# Algorithms for Drug Sensitivity Prediction
## 1 Introduction  
In recent years, the study of predictive modeling of drug sensitivity has received a boost due to the ever-growing interest in precision medicine and the availability of large-scale pharmacogenomics datasets. Drug sensitivity prediction is an integral part of personalized medicine that refers to therapy tailored to an individual patient, rather than a one-size-fits-all approach designed for an average patient. The idea of personalized medicine has been in existence since the time of [PERSON] in the fifth century BC, who treated patients based on their humor (bodily fluids) imbalances [1]. The humor approach held sway in personalized treatment for two millennia, but was debunked in the later part of the second millennium based on advanced understanding of human anatomy and physiology. Personalized therapy in the 20 th century was primarily based on advanced visualization approaches, such as X-ray or Magnetic Resonance Imaging (MRI) scans along with multiple pathology tests. The ability to measure genomic features on an individual basis in the last two decades opened up numerous possibilities for personalized therapy. The genomic characterizations provide substantially more detailed information on a patient with a genetic disease as compared to phenotypic observations or non-molecular tests. If we are to define personalized therapy in the current era, the incorporation of genomic information in designing personalized therapy is quite certain. For instance, the U.S. Food and Drug Administration (FDA) considers personalized medicine (or precision medicine) to entail \"disease prevention and treatment that takes into account differences in people's genes, environments and lifestyles\" ([[http://www.fda.gov/ScienceResearch/SpecialTopics/PrecisionMedicine/default.htm](http://www.fda.gov/ScienceResearch/SpecialTopics/PrecisionMedicine/default.htm)]([http://www.fda.gov/ScienceResearch/SpecialTopics/PrecisionMedicine/default.htm](http://www.fda.gov/ScienceResearch/SpecialTopics/PrecisionMedicine/default.htm))). Similarly, the National Cancer Institute (NCI) defines personalized medicine as \"A form of medicine that uses information about a person's genes, proteins, and environment to prevent, diagnose,and treat disease\" ([[https://www.cancer.gov/publications/dictionaries/cancer-terms?cridrid=561717](https://www.cancer.gov/publications/dictionaries/cancer-terms?cridrid=561717)]([https://www.cancer.gov/publications/dictionaries/cancer-terms?cridrid=561717](https://www.cancer.gov/publications/dictionaries/cancer-terms?cridrid=561717))). An underlying theme of personalized medicine is the incorporation of patient's genomic information besides other characteristics in therapeutic decisions. This review primarily considers various algorithms that have been proposed to decipher and obtain relevant information from genomic and drug sensitivity characterization sources. These data sources are used to design models that can predict: (1) the sensitivity of a new tumor culture or cell line to an existing drug or combinations of them or (2) the sensitivity of an existing tumor culture to a new drug or to a drug combination. We should note that cell lines are the most commonly-used approach to study cancer biology and test various cancer treatments. Cell lines usually contain cells of one type and can be either genetically identical or diverse. These cell lines are easy to grow in a laboratory, and multiple research groups can have access to the same cell line for corroborating research findings and to build a body of knowledge based on a specific genetic type of cell. Cancer cell lines are expected to reflect the properties (such as genotypic and phenotypic characteristics, mutation status, gene expression and drug sensitivity) of the original cancer type from which they were cultured [2; 3]. However, the laboratory environment along with the absence of other regular cell types within the cell line restricts the ability of a cell line itself to mimic in vivo cancer growth, as well as the efficacy of anti-cancer drugs [4].