Stratification Of Healthcare
If you don’t think healthcare is about power, you haven’t been paying attention.
Don Berwick
The arrangement of any form of data in certain groups helps our understanding of that particular group. This strategy has been followed at so many levels of our life that it affects every minuscule activity of our day to day activity. Governments across the globe break down the land into states and districts for better governance and effective law enabling. Even our education system is divided into different classes and sub-classes for better and effective education. This particular method of stratification is being used in Healthcare for identifying subgroups of patients with distinct mechanisms of disease, or particular responses to treatments. It helps us to identify and develop treatments for different groups of patients which is pretty analogous to how governments deal with different states differently.
Stratified Healthcare has proven beneficial for a number of cancer cases. What’s not been said is the number of non-cancer cases that are being treated using Stratified Healthcare. It has led to an increase in research with terabytes of data being exported in the field of healthcare. This will also induce a fast-paced expansion of pharmacogenomics which is the use of genetic information to improve the efficacy, and/or reduce the side effects of the medicine.
The Basics of Genetic Medicine
The patients generally react to the gene variant attached to the particular drug. This gives rise to the analysis of the amount of dosage of the medicine and changes the amount given of dosage or drug. Scientists generally do this by identifying genetic loci associated with known drug responses and then testing individuals whose response is unknown. Modern approaches include multigene analysis or whole-genome single nucleotide polymorphism (SNP) profiles, and these approaches are just coming into clinical use for drug discovery and development. There are two determinants which the scientists look for:
- Amount of drug required to reach the target in the body.
- The response of the cells such as neurons or heart tissue.
AI in Play
Data Science is playing a significant role in bringing precision medicine into the market. The analysis of the effects of medicines on an individual and their genetic markers can lead to the creation of medicines for individuals. The advantages of personalized medicine are:
- Better medication effectiveness, since treatments are tailored to patient characteristics, e.g., genetic profile.
- Reduction of adverse event risks through avoidance of therapies showing no clear positive effect on the disease, while at the same time exhibiting (partially unavoidable) negative side effects
- Early disease diagnosis and prevention by using molecular and non-molecular biomarkers.
Hoopla or Reality
There’s been a lot of noise about Precision Medicine off late with the market is expected to reach USD 3.18 trillion by 2025 registering a CAGR of 10.6% over the forecast period, according to a new report by Grand View Research, Inc. published in June 2019. But how far have we fared in reality to produce some value to the research done? Let’s have a look at what we have achieved till now as published by Foley and Lardner LLP:
- A record number of personalized medicine approvals (42% of all 2018 new drug approvals) by the FDA. Twenty-five of the 59 new molecular entities FDA approved in 2018 were personalized therapies. Over 30% of all new drug approvals were personalized medicines. PMC defines personalized therapy as any treatment that uses a diagnostic test to determine which treatment is best for each patient.
- FDA’s approved the cancer drug Vitrakvi (larotrectinib) for the treatment of all solid tumors that express neurotrophic receptor tyrosine kinase (NTRK) gene fusion, wherein the use of the drug is based on the presence of the NTRK biomarker rather than the tumor type or tissue of origin of the tumor. This is the second approval of a cancer drug indication based on the presence of a biomarker.
- FDA recognized a public human genetic variant database to support claimed relationships between tested genetic variants and disease: the Clinical Genome Resource (ClinGen) database. The use of the public databases is reported to increase the use of real-world data for oversight purposes and reduce clinical development burden associated with the time and cost of developing personalized medicine diagnostics.
- FDA authorized the first direct-to-consumer pharmacogenetic and cancer risk-related genetic tests. FDA’s approval of 23andMe’s limited BRCA variant test for breast and ovarian cancer risk is the first available approved test for cancer risk without a prescription. FDA also authorized 23andMe’s Personal Genome Service Pharmacogenetic Reports that provide information about 33 genetic variants that may be associated with a patient’s ability to metabolize some medications.
Challenges
Clearly, stratified healthcare and precision medicine are of great advantage but there are a few challenges towards its adoption:
- The regulators are worried about the medical data to be shared.
- Costing is yet skeptical if it can be applied to masses, then we can reduce the cost. Else it could turn up very costly.
- Awareness and clinical adoption is also very slow.
While there has been good progress, a huge increase in pace is required to bring precision medicine to the masses. In the coming years, clinical development of CRISPR technology promises to change how we think about monogenic and polygenic diseases, though data is still in its infancy. While concerns regarding safety and administration of CRISPR persist and researchers are still seeking to better understand its effect on the body’s ability to recognize and repair damaged DNA, the applications for personalized medicine are exciting and wide-ranging.
