Drug interaction studies between paclitaxel (Taxol) and OC144-093 — A new modulator of MDR in cancer chemotherapy

Guns, Emma S. Tomlinson; Denyssevych, Tetyana; Dixon, Ross; Bally, Marcel B.; Mayer, Lawrence D.

doi:10.1007/bf03190426

Cited by 32 publications

(16 citation statements)

References 22 publications

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“…Some of the higher profile examples include: i) the cyclosporin A derivative valspodar (PSC-833); 6 ii) Vertex Pharmaceutical's biricodar (VX-710); 7 iii) the anthranilamide-based modulators XR9051 ( 2 ), 8 tariquidar (XR9576), 9-10 XR9577, 11 and WK-X-34; 11 iv) acridone carboxamide derivative elacridar (GF120918); 12 v) the heteroaryloxypropanolamines zosuquidar (LY335979), 13 dofequidar (MS-209) 14 and laniquidar; 15 and vi) the diarylimidazole ontogen (OC144-093, ONT-093). 16 The later generation inhibitors tended to be more potent and less toxic than the first generation compounds; however, multiple off-target issues remain.…”

Section: Introductionmentioning

confidence: 99%

A Selective ATP-Binding Cassette Subfamily G Member 2 Efflux Inhibitor Revealed via High-Throughput Flow Cytometry

et al. 2013

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Chemotherapeutics tumor resistance is a principal reason for treatment failure and clinical and experimental data indicate that multidrug transporters such as ATP-binding Cassette (ABC) B1 and ABCG2 play a leading role by preventing cytotoxic intracellular drug concentrations. Functional efflux inhibition of existing chemotherapeutics by these pumps continues to present a promising approach for treatment. A contributing factor to the failure of existing inhibitors in clinical applications is limited understanding of specific substrate/inhibitor/pump interactions. We have identified selective efflux inhibitors by profiling multiple ABC transporters against a library of small molecules to find molecular probes to further explore such interactions. In our primary screening protocol using JC-1 as a dual-pump fluorescent reporter substrate we identified a piperazine substituted pyrazolo[1,5-a]pyrimidine substructure with promise for selective efflux inhibition. As a result of a focused SAR-driven chemistry effort we describe compound 1 (CID44640177), an efflux inhibitor with selectivity toward ABCG2 over ABCB1. Compound 1 is also shown to potentiate the activity of mitoxantrone in vitro as well as preliminarily in vivo in an ABCG2 over-expressing tumor model. At least two analogs significantly reduce tumor size in combination with the chemotherapeutic topotecan. To our knowledge, low nanomolar chemoreversal activity coupled with direct evidence of efflux inhibition for ABCG2 is unprecedented.

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Section: Introductionmentioning

confidence: 99%

A Selective ATP-Binding Cassette Subfamily G Member 2 Efflux Inhibitor Revealed via High-Throughput Flow Cytometry

et al. 2013

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“…38 L/h for Case 2) when CL was not less than the standard. If CL is less than the standard, the CL fold change was defined as the negative ratio of the standard CL (38 L/h for Case 2) to the CL used in the simulation (CL sim ) as shown in equation (4 b n1, n2, ..., n10, n11 have the similar meaning as in footnote a except that they represent 11 numbers from 1 to 5 considering the bioavailability limit (0 to 1).…”

Section: Glossarymentioning

confidence: 99%

“…[2,3] The second category includes the cases in which AUC has larger fold changes compared to C max . [4][5][6][7][8] In cases of the third category, C max has larger fold changes compared with AUC. [9][10][11][12][13][14][15][16][17][18] It is interesting to note that rosuvastatin in three different studies with three different concomitant medications (gemfibrozil, [6] lopinavir/ ritonavir [7] and ciclosporin, [8] respectively) showed similar pattern with C max having higher fold changes compared to AUC fold changes, although the absolute fold changes were different (2.21, 4.7 and 10.6 for C max , respectively).…”

