Although the past three decades have seen significant increases in the cost and time associated with delivering new drugs for clinical use, efforts to increase the number of molecules arriving through the drug development pipeline have been underwhelming. But what if the solution has been hiding in plain sight?

New approaches to accelerating and refining the development of therapeutics are constantly being trialled across the globe. Here, we explore three different ways in which machine learning is being integrated into drug delivery and development.

The study highlighted the benefits of combining computational workflows with experimental validation for the rational engineering of desirable crystal habits, demonstrating how the collaboration is transforming pharmaceutical development and positively impacting patients' lives.

Where is the formulation and delivery of mRNA vaccines heading in the years after the pandemic?

The future of AI in drug discovery and development looks promising. However, pharma’s big players need to ensure they’re prepared to deploy AI-capable workforces in the coming years.

Inhaled biologics are being evaluated as an alternative to injections for treating rare and serious diseases. Experts touch on several challenges including making stable and safe formulations of inhaled biologics.

Key regulatory considerations for the application of AI to pharmaceutical manufacturing were outlined by the Center for Drug Evaluation and Research.

Here, we explore three emerging technologies and the ways in which they’re helping to revolutionise the development of drugs, such as treatments for COVID-19, cancer research, and dementia.

Cholesterol oxidation, working with regulators, drug loading nanoparticles, and functional excipients for specific pathways.

Machine learning approaches may help to accelerate the formulation process for long-acting injectables, improving treatment options and therapeutic availability for patients.

Incorporating AI/ML approaches into the holistic identification lifecycle can help to improve the efficiency of a compound reaching the next stage of validation, streamlining the initial identification process.

The Indian pharma giant comes out of inspection with eight observations in its generic manufacturing plant.