“Method And System For Hybrid Clinical Trial Design” in Patent Application Approval Process (USPTO 20240120037): Janssen Research & Development LLC

2024 APR 26 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventor Demirdjian, Levon (San Diego, CA, US), filed on October 4, 2023, was made available online on April 11, 2024, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application is assigned to Janssen Research & Development LLC (Raritan, New Jersey, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Controlled clinical trials have long been the gold standard for assessing the safety and efficacy of treatments. Now, medical researchers have access to hundreds of millions of real-world data (RWD) points about how people are using them, including electronic health records, health insurance claims and even mobile phone data. And, in some cases, these data points can be more useful to medical researchers than trial data. There are also situations in which reusing clinical trial data can reduce the need to conduct further trials. Researchers may also receive patient-level data, which is data about an individual person who is using a particular medication under investigation, for example. Researchers may also get access to aggregated data, which is a consolidation of data related to groups of people who use medications or treatments under investigation. Big data used in RWD may come from several sources, including insurance providers, as well as information that gets entered into electronic health records (EHR) when people visit healthcare providers, like physicians and nurse practitioners.

“As patient privacy is of utmost importance, the data used in RWD studies may be key-coded so medical researchers cannot see anything that would allow someone to recognize the person, although the patient may be re-identified if needed. Sometimes ages or a zip code of persons may be visible, but the person’s name, date of birth or Social Security numbers (and other patient-identifiable information) will not be visible.

“Researchers may also use data for predictive modeling, which is trying to understand whether or which patients are responding the best or benefiting the most, and which groups might or might not be developing more side effects with the medicine. Real-world data can assist in better predicting outcomes for-groups of patients presenting with similar characteristics, thereby maximizing the benefits of a therapy and minimizing the risks. Predictive modeling using big data may go beyond just averages, to allow researchers to examine many more people with different backgrounds and baseline risk factors. This enables researchers to study the effectiveness and the safety of products among a diverse population.

“Real-world data also allows researchers to take a closer look at populations that may not have been well represented in the original clinical trial and then study the impact of a particular treatment over a longer period of time. For example, pharmaceutical company Janssen developed a new model for translating RWD into real-world evidence (RWE) of health benefits by using de-identified patient information from four U.S. administrative claims databases as part of one of the largest and most comprehensive evaluations of the risk of hospitalization and other complications for certain type 2 diabetes treatments. See “Clinical Trials 2.0: 5 Ways Johnson & Johnson Is Helping Revamp-and Revolutionize-How They’re Conducted”, by Hallie Levine, Sep. 2, 2019, published at https://www.jnj.com/innovation/5-ways-johnson-johnson-is-helping-revolutionize-clinical-trials.

“Enacted in 2016, the 21st Century Cures Act expanded the use of real-world data (RWD) in the regulatory approval process. Life sciences companies are exploring how best to use RWD, including external control arm studies to support regulatory submissions. As mentioned above, the commonly accepted gold standard to evaluate efficacy of medical interventions is a randomized control trial or randomized clinical trial (RCT). In this method, researchers randomize study participants into two groups-one that receives intervention and one that does not. However, RCTs are time consuming, costly, and in some situations, not feasible. In an external control arm study, enrolled patients receiving the intervention are compared to patients outside of the study. The external control arm could be patients who received treatment earlier (historical) or a group treated at the same time but in a different setting (contemporaneous). Because some external control arm studies use RWD that is already collected, it can be an efficient way to evaluate the impact of an intervention.

“In some situations, it can be relatively simple to create an external control arm. For example, if patients in the intervention arm have similar characteristics to those in historical RCT data, researchers can use the historical data to create an external control arm. Because these data must be tightly aligned, it may only be valid for small sub-populations.

“Researchers could also use a microsimulation approach, in which models simulate disease progression, using RWD to tune parameters. These methods enable researchers to model long-term outcomes that might not be available in medical or claims data, but researchers must be rigorous in how they tune parameters and validate results.

