Natural Intelligence?

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My pulse will be quickenin'

With each drop of strychnine

We feed to a pigeon

It just takes a smidgin

To poison a pigeon in the park

Tom Lehrer, Poisoning Pigeons in the Park | video

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I’ll be reviewing the H2024 study (Occurrence of “Natural Selection in Successful Small Molecule Drug Discovery) in this post. Derek has already posted on the H2024 study which has been included in the BL2024 Virtual Special Issue on natural products (NPs) in medicinal chemistry. I'll also mention reviews here at Molecular Design of the the related studies (4) (see post) and (24) (see post). As is usual for Molecular Design reviews of literature I have used the same reference numbers that were used in H2024 and quoted text is indented with any comments by me in square brackets and italicised in red. Given the serious concerns I have about H2024 this is going to be a long post and there are a couple of disclaimers that I need to make before starting the review:

1. I regard identification and biological characterisation of NPs as vital scientific activities that should be generously funded and Derek puts it very well in his recent post ("When you see specific and complex small molecules that living creatures are going to the metabolic trouble to prepare, there are surely survival-linked functions behind them."). In particular, I see it as important that NPs be screened in diverse phenotypic assays and here’s a link to the Chemical Probes Portal. While my criticisms of H2024 are certainly serious it would be grossly inaccurate to take these criticisms as indicative of an anti-NP position.
2. Automation of workflows (N2017) and generation of datasets from databases such as ChEMBL are far from trivial and (33), which highlights some of the challenges faced by researchers in this area, was the subject of a recent post at Molecular Design. I consider method development in this area to be an important cheminformatic activity that should be adequately supported. It must also be stressed that the design, building and updating of databases such as ChEMBL (G2012 | B2014 | P2015 | G2017 | 23) are vital scientific activities that should be generously funded (had it not been of the vision and foresight of the creators of the PDB over half a century ago it is improbable that the 2024 Chemistry Nobel Prize would have been awarded for “computational protein design” and “protein structure prediction”). While my criticisms of H2024 are certainly serious it would be grossly inaccurate to take these as criticisms of the automated dataset generation described in the study (and recently published in H2024b) or of the contributions by a number of individuals that have made ChEMBL an invaluable resource for drug discovery scientists and chemical biologists.

Hampi, November 2013

Having made the disclaimers, I’ll open my review of H2024 with some general observations. First, I do not consider that H2024 presents any insights of practical value to medicinal chemists nor do I consider the analyses presented in the study to support the assertion that “there is untapped potential awaiting exploitation, by applying nature’s building blocks─’natural intelligence’─to drug design” (in my view the use of the term “natural intelligence” does rather endow the study with what I’ll politely refer to as a distinctly pastoral odour). Second, the results of the analyses presented in H2024 do not demonstrate any tangible benefits from the drug design perspective of incorporating structural features that have been anointed as 'natural' by the authors (my view is that it would be extremely difficult to design data analyses to address the relevant questions in an objective manner). Third, the authors of H2024 present a ‘scaffold-centric’ view of NPs in which the naturalness of NPs is due to cyclic substructures present within their chemical (2D) structures (it is almost as if these 'natural' substructures are considered to be infused with 'vital force') and I would question whether this is a realistic view from the molecular recognition and physicochemical perspectives.  Fourth, the meaning of what the authors of H2024 are calling 'enrichment' of pseudo-NPs (PNPs) in clinical compounds is unclear and, in any case, the 'enrichment' values do seem rather low (never more than twofold) when you consider the numbers of compounds that successful discovery project teams typically have to synthesize in order to deliver a drug that gets to market.

It's not clear (at least to me) what the authors of H2024 mean by ‘natural selection’ and at times their view of natural selection appears to be closer to Lysenkoism than Darwinism. For example, they assert in the conclusions section of H2024 that “NP structural motifs are provided predesigned by nature, constructed for biological purposes as a result of 4 billion years of evolution.” Design actually has no place in natural selection and perhaps the authors are thinking of 'Intelligent Design' which is a doctrine with many adherents in the Creationist community.  While I don’t dispute that the chemical structures of many clinical compounds contain substructures that are also found in the chemical structures of NPs, I think that it would be extremely difficult to objectively compare different explanations for the observations (it's worth remembering that correlation does not imply causation). The explanation favoured by the authors of H2024 is that compounds assembled from Nature’s building blocks are ‘better’ and a stated aim of the study is “to seek further support for the existence of ‘natural selection’ in drug discovery” (this video will give readers an idea of what the late great Dave Allen might have made of this). In my view the data analyses presented in H2024 are not actually based on statistics and are therefore unfit for the purpose of testing hypotheses. Put another way, if you're going to use data analysis to look for something then it would be a good idea to use methods capable of telling you that you that haven't found what you were looking for.    

 

The data analyses in H2024 are largely based on quantities (PNP_Status | Frag_coverage_Murcko | NP-likeness) that are calculated from the chemical (2D) structures of compounds.  However, the authors do not state which software was used to perform the calculations and, had I been a reviewer, I would have drawn their attention to the following directive in the Data Requirements section in the J Med Chem Author Guidelines (accessed 27-Dec-2024):

9. Software. Software used as a part of computer-aided drug design should be readily available from reliable sources, and the authors should specify where the software can be obtained.

As was the case for my review of (24) I see much of the analysis in H2024 as relatively harmless “stamp collecting” (in contrast, as discussed in KM2013, I consider presentations and analyses of data that exaggerate trend strength, such as those used in the HMO2006, LS2007, LBH2009, HY2010 and TY2020 studies to be anything but harmless). The analyses that I’ll be examining in this post are of comparisons between clinical compounds and reference compounds although I'll comment in general terms on the analyses of time-dependencies of characteristics of clinical compounds. My general criticism of H2024 is not that the analyses presented by its authors are necessarily invalid but that they fail to provide any useful insight and I’ll share an insightful observation by Manfred Eigen (1927-2019):

A theory has only the alternative of being right or wrong. A model has a third possibility: it may be right, but irrelevant.

