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Official websites use. Share sensitive information only on official, secure websites. This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology. The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Many biological or chemical agents when combined interact with each other and produce a synergistic response that cannot be predicted based on the single agent responses alone. However, depending on the postulated null hypothesis of non-interaction, one may end up in different interpretations of synergy. Two popular reference models for null hypothesis include the Bliss independence model and the Loewe additivity model, each of which is formulated from different perspectives. During the last century, there has been an intensive debate on the suitability of these synergy models, both of which are theoretically justified and also in practice supported by different schools of scientists. More than 20 years ago, there was a community effort to make a consensus on the terminology one should use when claiming synergy. We review the theoretical relationships between these models and argue that one should combine the advantages of both models to provide a more consistent definition of synergy and antagonism. Keywords: definition of synergy, drug combinations, Bliss and Loewe models, interaction barometer, consensus agreement. Evaluation of interaction effects between biologically active agents has become an important topic in many disciplines, including pharmacology Cokol et al. The interaction between multiple agents is often classified as either synergistic or antagonistic, depending on how much the observed combination response differs from the expected response under the null hypothesis that the two agents are non-interacting. Multiple reference models have been formulated based on a distinctive set of empirical or biological assumptions see e. These assumptions, albeit difficult to validate a priori due to the lack of precise knowledge of the mechanisms of action, are often justifiable as long as they provide biologically plausible reasoning about the nature of non-interaction. However, the inherent differences in the model assumptions have inevitably led to inconsistency in the quantification of the degree of interaction, contributing to a major source of confusion and controversy on the definitions of synergy and antagonism. Rather than continuing the debate on the appropriateness of the model assumptions, the agreement called for a compromise between the advocates of the models, and proposed a practical guideline for reporting synergy or antagonism, where the underlying reference model should be explicitly described to avoid any ambiguity. For the rest of the review, we will use drug combination as an example of interaction data. A drug's effect y is often measured at a certain dose x as the percentage of biological response, i. Let us consider that, drug 1 at dose x 1 produces a response y 1 and drug 2 at dose x 2 produces a response y 2. Next, we combine the two drugs at the dose pair x 1 , x 2 and observe a combination response y c. To quantify the degree of drug interaction, we need to formulate a reference model to answer the following question: if there is no interaction between the drugs, what would be the expected combination response y EXP at x 1 , x 2? So far, two major reference model classes have been proposed, the Bliss independence model Bliss, and the Loewe additivity model Loewe, While each having its own logical basis, the underlying assumptions behind these two models are relatively distinct. The Bliss independence model adopts a probabilistic perspective by treating a drug combination under non-interaction as a joint action of independent, yet competing perturbations by the individual drugs. Such a probabilistic independence allows the expected combination response to be computed as the product of the individual drug responses:. An observed combination response greater or smaller than y BLISS can be interpreted as a departure from the probabilistic independence, which thus implies an interaction between the two drugs. The Loewe additivity model, on the other hand, requires additional information about the dose-response relationships of the individual drugs. The assumptions and performance of the two reference models have been compared and discussed in many review articles e. There have been attempts to distinguish the Bliss and Loewe models in terms of mechanistic implications Shafer et al. The Bliss independence model is expected to hold true for non-interacting drugs that elicit their responses independently, e. Loewe additivity, in contrast, is more compatible with the case where the drugs have similar modes of action on the same targets or pathways. However, little is known about whether such mechanistic justifications for the Bliss and Loewe models reflect the reality. Rather, Loewe and Bliss models should be used as data exploratory approaches, with a major purpose to identify potential synergistic drug combinations that warrant further mechanistic investigation, but not the other way around, i. Namely, depending on which model is used, a combination response greater or less than y EXP will be termed as Loewe synergy, Loewe antagonism, Bliss synergy or Bliss antagonism, respectively. Following these recommendations, the controversy over the definitions of synergy and antagonism seemed subsided. More