??Q2 new modeling & simulation features on jinkō

During the last few months, we introduced significant enhancements in our modeling and simulation features, leading to more sophisticated exploration of trial outcomes. Key developments include the implementation of jinkō's virtual population sub-sampling, comprehensive trial versioning,? phase portrait and the preliminary phases of our new calibration module.

?? Sub-sampling: iterate on your simulations with refined virtual populations

Use outcomes of your trial simulations and jinkō’s embedded logic to identify subsets of patients with characteristics of interests, on both inputs and outputs of your models, and re-run simulations on these populations. Once a trial is completed on an initial virtual population, you now have the ability to select scalar measures (typically used in your initial trial) to? apply constraints that will result in a new subset of patients. Jinkō’s algorithm, based on Simulated Annealing (Reeves, Colin R., ed. Modern heuristic techniques for combinatorial problems, John Wiley & Sons, Inc., 1993; implemented via the GNU Scientific Library) will optimize the selection of patients, based on various options, in order to maximize likelihood between the subset of patients and the constraints.

?? Phase portrait: visualize your system’s dynamics

A phase portrait offers a visual representation of the trajectories of a dynamical system within a multidimensional phase space. Observing these trajectories allows for the inference of the system's dynamics, such as stability, chaos, or periodicity.

?? New trial versioning capabilities: easily and safely apply variations in your trials

The trial editor is now fully versioned in jinkō, enabling you to make as many variations as you like to a trial, for instance as we have seen above re-running it with a sub-sampled population, then observing results and iterating again.

??? Model calibration: the beta version of our new module is out!?

Calibration is a process in which a priori unknown and estimated input descriptors of a computational model are refined through a numerical procedure, more specifically optimization routines in order to achieve a desired behavior of the model. This behavior is characterized by the Objective function to minimize.

This is a critical step when wanting to measure the coherence of your model in observation of samples of real-life data from clinical trials. We are launching our first installment of the calibration module in jinkō, which allows the following (from a model):?

?? Optimization of the fitness criteria: this is a metric, maximized by our calibration algorithm, of the match between outputs of the simulation and the expected behavior covered by a Scorings set and/or a data table used as inputs.

?? Creation of variations in the simulation via a choice of outputs / measures and the configuration of protocol arms

?? Options refining, such as inputs to calibrate, solving options, population size or completion conditions for the simulation.?

Please note that this feature is at the moment only released for selected users, but? you can register to become a beta tester of the feature here:

?And more!

See our full release notes on jinkō'mmunity.?