I first encountered analyses of time-dependencies of drug properties about two decades ago and rapidly came to the conclusion that some senior medicinal chemists where I worked had a bit too much time on their hands.  The fundamental flaw in the interpretation of these analyses is that time-dependencies of the properties of drugs and other clinical compounds are presented as causes rather than effects and it has never been clear how medicinal chemists working on drug discovery projects in the real world should use the results from such analyses. The authors claim that “changes to drug properties over time are significant” and I would challenge them to present even a single example of such analysis being used to meaningfully inform decision-making in a drug discovery project. It must be stressed that my criticism of analyses of time-dependency of the properties of drugs and other clinical compounds is simply that they don't provide useful insights and not that the analyses are necessarily invalid. That said, I do have general concerns about how time-dependencies are compared when some of the properties are expressed as logarithms and some are not. As reviewer I would have recommended that the vertical axis of the plot in the graphical abstract be drawn from 0% to 100% rather than from 30% to ~67%.

As is the case for analyses of time-dependency, my criticism of analyses of the differences between clinical compounds and reference compounds is that they don’t provide useful insight and there is no suggestion that the analyses are necessarily invalid. Before looking at the analyses presented in H2024 I’ll quote from the abstract of (24) because this will give you an idea of what I mean by analyses not providing useful insight:

Drugs are differentiated from target comparators by higher potency, ligand efficiency (LE), lipophilic ligand efficiency (LLE), and lower carboaromaticity.

As I noted in this post (this focused principally on the invalidity of the LE metric as discussed in NoLE) reporting that an analysis has shown drugs to be differentiated by potency from target comparators does seem to be stating the obvious and, given how LE and LLE are defined, it is perhaps not the most penetrating of insights to observe that values of these efficiency metrics tend to be greater for drugs than for comparator compounds. While the observation of lower carboaromaticity of drugs relative to comparator compounds is non-obvious, it does not constitute information that can be used for medicinal chemistry decision-making in specific discovery projects (as we noted in KM2013 carboaromaticity and lipophilicity can both be reduced simply by replacing a benzene ring with benzoquinone).

Let’s take a look at how this type of analysis is used in H2024. The authors of H2024 note that “comparing Figure 3a,b shows a clear ‘enrichment’ of PNPs in clinical compounds versus reference compounds in the post-2008 period” and two of these authors, writing in (17), assert that “PNPs have increasingly been explored in recent drug discovery programs, and are strongly enriched in clinical compounds”.  What the authors of H2024 are calling 'enrichment' is rather different to the enrichment in structural features that results from high-throughput screening (HTS) and it’s important to understand the difference. Let’s suppose that we’ve screened a library of compounds of which 1% are pyrimidines and 1% are pyrazines and we find that 10% of the hits are pyrimidines and 0.1% are pyrazines (to simplify things you can assume there is no compound in the library with a pyrimidine and a pyrazine in its chemical structure). In this case we would conclude that the process of screening has resulted in a tenfold enrichment for pyrimidines and a tenfold impoverishment for pyrazines. Now let's create a 'selected azines' category by combining the pyrimidines and pyrazines which as a structural class comprise 2% of the screening library compounds but 10.1% of the hits. What I'm getting at here is that enrichment of an more inclusive structural class such as 'selected azines' (or PNPs) does not imply that each and every one of the structural classes covered by the inclusive structural class definition will also be enriched.

Now let’s take a look at how the 'enrichment' of PNPs in clinical compounds is assessed in H2024. First, a set of reference compounds is generated for each clinical compound (this is discussed in detail in H2024b) and the sets of reference compounds are combined. 'Enrichment' is then assessed by comparing the fraction of clinical compounds that are PNPs with the fraction of compounds in the combined reference sets that are PNPs. When we assess enrichment of chemotypes in HTS the hits are all selected (by the screening process) from the same reference pool of compounds. In contrast, each clinical compound in the H2024 analysis is associated with a different reference set of compounds (from the perspective of data analysis combining reference sets defined in this manner gratuitously throws information away). As a reviewer I would have pressed the authors to enlighten readers as to how they should interpret the proportions of PNPs in the reference sets for individual compounds.

It's worth thinking about what the reference compound set might look like for a clinical compound that is a PNP. The proportion of PNPs in the reference set will generally be influenced by factors such as availability of data, the ‘rarity’ of the structural features of the drug and the ‘tightness’ of the structure-activity relationship (SAR).  A more permissive definition of ‘activity’ would generally be expected to make SAR appear to be less ‘tight’ (or ‘looser’ if you prefer). Compounds were defined as ‘active’ for the analysis on the basis of a recorded pChEMBL value against one of the clinical compound’s targets (as a reviewer I’d have suggested that the authors define the term ‘pChEMBL’) which means that a compound might have been selected for inclusion in a reference set on the basis of an IC₅₀ value of 100 μM.

Let’s define 'enrichment' by dividing the fraction of the clinical compounds that are PNPs by the fraction of reference compounds that are PNPs. When we select a reference set for a clinical compound that is a PNP then it’s extremely unlikely that every single compound in the reference set will also be a PNP (especially if we’re accepting compounds with IC₅₀ values 100 μM as ‘active’) and it’s even less likely that every single compound in the combined reference sets will be a PNP. This means that we should generally expect the clinical compounds that are PNPs to be ‘enriched’ in PNPs when compared with their combined reference sets. We can apply exactly the same logic to conclude that we should expect that the combined reference sets for the clinical compounds that are not PNPs  (under this scenario we would conclude that the set of clinical compounds that are not PNPs are infinitely impoverished in PNPs when compared with their combined reference sets). This means that we should expect that the 'enrichment' of PNPs in the clinical compound set in comparison with their combined reference sets will increase with the fraction of clinical compounds that are PNPs.

