Synergy and antagonism in natural product extracts: when 1 + 1 does not equal 2

Abstract

Covering: 2000 to 2019 According to a 2012 survey from the Centers for Disease Control and Prevention, approximately 18% of the U.S. population uses natural products (including plant-based or botanical preparations) for treatment or prevention of disease. The use of plant-based medicines is even more prevalent in developing countries, where for many they constitute the primary health care modality. Proponents of the medicinal use of natural product mixtures often claim that they are more effective than purified compounds due to beneficial "synergistic" interactions. A less-discussed phenomenon, antagonism, in which effects of active constituents are masked by other compounds in a complex mixture, also occurs in natural product mixtures. Synergy and antagonism are notoriously difficult to study in a rigorous fashion, particularly given that natural products chemistry research methodology is typically devoted to reducing complexity and identifying single active constituents for drug development. This report represents a critical review with commentary about the current state of the scientific literature as it relates to studying combination effects (including both synergy and antagonism) in natural product extracts. We provide particular emphasis on analytical and Big Data approaches for identifying synergistic or antagonistic combinations and elucidating the mechanisms that underlie their interactions. Specific case studies of botanicals in which synergistic interactions have been documented are also discussed. The topic of synergy is important given that consumer use of botanical natural products and associated safety concerns continue to garner attention by the public and the media. Guidance by the natural products community is needed to provide strategies for effective evaluation of safety and toxicity of botanical mixtures and to drive discovery in botanical natural product research.

Fig. 1

Chromatograms (obtained with liquid-chromatography coupled to mass spectrometry) of a complex extract of the botanical Salvia mittiorrhiza (Chinese red sage or Danshen). The full chromatogram is shown in (A), while (B) shows a zoomed in version of the baseline that demonstrates the immense complexity of the mixture. Counts for numbers of ions detected are shown at the right, and it is observed that the number of ions detected increases by ~10-fold with each 10-fold decrease in the cutoff for peak area. Notably, each mixture component may be represented by more than one ion, making it difficult to assign specifically the number of mixture components. Nonetheless, the data indicate the immense complexity of the botanical extract.

Fig. 2

Example of isobolograms for antagonistic, additive, and synergistic components. Axes represent the doses of individual agents, and the points represent the combination of concentrations of the two agents required to reach a particular fixed effect.

Fig. 3

Example of synergistic (top) and antagonistic (bottom) interaction landscapes using delta scores (δ) calculated with the zero interaction potency model of compounds in combination with ibrutinib, an approved anti-cancer drug targeting Bruton's tyrosine kinase. (A) Interaction map between anti-cancer activity of ispinesib (a selective kinesin spindle protein inhibitor) and ibrutinib. Average delta across the dose–response matrix (Δ) is 17.596, indicative of overall synergy. (B) Interaction map between canertinib (an epidermal growth factor receptor family inhibitor) and ibrutinib. The Δ value is −14.038, indicative of overall antagonism. Figure is reprinted with permission from Yadav et al. 2015.

Fig. 4

Bacterial resistance mechanisms that could be targeted with combination therapy enabling re-sensitization of resistant organisms to existing antibiotics.

Fig. 5

Synergy-directed fractionation workflow. Reproduced with permission from Junio et al. 2011.

Fig. 6

Bioactive molecular networking in which nodes connected in a network represent structurally related compounds based on MS/MS fragmentation patterns, and the size of nodes represents the correlation of compound peak areas with biological activity of interest. Figure is reprinted with permission from Nothias et al. 2018.

Fig. 7

Compound activity mapping workflow. (A) Network analysis of the full chemical space of the tested actinobacterial extracts. Light blue nodes represent extracts connected to all m/z features (red), illustrating the immense chemical complexity of the extract library. (B) Activity histograms and cluster scores for all m/z features. (C) Compound activity map, displaying only extracts and m/z features predicted to be responsible for consistent phenotypes of interest. (D) Close up of a specific bioactive cluster, belonging to the staurosporine natural product family. This figure is reprinted with permission from Kurita et al. 2015.

Fig. 8

Selectivity ratio plots for first, second, and third stages of fractionation [(A–C), respectively] of the botanical Hydrastis canadensis. Growth inhibition data were used to guide selectivity ratio analysis, so variables with negative selectivity ratio are most likely to possess additive or synergistic activity. Known flavonoids (likely to be synergists) are marked in green, while known alkaloids (likely to be additives) are marked in red. First-stage (A) and second-stage (B) models were not able to identify known compounds as contributing to activity. However, the third-stage model (C) predicted seven flavonoids (1, 2, 3, 5, 6, 8, 29) and three alkaloids (10, 22, 23) to possess additive or synergistic activity. With this approach, a new synergistic flavonoid (29) was identified in H. canadensis, and known flavonoids and alkaloids not previously known to possess additive or synergistic activity were prioritized for future studies. This figure is reprinted with permission from Britton et al. 2017.