It’s hard to overstate the role artificial intelligence (AI) is playing in the pharmaceutical industry.
The revolution has arrived rapidly and both pharmaceutical and biotech companies are increasing the adoption of such technologies, including machine learning, to improve the accuracy and speed of their data-driven decisions.
AI technology can process and find patterns within vast amounts of data far more quickly and skilfully than any human could. Each different form of AI has a varying application in the field of pharmaceuticals, from research and development to distribution. While data science algorithms are already revolutionising the research and analysis stages of drug development, with outcomes being measured more accurately and rapidly, machine learning and deep learning are two forms of AI that will rise in the future.
What is machine learning and deep learning?
Machine learning relies on neural networks which mimic the human brain and uses algorithms that can essentially ‘run on their own’, calculating and predicting outcomes without having specific programming.
Deep learning is a step further along and has the potential to revolutionise patient diagnostics. While also relying on neural networks, deep learning can accurately analyse imagery and combine this analysis with various other forms of data to create predictive outcomes. For example, there is a potential for deep learning AI to be able to analyse an MRI scan and, allied with the patient’s history and medical data profile, correctly diagnose an illness without the intervention of a human.
5 ways artificial intelligence is being used in the pharmaceutical industry
Research and development
The costliest and most time-consuming part of drug creation is the research and development part. In 2020, we saw how data analytics greatly improved the production speed and efficacy of the number of viable COVID19 vaccines and even helped in the distribution phase. AI continues to play a huge role in reducing the time it takes for a drug to not only be discovered but also to reach the market.
Data analysis and processing
Throughout any research, development or trialling stage, huge volumes of data are produced and require analysis, often from labs across the world. AI can vastly reduce the time taken to complete this analysis by intelligently examining the data to either validate or reject hypotheses that would have taken a group of human pharmacists much longer, also removing the margin for human error.
AI has the ability to simultaneously and accurately process multiple data sources and find patterns between them, something that humans aren’t always as adept at.
Identifying clinical trial candidates
All drugs undergo clinical trials prior to being rolled out into the marketplace and finding suitable participants for these trials can be time consuming. But utilising AI’s predictive analytics, the appropriate patients for clinical trials can be extracted and a suitable sample size identified.
The ability for AI to read patient files and combine several data streams intelligently to form a cohesive picture of the suitability of a patient is ground-breaking for the clinical trial stage, and saves considerable time that would have otherwise been spent picking a sample group manually.
Analysing treatment results and predicting outcomes
AI also can match drug interventions with individual patients, reducing work that previously involved trial and error. Machine learning can predict a patient’s response to drug treatments by inferring potential relationships among factors that might be affecting the results, such as the body’s ability to absorb the compounds, the distribution of those compounds around the body, and a person’s metabolism.
Treating new diseases with drug repurposing
According to Healx, a Cambridge-based technology company working to develop innovative drugs for rare diseases, there are 350 million people globally who are affected by 7,000 known rare diseases. 95% of these rare diseases currently remain without effective treatment.
One of Healx’s co-founders was also a co-creator of the drug Viagra, initially intended to lower blood pressure. When the results were unsatisfactory, the drug was repositioned as a male impotence cure due to one of the side effects from trials. Healx’s strategy is to follow this repurposing route using technology and artificial intelligence with its predictive modelling capabilities to repurpose current drugs to help cure rare diseases.
With the many benefits on record for the usage of artificial intelligence in the pharmaceutical industry in terms of cost-saving, time-saving and efficacy, AI is set to be the next big thing in the pharma industry. Those who adapt to these developments and adopt new processes will have distinct a strategic advantage in the race to find cures and treatments.