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jeudi 28 mai 2020

WL Samchundang Pharm Co., Ltd. MAY 13, 2020

1. Your firm failed to establish and document the accuracy, sensitivity, specificity, and reproducibility of its test methods (21 CFR 211.165(e)).
Your firm manufactures and aseptically fills (b)(4) drug products for distribution to the U.S. You did not establish the suitability of the sterility test method used for final release testing of (b)(4) of your finished drug products. In addition, you did not determine the suitability of the in-process bioburden test performed for each of your drug products.
Suitability testing must be performed for each drug product to ensure the sterility test method is valid. Suitability testing establishes that contamination, if present, will be detected. When inhibition is encountered during suitability testing, test method modifications allow for optimized recovery...
2. Your firm failed to establish an adequate system for monitoring environmental conditions in aseptic processing areas (21 CFR 211.42(c)(10)(iv)).

You did not routinely identify isolates recovered during environmental monitoring of your aseptic processing areas where your sterile drug products are filled. Per your procedure, SOP for Microbial Identification Management (QS-508), recovered isolates are grouped according to visual morphology. From the grouping of isolates with similar morphology, only one isolate is routinely identified for species determination.
In addition, your personnel monitoring program specifies alert and action limits of three CFU/plate and four CFU/plate, respectively, for personnel working in the aseptic processing operation, including (b)(4) samples. Manufacturing personnel who perform operations in aseptic processing spaces should normally maintain contamination-free (b)(4) throughout operations. It is important to set action limits accordingly.
Inadequate environmental and personnel monitoring practices may obscure the type and level of microbiological contamination in your aseptic processing facility. Vigilant environmental and personnel monitoring provides ongoing information on the state of control of your facility. Growth observed on (b)(4) samples taken from personnel who can perform any activities within the ISO 5 areas should trigger an appropriate investigation...

WL Altaire Pharmaceuticals, Inc. MARCH 12, 2020

1. Your firm failed to establish an adequate system for monitoring environmental conditions in aseptic processing areas (21 CFR 211.42(c)(10)(iv)).
Your firm manufactures sterile ophthalmic drug products which are subject to approved FDA applications for human and veterinary drug products. Additionally, your firm manufactures sterile ophthalmic over-the-counter (OTC) and homeopathic drug products.
Our inspection revealed serious data integrity breaches and other serious violations relating to environmental and personnel monitoring.
We found that plates taken from ISO 5 areas exceeded action limits, but your firm failed to initiate investigations.
Furthermore, laboratory technicians falsified this data which is critical to maintaining an ongoing state of control in your aseptic processing facility. For instance, an environmental monitoring plate was recorded by your technician as “0” on your viable surface monitoring report form. The discarded plate was retrieved that same day and observed to contain colonies too numerous to count.
In addition, although you failed to conduct “post activity” personnel monitoring for up to a year, your technicians repeatedly recorded results of “0” on the personnel (b)(4) report form. Personnel monitoring samples are critical because they indicate whether or not personnel in the aseptic processing environment are adversely affecting quality.
Due to this lack of authentic data relating to the microbial control of personnel who perform aseptic processing operations, you lacked information that is basic to determining aseptic processing control. For up to a year, you lacked the ability to identify microbial contamination risks posed by personnel.
Your failure to reliably record data, the systemic flaws that led to these fundamental data integrity breaches, and your lack of sufficient investigations into both, raises questions regarding integrity of data throughout your operation...
2. Your firm failed to establish laboratory controls that include scientifically sound and appropriate specifications, standards, sampling plans, and test procedures designed to assure that components, drug product containers, closures, in-process materials, labeling, and drug products conform to appropriate standards of identity, strength, quality, and purity (21 CFR 211.160(b)).
You falsified laboratory data used to make batch release decisions for sterile ophthalmic drug products. In addition, you did not perform a required (b)(4) test to determine whether (b)(4) contained viable microorganisms. Your firm released these products to the market despite the lack of accurate and reliable analyses for products purporting to be sterile.
Specifically, your laboratory technicians recorded no growth (“NG”) for (b)(4) of sterility test media that were observed during the inspection to be turbid. Although (b)(4) were found to be turbid, you did not deem the product to be non-sterile or perform the additional (b)(4) step that is critical if a (b)(4) inherent turbidity issue is suspected. Regarding the latter, if a laboratory suspects that the turbidity could be of a non-microbial nature, (b)(4) is performed to ensure that such inherent turbidity is not masking microbial growth. Notably, your firm’s procedure (b)(4).
Your firm believes deficient laboratory practices relating to preparation of both of the sterility test media led to the turbid appearance of sterility test canisters. You stated that at the time of the inspection “(b)(4) was not prepared to be particulate free” and that the (b)(4) sometimes appeared with tiny black particulates.” You “determined that the source of such was overheating the glassware which burned some of the medium at the bottom of the vessels.” Your response stated that you will formalize a procedure for “when and how to perform (b)(4).”
We also note that you committed to have all products currently within expiry sterility tested by outside laboratories. Note that finished product sterility testing has limitations: it does not, on its own, establish the sterility of all units in a given batch because contamination is not normally uniformly distributed. Examples of other essential information to be evaluated along with the sterility test include data on the capability of the process to produce a sterile product, as well as data on the facility and process conditions associated with a given cycle of batch production.
In addition, the method suitability data you submitted for your contract lab, (b)(4), was insufficient. It is your responsibility to ensure contract laboratories are qualified, including but not limited to their use of methods and equipment that are validated and suitable for the analysis of your drug products...

