# Algorithms for Drug Sensitivity Prediction
## 4 Predictive Models
### Integrated Functional and Genomic Characterization-Based Models  
The predictive modeling approaches based on genomic characterizations alone are often restricted in their accuracy as the genomic characterizations observed under normal growth conditions can only provide a single snapshot of the biological system. The model inferred from analyzing multiple samples is primarily an aggregate model with the expectation that new tumor samples will activate distinct parts of the model, which will be sufficient to distinguish their diverse responses to drugs. This methodology will provide reliable results when the tumor samples have limited variations in their pathways and the tumor type is well characterized. However, for less studied tumors like sarcoma and tumors exhibiting numerous aberrations in the molecular pathways, it is desirable to investigate further the personalized inference of pathway structure. Perturbing the patient tumor culture using various known disturbances in the form of targeted drugs and observing the responses can be utilized as additional information to gain further insights on the specific patient tumor circuit. Based on this idea, a computational modeling framework to infer target inhibition maps utilizing responses of a tumor culture to a range of targeted drugs was considered in [32; 47]. The target inhibition profiles of the drugs and the functional responses are utilized to infer potential tumor proliferation circuits that can assist in the generation of synergistic drug combinations. The previously-described drug sensitivity prediction approaches are primarily based on genetic and epigenetic characterizations alone without the incorporation of drug target profiles. As an alternative perspective, rather than designing a model based on the response of a single drug to multiple different genetic samples, the response of one genetic sample to multiple different drugs is considered. The multiple drugs are applied through a drug screen with known target inhibition profiles, and their steady state response allows us to create the target inhibition map, which can predict the sensitivity to any combination of target inhibitions. The drugs included in the drug screen are often kinase inhibitors, and thus, the starting number of targets is limited to around 500 kinases of the human kinome. The difference in the datasets used in this approach as compared to earlier approaches can be illustrated through Figure 1, which shows the details of the various datasets used for generating drug sensitivity models. \(G\) denotes the matrix of genetic characterizations of size \(m\times N\), where \(m=\) number of cell lines or tumor cultures and \(N=\) the total number of genomic features. \(R\) denotes the drug response matrix of size \(m\times D\) in some form of characteristics of the drug response curves (such as \(IC_{50}\), area under the curve, etc. ), where \(D=\) the number of drugs. \(K\) denotes the drug target profile matrix of size \(L\times D\) where \(L\) denotes the total number of drug features. The currently-available prediction approaches based on genetic characterizations primarily consider the matrix \(G\) and a column of \(R\) to generate a model that can predict the response to that drug for a new genomic characterization. The integrated approach considers \(K\) and a row of \(R\), along with the corresponding row of \(G\) to generate a predictive model that can predict the response to a new drug or drug combination with known target inhibition profiles. To decipher a predictive model from \(K\), \(R\) and \(G\), a number of biological constraints were considered in [32; 47]. The objective elements of molecularly-targeted drugs [102; 103; 104; 105; 106] are primarily oncogenes (genes with potential to cause tumor proliferation while getting stuck in a constant activation state), and thus, increased inhibition of an oncogene is expected to increase the sensitivity (reduce cell viability). This prior biological knowledge can be incorporated as constraints in the framework.