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STAT+: FDA cites Dr. Reddy’s for quality control problems at a biologics plant

STAT

Food and Drug Administration for a host of manufacturing violations at one of its plants in India, the latest instance in which the company was tagged by the regulator for quality-control problems.

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A REVIEW ON COMPUTER AIDED DRUG DESIGN (CAAD) AND IT’S IMPLICATIONS IN DRUG DISCOVERY AND DEVELOPMENT PROCESS

Pharma Tutor

A REVIEW ON COMPUTER AIDED DRUG DESIGN (CAAD) AND IT’S IMPLICATIONS IN DRUG DISCOVERY AND DEVELOPMENT PROCESS. Read more about A REVIEW ON COMPUTER AIDED DRUG DESIGN (CAAD) AND IT’S IMPLICATIONS IN DRUG DISCOVERY AND DEVELOPMENT PROCESS Log in or register to post comments About Authors. Sayyed , Ms. Wed, 07/13/2022 - 15:34.

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Timeular Review - A fun system to keep track of daily work productivity

The Digital Apothecary

This included only being available through a browser and need to tracking time after the event, lack of syncing between a mobile device and computer, data collection and reporting were only offered under a premium service, etc. This was a must, and I what I like is I can #tag actions (e.g., Ability to keep notes on activities.

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RFID: The future of smart labelling?

Pharmaceutical Technology

The pharmaceutical industry began using radio frequency identification (RFID) tags in the early 2000s. Pfizer was the first to use the tech, adding RFID tags to track a Viagra (sildenafil) shipment circa 2006. As computing has developed in the past decade, so has the potential to store and use information in the cloud.

Labelling 105
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Viagra ‘should be tested as Alzheimer’s therapy’

pharmaphorum

They used computational techniques to sift through genetic and other biologic data as well as insurance claims information from a database of more than 7 million patients, and found that sildenafil had the greatest potential as an Alzheimer’s drug.

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Using Technology for Continuous Process Verification 4.0

ISPE

For example, based on benchmarking different cases, inserting four additional data points as tags into an existing data store would require an entire week of IT and QA collaboration. needs a lot of computation power for ML model training and predictive analysis. This ad hoc strategy is rigid, expensive, and slow.