jeudi 21 mai 2020

La mainmise des données santé en France par Microsoft inquiète Edward Snowden

Technologie : Le lanceur d'alerte le plus connu de la planète se montre critique à l'égard du choix d'hébergeur des données santé en France.
La mainmise des données santé en France par Microsoft inquiète Edward Snowden
Edward Snowden ne cache pas son indignation en apprenant l'alliance entre la future plateforme de santé française (appelée "Health Data Hub") et Microsoft, qui est devenu le premier acteur du Cloud public à recevoir la certification hébergeur de données de santé.
« Il semble que le gouvernement français capitulera face au cartel du Cloud et fournira les informations médicales du pays directement à Microsoft » a déclaré mardi soir (en français, s'il vous plaît !), le fugitif américain réfugié à Moscou sur son compte Twitter. « Pourquoi ? C'est plus simple », constate ironiquement le lanceur d'alerte.
Le projet controversé "Health Data Hub", dont l'architecte, Jean-Marc Aubert, était parti rejoindre en décembre dernier la société Iqvia spécialisée dans l'exploitation des données de santé, a connu une accélération ces dernières semaines. Cette plateforme vise à mettre à disposition des données de santé aux entités tierces opérant notamment dans le domaine de la recherche médicale. Au travers de cette plateforme, les organismes et sociétés de recherche médicale pourront accéder aux données de santé anonymisées des citoyens...

mercredi 20 mai 2020

WL Blaine Labs Inc MAY 05, 2020

1. Your firm failed to thoroughly investigate any unexplained discrepancy or failure of a batch or any of its components to meet any of its specifications, whether or not the batch has already been distributed (21 CFR 211.192).
A. Your investigations into out-of-specification (OOS) test results were inadequate. For example, you did not adequately investigate the failing viscosity test result obtained at bulk stage of Terpenicol AFC 13% topical cream lot BL2534. Although your Quality Unit (QU) was aware of the drug quality failure, no investigation of manufacturing was performed and the lot was approved for release to customers.
Inadequate investigation of viscosity failures was also cited during our November 2015 inspection...
4. Your firm failed to establish an adequate quality unit and the responsibilities, and procedures applicable to the quality control unit were not in writing and fully followed (21 CFR 211.22(a)&(d)).
Your QU did not fully exercise its authority and responsibilities. Your QU failed to ensure that you have adequate procedures and oversight of your manufacturing activities. For example, you lacked written or approved procedures describing process validation, equipment qualification, OOS investigations, stability program, and change control for your manufacturing processes.
Your firm failed to follow proper documentation practices to ensure the accuracy of your CGMP records. For example, you had multiple versions of your bulk material test report template which were not approved by your QU. In addition, your document change control report did not adequately capture all changes made to your bulk material test report template.
Further, your quality control laboratory personnel used loose paper to document raw test data, which was later transcribed onto test reports. The loose paper was not retained or reviewed, and laboratory notebooks or worksheets were not used. Laboratory personnel signed off on their own test reports without secondary verification.
Your response lacked documentation and sufficient detail to support that you are establishing appropriate operational functions, systems, programs, and related procedures to ensure product quality. You also failed to address the potential impact that your lack of quality oversight had on the quality of all drugs that you manufacture...

