4.23.5. The Paradigm of Personalized Medicine

The paradigm of personalized medicine can be illustrated as follows:

Figure 4.57: Schematics of “Clinical Analytical Framework” for assessing future medical technologies

This arrow reflects the current and projected flow of healthcare services, and changing points of intervention, as medicine becomes more personalized. Early detection testing will continue based on large population risk (e.g., mammograms). The new forms of risk assessment will be incorporated such as the determination of the patients who carry the genetic variation that increases their risk for developing cancer will be done. Though true prevention must occur before disease symptoms are present, better risk assessment enables more targeted monitoring. As for example the patient with the genetic variation should have more frequent mammograms. Then the symptom-driven diagnosis will be done which could possibly identify disease subtypes that cannot be clinically determined. Such diagnosis may or may not lead to targeted therapy. However, in any case we may also benefit from improvements in monitoring a patient’s response to a particular therapy.

 

 

 

 

 

4.23.6. The Three Promises of Personalized Medicine

Personalized medicine has the potential to change the way we think about, identify and manage health problems. It is already having an exciting impact on both clinical research and patient care, and this impact will grow as our understanding and technologies improve.
It is already clear that personalized medicine promises three key benefits:

1. Better Diagnoses and Earlier Interventions: Molecular analysis could determine precisely which variant of a disease a person has, or whether an individual is susceptible to drug toxicities, to help guide treatment choices. For preventive medicine, such analysis could improve the ability to identify which individuals are predisposed to develop a particular condition-and guide decisions about interventions that might prevent it, delay its onset or reduce its impact.
2. More Efficient Drug Development: A better understanding of genetic variations could help scientists to identify new disease subgroups or their associated molecular pathways. Thus, it would help in designing drugs that target those subgroups. Molecular analysis could also help to select patients for inclusion in/ exclusion from the late stage clinical trials which would help to approve drugs that might otherwise be abandoned because they appear to be ineffective in the larger patient population.
3. More Effective Therapies: Currently, physicians often have to use trial and error to find the most effective medication for each patient. As we learn more about which molecular variations best predict how a patient will react to a treatment, and develop accurate and cost-effective tests, doctors will have more information to guide their decision about which medications are likely to work best. Testing is already being used to find the one in four women likely to respond to a particular breast cancer drug. In the future, tests will help in identifying the one in ten patients who for tumor-specific molecular reasons will benefit from a new lung cancer drug. In addition, testing will help to predict the best dosing schedule or combination of drugs for a particular patient.

4.23.7. Case Study of Personalized Medicine: Genomic Testing in Cardiovascular Disease: Antithrombotic Drugs