As RWE continues to influence the future of drug development, pharma companies will inevitably encounter hurdles. But with the right approach, they can be overcome to unlock critical insights and bring better therapies to patients faster.
Using RWD to understand SDOH factors faced by patients with non-small cell lung cancer paves the way for improving health equity and more effective treatments for more people.
Across the nation, state-based health insurance exchanges are searching for ways to better serve their communities while making enrollment easier, more personal, and more accessible for everyone.
AI is no panacea, but it can have a role in turning artisanal, trial-and-error drug development into a rapid, cost-effective, data-driven process.
The webinar, sponsored by PurpleLab and scheduled for May 22, will explore how healthcare sector companies can leverage real world data (RWD) with a focus on lung cancer.
Deploying a robust continuous improvement methodology is especially beneficial in the earliest phases of drug development. It can help ensure the delivery of high-quality, reliable, timely bioanalytical and clinical data to make informed decisions about either moving on to the next drug development milestone or pivoting to another strategy.
The way ahead for HEOR in biopharma should be built on increasing C-suite visibility and understanding of the value contribution that HEOR makes and demonstrating that a failure to invest in HEOR puts companies at a competitive disadvantage, especially in the face of rising regulatory and payer expectations for evidence.
In a landscape where complexity has long been the norm, the power of one lies not just in unification, but in intelligence and automation.
From the executive boardrooms of pharma companies to academic research centers, one fact is becoming unmistakably clear: making radiopharmaceuticals is an inherently complex, high-stakes endeavor, and the industry is racing to manage the risks of these perishable therapies before the clock runs out
Improving the identification and validation of disease-specific drug targets in a cell-type and patient-specific manner early on will not only reduce the failure rate and cost that is so inherent in current drug development processes but also allow the development of more effective precision medicines, improving patient outcomes.
The clinical trial process is complex, long, and always arduous. But the results of the drug development process can be amazing, new, safe, effective, life-changing, and life-enhancing therapies.
As the FDA stakes its claim as the primary regulator of AI in healthcare, it must navigate the delicate balance between fostering innovation and ensuring safety and responsibility.
In a recent interview, Esperion president and CEO, Sheldon Koenig talks about the reality of statin intolerance and his company’s expansion into new diseases
Moving from spreadsheets to automated data collection and processing is far from easy, but it could revolutionize drug development. It’s time for the biopharma industry to embrace end-to-end digitalization.
The next era for mRNA-based therapeutics and vaccines is here, and it’s time to move from an R&D mindset to a focus on long-term success with mRNA and bringing these innovative therapeutics to patients at scale.
Multiple studies have shown no correlation between R&D costs and the launch price of new drugs though that is an argument often used by drugmakers to prevent a cap on pricing. Still, one way to keep costs low, is for organizations to leverage health technology like AI and improve formulary compliance.
Government funding plays a major role in the advancement of drugs, including monoclonal antibodies, which are extremely expensive to produce and can take months to develop.
A drug development approach that makes use of hybrid AI can de-risk drug development while simultaneously removing other barriers to success. In other words, it has the power to significantly reduce the drug development timeline, and ultimately, save more lives.