Month: December 2022

Explosive growth, high demand and limited supply: these are challenging times for the life sciences industry. The talent crunch has hit the sector hard, and it could not have come at a worse time. In the midst of unprecedented growth in innovation and research, pharmaceutical and medical research companies urgently need to scale operations without scaling costs. And the traditional method of throwing more people at the problem is simply no longer an option in a tight labor market, with recruitment pipelines already running dry.

The challenge is twofold: how do companies get around R&D and data talent shortages? Perhaps even more urgently, how can they optimize business processes in order to empower their existing talent to spend less time on manual searches and more time making decisions and innovating?

In this article, we’ll outline the various ways in which AI and machine learning solve both problems at once, allowing companies to muscle in on the opportunities that rapid, ongoing growth provides.

Talent shortages in the life sciences industry: water, water, everywhere – but not a drop to drink.

In the wake of world historic levels of biopharma innovation during and after the COVID-19 pandemic, the industry has been left in a kind of paradox: growth and investment are up, but confidence is tepid. There is serious concern about whether this can be sustained, given the available crop of STEM talent in many developed countries. In the US, there are roughly twice as many job postings for life sciences positions as there were before the pandemic. Unsurprisingly, executives have been sounding the alarm: according to Randstad, 33% have identified talent scarcity as a major pain point, with 45% ramping up hiring to protect their businesses against staff attrition. 

The life sciences enter the global race for digital skills 

Part of the problem stems from the fact that the industry is so hungry for data and engineering skills – skills that are already in high demand across many other industries. And the urgency of this need is compounded by the skills gap that has been noted by the Association of the British Pharmaceutical Industry (ABPI): 43% of respondents in a recent survey said that digital literacy was a concern. These findings suggest that life science professionals are not currently receiving adequate training in digital and data skills, making it all the more urgent for companies to hire outside traditional boundaries. 

Bridging the gap and empowering people: the promise of AI

Advances in AI, machine learning and Natural Language Processing (NLP) are providing a much-needed way forward. One of the areas in which it’s making the biggest impact is in revolutionizing the way researchers work. In place of labor-intensive manual search, platforms like Similari offer smooth, automated processes that are more accurate and more scalable. 

By leveraging machine learning algorithms, researchers are able to sift through large volumes of data much faster than they could before, identifying patterns that would have otherwise been invisible. They can also avoid costly mistakes in clinical trial design by having unfettered, instantaneous access to results of relevant trials from around the world.

Faster, better drug discovery through enhanced technical data monitoring

AI also has the potential to revolutionize drug discovery and development. Data-driven algorithms and machine learning techniques make it easier to identify new drug candidates more quickly and accurately than ever before. Going even further, AI can be used to optimize existing drugs for improved efficacy and safety profiles. 

For example, AI can analyze large datasets at a much faster rate than humans can, making it easier for researchers to identify promising compounds. It can also help reduce costs associated with drug discovery by automating mundane tasks such as data entry and analysis. Even better, AI systems can learn from each iteration of the drug discovery process, enabling them to refine their strategies over time.  

AI in life sciences: unlocking human potential

But the benefits of AI go beyond simply reducing the need to grow R&D headcounts. The shift to AI-enhanced monitoring and research isn’t even about replacing people. Rather, it’s about augmenting human capital, and empowering human researchers to do their jobs better. And because AI-based systems can be “always on” in ways that humans cannot (and should not) be, they allow researchers to keep pace with a rapidly evolving environment.

Amidst dangerously high staff turnover levels, and the looming threat of burnout dogging the industry at large, it makes strategic sense to equip teams with tools that make their lives easier and their workflows more efficient. Companies who do so will enjoy higher talent retention rates, a priority in the current climate.

Similari: best-in-class software for R&D teams

In the world of life sciences research, AI has gone from nice-to-have to essential in a fairly short space of time. At Similari, we’ve brought together leading expertise in AI, machine learning and NLP to create the solution that the industry needs. With Similari’s always-on, intelligent surveillance of technical data, your teams can stay in the know at all times, while offloading up to 80% of the time they’re currently spending on research.  

