APR March 2024 - 26

» FORMULATION AND DEVELOPMENT
»
alternative to more traditional treatment. While the approach entails
costly steps initially, such as high-tech diagnostic tests, the increased
accuracy of these tests means that patients may be able to avoid
further unnecessary procedures and prescriptions. Such benefits
boost the efficiency of physicians and healthcare teams by reducing
the need to adjust prescriptions and treatment plans.
Machine Learning's Role in Aiding
Precision Medicine
ML will play a role in the realization of precision medicine's potential
to move away from a one-size-fits-all approach to developing patient
treatment plans. Researchers predicted that
" the convergence
of artificial intelligence (AI) and precision medicine promises to
revolutionize healthcare. " 7
One of precision medicine's primary
benefits is its ability to use advanced computation to help physicians
identify the phenotypes of patients with unique needs and those who
might respond to treatment differently than similar patients and assist
clinicians in making care decisions based on that information.
ML's capability to handle " large and complex datasets makes them
suitable for applications in precision medicine. " 7
Using this evolving data, clinicians can put adaptive interventions
in place and ensure that treatment plans are adjusted promptly to
respond to changes in a patient's condition. ML models can also predict
patient engagement and behavior, assessing patient preferences
and adherence patterns, for instance, to predict how a patient might
respond to various interventions. This information is valuable to
clinicians who can design personalized preventive strategies and
interventions that fit specific patient preferences and motivations.
On a broader scale, healthcare systems rely on ML to analyze
population health data, uncovering trends, patterns, and areas of
concern. Understanding the risks and factors that contribute to
disease at the population level is crucial because it helps healthcare
providers implement targeted preventive measures and public
health interventions.
Clinicians use ML to analyze medical image data gleaned from MRIs,
computed tomography scans, and X-rays to detect early signs of
disease, quantify its progression, and gain insight to design more
personalized treatment plans.
Specifically, the use of Challenges and Considerations
ML can expand applications of feedback control to " direct the response
of complex biological systems predictability and automatically, "
according to the investigators, adding that this capability " paves the
way " for more sophisticated treatments that adapt to the patient's
response. Additionally, elements of control that can be improved
through ML include sensor data analysis, modeling, and methods of
reconfiguring the control algorithm " on the fly. " 8
From an efficiency standpoint, the real-time analysis of patient data
enabled by ML aids clinicians in making informed decisions in a
timelier fashion and helps better predict disease risks and outcomes,
transforming early intervention and preventive strategies. Applying
ML in drug development also accelerates the identification of potential
therapeutic targets and streamlines the drug discovery process.
ML has the potential to revolutionize early intervention and preventive
strategies. For example, ML can be a tremendous asset in devising
risk prediction and stratification approaches. ML algorithms analyze
patient data to predict patients' risk of developing specific diseases.
By stratifying individuals based on their risk profiles, clinicians can
prioritize preventive measures for those at higher risk and subsequently
facilitate early intervention to mitigate or delay the onset of disease.
ML models are proficient at identifying subtle patterns and irregularities
from diverse datasets. This capability improves clinicians' ability to
detect and diagnose diseases earlier by analyzing biomarkers, imaging
data, and other clinical information. This translates to more timely and
targeted interventions before the disease progresses.
The real-time monitoring of patient data that ML facilitates allows
healthcare professionals to identify subtle changes in health status.
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| March 2024
There are challenges associated with the adoption of precision
medicine, such as cost considerations. While precision medicine
techniques decrease the need for additional appointments and
diagnostic tests, the initial costs of genetic testing, for instance, can
be significant and might prohibit some patients from receiving more
personalized treatment.
Precision medicine is not yet widely accessible, nor are all physicians
well-versed in how to deliver more personalized treatment. These
factors could equate to limited availability for some patients, such as
those outside urban or suburban areas or without the financial resources
to travel for treatment. Given its limited accessibility, there is a lack of
standardization in precision medicine, which can make comparing
patient treatments and outcomes problematic and determining the
most effective treatment for a patient more challenging.
Ethical issues could also arise as precision medicine becomes
more commonplace. Genetic tests can divulge sensitive patient
health information that could be used by third parties to
discriminate against a patient seeking employment or insurance
coverage, for example. Compliance with evolving regulations and
standards related to the ethical use of patient data and emerging
technologies associated with precision medicine adds additional
difficulty for clinicians. Other concerns include data complexity and
the intricate and diverse datasets encompassing genetic, clinical,
and environmental factors.
There are interoperability issues to overcome to successfully integrate
data from various healthcare systems and sources, which is necessary
to create in-depth patient profiles that help inform clinicians'

