# Digital twins: The New Frontier for Personalized Medicine?
## 7 DHT and Pharma Industry: Drug Development  
DHTs promise to revolutionize the pharmaceutical field not only in clinical applications but especially in drug development. As in the Phase I study clinical dose-finding of new drugs can be difficult primarily due to the small sample size, the simulation of the molecular landscape of an organism derived from the integration of genetic, transcriptomic, and metabolomic information will allow forecasting the effects of new drugs or interactions between drugs on a virtual body, allowing researchers to test the safety and efficacy of new medicines before conducting clinical trials, protecting humans from potential adverse reactions by discarding potentially harmful molecules in advance and potentially reducing costs for pharmaceutical companies [19].  
In a recent study, [PERSON] et al. have challenged this paradigm by developing a virtual model of non-Hodgkin's lymphoma to facilitate the phase I study of a specific antibody drug (monsunetuzumab). The authors used a DHT to study clinical dose-response relationships and to explore potential underlying biomarkers of treatment response. Virtual patients reproduced the clinical measurement for the corresponding clinical patient and were derived by combining specific clinical data of real patients enrolled in the Phase I study (e.g., pharmacokinetics, tumor size, and biomarker data) with other in vitro/in vivo data obtained with a quantitative systems pharmacology model. To account for biological variability and model uncertainty, multiple DHTs with different model parameterizations were identified for each clinical patient. In this way, they were able to predict the effective dose and to compare responder vs. non-responder patterns, highlighting the key role of tumor size, proliferation rate, and baseline T-cell infiltration in determining response to the drug [35]. Despite promising results, open issues remain the characterization of disease-associated environmental variables (\"exposome\") is still difficult, especially for the large amount of data required to build a built reality and reproducible DHT.  
In neurosciences, [PERSON] et al. described the main application of DHT in mental health; they affirm that the physical asset (the alliance between patient and therapist) and its DHT are two connected dynamical systems that evolve across their separate state spaces over time [36]. They employed a probabilistic graphical model (dynamic Bayesian network with decision nodes) to establish the constituents of this coupled dynamical system over therapy sessions (treatment efficacy, strength of alliance at the end of each session, and rupture during the session). The digital states are the results of validated scales that measure the efficacy and effectiveness of treatment as well as the strength of the alliance in each session. These values are determined by a previously trained statistical/machine learning model that calculates these quantities based on current and predicted nonverbal synchrony values calculated over sessions [36]. Ruptures in the alliance are described as deterioration or tension in the alliance, evidenced by a disagreement between the patient and therapist on treatment objectives, a lack of participation in therapeutic activities, or pressure on their emotional connection [37]. Ruptures are correlated with improved therapy outcomes when they are solved, to resolve a rupture, therapists must first recognize it, then they must use specific resolution procedures to heal it; as a result, anticipating the progression of therapy using dt enables the implementation of strategies aiming at a certain outcome.