Risk analysis by FMEA as an element of analytical validation

Analytical Quality by Design (AQbD) Approach to the Development of Analytical Procedures for Medicinal Plants

Scientific regulatory systems with suitable analytical methods for monitoring quality, safety, and efficacy are essential in medicinal plant drug discovery. There have been only few attempts to adopt the analytical quality by design (AQbD) strategy in medicinal plants analysis over the last few years. AQbD is a holistic method and development approach that understands analytical procedure, from risk assessment to lifecycle management. The enhanced AQbD approach reduces the time and effort necessary to develop reliable analytical methods, leads to flexible change control through the method operable design region (MODR), and lowers the out-of-specification (OOS) results. However, it is difficult to follow all the AQbD workflow steps in the field of medicinal plants analysis, such as defining the analytical target profiles (ATPs), identifying critical analytical procedure parameters (CAPPs), among others, because the complexity of chemical and biological properties in medicinal plants acts as a barrier. In this review, various applications of AQbD to medicinal plant analytical procedures are discussed. Unlike the analysis of a single compound, medicinal plant analysis is characterized by analyzing multiple components contained in biological materials, so it will be summarized by focusing on the following points: Analytical methods showing correlations within analysis parameters for the specific medicinal plant analysis, plant raw material diversity, one or more analysis targets defined for multiple phytochemicals, key analysis attributes, and analysis control strategies. In addition, the opportunities available through the use of design-based quality management techniques and the challenges that coexist are also discussed.

Quality-by-design in pharmaceutical development: From current perspectives to practical applications

Current pharmaceutical research directions tend to follow a systematic approach in the field of applied research and development. The concept of quality-by-design (QbD) has been the focus of the current progress of pharmaceutical sciences. It is based on, but not limited, to risk assessment, design of experiments and other computational methods and process analytical technology. These tools offer a well-organized methodology, both to identify and analyse the hazards that should be handled as critical, and are therefore applicable in the control strategy. Once implemented, the QbD approach will augment the comprehension of experts concerning the developed analytical technique or manufacturing process. The main activities are oriented towards the identification of the quality target product profiles, along with the critical quality attributes, the risk management of these and their analysis through in silico aided methods. This review aims to offer an overview of the current standpoints and general applications of QbD methods in pharmaceutical development.

Complete intra-laboratory validation of a LAL assay for bacterial endotoxin determination in EBV-specific cytotoxic T lymphocytes

Endotoxin content is a critical factor that affects the safety of biological pharmaceutical products. International pharmacopoeias describe several reference methods to determine endotoxin levels in advanced therapy medicinal product (ATMP) preparations. Administration of ATMPs must be done as rapidly as possible to ensure complete viability and potency of the cellular product. To evaluate the endotoxin content in the shortest time possible, we chose to validate an alternative method based on the use of the Charles River Portable Testing System (PTS) and FDA-approved cartridges, compliant with the requirements of the European Pharmacopoeia and providing results in <20 min. Here, we describe a unique and complete validation approach for instrument, personnel, and analytical method for assessment of endotoxins in ATMP matrices. The PTS system provides high sensitivity and fast quantitative results and uses less raw material and accessories compared with compendial methods. It is also less time consuming and less prone to operator variability. Our validation approach is suitable for a validated laboratory with trained personnel capable of conducting the ATMP release tests, and with very low intra-laboratory variability, and meets the criteria required for an alternative approach to endotoxin detection for in-process and product-release testing of ATMPs.

Risk assessment of the total testing process based on quality indicators with the Sigma metrics

Background Evidence-based evaluation of laboratory performances including pre-analytical, analytical and post-analytical stages of the total testing process (TTP) is crucial to ensure patients receiving safe, efficient and effective care. To conduct risk assessment, quality management tools such as Failure Mode and Effect Analysis (FMEA) and the Failure Reporting and Corrective Action System (FRACAS) were constantly used for proactive or reactive analysis, respectively. However, FMEA and FRACAS faced big challenges in determining the scoring scales and failure prioritization in the assessment of real-world cases. Here, we developed a novel strategy, by incorporating Sigma metrics into risk assessment based on quality indicators (QIs) data, to provide a more objective assessment of risks in TTP. Methods QI data was collected for 1 year and FRACAS was applied to produce the risk rating based on three variables: (1) Sigma metrics for the frequency of defects; (2) possible consequence; (3) detection method. The risk priority number (RPN) of each QI was calculated by a 5-point scale score, where a value of RPN > 50 was rated as high-risk. Results The RPNs of two QIs in post-analytical phase (TAT of Stat biochemistry analyte and Timely critical values notification) were above 50 which required rigorous monitoring and corrective actions to eliminate the high risks. Nine QIs (RPNs between 25 and 50) required further investigation and monitoring. After 3 months of corrective action the two identified high-risk processes were successfully reduced. Conclusions The strategy can be implemented to reduce identified risk and assuring patient safety.