Let’s take another look at the plot in the graphical abstract which shows the fractions of clinical compounds and reference compounds that are PNPs as a function of time. Notice how the lines tend to be furthest apart when the fraction of clinical compounds that are PNPs is relatively high. As a reviewer, I would have required that the authors examine the correlation between the logarithm of the fraction of clinical compounds and the logarithm of the enrichment (a relatively strong correlation would indicate that the information added by the combined reference sets is minimal). The 'enrichments' calculated from the plot in the graphical abstract are underwhelming (the highest degree of enrichment is the 2014 value of just over 1.5-fold and this value seems very low when you consider the numbers of compounds that successful discovery project teams typically need to synthesize in order to get drugs approved).  From 2011 the fraction of clinical compounds that are PNPs exceeds 50% but I wouldn't consider it accurate to use the term "strongly enriched" (17) because the fraction of reference compounds that are PNPs is 40% or greater for this time period (plotting the vertical axis in the graphical abstract from 30% to ~67%  creates the illusion that the 'enrichment' is greater than it actually is).

I do have a number of other gripes about the data analysis in H2024 but I do also need to take a look at PNPs and the following assertion by the authors is an appropriate point at which to start this discussion:

The PNP concept has been validated by its appearance in the literature (16,17) and by the design of several new classes of biologically active compounds. (18,19) [As a reviewer I would have pressed the authors to clearly articulate the “PNP concept” (just as I would have pressed the authors of this Editorial to clearly articulate the new principles that their nominees for the Nobel Prize in Physiology or Medicine had introduced).  My view is that it is verging on megalomania to claim that a concept “has been validated by its appearance in the literature” and I don’t consider (18) to support the claim for “design of several new classes of biologically active compounds”. To support such a claim, one would ideally need to demonstrate that screening of libraries of compounds designed as PNPs resulted in the discovery of viable lead series against a range of therapeutic targets. At absolute minimum, one would need to show that libraries of compounds designed as PNPs exhibited exploitable activity across a range of target-related assays (although interesting, the results from the “cell painting assay” would not by themselves support a claim for “design of several new classes of biologically active compounds”). I should also mention that some in the compound quality field (see B2023 and my review of that article) interpret activity against multiple targets for a set of compounds based on a particular scaffold as evidence for pan-assay interference even when the individual compounds don’t themselves exhibit frequent-hitter behaviour. I don't have access to (19) and am therefore unable to assess the degree to which that article supports the authors claim for “design of several new classes of biologically active compounds”.]

The PNP status of a compound is determined by how “NP library fragments” (these are cyclic substructures extracted from the chemical structures of compounds in an NP-focussed screening library that had been generated over a decade ago for fragment-based drug discovery) are combined in its chemical structure.

 

PNP_Status. Compounds were assigned to one of four categories according to their NP fragment combination graphs. (16,17) The NP library fragments used for this purpose are Murcko scaffolds (26) [It would be actually more appropriate to refer to these as ‘Bemis scaffolds’ in order to properly recognize the corresponding author of this article.] (the core structures containing all rings without substituents except for double bonds, n = 1673) derived (16) from a representative set of 2000 NP fragment clusters. (15) [I see this approach as unlikely to capture all the relevant cyclic substructures present in NPs.  My view is that it would have been better to first extract the relevant cyclic substructures from the chemical structures of all NPs for which this information is available, and then do the selection and filtering in one or more subsequent steps. The other advantage of doing things this way is that you’ll get a better assessment of the frequencies with which the different cyclic substructures occur in the chemical structures of NPs.]  Because of their ubiquitous appearances in NPs, the phenyl ring and glucose moieties were specifically excluded as fragments. (16) [I would expect exclusion of the benzene ring (I consider ‘benzene ring’ more correct than ‘phenyl ring’ in this context) as a fragment to result is a significant reduction in number of the number of compounds that are considered to be PNPs (and, by implication, the ‘enrichment’ associated with membership of the PNP class).  Even though the benzene ring has been excluded for the purpose of assigning PNP status it should still be considered to be one of Nature’s building blocks.]

As I mentioned earlier in the post, the view of NPs presented in H2024 is ‘scaffold-centric’ and I would question how realistic this view is given that non-scaffold atoms at the periphery of a molecular structure will generally be more exposed to targets (and anti-targets) than scaffold atoms at the core of the molecular structure. What I’m getting at here is that it is far from clear how much of a compound’s pharmacological activity can be attributed to the presence of individual substructural features in the chemical structure of the compound (modifying a point made in NoLE, I would argue that the contribution of a structural feature to the binding affinity of a compound is not actually an experimental observable). This is one reason that unless matched molecular pairs are available it would not generally be possible to demonstrate the superiority of one structural feature over another in an objective manner.

Something that you need to pay very close attention to when extracting substructures from chemical structures of compounds is the ‘environment’ of the substructure (I prefer to use the term ‘substructural context’). For example, two piperidine rings linked through nitrogen look very different from the perspective of a therapeutic target protein depending on whether the link is a carbonyl carbon or a tetrahedral carbon (most medicinal chemists will be aware that the protonation states differ but there are also subtle, although still significant, differences in the shape of the piperidine ring in the two substructures). You also need to be aware that fusing rings can have profound effects on physicochemical characteristics and I would consider it a bad idea to extract monocyclic substructures from fused or bicyclic ring systems.

There are some things that don't look quite right and I would have flagged these up if I’d been reviewing the manuscript. Let’s take a look at the first entry (Sotorasib) in Table 1 and you can see that the oxygen of the 2-pyrimidone substructure is coloured lilac indicating that this substructure can be found in the chemical structures of one or more NPs (I would still challenge the view that the result of fusing 2-pyrimidone with pyridine should be considered 'natural' on the basis that the heterocycles from which it is derived from are both found in chemical structures of NPs). Now take a look the second entry (Dolutegravir) in Table 1 and you'll notice that the oxygen in the 4-pyridone substructure is not coloured green. This implies that 4-pyridone does not occur in the chemical structure of any NP and, in the absence of  information, I can only assume that it has been anointed as 'natural' because of its structural analogy with pyridine (while there is a nitrogen atom and five trigonal carbon atoms in each substructure the molecular recognition characteristics of the two substructures differ far too much for them to be regarded as equivalent from the perspective of assigning PNP status). Six of the substructures in Figure 5 appear to be in unstable tautomeric forms (first, fifth, ninth, twelfth entries in line 2 | seventh entry in line 3 | first entry in line 5).   

