This module is designed to provide a single snapshot of clinical trial progress, allowing the clinical trial professional to immediately identify areas of concern that require further action. Issues requiring attention will be flagged and action plans will be recorded and monitored on the ClinTex blockchain.

These snapshots will then be integrated into the Predictive Analytics application (CTi-PDA) to allow for emerging trends to be identified before they impact overall clinical trial efficiency.

The broad range of data collected during a clinical trial creates huge opportunity to predict the probability of key events that impact the ability of a clinical trial to deliver the clinical data that regulators accept as proof of efficacy and safety of the drug being tested.

The CTi-PDA application will combine all operational and clinical data sources and use predictive modelling to forecast issues and events before they happen. This is the risk-based approach supported by the FDA but to date it has not been utilised to its full potential as predictive modelling is not yet routinely used to forecast risk. Utilising the KPIs generated from the CTi-CDV and CTi-OEM application, the CTi-PDA application will reveal hidden correlations across all datasets, thereby enabling the pharmaceutical company to take pro-active actions.

For example, being able to predict that a patient will withdraw from a clinical trial would enable the pharmaceutical company to take pre-emptive action around patient retention so that the cost and time-delays as a result of patient withdrawal are avoided.

As is the case with patient recruitment, there is a lot of competition between pharmaceutical companies to recruit experienced investigators to conduct their clinical trials.

The CTi-SIM application works in two ways to support the recruitment and management of clinical investigators. Secondly, investigators (clinical research physicians) are reimbursed for their participation in a clinical trial. Reimbursement to investigators can be done using cryptographic tokens, with payments triggered based on key indicators such as number of patients recruited, number of patients reaching defined points in the study, and data quality and evaluability of patients.

The CTi-SIM (in combination with CTi-PRR/CTi-CDV) evaluates attainment of these milestones and can automatically trigger payments to investigators. This will be executed by smart contract, removing the requirement and associated cost of manual intervention in the payment process, and ensuring that payments are released based on data quality and data currency.

Achieving clinical trial research patient enrolment is clearly essential to conducting a successful trial. Without sufficient patient recruitment and retention from the time of study initiation to closeout, the number of completing patients may prove to be too small to derive conclusive proof of the safety and efficacy of the medicine, and therefore will lead to failure in securing FDA approval/marketing authorisation.

As such, there is a lot of “competition” for patients. Difficulty in patient recruitment and retention has resulted in nearly 80% of clinical trials overrunning enrolment timelines by an average of 10.8 months. This translates into as much as $8m in lost revenue for each day a drug is delayed and it also means that cutting-edge new medications are significantly delayed in their journey to the patients who need them most. Our CTi-PRR application provides the solution.

Third-party vendors manage pharmacokinetic/ pharmacodynamic, serology, stool and blood analysis, electronic diaries, ECG data etc. during clinical trials.

They provide different types of services based on the specific therapeutic area being trialled. For example, serology data is important for vaccine trials.

Although outsourcing these services is considered to be advantageous for clinical trials, there are some risks involved. Hence, pharmaceutical companies proactively select, track and evaluate third-party vendors on a regular basis before, during and after the completion of the contract.

This is where the CTi-VMM application has a significant role to play in effective management of third party vendor data. CTi-VMM integrates CTi-CDV to provide oversight on data quality from third-party vendors. Furthermore, CTi-VMM manages token payment compensation by the pharmaceutical company to the third-party vendors, triggered automatically by milestones and KPIs such as number of assessments performed, data quality and data timeliness.

This pay out to vendors will also be managed by smart contract within the Clinical Trials Intelligence.

Site monitoring accounts for an average of between 9% and 14% of total trial costs and involves the pharmaceutical company representative (site monitor) actually attending the site to examine site quality and data records on a regular basis (usually every 4- 8 weeks).

This represents a cost of between $3 million and $10 million per average Phase III clinical trial. Further building upon the CTi-PDA application, the Clinical Trials Intelligence will include a Risk Based Monitoring (RBM) feature.

Although some RBM tools currently exist, many are based on a cumulative picture of clinical and operational data and use this data to indicate early warnings that suggest the need for a site monitoring visit. The CTi-RBM application works differently. Rather than reacting to signals in the data, the Clinical Trials Intelligence will combine current and historical data to predict specific risks, thereby allowing for tactical deployment of the pharmaceutical company’s resources to take preventative action rather than utilising significant resources and effort to “clean-up” and deal with an issue that has already occurred.

This predictive risk-based approach can significantly reduce monitoring costs.

Traditionally, medical review of clinical data was performed based on “line listings” of data generated from clinical trials. After line by line reviews, issues identified are manually raised with the investigator site in question.

Queries and other corrective actions are then manually resolved. Data quality is monitored similarly with programmed checks and controls, the output of which is reviewed manually.

This approach leads to missed issues during data reviews and delays in the clinical trial (waiting for corrective actions to be completed). With the CTi-CDV, medical review will be significantly enhanced through the use of data visualisations output from information contained in ClinTex’s blockchain.

These visualisations will enable the improved identification and sharing of issues that may impact on trial integrity and safety of patients. Data quality will be monitored using visualisations (e.g. identification of outliers) and statistical monitoring. Furthermore, CTi-CDV will facilitate flagging of these issues as well as corrective actions to be recorded and monitored.