Ex Vivo Assessment of Drug Activity in Patient Tumor Cells as a Basis for Tailored Cancer Therapy

Blom, Kristin; Nygren, Peter; Alvarsson, Jonathan; Larsson, Rolf; Andersson, Claes

doi:10.1177/2211068215598117

Cited by 32 publications

(28 citation statements)

References 15 publications

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“…The combinatorial explosion makes exhaustive testing of drug dose combinations unfeasible. This problem is especially acute in cases where material is scarce, such as testing of patient-derived samples (26)(27)(28)(29). Hence, models for predicting high-order effects are essential.…”

mentioning

confidence: 99%

Prediction of multidimensional drug dose responses based on measurements of drug pairs

Zimmer

Katzir

Dekel

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

152

174

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Finding potent multidrug combinations against cancer and infections is a pressing therapeutic challenge; however, screening all combinations is difficult because the number of experiments grows exponentially with the number of drugs and doses. To address this, we present a mathematical model that predicts the effects of three or more antibiotics or anticancer drugs at all doses based only on measurements of drug pairs at a few doses, without need for mechanistic information. The model provides accurate predictions on available data for antibiotic combinations, and on experiments presented here on the response matrix of three cancer drugs at eight doses per drug. This approach offers a way to search for effective multidrug combinations using a small number of experiments.drug combinations | drug cocktails | cancer treatment | mechanism-free formula | predictive formula T o kill cancer cells or bacteria, combination therapy can be more effective than individual drugs (1-6). Combination therapy is thought to allow increased efficacy at low doses, thus reducing side effects and toxicity; it is also believed to minimize the chances of resistance (7-9), a pressing problem in treating cancer and infectious diseases.Much work has been devoted to classifying how pairs of drugs interact (10)(11)(12)(13)(14). Across systems, a good first approximation is the Bliss independence model (15,16), in which the pair effect is the product of the individual drug effects: If the effect of the drugs are g 1 and g 2 , the effect of the combination is g 12 = g 1 · g 2 . The Bliss model ignores interactions in which drugs enhance each other effectssynergism-or inhibit each other's effects-antagonism. Some drugs even inhibit each other so much that the combined effect is lower than either drug alone, an effect called hyperantagonism (17)(18)(19)(20)(21).Going beyond drug pairs has been difficult. Experimentally testing high-order combinations beyond pairs in a systematic way is challenging because it requires an exponentially large number of experiments (22-25): For N drugs at D doses, one needs D N experiments. For N = 10 drugs and D = 8 doses, this means ∼10 9 measurements. The combinatorial explosion makes exhaustive testing of drug dose combinations unfeasible. This problem is especially acute in cases where material is scarce, such as testing of patient-derived samples (26)(27)(28)(29). Hence, models for predicting high-order effects are essential.Apart from detailed simulations of particular systems (22,30,31), there has been little study of general mechanism-independent models for multiple drugs. An exception is the elegant study by Wood et al. (32) that showed that combinations of antibiotics can be predicted by an Iserliss-like formula that uses pair effects to predict the effects of higher-order mixtures. For example, the effect of a drug triplet is modeled as g 123 = g 12 g 3 + g 13 g 2 + g 23 g 1 − 2g 1 g 2 g 3 . This formula has not been tested on cancer drug mixtures, to the best of our knowledge.Another line of research u...

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mentioning

confidence: 99%

Prediction of multidimensional drug dose responses based on measurements of drug pairs

Zimmer

Katzir

Dekel

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

152

174

View full text Add to dashboard Cite

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“…As previously described by Blom et al . [37], AML cells and peripheral blood mononuclear cells (PBMC) from healthy donors were isolated from bone marrow or peripheral blood by 1.007 g/ml histopaque (Pharmacia Biotech, Uppsala) density gradient centrifugation. Cells were counted using toluidine blue and resuspended in RPMI 1640 cell culture medium, supplemented with 10% HI-FCS and 2% pest/glut (all Sigma-Aldrich), to a concentration of 0.4 × 10 6 cells/mL for use in the FMCA.…”

Section: Methodsmentioning

confidence: 99%

In vitro and in vivo anti-leukemic activity of the peptidase-potentiated alkylator melflufen in acute myeloid leukemia

et al. 2016

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The novel aminopeptidase potentiated alkylating agent melflufen, was evaluated for activity in acute myeloid leukemia in a range of in vitro models, as well as in a patient derived xenograft study. All tested AML cell lines were highly sensitive to melflufen while melphalan was considerably less potent. In the HL-60 cell line model, synergy was observed for the combination of melflufen and cytarabine, an interaction that appeared sequence dependent with increased synergy when melflufen was added before cytarabine. Also, in primary cultures of AML cells from patients melflufen was highly active, while normal PBMC cultures appeared less sensitive, indicating a 7-fold in vitro therapeutic index. Melphalan, on the other hand, was only 2-fold more potent in the AML patient samples compared with PBMCs. Melflufen was equally active against non-malignant, immature CD34+ progenitor cells and a more differentiated CD34+ derived cell population (GM14), whereas the stem cell like cells were less sensitive to melphalan. Finally, melflufen treatment showed significant anti-leukemia activity and increased survival in a patient derived xenograft of AML in mice. In conclusion, melflufen demonstrates high and significant preclinical activity in AML and further clinical evaluation seem warranted in this disease.

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“…Special techniques include primary cells from patients, preparation culturing and testing in various cell assays [24], 3D-cell culture techniques in 384-format [25], transcriptomics screening in 384-format (in collaboration with Justin Lamb) [26], Cmap analysis [27,28], imaging, time-lapse, studying confluence, apoptosis, growth patterns, morphology, subcellular structures [29,30], drug combination studies on viability/proliferation, differentiation, subcellular morphology. Multivariate data analysis includes machine learning and mass spectrometry-based metabolic and proteomic profiling.…”

Section: In Vitro and Systems Pharmacologymentioning

confidence: 99%

Institutional Profile: the National Swedish Academic Drug Discovery & Development Platform at Scilifelab

Arvidsson

Sandberg

Sakariassen³

2017

Future Sci. OA

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The Science for Life Laboratory Drug Discovery and Development Platform (SciLifeLab DDD) was established in Stockholm and Uppsala, Sweden, in 2014. It is one of ten platforms of the Swedish national SciLifeLab which support projects run by Swedish academic researchers with large-scale technologies for molecular biosciences with a focus on health and environment. SciLifeLab was created by the coordinated effort of four universities in Stockholm and Uppsala: Stockholm University, Karolinska Institutet, KTH Royal Institute of Technology and Uppsala University, and has recently expanded to other Swedish university locations. The primary goal of the SciLifeLab DDD is to support selected academic discovery and development research projects with tools and resources to discover novel lead therapeutics, either molecules or human antibodies. Intellectual property developed with the help of SciLifeLab DDD is wholly owned by the academic research group. The bulk of SciLifeLab DDD's research and service activities are funded from the Swedish state, with only consumables paid by the academic research group through individual grants.

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Ex Vivo Assessment of Drug Activity in Patient Tumor Cells as a Basis for Tailored Cancer Therapy

Cited by 32 publications

References 15 publications

Prediction of multidimensional drug dose responses based on measurements of drug pairs

Prediction of multidimensional drug dose responses based on measurements of drug pairs

In vitro and in vivo anti-leukemic activity of the peptidase-potentiated alkylator melflufen in acute myeloid leukemia

Institutional Profile: the National Swedish Academic Drug Discovery & Development Platform at Scilifelab

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