A shot in the arm for antimalarial drug discovery?

While heart disease, cancer and Alzheimer's continue to grab the headlines, malaria and tuberculosis continue to quietly do their deadly work behind the scenes. Diseases that disproportionately affect sub-Saharan Africa are not exactly priorities for drug companies. But they pose a tremendous unmet need. Especially malaria, which kills an unbelievable 800,000 people every year, has fought back against almost every traditional drug. The fight against the disease has boiled down to one class of drugs- the artemisinins. If the parasite develops resistance against these, nobody knows how fast and wide it will spread.

Since pharma companies often get bad press for neglecting....neglected diseases, this makes the duo of papers in this week's issue of Nature especially impressive. The papers talk about GSK collaborating with a host of academic laboratories to discover literally hundreds of hits against malaria through phenotypic screening. The sheer multidisciplinary effort put into this endeavor is laudable. Phenotypic screening is an effective method for drug discovery since it does not care about the target of a drug, at least in the beginning. It's a more top down approach that complements bottom-up rational drug design. The goal is to simply watch out for a particular kind of response, which could be anything from fluorescence to cell shrinkage. In this case it was 80% inhibition of growth of the parasite in the asexual stage in red blood cells. Target identification can come later.

The company screened its proprietary collection of about 2 million compounds. The compound library was chosen for diversity of scaffolds and novel chemotypes. The assay looked for 80% inhibition of the P. falciparum parasite, and came up with hundreds of diverse compounds. The scientists seemed to have taken due care to minimize false positives. They sought to eliminate promiscuous, lipophilic compounds from the list. They also screened their compounds against well known targets and processes that the malarial parasite exploits to subdue its host. One of these was particularly eye-opening for me; apparently, the insidious little weasel can hack up the amino acids from hemoglobin molecules in the host to assemble its own proteins. Now that's stealth for you. More interestingly, the group then screened the selected molecules against seven novel malarial targets and found encouraging inhibition profiles against these targets. Infectious disease are best treated when you can hit the causative agent in multiple places. Paucity of targets has especially been an issue for malaria and TB, and these chemotypes along with their suggested targets provide promising leads. As a final act, the first paper co-authored by Guiguemde et al. also demonstrates favorable pharmacokinetic properties for one of their hits.

Especially interesting is the report in the second paper authored by Gamo et al. where the authors follow a similar procedure but discover that the novel target list for the purported antimalarial candidates is enriched in kinases. They take due care to investigate that this enrichment is not a chance enrichment. Unlike the human genome which has about 500 kinases, the malarial genome has about 80. But finding kinases among the targets of these novel chemotypes has rich implications, since kinases have already been intensely investigated, the targets are well-understood and there are literally thousands of kinase inhibitors out there waiting to be tested. Testing kinase inhibitors against malaria would open up a whole new chapter for antimalarial drug discovery.

Finally, and this is the kicker most talked about, GSK has made the entire list of hits freely available to the public. This is a very laudable act. In an age where corporations are routinely derided for their emphasis on secrecy and profit-making, such a decision should drive home the good work that corporations can potentially do. It also underscores the tremendous opportunities for drug discovery against neglected diseases gained from academic-corporate collaboration. While it remains to be seen how many of these promising candidates become bona fide drugs, it provides many promising starting points for further efforts. Malaria is about as insidious a disease as you can have, lurking in the shadows and waiting to pounce on you. The more the hands that try to squeeze its neck, the better.

Guiguemde, W., Shelat, A., Bouck, D., Duffy, S., Crowther, G., Davis, P., Smithson, D., Connelly, M., Clark, J., Zhu, F., Jiménez-Díaz, M., Martinez, M., Wilson, E., Tripathi, A., Gut, J., Sharlow, E., Bathurst, I., Mazouni, F., Fowble, J., Forquer, I., McGinley, P., Castro, S., Angulo-Barturen, I., Ferrer, S., Rosenthal, P., DeRisi, J., Sullivan, D., Lazo, J., Roos, D., Riscoe, M., Phillips, M., Rathod, P., Van Voorhis, W., Avery, V., & Guy, R. (2010). Chemical genetics of Plasmodium falciparum Nature, 465 (7296), 311-315 DOI: 10.1038/nature09099

Gamo, F., Sanz, L., Vidal, J., de Cozar, C., Alvarez, E., Lavandera, J., Vanderwall, D., Green, D., Kumar, V., Hasan, S., Brown, J., Peishoff, C., Cardon, L., & Garcia-Bustos, J. (2010). Thousands of chemical starting points for antimalarial lead identification Nature, 465 (7296), 305-310 DOI: 10.1038/nature09107

Life anew?

The recent creation of a "synthetic organism" by Craig Venter and his colleagues his hit the headlines. By all accounts it is a thoroughly impressive piece of work, a tour de force that designed a genome from scratch, literally by writing it the way a piece of computer code is written. The perseverance and ingenuity put into the process deserve ample applause. And it should rightly catapult the emerging field of synthetic biology into the public discourse.

