Definition Of Synergy Research Articles

Abstract 1907: Combocat: A high-throughput framework for drug combination studies

Abstract Drug combinations are the basis of treatment for modern diseases but arriving at successful combination therapies is fraught with challenges. Decades ago, the limited number of drugs represented a tractable candidate list from which to design combination experiments. However, the current pool of single-agent drugs to potentially combine is far too large to brute-force screen, and purely computational predictions have performed poorly. Suitable screening methods are needed, but the design of experimental approaches has proven to be highly complex; researchers need to carefully balance many variables such as appropriate drug concentration ranges, number of doses, inclusion of replicates, and throughput. Perhaps the most significant obstacle facing these studies is the approach to data analysis, where conflicting definitions of synergy and unintuitive metrics serve to confuse researchers and render largely uninterpretable results. Collectively, these challenges hamper the progress of drug combination research and ultimately translational impact. To overcome these limitations, we have developed a fully self-contained framework to handle both the experimental design and analysis of drug combination experiments. Our method, called Combocat, provides a straightforward way to test and analyze any number of drug combinations and samples, and is suitable for high-throughput. Combocat provides a high-resolution of concentration combinations compared to most current approaches. This is automated by common instruments and uses scripts included within our protocol. Through careful template design, we were able to include 3 replicates of each 10x10 matrix, single-agent drugs, and controls - all within a single 384-well plate. We found our method to work robustly with varying sample types (Human cancer, bacteria, fungi), and readouts. After data generation, files can simply be dragged into our Combocat analysis tool directly. We provide a free, web-based software suite to fully automate the analysis after data collection. The Combocat web tool is intuitive and facilitates interactive exploration of synergy. It also provides a rich array of information such as dose-response curves, IC50 values, synergy matrices, ranked hit plots, and more. Data normalization, synergy algorithms, scoring functions, and other complex calculations are run swiftly and automatically in the background with no need for user input. Notably, we employ statistical testing by taking advantage of experimental replicates, which is a feature we found lacking in most methods. We use a well-documented synergy metric but also decided to formulate our own Combocat score which considers statistical measurements and assay quality. The Combocat score provides an easy interpretation of results and facilitates quick identification of top hits. Collectively, our platform will be used to enhance and expedite the selection of effective drug combinations. Citation Format: William C. Wright, Min Pan, Hyeong-Min Lee, Gregory A. Phelps, Jonathan Low, Duane Currier, Richard E. Lee, Taosheng Chen, Paul Geeleher. Combocat: A high-throughput framework for drug combination studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1907.

In search for a synergistic combination against pandrug-resistant A. baumannii; methodological considerations.

Pending approval of new antimicrobials, synergistic combinations are the only treatment option against pandrug-resistant A. baumannii (PDRAB). Considering the lack of a standardized methodology, the aim of this manuscript is to systematically review the methodology and discuss unique considerations for assessing antimicrobial combinations against PDRAB. Post-hoc analysis of a systematic review (conducted in PubMed and Scopus from inception to April 2021) of studies evaluating antimicrobial combination against A. baumannii, based on antimicrobials that are inactive in vitro alone. Eighty-four publications were reviewed, using a variety of synergy testing methods, including; gradient-based methods (n = 11), disk-based methods (n = 6), agar dilution (n = 2), checkerboard assay (n = 44), time-kill assay (n = 50), dynamic in vitro PK/PD models (n = 6), semi-mechanistic PK/PD models (n = 5), and in vivo animal models (n = 11). Several variations in definitions of synergy and interpretation of each method were observed and are discussed. Challenges related to testing combinations of antimicrobials that are inactive alone (with regards to concentrations at which the combinations are assessed), as well as other considerations (assessment of stasis vs killing, clinical relevance of re-growth in vitro after initial killing, role of in vitro vs in vivo conditions, challenges of clinical testing of antimicrobial combinations against PDRAB infections) are discussed. This review demonstrates the need for consensus on a standardized methodology and clinically relevant definitions for synergy. Modifications in the methodology and definitions of synergy as well as a roadmap for further development of antimicrobial combinations against PDRAB are proposed.

Synergetic effects in working teams

PurposeWhereas motivation and coordination losses in teams have been investigated for quite some time, systematic research on performance gains in teams (often called “synergetic effects”) only emerged recently. The purpose of the present paper is to clarify the concept of process gains (or synergy) in teams, and to introduce recent findings from basic psychology that can be very valuable for the management of high performing teams.Design/methodology/approachBased on the definition of synergy as process gain during teamwork compared with a clear baseline (team potential), this review develops specific requirements for the empirical demonstration of synergetic effects in teams. Moreover, a brief history of research on process gains in teams is provided, followed by an outlook on current and future trends in this field.FindingsAlthough this research is still in its pioneering days, various triggers of process gains in teams have been already derived theoretically and/or demonstrated empirically, among them social support from fellow team members, perceived indispensability for the team outcome, the development and/or selection of experts for task and team processes, use of multiple perspectives and information, team learning, and social identification processes.Practical implicationsUnderstanding the preconditions and underlying mechanisms of process gains in teams enables managers to trigger performance levels of teams that exceed what can be expected based on the individual team members' capabilities alone. Moreover, the estimation of a team's potential provides a helpful standard for the assessment of the ongoing team performance.Originality/valueProcess gains in teams and related laboratory research have been largely neglected in the managerial literature so far. This paper and the current special issue are among the first to introduce a clear definition of process gains in teams, and to suggest concrete trigger factors of synergetic effects in teams based on systematic research.

