Digitalization of biotech: why it’s here to stay

The digitalization or virtualization of biotech can be seen as a natural response to the dilemma faced by life-sciences companies.

Biotech and pharmaceutical companies must strike a balance between the cost and expected reward of breakthrough discovery.  Assuming no failed projects, it costs over $300 million to bring a drug to market. In practice, this number soars to $2.6 billion dollars when accounting for failures along the way. Not to mention, in order to receive FDA approval, drug companies must produce drugs that are significantly better than existing drugs or provide novel treatments, all the while complying with stringent regulations. An apt analogy is that if each drug campaign were a new musician, one must be “better than the Beatles.” The drug must be better than anything that has come before. 

Smaller biotech startups are particularly affected by this dilemma. They must push out drugs as quickly as possible to receive approval within their runway while competing with more established big pharma offerings. Startups only have the ability to bet on a few drug programs, rather than dispersing risk across a large portfolio of drugs. In contrast, large pharma companies such as Johnson & Johnson can work on everything from vaccines to over-the-counter medications to drugs for rare diseases. Biotech startups need to focus on their competitive edge and outsource non-core technologies wherever possible. 

Thus, smaller biotechs must rely on processes to cut time, save costs, automate experiments, and streamline communications with outside contractors. Digitalization of biotech is the integration of digital technology in all areas of biotech. It is a way for emerging biotech companies to become more efficient and competitive. 

In this article, we break down digitization of biotech into three aspects. The first aspect of the digitization of biotech involves collecting and processing more data during R&D. Another important component of the digitization of biotech is how relationships between various parties in the industry are streamlined and digitized. The third part of the article will cover common obstacles to digitization and how to approach them. 

1. The trend towards data

2. Streamlined relationships 

3. Strategies for accelerating digitization

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I. The trend towards data

Imagine a future where all aspects of drug development are digitized, from basic bench research to clinical trials. 

Today, it is estimated that 70% of biomanufacturing data is still not used due to outdated, paper-based methods. Deciphering your labmates’ hurried paper lab notebook blotted with unknown reagents is not only inconvenient, but also contributes to longer drug development timelines.

For example, suppose during a study scientists observed white bubbles that commonly appear in the slice of an animal organ but did not record whether the bubbles appeared in the control or test group. If a different study were to demonstrate increased frequency of the bubbles with the same intervention, it would be impossible to know if the new study confirmed or contradicted the original one.

However, if every single aspect of research were digitized and recorded real-time, there would be a system of record that could help answer this question. Better data infrastructure could help avoid this situation in its entirety by standardizing the data collected across scientists and assays.

More data also makes it possible to reveal subtle patterns that would have gone unnoticed in more traditional wet labs. The value of big data is clear, as the broad adoption of high throughput screening (HTS) has shown. HTS is a technique that uses automated equipment or clever science to rapidly test thousands to millions of samples in parallel. HTS requires more complex data collection systems for recording and collecting data due to the scale of experimentation.

While HTS started with compound libraries, a larger trend in the biotech industry has been the development and adoption of technologies that generate high throughput datasets such as CRISPR, multiplexing/barcoding, and imaging. These have led to the boom in biological data and created the need for better tools to process mass data. Some have coined the term techbio to describe the class of companies that have spawned from this trend of applying engineering to bio in order to create, process, and apply big data in the life sciences. 

Without digital methods, companies miss the opportunity for insightful analytics, which can help companies understand what they can do to improve operations and avoid repeating the same mistakes. 

Finally, keeping record of data and quality systems is extremely important for FDA submissions such as an Investigational New Drug (IND). Filling these applications involves gathering large amounts of data including lab notebooks, protocols, and results scattered across laptops – no easy task. Digital solutions can help companies keep track of data from the get go, including the entire editing history. This is the most cost-effective way to comply with regulations. 

The digitization of biotech is tied to the trend of more data being used to accelerate scientific discovery. This drive towards more data is not new, but it is accelerating at an exciting rate. Typically, when we think of scientific innovation, we picture scientists working at a wet lab. With faster data capture, it is more important than ever that computational teams work hand in hand with wet labs.

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II. Streamlined relationships 

Many vendors and partners are involved in a R&D pipeline. Most drugs have multiple players and companies that contributed to different parts of the journey from lead generation to clinical trial design to marketing. Digitalization of biotech can streamline these relationships and maximize the chance of a successful partnership. 

Contract Research Organizations (CROs) and Contract Development and Manufacturing Organizations (CDMOs) are vendors that perform a particular scientific service for other biotech and pharmaceutical companies. DNA sequencing, animal model experiments, bioinformatics, clinical trial recruitment – name an area in the industry, and there will be a contract service provider that specializes in that area. As the life sciences industry matures and the complexity of research expands, specialization is happening everywhere. 

