Virtual Patient Generation using Physiological Models through a Compressed Latent Parameterization

Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer

“…As a result, larger-than-reality ranges could be generated for parameters and model outputs, and without a sufficiently large dataset of relevant clinical measurements it is hard to apply a robust algorithm to narrow down the virtual patient population while preserving the desirable inter-individual heterogeneity. In addition, covariance between parameters was not considered because of the lack of patient data, as parameters are independently sampled during virtual patient generation, and thus could result in virtual patients that would not have existed in reality ( Tivay et al., 2020 ). In summary, more precise selection criteria and screening methods need to be added into the overall virtual patient generation workflow, as additional data become available, to improve the predictive power of such model-based in silico trials in immuno-oncology research.…”

Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition

“…Although most of these clinically measured characteristics are not present in the QSP model, the covariate effect on PK was included during reproduction of the PK data to be fitted with the QSP model. Since immunogenomic data used to select virtual patients above were not coupled with PK-related data, we independently generated PK parameters for the virtual patients via compressed latent parametrization (23). This optimization method added an additional term to the mean-squared-error cost function to limit deviations from the group-average model (see Methods), and thus allowed us to maintain the PK parameters within a physiologically reasonable range.…”

Closed-Loop Controlled Fluid Administration Systems: A Comprehensive Scoping Review

“…With regard to future directions in this field, there are several trends that might be expected: Further maturation of the described systems as well as introduction of new ones; Increased adoption of closed-loop controlled fluid administration. The first scenario, where these systems can be safely operated, will probably be the operating room, where constant supervision by an anesthesiologist provides an important safety net; Deeper understanding of fluid dynamics and their translation to ever-more-complex computational models, meant for better accuracy and validity of both controllers and in silico testing platforms [ 18 , 79 , 80 ]; Introduction of new modalities of artificial intelligence, such as reinforcement learning [ 81 , 82 ] and other deep-learning modalities. While there’s increasing use of deep-learning for anesthesia and critical care-related applications [ 83 , 84 ], we have not identified detailed reports on deep-learning-based systems matching our inclusion criteria, meaning we have not identified a system that incorporates deep-learning-based capabilities into a CL system (or, for that matter, a DS system with a feedback loop of repeat evaluations); Continuing formation of a regulatory pipeline dedicated to autonomous and semi-autonomous controlled systems; Increased use of non-invasive sensors in closed-loop fluid administration systems, as their reliability will gradually increase [ 43 , 85 ], as well as artificial intelligence-based advanced sensing modalities (specifically, feature extraction), such as arterial waveform feature analysis [ 77 , 78 ], aimed at providing personalized resuscitation goals; Gradual increase in the degree of automation—from a regulatory standpoint, decision support systems are generally considered safer and easier to approve.…”

“…Deeper understanding of fluid dynamics and their translation to ever-more-complex computational models, meant for better accuracy and validity of both controllers and in silico testing platforms [ 18 , 79 , 80 ];…”

“…Further maturation of the described systems as well as introduction of new ones; • Increased adoption of closed-loop controlled fluid administration. The first scenario, where these systems can be safely operated, will probably be the operating room, where constant supervision by an anesthesiologist provides an important safety net; • Deeper understanding of fluid dynamics and their translation to ever-more-complex computational models, meant for better accuracy and validity of both controllers and in silico testing platforms [18,79,80];…”