Die Mischarznei

Loewe, S.

doi:10.1007/bf01890305

Cited by 101 publications

(35 citation statements)

References 2 publications

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“…A graphical tool is the isobologram (25)(26)(27)(28). The isobologram is based on the simplified model of a direct relationship between drug dose and a biomarker response and generates a Bcurve of additivity^which can be used to distinguish additivity from synergy.…”

Section: Introductionmentioning

confidence: 99%

Tumor Static Concentration Curves in Combination Therapy

Cardilin

Almquist

Jirstrand

et al. 2016

AAPS J

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Abstract.Combination therapies are widely accepted as a cornerstone for treatment of different cancer types. A tumor growth inhibition (TGI) model is developed for combinations of cetuximab and cisplatin obtained from xenograft mice. Unlike traditional TGI models, both natural cell growth and cell death are considered explicitly. The growth rate was estimated to 0.006 h −1 and the natural cell death to 0.0039 h −1 resulting in a tumor doubling time of 14 days. The tumor static concentrations (TSC) are predicted for each individual compound. When the compounds are given as single-agents, the required concentrations were computed to be 506 μg · mL −1 and 56 ng · mL −1 for cetuximab and cisplatin, respectively. A TSC curve is constructed for different combinations of the two drugs, which separates concentration combinations into regions of tumor shrinkage and tumor growth. The more concave the TSC curve is, the lower is the total exposure to test compounds necessary to achieve tumor regression. The TSC curve for cetuximab and cisplatin showed weak concavity. TSC values and TSC curves were estimated that predict tumor regression for 95% of the population by taking between-subject variability into account. The TSC concept is further discussed for different concentration-effect relationships and for combinations of three or more compounds.

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Section: Introductionmentioning

confidence: 99%

Tumor Static Concentration Curves in Combination Therapy

Cardilin

Almquist

Jirstrand

et al. 2016

AAPS J

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“…Therefore, each drug is an agonist that displays dose dependence. As studies of drug combinations have become more common, there has emerged an increased use of the isobologram, a graph that was introduced many years ago (Loewe, 1927(Loewe, , 1928. That graph, constructed on a coordinate system composed of the individual drug doses, commonly contains a straight "line of additivity" that is employed to distinguish additive from synergistic and antagonistic interactions.…”

Section: Introductionmentioning

confidence: 99%

“…The individual doses that produce the specified effect are determined from the dose-effect graphs (Fig. 1A), and these doses are plotted as the axial points in a Cartesian coordinate plot termed the isobologram, a plot that was popularized by Loewe (1927Loewe ( , 1928Loewe ( , 1953 and shown in Fig. 1B.…”

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confidence: 99%

An Overview of Drug Combination Analysis with Isobolograms

Tallarida

2006

J Pharmacol Exp Ther

455

434

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Drugs given in combination may produce effects that are greater than or less than the effect predicted from their individual potencies. The historical basis for predicting the effect of a combination is based on the concept of dose equivalence; i.e., an equally effective dose (a) of one will add to the dose (b) of the other in the combination situation. For drugs with a constant relative potency, this leads to linear additive isoboles (a-b curves of constant effect), whereas a varying potency ratio produces nonlinear additive isoboles. Determination of the additive isobole is a necessary procedure for assessing both synergistic and antagonistic interactions of the combination. This review discusses both variable and constant relative potency situations and provides the mathematical formulas needed to distinguish these cases.This communication is concerned with the analysis of combinations of two drugs that produce overtly similar effects that are measurable. Therefore, each drug is an agonist that displays dose dependence. As studies of drug combinations have become more common, there has emerged an increased use of the isobologram, a graph that was introduced many years ago (Loewe, 1927(Loewe, , 1928. That graph, constructed on a coordinate system composed of the individual drug doses, commonly contains a straight "line of additivity" that is employed to distinguish additive from synergistic and antagonistic interactions. This graphical construction is based on the assumption of a constant relative potency.In a previous review, Tallarida (2001) discussed the use and construction of the common (linear) isobole, the set of points (dose pairs) that give a specified effect magnitude. A subsequent study (Grabovsky and Tallarida, 2004) considered combinations of a full and partial agonist, a situation that necessarily means a variable relative potency. That situation was shown to lead to nonlinear isoboles of additivity instead of the widely applied linear isobole and demonstrated that experimental results in this case could be mistaken for synergism or antagonism. That result (nonlinearity), which represents a departure from the common use of isobolograms, prompted further attention to other situations of variable relative potency. However, the variability condition was not explicitly connected to Loewe's concept of dose equivalence in the author's previous review. In this review, we make this explicit by showing (for the first time) that the derivation leading to curved isoboles is consistent with the same concept of dose equivalence that was employed by Loewe. A further consequence of this concept is in its application to two full agonists, with varying relative potency, a case that is shown here to lead to not just one but two nonlinear but symmetric isoboles. The demonstration (proof) of symmetry of this pair of isoboles, presented here for the first time, provides a new criterion for distinguishing between additive and nonadditive interactions.The theoretical basis for this graphical procedure, i.e., t...

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“…As introduced by Loewe (1927Loewe ( , 1928Loewe ( , 1953, it is a graph in Cartesian coordinates in which the axes are the doses of the respective drugs. These are drugs or compounds that produce overtly similar effects (e.g., two analgesics, two antihypertensives, etc.)…”

Section: Introductionmentioning

confidence: 99%

Revisiting the Isobole and Related Quantitative Methods for Assessing Drug Synergism

Tallarida¹

2012

J Pharmacol Exp Ther

128

136

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The isobole is well established and commonly used in the quantitative study of agonist drug combinations. This article reviews the isobole, its derivation from the concept of dose equivalence, and its usefulness in providing the predicted effect of an agonist drug combination, a topic not discussed in pharmacology textbooks. This review addresses that topic and also shows that an alternate method, called "Bliss independence," is inconsistent with the isobolar approach and also has a less clear conceptual basis. In its simplest application the isobole is the familiar linear plot in Cartesian coordinates with intercepts representing the individual drug potencies. It is also shown that the isobole can be nonlinear, a fact recognized by its founder (Loewe) but neglected or rejected by virtually all other users. Whether its shape is linear or nonlinear the isobole is equally useful in detecting synergism and antagonism for drug combinations, and its theoretical basis leads to calculations of the expected effect of a drug combination. Numerous applications of isoboles in preclinical testing have shown that synergism or antagonism is not only a property of the two agonist drugs; the dose ratio is also important, a fact of potential importance to the design and testing of drug combinations in clinical trials.

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Die Mischarznei

Cited by 101 publications

References 2 publications

Tumor Static Concentration Curves in Combination Therapy

Tumor Static Concentration Curves in Combination Therapy

An Overview of Drug Combination Analysis with Isobolograms

Revisiting the Isobole and Related Quantitative Methods for Assessing Drug Synergism

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