Notions of synergy for combinations of interventions against infectious diseases in heterogeneously mixing populations

In public health programmes interventions are frequently combined with hoped for ‘synergies’ [22]. However, there is not yet a precise definition for synergy between interventions that captures the idea that there is added benefit at the population-level in using them together. To explore the synergy between interventions in the context of endemic disease, we consider a general model of infection spread in a heterogeneously mixing population. We consider interventions which may alter individuals’ infectiousness, susceptibility, profile of infectiousness through time and survival while infected. Allowing general patterns of overlap and targeting in those receiving the interventions, we show how to compute changes to epidemiological indices such as R 0, and introduce a simple technique for calculating equilibrium prevalences and incidences via an iterated map. We argue for a particular definition of synergy and investigate its behaviour, both analytically and numerically, concluding that it is easiest to achieve synergy between interventions which perform poorly in isolation; implementation strategies that minimize the overlap of different interventions in the population tend to achieve more synergy; and that in populations with heterogeneous risk, interventions that are redundant when universally targeted can regain substantial synergy when applied in a targeted manner.

Abstract 5398: Drug combination synergy quantification in animals and clinics using computerized combination index method

Abstract Drug combination has been used for thousand of years, and currently widely used in the treatments of cancer, AIDS, and other diseases. Drug combination studies have been carried out mostly in vitro but rarely in animals and clinics due to cost, efficiency, ethical considerations, etc. However, the “definition of synergy” is universal and can be quantitatively determined as indicated by Chou & Talalay in (i). Adv. Enz. Regul. 22: 27-55, 1984 and by Chou in (ii). Pharmacol. Rev. 58: 621-681, 2006, (iii). Leukemia & Lymphoma 49: 2059-2080, 2008, and (iv). Cancer Res. Perspective Article 2009 (in press). By definition, synergism is more than the additive effect, however, additive effect cannot be determined by the arithmetic sum of effect, nor by the fractional product method, nor by statistics, p value, but rather by the combination index (CI) where CI = 1, < 1, and > 1 indicate additive effect, synergism, and antagonism, respectively. The theory, experimental design, and computerized simulation have been discussed or illustrated in (i-iii), and in (iv) outlined the pitfalls, common errors, and frequently asked questions. The CI method of Chou-Talalay has been cited in over 400 journals and in (i) alone has been cited over 1,916 times with citations over 1,000-fold higher than the journal's impact number. The CI method, based on the median-effect principle of the mass-action law, is quantitative, efficient, simple and economical which allows using small number of data points. This paper will illustrate how synergism of two anticancer drugs can be determined in nude mice bearing mammary carcinoma MX-1, and why two similar anti-HIV clinical trials, where both used surrogate markers and fractionated doses, were published and had different results; one used 366 patients (N. Engl. J. Med. 333: 1662-1669, 1995) but was unable to determine synergism due to the experimental design, and another, which applied the CI method, used only 36 patients (Antivir. Ther. 1: 77-88, 1996), and yet was able to quantitatively determine synergism. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5398.

Correlation of antibiotic synergy in vitro and in vivo: use of an animal model of neutropenic gram-negative sepsis.

The predictive value of in vitro studies of antibiotic interaction for clinical drug interactions is unclear. Five clinical isolates (two Klebsiella, two Pseudomonas aeruginosa, and one Serratia marcescens) were evaluated by the time-kill curve method for in vitro synergy between amikacin and imipenem. When we used the stringent definition of synergy of Hallander et al., no synergy was present for any study strain; however, when we used a more-conventional definition of synergy, these drugs interacted synergistically against all study strains. The results of these in vitro studies were correlated with in vivo interactions by using neutropenic infant rats injected ip with study organisms and given various treatment regimens. For 80% of the study strains, treatment of rats with amikacin and imipenem resulted in significantly greater survival than did therapy with either drug alone or than could be predicted by addition of survival rates achieved with either agent alone (P less than .005). In vitro studies predicted this in vivo synergy in 80% of the cases when the more-conventional definition of synergy was used, whereas they were not predictive when the more-stringent definition of synergy was used. This rat model of neutropenia and gram-negative sepsis may provide more insight into in vivo drug interactions than do current methods.