The shift to intelligence driven pharma 

Pharmaceutical companies are experiencing a substantial transformation compared to how they operated in prior decades. Previously, medications were developed with an approach that targeted large populations. While this produced medications for mass distribution, there wasn’t always an ideal response from every patient. The reason is that different patients respond to medication differently due to variables like genetics, lifestyle choices, age, etc. The beginning of Artificial Intelligence (AI) is enabling pharmaceutical companies to analyse large data sets to determine how various patients may respond to the same medication. As a result, the industry is gradually moving to a more tailored approach, as evidenced by new treatments (e.g., CAR T and other targeted therapies). That is where data and AI are playing crucial role for future to set the approaches and horizons. 

Historically, AI has primarily been used for automating business processes. Increasingly, AI has become a critical component of drug discovery, development, manufacturing, and regulatory processes. As a result, scientists are making faster, more informed decisions, thus reducing the time required to complete a particular project. As a result, the attention of pharma companies is shifting from the development of general treatments to the development of treatments that focus specifically on individual patients. Also, the data driven analysis and automation guide various cross function departments to easy their task with insightful guide. 

Faster drug discovery and better formulation 

The time and expense involved with drug discovery has always been significant, but companies today can utilise AI technology to streamline chemical experimentation, clinical trials plan, execution and monitoring. For instance, usually, many pharma companies perform thousands of tests to identify viable targets for drug development. With AI, however, they have access to sophisticated computational predictive tools that allow them to determine which molecules will potentially work before ever synthesising them, resulting in reduced time spent (and costs incurred) on testing many different varieties of molecules and enabling the focus to only be placed on those that are most promising. Ultimately, the time saved on traditional testing can greatly improve your chance for success. 

Within formulation development as well, the cost associated with trial-and-error testing has also been reduced significantly using AI based tools. Using an AI system allows for the simulation of how various ingredients will perform as part of a given drug formulation, leading to quicker selection of raw materials and composition of an optimal drug combination. AI-based technology is also extremely effective in developing biologic and complex pharmaceutical products due to their inherent sensitivity and handling requirements. By having an indication of how long a given drug product will maintain its stability, AI will also allow researchers to determine appropriate storage conditions and minimise risk to patient safety using stable drug formulations. 

Smarter clinical trials, manufacturing and quality 

Clinical trials are often lengthy processes that require extensive planning and selection of appropriate participants. AI can aid in this process by helping to identify suitable participants as well as predicting their pharmacokinetic and pharmacodynamic responses; it can also facilitate better designs for clinical trials and reduce delays while improving overall success rate. However, it is important to emphasise that although AI can support clinical trial decision-making, it cannot displace clinical trials; test-based, regulatory requirements are still being reinforced by the regulators. 

In production, AI enhances operational efficiency and product quality. One of the significant areas of pharma is technology transfer; that is, the step of moving a process from the laboratory to commercial scale is extensive with potential challenges. AI can help mitigate these problems by allowing how a process will behave at a commercial scale before we initiate technology transfer; this significantly reduces the potential for error and enhances the probability of success. Furthermore, digital models (CFD/DEM, etc.) of pharma processes can be built and simulated to test various parameters before initiating any manufacturing work; again, this greatly reduces the potential for error and increases the probability of success. Additionally, predictive maintenance can be enabled through constant monitoring of machinery and equipment, allowing companies to determine the cause of equipment failure prior to actually failing. Companies are then able to limit their production downtime due to machine failures or other production-related process breakdowns; thus, increasing productivity. And, through data analysis and early detection of quality issues, AI can help assure that problems are prevented prior to occurrence rather than later. Overall, these improvements contribute to higher product quality and compliance levels. 

India’s opportunity and the road ahead 

The Indian pharma industry has a strong reputation as one of the world’s leading pharma producers with an estimated value of over $75 million. This reputation is due to India being known as one of the largest producers of generic drugs globally (approximating 40 per cent of global generic drug production), having a large number of approved manufacturing sites and also the existing large domestic and export market size of generic drugs manufactured in India; therefore these factors are establishing the foundation for a very successful future for growth within the industry.

The pharma industry is currently transitioning from Pharma 4.0 to Pharma 5.0. Pharma 4.0 primarily focuses on automation and digital technologies versus Pharma 5.0, which extends beyond automation and digital technologies, leveraging human intelligence and artificial intelligence to create new healthcare solutions that are smarter, faster, and tailored than anything we have experienced previously. 

Companies in India have already begun utilising AI technologies within their research and manufacturing processes. However, significant improvements will be needed to realise the full benefits of AI integration in the industry. These include improving both the quality and availability of data used to develop AI algorithms, investing in digital infrastructures that allow for AI research to be performed, and training people on newly developed digital technologies is essential. The final, and arguably most important, requirement for the successful integration of AI into the industry is to change and promote an accepting ‘mindset’ among the employees about their contributions to the AI development process. 

In conclusion, AI will allow for quicker, smarter, and efficient development of pharmas. AI will also allow for the development of less expensive, higher quality, and better focused products to meet the needs of patients. Companies successfully utilising appropriate AI technologies combined with appropriately designed systems and compliant processes will lead the pharma industry over the next several years. 

“By identifying and acting upon these opportunities, India will be able to make the transition from a volume-based supplier to a leader in innovation. If the right actions are taken today, India can become not only ‘the pharmacy of the world’, but also a center for personalised and cutting-edge healthcare.”