Section: Introductionmentioning

confidence: 99%

Drug-Drug Interaction Pattern Recognition

Duan

2010

Drugs R D

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Background and Objective: Drug-drug interaction (DDI) is an important aspect of drug development, especially for safety. When a drug is used concomitantly with other drug(s), one of the major concerns is the change of exposures, including the rate and extent of drug absorption, distribution, metabolism and elimination. To address the concerns, a common practice is to measure and report the differences between the exposure in the presence and in the absence of concomitant medication (COMED). The area under the plasma concentration versus time curve (AUC), maximum plasma concentration (C max ) and time to reach the C max (t max ) changes are usually measured in DDI studies. A usual observation is the different extents of changes among AUC, C max and t max , which may raise concerns in certain therapeutic areas or some special agents. The objective of this study was to investigate the variation among changes of AUC, C max and t max in DDI studies, and its pharmacokinetic manifestation. Data Sources: Based on a list of DDI results from the literature, with the assumptions that the primary parameters of a drug of interest were altered during a DDI, two sets of simulated data were generated according to a single oral dose, one-compartment model. The first set including 24 cases with different half-lives and absorption constants (k a ) considered the exposure changes upon independent variation of bioavailability (F), clearance (CL), volume of distribution (V d ) and k a up to 50-fold increases or decreases. The second set considered the exposure changes with simultaneous variation of F, CL, V d , and k a within 5-fold range (increase or decrease) for a case selected from the first set. Study Selection, Data Extraction and Synthesis: Parameter fold changes (defined in a fashion showing fold increase or fold decreases, including CL fold change, F fold change, V d fold change and k a fold change) and exposure changes (AUC fold change, C max fold change, t max fold change and fold change difference [AUC fold change -C max fold change]) were used to generate plots demonstrating various relationships between parameter fold changes and exposure changes. Based on the observations that AUC was influenced by CL and F, C max was affected by all four parameters, t max was mainly determined by CL and k a , F did little for t max and k a was unrelated to AUC, a chart was created for DDI pattern recognition.

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“…[2,3] The second category includes the cases in which AUC has larger fold changes compared to C max . [4–8] In cases of the third category, C max has larger fold changes compared with AUC. [9–18]…”

Section: Introductionmentioning

confidence: 99%

Drug-Drug Interaction Pattern Recognition

Duan

2010

Drugs R D

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Background and Objective: Drug-drug interaction (DDI) is an important aspect of drug development, especially for safety. When a drug is used concomitantly with other drug(s), one of the major concerns is the change of exposures, including the rate and extent of drug absorption, distribution, metabolism and elimination. To address the concerns, a common practice is to measure and report the differences between the exposure in the presence and in the absence of concomitant medication (COMED). The area under the plasma concentration versus time curve (AUC), maximum plasma concentration (Cmax) and time to reach the Cmax (tmax) changes are usually measured in DDI studies. A usual observation is the different extents of changes among AUC, Cmax and tmax, which may raise concerns in certain therapeutic areas or some special agents. The objective of this study was to investigate the variation among changes of AUC, Cmax and tmax in DDI studies, and its pharmacokinetic manifestation.Data Sources: Based on a list of DDI results from the literature, with the assumptions that the primary parameters of a drug of interest were altered during a DDI, two sets of simulated data were generated according to a single oral dose, one-compartment model. The first set including 24 cases with different half-lives and absorption constants (ka) considered the exposure changes upon independent variation of bioavailability (F), clearance (CL), volume of distribution (Vd) and ka up to 50-fold increases or decreases. The second set considered the exposure changes with simultaneous variation of F, CL, Vd, and ka within 5-fold range (increase or decrease) for a case selected from the first set.Study Selection, Data Extraction and Synthesis: Parameter fold changes (defined in a fashion showing fold increase or fold decreases, including CL fold change, F fold change, Vd fold change and ka fold change) and exposure changes (AUC fold change, Cmax fold change, tmax fold change and fold change difference [AUC fold change — Cmax fold change]) were used to generate plots demonstrating various relationships between parameter fold changes and exposure changes. Based on the observations that AUC was influenced by CL and F, Cmax was affected by all four parameters, tmax was mainly determined by CL and ka, F did little for tmax and ka was unrelated to AUC, a chart was created for DDI pattern recognition.Conclusion: An approach, named DDI pattern recognition, is proposed for didactical purposes. It provides a quick initial estimate for interpreting the DDI results based on the exposure changes. This approach entails the following stages: (i) performing a drug interaction study; (ii) calculating the exposure changes in the presence of COMED compared to those in the absence of COMED, and the fold change difference; (iii) selecting the parameter fold changes that may play important roles in a specific DDI, by estimating their possible ranges; and (iv) interpreting the DDI by integrating all the information available, such as the possible mechanism involved. A quic...

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Drug interaction studies between paclitaxel (Taxol) and OC144-093 — A new modulator of MDR in cancer chemotherapy

Cited by 32 publications

References 22 publications

A Selective ATP-Binding Cassette Subfamily G Member 2 Efflux Inhibitor Revealed via High-Throughput Flow Cytometry

A Selective ATP-Binding Cassette Subfamily G Member 2 Efflux Inhibitor Revealed via High-Throughput Flow Cytometry

Drug-Drug Interaction Pattern Recognition

Drug-Drug Interaction Pattern Recognition

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