From Lab to Shelf: Artificial Intelligence in Drug Development
Francesca Morgan
Industry Snapshot
12:58, 15th May 2019

                                         

Technologies are rapidly thriving in the evolution towards big data, cloud computing and machine learning, with their employment promising to drive global-scale developments across myriad industries. 

The vast “med-tech” sector has characterised AI as an essential game changer and the collective mind within the sector and mainstream media has tapped into the many ways AI and big data will cultivate this growth, with Accenture predicting annual savings at around $150 billion for the US healthcare economy alone. 

The very nature of AI-centric innovations will see its greatest value as catalysts for scientists and researchers, and today, with the essence of developing drugs inextricably linked to data-driven decisions, there's little doubt in the inevitability of AI integration as the next logical step for the pharmaceutical space.

So, with patient data presenting such an invaluable opportunity in healthcare, and AI finding its greatest value yet in new drug discovery - why, in the realm of this burgeoning market, does this application of AI remain largely untapped? 

Principally, the very nature of drug discovery and its value is why its application will be slower to adopt than in other industries.

Pharma companies will need to implement viable strategies between machine-tools and scientists, and the mechanics for doing so will primarily rely on education and knowledge for leveraging data accurately. From a healthcare perspective, it seems that more pharma players are building the necessary foundations to see where and how AI will extract its greatest value.

So, as consumers who lie at the receiving end of this value chain, AI promises to transform the ubiquitous industry, and it remains a universal interest to understand the real implications for driving this discovery. 

We interviewed Jackie Hunter, Board Director of bioscience division BenevolentAI, for our latest episode of Industry Experts, helping us to pinpoint the fundamentals for a consumer narrative. With Jackie's insight, we've accumulated some essential starting points for understanding the real implications and value of AI in advancing drug discovery.

1. AI can assist Pharma companies in getting medicines to market faster, minimising the overall drug lifecycle. 

With the pharmaceutical industry relying on machine learning to drive medical research, researchers will gain the ability to process, assimilate and link valuable data faster. With the pace of pipeline propelled forward by AI with enough data, this will mean reducing the former process by some years. 

From initial discovery all the way through to clinical development and bringing to market, a lengthy procedure for one successful drug making it from lab to shelf can take between ten and twenty years.

Machine learning will aid organisations by streamlining all labour-intensive stages of drug development. By reducing the time to market, patients are likely to see the drugs they need on the shelves faster. 

2. Time is money, so faster research results will mean significantly lower costs.

With AI standardising and streamlining data, improving the speed and efficiency of its former process will find equal effect on the costs of its drug pipeline. With the cost of discovering, developing and bringing a new drug to market typically falling between $2 billion and $3 billion, leveraging these new technologies will lead to tangible economic benefits. 

In fact, in her interview, Jackie Hunter admitted that looking ahead, it would be hard to imagine Pharma and healthcare companies as being financially viable without integrating AI - It seems a valid point when you consider the effects of cost behind drug discovery, with most businesses barely reflecting a positive return on investment. 

And in fact, this loss seems to have slumped in recent years according to industry leaders, with Andrew Hopkins, Chief Executive of Exscientia stating that, “In 2010 the industry had an internal rate of return on investment of 10 per cent. That is now down to 1.9 per cent.”

Why is the return so astronomically low? It often goes unnoticed that whilst one drug will take x amount of years to bring to market, with less than an overall 10% success rate, the majority of costs are actually covering the cost of failure

3. Replacing traditional methods will mean a higher success rate, even with complex targets. 

AI will subsequently aid the researcher’s ability to aggregate and hypothesise data libraries for targeted results, and faster solutions. This will facilitate more efficient processes, for instance, application into the earliest stages of drug discovery will have a big impact in target identification. 

Robust Target Identification for Drug Discovery investigates the importance of target identification in the development of new pharmaceutical drugs. The step is a crucial stage for distinguishing direct targets of bioactive compounds from all other gene products that respond indirectly to the drug targets. 

The value ushered in by AI will infiltrate all levels of development, optimising the overall success rate in discovery. Locating the efficacy targets faster will have a knock on effect for optimising and developing the drugs in the later stages more rapidly. For instance, using data to assess a drug’s potential or optimising patient stratification will allow resources to be reallocated more efficiently. 

4. Enhancing the value chain means getting the right drug to the right person. 

From a patient perspective, the value of AI is crucial and this is crux of the technology’s impact. Today, 30-50% of drugs that are on the market will have no effect to the patient they are prescribed to. This is essential since this deficit is representative of an impaired system that will continue to exist without the impact of innovative technologies. 

Not to mention, there remains around 9,000 untreated diseases, and there is a huge opportunity here to address these highly critical unmet medical needs, faster. 

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So, the industry is nevertheless accumulating traction, and it seems Big Pharma companies will need to present a convincing case for investment if they want to avoid falling behind. 

The general trajectory of digitalisation will see all corners of business acclimatising to Industry 4.0, with AI driving particular value for organisations. Whilst the pace of application will rely externally on exertion from governments, funders and regulators alike, the value chain of potential alongside the inevitable evolution of this industry makes a persuasive case for the backing of its capabilities. 

It is, therefore, only a matter of time before widespread integration becomes in full swing, and those willing to embrace its inevitability will position themselves and such businesses as the leaders in the field. 

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Where can I find the interview?

You can listen to the full interview with Dr Jackie Hunter here

Want to know more? 

With countries today racing to be global leaders in AI, governments continue to lean towards its use, with investment in the sector growing steadily. For our Spotlight segment we turned to healthcare experts to help guide us through the opportunities, challenges, and serious investment opportunity that AI in Medtech presents - you can listen to the episode here

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The author is not a registered investment adviser, nor authorized or regulated by the Financial Conduct Authority and does not provide investment advice or recommendations. Prices of the investments referred to in the article (if any) are indicative only and may go down as well as up and investors may realise losses on them. This article should not be read as an offer or recommendation to buy or sell or a solicitation of an offer or recommendation to buy or sell any securities. Although based on information believed to be accurate and complete, the content of this article is not guaranteed to be accurate or current and the reader should conduct independent verification. No liability is accepted for any loss or damage incurred from acting or not acting on this article which is educational only and does not constitute legal, investment or financial advice.

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The information, investment views and recommendations in this article are provided for general information purposes only. Nothing in this article should be construed as a solicitation to buy or sell any financial product relating to any companies under discussion or to engage in or refrain from doing so or engaging in any other transaction. Any opinions or comments are made to the best of the knowledge and belief of the writer but no responsibility is accepted for actions based on such opinions or comments. Vox Markets may receive payment from companies mentioned for enhanced profiling or publication presence. The writer may or may not hold investments in the companies under discussion.

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