recently, a variety of interaction assessment methods have been further developed and applied to a wide range of biological research fields. Notably, most of these methods can be traced back to the two basic model classes. For example, variants of the Loewe additive model include combination index Lee et al. Paradoxically, even with the clear distinction that has been made between the reference models, we feel that the fundamental question still remains unanswered, if not becoming even more serious: What is synergy after all? Since the expected combination responses y BLISS and y LOEWE most often are not identical Berenbaum, , choosing the model to use has become a practical burden for a researcher who tries to draw solid biological conclusions out from the data. Due to the lack of practical guidelines, the model selection has become a personal preference or largely a convention that has been followed in a particular research field without clear reasons Lee et al. There has been a tendency to favor a model that yields a lower expected combination response, as it results in a higher likelihood of detecting stronger synergy. To make matters even worse, there has been often a dilemma when a drug combination is classified as synergistic according to one model but antagonistic according to the other Cokol et al. To ease the model selection burden, we propose here the use of new terminology that incorporates both of the two reference models, together with the single drug responses, to distinguish non-interaction, synergy and antagonism. We note that, max y 1 , y 2 is also the expected response from a popular reference model, called highest single agent HSA model Berenbaum, If the combination response y c is lower than max y 1 , y 2 , then one would intuitively infer antagonism. Therefore, we may use max y 1 , y 2 , to distinguish antagonism from non-interaction. Similarly, one can use the response of the less effective single drug, that is min y 1 , y 2 , to further distinguish between weak and strong antagonisms. There has been considerable interest in the mathematical relationships between the Bliss independence and the Loewe additivity models to understand how much difference in the characterization of drug interaction one can expect when choosing one model over another see e. Given such a classification, one may continue to develop statistical testing methods for evaluation of its significance for replicate data. To facilitate better understanding of these definitions, we designed an interaction barometer that enables a systematic comparison of these proposed interaction terms along an axis of drug combination response y c Figure 1. The proposed terminology for classifying drug interactions. Using the interaction barometer allows a direct comparison between different drug combinations in terms of their degrees of interaction as well as their combination responses. The benefits of adopting the proposed terminology for the degree of interaction are two-fold. First, the definitions of synergy and antagonism are based on a simultaneous evaluation of the two reference models, as well as the individual drug responses. Such a data-driven approach avoids any pre-defined preference either for the Bliss independence or Loewe additivity when characterizing drug interactions, and thus it minimizes the biases toward either of the models. Further, this terminology enables a more intuitive definition of non-interaction, under which the combination response may be higher than the single drug response, but not as high as the expected responses from the Bliss independence and the Loewe additivity models. Note that a drug combination falling into such an interval would be classified as antagonistic according to both of the two models, but since it produces a higher response than the single drugs, one would rather characterize it as an additive effect or non-interaction. For the sake of clarity, we would call it non-interaction, and in fact discourage the terms additive or additivity since these may be confused with the additivity implicated by the Loewe additivity model. The interval of non-interaction shown in Figure 1 is positioned at the center of the barometer as a gray zone for those drug combinations with no clear evidence in support of either synergy or antagonism. Secondly, the different interaction terms are positioned along the common response axis e. With the proposed interaction barometer Figure 1 , one can immediately tell the differences between the drug combination response y c and the responses of individual drugs y 1 , y 2 , as well as the expected combination responses y BLISS , y LOEWE based on the two reference models. The clear correspondence between the degree of synergy and the combination response is in many ways superior to the use of an interaction index, such as combination index or other similar approaches see e. For example, a combination index of 0. In contrast, with the interaction barometer one can easily visualize the levels of boosted response of the combination compared to the single drugs or reference models. From the model development perspective, the graphical representation of the competing reference models in the interaction barometer may facilitate a more systematic comparison among different approaches. For example, when new reference models are introduced, one can always position its expected combination response onto the barometer to enable better