So I figured that since I am attending the drug discovery conference at Northeastern University I might as well jot down a few thoughts about the talks.
Leroy Hood: Institute for Systems Biology (Seattle).
Lots of optimism, which is usually the case with systems biology talks; being able to distinguish "wellness" genes from "disease" genes for everybody in about ten years, being able to map all disease-related biomarkers from blood analysis etc. But there were some interesting tidbits:
- Noise - especially biological noise - cannot be handled by traditional machine learning approaches. Signal to noise ratio is very low especially when picking biomarkers.
- SysBio can help pharma pick targets (which it is increasingly getting worse at).
- Cost can be minimized in optimal cases; eg. FDA approved Herceptin specific for 20% of patients in only 40-patient sample (Genentech).
- Descriptive and graphical models can be enormously useful; in fact complexity often precludes mathematical modeling.
- Example of prions injected into mouse: expression of 33% genes changed. Biological noise can be “subtracted” by judiciously picking strains that get rid of one effect and preserve others.
My own take on systems biology has always been that, while it is likely to become quite significant at some point:
a. It's going to take longer than we think.
b. Separating signal from noise and honing in on the handful of approaches which will be robust and meaningful is going to give us a lot of grief. This will likely be Darwinian selection at its best.
Patricia Hurter (Vertex): Formulation
For people like me in discovery, formulation is a whole new world. Compaction, rolling, powder flow, force-response curves; engineers would feel right at home with these concepts, and in fact they do. And of course, you don’t talk about anything less than 25 kilograms.
Eric Olson (Vertex): Cystic Fibrosis
- Most common mutation is F508del (targets 88% of patients)
- Two potential drugs; potentiators (for restoring function) and correctors (for localizing protein from ER to membrane surface).
- However, only potentiators needed for G551D mutation (targets 4% of patients). Ivacaftor increases probability of channel being open; more beating cilia (nice video).
- Development challenges: little CF expertise, limited patient pop, no defined preclinical and regulatory path, outcomes for proof-of-concept and phase 3 not well established for mechanism-of-action.
I thought that the development of Vertex's CF drugs is a model example of charting out drug development in a novel, unexplored area.
Arun Ghosh (Purdue): Darunavir
From a medicinal chemistry standpoint this was probably the most impressive. Ghosh is one of the very few academic scientists to have a drug (Darunavir) named after them. He described the evolution of Darunavir from the key idea of targeting the backbone of HIV protease; the belief was that while side-chains are different between HIV mutants, the backbone stays constant and therefore compound binding to the backbone would be effective against resistant strains.
This idea turned out to be remarkably productive, and Ghosh described a series of studies that just kept on improving potencies against virtually any mutant HIV strain that the biologists threw at the compound. It was a medicinal chemist’s dream; there was a wealth of crystal structure data, compounds routinely turned out to have picomolar potencies, and almost every single modification that the chemists designed worked exactly as expected. Some of this success was of course good luck, but that’s something that’s usually a given in drug discovery. Darunavir and its analogs got fast-track FDA approval against HIV strains that had failed to respond against every other medication. Ghosh’s study was a powerful reminder that the right kind of design principal can lead to exceptional success, even against a target that's been beaten to death.
George Whitesides (Harvard): Challenges
Interesting talk by Whitesides. A pretty laid back speaker. The first half was a general rumination on the state of pharma and drug discovery ("the current model of capitalism is not working"; "the FDA has become unreasonable"; "if the best we can do in cancer is to invent a drug that gives someone 3 extra months with a lot of side effects, then we are doing something wrong").
The second half concerned his work on the hydrophobic effect. The papers deal with ligand binding to carbonic anhydrase. Basically he found out that the so-called entropic signature of the hydrophobic effect (an increase in entropy from release of bound water molecules) is more complicated.
A few notes:
- Designing drugs is hard because we are robust, multi multiplexed complex systems.
- Cost of healthcare in the US is ~17% of GDP: also, no correlation between health cost and quality, as evidenced by low standing of US.
- Quoted Anna Karenina’s happy and unhappy families; has something to do with drug development. Every successful drug has its success in common, unsuccessful drugs are unsuccessful in their own way.
- Pharmaceutical crisis has nothing to with per se with science, everything to do with costs.
Finally, he made an important point: biochemists have always done experiments in dilute phosphate buffer. Interior of cell is anything but.
Favorite quote, regarding the limitations of animal models: "Whatever else you may think of me, I am not a large, hairless mouse”
Leroy Hood: Institute for Systems Biology (Seattle).
Lots of optimism, which is usually the case with systems biology talks; being able to distinguish "wellness" genes from "disease" genes for everybody in about ten years, being able to map all disease-related biomarkers from blood analysis etc. But there were some interesting tidbits:
- Noise - especially biological noise - cannot be handled by traditional machine learning approaches. Signal to noise ratio is very low especially when picking biomarkers.
- SysBio can help pharma pick targets (which it is increasingly getting worse at).
- Cost can be minimized in optimal cases; eg. FDA approved Herceptin specific for 20% of patients in only 40-patient sample (Genentech).
- Descriptive and graphical models can be enormously useful; in fact complexity often precludes mathematical modeling.
- Example of prions injected into mouse: expression of 33% genes changed. Biological noise can be “subtracted” by judiciously picking strains that get rid of one effect and preserve others.
My own take on systems biology has always been that, while it is likely to become quite significant at some point:
a. It's going to take longer than we think.
b. Separating signal from noise and honing in on the handful of approaches which will be robust and meaningful is going to give us a lot of grief. This will likely be Darwinian selection at its best.
Patricia Hurter (Vertex): Formulation
Eric Olson (Vertex): Cystic Fibrosis
- Most common mutation is F508del (targets 88% of patients)
- Two potential drugs; potentiators (for restoring function) and correctors (for localizing protein from ER to membrane surface).
- However, only potentiators needed for G551D mutation (targets 4% of patients). Ivacaftor increases probability of channel being open; more beating cilia (nice video).
- Development challenges: little CF expertise, limited patient pop, no defined preclinical and regulatory path, outcomes for proof-of-concept and phase 3 not well established for mechanism-of-action.
I thought that the development of Vertex's CF drugs is a model example of charting out drug development in a novel, unexplored area.
George Whitesides (Harvard): Challenges
Interesting talk by Whitesides. A pretty laid back speaker. The first half was a general rumination on the state of pharma and drug discovery ("the current model of capitalism is not working"; "the FDA has become unreasonable"; "if the best we can do in cancer is to invent a drug that gives someone 3 extra months with a lot of side effects, then we are doing something wrong").
The second half concerned his work on the hydrophobic effect. The papers deal with ligand binding to carbonic anhydrase. Basically he found out that the so-called entropic signature of the hydrophobic effect (an increase in entropy from release of bound water molecules) is more complicated.
A few notes:
- Designing drugs is hard because we are robust, multi multiplexed complex systems.
- Cost of healthcare in the US is ~17% of GDP: also, no correlation between health cost and quality, as evidenced by low standing of US.
- Quoted Anna Karenina’s happy and unhappy families; has something to do with drug development. Every successful drug has its success in common, unsuccessful drugs are unsuccessful in their own way.
- Pharmaceutical crisis has nothing to with per se with science, everything to do with costs.
Finally, he made an important point: biochemists have always done experiments in dilute phosphate buffer. Interior of cell is anything but.
Favorite quote, regarding the limitations of animal models: "Whatever else you may think of me, I am not a large, hairless mouse”
