Biology subjects

Damoiseaux, R.

Publications and source records attributed to Damoiseaux, R..

3 recordsLinked to original sources

bioRxiv2018

Transitions in interaction landscapes of multidrug combinations

Drug combinations are a promising strategy to increase killing efficiency and to decrease the likelihood of evolving resistance. A major challenge is to gain a detailed understanding of how drugs interact in a dose-specific manner, especially for interactions involving more than two drugs. Here we introduce a direct and intuitive visual representation that we term \"interaction landscapes\". We use these landscapes to clearly show that the interaction type of two drugs typically transitions smoothly from antagonism to no interaction to synergy as drug doses increase. This finding contradicts prevailing assumptions that interaction type is always the same. Our results, from 56 interaction landscapes, are derived from all possible three-drug combinations among 8 antibiotics, each varied across a range of 7 concentrations and applied to a pathogenic Escherichia coli strain. Such comprehensive data and analysis are only recently possible through implementation of an automated high-throughput drug-delivery system and an explicit mathematical framework that disentangles pairwise versus three-way as well as net (any effect) versus emergent (requiring all three drugs) interactions. Altogether, these landscapes partly capture and encapsulate selective pressures that correspond to different dose regions and could help optimize treatment strategies. Consequently, interaction landscapes have profound consequences for choosing effective drug-dose combinations because there are regions where small changes in dose can cause large changes in pathogen killing efficiency and selective pressure.

evolutionary biology↗

bioRxiv2017

Prevalence and patterns of higher-order interactions

Interactions and emergent processes are essential for research on complex systems involving many components. Most studies focus solely on pairwise interactions and ignore higher-order interactions among three or more components. To gain deeper insights into higher-order interactions and complex environments, we study antibiotic combinations applied to pathogenic Escherichia coli and obtain unprecedented amounts of detailed data (251 two-drug combinations, 1512 three-drug combinations, 5670 four-drug combinations, and 13608 five-drug combinations). Directly opposite to previous assumptions and reports, we find higher-order interactions increase in frequency with the number of drugs in the bacterias environment. Furthermore, we observe a shift towards net synergy (effect greater than expected based on independent individual effects) and towards emergent antagonism (effect less than expected based on lower-order interaction effects). These findings have implications for the potential efficacy of drug combinations and are crucial for better navigating problems associated with the combinatorial complexity of multi-component systems.

systems biology↗

bioRxiv2017

Miniring approach for high-throughput drug screenings in 3D tumor models

There is increasing interest in developing 3D tumor organoid models for drug development and personalized medicine applications. While tumor organoids are in principle amenable to high-throughput drug screenings, progress has been hampered by technical constraints and extensive manipulations required by current methodologies. Here, we introduce a miniaturized, fully automatable, flexible high-throughput method using a simplified geometry to rapidly establish 3D organoids from cell lines and primary tissue and robustly assay drug responses. As proof of principle, we use our miniring approach to establish organoids of high-grade serous tumors and one carcinosarcoma of the ovaries and screen hundreds of protein kinase compounds currently FDA-approved or in clinical development. In all cases we could identify drugs causing significant reduction in cell viability, number and size of organoids within a week from surgery, a timeline compatible with therapeutic decision making.

cancer biology↗