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Synergia - A web application for adverse event management in clinical trials
#1
Almost everyone associated with the healthcare domain is well aware of the concept of '13' & '7'. 13 is the average time needed for a new pharmaceutical drug, to be ready to enter into the market, and considering the patent protection period to be of 20 years, the pharma firm has only an average of 7 years to market the drug exclusively. To speed up the clinical trial process, the company taps each and every step of the drug discovery and development. In recent times, the focus has been more on the clinical trial phase, in order to speed up the process. The focus has been more because this is the phase when the investigational drug candidate is tested on humans, and secondly, this phase consumes 50-70% of the cost and time of entire clinical trial budget.

In the clinical trials, the focus has been more on the adverse event management. The occurrence of adverse events may operate to extend the length of the trial and result in increased costs, and may be responsible, in part, for the high rate of clinical attrition, with a 40-45%failure rate experienced in Phase-III trials – where most of the failures are attributed to lack of sufficient efficacy and safety data. Building a genomic profile of the patient might help in this case.

The clinical trial process is further complicated by the need for patient monitoring, which is generally performed by doctor visiting the study or testing site.  However, such methods are not well suited for serious adverse events, requiring immediate medical attention.

Accordingly, a need exists for a remote monitoring tool that provides for early and targeted stratification of patients, which may result in improved drug success rates, increased drug response predictability, and improved identification of causal links between drug treatments and adverse events.

“Synergia” utilizes “genomics” along with “remote patient monitoring” data, stored in the cloud, use analytics to form a uniform consolidated output, thus assisting in smarter adverse event prediction.
The process may begin by building a patent gene expression database, which may involve testing the patient for inclusion and exclusion criteria.  If the patient meets the inclusion criteria, they are enrolled in the study.  The percentage of expression for a particular set of genes is calculated by means of microarray analysis (also known as SNPchip analysis).  This genetic profile database may be stored in a standardized format in the cloud. In some embodiments, the system may utilize a subscription to the cloud storage service, array plates (e.g., Affymetrix Axiom Genotyping) and a sputum sample and analysis kit, for extraction of DNA and for analysis through microarray experimentation.

The database having patient records with their gene expression level is taken into consideration for each therapy for a defined therapeutic area.  This may compare a baseline level for the gene expression defined by the user or investigator in an application to a patients’ gene expression levels and may reflect this in terms of a R-A-G (Red, Amber, Green) status, explained by means of a bar graph in a UCO page (Uniform Consolidated Output) provided through the application. The investigator may be able to identify and tag different patient clusters, depending on which category the patient belongs to (i.e., red, amber, green), which may be defined in part by the baseline value and the patients gene expression levels. This is information may be captured in a genetic predisposition database or table. By way of example, for a given therapeutic area, breast cancer, and a specific therapy, the drug Bevacizumab, some patients may respond favorably, some patients may not respond at all and yet other patients may display an adverse reaction, for example, a sudden increase in the patient’s blood pressure when the drug is administered.  These dispositions are associated with the patients’ genetic expression profile, and an investigator may use this information to identify patients who might require extra care as they may be predisposed to extreme hypertension, or in other cases may exclude the patient (who otherwise met the inclusion criterion) from the clinical study.  While the above example is context specific, the process may generally make it possible to “tag” a patient according to his or her genetic makeup.

Currently, the patients are subjected to randomization immediately after they pass for inclusion/exclusion criteria test, but without prior genetic testing done the investigator is not aware of the chance that the patient may be a “wrong patient”, which may unnecessarily increase the risk of an adverse event in the future when the drug is administered by the patient.  By “tagging” the patient, the genetic predisposition data may help to determine the “right patient for the drug” and may help to narrow down results and pin-point patients who might not be able to respond to a drug or who may give an undesirable response towards a therapy.

Once the investigator selects the patients who should be given the therapy of the drug under test, the patients may be subjected to continuous remote patient monitoring by means of vital sign monitoring through wearable devices. , information regarding the vital signs may be sent to a data platform every 20 minutes before and after a dose is given.  These readings may be captured and stored in a database maintained at the back end of the application or server in a pre-defined format.  The system may then calculate the percentage change in the average vital sign reading, at the backend,for display on the front-end application.  The percentage change may be based on a pre-dose baseline, steady state dose baseline, patient population to sub-population average, or other normative baseline for comparison.  The investigator may have the ability to set the baseline value for each parameter. Depending on the baseline value, a graph will be displayed through the UCO illustrating the percentage change in the average vital sign recording exhibited in terms of a R-A-G status.  If the percentage change of a parameter goes beyond a baseline value, an “ALERT” is displayed on the “dashboard” page of the application. By clicking the “ALERT” icon on the dashboard, the investigator may be able to select or filter those alerts based on patient commonality, for example, based on the rise of any one of the vital signs.  For example, if the percentage change in average blood pressure value is more than 35% (the baseline value set by the investigator), then the information dashboard will be populated with an “ALERT”.Clicking on the “ALERT” button may take the investigator to the UCO (Uniform Consolidated Output) page.

In current arrangements, the doctor and patient have to commonly visit the trial site to monitor the patients, record their vital signs and feed the electronic data into the system, but the risk of human error remains and the doctors predictive abilities are limited in that it fails to capture adverse events taking place between or outside of visits to a trial site.  By continuously remotely monitoring a patient, the patient is allowed to stay at home rather than remaining in a controlled clinical setting (which could reduce the cost of his maintenance), and an unexpected adverse event may be more readily detected in any of the parameter readings, because of the continuous, almost real time nature of remote patient monitoring. The system may consolidate the gene expression profile data with the real time vital sign recording data, which may help the investigator reach causal or other inferential medical conclusions, at an individual level and more broadly across trial sites.

As noted above, the investigator may be provided with a UCO that may allow him to compare the vital sign readings of a patient with the patient percentage gene expression level data, and may provide insight (e.g., through statistical analysis) into deeper relationships that may be present.  For instance, an investigator may consider the R-A-G indicator of gene expression and vital sign readings, and may match parameters and genes with similar status indicators (e.g., matching a red bar of a graph for gene expression with a red bar for a graph of percentage change in vital sign reading.  The investigator may also use the tool to identify the patients who are at a higher risk of adverse event in a quicker and more efficient manner.

As noted, the investigator may be provided with deeper insights behind the gene expression values and vital sign readings, and may look for statistically significant values that may justify making a particular decision.  In such situations, it may be useful to look at information of a broader population, and by clicking on the “gene ID” in a graph in the UCO, the investigator may be able to navigate to a site-wise depiction of the genetic predisposition data capturing the gene expression levels across sites. The investigator could use this information to form a higher level connection between a particular drug or treatment and genetic predisposition data, and may further allocate resources to patients who may need extra care or identify patients who should not be given the drug at all.

The system may serve to reduce the adverse event occurrence rate by increasing treatment predictability, which may save money in terms of compensation costs paid to patients’ family, and may even serve as evidence of a drug’s efficacy, which may help the company overcome lesser rejections and speed up the clinical trial process.

This process has been patented and is in the process of getting launched very soon in the market.
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#2
I have submitted this and got approved as a US patent.
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#3
Has anyone heard of this concept before/any time recently?
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Synergia - A web application for adverse event management in clinical trials00