There is a saying in medicine: treat the patient, not the disease. This caveat is commonly preached to new medical residents as a reminder that holistic care is essential and that the same disease process in a different patient is essentially a different condition. But what if you could treat not just the disease and not just the patient but rather address the patient’s precise disease process? That is the reality of precision medicine made possible through DNA sequencing and more specifically, pharmacogenomics.
The Harsh Reality of Drug Development
We don’t have to tell you the difficulties of drug development. If a recent report from the FDA is any indication, then it is only getting harder. In 2016, there were only 22 FDA decisions on new drugs, and fewer are expected to be issued in 2017. The reasons contributing to this decline include challenges like increasingly complex diseases, worsening regulatory requirements, increased clinical trial sizes, and late-phase clinical trial failures.
The Innovation Gap
The traditional answer, the business approach, is to throw more money at a problem to find a solution. However, despite the increasing expenditure on R&D over the last 30 years, output has declined –regarding the number of compounds receiving FDA approval. This phenomenon is being called the “Innovation Gap.” New solutions are needed to bridge this gap and see an increase in the amount and success of new drug applications.
The fact is this: drugs work for some patients and not others. This principle has been confounding clinicians since, probably, the beginning of time. In drug development, this factor is one of the chief reasons for late-phase drug failure. Pharmacogenomics, the study of genetic variations that influence a human’s inter-individual response to a drug, is instrumental in today’s move toward personalized medicine.
Pharmacogenomics From a Clinical Perspective
Pharmacogenomics holds so much promise not only because it removes the uncertainty from drug selection –a foremost challenge when prescribing, but it also offers more predictable recovery time and inherently decreases the likelihood of adverse drug reactions. Additionally, data about an individual’s disease susceptibility allows for drugs to be introduced at a precise phase in the disease process -ultimately this results in increased pharmacoefficiency.
The Researcher’s Dilemma
Genetic testing has become more standard over the past several decades as the causative genetic mutations for various diseases have been discovered. The implications of using genomics in drug research and development are extensive and varied. And though we have witnessed a decrease in the cost of testing as molecular-based technologies have advanced, Genomics in the development phases of pharma R&D remains somewhat underutilized.
Due in part, to a lack of understanding in the field as to the availability, affordability, and questions about the efficacy of such use. Other barriers to implementation of clinical pharmacogenetics include utility, education, as well as issues with regulatory use and reimbursement. Another obstacle has been the challenges associated with forming the necessary strategic partnerships with informatic, big data, and IT solution providers.
Pharmacogenomics and Big Data
We know that tapping into big data can create real-time solutions for disease prevention and therapies. Currently, the proliferation of big data is continuing, from genome sequencing to the advanced adoption of EHRs including imaging data. Data transformation and analysis has become important in the successful research and clinical development of new drugs. With the goal of accelerating the discovery of new and useful therapies, the emerging field of Bioinformatics faces multiple challenges in the integration of molecular biological data with relevant patient information. As researchers find cost-effective ways to sequence, analyze, and test these enormous troves of data, the quest for personalized healthcare and improved drug development processes are becoming a reality.
In conclusion, drug development is a complex, costly, taxing, and, all too often, disappointing process. Drug development companies poised for success are exploring new ways to reduce their development times and improve the odds of approval by utilizing new research models, more adaptive trial designs, and methodologies like pharmacogenomics in combination with big data analytics. They are bridging the innovation gap by developing partnerships with vertical industries that provide precise solutions to improve efficiency and outcomes while reducing costs.