Identification and prediction of failures early in the development cycle of clinical trials are of great value in drug development . To facilitate the classification of 'Fail' samples, in this method, we use p observations in the test set and all the remaining ones in the training set. In particular, we set the value of p as 2, wherein a pair of data points is set aside for validation, there is one sample from each class. Though the Leave-p-out scheme is computationally expensive due to repeated training, it is reliable and powerful for smaller datasets and AUC estimations in a binary classifier . This approach aims to have a fair estimate in predicting the failed trials ignoring the computational cost. To inspect the behavior of the model on interventions that have more than one trial, we analyze the performance of recall when the intervention and all its trials are in the Test set against the scenario when only one intervention is in the Test set. We believe this analysis can provide deeper insights on the likely outcome of clinical trials for drugs in re-purposing studies.