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

Anat Zimmer; Itay Katzir; Erez Dekel; Avraham E. Mayo; Uri Alon; Iair Arcavi; P. Challis; K. C. Chambers; T. -W. Chen; P. S. Cowperthwaite; Avishay Gal-Yam; G. Hosseinzadeh; D. A. Howell; C. Inserra; E. Kankare; E. A. Magnier; K. Maguire; P. A. Mazzali; C. McCully; D. Milisavljevic; K. W. Smith; S. Taubenberger; S. Valenti; R. J. Wainscoat; Ofer Yaron; D. R. Young

doi:10.1073/pnas.1606301113

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

Anat Zimmer, Itay Katzir, Erez Dekel, Avraham E. Mayo, Uri Alon, Iair Arcavi, P. Challis, K. C. Chambers, T. -W. Chen, P. S. Cowperthwaite, Avishay Gal-Yam, G. Hosseinzadeh, D. A. Howell, C. Inserra, E. Kankare, E. A. Magnier, K. Maguire, P. A. Mazzali, C. McCully, D. MilisavljevicK. W. Smith, S. Taubenberger, S. Valenti, R. J. Wainscoat, Ofer Yaron, D. R. Young

Weizmann Institute of Science

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	10442-10447
Number of pages	6
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	113
Issue number	37
DOIs	https://doi.org/10.1073/pnas.1606301113
State	Published - 13 Sep 2016

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cancer treatment|mechanism-free formula
Drug cocktails
Drug combinations
Predictive formula

All Science Journal Classification (ASJC) codes

General

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10.1073/pnas.1606301113

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Cite this

Zimmer, A., Katzir, I., Dekel, E., Mayo, A. E., Alon, U., Arcavi, I., Challis, P., Chambers, K. C., Chen, T. .-W., Cowperthwaite, P. S., Gal-Yam, A., Hosseinzadeh, G., Howell, D. A., Inserra, C., Kankare, E., Magnier, E. A., Maguire, K., Mazzali, P. A., McCully, C., ... Young, D. R. (2016). Prediction of multidimensional drug dose responses based on measurements of drug pairs. Proceedings of the National Academy of Sciences of the United States of America, 113(37), 10442-10447. https://doi.org/10.1073/pnas.1606301113

@article{f2349dbaf9104fa285d8b3f33093db01,

title = "Prediction of multidimensional drug dose responses based on measurements of drug pairs",

abstract = "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.",

keywords = "Cancer treatment|mechanism-free formula, Drug cocktails, Drug combinations, Predictive formula",

author = "Anat Zimmer and Itay Katzir and Erez Dekel and Mayo, \{Avraham E.\} and Uri Alon and Iair Arcavi and P. Challis and Chambers, \{K. C.\} and Chen, \{T. -W.\} and Cowperthwaite, \{P. S.\} and Avishay Gal-Yam and G. Hosseinzadeh and Howell, \{D. A.\} and C. Inserra and E. Kankare and Magnier, \{E. A.\} and K. Maguire and Mazzali, \{P. A.\} and C. McCully and D. Milisavljevic and Smith, \{K. W.\} and S. Taubenberger and S. Valenti and Wainscoat, \{R. J.\} and Ofer Yaron and Young, \{D. R.\}",

note = "Clore-Katz-David Foundation; Fela Shapell Family Foundation; Israel National Center for Personalized Medicine (INCPM)",

year = "2016",

month = sep,

day = "13",

doi = "10.1073/pnas.1606301113",

language = "الإنجليزيّة",

volume = "113",

pages = "10442--10447",

number = "37",

}

Zimmer, A, Katzir, I, Dekel, E, Mayo, AE, Alon, U , Arcavi, I, Challis, P, Chambers, KC, Chen, T-W, Cowperthwaite, PS, Gal-Yam, A, Hosseinzadeh, G, Howell, DA, Inserra, C, Kankare, E, Magnier, EA, Maguire, K, Mazzali, PA, McCully, C, Milisavljevic, D, Smith, KW, Taubenberger, S, Valenti, S, Wainscoat, RJ, Yaron, O & Young, DR 2016, 'Prediction of multidimensional drug dose responses based on measurements of drug pairs', Proceedings of the National Academy of Sciences of the United States of America, vol. 113, no. 37, pp. 10442-10447. https://doi.org/10.1073/pnas.1606301113

TY - JOUR

T1 - Prediction of multidimensional drug dose responses based on measurements of drug pairs

AU - Zimmer, Anat

AU - Katzir, Itay

AU - Dekel, Erez

AU - Mayo, Avraham E.

AU - Alon, Uri

AU - Arcavi, Iair

AU - Challis, P.

AU - Chambers, K. C.

AU - Chen, T. -W.

AU - Cowperthwaite, P. S.

AU - Gal-Yam, Avishay

AU - Hosseinzadeh, G.

AU - Howell, D. A.

AU - Inserra, C.

AU - Kankare, E.

AU - Magnier, E. A.

AU - Maguire, K.

AU - Mazzali, P. A.

AU - McCully, C.

AU - Milisavljevic, D.

AU - Smith, K. W.

AU - Taubenberger, S.

AU - Valenti, S.

AU - Wainscoat, R. J.

AU - Yaron, Ofer

AU - Young, D. R.

N1 - Clore-Katz-David Foundation; Fela Shapell Family Foundation; Israel National Center for Personalized Medicine (INCPM)

PY - 2016/9/13

Y1 - 2016/9/13

N2 - 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.

AB - 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.

KW - Cancer treatment|mechanism-free formula

KW - Drug cocktails

KW - Drug combinations

KW - Predictive formula

UR - http://www.scopus.com/inward/record.url?scp=84986879765&partnerID=8YFLogxK

U2 - 10.1073/pnas.1606301113

DO - 10.1073/pnas.1606301113

M3 - مقالة

C2 - 27562164

SN - 0027-8424

VL - 113

SP - 10442

EP - 10447

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

IS - 37

ER -

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

Abstract

UN SDGs

Keywords

All Science Journal Classification (ASJC) codes

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