Solving Key Data Challenges in Managing a Biotech Lab
One of the biggest challenges to managing a lab is managing data and improving access to it
Kashef Qaadri, software technology leader for Bio-Rad Life Science Research, discusses key challenges biotech labs face, important software tools to manage those challenges, and the role AI will play in addressing future challenges.
Qaadri previously led business development, sales, commercial operations, and product at several bioinformatics companies including Benchling, OneCodex (Invitae), QIAGEN's Ingenuity Systems, and Biomatters (Insightful Science). He is also the host of the BioRad.io podcast, focusing on research informatics within biopharma. He studied molecular genetics and health care management.
Q: What are some of the major challenges when it comes to managing a biotechnology lab?
A: One of the biggest challenges to managing a lab is managing data and improving access to it, connecting disparate data sources in a single, consolidated, and connected environment. With the variety and volume of data, this has become increasingly challenging. Business applications, end users, and analysis tools all need to securely access and process data stored across different locations to make informed decisions. Data transfer, particularly taking data off the instrument, plays a critical role in this challenge.
The management of instruments has always been a challenge. With the cloud and connected instruments, leveraging artificial intelligence (AI) could deliver cost-saving benefits with predictive maintenance to reduce downtimes.
Furthermore, with ongoing supply chain issues, harnessing the cloud could improve production and lower cost with just-in-time access to consumables and reagents, with full traceability.
Achieving reproducibility and scalability is also key. In an era of high-throughput screening, relying exclusively on manual experiments is slow, costly, and inconsistent. To remain competitive, biopharma organizations are looking to outsource and automate portions of their R&D.
Finally, increased quality compliance and regulatory scrutiny is another major challenge. Regulatory agencies, who ensure the safety, efficacy, and quality of drugs, expect that the research data submitted by sponsor organizations are both reliable and accurate. As such, data integrity—the completeness, consistency, and accuracy—is critical, and must also be done in a convenient manner.
Q: What are some key solutions to these challenges?
A: Cloud-connected instruments and cloud-based storage help not only with data capture (acquisition), but also support seemingly unlimited scalability and computational resource access with improved security benefits. In our post-COVID era of research, we are seeing increasingly geographically dispersed teams, outsourcing, and collaborations. There are several recent examples of crowd-sourced science in the context of COVID research, and large technology companies have made advances in the sharing and collaboration of scientific data. Though there are further IT challenges associated with sharing data, these can be overcome by using cloud-based scientific data management systems (SDMS).
Additionally, many labs have already been centralized using a “core” model and, over time, this trend of highly specialized core facilities that focus on a limited number of areas will increase.
Having solutions that seamlessly plug into existing IT ecosystems is also critical, as most workflows aren’t isolated. Connected systems, which can programmatically interface with other systems, help with security, documentation, and compliance. For example, this could include connecting new instrument systems with permanent data stores (laboratory information management systems) as well as audit and tracking solutions.
Automation of routine experiments reduces the manual steps involved, decreasing costs while increasing reproducibility and throughput. This trend toward automation is fully expected to grow over the coming years, as more lab work is conducted using advanced technologies.
Q: With so many options to choose from, how can labs ensure they are choosing the right solutions for their facility and application(s)?
A: With the speed of technological advances in instrumentation, it’s important for labs to future-proof themselves as best as possible. Though labs may often prioritize short-term gains, selecting long-term, trusted partners who have a proven history of supporting customers is important. When evaluating instruments, for example, it’s important to ensure that both your instrument and accompanying software will be supported for however long you intend to use it.
Q: What are some of the major trends in biotech now and how are those trends impacting the management of these labs?
A: One major trend in biotech is large molecules (biologics). The entire R&D process has transformed from small molecules (chemical drugs) to large molecules. This shift has created new challenges around capturing and analyzing data along the drug development pipeline, from discovery and development to preclinical and clinical research. Large molecule manufacturing has also changed, requiring new tools, techniques, and instruments.
Looking forward, there will be a key role for AI in augmenting lab automation, not only in selecting the highest value experiments in the design phase, but also in the analysis and interpretation of data. Automation will also play a huge role in the lab of the future.
Q: What challenges do you expect biotech lab managers will face going forward and how can they prepare for those challenges?
A: Increased automation, AI, and high-throughput screening will create massive amounts of data. This will limit an organization’s ability to manage it, making it difficult for researchers to analyze and deduce any useful conclusions. This data surge will require lab managers to partner more closely with their research informatics counterparts, and researchers will need to have improved knowledge of cloud computing and software programing. Involving IT partners early and often is imperative for building a working relationship; otherwise, basic IT support of data could become a bottleneck in the research process.
Q: Was there anything you wanted to add?
A: As scientists, we must embrace new technologies to help accelerate research. These advances will shift several tasks currently performed by researchers, from experimental design to data analysis.