Virtual Patients and Virtual Doctors for in silico clinical trials
ISCTS is a suite of software services supporting medical scientists in carrying out clinical trials for new treatments completely in silico, that is by means of computer simulations over a population of virtual patients.
In silico clinical trials allow medical scientists to reduce and postpone invasive, risky, costly and time-consuming in vivo experiments on animals and humans to much later stages of the process, when a deeper knowledge of the effectiveness and side-effects of the new treatment has been acquired via simulations.
In silico clinical trials are a disruptive key innovation and a highly supported research field in medicine, with a strong potential for improvement of clinical and therapeutical approaches as well as cost reduction in the development of new treatments.
By putting together a VPH model (and its associated population of Virtual Patients) and the model of a new treatment (Virtual Doctor), Paeon ISCTS allows medical scientists to evaluate how the treatment performs on the input population of Virtual Patients and to search for treatment variations optimised for a particular class of Virtual Patients.
ISCTS can be used to verify how a given treatment protocol (modelled as a Virtual Doctor) performs on a population of Virtual Patients.
ISCTS simulates the correct administration of the treatment protocol to all virtual patients and computes suitable statistics.
Actual fertility treatments currently in use at the Division of Reproductive Endocrinology of University Hospital Zurich have been successfully defined as Virtual Doctors and verified through ISCTS on the entire population of virtual patients associated to the Paeon VPH Model.
ISCTS can also be used to optimise the performance of a given treatment (modelled as a Virtual Doctor) on a certain class of Virtual Patients. To this end, ISCTS searches for variations of the input treatment model which maximise performance on a given set of Key Performance Indicators.
Variations with respect to the input treatment may differ in terms of, for example, the timings and administered doses in the various circumstances and/or for the patient classification criteria. Users can easily annotate the Virtual Doctor modelling the reference treatment to define which variations ISCTS will consider.
Like all software services developed within Paeon, ISCTS is not restricted to fertility treatments, as it is ready to be used with other VPH models and other treatment protocol models.
During a classical in vivo clinical trial, medical scientists administer the treatment under test to a set of patients (volunteers). For each patient, an iterative process is devised, where the doctor, on the basis of some clinical measurements on the patient, decides the next clinical actions to perform, as defined by the treatment protocol.
At the end of the clinical trial, performance of the treatment protocol under test are analysed and suitable statistics are computed.
ISCTS allows medical scientists to shift this workflow into an in silico environment, by allowing them to automatically administer a model of the treatment protocol (a Virtual Doctor) on a set of Virtual Patients.
ISCTS supports medical scientists in performing in silico clinical trials with two main software services allowing, respectively, in silico verification and in silico optimisation of treatment protcols.
ISCTS services are computationally very intensive. To this end, suitable algorithms have been devised to take benefit from high-performance parallel computational infrastructures in the cloud.
During the project, three fertility treatment protocols used by University Hospital Zurich (UZH) in daily clinical practice have been modelled as Virtual Doctors.
Such treatment protocols have been verified and optimised in silico through ISCTS on the population of Virtual Patients associated to the Paeon VPH Model.
When verifying a new treatment protocol in silico via ISCTS, the treatment protocol model (Virtual Doctor) is automatically administered to all virtual patient in the input population, also evaluating patient inclusion/exclusion criteria.
Beyond suitable statistics, ISCTS also provides the medical doctors with means to access the log of the (in silico) treatment administration for each Virtual Patient.
The figure shows an example of treatment (in silico) administration log for the UZH Long Treatment Protocol on a Virtual Patient satisfying the treatment inclusion criteria and for whom the treatment succeeds.
In particular, the figure shows:
Unfortunately the in silico verification of the Virtual Doctors modelling the UZH Long protocols did not succeed for all Virtual Patients. This is not surprising, as the state-of-the-art success rates of fertility treatments is far below 100% also in the most renowned fertility clinics as UZH.
The figure shows an example of treatment (in silico) administration log for the UZH Short Treatment Protocol on a Virtual Patient satisfying the treatment inclusion criteria and for whom the treatment fails.
In particular, on this Virtual Patient the treatment protocol (modelled as a Virtual Doctor) fails to make enough follicles become mature within the defined deadline (day 13 of the stimulation phase), although it tries hard (see left part) to push follicle maturation by constantly increasing the dose of the stimulation drug, after having verified that all safety thresholds regarding hormone blood levels are satisfied (see middle part).
By returning to the user such administration logs, ISCTS supports the medical scientists running the in silico clinical trials with effective means to analyse the reasons of the treatment protocol failures. The treatment model can then be improved for a set of Virtual Patients for whom it fails either manually or automatically by ISCTS itself, through the Treatment Optimisation service described next.
When optimising a treatment protocol in silico via ISCTS, medical scientists start by annotating the reference treatment protocol model (Virtual Doctor) in order to define which variations ISCTS will consider, in terms of, for example, variations in drug doses, timings, patients classification criteria.
When launched on the annotated reference treatment model and on a population of Virtual Patients, ISCTS intelligently searches for variations of the treatment protocol (according to user annotations) and returns the set of Pareto Optimal variations, that is those performing better than others in the output set for at least one of the given Key Performance Indicators.
To enable further analyses, ISCTS also provides the medical doctors with means to access the logs of the (in silico) administration of all treatment variations for each Virtual Patient.
The figure shows the result of running in silico optimisation of the UZH Long Treatment Protocol on the entire population of Virtual Patients associated to the Paeon VPH model and satisfying the treatment inclusion criteria.
ISCTS was run on the Virtual Doctor modelling the reference UZH Long Treatment Protocol, suitably annotated to define the possible variations to be considered, as follows:
ISCTS evaluated the considered treatment variations of the UZH Long Protocol using the following set of Key Performance Indicators (KPIs):
The final set of Pareto Optimal treatment variations computed by ISCTS is shown in the figure. It can be observed how the reference treatment somewhat averages its performance on all KPIs, although there exist interesting treatment variations seeming to have better performance on a subset of KPIs. Some of such variations are currently under investigation at UZH for their possible deployment in clinical practice.