Cartilage Tissue Engineering by Extrusion Bioprinting: Process Analysis, Risk Evaluation, and Mitigation Strategies

Extrusion bioprinting is considered promising in cartilage tissue engineering since it allows the fabrication of complex, customized, and living constructs potentially suitable for clinical applications. However, clinical translation is often complicated by the variability and unknown/unsolved issues related to this technology. The aim of this study was to perform a risk analysis on a research process, consisting in the bioprinting of a stem cell-laden collagen bioink to fabricate constructs with cartilage-like properties. The method utilized was the Failure Mode and Effect Analysis/Failure Mode and Effect Criticality Analysis (FMEA/FMECA) which foresees a mapping of the process to proactively identify related risks and the mitigation actions. This proactive risk analysis allowed the identification of forty-seven possible failure modes, deriving from seventy-one potential causes. Twenty-four failure modes displayed a high-risk level according to the selected evaluation criteria and threshold (RPN > 100). The results highlighted that the main process risks are a relatively low fidelity of the fabricated structures, unsuitable parameters/material properties, the death of encapsulated cells due to the shear stress generated along the nozzle by mechanical extrusion, and possible biological contamination phenomena. The main mitigation actions involved personnel training and the implementation of dedicated procedures, system calibration, printing conditions check, and, most importantly, a thorough knowledge of selected biomaterial and cell properties that could be built either through the provided data/scientific literature or their preliminary assessment through dedicated experimental optimization phase. To conclude, highlighting issues in the early research phase and putting in place all the required actions to mitigate risks will make easier to develop a standardized process to be quickly translated to clinical use.

Failure mode and effects analysis for proactive healthcare risk evaluation: A systematic literature review

RATIONALE, AIMS, AND OBJECTIVES

Failure mode and effects analysis (FMEA) is a valuable reliability management tool that can preemptively identify the potential failures of a system and assess their causes and effects, thereby preventing them from occurring. The use of FMEA in the healthcare setting has become increasingly popular over the last decade, being applied to a multitude of different areas. The objective of this study is to review comprehensively the literature regarding the application of FMEA for healthcare risk analysis.

METHODS

An extensive search was carried out in the scholarly databases of Scopus and PubMed, and we only chose the academic articles which used the FMEA technique to solve healthcare risk analysis problems. Furthermore, a bibliometric analysis was performed based on the number of citations, publication year, appeared journals, authors, and country of origin.

RESULTS

A total of 158 journal papers published over the period of 1998 to 2018 were extracted and reviewed. These publications were classified into four categories (ie, healthcare process, hospital management, hospital informatization, and medical equipment and production) according to the healthcare issues to be solved, and analyzed regarding the application fields and the utilized FMEA methods.

CONCLUSION

FMEA has high practicality for healthcare quality improvement and error reduction and has been prevalently employed to improve healthcare processes in hospitals. This research supports academics and practitioners in effectively adopting the FMEA tool to proactively reduce healthcare risks and increase patient safety, and provides an insight into its state-of-the-art.

Developing a new BWM-based GMAFMA approach for evaluation of potential risks and failure modes in production processes

Purpose

Nowadays, the risk assessment and reliability engineering of various production processes have become an inevitable necessity. Because if these risks are not going to be evaluated and no solution is going to be taken for their prevention, managing them would be really hard and costly in case of their occurrence. The importance of this issue is much higher in producing healthcare products due to their quality's direct impact on the health of individuals and society.

Design/methodology/approach

One of the most common approaches of risk assessment is the failure mode and effects analysis (FMEA), which is facing some limitations in practice. In this research, a new generalized multi-attribute failure mode analysis approach has been proposed by utilizing the best–worst method and linguistic 2-tuple representation in order to evaluate the production process of hemodialysis solution in a case of Tehran, Iran.