“When the external patient population differs from the trial participants, researchers need to use more advanced methods. For example, using RWD such as linked claims and electronic medical record data, researchers could identify a large cohort of patients diagnosed with a specific disease. These patients likely represent a broader range of disease severity and treatment patterns when compared to patients with the same diagnosis enrolled in a RCT. In these situations, researchers must use statistical matching and weighting methods to find a subset of patients that mirror the intervention group. The goal of these methods can help generate balanced pools of participants and estimate a variety of treatment effects. This type of study is likely the most common application of external control arm studies that would be utilized in regulatory submissions.

“The next step for researchers is to evaluate study feasibility and methodology. During this phase, it is important to determine if the intervention arm and the external control arm of the study are appropriately aligned on patient selection criteria and baseline patient characteristics. This step requires evaluating potential data sources (e.g., insurance claims, medical records, linked claims-electronic medical records, patient registries) for use as the external control arm. Researchers should look for potential sources of bias between the different data sources. They should also select a data source for the external control arm that enables equal ascertainment of study outcomes in both arms of the study.

“Researchers may rely on human clinical trials for information before products are approved, but rely on data after a product is approved to really understand its benefits in the “real world.” The data are complementary. However, RWD is generally available in the therapeutic area years after the new drug application (NDA) has been filed with the regulatory authorities such as the U.S. Food and Drug Administration.

“The regulatory landscape for external control arm studies is new, complex and quickly evolving. Early conversations with regulatory authorities, coupled with a rigorous and well-designed external control arm strategy can help prepare life sciences organizations for this application of real-world data. Medical researchers may continue to engage with agencies and others to explore how hybrid clinical trial design can inform regulatory and clinical decisions. Thus, methods of designing hybrid clinical trials combining real-world data with clinical in an external control arm (ECA) study are needed.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventor’s summary information for this patent application: “This approach in clinical trial design and methodology could be used in any indication in support of the 21^st Century Cures Act and recent FDA guidance on RWD/RWE studies. The ECA developed in embodiments of the hybrid clinical trial design disclosed herein combines the use of both real-world data and clinical trial data in a hybrid approach, including both data captured in randomized clinical trials (RCT) and data from the real-world setting. Considering that clinical trials do not perfectly reflect practices and circumstances in the real-world setting and that data collected in the real-world setting may lack critical information needed for robust comparison of outcomes collected in the RCT, a hybrid approach is needed.

“Moving the timeline to close the gap between agency approval and RWD availability would assist clinicians with providing effective and safe medical therapeutic pharmaceutical treatments. In one embodiment of the disclosed methods, the timeline for the ECA protocol may be moved into the Phase 3 of the FDA New Drug Application process, including the RCT collection of data. In addition to this, introducing a trifecta of outlier detection, dynamic adjustment of patient recruitment, and propensity score modeling (PSM) of the ECA (external control arm) to the randomized controlled trial (RCT) provides a novel approach to a hybrid clinical trial design.

“In one embodiment of the disclosed methods, a safety comparison of orexin antagonist medication in the RCT to standard of care (SOC) in the ECA may be performed. However, there are inherent differences between the RCT and real-world cohorts (e.g., differences in healthcare seeking behaviors, baseline depression severity, etc.). To ensure that the comparison is valid, it is important to limit and control for the effects of measured confounders. A confounder, otherwise known as a confounding variable, confounding factor, or lurking variable, is a variable that influences both an independent variable and a dependent variable in an observational study and leads to a false correlation between the dependent and independent variables. Confounding is a problem common in observational studies.

“Methods of designing a hybrid clinical trial including an external control arm (ECA) study located in at least one site, to support a randomized clinical trial (RCT) study for a treatment of a condition are disclosed. A Mahalanobis distance value is calculated based on a point comprising a set of values corresponding to a first plurality of feature variables corresponding to at least one ECA candidate; and a distribution of points comprising a set of values corresponding to the first plurality of feature variables of each of a plurality of RCT participants that have received the treatment. ECA candidates may be deemed outliers and excluded as ECA participants based on the Mahalanobis distance value. Recruitment is dynamically adjusted into at least one ECA participant site database by comparing sets of feature variables in at least one ECA participant site database to corresponding sets of feature variables in the RCT participant database.