But it's still not a "synthetic cell" in my opinion. The genome was inserted into a cell where it started working exactly as expected. I would not hold my breath before we can design completely synthetic genomes that can do whatever we want, including eating CO2 or producing Lipitor.

To this non-expert, the reason looks simple: everything in molecular biology that we have encountered until now has turned out to be more complex than expected. Notice what's happened to AIDS vaccines, gene therapy and treatments for Alzheimer's disease, all of which would supposedly be simpler than designing a synthetic organism. In each of these cases, what seemed obvious and straightforward has turned out to be a maze of unexpected challenges and unexplained observations. The fact is, designing a genome is one thing, making it produce proteins that will interact with each other in a carefully orchestrated manner, will find binding partners with exquisite specificity and accomplish the extremely complex and often non-intuitive cascades of signal transduction is quite another. A cell is not just the genome, it's really about interactions. And I am willing to bet that we are a long way off before we can generally design all those countless specific interactions which give rise to that entity named a "cell".

Perturbed by Free Energy Perturbation?

Family matters kept me away for sometime, but this topic seems apt to jump into the fray again. In the Pipeline has an interesting slew of comments about the role of computational chemistry in drug design and discovery. The comments were in response to a question by Derek about how useful Free Energy Perturbation (FEP) could be in drug design. FEP is a kind of holy grail for drug hunters. If you could really predict the absolute free energy of binding of a series of diverse drug-like molecules to a protein, it would comprise an unprecedented breakthrough. It may not instantly make it possible to put two new cancer drugs a week on the market, but predicting the affinity of compounds without making them would certainly lead to unimaginable savings in cost and money for the pharmaceutical industry. Not surprisingly, many erstwhile knights are pursuing this dream with vigor. To me it seems interesting to summarize what my reading of some of the major challenges in the field are. This is a personal evaluation, feel free to enlighten in the comments section.

1. Our understanding of protein structure and conformation is still significantly inadequate: This may be the single-most daunting challenge in doing FEP. We don't know how to calculate the entropy and enthalpy of proteins binding to drugs that arises from their motions. Induced fit effects have long been recognized as being very important in dictating protein-ligand binding. Yet, most docking programs that try to fit ligands into protein pockets do so while considering the protein rigid. Movements of side chains, loops and sometimes even large scale movement of helices can be significant yet subtle, and it's an uphill task to include these into a docking calculation. Some docking programs have made impressive advancements in predicting induced fit, but a lot remains to be done. However, the core problem with doing any of this really leads us to the biggie in the field- protein structure prediction. Convoys of experimentalists and theorists have been trying to do this for decades. Sucess has been impressive, but still not general enough.

The general problem has huge implications for understanding protein folding, misfolding and of course, protein-drug binding. It's significiant and appreciated enough that at least one man, who happens to be the richest man in the world, has decided to put his money on it. Bill Gates recently announced that he is investing 10 million dollars in the computational drug design company Schrodinger, specifically with a view to supporting developments in protein structure prediction and related issues. That must mean something. In any case, unless we can capture the dance of proteins even as they bind to a drug, our dream of FEP will be a distant spot on the horizon. If an x-ray structure is available, such efforts become more feasible. And yet for some of the most important proteins like GPCRs, only a handful of structures exist. Homology modeling can and is supplying some of the missing structures, but the process involves tremendous guesswork and the devil in the details often thwarts your best efforts. In the end, computational prediction of protein structure can only come from an enhanced basic understanding of the basic properties of proteins, and both theory and experiment will need to massively intertwine in this quest.

2. Our understanding of ligand conformations is much better, but still not perfect: Compared to protein conformation prediction, we are orders of magnitude better with ligand conformations prediction, primarily because of the small size of the ligand. But even here challenges lurk. Ligands usually exist as multiple conformations in solution. One of these conformations is the bioactive one that binds to the protein. Often this is only 2-3% percent, which means it's virtually impossible to detect by NMR. While several methods exist for generating relevant ligand conformations, it is prima facie very difficult to say which one is the bioactive one. Plus, ligand and protein have to expend strain energy for the ligand to adopt the right conformation. One never knows how much strain energy the ligand can pay, although recent estimates have suggested a maximum cap of a few kcal/mol. Beyond all this, it's worth noting that drastic changes in activity can sometimes result from small changes in ligand conformation. Docking cannot always capture these small changes, although in some cases as I demonstrated before, docking can capture non-intuitive ligand conformations that only crystal structures can reveal. The bottom line is that even though we have a much better handle on ligand conformations compared to protein conformations, locating the bioactive conformation is still trying to locate a needle among a haystack of needles.