WL International Trading Pharm Lab Inc MARCS - APRIL 24, 2020

1. Failure of your quality unit to ensure that drugs are appropriately tested and the results are reported.
Your quality unit did not provide appropriate oversight to laboratory operations. Several chromatographic injections of samples and standards associated with an out-of-specification (OOS) investigation were not included in your investigation, reviewed by your quality unit, and communicated to your client.
For example, four (b)(4) samples tested OOS for assay on October 24, 2017. As part of the OOS investigation, they were all retested on November 11, 2017. One of the four samples you retested as part of your OOS investigation, (b)(4) sample ID (b)(4), was re-injected in
duplicate under a separate series for assay approximately 14 hours later that same day. The second data set was not captured in the analyst’s notebook, it was not included as part of your documented OOS investigation, and your quality unit (QU) was unaware of the sample re-injection.
In addition, (b)(4) sample ID (b)(4) was tested a second time with injections labeled in part “Experimental” with unknown results obtained, and also was not included as part of your official OOS investigation...
2. Failure to establish and follow written procedures for investigating critical deviations or the failure of API batches to meet specifications.
Your firm’s investigation of OOS results was closed without adequate scientific justification. For example, OOS results were obtained during testing of four (b)(4), United States Pharmacopeia (USP) samples starting on October 24, 2017. Your investigation determined there was an unknown peak co-eluting with the (b)(4) peak. However, this determination was not scientifically justified: the sample solution determined to have a co-eluting peak was approximately 15 days old when it was tested. You lack data on solution stability to show that the co-eluting peak was not caused by the age of the sample solution...
3. Failure to verify the suitability of analytical methods.
Your firm failed to ensure that methods used for analyzing drug samples had been verified as suitable for their intended use. For example, your firm conducted (b)(4) assay, (b)(4) assay, and identity A testing on multiple (b)(4) API samples following USP methods without verifying their suitability under actual conditions of use...
4. Failure to exercise sufficient controls over computerized systems to prevent unauthorized access or changes to data, and failure to have adequate controls to prevent omission of data.
Your firm lacks controls to assure the integrity of electronic test data generated by high performance liquid chromatography (HPLC) and gas chromatography (GC) systems. For example, during the inspection our investigator observed that stand-alone computers used to run an HPLC and a GC allowed analysts who test drug samples the ability to delete raw data files and alter date and time stamps. In addition, audit trails were not enabled, so there would be no way to determine whether analysts manipulated data.
Customers rely on the integrity of the laboratory data that you generate. You also need traceability of actions for investigational purposes. It is important to maintain strict control over CGMP electronic data to ensure that all additions, deletions, or modifications of information in your electronic records are authorized and appropriately documented...

mardi 19 mai 2020

EMA notice to sponsors on validation and qualification of computerised systems used in clinical trials

Note: This notice should be read in conjunction with Q8 and Q9 from the good clinical practice (GCP) Q&As published on the EMA website:
https://www.ema.europa.eu/en/human-regulatory/research-development/compliance/good-clinical-practice/qa-good-clinical-practice-gcp

Introduction:

The integrity, reliability and robustness of data generated in clinical trials, e.g. data submitted to support marketing authorisation applications (MAAs), are essential to regulators. Most clinical trial data supporting MAAs are now collected through computerised data collection tools, e.g. electronic case report forms (eCRFs) and electronic patient reported outcomes (ePROs). In addition, a wide range of computerised media and systems are used in the conduct of a trial, such as safety databases, systems for electronic interactive response technology (eIRT), clinical trial management systems (CTMSs) etc., the use of which will increase in the future.

Given recent inspection findings and the implications they have had on the integrity, reliability, robustness and acceptability of data in the context of MAAs, the GCP Inspectors Working Group (IWG) in cooperation with the Committee for Medicinal Products for Human Use (CHMP) sees the need to emphasize requirements for sponsors/vendors providing computerised systems or services as well as for the qualification and validation of computerised systems used to manage clinical trial data.

Link to document - CLICK HERE

vendredi 15 mai 2020

Data Integrity In A Cloud-Based World: Regulations & Best Practices

By Kip Wolf, Tunnell Consulting, @KipWolf
Cloud
To begin, it is important to understand that data quality and data integrity are not the same thing.
Data quality may be defined as the general utility of a data set as a function of its ability to meet the requirements for its use. This definition includes relativity that may also be explained as bias, which simply means that context is necessary to fully interpret and understand the data. Data quality is very specific to the data set and the data itself and, if measured to be poor, may be improved through verification, transformation, and/or cleanup.
Data integrity is about trust and is as much about the supporting systems and processes as it is about the data set and the data itself. Data integrity relates to the state of the data or the sensitivity of data to external influence or change...

