Revolutionizing Drug Development: How AI Shortens the Journey from Lab to Lives
The journey of progressing a drug molecule from concept to commercialization is a lengthy and intricate process, typically spanning 10-15 years. This period encompasses the initial discovery phase, through to clinical trials and ultimately, regulatory approval.
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into this process is transforming the pharmaceutical industry, offering a beacon of hope for accelerating the traditionally slow and costly pathway of drug development. With development costs potentially reaching up to $2 billion per launched drug, the adoption of AI and ML technologies presents a strategic advantage. These technologies are leading the way in broadening the search for new compounds, speeding up the calculation of complex properties, and providing insights into incomplete data, thereby improving the efficiency of drug discovery.
AI and ML are revolutionizing the selection process of candidate molecules for new chemical entities (NCEs) by enabling the analysis of vast virtual libraries. This capability significantly streamlines the candidate analysis, sparing scientists from the daunting task of manually sifting through billions of potential drug candidates. Additionally, AI-driven technologies are refining the prediction of complex calculations, such as quantum mechanics and binding energies, making these analyses feasible in milliseconds instead of hours.
Another noteworthy application of AI in drug discovery is in the generation of synthetic routes. AI models, trained on literature examples of synthetic reactions, can rapidly identify viable synthesis pathways, optimizing routes for better yields or lower costs. This application showcases AI’s potential to mirror the proficiency of experienced synthetic organic chemists but in a fraction of the time.
Despite the progress, the full potential of AI and ML in drug discovery faces challenges, primarily due to limitations in data availability and the scarcity of experts proficient in both drug discovery and AI. Moving forward, the pharmaceutical industry is likely to adopt a more open approach to data sharing, enabling AI and ML tools to more accurately predict complex molecular properties and streamline the discovery process.
As AI and ML technologies continue to evolve, their integration into drug discovery platforms promises to revolutionize the field, making drug development more efficient and accessible, thereby shortening the lengthy 10-15 year journey from concept to commercialization.
To that end, PRISM has put the spotlight on three small cap players in the AI powered drug sector.
Recursion Pharmaceuticals (NASDAQ: RXRX) is a clinical stage TechBio company focused on transforming drug discovery by decoding biology. It utilizes the Recursion OS, a platform that combines diverse technologies to grow a large proprietary dataset of biological and chemical information. The company employs advanced machine learning algorithms to analyze this dataset, identifying a vast number of biological and chemical relationships without human bias. Recursion conducts millions of wet lab experiments every week and operates one of the world’s most powerful supercomputers to support its research efforts. Recently, Nvidia (NVDA) disclosed a $76 million investment in Recursion Pharmaceuticals, following a previous $50 million investment. Recursion’s market capitalization is approximately $2.7 billion.
Predictive Oncology (Nasdaq:POAI) is at the forefront of oncology drug discovery, combining scientific expertise with machine learning to improve drug development. It leverages a vast biobank of over 150,000 tumor samples and the PEDAL AI platform, which predicts tumor responses to drugs with 92% accuracy, to enhance early-stage drug discovery. The company provides comprehensive solutions including tumor models, biologics development, and access to GMP and CLIA-certified facilities. Through collaborations, such as with Cancer Research Horizons and UPMC Magee-Womens Hospital, Predictive Oncology has shown its capability to identify effective cancer treatments and tailor therapies to individual patients, advancing personalized medicine in oncology. Predictive’s capitalization is approximately $13.2 million.
Lantern Pharma, Inc. (Nasdaq:LTRN), a clinical-stage company, is focused on developing precision cancer therapies with new mechanisms of action. It uses machine learning and genomic data to revive failed cancer drugs and improve oncology drug development’s success rates and costs. By leveraging its RADR platform, Lantern Pharma enhances patient stratification and understanding of drug mechanisms. The company prioritizes personalized cancer treatments, utilizing AI and genomic data for better patient selection.
Lantern recently announced progress in its unique Antibody Drug Conjugate (ADC) program for treating multiple solid tumor cancers. This initiative, developed with academic partners, focuses on a cryptophycin-based ADC with high potency in preclinical studies. Leveraging the RADR® AI platform for targeted drug development, Lantern aims to advance this program to IND development in 2024, targeting tumors resistant to current treatments. ADCs, an emerging therapy class, are projected to generate significant revenue by 2030, highlighting their growing importance in oncology. Lantern’s market capitalization is approximately $43 million.
