How AI is Revamping the Product Launch

Ken McLaren
Dat and AI partner
Frazier Healthcare Partners

Commercializing new drug therapies has never been more critical–and complex. With one-third of drug launches between 2012 and 2021 missing their stated goals¹–and patent expirations increasing to 4-6% of industry revenues², up from 1-2%³–product launch teams are eager to try any and all novel approaches to optimizing launch success.

Traditionally the Awareness, Trial, and Usage (ATU) study provided foundational insights from which a myriad of launch activities evolve. ATUs provide fresh data on a wide range of elements including the physicians’ indications for prescribing, barriers to adoption, critical early patient experiences, competitive positioning, and message optimization.

However in today’s highly competitive environment, teams can’t wait the four to six weeks required for an updated ATU while decisions need to be adjusted daily.

AI: The Great Launch Enabler

Artificial Intelligence (AI) is transforming how organizations work and stay ahead of fast-changing therapeutic areas. AI can streamline workflows, automate repetitive tasks, and expedite prescriber and patient access, in ways that help teams better navigate to a successful launch.

Whether it’s analytic AI, where algorithms classify data more efficiently–or generative AI (GenAI), where large language models (LLMs) trained on data perform near-level human language tasks at scale–in a world where time is money, applications of AI and ML have the potential to save both, in impressive quantity.

Streamlining Market Insights

AI is already expediting multiple steps in the product launch process. In the market insights field in particular, AI empowers teams to make needed launch decisions sooner, respond in an agile manner, and streamline the path to revenue.

Monitoring brand perceptions. When executives review weekly Rx data and note a surprise, their teams can ask questions on the fly of prescribers and KOLs, to explore possible dimensions behind a change in prescribing pattern that may impact the debut of a first-in-class treatment. They can use new GenAI tools to seamlessly sift through the noise for signals that help launch teams find perceptual shifts and respond fast.

Smarter prescriber and KOL sampling. Brand managers establish target prescribers by specifying HCP credentials, caseloads, and patient indications. Proprietary machine learning (ML) algorithms can match these best-fit target prescribers to the insights needed for the launch. Using AI for smarter sampling in a recent project meant the typical 6- 8-week ATU delivery cycle was reduced to 24 to 48 hours–permitting 50% fewer survey invites to be sent while reducing median survey fielding duration by 20%.

Joe Baldini
Chief technology officer
Apollo Intelligence

Adverse event (AE) reporting. When market insights efforts uncover potential adverse events (AEs), pharmas are mandated to report them to the FDA. LLMs can perform a first-pass analysis of open-text verbatim survey answers that appear to indicate a possible patient AE. A human analyst then can review and determine potential AE candidates for follow-on reporting.

Real World Data (RWD) integration. Pharmas often treat ongoing data projects and launch-specific market insights as silos with separate budgets. Technology makes it far easier to combine de-identified, HIPAA compliant data from databases, web logs or other sources, with primary market research data, such as payer or prescriber perceptions. Doing this connects the “What” with the “Why,” helping decisionmakers better understand and engage a target community strategically, with tactics such as messaging development.

Prescriber and patient engagement. AI can empower a chat agent to hold a more natural dialogue with a site visitor, while providing vital information for marketers. For example, an LLM can help define information that is offered to the HCP in various scenarios for a given drug. Analysis of the HCP’s decision pathways can allow pharma marketers to understand which factors the HCPs prioritize in their decision-making process.

The Agility to Outperform

Powered by AI, the cross-functional launch team has the agility to be more responsive to market conditions. It can see beyond the data to spot trends that otherwise may be invisible – and respond faster to help deliver a competitive advantage.

Joe Baldini is chief technology officer at Apollo Intelligence. Ken McLaren is data and AI partner at Frazier Healthcare Partners.

Sources

1. “Drug Launches Reflect Overall Company Performance,” Deloitte Center for Health Solutions, August 25, 2023, as posted at https://www2.deloitte.com/us/en/insights/industry/health-care/key-factors-for-successful-drug-launch.html
2. “Worldwide Prescription Drug Revenue – Market Risk from Patent Expiration, 2023-2028,” Matej Mikulic, Statista, October 6, 2023, as posted at https://www.statista.com/statistics/309407/global-prescription-drug-revenue-percentage-at-risk-due-to-patent-expiration/
3. https://www.pharmavoice.com/news/drug-launches-jim-lang-commercialization-trends/717714/