New Treatments for Opioid Addicts

Barcelona Supercomputing Center

Barcelona, Spain

Barcelona International Youth Science Challenge

Year

2018

My team and I tested biochemical ligand-receptor interactions and binding energy analysis through computer programming (SwissDock).
We modeled 3D molecules using Schrödinger Maestro and VMD (Visual Molecular Dynamics).
I was the designated final presentation speaker and group leader.

All credit to my team and amazing mentors, all names credited on final slide.

On this map we see the use of opioids in the world and how this is not an isolated issue but a worldwide problem. Drugs are getting more easily available. In order to fight this problem, we need to find drugs that have the necessary effects to bypass total abstinence, but without the side effects that regular drugs have.

Dopamine production spikes result in a tendency to develop dependency, addiction, withdrawal symptoms, etc.

Thus, a way to go about curing opioid addiction is by using a lighter, less addictive opioid substance; methadone. This can be safe in the right hands, but it can still just as easily be abused, and is statistically one of the highest abused drugs in the world due to the fact that it can easily be acquired compared to more lethal, potent drugs of similar nature.

G-Proteins initiate a signaling cascade.

Opioids bind to and activate a G Protein-Coupled micro-opioid receptor in the central nervous system.

PELE uses a set amount of high speed CPUs running a continuous iterative simulation and simulating movement.

In unconstrained ligand migration: focus is on finding the position of an active site, and an attempt in finding the most stable entry into the active site.

In constrained ligand migration: focus is on entering the active site in the most stable way, steps are focused in the area of the binding site.

A successful modified drug should have roughly the same binding energy as morphine (-45 as lowest ligand bind value)

It should also have higher SASA values so that it is not as deeply embedded and thus has lower binding energy (lower SASA values indicate that the addictive drug is deeply embedded within the active site, meaning higher binding energy, meaning more addictive).

Krokodil is far more addictive than morphine, for example.

Here, we can see that the modification has resulted in higher SASA values, making the drug slightly less addictive as the ligand seems to be interacting less strongly with the receptor.

Here, we can see that the modification has resulted in lower (more negative) binding energy values, making the drug more addictive as the ligand is binding more strongly to the active site of the receptor.

The formation of new and useful drugs is pretty random, even in regular wet labs, so the main benefit and purpose of these simulations is to reduce the amount of testable drugs by running very efficient simulations rather than actually creating the new drugs.

Mentors:

- Ferran Sanchjo

- Gerard Santiago

- Martí Roset Julià

- Pep Amengual Rigo

Fellow Research Peers:

- Meliani Martina

- Agnés Masip

- Belen Gutierrez

- Camryn Clutters

- Daniel Arroyo

- Dae Hurn Kang

- Makeda Maduka