Diseases can be caused by various things. Those can be determined by research into patients, comparing them with healthy individuals and identifying what works and what doesn’t. Such research is hard and expensive, but it is only the beginning in the drug discovery process.
This is because, when researchers identify molecular/genetic pathways that lead to a specific disease, they can’t always be sure which molecules cause the symptoms, and which ones simply “coexist” or are simply not the main causes. As this great article says, a good example for this, is HDL and LDL. HDL can be associated with reduced risk for cardiovascular disease, while LDL can be associated with an increased risk. And indeed this is true, but when patients take drugs that increase HDL, the symptoms don’t magically go away. As the author of the article says, correlation is not causation. Simply, some things may correlate, but one doesn’t necessarily cause the other.
This can delay the first step of the drug designing process, since it makes it more complicated for researchers to identify which components of a pathway that is linked to a disease is the main cause of the rest of the events. But when a target is found, there needs to be a compound that binds to that target, and hopefully stops the symptoms of the disease.
Here, organic chemistry helps us identify which molecules may work in each case. A drug company might spend millions, testing millions of molecules in the lab before finding a few thousand of them that work best. Out of those only some may end up being safe and effective enough for clinical trials. But even then, the majority of drugs do not get approved.
Another approach is to more carefully design molecules. This works best for smaller companies/labs, that don’t have huge budgets for millions of tests. There, most of the effort is put to the molecule design, since only a few thousand of them will end up being tested. Obviously a small lab cannot play with chances like a big pharmaceutical company can, and their budget has to be spent carefully.
Over the years drug design has been done in different ways and ultimately, drugs get disapproved in the clinical trials most of the time. This costs a lot, discourages young companies from even trying and slows down the industry.
A new approach, Mendelian randomization, allows researchers to study populations and identify which molecules in a pathway do indeed lead to the symptoms. Basically bypassing the correlation-causation issue, thus speeding up the drug discovery process. While the last steps of the process cannot be sped up for now, or at least until 3D bioprinting improves for drug testing, improvements in the initial steps, in the identification of targets for new drugs will be a huge advantage.
Mendelian randomization is a very simple technique that only involves genetic screening and data analysis. If a researcher identifies a target protein that is thought to cause the disease, he can look for individuals with a polymorphism that causes this protein to be under or over-expressed. If this irregularity improves or worsens the condition of that individual, compared to someone lacking that mutation, then the researcher can assume that this protein is indeed an essential part of the disease, and it is maybe worth it to develop drugs targeting that protein. By looking at populations with abnormal genes for the target protein, we can gather data and therefore determine if our target is indeed a target worth targeting. The following video does a great job at explaining this concept.
My point with the title of this article, and this story, is that data is becoming more and more important in every field. And it seems that the most successful companies right now are the ones gathering and analyzing tons of data. It used to be that data meant better advertising, it had value for marketing people. Now companies like 23andme have started gathering genetic data that when analyzed can potentially accelerate drug discovery. Thus data has suddenly become something with enormous value.
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