The right timing of treatment keeps resistant cells in check, yeast experiments suggest

This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: The development of treatment resistance is one of the key challenges in modern cancer and antibiotic therapy. New therapeutic approaches aim to curb the spread of resistance through the targeted timing of treatment cycles.

Researchers at the Max-Planck-Zentrum für Physik und Medizin (MPZPM) have demonstrated how a deeper understanding of the physical processes in growing tumors and microbial biofilms can contribute to the success of "evolution-based" therapies. Solid tumors and microbial biofilms consist of densely packed cells. Nutrients, oxygen and other resources essential for growth decrease from the periphery of the cell population toward the center.

This declining supply of resources regulates cell growth. Resistant cells that arise deep within a population are restricted in their growth and can spread only slowly. Treatment fundamentally alters this situation, sometimes with serious consequences for the course of therapy.

An interdisciplinary team of scientists led by Dr. Jona Kayser, head of the Emmy Noether Research Group "Cellular Evolution" at MPZPM, is investigating how fundamental biophysical processes influence the emergence and development of resistance in dense cell aggregates. Their new study in Nature Ecology & Evolution shows that physical processes play a decisive role in determining the optimal timing of treatment cycles.

The team combines microbial model systems and computer simulations to observe the "evolution" of resistance and decipher the underlying mechanisms. "We use a 'Real-To-Sim-To-Real' approach. In this approach, therapy-mimicking experiments with genetically modified yeast cells serve as the basis for digital twins.

The therapeutic strategies optimized on the computer are subsequently tested in experiments," says Nico Appold, one of the two lead authors. Using this approach, the researchers showed how the physical coupling of environmental and growth dynamics divided treatment outcomes into two distinct classes. The "sweet spot"—the range of optimal therapeutic efficacy—is located precisely at their boundary.