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So: Moderna says it was blindsided by the FDA, and Vinay Prasad, on its mRNA flu vaccine. Meanwhile, regulators are moving aggressively against Hims & Hers, and midsized biotechs have come together in solidarity — saying they could be crushed by President Trump’s drug pricing policy. Needless to say, it is not business as usual in Washington.
Out west, STAT’s Jonathan Wosen spoke with Novartis’ chief of biomedical research, who was in San Diego for the groundbreaking on a $1.1 billion research hub. She explained why company is pruning its pipeline and how it’s harnessing AI.
Hello, everyone, and welcome to the middle of the week. Congratulations on making it this far. It is an accomplishment, after all. The next step is to… keep going. And why not? Just consider the alternatives. On that optimistic note, please join us for a needed cup or three of stimulation. Our choice today is coconut rum. Meanwhile, here are some items of interest to get you going. Have a wonderful day and do drop us a line when you hear something juicy …
The U.S. Food and Drug Administration refused to review Moderna’s application for a new influenza vaccine, a surprise decision that could raise concerns about the agency’s posture toward drug companies and the Trump administration’s policies on vaccines, STAT writes. Moderna, revealing the rejection, took the unusual step of releasing the letter it had received from Vinay Prasad, who heads the FDA’s biologics division. They also issued a strongly worded statement from its chief executive officer Stephane Bancel, who said the decision “does not further our shared goal of enhancing America’s leadership in developing innovative medicines.” At the heart of the dispute is what existing influenza vaccine Moderna should have used as a control when testing the efficacy of its new shot, which utilizes the same mRNA technology the company used in its Covid-19 vaccine.
The recent moves by the Trump administration against Hims & Hers might only be the start of a crackdown on compounding, STAT explains. In recent days, the Food and Drug Administration issued a warning, the Department of Health & Human Services asked the Department of Justice to open an investigation and, meanwhile, Novo Nordisk filed a patent infringement lawsuit against the company. But while compounded weight-loss drugs proliferated during recent shortages and continued to remain available, the flurry of developments underscores growing unease among regulators with mass-marketed compounded drugs sold by national, vertically integrated telehealth platforms. The FDA has so far focused publicly on misleading marketing, but signs that it may scrutinize compounding practices themselves have the industry on edge, given how many telehealth companies rely on compounded versions of everything from acne treatments to libido drugs.
Agentic AI in healthcare is graduating from answering prompts to autonomously executing complex marketing tasks – and life sciences companies are betting their commercial strategies on it.
According to a recent report cited by Capgemini Invent, AI agents could generate up to $450 billion in economic value through revenue uplift and cost savings globally by 2028, with 69% of executives planning to deploy agents in marketing processes by year’s end.
The stakes are particularly high in pharmaceutical marketing, where sales representatives have increasingly limited face-time with healthcare professionals (HCPs) – a trend accelerated by Covid-19. The challenge isn’t just access; it’s making those rare interactions count with intelligence that’s currently trapped in data silos.
The fragmented intelligence problem
Briggs Davidson, senior director of digital, data & marketing strategy for life Sciences at Capgemini Invent, outlines a scenario that will sound familiar to anyone in pharma marketing: An HCP attends a conference where a competitor showcases promising drug results, publishes research, and shifts their prescriptions to a rival product – in a single quarter.
“In most companies, legacy IT infrastructure and data silos keep this information in disparate systems in CRM, events databases and claims data,” Davidson writes. “Chances are, none of that information was accessible to sales reps before they met with the HCP.”
The solution, according to Davidson, isn’t to connect these systems, it’s deploying agentic AI in healthcare marketing to autonomously query, synthesising and acting on unified data. Unlike conversational AI that responds to queries, agentic systems can independently execute multi-step tasks.
Instead of a data engineer building a new pipeline, an AI agent could autonomously query the CRM and claims database to answer business questions like: “Identify oncologists in the Northwest who have a 20% lower prescription volume but attended our last medical congress.”
From orchestration to autonomous execution
Davidson frames the change as moving from an “omnichannel view” – coordinating experiences in channels – to true orchestration powered by agentic AI.
In practice, this means a sales representative could have an agent assist with call and visit planning by asking: “What messages has my HCP responded to most recently?” or “Can you create a detailed intelligence brief on my HCP?”
The agentic system would compile:
Their most recent conversation with the HCP,
The HCP’s prescribing behaviour,
Thought-leaders the HCP follows,
Relevant content to share,
The HCP’s preferred outreach channels (in-person visits, emails, webinars).
