Pharmaceutical Trends Decoding Data in the Pharma Sector with Advanced Analytics

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Pharmaceutical companies are steadily adopting advanced analytics to understand their huge data sets in order to make better business decisions with greater accuracy, efficiency and speed. PROCESS Worldwide offers an insight into the application and requirements of this unique analytical technique in the pharma sector.

Numerous industries across the globe are under tremendous pressure, especially in today’s Covid-19 scenario, to make the right decisions for the growth of their business and company. However, forecasting the future through a crystal ball or reading horoscopes are not promising enough and this is where advanced analytics of data comes in.

Advanced analytics is the process through which large amounts of data are analysed and then transformed into relevant KPI’s (Key Performance Indicators) which can be followed in real-time. This allows one to predict possible future market trends, behaviour or events which assist companies to make better business decisions with greater accuracy, efficiency and speed.

The pharmaceutical industry is one sector that has adopted this data analytics approach in its processes in order to stay ahead in the market. According to a survey conducted by Accenture and IDC Health Insights in 2019, 94 % of the respondents belonging to the US and UK pharmaceutical and biotechnology sector showed strong consensus around the importance of applying advanced analytics and/or AI to data from across the organisation. In addition to this, advanced analytics could improve EBITDA (Earnings before interest, taxes, depreciation and amortisation) for pharmaceutical companies by 45 – 75 %, states a McKinsey&Company report.

Role of Advanced Analytics in the Pharma Industry

Pharmaceutical companies can utilise this data analytics method across diverse areas such as research and drug development, manufacturing & supply chain, market access, and other related segments.

• Fast Track Drug Discovery and Development

Pharmaceutical companies have to bear extremely high costs for introducing a new drug in the market. However, with advanced analytics the cost can be reduced via its predictive analytics technique as it can browse across different datasets of patents, clinical trials and scientific publications and enable firms to make intelligent decisions inorder to speed up the data discovery process.

The US pharmaceutical firm, Merck is also using Seeq’s analytics for its continuous manufacturing process. Lisa J. Graham, VP – Analytics Engineering, Seeq Corporation says, “Regulatory support and innovations in technology have fueled a transition to continuous manufacturing processes in the pharmaceutical industry which enables increased production within multi-use facilities, reduced scale-up risk, and higher quality product.”

She adds, “Advanced analytics on plant time-series data is foundational to developing process understanding, creating models and developing control strategies to support a move to continuous, complete lifecycle, manufacturing by delivering data-based decisions and thus process improvements.”

Similarly, the German pharmaceutical company Merck Group is using advanced analytics to improve the process development of its biotech medicines and is also making use of real time multivariate analysis for biotech manufacturing.

• Clinical Trials

The number of clinical trials conducted around the world has significantly increased in the past few months as pharma companies are racing to find the cure for Covid-19. The trials are again costly and time consuming; hence, it’s very important for firms to get the ideal group of patients for the trial to become a success. Advanced analytics is capable of suggesting the perfect mix of patients for the trial based on their demographics, historical data, previous clinical trials, etc.

This year, the US pharma major Pfizer partnered with Saama, an AI-powered clinical analytics cloud platform company to better understand its clinical trial models. The pharmaceutical firm utilised Saama’s Life Science Analytics Cloud (LSAC) platform to aggregate, transform, analyse, model and predict clinical data queries. With the assistance of the technology platform, Pfizer was able to identify efficiencies to improve processes and experiences for its clinical research partners.

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• Understanding Patient Behaviour

With advanced analytics, it is now possible for pharmaceutical companies to go deeper into multiple data sets and understand the behaviour of their patients. This information helps the organisations to develop related services and also reach out to their target audience across different demographics more effectively inorder to enhance the efficacy of treatment.

Last year, the French pharmaceutical company Sanofi collaborated with Google and created a new virtual healthcare innovation lab wherein it used advanced data analytics to develop new treatments, better understand its patients and diseases, increase the company’s operational efficiency, and enhance the overall experience for Sanofi’s patients and customers. In a Sanofi press release, Ameet Nathwani, M.D., Chief Digital Officer, Chief Medical Officer & Executive Vice President, Medical at Sanofi stated, “We stand on the forefront of a new age for biology and human health, with the opportunity to transform healthcare through partnerships with pioneering technology and analytics companies”.

• Personalised Treatment

Different patients respond differently to various treatments and hence, pharmaceutical firms apply analytics to data in order better understand and develop more targeted or personalised medications for patients with similar features. This enables drug companies to optimise patient care and also reduce healthcare costs for the patients. Novartis has teamed up with Microsoft to develop its AI innovation lab which aims at accelerating the discovery and development of transformative medicines for patients across the globe.

Requisites of Advanced Analytics

There are also instances where companies heavily invest in advanced analytics but fail to get the desired results despite having huge data sets. Graham explains, “The first requirement of advanced analytics to enable improved outcomes is it must be accessible and useful to the front-line decision makers and support the collaboration, knowledge capture, and workflows of these employees. Analytics must be democratised, self-service, and – even – easy to use,” She further adds three more requirements.

• Data Connectivity and Access: Time series and contextual data is typically stored in disparate source, so access to all potential data sources must be provided while at the same time honoring data governance regulations and practices.

• Iterative and Agile Processes: Production optimisation is an iterative process: advanced analytics must be able to support changing organisational priorities and context as opposed to delivering a one-time snapshot of operational status.

• Organisation Support: Having access (and enough) data, quickly generating insights, and enabling data-based decision making does not always equal execution. Leadership and employee objectives must support a move to an analytics-centric culture.

In Conclusion

With numerous plus points, the innovative analytics technique has attracted attention from major pharma companies across the globe. Exploring new as well as existing dimensions with greater efficiency and accuracy in the pharmaceutical industry has now become possible with the assistance of advanced analytics and this data science is all set to revolutionise the industry like never before.

* The author is an editor at PROCESS Worldwide. Contact: ahlam.rais@vogel.com

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