Synergistic and antagonistic drug combinations depend on network topology.
Drug combinations may exhibit synergistic or antagonistic effects. Rational design of synergistic drug combinations remains a challenge despite active experimental and computational efforts. Because drugs manifest their action via their targets, the effects of drug combinations should depend on the interaction of their targets in a network manner. We therefore modeled the effects of drug combinations along with their targets interacting in a network, trying to elucidate the relationships between the network topology involving drug targets and drug combination effects. We used three-node enzymatic networks with various topologies and parameters to study two-drug combinations. These networks can be simplifications of more complex networks involving drug targets, or closely connected target networks themselves. We found that the effects of most of the combinations were not sensitive to parameter variation, indicating that drug combinational effects largely depend on network topology. We then identified and analyzed consistent synergistic or antagonistic drug combination motifs. Synergistic motifs encompass a diverse range of patterns, including both serial and parallel combinations, while antagonistic combinations are relatively less common and homogenous, mostly composed of a positive feedback loop and a downstream link. Overall our study indicated that designing novel synergistic drug combinations based on network topology could be promising, and the motifs we identified could be a useful catalog for rational drug combination design in enzymatic systems.
Center for Quantitative Biology, Peking University, Beijing, China.
Ning
N
Yin
Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America.
Wenzhe
W
Ma
Center for Quantitative Biology, Peking University, Beijing, China.
Jianfeng
J
Pei
Center for Quantitative Biology, Peking University, Beijing, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China; School of Physics, Peking University, Beijing, China.
Qi
Q
Ouyang
Center for Quantitative Biology, Peking University, Beijing, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China; School of Physics, Peking University, Beijing, China.
Chao
C
Tang
Center for Quantitative Biology, Peking University, Beijing, China; BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
Luhua
L
Lai
D004338:Drug Combinations; D015195:Drug Design; D004347:Drug Interactions; D004357:Drug Synergism; D008962:Models, Theoretical
D004338:Drug Combinations