Drug Combination Modeling

Anna H.C. Vlot; Daniel J. Mason; Krishna C. Bulusu; Andreas Bender

doi:10.1016/B978-0-12-801238-3.11569-7

Drug Combination Modeling

Anna H.C. Vlot, Daniel J. Mason, Krishna C. Bulusu, Andreas Bender

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Combination effects of compounds are commonly discussed in relation to the treatment of complex and treatment resistance-prone illnesses like cancer, (auto-)immune disorders, and infectious diseases. However, traversing the whole combinatorial space experimentally is infeasible. Therefore, in the last decades, substantially efforts have been made in the field of (predictive) combination modeling. In this reference module, we will discuss the goals of drug combination modeling, the features and endpoints that are used to address combination effect, and the modeling strategies which have been developed. We conclude that data availability is the largest bottle neck in the combination modeling pipeline. Improved data management and availability is essential for drug combination modeling to provide treatment benefit.

Original language	English
Title of host publication	Systems Medicine
Subtitle of host publication	Integrative, Qualitative and Computational Approaches: Volume 1-3
Publisher	Elsevier
Pages	269-282
Number of pages	14
Volume	1-3
ISBN (Electronic)	9780128160770
ISBN (Print)	9780128160787
DOIs	https://doi.org/10.1016/B978-0-12-801238-3.11569-7
State	Published - Jan 1 2020
Externally published	Yes

Keywords

Antagonism
Cancer
Clinical benefit
Combination therapy
Deep learning
Drug discovery
Drug repurposing
Drug synergy
Infectious diseases
Machine learning
Network modeling
Predictive modeling
Synergy
Translational drug research

UN SDGs

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

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10.1016/B978-0-12-801238-3.11569-7

Cite this

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title = "Drug Combination Modeling",

abstract = "Combination effects of compounds are commonly discussed in relation to the treatment of complex and treatment resistance-prone illnesses like cancer, (auto-)immune disorders, and infectious diseases. However, traversing the whole combinatorial space experimentally is infeasible. Therefore, in the last decades, substantially efforts have been made in the field of (predictive) combination modeling. In this reference module, we will discuss the goals of drug combination modeling, the features and endpoints that are used to address combination effect, and the modeling strategies which have been developed. We conclude that data availability is the largest bottle neck in the combination modeling pipeline. Improved data management and availability is essential for drug combination modeling to provide treatment benefit.",

keywords = "Antagonism, Cancer, Clinical benefit, Combination therapy, Deep learning, Drug discovery, Drug repurposing, Drug synergy, Infectious diseases, Machine learning, Network modeling, Predictive modeling, Synergy, Translational drug research",

author = "Vlot, {Anna H.C.} and Mason, {Daniel J.} and Bulusu, {Krishna C.} and Andreas Bender",

year = "2020",

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doi = "10.1016/B978-0-12-801238-3.11569-7",

language = "English",

isbn = "9780128160787",

volume = "1-3",

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TY - CHAP

T1 - Drug Combination Modeling

AU - Vlot, Anna H.C.

AU - Mason, Daniel J.

AU - Bulusu, Krishna C.

AU - Bender, Andreas

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Combination effects of compounds are commonly discussed in relation to the treatment of complex and treatment resistance-prone illnesses like cancer, (auto-)immune disorders, and infectious diseases. However, traversing the whole combinatorial space experimentally is infeasible. Therefore, in the last decades, substantially efforts have been made in the field of (predictive) combination modeling. In this reference module, we will discuss the goals of drug combination modeling, the features and endpoints that are used to address combination effect, and the modeling strategies which have been developed. We conclude that data availability is the largest bottle neck in the combination modeling pipeline. Improved data management and availability is essential for drug combination modeling to provide treatment benefit.

AB - Combination effects of compounds are commonly discussed in relation to the treatment of complex and treatment resistance-prone illnesses like cancer, (auto-)immune disorders, and infectious diseases. However, traversing the whole combinatorial space experimentally is infeasible. Therefore, in the last decades, substantially efforts have been made in the field of (predictive) combination modeling. In this reference module, we will discuss the goals of drug combination modeling, the features and endpoints that are used to address combination effect, and the modeling strategies which have been developed. We conclude that data availability is the largest bottle neck in the combination modeling pipeline. Improved data management and availability is essential for drug combination modeling to provide treatment benefit.

KW - Antagonism

KW - Cancer

KW - Clinical benefit

KW - Combination therapy

KW - Deep learning

KW - Drug discovery

KW - Drug repurposing

KW - Drug synergy

KW - Infectious diseases

KW - Machine learning

KW - Network modeling

KW - Predictive modeling

KW - Synergy

KW - Translational drug research

UR - http://www.scopus.com/inward/record.url?scp=85151211142&partnerID=8YFLogxK

U2 - 10.1016/B978-0-12-801238-3.11569-7

DO - 10.1016/B978-0-12-801238-3.11569-7

M3 - Chapter

AN - SCOPUS:85151211142

SN - 9780128160787

VL - 1-3

SP - 269

EP - 282

BT - Systems Medicine

PB - Elsevier

ER -

Drug Combination Modeling

Abstract

Keywords

UN SDGs

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