Drug DesignPosted by Jonas Boström Wed, November 18, 2015 23:37:02
This was first posted: here
One key to successfully progress a drug discovery project is to make first-rate decisions (hopefully) based on unambiguous data. This is not trivial since our scientific problems are often very complex and data can be fuzzy. In drug design we try to approach this uncertainty by being rational. It is however sometimes forgotten that our rational approaches may not be that rational after all – decisions may well be based on personal preferences and intuitive biases.... perhaps unconsciously made on biased data.
In their great paper “Judgment under Uncertainty” the behavioral scientists Kahneman and Tversky elaborated on decision making and how people deal with uncertain events. It was shown that we tend to push ahead with confidence even though lacking enough information (to make informed decisions). There’ also the entertaining (?) and somewhat controversial notion that scientific facts can be constructed in a tribe-like fashionin laboratory settings. In drug discovery, a number of psychological biases that pose risks to good decision making was recently highlighted by Segal and Chadwick.
“The Halo Effect” is a specific type of confirmation bias that makes us perceive someone (or something) favorably because of one very positive quality in that person/thing. That is, one good feature lends its attractiveness to other properties of a person’s character: “that Hollywood actress is beautiful, so she must also be clever/happy/fill in the blank“. We tend to make attributions based on other data that we for some reason believe are reliable, and it can cloud our judgment and infer with decision-making.
David Beckham is looking good and few in the world can kick the ball like him. He's likable and extremely popular. It is easy to think that he's all good. Nonetheless, he has recently been accused of triggering a halo effect around unhealthy drinksby endorsing Pepsi. Is there also a halo(gen) effect in para-substituted phenyl rings? They are (on average) metabolically more stable than their ortho and meta regioisomeric partners and (perhaps therefore) the most popular regioisomer among medicinal chemists. Yet, para-substitution is (on average) the worst regioisomer with respect to hERG binding and aqueous solubility.
Dean Brown, a colleague of mine, recently discovered an unexpected biasin most (if not all) drug databases by performing exhaustive population analysis of phenyl-ring substitutions. It could be concluded that para-substitution are significantly more often occurring than meta and ortho. In attempt to gauge AstraZeneca medicinal chemists personal preferences regarding aromatic substitution pattern we set up a survey. The result was clear – the primary choice was indeed para. The two main reasons for this preference were: (a) para-substitution provides better protection against metabolism than ortho/meta; (b) the para-position was most likely to boost potency. The first reason was confirmed true whilst the second not.
There could be many reasons for this bias, such as the Topliss work that promoted para-substitutions, a range of possible DMPK (solubility, metabolism) and Safety (hERG) property differences, as well as ligand-binding effects (potency). Other possible factors are synthetic accessibility, cost differences for chemical reagents and historically different design strategies (classic pharmacology vs. target-based design). All of these were scrutinized and it was concluded that the para bias could not be attributed to one single factor. What we do know, however, is that personal preferences and subjectivity still play a pretty big role when selecting reagents for syntheses. In fact, a range of possible preconceptions was recently highlighted when the Dean Brown article was inthepipelined (it’s verb right?). Not to mention that luck influences most everything of what we do.
Why does this matter? Using skewed molecular databases can be risky if one is not aware of any uneven distributions. For example, if there are more para-substituted phenyls in a database than ortho/meta there will be more para hits (from a screen) out of sheer probability. This could in turn lead the inexperienced scientist to assume that the screened target favors para-substitution. Luckily there are remedies – statistical approaches combined with cheminformatics can be used to avoid these issue.
Relying on our intuition is often effective, when making decisions in situations of uncertainty. However, failing to understand the underlying reasons can lead to systematic and predictable errors as the one just described (ease of synthesis is not the reason for the para bias). We hope that our analysis will lead to a broader awareness of unevenly populated databases, a better understanding of how to deal with them to improve our judgments and decisions in medicinal chemistry. To learn more about this, a biased suggestion would be to read our article to see if any of your potential prejudices (regarding phenyl substituents) are supported by data.
Drug DesignPosted by Jonas Boström Wed, November 18, 2015 23:27:56
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.
Drug DesignPosted by Jonas Boström Wed, November 18, 2015 22:58:21
This was first posted: 05/07/2013 here
In the business literature and in various seminars, conferences and workshops, we are continuously bombarded with the message that originality and new ideas are good, while copied and old ideas are bad or unethical – criminal even. Such stupidity. Such complete and utter idiocy!
