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Updated: Apr 11, 2026

Quadruple-Checkerboard: A Modification of the Three-Dimensional Checkerboard for Studying Drug Combinations
Published on: July 24, 2021
Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multidrug resistance
Stefany Moreno-Gamez1, Alison L Hill2, Daniel I S Rosenbloom3
1Program for Evolutionary Dynamics, Department of Mathematics, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138; Theoretical Biology Group, Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, 9747 AG, The Netherlands;
Abstract:
Infections with rapidly evolving pathogens are often treated using combinations of drugs with different mechanisms of action. One of the major goal of combination therapy is to reduce the risk of drug resistance emerging during a patient's treatment. Although this strategy generally has significant benefits over monotherapy, it may also select for multidrug-resistant strains, particularly during long-term treatment for chronic infections. Infections with these strains present an important clinical and public health problem. Complicating this issue, for many antimicrobial treatment regimes, individual drugs have imperfect penetration throughout the body, so there may be regions where only one drug reaches an effective concentration. Here we propose that mismatched drug coverage can greatly speed up the evolution of multidrug resistance by allowing mutations to accumulate in a stepwise fashion. We develop a mathematical model of within-host pathogen evolution under spatially heterogeneous drug coverage and demonstrate that even very small single-drug compartments lead to dramatically higher resistance risk. We find that it is often better to use drug combinations with matched penetration profiles, although there may be a trade-off between preventing eventual treatment failure due to resistance in this way and temporarily reducing pathogen levels systemically. Our results show that drugs with the most extensive distribution are likely to be the most vulnerable to resistance. We conclude that optimal combination treatments should be designed to prevent this spatial effective monotherapy. These results are widely applicable to diverse microbial infections including viruses, bacteria, and parasites.
Insights
Mismatched drug coverage in combination therapy can accelerate the evolution of multidrug resistance (MDR). Optimizing drug penetration profiles is crucial to prevent spatial monotherapy and reduce resistance risk in treating infections.
Area of Science:
- Microbiology
- Evolutionary Biology
- Pharmacology
Background:
- Combination therapy is a key strategy to combat rapidly evolving pathogens and reduce drug resistance.
- However, combination therapy can inadvertently select for multidrug-resistant (MDR) strains, especially in chronic infections.
- Imperfect drug penetration creates spatial heterogeneity, leading to regions with suboptimal drug concentrations.
Purpose of the Study:
- To investigate how spatially heterogeneous drug coverage influences the evolution of multidrug resistance within a host.
- To determine if mismatched drug penetration profiles accelerate resistance development compared to matched profiles.
Main Methods:
- Development of a mathematical model simulating within-host pathogen evolution.
- Analysis of pathogen evolution under spatially heterogeneous drug coverage scenarios.
- Comparison of resistance risk associated with matched versus mismatched drug penetration.
Main Results:
- Even small regions with single-drug coverage significantly increase the risk of multidrug resistance evolution.
- Mismatched drug penetration dramatically accelerates the accumulation of resistance mutations.
- Drugs with broader distribution are more susceptible to resistance selection.
Conclusions:
- Optimal combination treatments should prioritize matched drug penetration profiles to prevent spatial effective monotherapy.
- Designing treatments to avoid localized single-drug efficacy is critical for minimizing resistance.
- Findings are applicable to diverse microbial infections, including viral, bacterial, and parasitic pathogens.
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