Sanobar Syed
University of Central Florida, Canada
Title: Is Pharmaceuticals Ready for the Rapid Increase in Commercial Analytics and Forecasting?
Biography
Biography: Sanobar Syed
Abstract
“Difficult to see. Always in motion is the future.” – Yoda (Star Wars)
As Cryptocurrency is to Fintech so is Bigdata in pharma.
Some call it the Fourth Revolution combining Data and Science together.
AI is shaping the future of pharmaceuticals and how.
According to Global Data, global AI revenues in the pharmaceutical, medical, and healthcare sectors are expected to reach almost $21 billion by 2025.
AI has entrenched in pharmaceuticals drug discovery and clinical stage and how. Top companies such as AbbVie, Novartis, Pfizer, Sanofi, GSK, AstraZeneca and the likes are either collaborating with the AI companies or acquiring the AI technologies. Therefore, the heavy investments by the top healthcare companies are exponentially fueling the growth of the global AI in the pharmaceutical market. The AI in the pharmaceutical market witnessed a sudden spike in 2019-2020 owing to the increased investments in the AI for discovering the drugs for the COVID-19 disease.
Drug discovery is a time-consuming process but with the implementation of the AI in the drug discovery procedure, the drug discovery method can be boosted, and time and cost can be significantly reduced. This has fostered the growth of this segment. Clinical trial is expected to be the fastest-growing segment during the forecast period. The increased drug discovery activities are resulting in the rising number of the clinical trials, which fosters the demand for the AI in the clinical trials.
Digital adoption and transformation enabled by AI and machine learning is affecting virtually every aspect of the value chain across geographies. AI is applied to big data to reshape business models, streamline biopharma manufacturing, and enhance everything from clinical research to supply chain & inventory management to KOL intelligence. For Oncology, Rare diseases and Cell & gene companies it is proving to be a boon to develop more personalized and authentic medicines, engagements across the key stakeholders namely health care professionals, patients, and policy makers.
The Pharmaceutical future will require a greater understanding and interpretation of available information from multiple sources including electronic health records, digital and big data sources. The pipeline of potential oncology and rare disease products continues to grow significantly and holds great promise for novel interventions due to advances in clinical trial design and data analyses. Expanding diagnostic procedures with improved sequencing methods will speed up the diagnosis for these critical diseases. There is a huge development of predictive analytics algorithms in forecasting in these areas. The clinical side of the pharmaceutical industry has lapped the usage of AI to predict the next blockbuster. Pharma investment in AI grew from less than $1 billion in 2015 to more than $7 billion in 2021, according to a report from life sciences consultancy McKinsey & Company.
But what about AI in commercial functions? Are we still ahead or lagging? Can AI predict the causes of the increase or decrease in market demand? If AI software sees the data for 2020 and 2021 chances are that it might predict the sales to go down as the market has never seen that volatility in a long time. This is certainly not the case as those years were the COVID years and we all know that market is bouncing back to the same normalcy. With that there is certainly a manual “forecaster” override needed.
With more and more advent and usage of technology in the pharmaceutical industry, one of the challenges remains as to “are we really technology ready yet “or “are we still north of the preparedness”?
These are some of the valid challenges faced by the pharmaceutical world which is still warming upto the usage of AI & machine learning in commercial functions like marketing, forecasting and business analytics.
The promise of smarter, faster, and sharper insights produced by big data is lucrative but there we are still have miles to conquer.