understanding of its relationships with the existing models. So far, we have merely discussed about the assessment of drug interactions using the difference between the observed combination response and its theoretical expectation, i. However, a drug combination can be also classified as either effective or ineffective based solely on its actual combination response y c. It is important not to confuse these two concepts, synergy and efficacy, as the nomenclatures are related but should not be treated the same. Synergy is a measure of the degree of drug interaction, while efficacy is a measure of phenotypic response of a drug combination. It is possible that a drug combination is highly synergistic, while its actual response may be insufficient to reach therapeutic efficacy. On the other hand, a drug combination that exhibits strong response does not necessarily imply a synergistic interaction. For instance, only one of its component drugs may produce the response, while the other one is simply lowering the adverse effect of the first drug without affecting its on-target activity. In preclinical testing, a drug combination with strong synergy and efficacy should be prioritized for further mechanistic investigation, with an additional requirement of tolerable toxicity profile Fitzgerald et al. Accordingly, the dosages of a drug combination are also important factor for clinical feasibility and for maintaining acceptable side effects. For instance, the concept of therapeutic synergy compares the therapeutic windows of the single agents to that of their combinations, instead of using compound efficacies alone Kashif et al. However, the main focus of this review was the definition of synergistic interaction, and we refer those readers interested in the therapeutic significance of synergy in drug discovery to previous reviews Fitzgerald et al. The definition of synergistic interaction is still under debate. After a careful investigation of the Bliss independence model and the Loewe additivity model, we argue that, without jeopardizing the validity of both models, a more consistent terminology for classifying synergy and antagonism can be made. By comparing the observed combination response with the expected combination responses from the two models, as well as the single drug responses, one can classify the drug combination into five categories including strong antagonism, weak antagonism, non-interaction, weak synergy, and strong synergy. We propose the use of the interaction barometer to visualize the degree of interaction on the common axis of drug response, which has been shown to facilitate the interpretation and comparison between different combinations. We acknowledge that our proposal is not yet solving the practical issues for analyzing real data which typically contain combination responses tested at different dose ranges. How to maximize the benefits of the interaction barometer to summarize the interaction patterns of a drug combination would be a source of future research initiatives. We hope that such a classification scheme will raise more discussions about the standardization of the interaction assessment, toward finally reaching a consensus not only on the definition itself, but also on the other important issues, such as the experimental design of combination experiments, their quality control and the statistical evaluation of synergy and antagonism. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. As a library, NLM provides access to scientific literature. Front Pharmacol. What is synergy? Find articles by Jing Tang. Find articles by Krister Wennerberg. Find articles by Tero Aittokallio. Edited by: Thomas J. Received Jun 29; Accepted Aug 11; Collection date Open in a new tab. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. 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The fire only affects the store, not the accommodations. Accommodation activities will continue as normal. Monday Neste is also rapidly expanding its product range due to the situation. We are now doing our best with the municipality of the area to be able to provide temporary facilities for Kuukkeli as soon as possible. I would like to emphasize that entrepreneurs in the area are still able to provide tourists with all the services they need. Ski holiday weeks are extremely important to our region and after difficult Corona years, we now need visitors more than ever. The municipalities and entrepreneurs in the area have worked tremendously to ensure that holidaymakers can enjoy our unique area and I really hope that no-one cancels their trip to our area due to an unfortunate incident. The range of products will be expanded to better match the range that was available at Kuukkeli store. You can also order products and prescription drugs for the library bus via online pharmacy at Ivalo Pharmacy. Taxi can be booked from for example following service providers: Taxi Briitta Pajari. Search Search. Share Facebook Twitter. News Searching. See all news See all events. X Subscribe to our newsletter. By subscribing to Lapland North Destinations newsletter you will receive news from the region. By subscribing I give Lapland North Destinations the right to use my contact information for direct marketing to provide news and offers. Subscription can be ended any moment. This field is for validation purposes and should be left unchanged.
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