ZMI ALPHA:Saama, Oracle partner on integrated clinical trial technology

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ZMI ALPHA:Saama, Oracle partner on integrated clinical trial technology

Saama Technologies is collaborating with Oracle to integrate Saama’s smart applications with the Oracle Health Sciences Clinical One platform. The collaboration is intended to enable pharmaceutical companies and their research partners to harness artificial intelligence (AI)-driven insights to expedite clinical trials.

Saama chief strategy officer Sagar Anisingaraju spoke with Outsourcing-Pharma about the technological integration, and how advanced technologies like artificial intelligence and machine learning are transforming research.

OSP: Could you please talk about how the understanding and use of AI has expanded and evolved in clinical trials over the past few years?

SA: Artificial intelligence (AI) has largely been underutilized in clinical trials, with pharma companies choosing instead to focus on AI in research. Typical use of machine learning (ML) in clinical trials, to date, has been quite simple, deploying bots to perform manual tasks such as copying from one system screen to another.

In addition, AI/ML has been explored in drug safety and regulatory in similar ways. The focus on applying AI to the data collected in a clinical trial is fairly new, and Saama has explored a number of ways to directly impact the performance of the trial itself such as:

  • Forecasting milestones and delays for proactive trial management
  • Automating manual and error-prone steps in clinical data management
  • Speeding up timeframes for data submission to the FDA for drug approval

OSP: How does such technology increase the speed of trials?

Sagar Anisingaraju, chief strategy officer, Saama Technologies

SA: Generally, AI and ML take difficult, manual, repetitive tasks away from end-users to make business processes more efficient. The application of AI in clinical trials aims to do that with the specific outcome of reducing the cycle times of the data as it flows into decision-making processes as well as improving the quality of insight that can be gained from that data.

Today, too much effort and time is spent with cleaning, formatting, and verifying data collected in clinical trials. This is where Saama’s Smart AutoMapper and Smart Data Quality applications help to reduce the manual burden of traditional clinical data management.

In addition, new analytics techniques are able to leverage AI over vast amounts of real-world epidemiological data sets, cross-referencing with disparate investigators or site databases for identifying sites, investigators, and patients for study planning. One such example of this is utilizing scientific publications to cross-reference investigators who may have an interest in running a clinical trial in the same disease area, indication, or molecule class as your investigational drug.

OSP: Could you please share some of the ways in which Saama’s smart applications stand out from similar solutions available through other providers?

SA: First, Saama’s smart applications are backed by the experimental work we proactively do in our AI Research Labs. Most of that early experimental work is peer-reviewed and published. Second, the pharma industry has collaborated actively with us in providing the clinical data sets to train our models, validate the results, and curate them over a period by domain experts.

Third, Saama’s smart applications are architected to co-exist with existing digital applications in very targeted ways. We do this by surgically inserting micro-services into the existing IT landscape at a pharma company, complementing their transactional systems and data landscape.

Last, Saama’s smart applications are context-aware with a human-in-the-loop AI approach. This makes it easy to integrate and adopt the insights from these applications to other downstream systems and processes. An example is where we were able to efficiently and seamlessly integrate insights from our smart application into Pfizer’s clinical infrastructure for the time-sensitive vaccine trial.

OSP: You’d mentioned the value of collaboration in advancing drug development and clinical research. Could you please elaborate on how such partnerships benefit the companies involved, as well as clients and (ultimately) patients?

SA: Getting drugs to market quickly and efficiently while not jeopardizing safety and efficacy is the goal for any pharmaceutical company.

The challenge of clinical development today is that key decisions affecting the success and speed of a drug’s approval require insight to be derived across many different and siloed data sets. Additionally, data privacy and security concerns have made it hard and cumbersome for researchers and pharma companies to collaborate.

The partnership between Saama and Oracle brings leading AI and ML modeling, coupled with leading data integration technology, to streamline the data flow in a clinical trial to get faster insights and outcomes. This helps speed up critical decisions on the quality and effectiveness of the investigational drug which has a real impact on patient lives.

The ability to share learnings and AI models between pharmaceutical companies will ultimately reduce the overall R&D timeline significantly, changing the way clinical operations and clinical data management are executed in the future.

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