Manish Menon on India’s Pharma Innovation, AI Integration, and Future Prospects

In an exclusive interview with Manish Menon, Office Managing Principal at ZS, shares his expertise on the evolving landscape of India’s pharmaceutical industry. With a focus on digital transformation, data integration, and the rise of Global Capability Centers (GCCs), Manish provides a deep dive into how India is positioning itself as a global hub for pharmaceutical innovation. 

He discusses the challenges and opportunities within the industry, the crucial role of AI and machine learning, and the importance of building agile processes within regulatory frameworks. The conversation also explores the essential skills for professionals in the pharma sector and envisions the future of India's pharmaceutical innovation on the global stage.

Excerpts:

DQ: How has India's pharmaceutical industry evolved to become a global innovation hub?

Manish: Before we dive into it, it’s important to define what we mean by the Indian pharmaceutical industry. There are two ways to look at it. First, there’s the pharmaceutical industry with an affiliate in India focused on the Indian market. Second, there are pharmaceutical companies doing work in India through GCCs—work that still serves their global clients, not just the Indian affiliate. They are creating capabilities that can impact on a global scale.

If we focus on how the Indian pharmaceutical market is doing, where we are essentially talking about pharmaceuticals being sold in India, it’s clear that India is a huge market, but it comes with its own set of challenges. For instance, you have fragmented data sources and a dual healthcare system—both government and private health providers. In India, there is a higher propensity to use private services, especially for hospitals. The market itself is diverse, with chronic diseases like diabetes on one end and very rare or super-specialty diseases like advanced cancer cases on the other.

Given the size of the population, India offers a wide spectrum of areas where patients need help. Many Indian companies are adopting best practices from global markets and bringing them to India. Another significant aspect of the Indian market is its potential for clinical trials. Globally, there’s a big push to increase diversity in clinical trials, and places like India, the African continent, and other developing nations are ideal for this. So, India is an attractive market for both commercial and R&D activities.

On the GCC front, you might have heard that GCCs are becoming increasingly important in India. Globally, there’s a lot of confidence in setting up capabilities here that can service the global organization. It’s an exciting time for the industry.

DQ: What are the primary ways data is being utilized to drive business value in pharma? How do data silos impact the effectiveness of data collection and analysis?

Manish: In pharma, there are primarily two or three ways to collect data. First, you get data from pharmacies—information on what has been sold, for what disease, and what medication. This tells you what patients are buying. The second source is medical claims data. As you know, many of us have health insurance, whether private or through government schemes. This data provides a longitudinal view of the patient’s journey, including hospital admissions, treatments, and outcomes.

There are also other sources like clinical trial data, which falls under the R&D part of pharma. However, all these data sources in India, and most of Asia, are still siloed. For example, if a patient is part of a clinical trial and later buys a drug outside of that trial, we currently can’t link those data sets in India. In contrast, in markets like the US, technologies, and companies exist that help link these data sets in a compliant way, keeping patient consent intact. This linked data can then be used for research to identify disease patterns, treatment outcomes, and more.

While siloed data still provides value, we at ZS strongly believe that a more integrated view would allow the pharma and healthcare industries to be more coordinated in patient care. It would also lead to more meaningful and impactful research outcomes.

DQ: How can pharmaceutical companies ensure data integrity and security while leveraging new technologies?

Manish: The integration of data primarily involves two major capabilities: data management and data consumption. In data management, the focus is on bringing data together in a standardized, high-quality format that can be easily consumed in analytics. For example, when pulling data from different sources like private health insurance companies or government schemes, we standardize and harmonize the data, ensuring consistency across disease codes, product codes, and even doctor names.

Security is paramount, and it’s implemented in at least two layers. First, the infrastructure itself—whether it’s hosted on AWS, Azure, or any other platform—is highly secure. Then, within the platform, we control who has access to what data, ensuring that only authorized personnel can view or manipulate sensitive information.

On the data consumption side, a lot of work is being done with AI and machine learning models. These models predict patient outcomes, such as the likelihood of a heart attack in cardiovascular patients or a seizure in epilepsy patients. These predictive models guide the next best action, whether it’s for healthcare providers or patient support programs. The insights derived are also used in traditional dashboards and reports, helping pharma companies and GCCs make more informed decisions.

DQ: How does the adoption of AI and machine learning enhance pharmaceutical research and development?

Manish: AI, particularly generative AI (GenAI) and classical AI is playing a significant role in pharma. In R&D, GenAI is being used to consolidate and summarize existing research. For example, if you’re planning a clinical trial or developing a new protein for a disease, GenAI can sift through millions of papers and summarize the key findings, drastically reducing manual effort. Another application is in clinical trial planning and execution. AI can help identify the best hospitals for patient recruitment based on existing data, ensuring that trials are conducted efficiently.

On the commercial side, GenAI is used to enhance interactions between medical representatives and doctors, summarizing discussions and suggesting the next best action. Additionally, there’s a growing interest in multimodal analysis, where AI combines different types of data, such as X-rays and radiology notes, to make more accurate predictions. For example, AI can analyze an x-ray of a lung and predict the likelihood of lung cancer. These use cases demonstrate how AI, in its various forms, is revolutionizing the pharmaceutical industry.

DQ: How are Indian pharma companies addressing the challenges of intellectual property and patent laws? What impact do regulatory frameworks have on India's pharmaceutical innovation?

Manish: Regulatory frameworks, while often viewed as challenges, are actually enablers that ensure we do the right thing, especially in drug development. These regulations are there to protect human lives, and the Indian pharmaceutical industry is not behind global standards in adopting these frameworks. Most clinical standards are harmonized across nations, with India often aligning with US standards, considered among the best in the world.

However, there’s still room for improvement, particularly in creating a data and analytics ecosystem that is agile within these frameworks. The challenge is to make processes more flexible while adhering to regulatory guidelines, ensuring that patient care and business needs are both met effectively.

DQ: What skills are essential for data analysts and scientists in the pharma sector? What tools and technologies do data scientists in pharma rely on most?

Manish: At ZS, we look at skills in two ways: aptitude and attitude. On the aptitude side, curiosity and learnability are crucial. The ability to question data, adapt to new technologies, and think critically about outcomes is vital. On the attitude side, a strong grounding in data, a good understanding of analytics, and a willingness to learn AI/ML tools are essential.

Beyond technical skills, domain knowledge is equally important. Knowing the industry, understanding patient needs, and being able to apply data insights meaningfully can significantly enhance the impact of any data-related work.

DQ: What are the future prospects for India's pharmaceutical innovation on the global stage?

Manish: India has tremendous potential, especially with the rise of GCCs, which are becoming hubs of innovation. However, we must move beyond being just back offices and become true centers of innovation. Additionally, the startup ecosystem in health tech needs to be nurtured, as these startups will play a critical role in bringing data together and solving key healthcare challenges. Lastly, India’s diverse population offers a unique opportunity for clinical trial development, which we must continue to leverage and improve.