Life Sciences research touches everyone’s lives in profoundly important ways. In the darkest days of the Covid-19 pandemic, lab teams’ pioneering work rode to society’s rescue with effective vaccines. More recently, Novo Nordisk’s anti-obesity drug may turn the tide of a more insidious public health epidemic.
The pharmaceutical industry lies directly downstream from Life Sciences innovation. Changes in how lab teams work will feed into boardrooms in the form of new business models and propositions. Right now, no technology is having a greater effect on biological research than Artificial Intelligence (AI). It has been predicted that AI will save the drug research and development industry $70 billion by 2028. But beyond the cost-savings, why does AI hold so much transformative potential for the pharmaceutical industry and the broader Life Sciences field?
Let’s take a closer look at how AI is enriching lab work and rewriting the whole value chain of the pharma industry, from drug discovery and development to manufacture.
Drug Discovery
Intelligent automation is powering a huge step forward in drug discovery. Research into new drugs is a long and costly endeavour, sometimes setting teams back ten years (and millions of dollars). That may now all be set to change. AI software can compress these processes and cut their price tags.
The new discipline of virtual screening is a gamechanger in this respect. Rather than physically testing the compounds of vast libraries of molecules, virtual screening tools process existing datasets to accurately simulate potential reactions. This will dramatically expedite the identification of potential drug candidates.
The algorithms involved can analyse the chemical structures of these compounds to predict which ones will be likely to make effective drugs. This filters down the list of compounds worth further analysis at a fraction of the time and cost of prior methods.
A whole new breed of companies, including Exscientia, Standigm and Genesis Therapeutics, are focusing exclusively on AI to discover new drugs, using their own, custom-built AI platforms.
An example of Exscientia’s AI platform for drug development
Drug Development
AI is now increasingly harnessed at the development stage, optimising the design of drug molecules for higher efficacy and lower toxicity. Algorithms can predict the three-dimensional structure of a protein and even design a drug that will be able to bind to it.
These effects are particularly pronounced in the context of pre-clinical studies. Tranches of the in vitro testing workflow can be automated according to the previous outputs of high-throughput screening assays. Algorithmic analysis then draws upon in vivo animal testing records to more accurately identify potential side effects of drugs in humans.
The most novel and transformative use case for AI in drug development is the new field of in silico testing. This promises to reduce reliance on real-world trials by using predictive learning models to computationally simulate tests in their entirety. In silico methods are still in their infancy, but when this technology comes to fruition, it will slash the time and expense of bringing new drugs to market.
Clinical trials
AI tools also have major applications for clinical trial data, helping identify new drug targets and optimising the efficiency of trial design. For example, algorithms can now analyse large datasets of patients’ electronic health records (EHRs) to identify those likely to respond to a particular drug.
In a similar vein, hugely exciting things are happening in the domain of computational drug repurposing. This relatively recent field puts AI to use on EHRs and other datasets to pre-identify new uses for existing drugs. By shrinking the unpredictable spans of time it takes for fresh applications to serendipitously emerge in clinical use, countless conditions could suddenly become more treatable. This simply was not possible before the advent of AI and Big Data.
Drug Manufacturing
Once a drug has been approved for use, AI allows for huge efficiencies in the manufacturing process. AI algorithms trained on process development data and applied at the Process Design and Scale-up stages can identify the optimal parameters. This reduces both development time and waste.
AI also has big implications in quality control. Using AI in combination with ultraviolet-visible or Fourier-transform infrared spectroscopy, critical process parameters can be monitored without human oversight. The result? A massive reduction in the time it takes to ensure drugs brought to market are safe and effective.
What This All Means
By optimising every stage of the time-to-market journey for safe, effective and repurposable drugs, AI could usher in a phase of exponential growth in the pharmaceutical industry’s output. Just another example of how next-gen tech can benefit pharma companies and patients alike.