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Page 4 of 7 McNamee. J Transl Genet Genom 2018;2:20. I https://doi.org/10.20517/jtgg.2018.24

Drug development
Advances in genomic medicine allow the pharma industry to move away from the development of block-
buster drugs that are only effective for a proportion of the population. Drug development has remained
relatively unchanged over the last 50 years but the cost (~$2.87 billion) and length of time it takes from dis-
covery to launch (12-15 years) of a new drug is no longer sustainable. The pharmaceutical industry is using
genomics to identify novel drug targets, and help create companion diagnostics, to identify those patients
most likely to benefit from a novel drug potentially reducing some of burden of the financial costs, and de-
[17]
velopment time, to get a new drug to market . Currently ~46% drugs fail at phase I trials, ~66% at phase
[18]
II and ~30% at phase III, leaving only ~8% lead compounds ever reaching the market place . Pre selecting
patients based on their genomic profile during phase one clinical trials helps industry reduce the number of
patients required to demonstrate drug efficacy and safety, limit the potential of drugs failing at the phase 2/3
hurdle, and help to overcome regulatory hurdles prior to licensing provides a strong case for the use of ge-
nomics in drug development.

Drug trials
Genomic medicine has a distinct role to play in developing innovative clinical trials that are more effec-
[19]
tive than traditional random control trials . Basket trials have been use for novel cancer drugs, where an
individual drug is trialed on a variety of tumors expressing a common single mutation to increase both the
scope of the trial and the number of patients eligible to join the trial. Bucket trials pool patients with a single
variant expressed in different cancer types into one trial to test a novel drug reduces the number of clini-
cal trials required and the length of time for the trail. Umbrella trials incorporate different treatment arms
within a single trial to test the impact of a range of drugs on various mutations within a single cancer type.
These innovative clinical trials are of great interest to industry as they reduce the time and costs of launch-
ing the novel drugs of the future.

CHALLENGES FOR PERSONALIZED GENOMIC MEDICINE
Patient support
Adoption of genomic medicine into clinical use provides many opportunities to improve healthcare but
there are several challenges to overcome before it becomes mainstream for all medical disciplines. Scientists
rely on patient samples and data for their research so support from the public and patients is essential to
[20]
move genomic medicine forward . Patients have an important role to play in focusing scientific research
to areas that will improve their condition to maximize productive research output. Issues around obtaining
the most appropriate level of consent to collect patient samples in biobanks, then ethical permission for aca-
demic and industry sectors to access those samples and data for research, have to be resolved. Educating the
public on how genomic data is collected, securely stored, and ethically utilised will demonstrate transpar-
ency, provide patients with confidence to donate their samples and data to researchers, plus raise awareness
of the benefits of genomic medicine. This raised awareness of the benefits of genomic medicine will also help
clinicians enroll patients in clinical trials to test the novel drugs of the future.

Patients are already bringing direct to consumer genetic test results to clinics and asking physicians to use
them to prescribe their medication. The regulators have a role to play in validating both the quality of such
tests, and to ensure there is sufficient evidence that the variants identified are clinically relevant and action-
able, for these direct to consumer test results to be of use for prescribing.

Multidisciplinary support
Physicians, and other healthcare professionals, require support from a wide range of different disciplines to
employ a personalized genomic medicine approach into their clinical practice. For example, bioinformatics
experts will have to adapt healthcare data systems to incorporate patient data into electronic records in a
suitable interoperable format. Experts in technology will be required to develop the decision support tools