I'll conclude my review of  H2024 by commenting on claims made by the authors:

This is further evidence that the three NP metrics can be considered as independent measures of clinical compound quality. [I would consider the claim that any of these “NP metrics” can be considered as a measure of“clinical compound quality” to be wildly extravagant (the authors haven't even stated how "clinical compound quality" is defined yet they claim to be able to measure it). I would argue that compound quality cannot be meaningfully compared for clinical compounds that have been developed for different diseases or disorders. Describing a compound as 'clinical' implies that a large body of measured data has actually been generated for it and the authors of H2024 might find it instructive to ask themselves why they think a simple metric calculated from the chemical structure of the compound would be of interest to a project team with access to this large of body of measured data One criticism that I make of drug discovery metrics is that they trivialize drug discovery and we noted in KM2013: “Given that drug discovery would appear to be anything but simple, the simplicity of a drug-likeness model could actually be taken as evidence for its irrelevance to drug discovery.” ]

The overall results are supportive of the occurrence of “natural selection” being associated with many successful drug discovery campaigns. [My view is that the authors of H2024 have not clearly articulated what they mean by“natural selection” in the context of this study.]  It has been proposed that NP-likeness assists drug distribution by membrane transporters, (21) [The author of (20c) asserts "Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low" and, by implication, that entry of the vast majority of drugs into cells is transporter mediated. I keep an open mind on this issue although I note that what is touted by some as a universal phenomenon does seem to have been remarkably difficult to observe directly by experiment. The difficulties caused by active efflux are widely recognized by drug discovery scientists and it may be instructive for the authors of H2024 to consider how an experienced medicinal chemist working in the CNS area might view a suggestion that compounds should be made more like NPs to increase the likelihood of being transporter substrates.] and we further speculate that employing NP fragments may result in less attrition due to toxicity, a major cause of preclinical failure. (55) [This does seem to be grasping at straws. The focus of the cited article is actually clinical failure and not preclinical failure.]

There is untapped potential for further exploitation of currently used and unused NP fragments, especially in fragment combinations and the design of PNPs, without the need to resort to chemically diverse ring systems and scaffolds. [This exemplifies what can be called the ‘Ro5 mentality’ (‘experts’ advising medicinal chemists to not explore but to focus on regions of chemical space that have been blessed by the ‘experts’). As I note in this blog post Ro5 (as it is stated) is not actually supported by data and in NoLE, I advise drug designers not to “automatically assume that conclusions drawn from analysis of large, structurally-diverse data sets are necessarily relevant to the specific drug design projects on which they are working.” An equally plausible 'explanation' for the observation that a high fraction of clinical compounds are PNPs is simply that medicinal chemists are working with what they're most familiar with (in this case the advice would be to look beyond Nature's building blocks for inspiration).] To exploit these opportunities, “NP awareness” needs to be added to the repertoire of medicinal chemists. [My view is that it would be more important for critical thinking to be added to the repertoire of medicinal chemists so they are better equipped to assess the extent to which conclusions and recommendations of studies like H2024 are actually supported by data.]

In short, applying nature’s building blocks─natural intelligence─to drug design can enhance the opportunities now offered by artificial intelligence. [In my view "natural intelligence" appears to be arm-waving that is neither natural nor intelligent.]  

This is a good point to wrap up and to also conclude blogging for the year. My new year wish is for a kinder, happier and more peaceful World in 2025 and I'll leave you with a photo of BB and Coco in the study here in Maraval. They had been helping me with this post before I unwisely decided to explain ligand efficiency to them. Let sleeping dogs lie I guess.

 

A time and place for Nature in drug discovery?

I’ll be reviewing Y2022 (The Time and Place for Nature in Drug Discovery) in this post and stating my position on natural products in modern drug discovery is a good place to start. I certainly see value in screening natural products and natural product-like compounds (especially in phenotypic assays) and there is currently a great deal of interest in chemical probes (I’ll point you toward an article on the Target 2035 initiative and a link to the Chemical Probes Portal).  In general, a natural product or natural product-like active identified by screening would either need to exhibit novel phenotypic effects or be significantly more potent than other known actives for me to enthusiastic about following it up. I would certainly consider screening fragments that are only present in natural product structures although these would need to still need comply with the criteria (typically defined in terms of properties such as molecular size, molecular complexity and lipophilicity) used to select fragments. I see significant benefits coming from the increased use of  biocatalysis, both in drug discovery and for manufacturing drugs, but I don’t see these benefits as being restricted to synthesis of natural products or natural product-like compounds. 

This will be a very long post (for which I make no apology) and it's a good point to say something about how the review is presented. I've used section headings (in bold text) used in Y2022 for my commentary and quoted text has been indented (my comments on the quoted text enclosed with square brackets and italicized in red). I'd like to raise four general points before starting my review: 

1. Proprietary data cannot accurately be described as “facts” or “evidence” and it’s not valid to claim that you’ve proven or demonstrated something on the basis of analysis of proprietary data.  
2. If continuous data such as oral bioavailability measurements have been made categorical (e.g., high | medium | low) prior to analysis then it’s generally a safe assumption that any trends "revealed" by the analysis are weak.  
3. If basing claims on analysis of locations or distributions within a particular chemical space it is necessary to demonstrate the chemical space is actually relevant to the claims being made. One way to do this is to build usefully predictive models of relevant quantities such as aqueous solubility or permeability using only the dimensions of the chemical space as descriptors.  
4. There are generally many ways to partition a region of chemical space into subregions with different average values for a measured quantity. Although the  boundaries resulting from these analyses typically appear to be well-defined (for example, as a line or curve in a 2-dimensional chemical space) it is a serious error to automatically interpret such boundaries as meaningful from a physicochemical perspective.    