To learn more about how Similari is changing the game businesses around the world, schedule a demo. Our team is ready to show you what lean, AI-enhanced research could do for your business. 

As of November 2022, 434 thousand clinical trials had been registered, approximately 35,000 more than 2021. This growth shows no signs of slowing down, and the proliferation of clinical data that these trials produce is driving innovation and drug discovery. That’s all to the good, but it also creates an urgent problem for the pharmaceutical industry at large: with so many trials ongoing, how do companies avoid duplicating research that has already been done?

In this article, we will discuss the costs and risks of redundant or duplicate clinical trials, and propose a much-needed solution for the problem of research waste. 

What exactly is a redundant clinical trial?

As the costs of clinical trials continue to balloon, cutting down on research waste should be an urgent priority for the entire industry. Regulators in many countries agree: China’s top regulator recently warned that it would be taking a harder line on redundant trials that waste resources. 

Broadly speaking, a clinical trial is considered redundant if the question it poses can or has been resolved on existing evidence. There is an important distinction between this kind of trial, phase IV trials that investigate side effects of treatments that have already entered the market. Phase IV trials are essential to assessing drug safety, and are, of necessity, justified by a review of existing data. Redundant trials, on the other hand, are not.

A significant 2020 study found, alarmingly, that half of Randomized Clinical Trials (RCTs) did not cite a Systematic Review (SR) before commencing the trial. This oversight radically increases the risk of research waste, with dire financial and ethical consequences.

The costs of redundant trials: the obvious and the hidden

The first and most obvious cost of wasted research is that of conducting the clinical trial itself. This includes all activities from planning to execution:

• pre-study preparation and writing protocols
• preparing for enrollment and recruiting participants
• conducting the study, administering interventions and collecting data
• analyzing results and writing reports
• obtaining regulatory approval for any products used during the clinical trial

In addition, further costs emerge after the trial: auditing by an independent third party, and the costs of marketing new products when they’re ready. The critical point here is that, for a truly redundant trial, all of this can and should have been avoided. The biggest cost of all, lurking behind all of the ones we have listed, is the opportunity cost incurred by pouring resources into dead-end research. That may be difficult to quantify, but it can be mission-ending.

Costly and harmful: a lose-lose for the pharmaceutical industry and patients alike

Perhaps more importantly, redundant trials expose patients themselves to unnecessary and unjustifiable risk. Risk is inherent to all clinical trials, and justifying that risk is an integral part of the systematic review process. Where that process is inadequate, or omitted altogether, human life and well-being are jeopardized. Recent research from China demonstrated the human cost of wasteful research: in one case, thousands of adverse events and hundreds of deaths. As regulators around the world move to close in on redundancy, companies can expect to face more scrutiny – and more penalties – for wasteful research in future.

Leveraging AI to safely navigate oceans of data

The way out of this crisis is obviously not to halt or even slow down innovation. What’s needed is more intelligent innovation that can avoid duplicating research and wasting resources. To achieve that, R&D professionals need a way to accurately survey existing data and identify genuine white spaces. But the rapid proliferation of data points has already made traditional methods of monitoring technical data obsolete. 

Artificial Intelligence is the solution that the industry desperately needs, and it holds the promise of revolutionizing the way research is conducted in the life sciences. Traditionally, pharmaceutical R&D teams are forced to rely on labor-intensive research practices that they simply cannot scale without drastically increasing personnel numbers. Artificial Intelligence is now able to replace much of that workflow, proactively scanning millions of data points and presenting them to the humans who need to make critical business decisions. 

Know everything you need to know, moment to moment, with Similari

At Similari, we’ve brought together industry leading expertise from AI, machine learning and NLP to create exactly the kind of innovation intelligence that enables pharmaceutical researchers to reliably identify what’s been done before, what’s currently being done, and where they can move in the future. 

To learn more about how to future-proof your innovation projects with cutting-edge data surveillance and monitoring tools, schedule a demo with the Similari team. We’re always ready – just like our technology – to show how much humans can achieve when they have all the information they need.

