ISPE Pharmaceutical Engineering article “AI’s Promise for ATMPs” honoured with 2022 APEX Award

Published: 29-Jul-2022

ISPE has been honoured as the winner of a 2022 "APEX Award for Publication Excellence in the Technical & Technology Writing" category for the article “AI’s Promise for ATMPs,” published in the November-December 2021 issue of Pharmaceutical Engineering

This is the third year in a row that Pharmaceutical Engineering has been recognised with an APEX Award.

The APEX Awards recognise achievement in writing, editing and graphics in a wide range of communications in non-profit and for-profit publishing and communications organisations with Awards of Excellence recognising exceptional entries in 100 subcategories and Grand Awards honouring outstanding work in 14 major categories.

AI’s Promise for ATMPs” was written by two ISPE members, William Whitford and Toni Manzano.

Whitford is a leader in research and development for biomedical and biomanufacturing applications, Industry 4.0, and digitalisation with more than 300 articles, book chapters and patents published.

Manzano has led software projects for pharmaceutical companies for more than 25 years and his current company provides big data and artificial intelligence (AI) software as a service (SaaS) platforms for the biotechnology and pharmaceutical industries.

“We are delighted to see Pharmaceutical Engineering content and authors recognised for this article, which addresses a major area of development and achievement for the pharmaceutical industry. We are honoured that PE has been recognised with an APEX Award for content for this third year in a row.”

“We have finally seen significant gains in pharmaceutical science and manufacturing operations through the application of AI. That AI’s power can apply to biopharmaceuticals is exemplified by AI’s heralded success in providing biomolecule structure prediction through, for example, Alphabet's/Google's DeepMind AlphaFold.”

“AI’s specific power in ATMPs include aiding in patient-distal autologous cell sample processing issues and supporting their continually evolving practices. The complexity and variability associated with the cellular process for patients, where in extreme cases, each batch would be related to a single patient, can only be adequately managed by AI mechanisms."

"Cytoskeletal organisation, cell morphology, and other biological characteristics can only be automated using AI to deliver the right drug at the right time to patients.”

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