Medical Design Briefs - November 2024 - 22

Optimizing Biopharma Workflows
As R&D workflows are impacted by
the rise of AI, new ethical considerations
are surfacing related to data privacy,
consent, and bias. Ensuring that
AI systems are designed and implemented
ethically is crucial for maintaining
public trust and achieving equitable
healthcare outcomes. Addressing
these ethical concerns involves implementing
measures to protect patient
data, obtain informed consent, and reduce
biases in AI models.
Real-World Healthcare Applications
The successful integration of AI into
biopharmaceutical workflows has already
led to significant advancements in
healthcare. And several real-world applications
demonstrate AI's potential to
transform the industry. One of the best
recent examples stems from the severe
acute respiratory syndrome human coronavirus
(SARS-hCOV) pandemic.
During the pandemic, AI played an
important role in accelerating vaccine
development. Companies like Moderna
used AI to quickly identify mRNA
sequences that could be used in potential
vaccines. During the development
process, AI-driven simulations and predictive
models helped optimize the
vaccine candidates, which significantly
reduced the time required for development
and approval. This vaccine development
example not only showcased
AI's capability to handle vast and complex
datasets, but also highlighted its
potential to address urgent global
health crises in a timely fashion. AI
proved itself to be a game changer.
AI is also revolutionizing cancer research
by enabling the analysis of largescale
genomic data. The 'IBM Watson Oncology
Platform' uses AI to analyze patient
data and recommends personalized treatment
plans. This system has been used in
clinical settings to support oncologists
make more informed treatment decisions.
The ability of AI to process and interpret
vast amounts of genetic data allows for
more accurate and individualized approaches
for cancer treatment, which
leads to improved patient outcomes.
In addition to facilitating new drug
discoveries, AI algorithms are being used
to repurpose new uses for existing drugs.
This can significantly reduce therapeutic
development timelines and costs. For instance,
AI-driven analysis of electronic
health records (EHRs) and biomedical
22
literature mining has led to the identification
of many existing drugs with potential
efficacy against neurological diseases
like Alzheimer's and Parkinson's.
By repurposing known and approved
compounds, the biopharma industry
can expedite the delivery of effective
treatments to patients.
AI is also improving the diagnosis of
rare diseases by analyzing genetic data
and identifying
patterns
associated
with these conditions. Platforms like
Face2Gene use facial recognition technology
and AI to help clinicians diagnose
rare genetic disorders based on
facial features.
This technology expedites the diagnostic
process and enables earlier intervention,
which is critical for managing
rare diseases. AI's ability to uncover
During the
development
process, AI-driven
simulations and
predictive models
helped optimize the
vaccine candidates.
subtle genetic patterns and correlations
that humans can easily miss, enhances
the accuracy and speed of diagnoses,
offering new hope to patients
with rare conditions.
Takeaway
The integration of AI into biopharmaceutical
workflows holds immense potential
to transform the industry, enhancing
research and discovery processes, and ultimately
improving patient outcomes. By
addressing bioinformatics challenges and
leveraging AI's capabilities in drug target
identification, compound screening, predictive
modeling, and personalized medicine,
the biopharma industry can achieve
remarkable advances in drug development
and healthcare delivery.
Realizing this potential requires concerted
efforts to ensure data quality,
foster interdisciplinary collaboration,
navigate regulatory landscapes, and
www.medicaldesignbriefs.com
address the ethical considerations. As
the biopharmaceutical industry continues
to embrace AI, it's poised to unlock
new possibilities in the fight
against diseases and pave the way for a
future where personalized, effective
treatments are available to all.
The integration of AI in biopharmaceutical
workflows is not just a technological
advancement, but a paradigm
shift that promises to completely revolutionize
how we research, understand,
and treat diseases. By harnessing the
power of AI, we can accelerate the pace
of scientific discovery, improve the efficiency
of drug development, and bring
hope to millions of patients worldwide.
References
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Dermatologist-level classification of skin cancer
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can still be wrong: Adopting a prevention
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3. Cornu H. (2023) " Machine learning to identify
and prioritise drug targets, " https://www.embl.
org/news/science/machine-learning-to-identify-and-prioritise-drug-targets/
4.
Dymala K. (2023) " Revolutionizing Drug Development
Through Artificial Intelligence, Machine
Learning " Pharm Times.
5. Zhang, L., Tan, J., Han, D., Zhu, H., and Zhou, X.
(2017). " Artificial intelligence in drug design. "
Scientific Reports, 7(1), 2820.
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and Blaschke, T. (2018). " The rise of deep learning
in drug discovery. " Drug Discovery Today,
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7. Vamathevan, J., Clark, D., Czodrowski, P., et al.
(2019). " Applications of machine learning in
drug discovery and development. " Nature
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8. Walters, W. P., and Murcko, M. (2018). " Assessing
the impact of generative AI on medicinal chemistry. "
Nature Biotechnology, 36, 136-137.
9. Silver, D., Huang, A., Maddison, C. J., et al.
(2016). Mastering the game of Go with deep neural
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11. The Atomwise AIMS Program. AI is a viable alternative
to high throughput screening: a 318target
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12. Topol, E. J. (2019). High-performance medicine:
The convergence of human and artificial
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(2020). " Artificial intelligence in oncology: Path
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319(13), 1317-1318.
This article was written by Christian
Olsen, Associate VP and Industry Principal
for Biologics at Dotmatics, Boston,
MA. For more information, visit https://
go.dotmatics.com.
Medical Design Briefs, November 2024
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Medical Design Briefs - November 2024

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