How Are Pharmaceutical Companies Leveraging Big Data and Predictive Analytics?

ucjournals

1 year ago

Global Research Journal of Pharmaceutical and Drug Discovery
Volume 1, Issue 2, May 2023, Pages: 20-22
Received: May 17, 2023; Reviewed: May 19, 2023; Accepted: May 22, 2023; Published: May 29, 2023

Author: Mrs. Sanobar Syed, Associate Director, BeiGene

How Are Pharmaceutical Companies Leveraging Big Data and Predictive Analytics?
The pharma industry is still in the early stages of its digital transformation journey. Most companies
have implemented some automation and online or at-line process and data analytics but have not yet
reached a point where those systems communicate with one another. There are companies which have
specialized teams and budgets set aside to increase the digital transformation. There are also firms
which run on very tight and limited budget and vision to be truly digital. These companies might have
eliminated paper-based records but not data silos. The widespread use of technologies, especially AI or
big data analytics, predictive analytics in a connected and integrated manner is yet to occur. This is also
an area where companies are learning to be more open and agile.
The pharmaceutical industry is running to be at the forefront of predictive analytics. Pharmaceutical
companies use advanced machine learning algorithms along with vast amounts of raw data to generate
predictive models. These algorithms and mathematical equations crunch data across a variety of
different variables or factors to forecast future outcomes, such as what’s the probability that certain
drugs will fail in the research and development phase, which patient characteristics are likely to lead to
adverse reactions when taking this drug, how quickly should we be able to produce medications before
they expire? Deep diving into commercial applications of Predictive analytics as see below we can see
the humongous transformation capabilities this field brings into a pharma companies’ success.

Drug Distribution and Sales
Using advanced forecasting and predictive analytics models, pharma companies can forecast potential
drug sales accurately. This allows for a more efficient distribution process overall through improved
inventory management – which also helps reduce both the risks of over-stocking and having too few
drugs available to meet demand. These mathematical models are used alongside historical data points
about past sales trends in various regions by different customer segments (e.g., hospitals vs.
pharmacies) and any other relevant variables that could contribute to future sales forecasts.
Predictive analytics can also help pharmaceutical companies identify and prioritize new sales
opportunities. This is particularly useful in identifying the most profitable customer segments for a
particular drug or medication, which could allow an organization to focus more on those with better
potential for return on investment.

Pharmaceutical Business Development, Sales and Marketing
In today’s world, pharmaceutical companies need to be as efficient as possible in advertising and
marketing their products. This means that they must rely heavily on predictive analytics models during
the early stages of product development – from identifying those patient populations most likely to
benefit from a new drug all the way through every step of its life cycle. Customer Relationship
Management (CRM) is also an essential aspect for any pharma company looking to improve customer
relations while better understanding how customers interact with various aspects of their business
operations throughout each phase or touchpoint within the overall sales process. Predictive modeling
coupled with advanced analytics can track this information throughout the entire customer experience
via numerous channels such as email campaigns, online ads, call center interactions, and more – giving
pharma companies the insights needed to market their products properly.

Batch manufacturing with Forecasting Models of Drugs Demand
Attaining a constant, full production is the goal of any manufacturing company in all situations.
However, in the case of pharma companies where demand for particular active pharmaceutical
ingredients or final therapeutics may go up or down significantly over time, achieving this objective
becomes difficult. In such cases, it is advisable to plan batch production with forecasting models to
calculate expected sales volume and help determine whether each proposed formula should be put into
production and when.
Predictions of drug demand can be made by using statistical models that take into account past orders
data, the number of patients suffering from a particular disease, prevalence and spread of this condition
within the target population, existing treatment options on the market, cost-effectiveness evaluations
for new drugs compared to other available therapies as well as their patent status. In cases where even
one factor deviates from its historical trend or some unforeseeable event such as a pandemic ( COVID-
19) or outbreak, it becomes challenging to predict future sales volume. However, when several factors
are taken under consideration at once and combined with additional big data about the company’s past
performance in managing production capacity for different products depending on actual demand
forecasts, more accurate predictions can be obtained, which will allow companies to optimize inventory
levels and reduce risk of suboptimal production. Predictions can be made for each product separately,
or they may concern the whole portfolio, which provides companies with an opportunity to optimize
their investment decisions by using different scenarios and anticipating possible market changes.
Forecasting models are also used in cases where various business objectives need to be fulfilled at once,
such as ensuring that margins remain stable over time, maximizing profit margin on new drugs while
taking into account cost-effectiveness analysis, or even just meeting demand without any delay when
required quantities cannot be produced fast enough due to some unforeseen circumstances.

The Future of Pharmaceutical Manufacturing is Predictive and Inclusive
Predictive analytics offers pharmaceutical manufacturers many benefits that directly impact the bottom
line, including increased yields, reduced waste and rework, better utilization of equipment and
workforce, and improved customer satisfaction and, therefore, revenues.
In short, small molecules are still a big deal – and will be for the foreseeable future. CDMOs with
specialized technologies and expertise will continue to help pharma companies advance their
compounds. The ability to tailor services to specific customers and drug programs will play an
increasingly important role in accelerating patient access to innovative drugs.Author Bio-
Sanobar Syed has over 14 years of proven achievements in establishing and leading business strategy
and forecasting in leading global pharmaceutical firms. She is considered a subject matter expert,
delivers speaker presentations in reputed conferences, guest lectures & has developed academic
modules in this field. By education she is Masters in Organic Chemistry and MBA (Marketing).

This abstract of Manuscript/Paper/Article is an open access Manuscript/Paper/Article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which allows and permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited and accepted.

This communication and any documents, or files, attached to it, constitute an electronic communication within the scope of the Electronic Communication Privacy Act (https://it.ojp.gov/PrivacyLiberty/authorities/statutes/1285)

To citation of this article: Mrs. Sanobar Syed, How Are Pharmaceutical Companies Leveraging Big Data and Predictive Analytics?, Global Research Journal of Pharmaceutical and Drug Discovery

References:

See, H.-Q., Chan, J.-N., Ling, S.-J., Gan, S.-C., Leong, C.-O., & Mai, C.-W. (2018). Advancing Pharmacy Service using Big Data – Are We Fully Utilising the Big Data’s Potential Yet?. Journal of Pharmacy & Pharmaceutical Sciences, 21(1), 217–221.
Chauhan, R., Jangade, R.: A robust model for big healthcare data analytics. In: International Conference-Cloud System and Big Data Engineering IEEE@2016
Predictive Analytics Techniques Using Big Data for Healthcare Databases, Manjula Sanjay Koti & B. H. Alamma, DOI: 10.1007/978-981-13-1927-3_71
Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 3 (2014)
https://www.iqpc.com/media/1001534/35903.pdf
https://intone.com/big-data-analytics-in-pharmaceutical-industry/