TECH ON DRUGS

Our first episode features world renowned epidemiologist, Prof. Ran Balicer, Chief Innovation Officer at Clalit Health Services, Israel’s largest healthcare organization. We talked about the mining of real-world data and how it guides healthcare policy. The impact on Covid is pretty mind-boggling!

The doctor in the clinic must build on molecular features and the bioinformatician in the lab must build on data from the clinic. Prof. Yehuda Chowers, a leading gastroenterologist, has been harping on this point for years. It's a two-way street that will benefit both drug makers and drug consumers. In this episode of TOD, Chowers talks to host Shai Shen-Orr about immunogenicity, clinical trials, and population models.

• The barriers to precision medicine (and how to overcome them).
• The potential of AI-designed antibodies (and how, just a few weeks ago, a cancer patient in North Carolina became the first American to receive treatment with such an antibody).
• The reasons why even in this golden digital era drug development is still insanely expensive (and how - to use a battle metaphor - we've mastered the intelligence phase, we have great ammunition, but we need to seriously up the ante in execution!).

What will the future of precision medicine look like 5-10 years down the line? And how will AI accelerate this future? I asked Dr. Emanuele de Rinaldis to answer this and many other questions. Emanuele is VP, Global Head of Precision Medicine & Computational Biology at Sanofi R&D. He believes that AI will help us define new taxonomies of diseases. We will identify different types of Asthma, different types of Fibrosis. And AI will help us create a new textbook – not one driven by just clinical observations, but a textbook driven by genetics and gene expression, together with real world evidence. This will take drug development and precision medicine to a level we have never seen before.

Our guest this time was Prof. Ido Amit, whose lab at the Weizmann Institute of Science pioneered single cell transcriptomic analysis and its application to the immune system. Amit’s research focuses on some of the most important questions in immunology – specifically those relevant to novel targets for immunotherapy in autoimmune diseases, neurodegeneration, and cancer. A fascinating discussion between two leading scientists about the potential of single cell RNA analysis to revolutionize target discovery and drug development.

This one was fun. In the latest episode of Tech on Drugs, Shai met with Prof. Eran Segal from the Weizmann Institute of Science. Eran heads a multidisciplinary team of computational biologists and experimental scientists working in the area of Computational and Systems biology. His lab at Weizmann aims to develop personalized nutrition and personalized medicine using machine learning, computational biology, probabilistic modeling, and analysis of heterogeneous high-throughput genomic and clinical data. Among other things, Eran and Shai talked about precision nutrition and preventative medicine, and the importance of large-scale, longitudinal health data for advancing personalized medicine and improving drug development efficacy.

While most neuroimmunologists try to understand how the immune system affects the brain, Prof. Asya Rolls has taken the road less traveled in her lab at the Technion. She and her team try to understand how the brain affects the immune system. In this episode of Tech on Drugs, Asya and Shai talked about the power of placebo and how brain activity, particularly in the reward system, can influence the immune system. They discussed the immense potential of harnessing brain-immune pathways for new treatment approaches. For example, research that shows that stimulating certain brain areas can boost immune responses and even reduce tumor sizes. But it's not so simple; as Asya put it, this is "complexity on steroids." To better understand the brain-immune connection and develop more holistic treatments, future research will require advanced computational models for different diseases and tissues, as well continuous data integration.

• What major shift did Keytruda's development introduce to the industry?
• How did pharma's 'Kodak moment’ come about?
• What needs to happen in pharma R&D so data is no longer JUST supporting decisions, but actually driving them?

• Lessons from lupus - the story of a failed lupus trial that revealed the critical importance of understanding disease heterogeneity and inspired new approaches in immunology.
• From trial and error to predictability - how AI and systems biology can transform drug development by embracing human complexity.
• Combination therapies and beyond - designing drugs with precision to improve patient outcomes and broaden therapeutic reach.