# Digital twins: The New Frontier for Personalized Medicine?
## 6 Targeted Therapies  
Many diseases with high morbidity and mortality rates have complex and multifactorial pathogenesis, with several intricate molecular pathways and environmental factors involved that can affect the efficacy of a treatment. The physician can hardly predict the effectiveness of therapy with inferences based on the patient's clinical data. For this reason,despite the extensive literature on the importance of targeted therapies, it is difficult to apply tailor-made management strategies. However, there is evidence especially in the oncology setting that targeted therapy is the key to improving patient survival in a variety of diseases. In cancer therapy, there is evidence that patients receiving targeted therapies have significantly improved overall survival and progression-free survival compared to those receiving generic treatments [31]. The development of DHTs could in the near future bridge the gap in treatment response between clinical trials and real-world clinical practice highlighting an unmet need to develop therapies tailored to individual patients. Indeed, despite the remarkable technical and functional advances in next-generation sequencing technologies that have led to a deeper understanding of the molecular basis of many diseases, including cancers and autoimmune diseases, each patient responds to targeted therapy differently [32]. DHTs can be used to create virtual models of individual patients with the ideal aim of digitally representing the unique and complex interaction between genetic, metabolic, and environmental factors. In this way, it would be possible to recognize and select highly effective personalized therapies to address the specific molecular mechanism of disease within the patient's unique metabolic, immune, and behavioral ecosystem. Each virtual twin could be treated with various drugs as a potential treatment for the disease, enabling the prediction of specific patient responses on the base of a vast number of variables. Powerful support for the construction of these models comes from quantitative imaging, driven by the extensive application of artificial intelligence to the analysis of imaging data. In oncology, this approach allows for extrapolating the unique tumor imaging signature and powerful non-invasive assessment of the intra- and peritumoral molecular landscape. Overall, the development of morphologically and functionally virtual replicas of the organism could bridge the gap between clinical practice and the ideal of personalized medicine, leading to a true patient-centered strategy: the right molecule for the right disease at the right dosage for the right patient [8]. DHT for the selection of target therapies are built on the integration of multilevel data omics and applications, which includes genomics and proteomics, and, from a radiological point of view, imaging features extraction with texture analysis, radiomics, and radiogenomics: these models can be used to simulate the effects of different treatments and predict outcomes, allowing doctors to develop new therapies and improve existing ones and tailor treatments to the individual patient (Figure 6) [33]. Since it is derived from computationally predicted effects of a patient's high-resolution model instead of previously observed effects on a small number of phenotypic and molecular variables in groups of patients, this prioritization would be far more precise than current treatment methods [19]. Figure 6: Therapies testing on DHT. Using the DHT, it is possible to test the effect of different drugs, at different dosages, simulating pharmacokinetic and pharmacodynamic interactions, until the correct therapy is correctly selected for that patient.The circles with different colors represent different possible therapies that are tested on a virtual model to find the right one for the patient  
In the clinical setting, a DHT of a type 1 diabetes pancreas model was created to adjust insulin dosage depending on continuous blood glucose monitoring [34]. [PERSON] et al.