Simulations Plus

Transporters play a critical role in drug absorption, distribution, and elimination, and their involvement in drug-drug interactions (DDIs) can lead to altered drug concentrations and unexpected adverse effects that hamper the effectiveness of the treatment.

Driving the development of your obesity drug assets efficiently is critical for success

In a fast-paced industry with big budget competition, you need every tool and approach to succeed.

Model-based meta analysis (MBMA) informs key drug development decisions by integrating data, published or unpublished, from multiple studies.

Understanding and predicting drug-drug interactions (DDIs) is crucial for ensuring patient safety and improving drug product development strategies.

Every day, scientists in the pharmaceutical industry are tasked with meeting regulatory expectations while also minimizing budget spend by identifying efficiencies for faster development of safer, more effective drugs.

The number of opioid overdose deaths in the United States has continued to increase for more than two decades.

Hepatotoxicity due to drugs and other xenobiotics, also known as drug-induced liver injury (DILI), is a primary reason for 1) the termination of drug development programs, 2) the delay of approving otherwise efficacious drugs by requiring large and expensive safety-focused clinical trials, 3) the restriction on the clinical use of approved drugs by the inclusion of black box warnings, and 4) the removal of approved drugs from the market.

This webinar, in Portuguese, will explore the IVIVC (in vitro–in vivo correlation) module on the innovative GPX platform.

The development of ophthalmic drug products is challenging due to the complexity of the ocular system, the lack of sensitive testing to evaluate the interplay of its physiology with ophthalmic drugs, and measurement limitations associated with ocular pharmacokinetics.

It is critical to understand the perspectives and key criteria of global regulatory agencies in the consideration of biowaivers, but many companies have knowledge gaps that hinder their success.

Pharmacological modeling plays a key role in drug development by guiding decisions on dosing to optimize safety and efficacy and ultimately streamlining the path to successful therapeutic interventions.

Artificial intelligence/machine learning (AI/ML) are increasingly being utilized in drug development.

Stay ahead of the competition with ADMET Predictor® 12!

Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease that affects more than five million people worldwide.

Get ready to turbocharge your scientific team’s PBBM PBPK modeling technology with the latest version of GastroPlus®!

It’s here—the latest version of GastroPlus®, GPX. And this is your chance to see it in action.

A major obstacle in drug development is the combinatorial complexity of available oncology therapies under development for solid tumors.

Identifying potential drug-drug interactions (DDIs) is critical to your program's success.

Identifying the right dose of the right drug for the right patient is a central objective of pharmaceutical drug development.