In a recent webinar ‘From Data Overload to Decision Clarity in Drug Discovery’ Ardigen’s experts Jan Majta and Blazej Szczerba discussed the challenges of effectively managing data flow in biotech and pharma research and offered solutions to streamline the processes. This blog post summarizes the key takeaways from the webinar and tips for effective data management practices. We encourage you to watch the full webinar for more details.

You can watch the full recording here

Combating data overload in drug discovery pipelines

The drug development process involves complex, iterative steps that require frequent adjustments based on initial results. High-throughput experimental techniques like next-generation sequencing and imaging produce enormous volumes of data. While that data fuels discovery, managing it effectively can be overwhelming and could even become a potential bottleneck for companies. 

Some biotech and pharma companies (especially startups) try to manage data on their own using tools like Excel spreadsheets or even writing custom Python scripts. But this can create challenges as the company grows and its data processing requirements increase. Larger organizations may have in-house data scientists or hire external companies for analysis. However, outsourcing your data management can introduce its own bottlenecks.

Here are some of the most common data management challenges we see working with biotech and pharma companies:

1. Lack of good tools: Many companies struggle with managing and integrating data across various sources, leading to inefficiencies. Trying to manage data on your own requires optimizing or creating customized workflows and setting up your own infrastructure, which is a very resource-intensive process for organizations.
   For example, in one case study, our client initially relied on Excel and CSV files, which became challenging to manage as their business grew. The solution Ardigen provided was to develop a centralized data system that allowed easy access and version control.
2. Data analysis bottlenecks: Similar to the case study above, many drug programs often face delays due to a backlog of data analysis requests, especially when dependent on limited bioinformatics resources. The burden falls on the bioinformatics team, who have to create product reports manually, creating a significant bottleneck. Automating repetitive parts of the process can lighten up your team’s load and reduce processing delays.
3. Broken feedback loops: A common pitfall of running high-throughput experiments is that researchers often miss the opportunity to use the data to develop insights that guide and optimize future experiments. By creating a Lab-Loop solution, where predictive models with experimental validation are integrated, each experiment can improve system accuracy and guide future research directions, saving both time and resources.

To tackle these challenges, companies should focus on: 

1. Establishing centralized data systems: Streamlining data sources into a single system can improve data access and version control, reduce confusion, and save time.
2. Automating repetitive processes: Automating routine analysis tasks, whether through custom applications or ML models, frees up bioinformatics resources for complex tasks.
3. Improving feedback loops: A continuous cycle of data-driven predictions and lab experiments allows for smarter experimentation, narrowing down viable drug candidates faster.

How do you overcome data management challenges in drug discovery?

Whatever type of raw data you are working with—whether it is sequencing, imaging, high-throughput screening or real-world evidence data such as electronic health records (EHRs)—you need to be able to efficiently organize, analyze and interpret it to make decisions on the next experiment. If you want to analyze hundreds of thousands of data points, it is really hard to do using your local resources. The solution is moving to the cloud.

What tools and practices can help you combat data overload?

In order to improve your efficiency and arrive at tangible research outcomes, you need to seamlessly connect raw data, and powerful computation. For this, you need a powerful Compute Bench to transform data into insights. The Compute Bench is the cornerstone of transforming data into meaningful insights, combining automation, AI and scalable computing to streamline your workflows.

The Compute Bench consists of an integrated solution that includes cloud infrastructure, data processing and sharing, AI models and an optimized user interface. For each of these elements, here are a few things you may want to consider.

1. Cloud infrastructure: With cloud solutions, you don’t need to invest in hardware—there are no up-front costs and you can scale and adjust your resource needs as you go. You can work in a secure, distributed environment that makes your data always available, facilitating collaboration and providing disaster and recovery solutions, as well as compliance (such as HIPAA or GDPR).
2. Data ingestion: Importing the raw data can involve different steps, depending on whether your data is structured or unstructured. Based on your specific type of data, we can recommend optimal data storage platforms. 
3. Automated data processing: The next step is creating highly optimized data processing pipelines that can be run on the cloud or on on-premise solutions. Nextflow or Airflow Apache offer ready-to-go pipelines and modules for analyzing your data. When multiple teams collaborate on projects, there are solutions that allow you to collaborate, share, and trace data with pay-as-you-go services that adjust to your needs.
4. AI models: The application of ML and AI in research has grown significantly in recent years. The most popular cloud services provide ready-to-go solutions to enhance your research with AI. ML models can help you predict experimental outcomes, improve your workflows and accelerate discovery, for example, by identifying new biomarkers. Continuous improvement of models by learning from historical data and past outcomes strengthens predictions over the time.
5. User interface: Presenting insights clearly so that scientists can easily understand and utilize the data they are generating is essential for your organization’s success. Custom user interfaces – such as data-driven web apps and dashboards – work well for both bioinformaticians and data scientists at pharma and biotechs.

Building a good data management system for biotech and pharma companies

Having a well-functioning data management system in place will help you prioritize hypotheses much faster with scalable, parallel processing. With cloud solutions, you can collaborate, share and effectively utilize data across your diverse teams. In addition, optimizing data management will help you not only lower the cost of data processing but reduce your R&D expenses as well by helping optimize your experimental workflows.

If you are looking for a trusted partner with expertise in both data management and biology, reach out to our team to discuss how Ardigen can help you set up customized and optimized data management solutions.