Findings

According to the results, entry of waste to the mixing tanker, impurity of raw materials and ingredients and fracture of the mixer screw have been identified as the most important potential failures. At last, the results of this research have been compared with the previous studies.

Originality/value

Some reinforcement attributes have been added to the traditional FMEA attributes in order to improve the results. Also, the problems of identical weights for attributes, inaccuracy in experts' opinions and the uncertainties in prioritizing the potential failures were improved. Furthermore, in addition to the need for less comparative data, the proposed approach is more accurate and comprehensive in its results.

The consistency analysis of failure mode and effect analysis (FMEA) in information technology risk assessment

FMEA is as a method for assessing IT risks. This research aimed to examine the consistency of both traditional FMEA and improved FMEA in IT risk assessment. Improved FMEA is the result of a synthesis framework to minimize consistency in traditional FMEA. Two sets of action research cycles (plan, act, observe, reflect) were applied in this research. Action Research 1 was used to examine and prove the consistency of traditional FMEA. On the other hand, Action Research 2 was applied to examine the consistency of improved FMEA. Tests were carried out by two different teams in the same case study. The consistency was observed in the gap of the RPN results in both teams, and the differences result in both action research cycles. Action Research 1 proved that traditional FMEA was not consistent. The gap in the amount of risk at a very high level was four risks. However, Action research 2 had the same amount of risk at a very high level. Based on the correlation test, the consistency of action research 1 was 0.848 (very large correlation), and the action research 2 was 0.937 (near-perfect correlation). The consistency of improved FMEA proved to be more consistent than traditional FMEA. The limitation of this study was memory issues because both action research cycles were carried out by the same team and with similar case studies. Further research is expected to compare traditional FMEA and improved FMEA in different case studies. The theoretical contribution was the improved FMEA synthesis based on limitations of traditional FMEA. The FMEA team may use Improved FMEA Framework.

Lifetime Estimation and Failure Risk Analysis in a Power Stage Used in Wind-Fuel Cell Hybrid Energy Systems

This paper presents a methodology based on the failure mode and effect analysis (FMEA) to analyze the failures in the power stage of wind-fuel cell hybrid energy systems. Besides, fault tree analysis (FTA) is applied to describe the probabilistic failures in the vital subcomponents. Finally, the reliability assessment of the system is carried out for a five-year operation that is guaranteed by the manufacturer. So, as the result, the reliability analysis proves that the metal oxide semiconductor field effect transistor (MOSFET) and electrolytic capacitor are the most critical components that introduce damages in the power circuit. Moreover, a comparative study on the reliability assessment by the exponential distribution and the Weibull distribution show that the B1 lifetime obtained by the Weibull distribution is closer to reality.

Risk-based approach for method development in pharmaceutical quality control context: A critical review

Pharmaceutical regulatory bodies increasingly require the implementation of systematic approaches in pharmaceutical product development. Quality control methods play a key role in the control strategy of drugs manufacturing to assure their quality. A risk-based approach in the analytical method development is strongly recommended to ensure that the method performances fit the purpose of the method during its entire life-cycle. In the last decade, analytical quality by design (AQbD), as risk management oriented methodology, has been progressively integrated with method development for fulfilling this objective. This approach has successfully allowed the quality to be designed into the analytical processes by obtaining a deep understanding of the procedures. In this paper the AQbD workflow and its application in the development of methods to be used for pharmaceutical quality control have been treated and discussed. Recent publications regarding how AQbD has been applied in separation techniques were reviewed. The different development strategies have been also showcased, highlighting their advantages and disadvantages, in order to give a useful overview.

