Lantern Pharma - Using AI to Transform The Cost & Risk of Cancer Drug Development

The following post was written and/or published as a collaboration between Benzinga’s in-house sponsored content team and a financial partner of Benzinga.

Artificial intelligence is one of the most fascinating and powerful technological advances to come along over the past decade. The idea that we can use machine learning and data to make a positive impact in all different types of industries is certainly attractive from an investing perspective. One of the more exciting applications of AI is in the healthcare industry, particularly in cancer research, since it can be used to dramatically improve medical care, lower costs, develop new medicines, and potentially save millions of lives.

Cancer takes nearly 10 million lives each year, that is almost 27,000 each day, and is an area where better and more targeted medicines are critically needed. The problem is that developing a cancer drug can take 10 years and nearly $2 billion. Additionally, nearly 90% of cancer drugs that enter clinical trials fail to ever reach the market.

Can AI help? Lantern Pharma LTRN certainly thinks so. This emerging company is using AI to tackle the problem and transform the cost, timeline and risk of developing new cancer therapies. Lantern is a clinical-stage biopharmaceutical company that is leveraging AI to fundamentally change cancer drug discovery and save patient lives by making cancer therapies more personalized and more affordable. The company's proprietary AI platform, RADR^®, has grown to over 4.6 billion datapoints and continues to expand its features, functionality and size. The ambitions of this AI platform are to: identify biologically significant mechanisms of action, discover potential drug combinations, and select patients based on their genomics that are most likely to benefit from the clinical trial. If achieved, it should help to shorten the drug development timeline and reduce clinical trial costs significantly. As RADR^® processes more data points, it is able to predict patient outcomes for a wider range of cancers and a wider range of therapies with greater precision. This is exactly what Lantern is doing in the development of its own pipeline and their pace of launching new clinical programs has been rapid. As of their June 2020 IPO, there were three clinical programs in development; today there are seven. With so many people affected by cancer every year, this type of accelerated development could have a tangible impact on the lives of cancer patients.

Within the RADR^® platform, Lantern curates, compiles, and tags data from genomic sequencing, clinical trials, tissue samples, drug sensitivity, cancer registries, and a range of other industry sources. After that, Lantern uses computational biology and machine learning to clarify the potential mechanisms of action for that compound and identify patient groups most likely to respond to that therapy. As RADR^® continues to grow, so will its ability to identify actionable opportunities for Lantern's own clinical portfolio as well as those for industry partnerships.

The recently announced R&D partnership is an example of how Lantern can expand the use of the RADR^® platform. During the last earnings call, Lantern's CEO discussed that similar partnerships could provide the company and its shareholders with equity and potential milestone payments from other biopharma companies that leverage the RADR^® platform.

Oncology drug development is a time-consuming, costly, and high-risk endeavor, which is a big reason why Lantern Pharma's data-driven approach stands out as they continue to make progress clinically. The Q1 earnings report stated that the Phase 2 clinical trial for non-smokers with NSCLC (Non-Small Cell Lung Cancer) utilizing LP-300 in combination with chemotherapy is scheduled to begin during the third quarter of 2021. Additionally, Lantern expanded potential indications for LP-184 to include ATRT pediatric brain cancers. These types of targeted trials are a timely example of how innovative AI technology has the potential to address areas of high unmet clinical need in oncology and deliver more personalized medicine to cancer patients.

The preceding post was written and/or published as a collaboration between Benzinga’s in-house sponsored content team and a financial partner of Benzinga. Although the piece is not and should not be construed as editorial content, the sponsored content team works to ensure that any and all information contained within is true and accurate to the best of their knowledge and research. This content is for informational purposes only and not intended to be investing advice.