If you were to envisage the drug discovery process, you may conjure up the image of a manic haired, lab coat adorned man pottering around, blowing things up and making potions bubble…It was this picture that intrigued me as a child but I know, it can be this that turns people off science.
In fact, drugs are not discovered as you may assume. Yes, many years ago drugs were founded by a man in a lab coat, testing materials in front of him and hoping for a positive result. Luckily, this is no longer the case (well, at least not most of the time, even if it feels like it).
Drug discovery has changed immeasurably over the past 50 years. Here I’ll explain the current methodology I use for drug design: computational chemistry. Hold on in there, I promise it’s way more interesting than it sounds!
Computational chemistry is where my area of research now lies. As the name suggests, we use computers to aid the design of new drugs for a target disease or condition. It is now possible to access a huge library of possible targets (proteins, like that seen here) (thanks to X-Ray crystallography), which are linked to causing certain conditions, such as cancer. Biologists identify the job of these proteins and demonstrate how preventing their function/s could stop a disease. Then with a moveable, 3D computer image of the protein, we as chemists can visualise the area which, if targeted with a well designed drug, could knock out the protein’s function and thus the disease or condition it causes.
Using the 3D image, we can see the target area and what it is formed of, formerly water loving (hydrophilic) or water hating (hydrophobic) areas. With this knowledge, we can design drug molecules which compliment these sites, creating a molecule that will bind to the desired area, halting it’s function (inhibition).
After the potential drug has been designed, it must be tested against the target. Traditionally this would be done experimentally in a lab, but we aim to speed up the process and decrease the number of attempts required by generating this data computationally. Some very clever people have developed computer modelling systems (I know, sounds fancy) which show the designed drug fitted into the known protein, as shown here. After this, other computer programmes can determine how well the drug binds to the protein (the affinity). When you have designed a library (a large collection of drugs), the programme will rank the binding ability of each drug from best to worst. From this we can determine which of the designed drugs are more likely to have the desired affect against the target protein; the better the binding affinity, the more likely the drug is to inhibit the protein.
By using computers to aid the drug design process, and harness the ability to predict which drugs are likely to work better than others, we speed up the time it takes to design and test a whole library of drugs in the lab. This should cut the time it traditionally takes to bring a drug through the research phase dramatically. Computional chemistry allows for more diverse drugs to be created, when compared to those currently in production as a wider range of avenues are open to exploration. It also holds opportunity for personalised medicines. If we have access to an individual’s protein structures, we can view the disease causing ones and fit drugs accordingly.
Computational chemistry has been investigated for roughly 30 years now, and I am very excited to be part of this movement in research.