Optimizing digitalization to accelerate your R&D

The size of the drug discovery market is expected to grow by over 9% CAGR from 2023 to 2030, reaching $120 billion by 2030. This growth is being driven by a number of factors, including the rising cost of treating an aging population that lives longer, the opportunities enabled by new advanced personalized therapies, the need to mitigate patent cliffs, and the drive to meet unmet clinical needs.

This has heightened demand across pharma and biotech organizations to innovate and launch more new products that meet an increasingly complex and segmented patient market faster and with a greater success rate. It is now common to see R&D pipelines chasing the next blockbuster therapy and tying up significant R&D investment on fewer, larger medicines. There is also a growing need to increase the efficiency of taking drugs to market so a larger portfolio of drugs with lower annual volumes can be delivered cost effectively. A key driver is to transition products from R&D into commercial manufacturing operations more smoothly and achieve optimum manufacturing output to meet patient demand and commercial success.

Drug development has made significant leaps in innovation in recent years with the introduction of personalized therapies and new treatments for previously incurable or complex diseases. The use of robotics and automation in labs has improved the quality and operational efficiency of the drug discovery and development processes. Furthermore, pharma companies have been scaling their data analytics capabilities and applying artificial intelligence (AI) and machine learning (ML) through manufacturing and operations for several years. The success of data analytics in manufacturing coupled with significant developments in data management has led pharma companies to recognize the value in leveraging this capability and applying it more extensively in R&D to create a seamless transition between R&D and manufacturing. The potential value is significant, especially if data analytics can be harnessed to improve the success rate of the drug development and improve speed and predictably of scale-up and release. The benefits of data analytics have also been recognized by regulatory bodies such as the FDA, which, with passage of the FDA Modernization Act 2.0, now allows more extensive use of computer models.

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