“While the invention is described with reference to the above drawings, the drawings are intended to be illustrative, and other embodiments are consistent with the spirit, and within the scope, of the invention.”

The claims supplied by the inventors are:

“1. A method of designing a hybrid clinical trial including an external control arm (ECA) study located in at least one site, to support a randomized clinical trial (RCT) study for a treatment of a condition, the method comprising: administering the treatment to at least some of a plurality of RCT participants, wherein each RCT participant is an individual person; detecting at least one outlier ECA candidate in a plurality of candidate records in an ECA candidate database located in the at least one site, wherein each candidate record corresponds to an individual person receiving a standard of care (SOC) treatment for the condition, by calculating a Mahalanobis distance value based on: a point comprising a set of values corresponding to a first plurality of feature variables obtained from the candidate record corresponding to the at least one outlier ECA candidate, wherein each candidate record corresponds to an ECA candidate and comprises information about administering the SOC treatment to the ECA candidate; and a distribution comprising a plurality of points, wherein each point comprises a set of values corresponding to the first plurality of feature variables obtained from each of a plurality of RCT participant records in a RCT participant database, wherein each participant record corresponds to an RCT participant and comprises information about administering the treatment to the RCT participant; excluding at least one outlier ECA candidate from at least one ECA participant database at the at least one site based on whether the Mahalanobis distance value meets a specified criteria; and dynamically adjusting recruitment into at least one ECA participant database at the at least one site that is recruiting participants into the ECA study by comparing a set of values corresponding to a second set of feature variables obtained from the ECA participant records in at least one ECA participant database to a set of values corresponding to the second set of feature variables obtained from the RCT participant records in the RCT participant database.

“2. The method of claim 1, further comprising fitting a propensity score model by calculating propensity scores using RCT and ECA participant records to adjust for one or more measured confounders.

“3. The method of claim 2, wherein the propensity score model is estimated using one or more of a logistic regression model, a machine learning based propensity score model, a probit model, neural networks, support vector machines, decision trees, or meta-classifiers.

“4. The method of claim 3, wherein propensity scores estimated using the propensity score model are used to match ECA participants to RCT participants based on the one or more measured confounders.

“5. The method of claim 3, wherein propensity scores estimated using the propensity score model are used to weight ECA and RCT participants based on one or more measured confounders.

“6. The method of claim 5, wherein the estimated propensity score on at least one ECA participant record is weighted downward if the propensity score model indicates that it is relatively dissimilar to one or more RCT participant records in the RCT participant database, and the estimated propensity score on the at least one RCT participant record is weighted upward if the propensity score model indicates that it is relatively dissimilar to one or more ECA participant records in the ECA participant database.

“7. The method of claim 6, wherein the propensity scores comprise real numbers greater than or equal to zero and less than or equal to 1.

“8. The method of claim 7, wherein patient data in the ECA and RCT participant databases is weighted by the propensity score in accordance with an overlap weighting methodology.

“9. The method of claim 7, wherein patient data in the ECA and RCT participant databases are weighted by the propensity score in accordance with an inverse-probability of treatment weighing (IPTW) methodology.

“10. The method of claim 1, wherein the at least one ECA candidate database comprises an electronic health records (EHR) database at a site.

“11. The method of claim 1, wherein the at least one ECA candidate database comprises both EHR data and non-EHR data.

“12. The method of claim 11, wherein the non-EHR data comprises a clinical database at a site.

“13. The method of claim 11, wherein the non-EHR data comprises a Patient Reported Outcomes (PROs) database.