A Lean Six Sigma approach to the improvement of the selenium analysis method

Reliable results represent the pinnacle assessment of quality of an analytical laboratory, and therefore variability is considered to be a critical quality problem associated with the selenium analysis method executed at Western Cape Provincial Veterinary Laboratory (WCPVL). The elimination and control of variability is undoubtedly of significant importance because of the narrow margin of safety between toxic and deficient doses of the trace element for good animal health. A quality methodology known as Lean Six Sigma was believed to present the most feasible solution for overcoming the adverse effect of variation, through steps towards analytical process improvement. Lean Six Sigma represents a form of scientific method type, which is empirical, inductive and deductive, and systematic, which relies on data, and is fact-based. The Lean Six Sigma methodology comprises five macro-phases, namely Define, Measure, Analyse, Improve and Control (DMAIC). Both qualitative and quantitative laboratory data were collected in terms of these phases. Qualitative data were collected by using quality-tools, namely an Ishikawa diagram, a Pareto chart, Kaizen analysis and a Failure Mode Effect analysis tool. Quantitative laboratory data, based on the analytical chemistry test method, were collected through a controlled experiment. The controlled experiment entailed 13 replicated runs of the selenium test method, whereby 11 samples were repetitively analysed, whilst Certified Reference Material (CRM) was also included in 6 of the runs. Laboratory results obtained from the controlled experiment was analysed by using statistical methods, commonly associated with quality validation of chemistry procedures. Analysis of both sets of data yielded an improved selenium analysis method, believed to provide greater reliability of results, in addition to a greatly reduced cycle time and superior control features. Lean Six Sigma may therefore be regarded as a valuable tool in any laboratory, and represents both a management discipline, and a standardised approach to problem solving and process optimisation.

A simulation framework for mapping risks in clinical processes: the case of in-patient transfers

OBJECTIVE

To model how individual violations in routine clinical processes cumulatively contribute to the risk of adverse events in hospital using an agent-based simulation framework.

DESIGN

An agent-based simulation was designed to model the cascade of common violations that contribute to the risk of adverse events in routine clinical processes. Clinicians and the information systems that support them were represented as a group of interacting agents using data from direct observations. The model was calibrated using data from 101 patient transfers observed in a hospital and results were validated for one of two scenarios (a misidentification scenario and an infection control scenario). Repeated simulations using the calibrated model were undertaken to create a distribution of possible process outcomes. The likelihood of end-of-chain risk is the main outcome measure, reported for each of the two scenarios.

RESULTS

The simulations demonstrate end-of-chain risks of 8% and 24% for the misidentification and infection control scenarios, respectively. Over 95% of the simulations in both scenarios are unique, indicating that the in-patient transfer process diverges from prescribed work practices in a variety of ways.

CONCLUSIONS

The simulation allowed us to model the risk of adverse events in a clinical process, by generating the variety of possible work subject to violations, a novel prospective risk analysis method. The in-patient transfer process has a high proportion of unique trajectories, implying that risk mitigation may benefit from focusing on reducing complexity rather than augmenting the process with further rule-based protocols.

Consistency of FMEA used in the validation of analytical procedures

In order to explore the consistency of the outcome of a Failure Mode and Effects Analysis (FMEA) in the validation of analytical procedures, an FMEA was carried out by two different teams. The two teams applied two separate FMEAs to a High Performance Liquid Chromatography-Diode Array Detection-Mass Spectrometry (HPLC-DAD-MS) analytical procedure used in the quality control of medicines. Each team was free to define their own ranking scales for the probability of severity (S), occurrence (O), and detection (D) of failure modes. We calculated Risk Priority Numbers (RPNs) and we identified the failure modes above the 90th percentile of RPN values as failure modes needing urgent corrective action; failure modes falling between the 75th and 90th percentile of RPN values were identified as failure modes needing necessary corrective action, respectively. Team 1 and Team 2 identified five and six failure modes needing urgent corrective action respectively, with two being commonly identified. Of the failure modes needing necessary corrective actions, about a third were commonly identified by both teams. These results show inconsistency in the outcome of the FMEA. To improve consistency, we recommend that FMEA is always carried out under the supervision of an experienced FMEA-facilitator and that the FMEA team has at least two members with competence in the analytical method to be validated. However, the FMEAs of both teams contained valuable information that was not identified by the other team, indicating that this inconsistency is not always a drawback.

A new scale for evaluating the risks for in-hospital falls of newborn infants: a failure modes and effects analysis study

We aimed to develop a new scale for evaluating risks and preventive measures for in-hospital falls of newborn infants, from admission to discharge of the expectant mother. Our study was prepared in accordance with Failure Modes and Effects Analysis criteria. The risks and preventive measures for in-hospital falls of newborns were determined. Risk Priority Numbers (RPNs) were determined by multiplication of the scores of severity, probability of occurrence, and probability of detection. Analyses showed that risks having the highest RPNs were the mother with epidural anesthesia (RPN: 350 point), holding of the baby at the moment of delivery (RPN: 240), and transportation of baby right after delivery (RPN: 240). A reduction was detected in all RPNs after the application of preventive measures. Our risk model can function as a guide for obstetric clinics that need to form strategies to prevent newborn falls.