This was first posted:09/29/2014 here

The popular Swedish video game commentator PewDiePie, hosting the currently most subscribed YouTube channel, recently highlighted a game called “Dumb Ways to Die”. One of the dumb ways to die is to press a red button. The only thing you need to do to progress in the game is to resist the urge to press a red button that appears on the screen. It’s surprisingly difficult not to push, and of course PewDiePie pushed it…and probably did many of his 30+ million (!) subscribers that as well.

As a matter of fact, it is likely that clever people such as yourself also have pushed similar buttons, with a clear expectation of what was going to happen. Some of the public buttons, that we push daily, don’t do anything at all. For example, in New York City around 75% of the pedestrian crossing buttons do nothing. Similar numbers have been reported for Los Angeles and for other big cities in the US. Around the globe, many traffic signals have been automated to optimize throughput in intersections. Reason being, if pedestrians were allowed to manually alter the signals (by pushing the buttons) traffic would run less smoothly. A more conspiratorial theory is that cities won’t pay to remove the push-buttons. It’s cheaper to cut the cord, and leave us with the buttons…and the illusion of control. Not a bad thing perhaps. We humans tend to feel better when we do something that we think we have control over, even if we don’t. These “placebo buttons” can occur in other places (e.g. the open/close buttons in elevators), as well as in other forms. For example, many top athletes have their rituals before important games, and stockbrokers often believe they can control the market.

In drug discovery, where uncertainty often is high and predictability can be low, we also tend to resort to a reductionist mode. The simplistic “one drug, one target, one disease” concept has, for example, dominated pharmaceutical research the last decades. Most efforts have been focused on hitting one particular target, hit it hard (nM) and selectively*. Now, this is somewhat difficult to understand. For most drugs we suspect and for lots we now know they interact with more than one target.

With large databases of biological activity becoming publicly available, drugs that previously have been claimed to be selective against one target, have later been shown to hit several targets. For example, the histamine H1 receptor was believed to be the only target for cetirizine and hydroxyzine, which both now have been reported to interact with other GPCRs (in in vitro assays). Similarly, celecoxib is referred to as a COX-2 selective inhibitor; although we now know that it interacts with at least two other targets (carbonic anhydrase II and 5-lipoxygenase).

On the other side of the scale we have the antipsychotic drugs (and multi-kinase inhibitors), where a plethora (>20) of targets have been known for a long time for most of them. Numerous mechanism-of-action hypotheses have been formulated, nearly all trying to tease out one particular target responsible for the therapeutic effect. But no selective drugs have reached the market, despite 50+ years of research for better antipsychotics. My own Ph.D. studies can serve as an illustrative example. In brief, the “dirty” drug clozapine had shown slightly more potency at the dopamine D4 receptor over D2, and hence the leading theory back then was that selective D4 antagonist would be beneficial. We (and others) managed to design selective and high-affinity D4 antagonists, but without the desired therapeutic effect.

It is very clear that not all diseases can be sorted under the “one target, one disease” theory. Humans are complex, and drugs are likely to affect several biochemical responses simultaneously, which in turn will cause feedback reactions on the effected pathways. The chance that the net result linearly correlates to a single target is almost negligible (not to mention degeneracy and robustness in biological system as a central survival mechanism). Not surprisingly, a lesson learnt from the fate of AstraZeneca’s drug pipeline was that 40% of the AZ internal drug projects lack a clear link between the main target and disease.

There are some other facts disfavoring the “one drug, one target” push-button theory. Drugs don’t have to be high-affinity to work; the most widely used drug aspirin has no high-affinity target reported. Some “off-target” activities have been reported to contribute to the efficacy of SSRI’s (e.g. fluoxetine). A drug can act through several different mechanisms and unrelated targets (e.g. ritonavir inhibits both the HIV protease and CYP2D6). Many anti-depressants show different target profiles, but result largely in the same therapeutic effect. Finally, it’s indeed possible to bring new drugs to the market without knowing their targets. There are many (30+) drugs with unknown mechanism of action.

And then there’s the relentless perception that the “one drug, one target” approach will provide inherently safer drugs. The assumption is that drugs cannot cause side-effects via other targets if they’re selective. However logical that may seem there is one thing wrong with that statement – it’s not right. Well, it hasn’t been proven and the jury is still out. Side-effects may indeed come from interaction with the therapeutic target itself, due to no (or little) separation between the efficacious dose and safety related outcomes. Another point that deserves to be stressed is that toxicity can arise from many different mechanisms, and that the term promiscuity in itself can easily be misinterpreted as the more targets you hit the greater the safety concern.

The scientific problems we are trying to solve (and understand) in drug discovery are extremely hard, and we need to recognize the complexity and powerful forces of randomness more. In hindsight, this reductionistic approach must have hampered drug discovery. It’s easy to understand the fondness and general acceptance of the theory. The desire to generate simple (and “not even wrong”) concepts is understandable, particularly when they are easy to measure. Trying to optimize for multiple targets, with optimal phys-chem properties may be viewed as a too challenging task to even start.

Nevertheless, there’s a willingness to challenge this view, and move away from the simplistic target definition view. To be fair, “polypharmacology” (a more fancy word for “dirty”) is used increasingly more often now, and it has been labeled as the “next paradigm in drug discovery”. Regulatory hurdles are fewer: the FDA have recently approved polypharmacological drugs (asenapine, sunitinib, and dronedarone) in several different therapy areas (anticancer, antipsychotics and antidepressants) for different target classes (kinases, GPCRs and ion-channels). In addition, multikinase drugs are believed to suppress mutations and expression changes and thus prevent drug resistance.

But still, at conferences, in the recent literature, and in current drug projects the “one target, one drug” approach very much dominates. There might well be cases when hitting one target will do the trick, but that’s likely to be the exception rather than the norm. Reflections on targets are important, but if we continue trying to find the “right target” and just push one button it’s quite possible that we, just like PewDiePie, end up not progressing…in the game of drug discovery.

* Although the “Term-Which-Must-Not-Be-Named” (Big Data) is indeed here the “compound x target” interaction matrix is still extremely sparse. The word “selective” is thus very much a relative concept, since it only refers to those targets we have data for. Both unexpected positive and negative mechanisms may come from hitting unknown targets. One “easy” solution out of this would of course be to expose our compounds to living animals as early as possibly in drug projects.