The Indian life sciences sector increasingly sees artificial intelligence (AI) as indispensable to propelling innovation across the value chain from drug discovery and clinical development to patient care, emerging as a strategic enabler accelerating insights and facilitating integrated decision-making. A connected approach is helping organizations move from individual AI projects to enterprise-wide transformation, creating greater value for researchers, healthcare providers, and patients.
AI’s Role in Drug Discovery
AI’s potential in drug discovery is well-documented. It can help researchers analyze complex biological and chemical datasets, identify potential targets, predict molecular properties, and prioritize promising compounds for further investigation. However, according to Abhishek Rungta, founder & CEO of Indus Net Technologies, the real opportunity lies in AI augmenting scientific judgment rather than replacing it. Rather than automating the entire process, AI can provide data-driven insights that assist and enhance human decision-making.
For instance, AI can help in designing molecules with desired properties by predicting how a change in structure will affect properties like solubility, toxicity, or bioavailability. It can also aid in identifying and validating druggable targets by analyzing large datasets of genomic, proteomic, and clinical information. By doing so, AI can significantly speed up the drug discovery process, reducing the time and resources required to bring new therapies to market.
Clinical Development and AI
Once a promising candidate enters clinical development, AI can support teams in various aspects. It can help in identifying suitable trial participants by analyzing electronic health records (EHRs) and other patient data to find individuals who match the trial’s inclusion and exclusion criteria. This can significantly streamline and accelerate the patient recruitment process.
AI can also analyze clinical datasets to detect patterns associated with trial risks. For example, it can identify subjects at higher risk of adverse events or those more likely to respond to the investigational drug. By doing so, AI can help in optimizing trial design, improving patient safety, and enhancing the chances of trial success.
Generative AI, with its large language models, presents another opportunity in clinical development. It can help organize, summarize, and retrieve information across complex datasets and documentation, including clinical trial protocols, case report forms, and investigator brochures. This can significantly improve the efficiency of clinical trial operations and data management.
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However, the bigger challenge lies in connecting the life sciences data environment. AI implementation without addressing data connections can lead to further fragmentation. According to Rungta, the goal is not just building an AI application but connecting AI and data engineering with cloud infrastructure, application engineering, cybersecurity, and digital experience to enable intelligence flow across workflows.
In the context of clinical development, this could mean integrating AI with electronic data capture (EDC) systems, clinical trial management systems (CTMS), and other clinical data platforms. It could also involve establishing secure data pipelines to transfer information between these systems and ensuring that AI models can safely and efficiently access and process data.
The industry is shifting from individual AI projects to enabling connected transformation across the entire life sciences value chain. This holistic approach is necessary to fully realize the benefits of AI. From supporting information-intensive workflows in clinical development to ensuring traceability and governance in regulatory operations, and improving patient experience in enterprise operations and patient engagement, the integration of AI requires a full strategy that considers the entire data ecosystem.
For instance, in regulatory operations, AI can help in predicting inspection outcomes, identifying potential safety signals, or automating the processing of regulatory submissions. However, to do so effectively, AI systems need to be integrated with regulatory databases, submission systems, and other relevant platforms.
In patient engagement, AI can personalize treatment plans, provide health coaching, or improve medication adherence through chatbots and other interactive tools. But to provide a seamless and effective patient experience, these AI-powered tools need to be integrated with patient portals, EHRs, and other patient-facing technologies.
The challenge, as Rungta noted, is no longer simply to build an AI application but to connect AI and data engineering with other enterprise systems. This requires a deep understanding of the organization’s data setting and a strategic approach to AI implementation that considers the entire value chain. By doing so, life sciences organizations can transform their operations, enhance their decision-making, and ultimately improve patient outcomes.
