Traditional in Vitro Synergy Models, Like Loewe Additivity or Chou-Talalay, Rely on Dose-Response Curves, which are Often Inapplicable In Vivo Due to Fixed Single-Dose Regimens

Abstract

Classic methods like Loewe additivity and the Chou–Talalay approach have long been used to study how drugs interact. These models help determine whether combining drugs leads to better, worse, or simply additive effects. Typically, researchers generate dose-response curves in lab settings to analyze these interactions. However, these laboratory-based methods don’t always translate well to real-world scenarios. In clinical and preclinical (animal) studies, it's common to administer fixed doses of each drug rather than exploring a range of doses. This poses a challenge, as many drug interaction models assume a smooth, testable relationship between dose and effect—something that rarely occurs in practical settings. Several real-world factors complicate the interpretation of combination therapies: pharmacokinetics (how drugs move through the body), drug distribution, site-specific activity, and toxicity levels all introduce variability. These complexities often make it difficult for simple mathematical models to accurately reflect what’s happening in the body. For researchers, a key challenge is applying drug interaction models in clinical contexts, where fixed-dose regimens are the norm. This disconnect has sparked growing interest in more advanced approaches, such as:

• Modelling tumour growth over time
• Using statistical frameworks to analyze biological systems
• Simulating drug behavior in the body (pharmacokinetic/pharmacodynamic modeling)

To make meaningful progress, scientists must strike a balance between simplicity and realism—developing models that are both accessible and biologically accurate. This balance is essential because the real value of combination therapies lies not only in theoretical synergy but also in demonstrated practical effectiveness at real-world doses.