PharmTech.com Regulating Innovation, Quality, and Risk eBook January 2024 Pharmaceutical Technology
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15
Regul
ations
ments, screening adverse event reports, and forecasting
the volume of incoming regulatory submissions. In the
future, both manufacturers and regulators might ben-
efit from the extensive data and analysis provided by
the expanded use of AI. International regulators have
projected using AI to detect false or misleading drug
information, scan scientific literature, identify safety
signals, and respond to public inquiries (7).
While there are potential benefits of AI, there are also
risks. Access to high-quality data is a fundamental re-
quirement for effective AI training or learning. AI can
be particularly sensitive to the characteristics of the data
used for training, testing, and validation. The process
analytical technologies providing data to AI systems
must be accurate and representative. For learning pur-
poses, data must represent not only process successes
but also process failures. It will be critical to ensure that
data used for AI training or learning are fit for use based
on quality, reliability, and representativeness.
Humans, and not machines, are ultimately respon-
sible for assuring that high-quality drugs are available
to patients. Humans must be able to interpret the infor-
mation generated by AI enough to ensure, for example,
adherence to current good manufacturing practice
requirements. Even with the best intentions, there are
risks for unintended consequences. Might some even
use AI for unethical outcomes?
Still, continued technology advancement is undeni-
able. There's already talk of an Industry 5.0 paradigm
which focuses on man and machine working together,
instead of separately, and considers the manufacturer's
impact on the workforce and environment (8). In looking
ahead, it feels only appropriate to ask the uber popular
AI chatbot, ChatGPT, how things will turn out, "Will AI
improve pharmaceutical manufacturing?" "Yes, AI has
the potential to significantly improve pharmaceutical
manufacturing."
The combined efforts of regulators, industry, and scien-
tists can now help to realize this potential while safeguard-
ing against risks to patients.
References
1. USM. Top 5 AI Use Cases in Pharma & Bio Medicine.
Usmsystems.com (accessed March 8, 2023).
2. FDA. Artificial Intelligence in Drug Manufacturing.
FDA.gov. (accessed April 25, 2023).
3. National Academies of Sciences, Engineering, and Med-
icine. Innovations in Pharmaceutical Manufacturing
on the Horizon: Technical Challenges, Regulatory Is-
sues, and Recommendations. The National Academies
Press online, DOI:10.17226/26009 (2021).
4. Kopcha, M. Beyond 'good' Practices. PharmaManufac-
turing.com (Oct. 20, 2022).
5. Arden, S. et al. Industry 4.0 for Pharmaceutical
Manufacturing: Preparing for the Smart Factories
of the future. Inter. J. Pharm. online, DOI:10.1016/j.ij-
pharm.2021.120554 (March 29, 2021).
6. FDA. Artificial Intelligence/Machine Learning As-
sisted Image Analysis for Characterizing Biotherapeu-
tics. FDA.gov. (accessed April 13, 2023).
7. International Coalition of Medicines Regulatory Au-
thorities. Horizon Scanning Assessment Report–Artificial
Intelligence (2021).
8. EC. Industry 5.0, https://research-and-innovation.ec.eu-
ropa.eu/research-area/industrial-research-and-innova-
tion/industry-50_en (accessed April 13, 2023). ■
Editor's Note: This article was previously published in Pharmaceu-
tical Technology's September 2023 issue.
Ajit Yeole,
senior director, Life Sciences
Manufacturing, Cognizant
Digital Transformation in
the Biopharma Industry
In this episode of the Drug Solutions Podcast, industry experts
discuss the challenges and advantages of the digitalization trend
in the biopharma industry, with particular emphasis on the im-
pacts to biomanufacturing. The trend toward digital transforma-
tion in biopharmaceutical manufacturing continues as advanced
technologies enter the scene and work their way through to
manufacturing operations. With the availability of technologies to
take automated biomanufacturing from preclinical to commercial
scale, the industry may start to see a wave of changeovers from
manual processes to an automated, digital environment. This
episode of the Drug Solutions Podcast will discuss the realities of
switching over from manual to digital, and what to expect.
For more podcast
episodes, visit our website!
Laks Pernenkil, principal and practice
leader, US Life Sciences Product &
Supply Operations, Deloitte
