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Audit, Formation et Accompagnement de vos projets réglementés

mercredi 15 janvier 2020

WL Apollo Health And Beauty Care, Inc. MARCS-CMS 593033 — DECEMBER 23, 2019

1. Your firm failed to routinely calibrate, inspect, or check according to a written program designed to assure proper performance and to maintain adequate written records of calibration checks and inspections of automatic, mechanical, electronic equipment, or other types of equipment, including computers, used in the manufacture, processing, packing, and holding of a drug product (21 CFR 211.68(a)).
Your firm contract manufactures over-the-counter (OTC) drug products, some of which are labeled to be used for children. During a review of an out-of-specification (OOS) investigation for (b)(4) content in your bulk (b)(4) lot (b)(4), our investigator identified multiple discrepancies between the human machine interface (HMI) data, and the entries made by operators into batch records. For example, the operator recorded (b)(4) the batch during Step (b)(4) for (b)(4) at (b)(4). However, HMI data indicated that (b)(4) were not operational at that time.

iBio aims to cut manufacturing costs and improve data management with AI

iBio will use blockchain tech to cut costs and improve traceability in a deal that may also see Mateon spin-out a biopharma AI specialist.
Plant-based biologics CDMO iBio asked EdgePoint AI – a unit of Mateon Therapeutics – to install artificial intelligence (AI) technologies at its manufacturing facility in Bryan, Texas.
The idea is that the systems – known as TrustPoint Vision Fabric and TrustPoint Smart Protocols – will monitor production operations conducted and store data in a “blockchain.”
Image: iStock/Andy

Cost savings

A blockchain is a digital record of transactions that can be viewed but not edited. In a drug manufacturing plant, it can track the transfer of raw materials from one unit operation to the next.
According to iBio initial deployment will be in raw material supply chain related functions.
The system will be used to automate the tracking of materials from time of receipt to manufacturing introduction...

mercredi 8 janvier 2020

Artificial Intelligence Takes Manufacturing Efficiency to the Next Level

Nov 21, 2018
Volume 11, Issue 12
In the pharmaceutical industry, increasing price pressures are driving the need for significant and sustainable improvements in manufacturing efficiency. One of the many areas that can be targeted for efficiency gains is overall equipment effectiveness (OEE). Fortunately, Industry 4.0 tools—including sensors that can connect production equipment to data collection systems via the industrial Internet of Things (IIoT), cloud storage for large data sets, advanced analytics to make sense of the data, and software to make the data understandable and visible to those who can use it—are commercially available. 

How data is changing the pharma operations world

By Thibaut Dedeurwaerder, Daniele Iacovelli, Eoin Leydon, and Parag Patel (Mc Kinsey)

Pharma companies have a great opportunity to turn a buzzword into exponential impact.

Aircraft today can be fully developed in a digital environment. They are designed using CAD software and tested in a virtual flight simulator, before any physical work happens. Imagine the same in pharma: a COO can model various product portfolios, swap out machines, or model utilization and schedules to optimize agility and cost—all using software and delivering quantifiable answers in seconds.

Science fiction? Yes and no. The technology exists today—including predictive analytics, robotic process automation, and AI-based tools, all digitally connected via the Internet of Things (IoT)—but no pharma company has fully leveraged it. Some companies apply point solutions and individual tools, but most get stuck in the pilot phase and struggle to scale up digital across the enterprise. This approach leads to limited results that might excite the CIO but not the CEO.

Link to article - CLICK HERE


Embracing the Digital Factory for Bio/Pharma Manufacturing

New technologies enhance quality, efficiency, and flexibility.
Mar 02, 2019
Volume 43, Issue 3, pg 16–21
Modern manufacturing technologies are being adopted by pharma and biopharma companies because of the value that they can provide in improving quality, efficiency, and flexibility, as well as profitability. Replacing manual activities with automated systems can remove error and increase the speed and accuracy of activities. Examples of such technologies range from automatic data capture and electronic batch records, which can improve data integrity, to using robots, which removes the potential for human error and reduces the exposure of operators to ergonomic or safety hazards. Connecting manufacturing systems and individual pieces of equipment using the industrial Internet of things (IIoT) improves data flow, so that decisions can be made more quickly and with more information, and data analytics tools create insights that enable improvements in many areas. Whether these technologies are labeled as advanced manufacturing technologies, Industry 4.0 (1), or the digital plant, they are poised to transform bio/pharma manufacturing.

Link to document - CLICK HERE

mardi 7 janvier 2020

MDCG 2019-16 - Guidance on Cybersecurity for Medical Devices (MDR / IVDR)

The primary purpose of this document is to provide manufacturers with guidance on how to fulfil all the relevant essential requirements of Annex I to the MDR and IVDR with regard to cybersecurity.

lundi 6 janvier 2020

Impact of Data Integrity Audits on Pharma Microbial QC Labs

Most people are aware of the requirements of the code of federal regulations 21 CFR Part 11 for computer software security, which have been a major pharmaceutical IT focus for approximately 10 years.

However, within the 21 CFR 11 requirements lurked another high-risk component: “Data Integrity”. Simply put, Data Integrity (“DI”) is the assurance that data records are accurate, complete, intact and maintained within their original context so as to make the data trustworthy.

