Chemical combination effects predict connectivity in biological systems

Lehár, Joseph; Zimmermann, Grant R.; Krueger, Andrew S; Molnar, Raymond A; Ledell, Jebediah T; Heilbut, Adrian; Short, Glenn; Giusti, Leanne C; Nolan, Garry P.; Magid, Omar A; Lee, Margaret; Borisy, Alexis A.; Stockwell, Brent R.; Keith, Curtis T.

doi:10.1038/msb4100116

Cited by 249 publications

(303 citation statements)

References 58 publications

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“…The results are complementary to detailed mechanistic models because they impose an upper limit on how much mechanistic information is required to predict bacterial growth. Mechanistic and empirical approaches remain essential to characterize the effects of specific drug pairs (18)(19)(20)(21)(22)(23). Remarkably, however, our results reveal that additional information is often not required to predict the effects of larger combinations of drugs.…”

Section: Discussionmentioning

confidence: 86%

Mechanism-independent method for predicting response to multidrug combinations in bacteria

Wood

Nishida

Sontag

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

137

177

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Drugs are commonly used in combinations larger than two for treating bacterial infection. However, it is generally impossible to infer directly from the effects of individual drugs the net effect of a multidrug combination. Here we develop a mechanism-independent method for predicting the microbial growth response to combinations of more than two drugs. Performing experiments in both Gram-negative ( Escherichia coli) and Gram-positive ( Staphylococcus aureus ) bacteria, we demonstrate that for a wide range of drugs, the bacterial responses to drug pairs are sufficient to infer the effects of larger drug combinations. To experimentally establish the broad applicability of the method, we use drug combinations comprising protein synthesis inhibitors (macrolides, aminoglycosides, tetracyclines, lincosamides, and chloramphenicol), DNA synthesis inhibitors (fluoroquinolones and quinolones), folic acid synthesis inhibitors (sulfonamides and diaminopyrimidines), cell wall synthesis inhibitors, polypeptide antibiotics, preservatives, and analgesics. Moreover, we show that the microbial responses to these drug combinations can be predicted using a simple formula that should be widely applicable in pharmacology. These findings offer a powerful, readily accessible method for the rational design of candidate therapies using combinations of more than two drugs. In addition, the accurate predictions of this framework raise the question of whether the multidrug response in bacteria obeys statistical, rather than chemical, laws for combinations larger than two.

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Section: Discussionmentioning

confidence: 86%

Mechanism-independent method for predicting response to multidrug combinations in bacteria

Wood

Nishida

Sontag

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

137

177

View full text Add to dashboard Cite

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“…In these models, the system is treated as a black box, and it does not require a complete characterization of the biological networks 37,38 . (iii) Model-based combinations in which biological measurements are used to build explicit models of a target network using simulations 39,40 . This approach seems to be one of the most successful in multidrug design.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Optimization of drug combinations using Feedback System Control

Nowak-Śliwińska¹,

Weiss²,

et al. 2016

Nat Protoc

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“…Non-linear dynamics including bistability and oscillation are involved in the CREB model. There are eight parameters in the model (16)- (17) and in total 28 possible distinct two-parameter combinations. For the bistable case, when the [CREB1]/[CREB2] ratio in the absence of parameter changes is chosen as the control, the percentage increase of the ratio over the control to perturbations of the 28 parameter pairs has been investigated and it has been shown that the parameter pair (v x , k dy ) exhibits a strong degree of synergism [28].…”

Section: Synergism In the Creb Modelmentioning

confidence: 99%

“…It also depends on the quantities of each perturbation in synergistic combinations. The determination of synergism is crucial to understand combinatorial control in molecular networks, e.g., how cells respond optimally to two or more mixed signals [12][13][14], how to optimize the combinatorial therapies in pharmaceutical development [15][16][17], and how to control the processes of fate decisions [18]. Prior works on synergism have mainly focused on combination drug therapies.…”

Section: Introductionmentioning

confidence: 99%

Bifurcation-based approach reveals synergism and optimal combinatorial perturbation

Liu

et al. 2016

J Biol Phys

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Cells accomplish the process of fate decisions and form terminal lineages through a series of binary choices in which cells switch stable states from one branch to another as the interacting strengths of regulatory factors continuously vary. Various combinatorial effects may occur because almost all regulatory processes are managed in a combinatorial fashion. Combinatorial regulation is crucial for cell fate decisions because it may effectively integrate many different signaling pathways to meet the higher regulation demand during cell development. However, whether the contribution of combinatorial regulation to the state transition is better than that of a single one and if so, what the optimal combination strategy is, seem to be significant issue from the point of view of both biology and mathematics. Using the approaches of combinatorial perturbations and bifurcation analysis, we provide a general framework for the quantitative analysis of synergism in molecular networks. Different from the known methods, the bifurcation-based approach depends only on stable state responses to stimuli because the state transition induced by combinatorial perturbations occurs between stable states. More importantly, an optimal combinatorial perturbation strategy can be determined by investigating the relationship between the bifurcation curve of a synergistic perturbation pair and the level set of a specific objective function. The approach is applied to two models, i.e., a theoretical multistable decision model and a biologically realistic CREB model, to show its validity, although the approach holds for a general class of biological systems.

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Chemical combination effects predict connectivity in biological systems

Abstract: Chemical synergies can be novel probes of biological systems.Simulated response shapes depend on target connectivity in a pathway.Experiments with yeast and cancer cells confirm simulated effects.Profiles across many combinations yield target location information.

Cited by 249 publications

References 58 publications

Mechanism-independent method for predicting response to multidrug combinations in bacteria

Mechanism-independent method for predicting response to multidrug combinations in bacteria

Optimization of drug combinations using Feedback System Control

Bifurcation-based approach reveals synergism and optimal combinatorial perturbation

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