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If you want quality outcomes from a clinical trial program, you have to focus on quality goals when you design the program. That is the essence of Quality by Design (QbD) principles. Yet the biopharmaceutical industry hasn’t invested enough time or thought into building that level of quality into their design process. 

Part of the problem is that teams tend to be formed on the basis of technical or therapeutic expertise, not on the basis of well-honed design skills. Furthermore, teams tend not to be incentivized to foster open approaches to design or to use structured design methodologies that lead to high quality and performance. 

Following a QbD approach, can help solve this problem. 

The biopharma manufacturing industry has used QbD principles for decades – making the efficient manufacture of a high-quality drug product the aim of project plans. But we’ve failed to translate that approach to R&D and clinical trials. Given the demonstrated success of QbD in manufacturing, it is both paradoxical and unfortunate that it has not yet become an integral component of this research and trial design, as incorporating QbD is one of the few strategies that the biopharmaceutical industry has seen deliver an improved probability of success. 

The reason QbD principles have not transferred to R&D is that clinical trials are expert-driven rather than process-driven. However, the key aspects of these two operational modes are not mutually exclusive. Within a structured process that facilitates efficient decision making, there is still room for expert input and creativity.

QbD Elements: Plan-Do-Check-Act 

Implementing a QbD approach to research and clinical trials requires a change in the way projects are planned and implemented. To achieve the change necessary, project teams should implement a “Plan-Do-Check-Act” framework. 

  • Plan – design diligence from the outset. In the early phase of study design, the protocol must focus on proactive quality risk-management and scientific risk assessments. This includes ensuring a rigorous recruiting process based on carefully determined inclusion and exclusion criteria, ensuring the safety of the participants who will be the study’s scientific objectives, and validating the assessments and procedures that will generate the data collected. • Operational risk assessments should further focus on feasibility considerations (e.g., can appropriate and sufficient investigational sites be secured?) and operational risk (e.g., supply chain issues, procedures such as imaging, patient reported outcomes, lab assays, data integrity). Operational plans can then be created for site/country selection, quality, data monitoring, and safety. 
  • Do – build a strong team and train them to focus on quality outcomes. In the “Do” phase of the cycle, training investigational sites, principal investigators, monitors, and clinical trial educators is a critical first step. Training establishes a clear baseline for knowledge and skills, and ensures team members understand the quality outcome goals. 
  • Once training is complete, you need to set up a rigorous process for overseeing trial execution, including prospective alerts, triggers, and risk mitigation plans that deliver against iterative project management plans. 
  • Check – implement state of the art data collection and reporting technology to avoid errors and track real-time results. As you execute your trial, the check phase should employ sophisticated reporting software within a central data-operations center to provide near-real-time access to blinded data at the participant level. Such technology investments are critical as they enable visualizations of core study indicators that can support real time decision making. Dashboards displaying expected versus actual enrollment, for example, are potent tools that provide detailed information in a readily assimilated manner. Alerts can also be programmed to indicate unacceptable values for multiple indicators, including safety concerns and endpoint accrual. Data cleaning status is also monitored and the quality assurance database assembled. 
  • Act – drawing upon real world data, make changes to achieve better outcomes. The act phase entails the final proactive (rather than reactive) step in QbD. It involves preemptive project management and proactive risk mitigation using the information gleaned from the check phase. Re-forecasting is conducted based on information gained to date and QA/quality management processes followed. 
Taking the Plan-Do-Check-Act approach to clinical trial design may require additional up front time and cost, but the long term value is worth the investment. It is a more robust approach to trial management that improves the probability of success. And ultimately, that should be the goal of everything we do.
Topics in this blog post: Biopharma, Clinical Trials, Data and Technology, R&D