I have a number of concerns about the Y2022 article and I’ll focus on the more serious of these in this post. I’ll also be commenting on the Rule of 5 (Ro5; see L1997), logP/logD differences, and the drug discovery “sweet spot” reported in the HK2012 article.  My view is that a number of the assertions and recommendations made by the authors of Y2022 are not supported by the analyses or the data that they’ve presented. Specifically, the authors present results of analyses that had been performed using proprietary and undocumented models and, in my view, they have grossly over-interpreted the predictions made using the models.  At times, the authors appear to be treating natural products as if these occupy a distinct and contiguous region of chemical space (this is a pitfall into which drug-likeness advocates also frequently stumble).  The authors of Y2022 discuss physicochemical properties at considerable length without making any convincing connection between this discussion and natural products. Reading the Y2022 article, I did detect a subliminal message that natural products might be infused with vital force and wouldn’t have been surprised to see Gwyneth Paltrow as a co-author.

I’ll make some general observations before examining Y2022 in detail. If you’re going to base decisions on trends in data then you need to now how strong the trends are because this tells you how much weight to give to the trends when making your decisions. In what I’ll call the ‘compound quality’ field you’ll often encounter data presentations that make it extremely difficult to see how strong (or weak) the trends in the data actually are (see KM2013: Inflation of correlation in the pursuit of drug-likeness). Since Ro5 was introduced in 1997 (see L1997) there has been a free flow of advice from self-appointed compound quality gurus as to how compounds can be made better, more developable and more beautiful (introduction of the term “Ro5 envy” in KM2013 appeared to cause some to spit feathers). This advice frequently comes in the form of dire warnings that exceeding a threshold value of a property, such as molecular weight or predicted octanol/water partition coefficient, will increase the probability of something bad happening. It’s actually very difficult to set thresholds like these objectively and you have to consider the possibility that some of these statements of probability are merely expressions of belief (to some “there is a high probability that God exists” will sound rather more convincing than “I believe in God”).

The graphical abstract is a good place to start my review of Y2022. I don’t know whether biotransformations exist that would convert the Core Scaffold into compounds that would match the Bios Collection generalized structure but a 1,3-diene in conjugation with a tertiary nitrogen is not the sort of substructure that I would want to see in a screening active that I had been charged with optimizing.  

Abstract

The authors of Y2022 state:  

The declining natural product-likeness of licensed drugs and the consequent physicochemical implications of this trend in the context of current practices are noted. [The authors do not make a convincing connection between natural product-likeness and physicochemical properties.]  To arrest these trends, the logic of seeking new bioactive agents with enhanced natural mimicry is considered; notably that molecules constructed by proteins (enzymes) are more likely to interact with other proteins (e.g., targets and transporters), a notion validated by natural products. [I consider this claim to be extravagant and it does need to be supported by evidence. The authors’ use of “validated” reminded me of the extravagant claim made in a Future Medicinal Chemistry editorial that “ligand efficiency validated fragment-based design”. Taking the statement literally, the authors appear to be suggesting that a compound would be more likely to interact with proteins if it had been isolated from natural sources than if it had been synthesized in a laboratory (I was reminded of the "water memory" explanation for why homeopathy works). If “molecules constructed by proteins” really are more likely to interact with other proteins then they’re also more likely to interact with anti-targets like hERG and CYPs. I’m guessing that the response of medicinal chemistry teams tackling CNS targets to suggestions that they should make their compounds more like natural products so as increase the likelihood of recognition by transporters might be to ask which natural products those offering the advice had been smoking.]

Introduction

The authors show time-dependence for the values of a number of parameters calculated for drugs in Figure 1. I see analyses like these as exercises in philately and, when I first encountered examples about two decades ago, I formed a view that some senior medicinal chemists had a bit too much time on their hands. The observation of significant time-dependency for a parameter calculated for drugs can mean one of three things. First, the parameter is irrelevant to drug discovery (however, the absence of a time-dependence shouldn't be taken as evidence that the parameter is relevant to drug discovery). Second, the old ways were best and the medicinal chemists of today have lost their way (I’m guessing this might be Jacob Rees Mogg’s interpretation if he were a medicinal chemist). Third, the old ways no longer work so well and the medicinal chemists of today have learned new ways.

I have a number of concerns about what is shown in Figure 1 (quite aside from these concerns I would question why 1b or 1c were even included in the study). The data values that have been plotted are actually mean values and, as we observed in KM2013, the presentation of mean value (or median) values without showing measures of the spread in the data, such as standard deviation or inter-quartile range, makes trends look stronger than they actually are (others use the term “voodoo correlations”).  This way of presenting data is specifically verboten by J Med Chem and Author Guidelines (viewed 18-May-2024) for that journal specifically state:

If average values are reported from computational analysis, their variance must be documented. This can be accomplished by providing the number of times calculations have been repeated, mean values, and standard deviations (or standard errors). Alternatively, median values and percentile ranges can be provided. Data might also be summarized in scatter plots or box plots.

However, the hidden variation in the response variables is not the only issue that I have with Figure 1. Let’s take a look at Figure 1a which shows “a temporal comparison of natural product likeness of approved drugs assessed by the Natural Product Scout algorithm (12) versus the year of the first disclosure of the drug” although it the caption for Figure 1a is “Natural product class probability. (8)”. I think that the authors do need to explain exactly what they mean by natural product class probability because the true probability that a compound is a natural product is either 1 (it’s a natural product) or 0 (it’s not a natural product). Put another way there are differences between natural products and Prof.  Schrödinger’s unfortunate feline companion. The measure of lipophilicity shown in Figure 1c is XLogP3 although no justification is given for the selection of this particular method for lipophilicity prediction nor is any reference provided.

