Can you provide a brief overview of Tempus as a precision medicine platform?

How does AI help alleviate challenges throughout drug development?

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How is Tempus addressing these challenges through the application of RWD in drug discovery, development, and delivery?

With this, we aim to not only speed up the process of identifying new targets but also enhance our ability to validate these targets and advance them through the development pipeline. When considering the platform in more depth, we are now able to utilize patient-derived organoids (PDO) within a lab-in-the-loop research ecosystem. This allows us to evaluate RWD insights alongside AI, machine learning tools, and other technologies to develop hypotheses, test those hypotheses in the organoid or wet lab setting, and then iterate in a more rapid fashion.

Tempus also utilizes our real-world data and AI capabilities in early clinical development and translational research. This phase is often referred to as the “Valley of Death,” indicating that the likelihood of progressing from an early phase to a late phase study is exceedingly low. In light of this, Tempus aims to work closely with our life sciences customers to address three distinct areas:

• Identifying and validating biomarkers
• Refining indications and patient populations
• Identifying inclusion/exclusion criteria for successful trials

One example of how we have successfully implemented this involved a life sciences company with a promising cell therapy application they wished to advance. Tempus harnessed our real-world data and developed an algorithm to identify human leukocyte antigen (HLA) loss of heterozygosity (LOH), and then again utilized RWD to understand the patient population and assist the company in identifying those patients in the real world. We assisted the company in refining their clinical trial and have helped them locate and enroll patients in the study.

Similar to HLA LOH, we can examine other standard biomarkers like tumor mutational burden (TMB) or microsatellite instability (MSI), but we are also capable of developing novel biomarkers. We plan to present data at the Society for Immunotherapy of Cancer (SITC) this year on a novel immune signature called Immune Profile Score (IPS)3, which leverages different parameters, including a combination of DNA and RNA gene signatures, to identify patients who may likely to respond favorably to immune checkpoint inhibitors.

We recognize that clinical trials are challenging and that fewer than 3% of patients enroll in a trial despite eligibility rates being much higher⁴. Tempus leverages its TIME network to identify patients who may be eligible for clinical trials and notify their treating physicians of the potential match.

As we look ahead, Tempus is focused on helping to close care gaps. Physicians often struggle to keep abreast of novel therapies they could be utilizing, new guidelines, or new trials available. To address this, we have introduced a new feature that is yet another example of applying AI algorithms to our real-world data. Tempus Next can identify care gaps and promptly alert physicians or care teams, providing actionable insights to support access to the most up to date, guideline-directed treatments . For instance, it is a common misconception in the industry that every patient is receiving genomic sequencing, when in fact, this is not the case. This is a scenario where we can readily flag to physicians and healthcare systems when clinical guidelines recommend testing to help close that care gap. Finally, for companies aiming to develop new drugs or that already have drugs on the market, this data can be highly informative in terms of real-world utilization and the adoption of the precision medicine they are developing.

In short, Tempus AI is constantly thinking about how to best utilize our continuously growing real-world dataset. We explore new ways to apply machine learning, AI, and novel algorithms alongside delivery mechanisms, to help deliver precision medicine that positively impacts patient care.