{"id":174298,"date":"2026-09-01T10:06:42","date_gmt":"2026-09-01T16:06:42","guid":{"rendered":"https:\/\/tecscience.tec.mx\/en\/?p=174298"},"modified":"2026-09-04T10:09:09","modified_gmt":"2026-09-04T16:09:09","slug":"new-paradigm-for-drug-discovery","status":"publish","type":"post","link":"https:\/\/tecscience.tec.mx\/en\/health\/new-paradigm-for-drug-discovery\/","title":{"rendered":"The New Paradigm for Drug Discovery"},"content":{"rendered":"\n

By <\/em>\u00d3scar Arias Carri\u00f3n<\/em><\/strong><\/p>\n\n\n\n

The history of modern medicine is often told as a succession of extraordinary molecules<\/strong>: penicillin, recombinant insulin, statins, monoclonal antibodies. However, that narrative is misleading<\/strong>. Major changes rarely depend on a single discovery; they occur when an innovation changes how knowledge is produced.<\/p>\n\n\n\n

That is probably what is happening with Eli Lilly.<\/p>\n\n\n\n

From a strictly economic perspective, the company’s success appears to be explained by the extraordinary performance of tirzepatide<\/a><\/strong> and a new generation of GIP and GLP-1 receptor agonists, originally developed to treat type 2 diabetes<\/strong> and later becoming some of the most successful anti-obesity medications<\/a><\/strong> in history.<\/p>\n\n\n\n

Demand for these drugs<\/strong> has been so intense that it has forced a massive expansion of manufacturing capacity and driven unprecedented investments in research, infrastructure, and artificial intelligence. However, reducing the phenomenon to a pharmaceutical success would overlook a deeper transformation.<\/p>\n\n\n\n

Biology Enters a New Era<\/h2>\n\n\n\n

For much of the 20th century, drug development was essentially empirical<\/strong>. Thousands of compounds were synthesized and experimentally tested in the hope of finding one with therapeutic activity<\/strong>. Later, molecular biology made it possible to identify specific targets and design molecules more rationally.<\/p>\n\n\n\n

Today, a third paradigm<\/strong> is emerging.<\/p>\n\n\n\n

The simultaneous availability of genomic sequences, three-dimensional protein structures, clinical databases containing millions of patients, medical images, cellular models, and artificial intelligence tools is changing the logic of biomedical discovery. Instead of searching for molecules one by one<\/strong>, it is becoming possible to explore virtually unmanageable chemical spaces using algorithms that can generate and evaluate millions of candidates before synthesizing even one.<\/p>\n\n\n\n

This is not simply about automating existing processes. It is about changing<\/strong> the order in which scientists generate hypotheses<\/strong>.<\/p>\n\n\n\n

This transition is reminiscent, in some respects, of the shift from descriptive natural history to experimental biology in the 19th century. The difference is that today’s capacity for observation comes from enormous volumes of data<\/strong> and computational models trained to identify patterns that are invisible to human analysis.<\/p>\n\n\n\n

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