Given the glaring challenge of managing bioprocess data, the question remains: Why have bioprocess data solutions been historically overlooked? This is particularly surprising when you consider that the last few decades have resulted in a lot of powerful software solutions for other aspects of biotechnological research, including tools for DNA design (like Cello, Asimov, and Snapgene) bioinformatics (like those from Nextflow, Latch, AWS, Geneious), and strain design (like, Gingko Bioworks’ custom-built recommendation engine), and beyond. Yet, software tools for managing bioprocess data are largely absent in the biomanufacturing sector, especially for earlier-stage companies powering the synthetic biology sector. As Prabha Ramakrishnan, VP of Partnerships & Strategy at Invert, puts it, “fermentation historically has been underpowered with software.”
This blog explores why bioprocess data management has received minimal devoted attention from software developers. In addition, this piece will address Invert’s bioprocess software and how it closes the remaining gap in the biotech software continuum.
A Lack of Volume
One explanation is that there was not enough volume (until recently) to justify a company devoting the resources towards developing a turnkey bioprocess data solution. With too few customers to sell to, Software-as-a-Service (SaaS) business models struggle to find a path to profitability.
Most major biotech innovations of the past few decades (like genome sequencing, CRISPR, etc.) and biotech IP are upstream of bioprocess development. So, every new biotech company starts with molecular biology and translational research, whereas bioprocess development comes much later. This explains why software developers have heavily weighted their efforts towards research tools.
Research software tools enjoy applications much further upstream than bioproduction, resulting in a wider user base made up of academic researchers and many companies engaged in early-stage research and development. Since their application is well ahead of the “valley of death,” they can de-risk their operations by drawing from a wider pool and remaining unaffected by user commercialization failures.
Even when the biotechnology community was much smaller, research software tools could thrive simply because they drew from more of the community. Though biomanufacturing efforts succeeded in the previous decades, there still were not enough companies or products to develop software around. From a business perspective, building R&D software has historically made more sense because they can serve a much wider audience and have a much larger target addressable market (TAM). Put succinctly, every biotech company does wet lab R&D, but not every biotech company runs bioreactors.
Only recently did the biotech industry grow much more prominent, leading to an explosion in biomanufacturing demand and capacity. Though the valley of death persists and failure rates remain high, a higher number of bioproduct companies now reach the commercialization stage. Thus, only now has the market for bioprocess data management software become sizable enough to encourage investment into a new solution.
Bioprocess & Software Engineering: A Venn Diagram Without Much Overlap
Separate from the available market, it’s quite challenging to build an effective bioprocess data management software without a deep understanding of biomanufacturing. Software development depends on engineers able to understand the context of what end-users need from the software. However, it’s already hard enough to recruit software developers, let alone those who also understand bioprocessing at an expert level. Simply put, these two groups only marginally overlap. For those with both knowledge sets, their talents are in demand, making them expensive and difficult to recruit, especially when large tech companies can hire them at high salaries.
With limited ability to recruit a team capable of building a proper bioprocess data solution, companies couldn’t create software that solves the challenge across the board.
Large Biopharma Companies Built Their Own Data Tools
Large pharma and biopharma companies heavily dominated the early stage of modern biomanufacturing, beginning in the 1980s through the 2010s. The first biologic therapeutics (like recombinant human insulin and blockbuster monoclonal antibody therapeutics) required organizations with deep pockets. Though no true commercial bioprocess data solution option existed, trailblazing big pharma companies had the resources to build and implement their own data management software to support greater productivity and ensure regulatory rigor. In addition, the profitability of the resulting approved biologics could greatly offset their capital expense.
Once completed, these companies had no incentive to share their software, which meant that new biomanufacturing players would also need to build their own, further entrenching decisions to build instead of buy. Large pharma companies also enjoyed the resources to hire and retain the few individuals with both software development and bioprocess expertise, which minimized attempts from others to harness the talented few to build more broadly applicable bioprocess data solutions.
Even still, it is important to note that these software didn’t solve every challenging aspect of managing bioprocess data. In effect, they only really met the last generation of data analytics needs well enough to just get the first biological drugs to the market. Since they designed the software in the context of specific leading biologics, the tools quickly became clunky and obsolete as the industry became more competitive and biologic drugs became more diverse. As the biomanufacturing industry continued to evolve, so too did its data analytics needs. Understanding new bioprocesses and making new bioproducts while keeping costs low required increasingly advanced data analytics and more flexible software.
