The pharmaceutical industry is one of the most affluent industries operating today – worth an estimated $450 billion globally (£30 billion in the UK) - treating health issues ranging from migraines to the most severe of illnesses, all with a simple pill in our palms. However, that pill in your hand hasn’t “simply” come to be; on average, it takes around a decade of research, and an estimated $2.6 billion, before a medication can be officially authorised for public consumption. And still, only 5 percent of these experimental drugs ever make it to the market.  

But pharmaceutical companies are now investing billions in tech companies to develop artificial intelligence (AI) that could revolutionise the industry, a drug seeking process that will exponential save on time and funds.  

In every stage of a medication’s lifecycle, from discovery to development, AI is working to change the pharmaceutical industry forever. It is expected that in the near future, the drugs we use to save human lives may very well have been discovered and developed through machines.

Where the near-impossible mountains of data to sift through was once a daunting human trial, AI offer’s the potential to effectively use that data at an unprecedented rate. Information that was once too challenging to analyse can now be exploited for multiple uses in discovering life changing medication for millions, if not billions, of people.

Machine learning has already been utilised for regulatory decision making, determining the safety of a drug, or detecting anomalies that might otherwise slow the progress of a drug’s development.

The trial’s before going to trial for medication development is usually found in the early stages of their research, where a disease must first be identified and then matched with a drug candidate for approval. This is a process that can take between four to six years, but, with AI working at rates never before seen, this daunting period could soon only take a year.

Medi-data

Another massive issue that the pharmaceutical industry is challenged with, as mentioned above, is the masses of data that accumulate as more and more products are created, their data gathered and then dispersed and stored across multiple systems. In order to access all of this to be useful in the production of future medication can be an extremely long and daunting process. Until now. AI is changing the way we gather and analyse data for development.

One example is called a generative adversarial network (GAN), which involves feuding two AI networks against one another in competition. As one network works as a generator to guess drug-like molecules, the other, called the discriminator, has to say yes or no to each guess from the generator. Although they start out with a basic knowledge, over time this competition develops their understanding and the two networks learn and become smarter at recognising patterns useful for medication development.

Is AI the prescription that the industry needs?

A drug has yet to be certified that was developed using AI, however, it is believed that soon implementing AI will be entirely necessary to keep up with competition in the pharmaceutical industry. With the issues mentioned above, it’s no wonder that companies are turning to AI in order to save themselves on wasted funding and time spent on rejected drugs.           

By deploying AI to give us a more comprehensive knowledge of how disease’s biologically tick, scientists will be propelled forward without the necessity for trial and error in the development of therapies. Gone are the days of hoping that a drug’s new trial will successfully eliminate a disease during tests, replaced by scientists who are able to accurately use AI to identify a disease and steamroll through the clinical trials with multiple experiments. By reducing the failure rate, it won’t just be money from trial error’s that is saved, but also human lives.

AI: Recommended dose

AI does pose its own issues however: Will the regulatory environment be able to change as quickly as AI is able to provide rapid results? Nevertheless, the future is speeding towards us and there is little that can put the brakes on the development and change that every industry is receiving through the utilisation of these exciting and intelligent technologies.

With machine learning gaining traction, it is becoming a useful tool for speeding up the medication lifecycle process, improving a trial’s reliability for a pharmaceutical organisation, and drastically cutting the costs of drug development. And those with concerns over corporations giving too much power to artificial intelligence as unsafe? Human insight can never be replaced. AI is to be used more as a tool that is used by scientists. Human’s will still be the one’s asking the questions, using artificial intelligence as a way to answer complex questions and compute the vast amounts of data.

In order for us to confidently develop the pharmaceutical industry, there must first be a change in attitudes and scepticism towards the technology. Pharmaceutical giant’s need to see that AI is being used to see the signs that humans cannot, to eradicate human error and scope out what may otherwise be limited by scientists. We need to understand how we answer questions, rather than trying to answer them; the rest is up to the artificial.

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