Simulations Plus

There’s no one “right” modeling or simulation approach—but some are better suited to answer or explore specific types of questions.

How Composer keeps AI-enabled MIDD workflows scientific and under your control

Regulatory perspectives increasingly emphasize the value of integrating modeling early in the drug development process rather than applying it retrospectively.

One of the more common narratives surrounding AI is the idea that automation will gradually reduce the importance of scientists and technical experts. Personally, I believe the opposite is true.

AI is the buzzword across every industry right now, and the pharma industry is not immune.

We have an exciting new drug target, we’ve validated the biology, we have a structure, now it’s time to design a compound.

Right now, using AI means something completely different depending on who you ask.

Regulatory reviewers are tasked with assessing whether the level of model validation is appropriate for its risk and impact, and their confidence is established when the overall modeling approach is proportionate, coherent, and easy to verify.

In drug development, trust in the models utilized is critical. Even when a model demonstrates acceptable predictive performance, certain recurring issues can significantly reduce reviewer confidence.

The question is not whether agentic AI is coming. It is this: can you stake a regulatory submission on it?

Model interpretability is not uniformly critical across all stages of model development; however, it becomes essential at key decision points where scientific understanding must support regulatory confidence.

In high-stakes decision-making, particularly within regulatory submissions to agencies such as the FDA, PMDA, EMA, Health Canada, MHRA, ANVISA, models serve as a weight of evidence approach rather than exploratory tools.

For decades, the pharmaceutical industry has pursued a reliable bridge between in vitro dissolution and in vivo performance.

Drug development is moving fast—and not everything deserves your attention.

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.