In pharmaceutical QC labs, there are often many manual steps in the performance of a routine QC analytic test to release a product (Figure 1). High risk areas were associated with the amount of human input required and how closely that input was monitored and verified.

Plus d'information ici.

Google mise sur l'IA pour lutter contre le cancer du sein




Google mise tout sur l'intelligence artificielle (IA). Le géant américain, qui s'intéresse de près au secteur de la santé, a travaillé avec des partenaires de recherche clinique au Royaume-Uni et aux Etats-Unis pour évaluer si l'intelligence artificielle pouvait être utilisée pour améliorer la détection du cancer du sein.

En collaboration avec le Centre impérial de recherche sur le cancer du Royaume-Uni, l'Université Northwestern et le Royal Surrey County Hospital, Google a créé un modèle d'IA pour la lecture des mammographies, qui sont des radiographies du sein, afin d'aider les radiologues à repérer plus précisément les signes du cancer du sein.

Selon l'American Cancer Society, les mammographies omettent environ 20 % des cancers du sein aux Etats-Unis, et les faux positifs sont fréquents, ce qui fait que les femmes sont rappelées pour d'autres examens, parfois même des biopsies.

Plus d'information ici.

ENISA : Cloud Security

In the past, organizations would buy IT equipment (hardware and/or software) and manage it themselves. Today many organizations prefer to buy IT services from an IT service provider. This trend is generally, and liberally, referred to as ‘going cloud’.
Our 2009 cloud security risk assessment is widely referred to, across EU member states, and outside the EU. Following up on this risk assessment we published an assurance framework for governing the information security risks when going cloud. This assurance framework is being used as the basis for some industry initiatives on cloud assurance. In 2011 ENISA published a report on security and resilience in government clouds...

Plus d'information ici

Nos meilleurs voeux pour 2020 !

Que cette nouvelle année vous garde en bonne santé et vous apporte le succès et le bonheur dans vos projets personnels et professionnels.

Que vos projets de mise en oeuvre et de conformité de vos systèmes informatisés se déroulent comme prévus, dans le respect des coûts et des délais et sans remarques d'inspection !


vendredi 3 janvier 2020

WARNING LETTER Cross Brands Contract Filling, LLC MARCS-CMS 589295 — DECEMBER 17, 2019

2. Your firm lacks an adequate quality control unit with adequate facilities and procedures to ensure that drugs are manufactured in compliance with CGMP regulations and meet established specifications for identity, strength, quality, and purity (21 CFR 211.22).
During the inspection, we observed that your Quality Unit (QU) did not provide adequate oversight over the manufacture of your drug products. For example, you lacked adequate written procedures describing your manufacturing operations and you failed to ensure that all batch and laboratory records are complete.
Our inspection found that your QU misrepresented results for the absence of Staphylococcus aureus and Pseudomonas aeruginosa on COA that were released to your customers. Your QU allowed distribution of these products.
Without complete laboratory and batch records, and adequate procedures, you cannot ensure the accuracy and reliability of your data.
Plus d'information ici.

WARNING LETTER GPT Pharmaceuticals Private Ltd MARCS-CMS 590938 — DECEMBER 17, 2019

3.  Your firm failed to exercise appropriate controls over computer or related systems to assure that only authorized personnel institute changes in master production and control records, or other records (21 CFR 211.68(b)).
Our investigator observed your laboratory equipment lacked appropriate controls. For example, from January 1, 2018, to June 25, 2019, audit trails from (b)(4) Agilent 1260 Infinity Series II high-performance liquid chromatography (HPLC) instruments showed a pattern of aborted runs and single run entries for testing (b)(4). Single run entries included analyses of multiple peaks or split peaks without documented investigations or adequate scientific justifications. Your employees used the Agilent Service Account login, with full administrative privileges, to abort HPLC testing runs without being attributable to a specific individual.
Your response identified the number of deleted, aborted, and single runs during your HPLC testing. However, your response did not provide adequate investigations or evidence of corrective actions put in place to prevent these data integrity issues from recurring.
Plus d'information ici

mercredi 25 décembre 2019

FDA & Data Integrity




Robert Marohn, Director of Quality Business Systems, Kite Pharmaceutical01.25.19

On December 13, 2018 the U.S. Food and Drug Administration (FDA) released the final version of its guidance released the final version of its guidance titled, “Data Integrity and Compliance with Drug CGMP: Questions and Answer.”1 The guidance is preceded by a statement released a day earlier from FDA Commissioner Scott Gottlieb, M.D., on “…the agency’s efforts to improve drug quality through data integrity and good manufacturing practice oversight.”2
The guidance and statement place an exclamation point on FDA’s focus on “ensuring data associated with drug manufacturing are complete, consistent, and accurate, and therefore reliable,” as emphasized by Commissioner Gottlieb. This article looks at the important elements of FDA’s final data integrity guidance, its impact to contract pharmaceutical organizations, and notable updates from FDA’s draft version of the guidance.

While FDA expects all data to be reliable and accurate, the introduction of the final guidance emphasizes that firms should “implement meaningful and effective strategies to manage their data integrity risks based on their process understanding and knowledge management of technologies and business models.” For more information on risk assessment and analysis, see Contract Pharma’s September 2017 feature, “Data Integrity: A Practical and Risk-Based Approach.”3

Plus d'information ici.