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5 Updates to the Simcyp Simulator You’ll Be Psyched About

The Simcyp™ Population-based Simulator streamlines drug development through the modelling and simulation of physiologically-based pharmacokinetics (PK) and pharmacodynamics (PD) in virtual patient populations. It incorporates numerous databases containing human physiological, genetic and epidemiological information.

By integrating this information with in vitro or clinical data, the Simulator can predict PK/PD behavior in ‘real-world’ populations. It can be used to select ideal dosing regimens; determine drug-drug interactions; and predict PK changes in special populations, such as children and the elderly. We’re always looking to improve this technology to make it even more responsive to our clients’ needs. I’m really excited about these most recent updates to the Simulator:

1. Enhanced oral absorption models

The Simcyp Simulator’s ADAM (Advanced Dissolution Adsorption Metabolism) module now contains extended features for handling fed versus fasted oral absorption models. These can account for some of the more complex food effects such as changes in viscosity. In addition, a Mechanistic Permeability (Mech Perf) model has been added to estimate drug permeability in different regions of the gastrointestinal (GI) tract, including the colon based on physiochemical inputs.

2. Improved gut wall modeling

A Nested-Enzyme-Within-Enterocyte (NEWE) model has been added to the Simulator to take into account the enterocyte turnover in the gut wall and the resulting changes in the level of intestinal drug metabolizing enzymes. Clients can now estimate a value for the proportion of unbound drug in the gut wall based on the physiochemical properties of the drug and the tissue composition of the gut wall.

3. Updated compound libraries

The version 14 platform contains new compound files for Pravastatin, Lorazepam, Probenecid and Cyclosporine. Moreover, the Rifampicin compound file has been modified to take account of the effect of OATP-mediated inhibition. As part of our continual refinement and updating of the compound file library, modifications were also made to the Metoprolol and Digoxin files.

In addition to providing compound files, Certara also creates defined patient populations to expedite virtual clinical trials. For example, the Simcyp Simulator has virtual populations based on ethnicity such as Japanese or Chinese, and specific physiological conditions, including pregnant women, patients with various severity of cirrhosis, and those with different levels of renal impairment.

4. Match virtual trials with real clinical trials

Clients increasingly want their in silico virtual trials to match more closely what they will do in real clinical trials. They want to mirror the complex dose scheduling and to combine multiple routes of administration. For instance, if the initial dose will be given as an injection and then subsequent doses as tablets, they want the same to happen in the virtual trial. In the new version of the Simulator, it is now possible to define specific output sampling times that match those in the clinical study, incorporate analytical error into PK/PD measurements and, importantly, estimate the population sample size needed to detect differences between different populations of individuals at a given level of power.

5. Monitor multiple metabolites

Clients can now monitor two drug metabolites at the same time without having to repeat the simulation. As drug metabolites can play significant roles in some aspects of drug safety and pharmacological response, this is a very practical addition to the platform.

Ready to learn more?

My colleague, Dr. Amin Rostami, recently gave a webinar that highlights how PBPK modeling and simulation may be useful for assessing risk during drug development. He also did a podcast with Clinical Pharmacology and Therapeutics on this topic. I hope that you’ll watch it and let us know what you think in the comments below.

I’d also recommend reading our white paper about how PBPK modeling can support drug development decisions, regulatory interactions, and drug labeling.

About the author

By: Iain Gardner

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