When testing a new medication, the last phase involves collecting large numbers of people who fit a specific profile. Once all of these people have been fully reviewed, they are often split into two groups. The intention is to provide the medication to one group and to give the second group an inactive agent like a sugar pill, otherwise referred to as a placebo.
The reason for doing so is that there is, as of today, no better way to study the positive and negative effects of a new medication before it is released to the general population. It is from such studies that key papers are generated that will make a statement such as “in the group that took the medication, there was a 30% drop in the risk of <pick a disease>”. Based on such data, the FDA will decide if there is enough evidence to safely release the medication for general use.
Unfortunately, this scientific method is not always successful. The science is solid. But other social, political and financial factors can disrupt the appropriate use of the new medication, and make it more dangerous than helpful.
Many years ago, two individual medications that were helpful in weight loss, were combined to increase their effect. The details are not critical. What is important to know is that the wide, unsupervised use of these two medications together led to serious side effects in a rare number of cases. Even though the severe complications were rare, both individual medications were pulled from the pharmacies. Two legitimate and tested medications were lost to the general public because of misuse. These medications would have been a significant help to the appropriate specific target audience they were designed for. But the general population had a different idea and effectively ruined the game for everyone.
There is an obvious question that is still rarely asked, because we still do not have a good answer. Very simply put, why do different people react differently to the same medication? Unquestionably, every human being is the product of a unique set of DNA. It is expected that every individual will have some differences in their physiology. But when designing medications that affect fundamental parts of our anatomy and cell biology, it is still surprising that there should be such a range of responses.
Of late, there has been a tremendous amount of advancement in personalized sequencing of an individual human’s set of genes. In other words, these days it is nearly practical to scan every patient’s collection of chromosomes in order to identify similarities and differences from others. Within our genetic code there is the information about how we will respond to a whole range of stimuli and interventions. Soon, it should be possible to predict how an individual will respond to a medication, without ever actually giving the medication to the person.
The significance of using genetic codes in this way is revolutionary. When the two groups of test subjects are set up, as I described above, there is no way of knowing if anyone in either group has a genetic propensity for benefiting from the medication or rejecting it. It almost seems self defeatist to try and test a new medication without knowing this critical piece of information about every test subject. But until very recently, it was not even imaginable that we could identify genetic codes on a grand scale, outside of a huge laboratory.
In the future, the grouping of test subjects will also be based on their genetic makeup. For example, if a certain group of people within the test groups have a propensity for quickly breaking down medications before the medications can act, it seems clear that such people should not be included in the study. It would prove nothing to say that the medication has no effect on such a group, since it is already known that their unique physiology would eliminate the medication before it can act. Contrarily, if scanning the genetic code of a person identifies a dramatic tendency to respond to a specific medication, then it may not be helpful to include such a person in the study. Such an individual’s positive response would be expected. Basically then, including people with a natural tendency to respond to or reject a new treatment would only muddy the waters of the study.
You can easily argue that, at the genetic level, we are all so unique that groupings of people for the purpose of any kind of medical test is almost doomed to failure. This realization could explain why large-scale studies can sometimes show very different results when performed by different researchers in different parts of the world. Both groups of researchers followed protocol and did everything right. But the genetic nature of their subjects was sufficiently different so as to lead to fundamentally different results.
Out of necessity, the day will soon come when medications will be tested on simulations of cellular biology. Then, when a patient presents, his or her genetic code will be tested and compared to various genetic codes that were included in the medication studies. When a match is found, the physician will be able to prescribe a medication that is truly customized to the need of the specific patient. And in this scenario, the patient can expect to benefit from the full effect of the medication, with very little in the way of side effects.
In order for this to be practical, there will need to be a tremendous amount of research and development into point of care systems that can take a sample of blood and do all of the sequencing and all of the comparisons, in order to advise on the best treatment for the specific patient. Initially, such systems will only exist in large clinical laboratories. But then, in time, such systems will be shrunk down to be appropriate even in a physician’s office.
Totally customized care at the point of physician–patient interaction is presently a dream. But it is a dream that will be realized within the next 1 to 2 decades. This will be just one more example of how future medical care will make present day physicians feel quite inadequate. The potential continues to seem endless.
Thanks for listening
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