“14. The method of claim 1, wherein dynamically adjusting recruitment from at least one site recruiting participants into the ECA comprises adding one or more ECA candidate records from at least one site-specific ECA candidate database to at least one ECA participant database when an imbalance is identified in the comparison of the set of values corresponding to the second set of feature variables obtained from the ECA participant records in the at least one ECA participant database and the set of values corresponding to the second set of feature variables obtained from the RCT participant records in the RCT participant database, wherein the imbalance is corrected to within a balancing range when the one or more ECA candidate records are added into the at least one ECA participant database.

“15. The method of claim 1, wherein the step of dynamically adjusting recruitment from at least one site recruiting participants into the ECA is performed at periodic time intervals for a time duration of the hybrid clinical trial.

“16. The method of claim 15, wherein the periodic time interval is at least monthly.

“17. The method of claim 14, wherein identifying the imbalance in the comparison of the set of values corresponding to the second set of feature variables obtained from the ECA participant records in the at least one ECA participant database and the set of values corresponding to the second set of feature variables obtained from the RCT participant records in the RCT participant database comprises: calculating an absolute standardized mean difference (aSMD) metric between at least one feature variable of the RCT participant records in the RCT participant database and the aSMD metric for at least one feature variable of the ECA participant records across the ECA participant databases of the sites in the ECA study after propensity score adjustments; and identifying an imbalance when the aSMD metric between at least one feature variable of the RCT participant records in the RCT participant database and the at least one feature variable of the ECA participant records across the ECA participant databases of the sites in the ECA study is greater than a threshold value.

“18. The method of claim 17, wherein the threshold value is at least 0.10.

“19. The method of claim 14, wherein the adjusting the imbalance within the balancing range comprises: contacting the at least one site wherein the aSMD metric between the RCT participant records in the RCT participant database and the ECA participant records at the site indicates an imbalance; and adding one or more ECA candidate records from the ECA candidate database at the at least one site into the at least one ECA participant database at the at least one site, wherein the set of values corresponding to the second set of feature variables obtained from the one or more ECA candidate records in the at least one ECA candidate database at the at least one site, when combined with the ECA participant records for the at least one ECA participant database at the at least one site, are in balance with the set of values corresponding to the second set of feature variables obtained from the RCT participant records in the RCT participant database.

“20. A non-transitory computer readable medium comprising processor-executable instructions that, when executed by one or more processors, perform a method of designing a hybrid clinical trial including an external control arm (ECA) study located in at least one site to support a randomized clinical trial (RCT) study for a treatment of a condition, the method comprising: administering the treatment to at least some of a plurality of RCT participants, wherein each RCT participant is an individual person; detecting at least one outlier ECA candidate in a plurality of candidate records in an ECA candidate database located in the at least one site, wherein each candidate record corresponds to an individual person receiving a standard of care (SOC) treatment for the condition, by calculating a Mahalanobis distance value based on: a point comprising a set of values corresponding to a first plurality of feature variables obtained from the candidate record corresponding to the at least one outlier ECA candidate, wherein each candidate record corresponds to an ECA candidate and comprises information about administering the SOC treatment to the ECA candidate; and a distribution comprising a plurality of points, wherein each point comprises a set of values corresponding to the first plurality of feature variables obtained from each of a plurality of RCT participant records in a RCT participant database, wherein each participant record corresponds to an RCT participant and comprises information about administering the treatment to the RCT participant; excluding at least one outlier ECA candidate from at least one ECA participant database at the at least one site based on whether the Mahalanobis distance value meets a specified criteria; and dynamically adjusting recruitment into at least one ECA participant database at the at least one site that is recruiting participants into the ECA study by comparing a set of values corresponding to a second set of feature variables obtained from the ECA participant records in at least one ECA participant database to a set of values corresponding to the second set of feature variables obtained from the RCT participant records in the RCT participant database.”

There are additional claims. Please visit full patent to read further.

URL and more information on this patent application, see: Demirdjian, Levon. Method And System For Hybrid Clinical Trial Design. U.S. Patent Application Number 20240120037, filed October 4, 2023 and posted April 11, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20240120037)&db=US-PGPUB&type=ids

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