In 1965, Gordon Moore predicted that the number of components in complex integrated circuits would double every two years, for a CAGR of 41%. In particular, he was talking about the number of transistors on microchips. As it turned out, he was right, and his prediction became “Moore’s Law”. It’s become synonymous with a kind of confident technological optimism: we expect computational progress to continue unabated, and for processes to get better, faster, and easier over time.

Cut to the world of drug discovery, where we would really hope to see this taking place, and it just isn’t. Drug discovery is actually becoming harder, slower and more costly over time, so dramatically that researchers have coined “Eroom’s Law”, the inverse of “Moore’s Law” to describe how bad things really are. Proponents of this view estimate that the cost of developing a new drug doubles every nine years. Diminishing returns indeed. 

How do we flip “Eroom” back to Moore?

The answer to this – as with many 21st-century challenges – lies in enhanced research through AI, machine learning and natural language processing. In this post we’ll discuss the process of drug discovery and show how new technology offers a promising way out of mounting costs and inefficiencies. 

What is the actual cost of drug discovery?

Estimates vary wildly, but producing a new drug costs anywhere between $314 million and $2.8 billion. The time it takes to get a new drug to market ranges between 10 and 15 years. The clinical trials phase absorbs the bulk of this time and money, but costs mount at each stage along the way. And long before a subject reaches the clinical phase, companies must dedicate substantial time and resources to Research and Development. The median capitalized R&D cost for new drugs is around $1.1 billion. It has never been more urgent for pharmaceutical and life sciences research companies to trim costs and boost efficiency – ideally both at once. Thankfully, that’s exactly the win-win that AI can deliver.

Speed: machine Learning finds patterns in huge amounts of data, in an instant.

The number of relevant data points in the life sciences continues to balloon, outstripping humans’ ability to ingest, process and categorize huge amounts of data. Machine learning answers the need for rapid, accurate pattern recognition across millions of data points. Even more importantly, artificial intelligence of this kind doesn’t need to be explicitly programmed. Instead, it learns over time and adapts to the user’s preferences. 

The use cases within drug discovery alone are numerous. Researchers can use this technology to identify correlations between certain molecules and adverse clinical outcomes. Or they can analyze thousands of research records, clinical trial results and articles in minutes to identify salient trends for directing research away from dead ends. 

On target: improving accuracy in Research and Development 

Minimizing human dependencies in the R&D process doesn’t just make it faster – it reduces error. Through predictive analytics, sophisticated software can predict which substances are likely to have therapeutic value based on their molecular structure or other properties. This eliminates much of the guesswork associated with traditional drug discovery methods, resulting in more accurate results that can help reduce waste during later stages of drug development. Some studies have shown that machine learning models can replace clinical trials with simulations, or eliminate the need for a trial altogether by retrieving relevant results of an existing trial that may otherwise have been missed. 

A way to beat the “Throw Money At It” tendency

As Scannel noted, one of the main tactics companies have employed to accelerate drug discovery has been to ramp up hiring of R&D personnel, thereby “throwing money” at the problem to make it go away. This is risky in the best of times – and we don’t live in the best of times for high tech hiring. Amidst ongoing, chronic and global talent shortages, companies (even large ones) can not afford to solve their speed problem by exacerbating their cost problem. 

If they adopt intelligent software instead, they won’t have to. AI has the potential to reduce costs associated with drug discovery processes by increasing efficiency throughout each step, from early discovery right through to the regulatory and surveillance phases. By reducing manual labor required for data analysis tasks, and leveraging predictive analytics to identify promising compounds earlier in the process, companies can realize significant savings across each stage of a given project. 

Similari: at the forefront of AI-enhanced drug discovery

With the right approach to AI, the pharmaceutical industry can finally make a dent in the multi-billion dollar price tag of developing new drugs. At Similari, we believe that can be done, quickly, and without scaling costs. We have brought together expertise from the domains of AI, machine learning and NLP to create a solution that enables accurate, always-on surveillance of life sciences data, on a scale that was previously out of reach for most pharmaceutical companies. 

To learn more about how Similari’s state-of-the-art platform and industry-leading capabilities could push your drug discovery process to new heights, schedule a demo with our team. We’re really excited about the future, and we’re ready to show you how we plan to get there.