Attempts to Solve The Bioprocess Data Management Challenge
As more precision fermentation and other biomanufacturing efforts cropped up, more companies needed to confront the challenge of competently managing the bevy of bioprocess data they generated. As a result of this critical mass, more bioprocess teams attempted to rectify the problem with varying success.
Like the big pharma biomanufacturing pioneers, most companies opt to create homegrown solutions to better manage bioprocess data. In these cases, engineers piece together patchworks of legacy software systems and data pipelines, build spreadsheets, and deploy process agreements to force organization, albeit imperfectly. Similarly, some opted to hire information technology companies to construct a solution specifically for them. Regardless, the expense of these custom-built solutions is significant and comes with the additional cost of service agreements to fix new problems and expand capabilities as needed.
Some custom-built systems sometimes stemmed from manufacturing execution systems (MES) bases, which originated in the 1990s. However, these MES systems were designed to drive manufacturing execution for enormous sectors like the petroleum and automotive industries. Thus, these systems struggled to manage the inherent complexity of biological systems and many different variables that must be diligently tracked and analyzed.
Over time, some bioprocessing technology companies began offering Process Analytical Technology (PAT) Software alongside their hardware. While these software could manage bioprocess data and perform analytics, they are often hardware-specific and sold as add-ons for specific bioreactor systems. So, maximizing the full impact of PAT software depends on using a single hardware provider, which may not benefit the biomanufacturer or may not be possible given the many different data sources.
In recent years, several electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS) have grown popular in research and laboratory settings, including at biotech companies. As a result, many users opted to rig up their existing ELN and LIMS systems to serve this purpose. To do so, they need to force-fit fermentation/biomanufacturing data to integrate it into the system. Unfortunately, ELN and LIMS systems companies did not design these handy products for this purpose. So, while this offered some improvement over manual pipelines and reduced costs from data management systems built from scratch, these piecemeal ELN/LIMS approaches never get to 100% integration, meaning that bottlenecks continue to hamper their efficacy.
Furthermore, ELNs and LIMS were not explicitly designed for environments like biomanufacturing, where different individuals or groups collect and analyze the data. So, they can struggle to provide appropriate context from scientific and engineering teams to decision-makers.
The Modern Solution: Invert
Increasingly, companies (especially those in synthetic biology) are realizing the importance of bioprocess development and commercialization. Only through well-designed bioprocesses can companies make enough product to sell at margins that turn a profit.
As more individuals target complex bioprocesses, the more they recognize the importance of accelerating process development, reducing risk along the commercialization path, and increasing the predictability of how their processes scale. More people than ever realize that these capabilities are the primary drivers of whether they will succeed or fail. But, to do this, bioproduct companies need to properly leverage their R&D and scale-up data.
Given the limitations of patchwork data systems and existing bioprocess software, the biotech industry needs a more seamless bioprocess data management and analysis software. So, we made one!
Combining the skillsets of both bioprocess and software development experts, Invert takes your bioprocess data from lab to production and shortens Design-Build-Test-Learn (DBTL) cycles by turning scattered data into actionable insights faster than ever.
Invert designed our bioprocess software for intelligent automated data ingestion no matter the source, allowing users to unify all their bioprocess data with minimal effort. Invert can connect to any bioreactor, off-line equipment, ELNs, LIMS, or other databases to readily sync data between your tools.
Invert can easily handle large sets, allowing users to readily analyze and compare on-line and off-line data across runs, scales, time, and events while creating advanced graphs, calculating derived parameters, and executing statistics.
Invert also provides complete process traceability and makes it easy to share data and information across teams and collaborators. Invert contextualizes bioprocess data while keeping all data and its full historical record secure (SOC2 compliant and ISO27001 certified). With end-to-end encryption, you can keep your single source of truth safe.
Plus, we designed our software to work with virtually any biological system and target bioproduct. Whether you’re in pharma, alternative protein, synthetic biology, or contract manufacturing, Invert can empower you to improve process outcomes.
If you’d like to learn how Invert’s bioprocess software can support your biomanufacturing efforts, reach out today!