It’s an exciting time for the research and development (R&D) sphere. The arrival of game-changing technologies has reformed the industry and continues to evolve at breakneck speeds, influencing trends in all areas, especially in life sciences. 

Advances have been rolled out and applied to almost all aspects of our lives, and a life sciences laboratory is no exception. Take a step back and you see trends emerging in the market, with innovative technology as their pillar.

From the equipment running experiments, and harvesting data in the most effective way, to simplifying workflows, life in the lab is significantly different compared to just a decade ago.

Heavy investments in budding therapeutic areas, as well as the uptake of cloud technology and AI, promise advancements that pique the imagination. But along with these changes and the benefits they bring, challenges also arise, as innovators navigate their implementation in a regulation-saturated market.

And as industry trends and tools change, regulations must adapt accordingly. So, what’s trending these days?

Single-use systems in the lab: unsustainable or pioneering tools?

Single-use bioprocessing technologies are widely used as alternatives to stainless steel bioreactors and fermentation vessels in fine chemical and pharmaceutical manufacturing. Conventional bioreactors provide a sterile environment for cell culture and growth and are designed to make large volumes of a product.

But now that the biopharmaceutical industry focus has migrated to more niche therapies and population groups, the end goal is smaller batches of many different products.

As the term suggests, single-use systems are disposable. While environmental advocates may scowl their disapproval, these systems are becoming the leading standard for manufacturing biologics. In fact, a recent report shows that the global single-use system market was valued at $2.8 billion in 2016 and is predicted to reach $9.3 billion by 2023.

Why are both small and large biopharma firms switching to this alternative? Single-use bioprocessing can credit its success to three main characteristics: cost-effectiveness, reduced risk of cross-contamination, and speed. Let’s take a closer look at each of these individually.

  • Cost-effectiveness: Single-use reactors are cheaper than traditional stainless steel, which requires extensive (and costly) sterilization, cleaning, and maintenance. Given the exponential increase in demand for biotherapeutics, and the rising development and production expenses, it’s logical to cut costs wherever possible.
  • Safety: They’re easy to clean—a major benefit. The product flow path is replaced after each batch, significantly reducing the risk of cross-contamination, and so boosting patient safety. Also, configuring a manufacturing facility using a single-use system offers greater flexibility to produce many different products at a single facility.
  • Speed: After a fermentation run is complete, it takes 24-48 hours to clean a conventional bioreactor, compared to just 10 minutes with a single-use system. Along with clean-up, these technologies speed up manufacturing by streamlining experiment preparation, set-up, validation, and reporting.

And while single-use bioreactors use plastic, they actually have a lower carbon footprint than their stainless-steel counterparts, as they consume less water and energy for cleaning.

AI technology will take drug discovery further

A hot topic these days, artificial intelligence (AI) has the biopharma world excited and apprehensive in equal measures. AI and machine learning have the potential to shake-up how life science labs operate. Harnessing algorithms could drive efficiencies in the lab and improve clinical care through image analysis, among other functions. Eventually, apps will be able to predict toxicity and drug response with precision.

The fact that the UK government has injected funds into two AI organizations, Chief.AI and Medicines Discovery Catapult, putting AI in reach of researchers in drug discovery, clearly shows the upward trend and potential in this area.

But lack of expertise in the area and anxiety over the high stakes have choked any real progress. For example, if Amazon’s prediction of our next online purchase might be completely off, this does not have a serious impact on our lives. An error in pharmaceutical decisions or data analysis, on the other hand, can have grave consequences to a patient’s health and set a company back hundreds of millions in costs.

Additional challenges in AI surround data and its accessibility—capturing data securely, collating data from multiple sources, and collaborating to analyze and interpret the data to make better informed decisions. What we need is to attract a new generation of scientists willing to use technology alongside their science and foster a culture change.

Re-thinking treatment: Is personalized medicine the future?

Is a radical overhaul of our approach to medicine long overdue? A major trend in the industry is replacing non-specific pharmaceutical medicines with more targeted treatments. Recently, there has been a greater focus on exploiting person data and customizing patient treatment, separating them based on their genomics or specific disease susceptibility. Cancer is the perfect contender for this tactic, as it is an agglomeration of hundreds of diseases.

To tackle personalized medicine, scientists need to ask different questions and interpret the answers from data. Questions such as “how does an individual’s genetic profile relate to the outcome of the disease?” and “can genetic markers be used to pick out the most effective option to treat the patient?” are important to understanding how to design an effective treatment.

These are questions that scientists are motivated to answer, using a plethora of science disciplines and technologies in combination to help advance research. Utilizing DNA-confirming technology with gene-editing tools including CRISPR, researchers can create a treatment based on a patient’s unique genetic make-up.

However, regulation remains a major barrier in this area. As with all of life sciences, the pharma/biopharma industries operate in a highly regulated environment, but strict regulatory compliance is slowing down the implementation of these technologies.

Large pharmaceutical companies are more likely to take the plunge, as they have large funds to invest in strategic partnerships with field experts. But data security continues to be a concern.

Increased focus on data integrity and cybersecurity

In 2017, the UK’s National Health Service (NHS) fell victim to the most disruptive cyber-attack in its history. The attack’s architects locked computer access for 24 GP trusts, prevented 40 hospitals from going online, and threatened to destroy patient data unless their demands were met.  

Today, data integrity and cybersecurity breaches continue to worry the life sciences industry. With the roll-out of GDPR, protecting personal data is paramount and requires a holistic approach to data governance.

To continue to drive growth through speedy product development and manufacturing, organizations are partnering up with tech companies to provide security. Data and technologies become shared assets, which poses security risks.

Establishing a robust data management process could be a possible solution—one where each revision in the flow chain is recorded, its impact analyzed, and measures put in place to safeguard data integrity of sensitive information.

Remaining competitive and secure with cloud technology

The amount of data generated by a single lab is phenomenal. But inefficiencies and bottlenecks to leveraging the data to gain better insight remain. Data on its own isn’t useful—it’s how the data is applied in the real world that drives innovation. Scientists must be smarter about collecting, analyzing, and using data to remove fragmented silos. This is where cloud software comes in.

Migrating to the cloud enables companies to stay ahead of the curve and bring new products to market faster. It allows organizations to connect and share information regardless of their own IT set-up while also being more secure. Using machine learning to provide conditional access based on risk, location, device, application, and user with a single sign-on, means:

  • threats can be identified in real time
  • responses are much faster
  • authorized personnel can access the system anywhere, anytime
  • high-risk users can be locked out
  • there is less disruption to users

In the face of growing competition, consumers expect high levels of quality, innovative tech, integrated solutions, speed, and transparency—all of which instill confidence in cloud technologies and play an important role in increasing ROI. 

What does the future hold?

It’s safe to say that regulations will have to adapt because these technologies are just in their nascent stage and explosive advancements are on the horizon. Pioneering tech will continue to streamline processes and decrease manual errors, boost product quality, and improve patient health through ground-breaking therapies.

Technologies offer the chance to realize scientific achievements we can only dream of, and those that embrace these technologies will take us into a new era of transformative opportunities.

 

Christian Marcazzo is general manager at IDBS.