# Algorithms for Drug Sensitivity Prediction
## 5 Biological Analysis  
The standard procedure used to compare drug sensitivity prediction algorithms consists of evaluating a fitness measure, such as a difference measure of a norm between experimental responses and predicted values or a similarity measure of the correlation coefficient between the vector of predicted and actual experimentally-derived sensitivities. Relative measurements, such as the Akaike Information Criterion (AIC) [118], Bayesian Information Criterion (BIC) [70], minimum description length [119] and Vapnik-Chervonenkis (VC) dimension [74], are also occasionally considered [67; 120; 121]. The sample selection approaches commonly considered in drug sensitivity predictive model evaluation involve techniques, such as \(k\)-fold cross-validation [32], bootstrap [48] or hold-out [25]. Other than evaluating the performance of a generated model using existing drug sensitivity data based on techniques discussed in the previous paragraph, new experiments can be conducted to validate hypotheses generated from the estimated models. For instance, [112] used in vitro DIPG cancer cell lines to validate model hypotheses of synergistic drug pairs. The commonly-used experimental approaches included the use of (a) in-vitro cancer cell lines: cell Lines are the most commonly-used approach to study cancer biology and test various cancer treatments. Some commonly-used cancer cell line databases are NCI60 [122; 123], the Cancer Cell Line Encyclopedia [23], the Genomics of Drug Sensitivity in Cancer [24] and the Cancer Therapeutics Response Portal (CTRP) [30]; (b) In vitro primary tumor cultures: Primary tumor cultures are the cell cultures established from the tumor biopsy of the patient and reflect the surroundings in the original tissue. As compared to cell lines, primary cell cultures are more heterogeneous and also contain a small fraction of normal stromal cells, which can be an advantage since tumor response is often influenced by an interplay of the tumor cells and the non-malignant cells in the tumor microenvironment; (c) In vitro three-dimensional cell cultures: 3D cell cultures attempt to provide an environment that is similar to what is available to in vivo tissues [124], including 3D scaffolding, nutrient-rich interstitial fluid, the exchange of growth factors and other biological effectors; (d) In vivo Genetically-Engineered Mice Models (GEMM): GEMM consists of mice whose genes have been modified to reflect genetic aberrations observed in a specific human tumor [125; 126]. GEMMs [127] have played a vital role in understanding tumor proliferation, as they effectively capture the tumor microenvironment along with the human tumor phenotype. However, the timelines to generate these models are longer, along with the expenses involved; (e) In vivo xenograft mice: Xenograft mice are created by transplanting human tumor cells into the organ type in which the tumor originated, in immunocompromised mice that accept human cells [128]. This approach has shown high drug efficacy predictive capability with respect to human clinical activity [129; 130; 131], but can fail to replicate the immune system of the human cancer and, thus, unable to model the lymphocyte-mediated response to the tumor [128]; (f) Other in vivo animal models have been used, such as dogs who have similarities to humans with respect to cardiovascular, urogenital, nervous and musculoskeletal systems [132], and they develop spontaneous tumors without genetic manipulations, as in the case of mice. Other than the predictive performance evaluation based on existing drug sensitivity data or new validation experiments, we can consider the biological analysis of the variables deemed significant by the generated predictive model. An approach to do that is to consider prior biological knowledge of Protein-Protein (PP) interactions or other forms like protein-DNA, protein-reaction, reaction-compound and protein-mRNA, among others, to evaluate the observed number of such interactions in the model of selected variables.