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University of Sydney will use AI to boost cancer drug research

Pharmaceutical Technology

The university and Pharos have drawn up a memorandum of understanding (MoU) to use AI technology to identify potential compounds for the rapid development of treatments. It is focused on the development of new compounds. It will provide the drug discovery initiative access to advanced algorithms and 230 million big data entries.

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Preparing for an Inspection or Accreditation Survey – Part 2

National Association of Boards of Pharmacy

Then, walk through each step of the practice of pharmacy such as data entry; drug utilization review; compounding; final association between the drug, the prescription, and the label (eg, the dispensing act); and patient counseling.

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Crowdsourced science refines AI prediction of clinical trial outcomes

pharmaphorum

The winning team relied on “handcrafted” features that incorporated their own insights into drug development timelines and which data entries should be discarded, according to MIT. The results were interesting, but the team wanted to do better.

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How using AI in clinical trials accelerates drug development

Pharmaceutical Technology

Once the target is identified, researchers work to develop a compound that can interact with it. After a promising compound has been identified, it’s tested in preclinical models to evaluate its safety and efficacy. AI also helps in interactions with patients, as it can be used for automating patient recruitment and data collection.

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Intelligent Automation in Pharmaceutical Industry (AI & ML)

PharmaShots

Major companies use this approach to find the right compound and check its effects on patients based on biological factors. In such cases, companies may initially begin by infusing small aspects of Robotic Process Automation (RPA) into essential areas like R&D, clinical trials, and patient data entry.

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Data integrity considerations in Pharma and Life Sciences

European Pharmaceutical Review

With the proliferation of digital tools, systems and platforms, it has become difficult to ensure the integrity of data throughout its lifecycle. This challenge is further compounded by the evolving regulatory landscape, requiring stringent data integrity requirements to be met.

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Typical GMP documentation in a quality control laboratory

GMPSOP

Data management policy : This policy governs activities for data entry, storage, and retrieval to ensure the integrity of laboratory results. Record keeping policy : this policy outlines procedures for documenting laboratory activities, including data, results, and observations.