APR March 2024

Table of Contents for the Digital Edition of APR March 2024

Message from the Editor
Editorial Advisory Board
BIOPHARMACEUTICALS - Getting to GMP-Quality Biotherapeutics From Today’s Bench-Scale Continuous Manufacturing Systems: A Gap Analysis
MICROBIOLOGY - Critical Behavioral Attributes and the Application of COM-B Framework in Aseptic Processing
FORMULATION AND DEVELOPMENT - Use of AUC in AAV Analysis in a GMP Setting
FORMULATION AND DEVELOPMENT - Precision Medicine in Clinical Trials: A Statistical Perspective
An Interview with Dan Smithey, PhD President & CEO, Serán
FORMULATION AND DEVELOPMENT - Still Early Days for AI in Drug Discovery...Says Who?
QC Corner - Enhancing Material and Equipment Availability in Production Isolators
BIOPHARMACEUTICALS - Technologies for Aseptic Filling: The Choice is Clear
Vendor Viewpoint - Data Integrity and Rapid Micro Methods: Transforming to a Modern Microbiology Lab
MICROBIOLOGY - Bacterial Spore Formers in Disinfectant Efficacy Testing
Partner Perspective - Nanoparticle Technologies: Enablers for Ocular Drug Delivery
DRUG DELIVERY - Your Nails and You
DRUG DEVELOPMENT - Battling Exorbitance: High Costs in Sickle Cell Gene Therapies and the Imperative of Global Patient Registries for Equity
FORMULATION AND DEVELOPMENT - How Pharma Companies Are Solving Regulatory Challenges with AI-based Technology
Event Preview - CPHI North America
Event Preview - Excipient World 2024
P.I.N. Points
Advertiser's Index
APR March 2024 - CoverTip01
APR March 2024 - CoverTip02
APR March 2024 - Cover1
APR March 2024 - Cover2
APR March 2024 - 1
APR March 2024 - 2
APR March 2024 - 3
APR March 2024 - 4
APR March 2024 - 5
APR March 2024 - Message from the Editor
APR March 2024 - Editorial Advisory Board
APR March 2024 - BIOPHARMACEUTICALS - Getting to GMP-Quality Biotherapeutics From Today’s Bench-Scale Continuous Manufacturing Systems: A Gap Analysis
APR March 2024 - 9
APR March 2024 - 10
APR March 2024 - 11
APR March 2024 - 12
APR March 2024 - 13
APR March 2024 - MICROBIOLOGY - Critical Behavioral Attributes and the Application of COM-B Framework in Aseptic Processing
APR March 2024 - 15
APR March 2024 - 16
APR March 2024 - 17
APR March 2024 - 18
APR March 2024 - FORMULATION AND DEVELOPMENT - Use of AUC in AAV Analysis in a GMP Setting
APR March 2024 - 20
APR March 2024 - 21
APR March 2024 - 22
APR March 2024 - 23
APR March 2024 - FORMULATION AND DEVELOPMENT - Precision Medicine in Clinical Trials: A Statistical Perspective
APR March 2024 - 25
APR March 2024 - 26
APR March 2024 - 27
APR March 2024 - An Interview with Dan Smithey, PhD President & CEO, Serán
APR March 2024 - 29
APR March 2024 - 30
APR March 2024 - FORMULATION AND DEVELOPMENT - Still Early Days for AI in Drug Discovery...Says Who?
APR March 2024 - 32
APR March 2024 - 33
APR March 2024 - QC Corner - Enhancing Material and Equipment Availability in Production Isolators
APR March 2024 - 35
APR March 2024 - BIOPHARMACEUTICALS - Technologies for Aseptic Filling: The Choice is Clear
APR March 2024 - 37
APR March 2024 - 38
APR March 2024 - 39
APR March 2024 - Vendor Viewpoint - Data Integrity and Rapid Micro Methods: Transforming to a Modern Microbiology Lab
APR March 2024 - 41
APR March 2024 - MICROBIOLOGY - Bacterial Spore Formers in Disinfectant Efficacy Testing
APR March 2024 - 43
APR March 2024 - Partner Perspective - Nanoparticle Technologies: Enablers for Ocular Drug Delivery
APR March 2024 - 45
APR March 2024 - 46
APR March 2024 - 47
APR March 2024 - DRUG DELIVERY - Your Nails and You
APR March 2024 - 49
APR March 2024 - 50
APR March 2024 - 51
APR March 2024 - DRUG DEVELOPMENT - Battling Exorbitance: High Costs in Sickle Cell Gene Therapies and the Imperative of Global Patient Registries for Equity
APR March 2024 - 53
APR March 2024 - 54
APR March 2024 - 55
APR March 2024 - FORMULATION AND DEVELOPMENT - How Pharma Companies Are Solving Regulatory Challenges with AI-based Technology
APR March 2024 - 57
APR March 2024 - Event Preview - CPHI North America
APR March 2024 - 59
APR March 2024 - Event Preview - Excipient World 2024
APR March 2024 - 61
APR March 2024 - P.I.N. Points
APR March 2024 - 63
APR March 2024 - Advertiser's Index
APR March 2024 - Cover3
APR March 2024 - Cover4
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