role the
gene-encoded protein plays in the disease. Then one has to identify a disease-related
tractable target – be it an enzyme, receptor or ion channel – using the best functional
genomics tools available. The diffi culty of this task is indicated by the fact that
almost a decade following the discovery of APOE as a disease susceptibility gene,
the precise role of this gene in Alzheimer’s disease has yet to be unraveled. Thus
moving from a gene to an understanding of its functional role in disease, and mov-
ing from there to optimal therapeutic targets and a therapeutic agent, is the next
great challenge for drug development. Genomics is expected to increase the number
of possible disease targets by a factor of 5–10. This increase will be driven mainly
by the genetic heterogeneity of many diseases. Thus there will be a need to develop
more potential medicines that are aimed at the patients’ underlying genotype, not
just the disease phenotype. This increase in targets generated by genomics is being
successfully met by the sophistication of technologies such as combinatorial chem-
istry and high-throughput screening.
Preclinical Prediction of Drug Effi cacy
Assays of drug action typically evaluate biochemical activity. However, accurately
matching therapeutic effi cacy with biochemical activity is a challenge. High-content
cellular assays seek to bridge this gap by capturing broad information about the cel-
lular physiology of drug action. Detailed information contained in genomic expres-
sion data is usually suffi cient to match the physiological effect of a novel drug at the
cellular level with its clinical relevance. This capacity to identify therapeutic effi -
cacy on the basis of gene expression signatures in vitro has potential utility in drug
discovery and drug target validation relevant to personalized medicine.
Knowledge of genetic variation in a target enables early assessment of the clinical
signifi cance of polymorphism through the appropriate design of preclinical studies
and use of relevant animal models. A focused pharmacogenomic strategy at the pre-
clinical phase of drug development can contribute to the decision-making process for
Pharmacogenomics and Drug Discovery