3. Water is still the big white elephant in the room: The most well-known solvent is still the least well-understood, especially in the context of its interaction with biomolecules. By some estimates, the displacement of water molecules by hydrophobic parts of a ligand is the single most important driver for binding affinity. Apart from the more obvious roles that water molecules can play in bridging ligand protein interactions and serving as well-placed displaceable entities that can be kicked out by ligand extensions with huge resulting changes in free energy, water also plays more subtle roles that we are just beginning to comprehend. Water can act as a kind of lubricant, 'massaging' proteins as they unfold and fold, gliding across hydrophobic and hydrophilic surfaces and helping them to form interactions. Plus, proteins usually are surrounded by a ghostly layer of bound water molecules that almost act as a virtual extension of their structure. These water molecules can exert important influences on protein conformational changes. Plus, the hydrophobic effect only gets more interesting every day, with recent findings suggesting that there is a 'dewetting transition' when two hydrophobic surfaces approach each other closer than a critical distance. To find out more, you can check out an excellent review of water's role in biology on the molecular level. Current methods for modeling water include implicit and explicit solvation models. The drawbacks of both are well-recognized. It seems astonishing that we are trying to predict the solvation of protein-ligand assemblies when we are still struggling to get the solvation of simple organic molecules right. In the end, correct accounting of water for specific systems is going to be key for accurate FEP calculation.

The real challenge in FEP comes from the exquisite, exponential dependence of free energy of binding on the dissociation constant of a protein ligand complex. Since a 1 kcal/mol change in ∆G can lead to a ten fold change in dissociation constant, we need to do at least as well as this number in predicting free energies accurately. Since hydrogen bonds are a few kcal/mol, hydrophobic and electrostatic interactions can contribute another few kcals, and the errors in these parameters effected by inadequate solvation, incomplete sampling of conformations and incomplete representation of things like entropy are all incremental, it's pretty clear that getting things correct to 1 kcal/mol is a decidedly uphill task. The methods just cannot include all the parameters from real life necessary to achieve this. Real life measurements of binding affinity are frequently conducted under messy conditions with mixed solvents, ions, buffers and inhomogeneous environments. Rest assured that your grandson will be trying as hard as you are to include these factors into a FEP calculation.

I have always thought that this glass ceiling of 1 kcal/mol really represents all the riches we can get from understanding the diverse factors that dictate protein-ligand binding. The magic number is like the mythical island of Ithaca. You may arrive there weary and old, and may even discover that the place does not exist, but the wisdom you would have gained on the way would be of permanent value. That's what counts.

It's truly the entropy that binds us together

Fragment-based Drug Design (FBDD) has emerged as one of the key strategies in drug design during the past two decades. FBDD hinges on the fact that fragments, as opposed to complete ligands, are easier to optimize and study since they possess lesser molecular complexity and have fewer binding interactions.

When fragments are optimized to bind to parts of a protein's active site, they can gain powerful binding affinity by being linked together. Usually fragments are relatively weak binders, and connecting them with linkers can provide orders of magnitude improvements in the free energy of binding. The reason for this affinity increase is usually stated to be entropic. The rationale is that when two fragments are linked together, the entropic cost that they would have to pay were they to separately bind is already paid for by the linker. Thus the combined free energy is much more favorable than the pairwise free energy. The increase in free energy is quantified by a number called the "linking coefficient", where a value of less than zeo indicates enhanced binding relative to the fragments.

However, such an analysis assumes that the contribution to the binding process from other factors is minimal, and the entropic advantage is the only major player in the affinity increase game. But as with protein-ligand interactions, the picture is more complex. There can be unfavorable enthalpic contributions from the fragments losing favorable binding interactions upon being constrained by linkers. There can sometimes be favorable enthaplic interactions from new contacts between the ligand and protein. And there can be enthalpic and entropic contributions from the linker itself. Thus, dissecting the factors that go into the free energy upon fragment linking is like the classic conundrums of physical organic chemistry which I encountered in college, where controlled experiments are deviously hard, and changing one factor inevitably changes another (when does it not?).

An ideal system for studying the contribution of entropy to FBDD would be a system of two fragments binding to a protein which can be linked together by a single bond and which retain all their existing contacts with the protein upon being linked. Such systems would admittedly be hard to design, but a group of Italian researchers has come up with a neat system linking two very simple fragments, a hydroxamic acid and a benzenesulfonamide, with a single bond.

The fragments and resulting molecule inhibit matrix metalloproteinase (MMP), a key enzyme implicated in cancer and inflammation. The authors perform x-ray crystallographic and isothermal calorimetry (ITC) to investigate the thermodynamics and binding of the individual fragments and their linked counterpart to MMP. They demonstrate that the two fragments preserve their binding modes even when linked together and observe a rather large free energy enhancement of almost 4 kcal/mole on fragment linking.

This is a nice case where a careful analysis and dismissal of other factors points accurately to entropy as the main contributor to enhanced binding of linked fragments.

Borsi, V., Calderone, V., Fragai, M., Luchinat, C., & Sarti, N. (2010). Entropic Contribution to the Linking Coefficient in Fragment Based Drug Design: A Case Study Journal of Medicinal Chemistry DOI: 10.1021/jm901723z