Prevailing drug discovery approaches focus on compounds with molecular selectivity, inhibiting disease-relevant targets over others in vitro. However in vivo, many such agents are not therapeutically selective, either because of undesirable activity at effective doses or because the biological system responds to compensate. In theory, drug combinations should permit increased control of such complex biology, but there is a common concern that therapeutic synergy will generally be mirrored by synergistic side-effects. Here we provide evidence, from 94,110 multi-dose combination experiments representing diverse disease areas and large scale flux balance simulations of inhibited bacterial metabolism, that multi-target synergies are more specific than single agent activities to particular cellular contexts. Using an anti-inflammatory combination, we show how multi-target synergy can achieve therapeutic selectivity in animals through differential target expression. Synergistic combinations can increase the number of selective therapies using the current pharmacopeia, and offer opportunities for more precise control of biological systems.

Therapeutic regimens that comprise more than one active ingredient are commonly used in clinical medicine. Despite this, most drug discovery efforts search for drugs that are composed of a single chemical entity. A focus in the early drug discovery process on identifying and optimizing the activity of combinations of molecules can result in the identification of more effective drug regimens. A systems perspective facilitates an understanding of the mechanism of action of such drug combinations.

Multicomponent therapies, originating through deliberate mixing of drugs in a clinical setting, through happenstance, and through rational design, have a successful history in a number of areas of medicine, including cancer, infectious diseases, and CNS disorders. We have developed a high-throughput screening method for identifying effective combinations of therapeutic compounds. We report here that systematic screening of combinations of small molecules reveals unexpected interactions between compounds, presumably due to interactions between the pathways on which they act. Through systematic screening of Ϸ120,000 different two-component combinations of reference-listed drugs, we identified potential multicomponent therapeutics, including (i) fungistatic and analgesic agents that together generate fungicidal activity in drug-resistant Candida albicans, yet do not significantly affect human cells, (ii) glucocorticoid and antiplatelet agents that together suppress the production of tumor necrosis factor-␣ in human primary peripheral blood mononuclear cells, and (iii) antipsychotic and antiprotozoal agents that do not exhibit significant antitumor activity alone, yet together prevent the growth of tumors in mice. Systematic combination screening may ultimately be useful for exploring the connectivity of biological pathways and, when performed with reference-listed drugs, may result in the discovery of new combination drug regimens.M odern biological research and much of drug discovery is often driven by the search for new molecularly targeted therapeutics (1-3). In this approach, a specific protein is studied in vitro, in cells and in whole organisms, and evaluated as a drug target for a specific therapeutic indication (3, 4). The refinement of this approach has resulted in the ability to discover compounds with great selectivity for a chosen protein target. The recent success of Gleevec (imatinib mesylate), an inhibitor of the breakpoint cluster regionabelson (BCR-ABL) kinase, and of selective cyclooxygenase-2 (COX-2) inhibitors Vioxx (rofecoxib) and Celebrex (celecoxib) are evidence that the target-based approach can be successful (5, 6).Systems biology, however, has revealed that human cells and tissues are composed of complex, networked systems with redundant, convergent and divergent signaling pathways (7-10). For example, the redundant function of proteins involved in cell-cycle regulation (11) has inspired efforts to intervene simultaneously at multiple points in these signaling pathways (12). A drug discovery approach consonant with this systems biology framework, and complementary to the target-based approach, entails identification of combinations of small molecules that perturb cellular signaling networks in a desired fashion.Recognition of the potential for multipoint intervention in biology and medicine has a long history. As early as 1928, Loewe (13) observed and quantified effects of combinations of compounds that were different from, and not predicted by, the activities of the constituents. The conc...

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.

Biological systems are robust, in that they can maintain stable phenotypes under varying conditions or attacks. Biological systems are also complex, being organized into many functional modules that communicate through interlocking pathways and feedback mechanisms. In these systems, robustness and complexity are linked because both qualities arise from the same underlying mechanisms. When perturbed by multiple attacks, such complex systems become fragile in both theoretical and experimental studies, and this fragility depends on the number of agents applied. We explore how this relationship can be used to study the functional robustness of a biological system using systematic high-order combination experiments. This presents a promising approach toward many biomedical and bioengineering challenges. For example, high-order experiments could determine the point of fragility for pathogenic bacteria and might help identify optimal treatments against multi-drug resistance. Such studies would also reinforce the growing appreciation that biological systems are best manipulated not by targeting a single protein, but by modulating the set of many nodes that can selectively control a system's functional state. Subject Categories: genomic and computational biology; metabolic and regulatory networks