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:
- 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.
- 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.
- 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.
- 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):
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 D7.4). (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 D7.4” 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 D7.4 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 D7.4 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 D7.4 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 D7.4 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 D7.4 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 D7.4 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 D7.4 < 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).
