Special considerations

C-QTc, DDIs, IVIVC, BE and BS, immunogenicity and other special considerations require special knowledge and methods, but have all in common to be able to be elucidated by leveraging modeling capabilities.


Determining whether a drug prolongs the cardiac QT wave (C-QTc), impacts the profile of another drug (DDI), or correlates in vitro and in vivo observations (IVIVC) are all challenges to drug development that can be greatly benefited by modeling and simulations.

Other special considerations relate to demonstrate bioequivalence (BE) or biosimilarity (BS) for small molecules and biologics, respectively, as well as evaluating the potential immunogenicity response associated with the administration of a large molecule. 

Drug development considerations that can be aided -reduced or replaced- by modeling, whether for biologic drugs (left) or small molecules (right)
“At Pharmetheus, we consistently aim at the maximal impact depending on the context of use and support in describing, reducing, or replacing trials.”


Waived trials or augmented power

In some considerations such as C-QTc or DDIs, the modeler may be able to propose and conduct an analysis that uses already existing information and grant the waiving of the trial.

Supporting a regulatory claim, such as establishing bioequivalence between a new formulation and a reference formulation, or demonstrating biosimilarity of a new monoclonal antibody to a commercialized product, is a context of use which requires appropriate planning, conduct, and analysis.

Similarly, proper effect testing in trials involving potential immunogenicity issues can help ensure that ​​the impact of an “antidrug antibody (ADA)” response can appropriately be accounted for in the filed dossier.

Recommended platforms

When addressing special considerations during trials, we can leverage the ways of working developed within the Pharmetheus Platforms:

To analyze both drug concentration (pharmacokinetic) and effects (pharmacodynamic) data using a population modeling approach. The adopted quantitative approach enables efficient integration of longitudinal information across subjects and across study protocols.

When the objective is to leverage accumulated knowledge to inform mechanistic analyses and or achieve predictions beyond observations.

A quantitative framework to support and inform development program strategies and decisions.