FROM BENCH TO BEDSIDE
When she was just five years old, Emily Whitehead was diagnosed with acute lymphoblastic leukemia. It’s the most common variety of childhood cancer, and hers proved particularly resilient in the face of conventional treatments. After Emily relapsed twice, her desperate parents enrolled her in a clinical trial for a promising new therapy that genetically reprograms white blood cells so they can better recognize and kill cancer cells.
It was the first time the experimental therapy had been used on a child, and the side effects nearly killed her. But ultimately it worked. In 2012, two years after her diagnosis, Emily’s cancer went into complete remission. However, the treatment severely weakened her immune system, permanently. That’s a comparatively small price to pay to save a child’s life; most parents would make that trade-off. But if scientists at the nonprofit California Institute for Biomedical Research (Calibr) have their way, future children like Emily will have access to a version of the treatment that doesn’t cause that kind of permanent damage—or have life-threatening side effects.
Calibr is the brainchild of its director, chemist and entrepreneur Peter Schultz, who aims to speed up the drug-discovery process by combining the strengths of the corporate pharmaceutical industry with the basic research conducted at universities. Headquartered in La Jolla, California, Calibr is developing drug therapies for an astonishing range of conditions: not just cancer but also multiple sclerosis, osteo-arthritis, malaria, and Parkinson’s, to name a few.
“Nature solves a lot of problems in biology by creating large numbers of candidate proteins or antibodies and selecting one that works,” Schultz says. “Whereas chemists tend to rationalize design. We have brought in this idea of creating large chemistry diversity and simply finding the right key in the haystack to solve problems.”
Schultz’s strategy: Do the basic research, identify promising molecules for targeting specific conditions, and then test those using high-throughput systems to find the best match. He calls this a “bench to bedside” model, and it lies at the