mercredi 13 mai 2020

WL Bedfont Scientific, Ltd. - FEB 12, 2020

c. Software changes are not adequately documented following your firm’s change control procedure (b)(4). The Product/Process Change Request form FRM-01 was not initiated for the change of the firmware (b)(4) and (b)(4).
The adequacy of your firm’s response cannot be determined at this time. Your firm’s Project Manager will arrange for the appropriate documentation to be completed to ensure that a record of the change is held and will train engineers on the importance of recording and approving changes in accordance with procedures. However, documentation of these activities has not been provided for review and you have not provided a timeline for the proposed corrective actions...

mercredi 6 mai 2020

WL Shriram Institute for Industrial Research APRIL 15, 2020

1. 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(a)).
Your firm serves as a contract testing laboratory analyzing both API and drug products. Your firm had not enabled the audit trail function on high-performance liquid chromatography (HPLC) units until on or about October 11, 2019, when this FDA inspection was announced.Your analyst acknowledged during the inspection that the audit trail function on the HPLCs units was not enabled until October 2019. This was a repeat observation of your August 2016 FDA inspection.
Despite written commitments after that inspection to install audit trails, you failed to enable audit trail functions on multiple analytical instruments, including your HPLC units.
Customers rely on the integrity of the laboratory data that you generate to make decisions regarding drug quality. It is important to maintain strict control over CGMP electronic data to ensure that all additions, deletions, or modifications of information in your electronic records are authorized and appropriately documented....



lundi 4 mai 2020

Bioprocessing 4.0 Accelerates Biological R&D Using Computer-Aided Biology

Computer-aided biology describes a growing ecosystem of tools that augment human capabilities in the laboratory. In this report we give two case study examples of how computer-aided biology has transformed industrial gene therapy bioprocessing. In this Special Report, the authors describe how Synthace’s Antha cloud-based software platform has enabled industrial collaborators Oxford Biomedica and the Cell and Gene Therapy Catapult to harness the power of Bioprocessing 4.0 by:

  • incorporating new process analytical technologies (PAT), such as Raman Spectroscopy, into their unit operations
  • automating the upload, collation, organization, structuring, processing, visualization, and analysis of large bioprocess datasets from various sources
  • precluding the need for data wrangling and reducing the time from data generation to high-value bioprocess insight from weeks to minutes.

vendredi 1 mai 2020

Increasing Transparency And Confidence Through Real-Time Data Sharing

By John Chapin, Senior Automation Engineer, and John Morse, QA Lead, Strategic Growth Investments and Engineering, Lonza Biologics
DNA digital health network (002)
Three industrial revolutions catalyzed by steam, electricity, and the computer, respectively, have occurred over the course of history and drastically changed the landscape of how goods are manufactured. The next transformation taking shape is being dubbed as Industry 4.0, which is the digitization of manufacturing utilizing data, machine learning, and artificial intelligence. Interconnectivity, real-time data sharing, and automation are used to increase transparency across an organization’s people, production line, and in the supply chain, leading to increased efficiency in manufacturing. This movement is fueled by more informed, and eventually autonomous, decision making as automation spreads throughout the business functions. A digitally connected plant is required for this transformation to occur.

mercredi 22 avril 2020

Google veut faciliter l'analyse des données sur la santé dans le cloud

Google a étendu la disponibilité de son API "Cloud Healthcare" dans le but d'améliorer l'interopérabilité des soins de santé et d'aider les fournisseurs à obtenir des informations à partir d'une myriade de sources de données médicales.
L'API Cloud Healthcare de Google permet aux organismes de santé de collecter et de gérer différents types de données médicales via le cloud, notamment les normes DICOM (Digital Imaging and Communications in Medicine), Health Level 7 et Fast Healthcare interoperability Resource. Ces données peuvent être alimentées par des programmes d'analyse et de machine learning afin que les prestataires de soins de santé puissent identifier des modèles qui pourraient les aider à améliorer les soins aux patients.

Implementing Data Quality By Design For Improved Data Integrity

By Kip Wolf, Tunnell Consulting, @KipWolf
puzzle
To begin, it is important to understand that data quality and data integrity are not the same thing.
Data quality may be defined as the general utility of a data set as a function of its ability to meet the requirements for its use. This definition includes relativity that may also be explained as bias, which simply means that context is necessary to fully interpret and understand the data. Data quality is very specific to the data set and the data itself and, if measured to be poor, may be improved through verification, transformation, and/or cleanup.
Data integrity is about trust and is as much about the supporting systems and processes as it is about the data set and the data itself. Data integrity relates to the state of the data or the sensitivity of data to external influence or change...