Before continuing with my review of Y2022 I also need to examine Ro5 and discuss the difference between logP and logD (the reasons for these digressions will hopefully become clear later). Ro5 which was based on physicochemical property distributions for compounds that had been taken into phase 2 of clinical development before 1997 (the year that L1997 was published). My view is that Ro5 certainly raised awareness of the problems associated with excessive lipophilicity and molecular size (A Good Thing) but I’ve never considered Ro5 to be useful in design. Although Ro5 is accepted by many (most?) drug discovery scientists as an article of faith, some are prepared to ask awkward questions and I’ll mention the S2019 study. Let’s take a look at how Ro5 was specified in the L1997 article (the graphic is slide #17 from a presentation that I gave late last year):

Ro5 is stated in terms of likelihood of poor absorption or permeation although no measured oral absorption or permeability data are given in the L1997 study and Ro5 should therefore be regarded as a statement of belief. I realise that to make such an assertion runs the risk of an appointment with the auto-da-fé and I stress that had Ro5 been stated in terms of physicochemical and molecular property distributions I would not have made the assertion.

Medieval cartographers annotated the unknown regions of their maps with “here be dragons” and Ro5’s dragons are poor absorption and poor permeation. However, there's another issue which I touched on in HBD3:

It is significant that attempts to build global models for permeability and solubility, using only the dimensions of the chemical space in which the Ro5 is specified as descriptors, do not appear to have been successful.

What I was getting at in HBD3 is that the chemical space in which Ro5 is specified was not demonstrated to be relevant to permeability or solubility (this relates to the third of the four points that I raised at the start of the post). It must be stressed that I'm definitely not denying that relationships exist between descriptors, such as logP, used to specify Ro5 and properties such as aqueous solubility and permeability that are more directly relevant to getting drugs to where they need to. It’s just that these relationships are weak (see TY2020) and, while we don’t exactly know exactly how weak the relationships are, we do know that they are weak because continuous data have been binned to display them (see also KM2013 and specifically the comments on HY2010). I would generally anticipate that these relationships will be stronger within structural series but in these cases you’ll generally observe different relationships for different structural series. In practical terms this means that a logP of 5 might be manageable in one structural series while in another structural series compounds with logP greater than 3.5 prove to be inadequately soluble. As I advised in NoLE:

Drug designers should not automatically assume that conclusions drawn from analysis of large, structurally-diverse data sets are necessarily relevant to the specific drug design projects on which they are working.

I also need to discuss the distinction between logP and logD since this is a source of confusion for medicinal chemists and compound quality 'experts' alike. Here’s a graphic (it’s slide #18) from the presentation that I did at SancaMedChem in 2019 (if the piranhas did venture into the non-polar phase they'd probably end up swimming backstroke):

The partition coefficient (P) is simply the ratio of the concentration of the neutral form of the compound in the organic phase (usually octanol) to the concentration of the compound in water when both phases are in equilibrium. The distribution coefficient (D) is defined analogously as the ratio of the sum of concentrations of all forms of the compound in the organic phase to the sum of concentrations of all forms of the compound in water. Values of P and D are usually quoted as their logarithms logP and logD. When interpreting logD values it is commonly assumed that that is that only neutral forms of compounds partition into organic phases and if we make this assumption the relationship between logD and logP is given by Eqn 1 (see B2017):

When we perform experiments to quantify lipophilicity it is actually logD that is measured. Values of logP and logD are identical when ionization can be neglected and logP values for ionizable compounds can be obtained by examination of measured logD-pH profiles although this is rarely done. It’s usually a safe assumption that logP values used by drug discovery scientists (and quoted in medicinal chemistry publications) have been predicted and these values vary with the method used for prediction of logP. For example, L1997 states that the upper logP limit for Ro5 is 5 when logP is calculated using the ClogP method (see L1993) but 4.15 when logP is calculated using the method of Moriguchi et al. (see M1992). Values of logD that you encounter in the literature may have been calculated or measured (you might need to dig around to see if you’re dealing with real data) and it’s also important to remember that logD depends on pH. I would argue that logD is less appropriate than logP for defining compound quality metrics because excessive lipophilicity can be countered simply by increasing the extent to which compounds are are ionized (I hope you can see why that would be A Bad Thing). Another way to think about this is to consider an amine with a pKa value of 8 bound to hERG at a pH of 7. Now suppose that you can change the pKa of the amine to 11 without changing anything else in the molecular structure. What effects would you expect this pKa change to have on affinity, on logD and on logP?

I’ll now get back to reviewing Y2022 and let’s take a look at Figure 2 which shows an adapted version of the "drug discovery sweet spot” proposed in the HK2012 study. As with Figure 1b and 1c, I would question why Figure 2 was included in the Y2022 study since the connection with natural products is tenuous. In my view the authors of the HK2012 study made a number of serious errors in their definition of the “sweet spot” and these errors have been reproduced in the Y2022 study. The authors of HK2012 claimed to have identified a “drug discovery sweet spot” in a chemical space defined by “Log P” and “Molecular mass” but they didn’t actually demonstrate that this chemical space is actually relevant to drug discovery (one way to demonstrate relevance is to build convincing global models for prediction of properties like permeability and aqueous solubility using only the dimensions of the chemical space as descriptors).

If claiming to have identified a drug discovery “sweet spot” it’s important that each dimension of the chemical space in which the “sweet spot” corresponds to a single entity. While “Molecular mass” is unambiguous the term “Log P” does not refer to the same entity for each of the data sets from which the “sweet spot” has been derived. As noted previously ClogP (see L1993) was used to specify Ro5 while the Gleeson upper Log P limit (see G2008) and the “μM potency Log P” (see G2011) were specified respectively by values of clogP (calculated logP from ACD) and AlogP (no reference provided). In contrast the Pfizer Golden Triangle (see J2009) is specified using elogD (proprietary logD prediction method for which details were ot provided).  The Waring low and high logP/logD values stated in W2010 are at least partly based on analysis of AZlogD7.4 values (proprietary logD prediction method; details not provided) reported in the WJ2007 and W2009 studies. The W2010 study states that “the optimal range of lipophilicity lies between ~ 1 and 3” but the these are not the values that are depicted in Figure 3 (or indeed in the original HK2012 study). The Gleeson upper limits for Log P and Molecular Mass stated in G2008 reflect the arbitrary schemes used to bin the data and should not be regarded as objectively-determined limits for these quantities. The authors of Y2022 have superimposed ellipses for "SHMs", "Antibiotic Space?" and "bRo5 /  AbbVie MPS space for higher MW" on the HK2012 "sweet spot" in the creation of Figure 2 although it is not clear how these ellipses were constructed.

