How Cloud Computing and AI are Shaping Life Sciences – with Pranav Joshi of Merck

Personalization and localization have become critical imperatives in the pharmaceutical industry. The shift from broad-spectrum therapies to precision medicine — rooted in pharmacogenomics and biomarker analysis — has demonstrated measurable improvements in treatment efficacy while reducing adverse drug reactions by up to 30%, according to a study by the Leiden University Medical Center known as  PREPARE. 

Yet pharmaceutical companies have lagged in localizing provider and clientele content, with 49% of industry leaders acknowledging a need to better utilize data for personalization, a 2024 research report from the Pharmaceutical Executive finds. 

Localization can address consumer gaps through culturally adapted patient education materials and region-specific personalization. Related compliance tasks, however, add to the already complex and time-consuming regulatory processes that most pharmaceutical companies cope with, according to an executive summary from Deloitte. 

Adapting to such circumstances requires a new framework that generative AI and advanced analytics are poised to enable. As genetic databases expand, pharmaceutical companies that master this synthesis will capture a greater share of the $869.9 billion personalized medicine market by 2030, per the Business Wire.

Emerj Editorial Director Matthew DeMello recently spoke with Pranav Joshi, Global Head of Experience Design Strategists at Merck, on the ‘AI in Business’ podcast to discuss how AI is revolutionizing life sciences spaces. Joshi focuses on drug design and patient-centric solutions in particular, outlining a vision for how AI can streamline operational burdens.

Merck, a global leader in healthcare and pharmaceuticals, focuses on developing innovative medicines, vaccines, and animal health products to improve lives worldwide. In 2023, Merck reported an annual revenue of $60.1 billion, reflecting a 1.4% increase from the previous year. With approximately 64,000 employees globally, Merck continues to drive advancements in oncology, vaccines, and other therapeutic areas.

In the following analysis of their conversation, we examine two key insights:

• Personalizing and localizing interactive tools: Leveraging generative AI and digital twins to hyper-personalize client engagement and improve medication adherence.
• Streamlining regulation compliance: Reducing procedural errors and delays by adopting advanced analytics and robotic process automation (RPA) to expedite regulatory management.

Listen to the full episode below:

Guest: Pranav Joshi, Global Head of Experience Design Strategists, Merck

Expertise: Supply Chain Management, Contract Negotiation, and Global Shipping.

Brief Recognition: Pranav Joshi has extensive experience leading user experience design and digital innovation in the pharmaceutical sector. He specializes in integrating AI and cloud technologies to bridge the gap between technical systems and the needs of patients and providers. He is instrumental in crafting Merck’s solutions that enhance operational efficiency and therapeutic outcomes.

Personalizing and Localizing Interactive Tools

Joshi begins the interview by discussing the role of generative AI at Merck in creating “digital twins” — virtual replicas of researchers that operate continuously to simulate molecule designs. 

The integration of generative AI with mRNA platforms enables researchers to simulate thousands of molecular interactions, leading to more targeted treatments and a reduction in the gap between drug discovery and patient delivery. 

Patient engagement, though, has largely clung to traditional methods of interaction, Joshi notes. The industry has missed the forest for the trees by neglecting systematic engagement challenges, such as health literacy gaps, linguistic differences, and outdated communication methods. 

The industry forfeits an estimated $1 trillion each year due to non-adherence, according to an article by Sanjay K. Rao, vice president at SRI Inc.

According to Joshi, generative AI and digital twins are being deployed at Merck to guide patients on medication usage through interactive digital tools, replacing traditional labeling methods and enhancing patient instruction.

“One company spends around $4 million annually on labeling for a single drug. What if we could eliminate labels entirely? Imagine an AI…guiding patients or nurses on how to use a particular medicine.”

– Pranav Joshi, Global Head of Experience Design Strategists at Merck

Localizing engagement strategies to tailor content to specific regions, languages, and populations will enhance patient understanding and retention, ultimately improving outcomes. 

According to Joshi, current AI-driven localization strategies include: 

• Labeling and Packaging: Translation of labeling and packaging for accessible understanding, formatting for ultra-legibility, and more precise indications of side effects
• Language Validation: Rigorous checking and re-runs to ensure translated content is accurate, consistent, and culturally appropriate 
• Digital Interactive Chatbots: Digital communication to provide more explicit medication instruction, reminders, and an accessible platform for concerns

A study sponsored by Merck corroborates its use of AI in driving engagement strategies. The study indicates that Merck used natural language processing (NLP) and AI to analyze patient experiences shared on social media platforms, allowing the company to: 

• Identify patient expectations and unmet needs
• Understand treatment expectations and miscommunications
• Predict drug-switch behavior
• Gain insights into non-adherence behaviors

Joshi also indicates that AI can be used to synthesize how drugs may interact with individual patient profiles, predicting efficacy and potential side effects. 

Streamlining Regulation Compliance

Joshi goes on to highlight how localization efforts introduce new complexities related to data management and compliance, adding to already overstretched regulation management systems. 

According to a case study by Automation Anywhere, high volumes of paperwork — amplified by up to 30 regulatory checkpoints per country — led to critical delays in Merck’s screening process and, ultimately, the product release. 

Joshi corroborates that the industry often produces significant volumes of paperwork, which can lead to substandard processing quality and subjectivity. Time-consuming documentation work took 6 – 8 months for the Merck team to process, according to Merck Head of Robotic Processing Automation Dr. Radhika Mahadev in an interview with CIO.inc. 

In light of these challenges, Joshi emphasizes the role of AI in streamlining regulatory processes, expediting processes like transfer pricing, and enhancing workflow efficiency. 

In 2021, Merck partnered with Automation Anywhere to expedite trade and regulatory compliance management via robotic process automation (RPA), as explained in the latter company’s previously cited use case documentation. 

RPA utilizes software bots to record, emulate, and interpret human actions involved in regulation compliance. Central to RPA are AI technologies, such as Intelligent Document Processing (IDP), which utilize advanced Optical Character Recognition (OCR) to extract data from both structured and unstructured regulatory documents accurately.

A review of the same case study from the publication CIO found evidence of several data inputs that Merck’s RPA system draws from: 

• Documentation Formats: Regulatory documents in various formats, such as PDFs, emails, faxes, and postal mail, are processed by bots to extract and structure data
• Regulatory Criteria: Country-specific compliance requirements — such as toxic vs. non-toxic product classifications and regulatory filing standards —  are integrated into the automation system
• Systems Data Applications: SAP, Oracle, Salesforce, and other enterprise system applications are automated to streamline compliance processes 

The CIO article claims that Merck’s automated regulatory compliance system delivered: 

• Expedited Regulatory Compliance Times: Reducing regulatory compliance schedules from 6 – 8 months to 1 month 
• Reductions in Compliance Errors: Raising accuracy in compliance documentation to 97% and a 60% reduction in turnaround time 
• Improvements in Product-to-market times: Reducing regulatory delays has boosted Merck’s ability to bring products to market faster

Beyond delivering tangible results, Joshi notes that AI can facilitate the synchronization of efforts between the public and private sectors. Multinational corporations operating across several regulatory jurisdictions, for example, may use analytics to access and process real-time updates on safety reporting requirements. In this way, AI can bridge the gap between decentralized data ecosystems and central governance imperatives.