The interaction index: a measure of drug synergism

Pain ◽  
2002 ◽  
Vol 98 (1) ◽  
pp. 163-168 ◽  
Author(s):  
Ronald J. Tallarida
Keyword(s):  
Drug Synergism ◽  
Interaction Index

scholarly journals Intramax and other objective functions: The case of Slovenia

2016 ◽  
Vol 24 (2) ◽  
pp. 12-25 ◽  
Author(s):  
Samo Drobne ◽  
Mitja Lakner
Keyword(s):  
Objective Function ◽  
The Other ◽  
Objective Functions ◽  
Interaction Index ◽  
Functional Regions ◽  
Hierarchical Aggregation ◽  
Administrative Units

Abstract The use of different objective functions in hierarchical aggregation procedures is examined in this paper. Specifically, we analyse the use of the original Intramax objective function, the sum-of-flows objective function, the sum-of-proportions-to-intra-regional-flows objective function, Smart’s weighted interaction index, the first and second CURDS weighted interaction indices, and Tolbert and Killian’s interaction index. The results of the functional regionalisation have been evaluated by self-containment statistics, and they show that the use of the original Intramax procedure tends to delineate operationally the most persuasive and balanced regions that, regarding the intra-regional flows, homogeneously cover the analysed territory. The other objective functions give statistically better but operationally less suitable results. Functional regions modelled using the original Intramax procedure were compared to the regions at NUTS 2 and NUTS 3 levels, as well as to administrative units in Slovenia. We conclude that there are some promising directions for further research on functional regionalisation using hierarchical aggregation procedures.

Informatics of Drug Synergism in Naturally Occurring Anticancer Agents

2011 ◽  
Vol 6 (1) ◽  
pp. 26-44 ◽  
Author(s):  
Ghazaleh Ghavami ◽  
Mohammad R. Kazemali ◽  
Soroush Sardari
Keyword(s):  
Anticancer Agents ◽  
Drug Synergism ◽  
Naturally Occurring

Statistical tests of differential susceptibility: Performance, limitations, and improvements

2017 ◽  
Vol 29 (4) ◽  
pp. 1267-1278 ◽  
Author(s):  
Marco Del Giudice
Keyword(s):  
Monte Carlo ◽  
Statistical Approach ◽  
Statistical Tests ◽  
Differential Susceptibility ◽  
Initial Response ◽  
Empirical Literature ◽  
False Negatives ◽  
Interaction Index ◽  
Future Studies ◽  
Highly Sensitive

AbstractStatistical tests of differential susceptibility have become standard in the empirical literature, and are routinely used to adjudicate between alternative developmental hypotheses. However, their performance and limitations have never been systematically investigated. In this paper I employ Monte Carlo simulations to explore the functioning of three commonly used tests proposed by Roisman et al. (2012). Simulations showed that critical tests of differential susceptibility require considerably larger samples than standard power calculations would suggest. The results also showed that existing criteria for differential susceptibility based on the proportion of interaction index (i.e., values between .40 and .60) are especially likely to produce false negatives and highly sensitive to assumptions about interaction symmetry. As an initial response to these problems, I propose a revised test based on a broader window of proportion of interaction index values (between .20 and .80). Additional simulations showed that the revised test outperforms existing tests of differential susceptibility, considerably improving detection with little effect on the rate of false positives. I conclude by noting the limitations of a purely statistical approach to differential susceptibility, and discussing the implications of the present results for the interpretation of published findings and the design of future studies in this area.

scholarly journals TAIJI: approaching experimental replicates-level accuracy for drug synergy prediction

2018 ◽  
Vol 35 (13) ◽  
pp. 2338-2339 ◽  
Author(s):  
Hongyang Li ◽  
Shuai Hu ◽  
Nouri Neamati ◽  
Yuanfang Guan
Keyword(s):  
Drug Combination ◽  
High Performance ◽  
Synergistic Effects ◽  
Field Performance ◽  
Supplementary Information ◽  
Drug Synergism ◽  
High Throughput Drug Screening ◽  
Current State ◽  
Candidate Drug ◽  
High Prediction

Abstract Motivation Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs. Results Here, we present TAIJI, a high-performance software for fast and accurate prediction of drug synergism. It is based on the winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a unique platform to unbiasedly evaluate the performance of current state-of-the-art methods, and includes 160 team-based submission methods. When tested across a broad spectrum of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction correlation (0.53), approaching the accuracy level of experimental replicates (0.56). The runtime is at the scale of minutes to achieve this state-of-the-field performance. Availability and implementation TAIJI is freely available on GitHub (https://github.com/GuanLab/TAIJI). It is functional with built-in Perl and Python. Supplementary information Supplementary data are available at Bioinformatics online.

Drug synergism of antihypertensive action in combination of telmisartan with lercanidipine in spontaneous hypertensive rats

2010 ◽  
Vol 33 (9) ◽  
pp. 1411-1418 ◽  
Author(s):  
Jung-Jin Lee ◽  
Chang-Yong Shin ◽  
Hong-Joon Park ◽  
Wei-Yun Zhang ◽  
Yohan Kim ◽  
...  
Keyword(s):  
Drug Synergism ◽  
Hypertensive Rats ◽  
Antihypertensive Action
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