Slow Drug Release From Cellular Automaton Pills
The pharmaceutical industry uses various systems for delivering drug doses over time. One of these is the matrix tablet.
These are made by mixing the drug in powder form with an inactive filler ingredient and a polymer binder, then compressing and heating the mixture to form a hard pill. When ingested, these tablets dissolve and crumble, releasing the drug into the body.
An important question is how quickly the drug enters the body. One way to assess this is experimentally, using an analogue of the human digestion system in a wetlab. But this process is messy, time consuming and expensive.
Today, Peter Hinow at the University of Wisconsin and a few pals suggest a new way to do this by computation. Their idea is to model a matrix tablet is if it were a pile of cubic building blocks, where each element is a cube of the active drug, the filler, a polymer binder, empty space or water. In fact, the whole system is like a three-dimensional chemical lego set.
By themselves, each block is stable but the cubes react with water in different ways. The empty blocks fill up at a certain rate while the filler, binder and drug blocks dissolve at certain rates. Once dissolved, these cubes become water blocks.
However, the rates depend on the number and type of neighbouring blocks– for example, a drug cube will dissolve more quickly when completely surrounded by water compared with when it shares a single face with a neighbouring cube of water.
It’s easy to see the similarity with a cellular automaton, where the state of each cell in the next round depends on its neighbours in the previous round. Knowing the various rates gives a straightforward way of calculating how the pill releases its drug over time.
An important factor, of course, is how the blocks of drug, binder, filler and empty space are distributed in a pill to start off with. Hinow and co assume they are distributed randomly in the proportions given in the pill’s formulation.
They then calculate how the process of drug release progresses on an iterative basis. It starts with the pill surrounded completely with water. The pill’s outer blocks then start to dissolve or fill with water. The model even allows for the pill fragmenting into parts.
The results, say Hinow and co, agree well with the values measured by experiment. ”We find that our simulations can reproduce experimental drug release profiles,” they say. And the simulations are much cheaper and easier to perform than the wetlab work.
Of course, there are limitations. The model assumes that the rate at which the filler and drug dissolves are independent but this may not always be the case, particularly when these substances are adjacent.
The model also assumes that a block of drug can dissolve completely in a block of water, which may not be the case either. These are factors that will have to be ironed out.
However, the work has important potential. Perhaps the most interesting aspect is how it could influence the way slow release pills are made in future. Hinow and co say the model could help pharmaceutical companies formulate and produce their pills more quickly because it does away with the need for some many wet tests.
But there is other potential here too. The cellular automaton approach suggests the possibility of far more complex drug release mechanisms, making different drugs available at different times for example.
But if pills really do work like cellular automaton (as opposed to being simulated by them) there is possibility of logic operations–if X then Y-type behaviour–which turns pill into chemical computers.
It’s not beyond the realms of possibility to imagine a pill manufacture using a kind of 3D printing technique which can create the complex assemblies necessary for this kind of work.
Far-fetched? Clearly. But exciting nonetheless.
Ref: arxiv.org/abs/1208.3447: Swallowing A Cellular Automaton Pill: Predicting Drug Release From A Matrix Tablet
The inside story of how ChatGPT was built from the people who made it
Exclusive conversations that take us behind the scenes of a cultural phenomenon.
Sam Altman invested $180 million into a company trying to delay death
Can anti-aging breakthroughs add 10 healthy years to the human life span? The CEO of OpenAI is paying to find out.
ChatGPT is about to revolutionize the economy. We need to decide what that looks like.
New large language models will transform many jobs. Whether they will lead to widespread prosperity or not is up to us.
GPT-4 is bigger and better than ChatGPT—but OpenAI won’t say why
We got a first look at the much-anticipated big new language model from OpenAI. But this time how it works is even more deeply under wraps.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.