Drug development is moving fast—and not everything deserves your attention.
These are the 10 blog posts from our experts that scientists like you read most in 2025. They cover everything from questions about modeling, regulation, earlier decision-making and more. If you want a quick read on what mattered most last year, start here.
Choosing Your PK/PD Modeling Software: Key Questions About Monolix, Answered
When it comes to PK/PD modeling, it makes sense that many researchers want to stick with the tools they know. Over time, however, that approach can mean missing out on new software and/or updates that deliver better results in less time.
In this blog post, you can read about common misconceptions and questions people ask about Monolix. You’ll learn what it does well and how it fits into model-informed decision-making when you need speed and rigor.
What You Need to Know About Using PBPK Modeling for DDI Interaction Assessments for GLP-1 Agonists
In her blog post, Dr. Xinyuan (Susie) Zhang, Vice President of Regulatory Strategies and former FDA expert in PBPK modeling and DDI risk assessment, explores the powerful role of in silico methods for assessing DDI risks, offering a more streamlined approach to meet regulatory requirements.
Clinical Pharmacology Considerations and Application of Model-Informed Drug Development in the Development of Drugs and Biological Products for Rare Diseases
Rare diseases are particularly challenging for treatment development. When your patient population is tiny, every trial decision has to count.
This blog post walks through how MIDD approaches—spanning PBPK, PopPK, and QSP—can support dosing, trial design, and regulatory strategy in rare disease programs where traditional evidence is harder to generate.
Drug-Induced Liver Injury: A Look at QST Modeling and AI Predictions
While AI-based models bring value in recognizing patterns across large datasets, they often fall short when it comes to mechanistic understanding and physiological realism. That is where quantitative systems toxicology (QST) modeling, particularly with DILIsym®, delivers a substantial advantage.
Read this blog post to learn how to leverage the best of both AI and QST for your program.
Decoding PBPK & PBBM: What You Need to Know for Effective Application
If you’ve ever wished for a clear, practical guide to what PBPK and PBBM really mean, this blog post is for you. In it, you’ll learn when each approach is most useful, and how teams can apply them confidently across FIH, absorption challenges, and formulation decisions.
Phasing Out Animal Testing: Responding to FDA and EMA’s Strategic Shifts
The question is no longer whether non-animal approaches are coming, it’s how fast you adapt.
Experts across multiple disciplines—Susie Zhang, Sandra Suarez Sharp, Lisl Shoda, Haiying Zhou, Grace Fraczkiewicz, Viera Lukacova, Priyata Kalra, Jeremy Jones and Brad Stefanovic—came together to break down FDA/EMA direction around new approach methodologies (NAMs) and highlight how modeling and simulation can support faster, more ethical, and increasingly regulator-aligned development strategies.
Understanding the FDA’s Draft Guidance on Artificial Intelligence to Support Regulatory Decision-Making
If you’re using artificial intelligence (AI) in your program, regulators are asking how you validate and explain them.
In this blog post, Vice President of Regulatory Strategies, Susie Zhang provides a practical walkthrough of FDA’s draft guidance, focused on what sponsors should document, how to think about transparency and performance, and where this fits in submissions for drug and biologic products.
Representing a Drug in a Complement QSP Model: Eculizumab as a Case Study
Drug developers need QSP models that don’t just explain biology, but can also reliably evaluate interventions across dose, target engagement, and downstream response.
Scientists Lara Clemens, Zachary Kenz, and Lisl Shoda use eculizumab to demonstrate how to incorporate a therapeutic into a complement QSP framework (COMPLEMENTsym) and validate it with observed data, creating a reusable approach for evaluating other complement therapies.
How to Improve Your Drug Candidate Quality Without Adding New Steps to Your Program
Everyone wants better candidates—but no one wants a longer workflow.
Learn how Roche has successfully embedded the HTPK (High-Throughput Pharmacokinetics) module from ADMET Predictor into their in-house drug design platform1. By doing so, their scientists were able to assess species-specific PK parameters—such as clearance, fraction unbound, and bioavailability—on-demand, without leaving their core design environment.
Understanding Food Effects in Drug Development: A PBBM Perspective
Food-effect surprises can wreck confidence in your exposure story—especially for oral drugs.
A practical PBBM-focused look at fed vs fasted physiology, how food alters BA/BE risk, and why mechanistic modeling is increasingly viewed as a smart way to anticipate food effects earlier.
These posts reflect where teams focused their attention in 2025—and why.
Whether you’re revisiting a familiar challenge or exploring something new, we hope this roundup helps you spend less time searching and more time making confident, informed decisions.
If you’d like to learn more about any of the software tools featured in these blogs, we’d be happy to chat.