More significantly, the AI agent would then create a custom call plan for each HCP based on their unified profile and recommend follow-up steps based on engagement outcomes. “Agentic AI systems are about driving action, graduating from ‘answer my prompt,’ to ‘autonomously execute my task,'” Davidson explains.
“That means evolving the sales representative mindset from asking questions to coordinating small teams of specialised agents that work together: one plans, another retrieves and checks content, a third schedules and measures, and a fourth enforces compliance guardrails – all under human oversight.”
The AI-ready data prerequisite
The operational promise hinges on what Davidson calls “AI-ready data” – standardised, accessible, complete, and trustworthy information that enables three abilities:
Faster decision making: Predictive analytics that provide near real-time alerts on what’s about to happen, letting sales representatives act proactively.
Personalisation at scale: Delivering customised experiences to thousands of HCPs simultaneously with small human teams enabled by specialised agent networks.
True marketing ROI: Moving beyond monthly historical reports to understanding which marketing activities are actively driving prescriptions.
Davidson emphasises that successful deployment starts with marketing and IT alignment on initial use cases, with stakeholders identifying KPIs that demonstrate tangible outcomes – like specific percentage increases in HCP engagement or sales representative productivity.
Critical implementation questions
The article frames agentic AI in healthcare as “not simply another technology-led ability; it’s a new operating layer for commercial teams.” But it acknowledges that “agentic AI’s full value only materialises with AI-ready data, trustworthy deployment and workflow redesign.”
What remains unaddressed is the regulatory and compliance complexity of autonomous systems querying claims databases containing prescriber behaviour, particularly under HIPAA’s minimum necessary standard. The piece also doesn’t detail actual client implementations or metrics beyond the aspirational $450B economic value projection.
For global organisations, Davidson says use cases “can and should be tailored to fit each market’s maturity for maximum ROI,” suggesting that deployment will vary in regulatory environments. The fundamental value proposition, according to Davidson, centres on bidirectional benefit: “The HCP receives directly relevant content, and the marketing teams can drive increased HCP engagement and conversion.”
Whether that vision of autonomous marketing agents coordinating in CRM, events, and claims systems becomes standard practice by 2028 – or remains constrained by data governance realities – will likely determine if life sciences achieves anything close to that $450 billion opportunity.
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Chem Biol Interact. 2026 Feb 7:111952. doi: 10.1016/j.cbi.2026.111952. Online ahead of print.
ABSTRACT
The scarcity of reliable biomarkers and predictive models for platinum resistance in lung adenocarcinoma (LUAD) poses a significant clinical challenge. This study endeavors to identify molecular subtypes related to platinum resistance and construct a robust predictive model through multi-omics techniques. We performed integrative analysis of public datasets using advanced bioinformatics strategies, including spatial transcriptome deconvolution and consensus clustering. Bulk RNA deconvolution analysis was conducted to characterize tumor microenvironment heterogeneity. Feature selection was performed using the Supervised Principal Component (SuperPC) algorithm, followed by diagnostic model construction validated through receiver operating characteristic (ROC) analysis. Functional validation was performed through cytological experiments measuring cisplatin IC50 alterations following gene manipulation in LUAD cell lines. Consensus clustering revealed distinct LUAD subtypes, with Cluster1 demonstrating significant platinum resistance. We first subtyped the patients in the bulk transcriptome data based on consistency clustering, and then analyzed the differences between different platinum-resistant subtypes (Cluster 1 and Cluster 2), so as to screen 333 isotype-specific differentially expressed genes and 15 platinum resistance-related (PRR) genes were selected through machine learning. A refined 5-gene signature (ANKRD29/CACNA2D2/DSP/HSD17B6/SPP1) achieved exceptional predictive performance (AUC=0.9639). Spatial transcriptomics demonstrated compartmentalized expression patterns: SPP1/DSP localized to tumor niches, HSD17B6/CACNA2D2 to epithelial regions, and ANKRD29 depletion in stromal areas. Cellular colocalization analysis revealed malignant epithelial PH proximity to myeloid and mast cells. Functional validation confirmed that ANKRD29/CACNA2D2 overexpression sensitized A549/DDP cells to cisplatin, while DSP/SPP1/HSD17B6 overexpression induced resistance. Experiments in nude mice have shown that these genes are closely related to cisplatin resistance in LUAD. This study identifies the Cluster1 subtype and malignant epithelial PH as crucial determinants of platinum resistance in LUAD. Our innovative 5-gene predictive model exhibits clinical-grade diagnostic accuracy, and spatial transcriptomic characterization offers mechanistic insights into the dynamics of the tumor microenvironment.
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