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When the Swiss chemist synthesized and tested the psychedelic compound LSD in the 1930s, he knew he had found a tool to explore the human mind.
In the newest episode of the BioRevolution podcast, Andreas Horchler and Louise von Stechow discuss the history, the promise, and the risks of psychedelics as treatments for psychiatric diseases with neuroscientist, psychedelics expert, and executive coach Galyna Pidpruzhnykova
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This time on the BioRevolution podcast Andreas Horchler and Louise von Stechow discuss science itself.
While recent surveys show persistently high trust in science worldwide, certain scientific topics have become the subject of intense societal and political debate. This politicization of science puts scientists in a difficult position when they are trying to argue based on facts.
The value of facts and evidence-based science seems to be increasingly questioned by those in power and by those in control of assets. If those who make laws and regulate science don’t see evidence and consensus as reliable resources anymore, what will become of progress in biomedicine, which feeds innovation in biotech, pharma, and medicine?
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In some respects, 2024 has been a record year for AI in biotech, with the $1 billion founding of the antibody-centered biotech company Xaira and the awarding of the Nobel Prize in Chemistry to DeepMind researchers Demis Hassabis, John Jumper, and David Baker. However, despite years of investment and innovation, we still lack an answer to the most pressing question: Will AI revolutionize drug development, or are we in a bubble that is about to burst?
In episode 31 of the BioRevolution podcast, Andreas Horchler and Louise von Stechow explore various use cases for AI in biotech—from discovery to clinical development, from chemistry to biology, and from data-centric to algorithm-centric AI strategies—trying to assess where the biggest payoff will be.
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2025: For millions of people, the new year starts with the self-promise to improve, to quit smoking, alcohol, to get in shape, to stay young and attractive, to basically create and become a new me. While traditionally new year vows meant to relinquish, today there seem to be many helpers in the form of biohacking.
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Modern humans are stressed creatures: overloaded schedules, constantly “on-call,” and social media haunting us into the late night.
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Science can be complex and difficult to grasp, yet public understanding is crucial for navigating issues like disease risk, climate change, and pandemics. Clear, engaging science communication can help prevent misinformation and conspiracy theories, offering facts in place of myths. But how can we make science more digestible and appealing?
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In the expensive and failure-prone process of drug development, artificial intelligence (AI) can serve both as an efficiency tool and as a creativity tool. Increased efficiency means shorter timelines, reduced investment, and earlier insights into success or failure. This can lead to more opportunities to bring new drugs to patients. Increased creativity means exploring new areas in drug discovery and development, such as identifying new patterns in targets, drug molecules, and patient populations that may be unintelligible to the human eye. While we are witnessing the first tangible milestones of AI in drug development, the hype in the field can lead to inflated expectations of its benefits.
To assess the true potential of the technology, we must also recognize its challenges, such as the algorithms' "black box" nature, their propensity for hallucination, and data bias. Only by being transparent about both the potential and the limitations of AI can we increase the trust of drug developers—and most importantly, our customers: patients.
Listen to episode 27 of We’re Doomed, We’re Saved to learn more about the potential of AI for pharma and biotech.
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How will the medicine of the future look? Healthcare and medicine are on the verge of transformative change, driven by new technologies such as artificial intelligence, telemedicine, and wearable devices. Alongside the technologization and virtualization of medicine, there is a shift from reactive approaches to preventative strategies and from one-size-fits-all treatments to personalized medicine.
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Some 36 months after the release of ChatGPT, the verdict is still out on the role that large language models (LLMs) will play in biotech, pharma, and medicine. On paper, the range of tasks that LLMs can perform in biomedical research and healthcare is vast—excavating relevant information for drug discovery from mountains of scientific literature, designing novel proteins, transcribing doctors' notes, aiding diagnostic decision-making, and acting as patient-facing chatbots.
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Rare or orphan diseases affect only a small percentage of the population and often lack effective treatments. While rare individually, in total, more than 350 million people worldwide live with rare diseases. Many of these are very hard to diagnose, let alone cure, and the rarity of patients challenges the development of novel treatments.
To make the development of drugs for rare diseases more efficient and successful, artificial intelligence (AI) could be an important ally not only for drug makers but also for patients.
In episode 24 of "We’re Doomed, We’re Saved," Andreas Horchler and Louise von Stechow discuss the benefits and challenges of using AI for drug development in rare diseases.
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Drug discovery
Diagnosis
Clinical research
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