The Physicochemical Characteristics of Drugs

The authors assert:

A principle advocated by Hansch that drug molecules should be made as hydrophilic as possible without loss of efficacy (47) is commonly expressed and utilized as Lipophilic Ligand Efficiency (LLE). (48) [If actually using this principle advocated by Hansch you would optimize leads by varying hydrophilicity and observing efficacy. While LLE is one way to express Hansch’s principle it is by no means the only way and (pIC₅₀ – 0.5 ´ logP) would be equally acceptable as a lipophilic ligand efficiency metric from the perspective of the Hansch’s principle.] This metric, widely accepted and exploited in drug discovery as a key metric in optimization, is expressed on a log scale as activity (e.g., −log₁₀[XC₅₀]) [The logarithm function is not defined for dimensioned quantities such as XC₅₀ (see M2011) and, while it may appear to nitpicking to point it out, this is the source of the invalidity of the ligand efficiency metric as was discussed at length in NoLE.] minus a lipophilicity term (typically the Partition coefficient or log₁₀ P or sometimes log D_7.4). (49) [Although it is common to see LLE values quoted in the drug discovery literature it’s much less clear how (or even whether) the metric was actually used to make project decisions. In many studies, however, the focus is on plots of pIC₅₀ against logP (or logD) rather than values of the metric itself. In lead optimization, medicinal chemists typically need to balance activity against properties such as permeability, aqueous solubility, metabolic stability and off-target activity. In these situations, experienced medicinal chemists typically give much more weight to structure-activity relationships (SARs) and structure-property relationships (SPRs) that they've observed within the structural series that they're optimising than to crude metrics of questionable relevance and predictivity. It is noteworthy that the authors of ref 49 use logD rather than logP to define LLE (which they call LiPE) and if you do this then you can make compounds more efficient simply by increasing the extent to which they are ionized.] The impact of lipophilicity on efficacy needs to be considered in the context that reducing lipophilicity (equating to increasing hydrophilicity) will generally increase the solubility, reduce the metabolism, and reduce the promiscuity of a given compound in a series. (50) [The relationships between these properties and lipophilicity shown in ref 50 are for structurally diverse data sets rather than for individual series. I consider the activity criterion (pIC₅₀ > 5) used to quantify promiscuity in ref 50 to be at least an order of magnitude too permissive to be pharmaceutically relevant.]

Let’s take a look at Figure 3 in which values of “Calc Chrom Log D_7.4” are plotted against “CMR”. This is what the authors of say about Figure 3 in the text of Y2022:

The distribution of marketed oral drugs in terms of their lipophilicity and size, shows a remarkably similar distribution to the set of compounds designed by Kell as a representative set of natural products to investigate carrier mechanisms (Figure 3). (64) [To state “shows a remarkably similar distribution” is arm-waving given that there are methods for assessing the similarity of two distributions in an objective manner.]

As is the case for Figure 1a, what is written in the text about Figure 3 differs significantly from the caption for this figure:

Figure 3. Natural products are found across most size lipophilicity combinations, as exemplified in a representative set designed and compiled by O’Hagan and Kell (64) superimposed on the Chrom log D_7.4 vs cmr training set of compounds with >30% bioavailability. (51) [It is unclear why this training set was restricted to compounds with >30% bioavailability.  The LDF is shown in this figure with “Limits of confidence” but the level of confidence to which these limits correspond is not given.]

The first criticism that I’ll make is that the authors of Y2022 have not actually demonstrated the relevance of chemical space specified by the axes of Figure 3 (this is the essence of the third of the four points that I raised at the start of the post and the same criticism can be made of Figure 4 and Figure 5). The authors note, with some arm-waving, that cmr “largely correlates with MW” which does rather beg the question of why they consider this particular measure of molecular size to be superior to MW for this type of analysis. The authors claim that “the GSK model based on log D_7.4 vs calculated molar refraction” (it is actually molar refractivity as opposed to molar refraction that was calculated) is a useful guide to predict oral exposure. I consider this claim to be extravagant because one would need to have access to the proprietary model for calculation of Chrom Log D_7.4 in order to use the model. The proprietary nature of the GSK model means that predictions made using this model cannot credibly be presented as “evidence”.

Details of the models for calculating Chrom Log D_7.4 and for prediction of oral exposure are sketchy and I regard each of these proprietary models as undocumented. A linear discriminant function (LDF) model was reportedly used for prediction of oral exposure but it is unclear how the model was trained (or if it was even validated). An LDF is a classification model and it is not clear what how the classes were defined for prediction of oral exposure. I’m assuming that the oral absorption classes used in GSK oral exposure model have been defined by categorization of continuous data (I’m happy to be corrected on this point but, given the sketchiness of details, I can be forgiven for speculation) and setting thresholds like these is difficult to achieve in an objective manner. If this was indeed the case I'd assume that the threshold value used to categorize the continuous data was arbitrary (you’ll get a different LDF model if you use a different threshold to define the classes). My view is that that an LDF is an inappropriate way to model this type of data because the categorization of the data discards a huge amount of information.

Here's the caption for Figure 4:

Figure 4. Proposed regions of size/lipophilicity space for an oral drug set, (51) using the effectual combination of Chrom Log D_7.4 vs calculated molar refraction (cmr) as a description of chemical space. [It’s actually molar refractivity as opposed to molar refractivity that was calculated. It is unclear what the authors mean by "bRo5 principles".] The highlighted regions suggest likely absorption mechanisms, based on ref (65) with compounds colored by binned NPScout probability scores. [The authors of Y2022 appear to be using a proprietary and undocumented LDF model of unknown predictivity to infer absorption mechanisms (this is what I was getting at in the fourth of the four points points that I raised at the start of the post). The depiction of data shown in Figure 4 would be much more informative had compounds known (as opposed to believed) by to be orally absorbed by one of these mechanisms been plotted in this chemical space.] Below the LDF line, then mean NPScout score is 0.45, (median 0.33) and above it (indicative of likely oral exposure) the mean is 0.31 and median 0.17 (p < 0.01) [It is unclear what (p < 0.01) refers to.]

