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Supply chain quality management optimal process and RACI

Supply chain quality management optimal process and RACI

These tools support sharing the optimal high-level steps to simplify a process and invite people into the discussion using a common language. The optimal process outlines the essential process steps and for each one lists the ‘who’ (i.e., which role) and the ‘how’ (i.e., what medium). The RACI supports people to understand where accountabilities and responsibilities lie.

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Accelerating multi-product biopharmaceutical manufacturing facility project lifecycle through modular design

Accelerating multi-product biopharmaceutical manufacturing facility project lifecycle through modular design

This paper uses a standardized, modular design and construction approach the benefits of a modular design approach to a multi-product viral vector (VV) facility at late-clinical/early-commercial scale, showing how significant speed benefits can be gained at each stage of facility design, procurement, construction, qualification and operation.

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Line clearance risk assessment in secondary packaging areas

Line clearance risk assessment in secondary packaging areas

This document describes the basis for performing a risk assessment for line clearance (LC) activities and failures. It contains a general description about the process and a template. This template is meant to support the documentation of the assessment of risks related to the LC process in secondary packaging lines handling pharmaceutical products and combination devices.

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Ultra high concentration formulation

This member only survey covers the challenges of working with ultra-high concentration formulations, including; low dose volume products, viscosity aspects related to manufacturing and delivery, manufacturing and filling, automated screening, analytical challenges, osmolality considerations (hyper and hypo), syringeability, acceptable injection forces.

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Data integrity challenges in the quality control (QC) labs

Data integrity challenges in the quality control (QC) labs

Data integrity is fundamental to comply with current good manufacturing practices (CGMP) and ensure patient safety. Unsurprisingly, quality control (QC) lab data are heavily scrutinized during audits as they directly support the quality, safety and efficacy of the product, and indicate the state of control of a facility. Most QC labs have worked hard to establish the fundamentals of data integrity. Many have digitized to eliminate paper, some systems have compliance features built in, and a series of mitigations and workarounds cover the rest, albeit with some inefficiencies and limitations. There have also been significant evolution of systems and business practices, rising expectations for efficient and thorough investigation capability, and the desire to go beyond compliance and get knowledge and insights through better access to data. Next steps in data integrity are key to the QC lab of the future and the digital maturity of the manufacturing capability as a whole.

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