MIT CAACB Risk Assessment Case Study: Assessing Virus Cross-Contamination Risk between Two Simultaneous Processes in an Open Biomanufacturing Facility.

Some members of MIT's Consortium on Adventitious Agent Contamination in Biomanufacturing (CAACB) previously published content on the "Quality Risk Management in the Context of Viral Contamination", which described tools, procedures, and methodologies for assessing and managing the risk of a potential virus contamination in cell culture processes. To address the growing industry interest in moving manufacturing toward open ballrooms with functionally closed systems and to demonstrate how the ideas of risk management can be leveraged to perform a risk assessment, CAACB conducted a case study exercise of these new manufacturing modalities. In the case study exercise, a cross-functional team composed of personnel from many of CAACB's industry membership collaboratively assessed the risks of viral cross-contamination between a human and non-human host cell system in an open manufacturing facility. This open manufacturing facility had no walls to provide architectural separation of two processes occurring simultaneously, specifically a recombinant protein perfusion cell culture process using the human cell line, HEK-293 (Process 1) and a downstream postviral filtration unit operation (Process 2) of a recombinant protein produced in CHO cells. This viral risk assessment focused on cross-contamination of the Process 2 filtration unit operation after the Process 1 perfusion bioreactor was contaminated with a virus that went undetected. The workflow for quality risk management that is recommended by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) was followed, which included identifying and mapping the manufacturing process, defining the risk question, risk evaluation, and risk control. The case study includes a completed Failure Mode and Effects Analysis (FMEA) to provide descriptions of the specific risks and corresponding recommended risk reduction actions.

An application for relating Legionella shower water monitoring results to estimated health outcomes.

Exposure models are useful tools for relating environmental monitoring data to expected health outcomes. The objective of this study was to (1) compare two Legionella shower exposure models, and (2) develop a risk calculator tool for relating environmental monitoring data to estimated Legionella infection risks and Legionnaires' Disease (LD) illness risks. Legionella infection risks for a single shower event were compared using two shower Legionella exposure models. These models varied in their description of partitioning of Legionella in aerosols and aerosol deposition in the lung, where Model 1 had larger and fewer aerosol ranges than Model 2. Model 2 described conventional vs. water efficient showers separately, while Model 1 described exposure for an unspecified shower type (did not describe it as conventional or water efficient). A Monte Carlo approach was used to account for variability and uncertainty in these aerosolization and deposition parameters, Legionella concentrations, and the dose-response parameter. Methods for relating infection risks to illness risks accounting for demographic differences were used to inform the risk calculator web application ("app"). Model 2 consistently estimated higher infection risks than Model 1 for the same Legionella concentration in water and estimated deposited doses with less variability. For a 7.8-min shower with a Legionella concentration of 0.1 CFU/mL, the average infection risks estimated using Model 2 were 4.8 × 10-6 (SD=3.0 × 10-6) (conventional shower) and 2.3 × 10-6 (SD=1.7 × 10-6) (water efficient). Average infection risk estimated by Model 1 was 1.1 × 10-6 (SD=9.7 × 10-7). Model 2 was used for app development due to more conservative risk estimates and less variability in estimated dose. While multiple Legionella shower models are available for quantitative microbial risk assessments (QMRAs), they may yield notably different infection risks for the same environmental microbial concentration. Model comparisons will inform decisions regarding their integration with risk assessment tools. The development of risk calculator tools for relating environmental microbiology data to infection risks will increase the impact of exposure models for informing water treatment decisions and achieving risk targets.

Three-Dimensional Printing for Planning of Structural Heart Interventions.

Three-dimensional (3D) printing is a process leading to the creation of a physical 3D model used for teaching, patient education, device evaluation, and procedural planning. 3D printed models of patient-specific anatomy can be generated from 3D transesophageal, cardiac MRI, or cardiac computed tomographic datasets. This article discusses the potential advantages of 3D printing, reviews the different modalities to acquire a 3D dataset, and highlights the application of 3D printing to enhance patient screening and procedural planning in structural heart intervention.