Here's the caption for Figure 5: 

Figure 5. Illustration of antibiotic drug space, expressed as Calculated Chrom Log D_7.4 vs cmr adapted from data in ref (65) colored by antibiotics (circles) and TB drugs (diamonds) which are sized by NP class probabilities and colored by prediction of likelihood of oral exposure (either side of the diagonal “linear discriminant function line” so to be oral, transporters a likely mechanism for the red colored compounds, which mostly have a high NPScout score). [As is the case for Figure 4, the authors of Y2022 appear to be using a proprietary and undocumented LDF model of unknown predictivity to infer absorption mechanisms. Stating that "mostly have a high NPScout score" is arm-waving.]  Vertical (cmr < 8) and horizontal lines (Chrom Log D_7.4 < 2.5) together represent likely boundaries for paracellular absorption. [The basis (measured data or belief) for this assertion is unclear. The depiction of data shown in Figure 5 would have been more convincing had compounds known to be and known not to be absorbed by the paracellular route been plotted in this chemical space. While the problems of achieving good oral absorption for antibiotics should not be underestimated, I see getting compounds into cells as the bigger issue and in some cases the transporters cause active efflux (see R2021). The depiction of data shown in Figure 5 would have been much more informative had compounds known (as opposed to believed) to exhibit active influx and active efflux been plotted in this chemical space. Although Figure 5 is presented as a description of antibiotic drug space, the study (ref 65) on which Figure 5 is based is actually focused on antitubercular drug space (one of the challenges to discovery of antitubercular drugs is that Mycobacterium tuberculosis is an intracellular pathogen; see WL2012). One article that I recommend to all drug discovery scientists, especially those working on infectious diseases, is the SM2019 review on intracellular drug concentration.]

The authors suggest:

A logical extension of this hypothesis would be to consider recognition processes with natural molecules, which are likely to have discrete interactions with carrier proteins and therapeutic targets. [The authors do need to articulate what they mean by "discrete interactions" and why "natural molecules" are likely to have "discrete interactions" with carrier proteins and therapeutic targets.] Small molecule drugs are noted to be relatively promiscuous, so making interactions with several proteins is a likely event. (76) [This assertion is not supported by ref 76 which is actually a study of nuisance compounds, PAINS filters, and dark chemical matter in a proprietary compound collection. Promiscuity of a compound is typically defined by a count of the number of targets against which activity exceeds a specific threshold and promiscuity generally increases with the permissiveness of the activity threshold (it’s therefore meaningless to describe a compound as “promiscuous” without also stating the activity threshold). The activity threshold for the analysis reported in ref 76 is ³ 50% inhibition at a concentration of 10 µM which is appropriate if you’re worried about assay interference but, in my view, is at least an order of magnitude too permissive if considering the possibility of off-target activity for a drug in vivo.]  It similarly is logical to consider that a molecule made by a recognition process in a catalytic enzyme may also interact with another protein in a similar manner. (77) [This is not quite as logical as the authors would have us believe since enzymes catalyze reactions by stabilizing transition states. A high binding affinity of an enzyme for its reaction product would generally be expected to result in inhibition of the enzyme by the reaction product.]

Natural Product Fragments in Fragment-Based Drug Discovery

The authors note:

Fragment-based drug discovery (FBDD) can be employed to rapidly explore large areas of chemical space for starting points of molecular design. (91 | 92 | 93) However, most FBDD libraries are composed of privileged substructures of known synthetic drugs and drug candidates and populate already well-explored areas of chemical space, (94 | 95 | 96) [I do not consider refs 94-96 to support this assertion (none of these three articles has a fragment screening library design focus and the most recent one was published in 2007).] often through the use of fragments with high sp2-character. (97)  Underexplored areas of chemical space can be rapidly explored by employing fragments derived from NPs that are already biologically prevalidated by evolution. [The authors appear to be suggesting that the physiological effects of natural products are more due to the fragments from which they have been constructed than of the way in which the fragments have been combined.] 

Molecular recognition

The authors state:

That the embedded recognition of natural products for proteins correlates with recognition of the biosynthetic enzyme is an increasingly validated concept. (118 | 119 | 120) [I have no idea what “embedded recognition” means and I’m guessing that the authors might be in a similar position.] The biosynthetic imprint translates to recognition of other proteins using similar interactions. [As I’ve already noted, high binding affinity of a natural product for the enzyme that catalysed its formation would lead to inhibition of the enzyme.] For example, the analysis of protein structures of 38 biosynthetic enzymes gave 64 potential targets for 25 natural products. (121) [Concepts are usually validated with measured data and not by making predictions.]

Conclusions and Prospects for Future Development

The authors assert:

More natural molecules will increase quality through their inherently improved permeability and solubility; [At the risk of appearing pedantic, permeability and solubility are properties of compounds as opposed to molecules. That said, the authors appear to be treating “natural molecules” as occupying a distinct and contiguous region of chemical space by making this claim and it is unclear what the improvements will be relative to. The authors do not present any measured data for permeability or solubility to support their claim.] this is a case of investing time and effort in the early stages of drug discovery to reap rewards with improvements in the later stages through more predictability in trials (and thus a greater chance of success, where quality rather than speed demonstrably impacts (170)) [Many, including me, do indeed believe that investing time and effort in the early stages of drug discovery increases the chances of success in the later stages. However, I would challenge the assertion by the authors of Y2022 that ref 170 actually demonstrates this.] and more sustainable manufacturing methods driven by the transformative power of biocatalysis. (171)

So that concludes my review of Y2022 and thanks for staying with me. I'll leave you with a selfie here in Trinidad's Maraval Valley with my faithful canine companions BB and Coco providing much-needed leadership (a few minutes earlier I had patiently explained to them why ligand efficiency is complete bollocks).