Simulations Plus

In drug development, the science never stands still. Constant research means there is constantly more to know and understand to stay current—but how can you filter through to what is most impactful?

Drug discovery has always been a balancing act, with scientists constantly striving for ‘Goldilocks’ compounds – the search for molecules that achieve on-target activity, while also avoiding hidden liabilities such as toxicity, poor exposure, or unfavorable pharmacokinetics, can seem insurmountable at times.

As anyone who has worked in the pharmaceutical industry knows, there is a constant push to accelerate and optimize development processes.

As scientists working in pre-clinical drug development, we are constantly balancing the need for rigorous, mechanistic modeling with the realities of fast-moving project timelines.

Training is one of the most critical parts of study start-up and yet, we often treat it like a box to check rather than the foundation of trial quality.

Learn how AI-powered tools--specifically GastroPlusGPT--can help physiologically based pharmacokinetic (PBPK) modelers work more efficiently, focus on deeper analysis, and ultimately be better scientists.

Selecting the optimal first-in-human (FIH) dose is one of the most challenging steps in drug development.

Despite systemic lupus erythematosus (SLE) clinical trials being conducted for over half a century, there are only two biologic therapies that are approved by the FDA to treat SLE: belimumab and anifrolumab.

Today more than ever, the role of thought leaders—also referred to as key opinion leaders (KOLs), external experts, and other designations—is critical.

The co-administration of oral drug products with food can lead to marked alterations in bioavailability (BA) and plasma concentrations when compared to the fasted state.

In systems biology, we often speak of emergence—how complex systems yield behaviors not apparent from their individual parts.

As every researcher in early development knows, there is constant pressure to identify high-quality drug candidates—while also increasing the speed and efficiency of the discovery process.

As AI tools gain traction in drug development, there is growing enthusiasm around their potential to predict drug-induced liver injury (DILI).

Both the U.S. Food and Drug Administration (FDA) (1) and the European Medicines Agency (EMA) (2) have articulated clear regulatory expectations for the implementation and advancement of non-animal methods, known as new approach methodologies (NAMs).

The challenges of developing drug and biological products for rare diseases

As the adage goes, “time is money,” and the drug development pipeline is no exception. Separately, both “time” and “money” are crucial considerations at every stage of drug development

In the complex world of R&D, acronyms like PBPK and PBBM often seem interchangeable—but they're not.

The pharmaceutical industry is on the brink of a transformative shift, with artificial intelligence (AI) increasingly being leveraged across the drug product lifecycle.

This blog post continues our ongoing series on leveraging quantitative systems pharmacology (QSP) to enhance drug development.

When it comes to PK/PD modeling, many researchers stick with the tools they know, even when those tools may not be the most efficient or insightful.