Following its fast-paced development in recent years, AI is transforming the future of industries. For pharma, in particular, AI is becoming an integral element of drug discoveries, device and packaging design, treatment plans and the pharmaceutical supply chain.
Enabling the industry to operate with increased accuracy and efficiency, AI has the potential to be the driving force behind major breakthroughs in healthcare.
However, the development of AI and the impact it’s having on pharma can be confusing. Whilst the combination of advanced intelligence and analytics is revolutionising the way medicines are developed and used, the exact mechanisms behind these advancements remain somewhat elusive, even to those with industry experience.
With an in-depth insight into how AI is changing pharma, what it means for healthcare professionals and what it can do for patients, Origin is leading the way when it comes to using artificial intelligence to maximise the capabilities of the industry.
How can AI and data support medicine creation and drug discoveries?
Drug discovery is fraught with complications, some of which pose a serious risk to patients. Whilst medicines must undergo clinical trials before being approved for use, even the most extensive experimental processes are unable to guarantee the elimination of possible harmful effects in every scenario.
With AI, however, researchers and clinicians are able to access more extensive and detailed data. When terbinafine was associated with high levels of liver toxicity in some patients, for example, researchers were unable to determine how the drug was producing the toxicity-inducing compound. It took over 20 years and the introduction of AI to establish the two-step process in which the drug could cause liver toxicity. By using a machine learning algorithm, all possible pathways could be identified. However, the algorithm was also able to determine which of these were most likely to occur. With this information, researchers were able to confirm how and why the drug caused liver toxicity in some patients.
Whilst terbinafine was approved for use before its harmful potential was identified, the use of similar AI algorithms during the creation process could greatly reduce, or even eradicate, the risk of new medications having adverse or dangerous effects on patients and trial participants.
Providing vast amounts of data can be input, advanced technology can use this to determine the exact number of possible outcomes, as well as the likelihood of them occurring. As a result, the use of data is integral to the efficacy of AI. Whilst humans are unable to process Big Data effectively, AI ensures this information can be put to use and evaluated to logical – and previously unknown – conclusions.
How can AI and data help with personalised data?
Personalised data is already being used in many areas and will become ubiquitous in the near future. For years, companies have sought to provide advice, information and product suggestions based on common traits associated with the individual’s demographic. With personalised data, however, businesses can tailor their recommendations to specific individuals.
For pharma, the personalisation of healthcare will change the lives of patients. Custom treatment plans and even bespoke medications could be produced and tailored to the individual’s specific needs, depending on their history, health issues, symptoms, allergies and previous treatment successes.
Again, the success of AI will depend on whether the equipment and algorithms used have enough relevant data to make accurate predictions and recommendations. In order to identify a tailored treatment plan, for example, a patient’s symptoms will need to be known. If any critical data is missing from the equation, the efficacy of AI-produced treatment plans could drop. With adequate data, however, AI can deliver more effective treatments, custom healthcare solutions and bespoke medications.
How data can help with changing current packaging and device design
Artificial intelligence, and the data which facilitates its use is already being used to improve packaging manufacturing processes. Increased automation and combining IoT with extensive data sets is allowing deep learning algorithms to improve and enhance the production of drug delivery devices, as well as medication packaging.
However, there are other ways that AI and data are revolutionising pharma packaging and devices. The development of intelligent packaging is fast-becoming the norm and, as a result, could minimise non-compliance and its consequences. With the ability to recognise when medication has been removed from its packaging and the facilities to store this data, real-time healthcare data can be obtained from any location. Furthermore, intelligent packaging can be used to actively remind patients how and when to take their next dose and ensure that the device they’re using is personalised to their healthcare needs.
How data can help in logistics and the pharmaceutical supply chain
It’s estimated that pharma companies have around 90% of their data siloed, which means they’re relying on a startling low amount of data to make crucial decisions. AI, and deep learning algorithms, in particular can process the entirety of this data effectively and enable businesses to make decisions accordingly.
With the full use of data, pharma companies can analyse costs, quality and supply in real-time and with increased accuracy. Furthermore, the use of external data sets, such as weather conditions and traffic updates, could ensure supply chains aren’t limited by external variables. With the ability to accurately predict when and how issues will become present, AI gives companies the opportunities to modify their strategies in advance, thus resolving a problem before it has a negative impact on their supply chain.
Is AI a new concept?
Although artificial intelligence seems to have become a hot topic recently, it’s actually been around since the 1950s. However, it’s the relatively recent use of deep learning algorithms which has led to increased possibilities for AI across many industries.
These algorithms can crawl and process vast amounts of data in real-time and make accurate calculations as well. Due to the amount of information involved with Big Data, this is something which simply cannot be undertaken by humans.
Whilst AI is being used to increase efficiency and eradicate human error in some areas when AI is combined with Big Data, artificial superintelligence is possible. Able to surpass human capabilities, AI will become commonplace in every facet of the pharma industry and will enable the industry to deliver safer and more effective solutions.