Mike Auerbach
Every year brings another round of headlines about pharma “drowning in data.” At this point, that’s less a warning than a permanent weather condition.
Melissa Mooney
Rich data sits across systems; actionable insight does not. Making clinical decisions on the back of mountains of collected data, without the systems to connect it all together, is like trying to understand the mood of a restaurant by looking at each patron individually.
Sajesh Parathath, Ph.D.
The history of oligonucleotide therapeutics illustrates that transformative medical progress occurs when foundational scientific breakthroughs are matched by equally robust advances in development infrastructure. Over the next decade, oligonucleotides are poised to expand their clinical footprint beyond reactive treatment and toward proactive preventive intervention for highly prevalent chronic conditions.
Mike Bruhns
Sponsors making decisions about device programs this year are setting more than just cost expectations for device procurement. They’re deciding whether devices will be a limiting factor on representative data or whether a partner who owns the full device strategy — not just the hardware — can enable broader, more diverse trials than would be possible by going directly to a manufacturer or managing it internally.
Michael Halaiko, Esq., CIPP/E, Alexandra Moylan, Esq., CIPP/US AIGP, Madison McMahan, Esq.
The national-security regulatory landscape now layers a geopolitical framework onto the data flows, sample logistics, and vendor relationships that make modern clinical research possible. This architecture will not simplify. The DSP’s country-of-concern list may expand, the BIOSECURE Act’s designated-entities list will likely grow, and state legislatures continue adding requirements.
Neelam Sharma, MS,, Lavanya Kundurthy, Hemant N. Joshi, Ph.D., MBA
This quarterly review on New Drug Applications contains data for applications approved for the first time during the second quarter of 2026, which includes New Molecular Entities (NMEs) and new biologics.
Jason Bryant, Samuel Wallis
An AI model can be right and still not be trusted. That’s what Bristol Myers Squibb (BMS) found six months into rolling out AI-powered adverse event processing: while F1 performance scores were tracking upward, the audit trail found reviewers still reopening cases the system had already cleared.
Dr. Chris Atherton
The CDMOs that bridge the fulfilment gap and truly become partners are likely the best choice for biotechs and pharma looking to develop their early-stage asset. While most CDMOs are well-versed in providing services with regular communication, sharing strategic objectives with clients, and ultimately providing the final material, it’s the “above and beyond” CDMOs that provide the real added value.
Early formulation decisions can make or break a program, yet they’re often made under significant data constraints. What are the most common mistakes sponsors make when they rush that decision, and what does it typically cost them downstream?
Sponsors sometimes optimize too narrowly for the next milestone instead of the full development path. A formulation may be sufficient for an early study but still be poorly positioned for robust scale-up, transfer, or validation. That’s where delays and added costs show up.
Leading pharmaceutical companies are predominantly applying AI to case intake and early case processing in PV, where rising Individual Case Safety Reports (ICSRs) volumes and diverse report formats have made manual handling increasingly unsustainable. AI is used to support data intake, extraction, and transformation up to the point where information can be integrated into the safety database, while downstream steps like case assessment and medical review largely remain human-led.
Gyöngyi Mezey
In large pharma, a staggering 40 to 50 percent of expert pharmacovigilance time is still consumed by tasks far removed from active, scientific PV - such as the mechanics of scheduling reports, tracking down contributors, and managing documents.
Across modalities and markets, patient-centric drug delivery is converging on three themes: self-administration at home, integration of drug delivery device platforms earlier in clinical trials, and alignment of formulations with device and human factors requirements.