Small biotechnology companies continue to be a significant force in the drug development market comprising 77% of the active products in development by biotech today.1 Nonetheless, the rise of clinical trial costs, resource scarcity, and fierce competition, along with limited availability of financing, requires small biotechs to be even more calculated in their decision process to bring their drug from bench to bedside.
Clinical Trial Challenges Faced by Small Biotech Companies
To stay competitive, small biotechs must enhance the value of their trials while keeping costs low. At the same time, they need to collect as much data as possible and demonstrate positive results for their investors. This is particularly important as funding is often provided on a milestone basis rather than a lump sum for the entire trial. Taking this into account, along with their preference to accelerate time-to-market as much as possible, small biotechs are shifting towards increasingly complex trial protocols, such as adaptive or decentralized trials.
Due to complex protocols, and the desire to gather as much information as possible, the expanding number of external data sources from different vendors (e.g., gene expression data, IRT [interactive response technology] data, ECG data, eCOA [electronic Clinical Outcome Assessment], and central lab data) introduces the risk of inefficient data management along with reduced data quality.2
Running these complex data-driven trials involves a multitude of stakeholders, such as the biotech sponsor, site staff, clinical study teams, external vendors, and Safety Monitoring Committee members. All of these require efficient cross-collaboration. Accessibility to critical trial data for each team, the ability to analyze it accordingly, and the use of visualizations to make operational and business decisions are crucial. However, the clinical trial industry operates with many different systems and requirements. With numerous different vendors, there is a high risk of misalignment on data definitions within the same clinical study. Inadequate knowledge of data standardization, such as CDISC (Clinical Data Interchange Standards), results in unstandardized data structures and can limit a small biotech company’s ability to submit its drug for approval to the US FDA.
In addition to standardization, it is critical to have the expertise to select the appropriate EDC (Electronic Data Capture) system along with other clinical trial systems, like CTMS (Clinical Trial Management System), to support the complex study protocol. Certain EDC systems do not provide the advanced features needed for complex data-collection forms, dynamic functions, automated remote support, as well as complex electronic edit check programming. The absence of suitable data capture systems increases costs due to excess repetitive data entry by site staff as well as the need for manual review by the study team, potentially affecting critical trial timelines and milestones.
Decisions made by the management of small biotech companies are data-driven, making metric reports and visualization tools essential. Using different vendors and involving multiple users can lead to risks of inconsistencies and inefficiencies due to the variety of data processing and analysis methods that may be utilized. Not having standardized processes and automated reports can lead to time-intensive manual work and potential data quality issues, opening the door to unreliable conclusions and faulty decisions.
Efficient Management of Clinical Trial Data: Strategies for Success
A clinical data management expert can recommend the most suitable EDC system that supports extensive data-collection forms, complex electronic edit checks, and dynamic features tailored to the trial-specific protocol, ensuring timely EDC Go-Live. Ideally, consolidating trial data into a single system provides both the client and the study team with all the necessary data points and the supporting reports that are essential for tracking and analyzing the outcomes during study conduct.
As clinical research organizations (CROs) with data management departments have extensive experience in numerous clinical studies across various therapeutic areas, often within the specific field of the small biotech company’s drug development program, they offer standardized Clinical Study Reports (CRFs) via CDISC, edit checks, and data reconciliation programs. These practices ensure more efficient data collection and analysis, leading to reduced costs and expedited timelines and allowing biotech companies to identify early clinical trial outcomes.
Additionally, during the Clinical Study Report (CSR) development, near the end of studies, adherence to CDISC data standards supports the efficient creation of Study Data Tabulation Model (SDTM); Analysis Data Model (ADaM) datasets and Table Listings and Figures (TLFs). Following CDISC requirements ensures the use of standardized Case Report Forms (CRFs) to enable data collection from multiple sources and their reuse across different phases of your clinical trial. Notably, CDISC standards are required for regulatory submission to the FDA, underscoring the importance of data standardization from the outset.
Lastly, with ongoing technological advancements, supporting tools like AI are being introduced into clinical trials. The utilization of these new technologies during study conduct can automate and streamline data analysis and data management, significantly improving efficiencies. AI can reduce the manual labor involved in data validation and analysis, uncovering trends that are not easily identified through human analysis alone. This capability enables sponsors to make strategic decisions and focus on the critical aspects of the study conduct.
Conclusion: Optimizing Clinical Trials through Effective Data Management and Automation
In the challenging landscape of clinical trials, small biotechs encounter significant hurdles, including increasing costs, limited funding, and resource constraints. Focusing on clinical data management, including the selection of the appropriate EDC system to implement the complex protocols and integrate data from multiple sources, alongside standardized data processes and CRFs can significantly enhance efficiency and accelerate timelines across the study.
The introduction of new technologies, such as AI-supported tools, can further increase efficiency by automating parts of the data review process and increasing data quality, thereby improving subject safety. Ultimately, this approach reduces costs and equips small biotech companies with essential data to make informed strategic decisions focused on the important data points and the critical aspects of their study.
Partnering with an expert data management team can alleviate challenges faced by small biotech companies. CDISC standardizations and robust data management practices will streamline processes, enhance efficiency, ensure regulatory compliance, and accelerate timelines.
To read more on this topic, check out this blog: “What are CDISC and What are CDISC Standards“.
Our Allucent Data Management A-Team can help you navigate complex clinical trial challenges providing cost-effective and scalable solutions tailored for your specific needs.
References
- Endpoints News. (2023). Spotlight on the Future of Drug Development for Small and Mid-Size Biotechs. Retrieved from https://endpts.com/sp/2023-spotlight-on-the-future-of-drug-development-for-small-and-mid-size-biotechs/
- Applied Clinical Trials Online. (n.d.). The Future of Clinical Trials: Turning Data Chaos into Trial Intelligence. Retrieved from https://www.appliedclinicaltrialsonline.com/view/the-future-of-clinical-trials-turning-data-chaos-into-trial-intelligence
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