AI-Powered Pharma: How Artificial Intelligence is Transforming Drug Development, Trials, and Patient Care
Introduction
Artificial intelligence (AI) has reshaped pharmaceutical development at an unprecedented pace, particularly in the wake of the COVID-19 pandemic. From drug discovery to clinical trials, supply chain optimisation, and patient-centric innovation, AI is revolutionising how pharmaceutical companies operate. But what does this transformation look like in practice? To cut through the hype and focus on real-world applications, Pharma IQ spoke with Ramji Vasudevan, Head of Pharma & Life Sciences at Altimetrik, to explore the most significant AI advancements, barriers to adoption, and the future of AI-driven innovation in pharma.
AI in Drug Discovery: A New Era of Target Identification
Pharma IQ: AI adoption in pharma has accelerated significantly since the pandemic. What’s one AI-driven advancement that has personally impressed you the most in the last few years?
Ramji Vasudevan: There are two aspects to this. The most obvious one is the emergence of Generative AI, but if we focus specifically on pharma, one of the biggest breakthroughs is how AI is transforming drug discovery, particularly in novel target identification. Historically, identifying protein structures for drug development was a slow and complex process. Pre-2022, the Protein Data Bank contained about 170,000 protein structures. But with AlphaFold 2, an AI model developed by Google’s DeepMind and Isomorphic Labs, that number expanded to over 200 million protein structures. This exponential increase in available data has armed researchers with a vastly improved foundation to identify new therapeutic targets much faster than ever before.
Overcoming AI Adoption Barriers in Clinical Trials
Pharma IQ: AI is also reshaping clinical trials, particularly in adaptive designs and real-time analytics. What are the biggest barriers preventing wider adoption, and how can companies overcome them?
Ramji Vasudevan: The biggest challenge is that most pharma organisations are not AI-ready when it comes to their data infrastructure. If AI is the ‘promised land,’ then data is the highway to get there. Many pharma companies sit on treasure troves of data, but it's often siloed, unstructured, and inaccessible for AI applications. Data scientists spend anywhere from 20% to 90% of their time just cleaning and preparing data before they can even begin building AI models.
To overcome this, pharma companies need to start treating data as a reusable asset rather than just a project-based resource. Implementing centralised data platforms and adopting frameworks like the FAIR data principles (Findable, Accessible, Interoperable, and Reusable) will allow multiple teams to leverage the same datasets more efficiently, reducing duplication and unlocking AI’s full potential.
AI for Supply Chain Resilience: Predicting and Preventing Disruptions
Pharma IQ: Supply chain disruptions have been a major issue in pharma. How is AI helping companies create more resilient and predictive supply chain networks?
Ramji Vasudevan: AI plays a crucial role in optimising demand forecasting, inventory management, and logistics planning. During COVID-19, the cost of shipping pharmaceuticals skyrocketed, in some cases increasing by six times. AI can help pharma companies optimise shipping routes based on a cost-effectiveness vs. speed trade-off.
For example, some drugs have a short shelf life, meaning they must be shipped quickly, whereas others have a three-year stability window, allowing for more cost-effective shipping strategies. AI-powered scenario planning enables pharma supply chains to anticipate potential disruptions and adjust distribution strategies dynamically. In the future, we expect AI-driven process mining to further enhance supply chain efficiency by identifying bottlenecks in real time.
Personalised Medicine & AI: The Road to Patient-Centric Innovation
Pharma IQ: Patient-centric innovation is another major focus area. How do you see AI transforming personalised medicine in the coming years?
Ramji Vasudevan: One of the most exciting developments is the rise of decentralised clinical trials. Traditionally, clinical trials are hugely expensive and logistically complex, often requiring patients to travel to specific trial sites. With AI-powered wearable technology, we can now conduct clinical trials remotely, allowing participants to contribute real-world data from their own homes.
This approach not only lowers the barrier to entry for patients but also enhances diversity in clinical trials. Historically, clinical trials have been heavily skewed toward specific demographics, with underrepresentation of Asian, Hispanic, and Latin populations. By integrating AI, real-time monitoring, and wearables, we can ensure a broader, more inclusive participant pool while also improving adherence rates and patient outcomes.
Agentic AI: The Next Frontier for Pharma Innovation
Pharma IQ: The conversation around ‘Agentic AI’—AI systems capable of autonomous decision-making—is gaining traction. How do you see this evolving in pharma?
Ramji Vasudevan: Agentic AI is still in its early stages, but I expect to see initial adoption in internal processes before it’s used in clinical decision-making. Areas like software development, process mining, and procurement are prime candidates for AI automation because they don’t directly impact patient safety or regulatory compliance.
In the long term, AI-powered digital twins of human biology could simulate clinical trials virtually, helping to optimise drug formulations before even reaching Phase 1 trials. If we reach a point where an AI agent can propose phase-ready drug candidates, that would be a game-changer for pharma R&D. While this is still speculative, it’s certainly a direction the industry is moving toward.
The Future of AI in Pharma: Ensuring Equity and Accessibility
Pharma IQ: Finally, if you could predict one major AI breakthrough or trend that will define the next phase of AI in pharma, what would it be?
Ramji Vasudevan: Beyond technological advancements, I believe the next big step for AI in pharma should be about equity and accessibility. AI has the potential to lower the cost of drug discovery, increase access to clinical trials, and improve patient care in low-resource settings. The industry needs to ensure that AI-driven innovations don’t just benefit high-income countries but are leveraged to make healthcare more equitable on a global scale.
Conclusion & Next Steps
AI is no longer a futuristic concept in pharma - it’s here, and it's driving real-world change across drug discovery, clinical trials, supply chain management, and patient care. However, as Ramji highlights, successful AI adoption hinges on data readiness, operational integration, and a focus on equity and accessibility. As pharma companies continue to invest in AI capabilities, the key to long-term success will be building the right data infrastructure and ensuring that AI serves all patients, not just a select few.
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