It is not true that original ideas are always the best, or that copying is always inferior. On the contrary, history is full of stories where original thinking failed completely, and copies managed to outdo originals. Take Google, for instance. Google got in the game at a stage of massive expansion, and was at the time just one search engine among many others. If you look at Google today, you’ll see a company famous for its many brilliant web-based services, but also a company where the most used ones tend to be copies or developments of things invented elsewhere. When I say that Google have copied en masse, I say it with praise and envy. Google are brilliant because they are amazing copiers! Copying can be a highly successful strategy, even though it might not sound quite as elegant and alluring as being recognized as a great original.
In a (rare) moment of clarity, I thought what would not be better than copying text myself to illustrate the power of copying. In fact, what you just read has been copied, word by word, from the book “Dangerous Ideas” , with permission from author Alf Rehn. Alf Rehn is a former professor of innovation and entrepreneurship. Thinkers 50, the listing of the world’s top 50 business thinkers, recently included him on their Guru Radar…and I love his provocative way of writing.
In the book, Alf’s lists eight “commandments” (or 7 since the 8th is a copy of the 7th) on how to copy better. One commandment reads “Sometimes you just need to change contexts…” – think current efforts on rescuing and repurposing drugs? Another one states “Small changes can generate big effects: “Dancing with the Stars is a copy of American Idol, but with famous amateurs dancing. Whoever thought of that little variation is rich today”. The analogy that comes to mind here are “follow-on” drugs. The most famous example of a “follow-on” drug is probably Levitra, which is basically a short nitrogen-walk from the original Viagra. Levitra sells for an enormous amount of money (total sales 2010: $242,446,000) , and helps people to a…eh…very natural way of copying.
Numerous drugs are “follow-ons”, and small changes can indeed make for important patient benefits. For example, replacing a twice-a-day with a once-a-day pill (Terazosin vs Prazosin ), and switching to ‘personalized’ medicines (some people respond well to Prozac but not Zoloft). Even so, the approach is most often mentioned with a negative connotation. “Follow-ons” are generally not considered to be truly innovative, as well as the relentless debate on their legal and financial aspects. In a recent review, we analyzed the DiMasi and Faden data set  of “first-in class–follow-on” pairs on the market. As many as 70% (N=74) of the pairs are characterized by minor structural variations . Thus, whereas it is generally accepted that large changes in molecular structure leads to large variations in properties, we tend to take too lightly on the fact that small molecular changes can also generate big effects.
It should not be forgotten that many astonishing scientific advances come from copying the science of Mother Nature itself. Evidently, many drugs are close analogues of native ligands or natural product. So, the design message is the following. Do not be afraid of seeking inspiration from competitor’s patent specifications, we have provided tools for that , or from nature. Biology and chemical space is to your advantage, increasing the odds that your optimized compounds will be novel with a significantly different, and improved, profile. And when encountering a medicinal chemistry related problem that you believe is specific to your structural series, take a moment to reflect. Your next candidate drug might be closer than you think, quite possibly only a few atoms away.
Some final words of wisdom from “Dangerous Ideas” :
I’m obviously not encouraging people to flout copyright law, and just as in any other activity, you need to ensure that you’re behaving in a sensible and ethical way when copying. But our reaction to this tends to be exaggerated and overly cautions, and just like no one wants to be queer zero, we’re all afraid of being seen as less than original. There is no shame in copying. On the contrary, it is a necessity. Instead of turning away, we should make copying our friend, create copying cultures in our organizations, and see how this approach can generate both brilliant new ideas and an understanding for the reinvention of wheels. We have nothing to lose but our preconceived notions.
Rock on!  Alf
- Rehn, A. (2011) “Dangerous Ideas: When Provocative Thinking Becomes Your Most Valuable Asset”,http://www.strikingly.com/dangerousideas
- Kyncl, J.J. (1986) “Pharmacology of Terazosin” Am. J. Med. 80, 12–19
- DiMasi, J.A. and Faden, L.B. (2011) “Competitiveness in follow-on drug R&D: a raceor imitation?”Nat. Rev. Drug Discov. 10, 23–27
- Giordanetto, F., Boström, J. and Tyrchan C. (2011) “Follow-on drugs: how far should chemists look?” Drug Discovery Today, 16, 722-732.
- Tyrchan, C. Boström, J. Giordanetto, F., Winter and Muresan S. (2012) ”Exploiting structural information in patent specifications for key compound prediction” J. Chem. Inf. Model. 52, 1480–1489.
- Personal communication with Alf Rehn.