On September 28, 2021 Lantern Pharma (NASDAQ: LTRN), a clinical stage biopharmaceutical company using its proprietary RADR artificial intelligence ("A.I.") platform to transform the cost, pace, and timeline of oncology drug discovery and development and Deep Lens, a digital healthcare company focused on enabling faster recruitment of the best-suited cancer patients for clinical trials at the time of diagnosis, reported that they have entered into a strategic collaboration that will leverage Deep Lens’ artificial intelligence clinical trial matching solution, VIPERÔ, creating an end-to-end A.I. enabled drug development pathway that is expected to accelerate trial enrollment for Lantern’s planned Phase 2 clinical trial for never-smokers with non-small cell lung cancer (NSCLC), utilizing LP-300 in combination with chemotherapy (Press release, Lantern Pharma, SEP 28, 2021, View Source [SID1234590408]).
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Lantern is developing oncology therapies by leveraging its proprietary RADR A.I. platform and machine learning to discover biomarker signatures that identify patients most likely to respond to its pipeline of therapeutics. Deep Lens’ proprietary A.I.-based platform, VIPER, identifies, triages and matches cancer patients to clinical trials in real time for which they may be eligible. Together, the companies are addressing two of the most complex and time-consuming parts of the drug development process: matching a novel molecule with a relevant indication and identifying the right patients to participate in clinical trials.
Panna Sharma, President & CEO of Lantern Pharma, stated, "The current drug development model is extremely expensive, with an estimated $2.6 billion in drug development costs for each Food and Drug Administration (FDA)-approved drug. Moreover, based on the estimated 5.3% success rate for oncology drugs, most therapies, will fail to reach commercialization, despite showing efficacy in certain subgroups. Not only do the majority of therapies in development fail to meet safety or efficacy endpoints, but an equally large number, 75% of clinical trials, fail to meet recruitment deadlines, due in large part to enrollment challenges. It is quite apparent that cancer treatment requires a lower cost of care and an increase in the choice and efficacy of precision therapies, which we believe we can deliver through a combination of A.I., machine learning, and large-scale biomarker analytics, with a goal of ultimately crushing the cost of cancer therapy development. For this reason, we are very excited to partner with Deep Lens and create an end-to-end A.I. enabled pathway from drug rescue to patient recruitment."
Mr. Sharma continued, "Our existing A.I. platform allows us to predict drug outcomes and response in very specific patient subsets, while Deep Lens’ VIPER serves as a tool to find and accelerate the enrollment of appropriate patients for clinical trials. We believe this accelerated and efficient process will help more cancer patients to have access to the right medicine at the right time. We hope to leverage this solution across additional trials and combine it with other advanced A.I. technologies that align with our mission of accelerating the timeline and reducing the costs of oncology drug discovery and development."
Lantern Pharma’s approach is to in-license and develop oncology therapies using genomic data, machine learning, and computational biology modeling to identify the patient groups most likely to respond to a therapy, and to clarify the potential underlying mechanisms of action. Lantern’s LP-300 is a small molecule entity that has been studied in multiple randomized, controlled multi-center NSCLC trials. In retrospective analyses of a multi-country Phase 3 trial, LP-300 with chemotherapy demonstrated substantial improvement in overall survival in the never-smoker subgroup. LP-300 is currently in preparation to enter a phase 2 clinical trial for use of LP-300 as a combination therapy for never-smoking NSCLC patients with histologically defined adenocarcinoma. Deep Lens will utilize the patient enrollment criteria identified by Lantern to find this subgroup of patients and match them to the LP-300 clinical trial across Deep Lens’ network of community oncology sites.
"Precision medicine has changed the way we think about treating and identifying certain types of cancer, but it has also significantly increased the complexity of clinical trials. Trials often have very narrow eligibility criteria, making enrollment objectives difficult to meet, and unfortunately, many companies will fail to move along the development path successfully," said Dave Billiter, Chief Executive Officer and Co-Founder of Deep Lens. "Deep Lens leverages their A.I. platform, VIPER, and supporting services to automate the patient identification and screening process, so that trials enroll faster and more efficiently. We believe that leveraging A.I. across multiple phases of drug development will decrease overall time-to-market timelines as well as associated costs. We look forward to partnering with Lantern to help them achieve their trial enrollment goals and to provide access to LP-300 to patients in need as quickly as possible."
Lantern is focused on developing LP-300 as a potential first-in-class combination therapy for never smoking NSCLC patients with histologically defined adenocarcinoma. NSCLC among never smokers has a distinct molecular profile and according to the American Cancer Society, as many as 20% of people who die from lung cancer in the United States every year have never smoked or used any other form of tobacco. Leading researchers have started to classify lung cancer in never and non-smokers as having unique and distinct clinical, biological and pathological characteristics that have the potential to be impacted by new therapeutic options. According to market research and data analytics firm, GlobalData, approximately $10 billion was spent on NSCLC therapies in 2020, across the leading eight markets (by annual drug sales), with approximately $4 billion of this drug spend in the U.S. alone.