Advancements in the technologies behind Data, Artificial
Intelligence (AI) and Machine learning have had an impact on every industry, as
our reliance on them grows ever larger. This rings true for Life Sciences, however
the take up in this industry has been somewhat slower than in others (Finance,
Marketing etc.). Here, we look at these areas and how greatly they could, and
in some cases are, revolutionising the approach to Life Sciences.
Data:
Pharmaceutical and Biopharmaceutical companies inherently
accrue and collate a massive amount of data, in the forms of research and
development results, patient records etc. This data requires a massive amount
of storage and resources to manage, but also presents a huge opportunity for
Life Sciences companies.
The evolution of this “Big Data” means that there are
petabytes of EMRs (Electronic Medical Records) providing huge sets of data ready
for mining. After the initial and crucial data
centralisation (the process of collating records into a “machine-readable”
format for analysis), this data can be used by companies to “gain insights…
into how their therapies are performing at a more granular level” (Forbes).
This analysis of patient symptoms, genealogies, reactions etc. can help provide
accurate insights into the efficacy of a medicine, making it easier for
companies to justify the worth and cost of their product. This could prove a
crucial time advantage in the increasingly saturated and competitive
pharmaceuticals market.
AI & Machine learning:
Once centralised, this data needs analysing. AI and Machine
learning provide a way of gaining previously unattainable insights, trends and
analyses from large sets of data. It can also be done using a fraction of the
time and resources as a manual process would – compare clicking a button and
receiving results instantly, to a group of people trawling through thousands of
documents. These new techniques for analysis can be applied to several areas
within Life Sciences, including:
·
Accelerating
development process – faster attained, actionable results will help speed
up processes such as R&D and clinical trials. This means the (sometimes 10+
year) process of getting a drug product to market can benefit from some
much-needed time saved. Being able to analyse data quickly within multiple
parameters will make process areas such as compliance, quality, risk management
and manufacturing .
· Optimising spend
and pricing
– identifying trends in processes will have a massive impact on quality and
compliance and can therefore be used to cut costs and save money. Analysing
errors and wastage will mean better yields in manufacturing as well as less
wastage in the manual processes. Additionally, machine learning can be utilised
in areas outside of production to make processes leaner, such as sales &
marketing or finance. Predictive analytics and trend analysis can also be used
in setting pricing models for products, predicting consumer spending habits through
predictive modelling.
·
Patient
suitability studies – having thousands of data points per patient analysed
as efficiently as possible will mean that choosing suitable patients for clinical
trials becomes a much easier and more accurate process. Having a large set of
EMRs studied and judged for feasibility at speed and with less errors than a
manual process will mean a faster procedure with less risk of harmful
unforeseen errors.
Data, AI and Machine learning have the potential to
completely change how the life
sciences industry conduct research and develop their products, causing the
same level of disruption and progression as the discovery of molecular biology
in the ‘70’s and the Human Genome project in the ‘90’s. Life sciences and
pharmaceuticals are no strangers to rapid growth and development however, and
are already starting to embrace all that these new technologies have to offer.
Find out more about our latest Life Sciences jobs or contact us for more information on any of our vacancies.
