APR Sept/Oct 2023 - 77

« FORMULATION AND DEVELOPMENT
Internal and external risk management: A top 10 pharmaceutical
company's product development and supply team needed a way to
improve its understanding of internal and external risk management
data to optimize the formulations, commercial supply, and postmarket
regulatory compliance of its products.
To fuel the initiative, the team developed a data lake to capture
important internal and external feeds. Internal feeds included
deviations, corrective and preventative actions (CAPAs), risks, and
response to questions (RTQs). External feeds included FDA warning
letters, biological license applications (BLA) review reports, white
papers, and industry benchmark repositories.
The team employed NLP to structure and generate this intelligence
data, extracting concepts, relationships, and sentiments embedded in
the information. The data's value to the team is further enhanced by
easy-to-understand visualizations, enabling end-users to drill down
and navigate the information. These data pipelines and workflows are
updated automatically and deliver a sustainable and scalable reporting
of the regulatory landscape, featuring key risks and recommendations
to act upon.
Semi-automated regulatory intelligence tracking: Often, compliance
teams depend on manual methods to monitor regulatory
affairs, such as having individual team members regularly perform
checks of relevant agency websites or subscribe to industry emails,
to stay up-to-date on recent guidelines, public consultations, and
meeting conclusions.
Although the process is important because it provides compliance
teams with essential intelligence to identify key concerns, deadlines,
events, and regulatory decisions for compounds of interest, it is
generally costly in terms of resources and time.
One pharmaceutical company surmounted these barriers by using
NLP to create a workflow to semi-automate information acquisition
and summaries. A key feature of the company's approach involved the
integration of NLP technology with Large Language Models (LLMs),
which served to enhance human teams' abilities and drive more
effective decision-making.
With these tools, the company used a combination of AI and human
capabilities to create a regulatory intelligence assistant, which
provided team members with user-friendly question-and-answer
access to updated regulatory information and risk categorization for
substances of interest. By employing this model, the team delivers
dynamic insights into various regulatory fields, highlighting major
areas of risk, by extracting, summarizing, and classifying information
for user-specified substances.
Access to drug labels for more effective authoring: A leading
pharmaceutical company is utilizing NLP technology to explore drug
label data efficiently. Various teams within the company, including
global labeling, regulatory affairs, medical, and safety, faced the
challenge of identifying and accessing labels and label content from
diverse sources and in multiple languages. To address this, a labeling
intelligence " hub " was implemented, incorporating FDA Drug Labels,
EMA Drug Labels, and local European databases, powered by NLP. The
tool enables users to conduct customized searches, refine results, and
export data for further analysis. Additionally, users can compare specific
labels through an interactive view and access original documents
directly. This solution streamlines the process of developing new
labels, updating existing ones, and expediting regulatory approval,
ultimately saving time for the teams involved.
NLP for identification of medicinal products and regulatory
master data management: IDMP (IDentification of Medicinal
Products) is a set of international standards, developed by the ISO,
to define the rules that uniquely identify medicinal product and the
relevant elements to identify them. IDMP is being adopted globally
by health regulatory agencies and provides a common language
to connect currently siloed data across R&D, safety and regulatory,
and supply chain systems. Many pharma companies are using IDMP
implementation to assist with master data management (MDM) across
the enterprise. But one of the key challenges is that many of the data
entities required are buried in unconnected silos of unstructured text.
A top 10 pharma consumer division used NLP to find and extract
over 140 IDMP data attributes from a range of regulatory documents
including Summary of Product Characteristics and regulatory dossiers
(eCTD sections 3.2.S and 3.2.P). The challenges are familiar to anyone
involved in IDMP compliance - varied document sets, some up to 50
years old, in mixed formats (Doc, Docx, image and text PDFs), across
different languages, integrating with SPOR vocabularies. The output
needed to be mapped to the regulatory MDM schema for their IDMP
submission and internal business use. NLP was highly effective for data
extraction and document processing, saving the team significant time
and resources.
Conclusions
Constant changes in regulations mean that companies require new
tools and solutions to assist with regulatory review and compliance,
to respond in an effective and timely manner. In some cases, meeting
the regulators' requirements is straightforward, while in other cases,
accessing the necessary data can take a significant amount of time,
money, and effort, while not necessarily increasing revenue. The
inefficient, laborious, and error-prone nature of traditional manual
search processes has led many pharmaceutical companies to use
AI-based technologies to provide relief to compliance teams. Due
to its ability to transform a wealth of internal and external data into
high-value, actionable insights, NLP is among the primary AI-based
technologies pharmaceutical companies are using to synthesize
information from many sources to deliver critical supporting evidence
for business decisions. Utilizing AI/ML tools, such as Natural Language
Processing, enables digital transformation to improve efficiencies in
regulatory disciplines. These innovative tools bring agility to regulatory
teams, enabling them to rapidly address critical business issues across
regulatory affairs.
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