Lantern Pharma aims to take drug to phase 3 for $100-200 million with AI-powered approach

Lantern Pharma’s AI-powered sprint 

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Lantern Pharma (NASDAQ: LTRN), a publicly traded clinical-stage biotech company with a market cap of around $79 million as of mid-March 2024, is shooting for developing $200 million drugs with a machine learning-based platform.

The oncology-focused firm Lantern Pharma, profiled last year, has developed a new drug (LP-284) in less than three years for under $3 million, which CEO Panna Sharma notes is “unheard of.” By using AI, Sharma projects that the company could develop a drug from concept to phase 3 trials for a price tag of $100–200 million — a small fraction of the typically $2.3 billion drug development price tag.

“We’re developing new drugs in less than two and a half years, from an idea through GMP manufacturing, to orphan indications, and multiple publications at ASH [American Society of Hematology] and SOHO [Society of Hem…

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2024 forecast: Navigating new frontiers in pharma with AI, synthetic data, and strategic partnerships

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In late 2022, we published a series of predictions, which, among other things, projected that 2023 would be a “massive showcase” for machine learning (ML) in drug discovery and development. And in many ways, 2023 was a pivotal year for AI in pharma. This evolution in AI and ML applications in pharma built upon the groundwork laid in 2022, a year in which the pharma sector embraced digital components, dynamic clinical trial designs, and advanced data science initiatives related to radiomics. Now, the ongoing focus on ML for processing large volumes of molecular, biochemical, and genomic data continues to lay the groundwork for innovation in 2024.

The predictions for 2024 further emphasize the importance of data, in both structured and unstructured formats, while also touching on a range of other themes related to everything from pharma partnerships with virtual care providers to the challenges and s…

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In data we trust: AI’s growing influence on drug development

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The journey to developing a successful drug, theoretically, may appear linear: you discover the right drug, find the suitable patient and administer it at the right time. The reality, however, often deviates from this straightforward path. Aligning these three variables remains notoriously difficult, often leading to elongated timelines strewn with failures, sometimes extending over a decade with costs often in the billion-dollar range.

In recent years, the use of AI in drug discovery and development has grown swiftly, marking a significant shift in how we understand, discover and develop new drugs. The technology promises to chip away at timelines and save the industry billions of dollars eventually. But those promises aren’t exactly new.  Even before ChatGPT became popular, many drug developers were working with high-end algorithms to “try to really hone in on databases,” said Andrew…

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