Facing the Wall in Computationally Based Approaches to Drug Discovery

JANET S. FINER-MOORE, JEFF BLANEY AND ROBERT M. STROUD*

1.1 The Promise, and the Problem

It has been 36 years since the first protein structure was determined and the promise that structure could guide drug discovery was born.' Yet today even the ability to design a molecule that will target a nominated site and bind there still remains a tantalizing and intellectually enticing prospect. Most good lead compounds fail for reasons to do with lack of efficacy, toxicity, or interference with metabolic pathways. These properties, too, are ripe for computational evaluation before synthesis rather than after. These important areas are being addressed by computational approaches. But the real challenge for drug design is in the intellectual process of appreciating what is actually coded within macromolecular interactions of the target with small ligands, and the nature of specificity for metabolic enzymes that degrade the compound. This code is at the heart of biology, as it is of chemistry.

Let us first recognize the impact that just one, rather incremental, computational approach could have on the process. If it were possible, we might be able to take the crystal structure of a good lead compound and predict where introducing a specific substituent would increase affinity of the compound with even a 10% chance of moving affinity in the favorable direction. This could provide for successive improvements in affinity of the lead compound with just 10 alternative chemical synthetic modifications at each round, one of which would be an improvement (presuming the 10% chance of success). But there is a wall beyond which the probability of success becomes very low. If we could even recognize the cusp at which that will happen ahead of hitting 'the wall', alternative scaffolds could be introduced. This would save enormous resources in chemistry. The same is true for many steps, such as predicting metabolic fate or toxicity of compounds. A small improvement coded in computational screening can reduce downstream efforts by factors of hundreds to thousands. Hence the rational encoding of valid principles, and even empirical wisdom, into the process will remain a key to human health, and one of the highest intellectual challenges of the next decade.

Another area of drug discovery that is hungry for computational assistance is the personalization of medicine. Why is it that some persons have devastating, even life-threatening, responses to Celebrex or Vioxx, while others find them to be the best treatment? Genetic screening is required, but the translation of this into therapeutic strategy is paramount to the salvage of vast potential in the efforts that have already been made to develop potentially good drugs, that could be highly valuable, though for only a defined cohort of the population. If we could anticipate the patients who would respond badly we would save billion dollar markets for present and future drugs. In many ways we are at an exciting frontier in drug discovery, and a frontier where computational biology will need to take the lead.

Lead discovery in the traditional pharmaceutical industry typically involves screening of up to 5 million chemical entities in a few weeks using high-throughput methods, at a cost of $0.50-$ 1.00 for each compound. A screen of that local library could easily cost $5000000. There is consequently real motivation to maximize the return on investment by finding fast and more effective ways to screen. The time and money are compounded by the need to test lead compounds and derivatives for absorption, distribution, metabolism, and excretion (ADME) and toxicity. The spectrum of activity against off-target proteins is something rarely built into even computational screens, and is often left simply to trials on cells, animals, and then humans. With such a wealth of knowledge gathered over decades, why are we not further along in proscribing affinity and favorable properties?

We are in the adolescence of drug discovery. As even empirical rules emerge it is increasingly pertinent that those rules be incorporated into computational analysis, then understood, and then translated into rational rules and algorithms. Ultimately the computer will extract everything we learn in our laboratories, and translate that knowledge into 'wisdom'. Can we blame the industrial leader who shuns the knowledge-based approach for the tried-and-true screening approach to lead discovery? As the knowledge base explodes, the intellectual ability to understand that knowledge and translate it into improved drug discovery has to catch up and demonstrate its contribution to enhanced drug design. Unfortunately much of the knowledge of the binding affinities of series of compounds is sometimes protected, or otherwise unpublished in the knowledge bases of pharmaceutical companies. Another great challenge for the industry and for computational approaches is to release and collate the combined knowledge across the...