Personalized computer modeling is a powerful, effective and non-invasive tool, that can be used to simulate the inducibility of ventricular tachycardia (VT) in scar-related cases. In this in silico study we propose a robust pipeline for VT risk prediction based on preclinical digital twins developed and parameterized from high resolution in vivo MR images and electro-anatomical datasets acquired in 5 pigs with chronic infarction. Specifically, we employed a modified Mitchell-Schaeffer computational model and calibrated per case the key parameter that tunes the action potential duration using the recorded intracardiac ECGs. To validate the predictions, we compared the measured VT cycle length (CL) per case to the simulated VTCL obtained from each calibrated digital twin by precisely replicating the experimental inducibility protocol. We further defined 100 pacing sites on the right and left endocardial surfaces, respectively, and investigated the impact of pacing location on the VT inducibility. Overall, results demonstrated that our pipeline was able to accurately predict VT inducibility after calibration, reproducing the experimental VT with a small error in CLs (i.e., <4% in 4 out of the 5 cases). In particular, the right ventricular pacing sites showed more susceptibility to sustained VT: (3/30 (10%), 1/30 (3.3%), 5/30 (16.7%) and 6/30 (20%) for Pigs # 1, 2, 3 and 4, respectively. Additionally, it was possible to induce sustained VT from the left ventricular surface for Pig #2 (5/70 (7.14%), #4 (3/70 (4.28%) and #5 (1/70 (1.4%), respectively. Future work will focus on implementing the numerical scheme on GPUs, to reduce the current simulation times (5h/case) and achieve clinical tractability enabling the pipeline translation into routine evaluations of VT risks in post-infarction patients.