For early stage biotech startups, using organizations that are already well suited and experienced in performing certain experiments can help them get through key experiments with limited resources. For big pharmaceutical companies, CROs and CDMOs help scale global operations for the commercialization and manufacturing of the drug. A significant portion of the production and distribution of the Pfizer or Moderna covid vaccines were handled by external vendors that complemented Pfizer and Moderna’s in-house capabilities.

One commonly proposed downside of performing research via an external vendor is that there is a lower likelihood of serendipitous discovery. Contract research and development organizations work on multiple clients’ studies and do not have the same ownership mindset as a drug discovery company. This leads to less attention to detail and severely diminishes the possibility of serendipitous discovery. 

This problem can be alleviated, however, if a significant amount of data is digitized and communicated across collaborators. Capturing and sharing the details of the experiment allows for the discovery of patterns and anomalies that otherwise might have been overlooked. CROs and other technology innovators are investing in solutions that allow for more seamless collaborations.

Moreover, digitization solves inefficiency in logistics and communication across research sites and patients. One good example of this is the rise of decentralized clinical trials (DCTs). In a fully decentralized trial, all trial procedures are conducted virtually so that participants can participate in the comfort of their homes. While more complex trial procedures (injections, MRIs, cell therapy) require in-person visits, many trials are following a hybrid model, where patients are able to visit local hospitals or mobile/retail sites instead of a central research site.

While DCTs have been around for at least the past decade, the pandemic has accelerated their adoption. One study found that in late 2019, 38 percent of pharma and contract-research organizations (CROs) expected virtual trials to be a major component of their portfolio, and 48 percent expected to run a trial with most activities conducted in participants’ homes. The numbers jumped to 100 and 89 percent respectively when asked the same questions a year later. 

Convenience is critical to patient enrollment and retention in clinical trials, especially for rare disease related studies. DCTs are expected to increase the sites and patients recruited for trials, resulting in more comprehensive data. 

With the digitalization of biotech, we see an increasing number of software solutions that can streamline relationships between separate entities. Scientific innovation relies on the ease of communication between collaborating parties, and digital solutions are the clear way to prevent communication problems. 

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III. Strategies for accelerating digitization

Despite these promises of digitization, the life sciences industry is slower than other industries when it comes to adopting new technologies. The conservatism is largely due to the importance of strict compliance with regulatory authorities in the industry. Prioritizing clinical safety is important. However, the COVID-19 pandemic has demonstrated that digitalization can accelerate deployment of key therapies without sacrificing patient safety. 

A smooth transition to digitization requires a cultural shift in the way researchers approach technology. Instead of viewing technology as a burden and a challenge to learn, digitalization of biotech must prioritize intuitiveness, delightfulness, and simplicity. 

Companies that are trying to digitize biotech must prioritize intuitive software design and invest in quality user experience (UX). Software that is easy to use encourages users to easily adopt it and advertise it to others researchers. Moreover, when new researchers try the technology for the first time, it must be easy enough to use that they do not need training from other researchers. 

Engineers in the life sciences often develop specialized tools for their research. However, most of their tools are difficult for someone without extensive training to use and lack clear documentation. Academia also doesn’t incentivize tool adoption or good documentation, instead rewarding novel tool creation. This leads to slow adoption of software tools and a big gap between the scientists and engineers in the field.  

Further, to achieve digitalization of biotech, data obtained from experiments must be easily communicable. The obstacle to universal adoption of EHRs in the healthcare industry is one example that demonstrates the importance of keeping data clean and organized. One reason why doctors are hesitant towards using EHRs is because of the massive deluge of data which can get overwhelming. According to the American Medical Association, “Physicians see virtually everything in a patient’s file every time they log in, regardless of where the patient is, how sick they are, and what kind of data the physician needs to see.” 

While massive amounts of important data should be stored securely, there must be a way to effectively surface the data that is essential for a researcher’s task at hand. Digital ways of collecting data should be seamlessly tailored to the researcher’s goals while also communicating clearly the essential information the researcher wants to know. 

An example of a software success story in the life sciences is CELLxGENE, a piece of open source software developed by Chan Zuckerberg initiative. CELLxGENE has democratized access to single cell RNA sequencing by allowing users to discover, explore, and analyze single cell data on the web without computational skills. It’s still early to see the full impact of software in this industry, but there’s no doubt it will be massive.

With the increasing need for biotechs to become more innovative amid regulatory and resource constraints, many biotechs have come to embrace the digitalization of R&D as a way to focus on their competitive edge. While adopting and developing such technologies can seem daunting, the improved speed and quality of discovery makes it clear that the digitization of biotech is unavoidable.