Originally published on pharmacompliancemonitor.com on Jan 15
With the release of FDA’s guidance on risk-based monitoring, the EMA Reflection paper on risk based quality management in clinical trials, and TransCelerate’s position paper, many biopharmaceutical enterprises and CROs are trying to establish solid risk assessment techniques and infrastructures to enhance clinical trial data quality, strategy, and reduce monitoring costs. In addition, with the introduction of novel technologies and data integration capabilities, the world of biopharmaceutical business conduct is changing drastically.
In order to mitigate risk and predict future clinical trial outcomes, successful biopharmaceutical enterprises are executing empirical data assessments on aggregated data sets and employ strategic decision making techniques in order to design high-quality and fast paced clinical trials that cost less. This blog will outline a few strategies on establishing strong risk assessments in clinical trials.
1. Define Key Performance and Key Risk Indicators (KPIs/KRIs)
Defining KPIs/KRIs helps sponsors/CROs optimize clinical trial costs/timelines, and establishes relevant quantitative definitions for risk-based monitoring. KPIs/KRIs must be meaningful from a study management and protocol perspective, be defined at the organizational level, and then differentiated by type of trial and indications studied. Standardized KPIs/KRIs built around operational activities should be complemented with disease / indication and type of molecular entity driven quality risk analyses to address impact of drug class effects.
To demonstrate, sponsors/CROs must evaluate risk on a broader level, and risk categories could include sponsor experience, scientific novelty, disease & population characteristics, trial methodology and trial development stages . Typical KPIs/KRIs on the clinical trial level could include the number of subjects enrolled & recruitment/site, number of protocol deviations, number of AEs/site, and trial master file quality . In order to make “the system” work for you and to design it to be acceptable for regulators, it is of the utmost importance, that KRIs/KPIs and their algorithms are based on data and not subjective opinions. Unspecific check questions as KRI such as, “sponsor experience, low, medium or high”, are likely to produce different “signals” when answered by different individuals. The solution for this conundrum is to make such questions specific and backed up by verifiable data.
It is up to the sponsor/CRO to determine relevant KPI/KRI portfolios and how they plan to implement these indicators for successful monitoring, quality enhancement and productive clinical trial execution. Sponsors/CROs can access analytical databases to conduct empirical assessments on Health Authority inspections for a specific disease and molecular entity and should evaluate historical data from previous clinical trials to unveil analytical patterns on specific factors that would increase risk. Risk identification must be complemented by prospective and properly designed and implemented corrective and preventive action plans.
Once the sponsor/CRO establishes KPI/KRI portfolios, management must leverage KPIs/KRIs by incorporating them in clinical trial risk-based monitoring plans in order to guide study teams and translate defined indicators into actions. Moreover, sponsors/CROs must define analytical parameters that would trigger unusual deviations and CAPA enablement for efficient quality improvement and monitoring.
KRIs / KPIs must be “validated” by other means such as structured monitoring and auditing activities to ensure that what they measure is meaningful, their signals are “true” whereas wrong “positive” signals are of less concern than false “negative” ones.
2. Establish Business Processes and IT Infrastructures
In order to successfully implement strong risk assessments, sponsors/CROs must have access to an IT infrastructure that efficiently accomplishes specific quality objectives and is able to receive data from clinical and pharmacovigilance operations (CTMS, safety database, etc.) and return reports. For example, if a sponsor has a temperature sensitive drug and it incorporates drug temperature excursions in its KPI/KRI portfolio, the sponsor/CRO must build and integrate the KRI/KPI system with drug distribution vendors and EDC systems in order to evaluate drug temperature variances between the drug distribution supplier and the study site. In addition, establishing such infrastructures allows study teams to rapidly and efficiently evaluate AE causes by drawing links between critical data points from one database. Establishing solid IT infrastructures allows sponsors/CROs and study teams to execute centralized and risk-based monitoring on a scalable level. One aspect that is very often forgotten during the planning phase are the contractual stipulations of data transfer from vendor to sponsor and back, including the roles and responsibilities of both defined regarding the KRI/KPI system, its reports and resulting CAPAs.
3. Measurement & Business Optimization
Successfully deploying efficient clinical trial risk assessments and monitoring requires developing analytical algorithms that aggregate KPI/KRI measurements for risk categorization, according to predefined business rules reflected in the algorithms. To elaborate, activating monitoring activities on individual KPI/KRI analytical parameters causes too much “noise;” oftentimes resulting in non-objectified monitoring, which is resource exhaustive. Alternatively, defining aggregate and weighted analytical parameters from a variety of KPI/KRI measures enables study teams to efficiently evaluate and address risk through objectified CAPA strategies, resulting in cost-efficient monitoring. Other pitfalls are to leave the decisions about follow-up actions that are triggered by a signal, to an individual or to allow for ad-hoc decision making.
If implemented sensibly, Risk based Study Management approaches enable sponsors/CROs to implement business and resource optimization models on monitoring activities in order to realize cost savings on risk-based assessments and maximize operational cash flows by reducing budgetary forecast variances.
Developing solid risk assessment strategies to improve quality and reduce clinical trial timelines requires planning, developing efficient business infrastructures and IT systems, and measuring/optimizing outcomes through data analysis. It is important to emphasize that the application of metadata analysis towards risk evaluation and business success requires equipping employees with sufficient analytical, statistical, business, and financial skills and tools. The change management effort to implement such strategies should not be underestimated.
Drs. Beat Widler and Peter Schiemann are the Managing Partners of WSQMS, and can be reached here. Moe Alsumidaie is the President & Chief Scientific Officer of Annex Clinical, and can be reached here.
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 Concepts for the Risk-Based Regulation of Clinical Research on Medicines and Medical Devices. Markus Hartmann and, Florence Hartmann-Vareilles. Drug Information Journal, September 2012; vol. 46: pp. 545-554, first published on August 8, 2012
 How a Data-Driven Quality Management System Can Manage Compliance Risk in Clinical Trials. Sina Djali, Stef Janssens, Stefan Van Yper, and Jan Van Parijs. Drug Information Journal, July 2010; vol. 44: pp. 359-373