Mapping the Efficiency and Physicochemical Trajectories of

Perspective pubs.acs.org/jmc

Cite This: J. Med. Chem. 2018, 61, 6421−6467

Mapping the Efficiency and Physicochemical Trajectories of Successful Optimizations Robert J. Young*,† and Paul D. Leeson*,‡ †

GlaxoSmithKline, Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K. Paul Leeson Consulting Ltd., The Malt House, Main Street, Congerstone, Nuneaton, Warwickshire CV13 6LZ, U.K.

‡

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S Supporting Information *

ABSTRACT: The practices and tactics employed in successful optimizations are examined, judged from the trajectories of ligand efficiency and property evolution. A wide range of targets is analyzed, encompassing a variety of hit finding methods (HTS, fragments, encoded library technology) and types of molecules, including those beyond the rule of five. The wider employment of efficiency metrics and lipophilicity control is evident in contemporary practice and the impact on quality demonstrable. What is clear is that while targets are different, successful molecules are almost invariably among the most efficient for their target, even at the extremes. Trajectory mapping, based on principles rather than rules, is useful in assessing quality and progress in optimizations while benchmarking against competitors and assessing property-dependent risks.

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INTRODUCTION Identification of small molecule leads in drug discovery over the past 20 years has been largely dependent on high throughput screening (HTS)1 against target proteins. The challenges associated with the introduction of HTS more than 2 decades ago led practitioners to define useful chemical quality guidelines and specific criteria at each step, from the identification of active compounds to confirmed hits, then from optimization to leads.2,3 Quality leads were sought, with the potential to deliver candidates in shortened time frames, driven by high throughput chemistry, to feed hungry drug development pipelines suffering high attrition rates. “Fail early, fail fast” was the mantra of the day. It is recognized now that this “high speed” approach contributed to further clinical attrition,4 but it is wrong to lay the blame at the door of HTS.1 The rule of five (Ro5)5 emerged because the HTS hits pursued at Pfizer6 in 1990s, while potent, tended to have poor solubility and permeability; this was not caused by HTS but by the decision-making processes employed in the selection of hits and their optimization.7 Candid retrospective analyses from large pharmaceutical companies, covering the period 2000−2010, have acknowledged that root causes of attrition in the clinic were often due to compound quality issues, including lack of exposure, 8 known preclinical toxicity risk, 9 and poor solubility.10 Many of these failings could have been addressed during the less expensive lead optimization phase. Encouragingly, the lessons are being learnt as demonstrated by increased focus on quality leading to improved success rates within a smaller, more focused discovery pipeline.11 The aim of hit and lead optimization efforts is to identify a molecule of appropriate affinity, selectivity, and efficacy (e.g., © 2018 American Chemical Society

agonist or antagonist) for the desired target while ensuring this can be achieved with acceptable pharmacokinetics, nontarget mediated toxicity, solubility, and permeability. When these parameters have been optimized, consequent candidate drug molecules should possess profiles that provide high confidence that, when administered to humans by the preferred route, the drug will reach and occupy the biological target for the required time at an acceptable dose and frequency, eliciting the desired pharmacological response with a suitable, disease-dependent, therapeutic index. Multiparameter optimization is necessary to achieve these aims; this is inherently complex, challenging to execute, frequently frustrating, and often unpredictable. Although full prediction of the effects of molecular structure on biological activity is likely to remain elusive, it is axiomatic that the molecular properties of drugs underlie their pharmacodynamic, pharmacokinetic, and toxicological behavior.12 A focus on measurable, predictable, and quantifiable physicochemical properties has established their value as indicators of compound quality and predictors of likely outcomes in their pharmacokinetic and pharmacological behavior.13−18 These studies provided helpful guidelines for the medicinal chemist, including, inter alia, the principle of minimal hydrophobicity,19 the Ro5,5,6 leadlike properties,20,21 and complexity;22,23 the rule of 3 for fragments;24 measures of ligand efficiency;25,26 drug efficiency;27 three-dimensionality as measured by fraction of sp3 carbon atoms,28,29 and aromatic ring count.30−32 Taken together, a combination of dose, lipophilicity, and solubility criteria has been established as an Received: February 3, 2018 Published: April 5, 2018 6421

DOI: 10.1021/acs.jmedchem.8b00180 J. Med. Chem. 2018, 61, 6421−6467

Journal of Medicinal Chemistry

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Figure 1. Optimization trends and physical properties. (a) Median cLogP and molecular weight of oral drugs and patented compounds: (in black) oral drugs by literature publication60 decade, updated to mid-2016 launches and restricted to drugs with available oral doses; (in red) median of median target data for 18 companies with >50 compounds patented per target in 2000−2010.48 (b) Median cLogP and molecular weight of “start to finish” optimization pairs. In blue: leads to drugs: (A,42 B22) historical drugs; (C) drugs launched post-1990;43 (D) first in class to follow-on drug.44 In red: literature optimizations: (E) early 2000s literature;45 (F) HTS hit-to-lead, 2000s literature;46 (G) 2014 literature not using lipophilicity.47 In green: property-aware optimizations: (H) 2014 literature using lipophilicity;47 (I) late 2000s literature using lipophilic ligand efficiency (LLE).26 (c) Median cLogP and molecular weight of screening sets and derived compounds: (in green) fragment hits and their optimizations54 and fragmentderived candidate drugs (calculated from the list in ref 40); (in red) HTS collections, leads from them, and linked patents [collected HTS and lead 2009 median data from GlaxoSmithKline, Novartis, Sanofi-Aventis, and Wyeth (HTS) and GlaxoSmithKline, Novartis, and Wyeth (leads)1 and corresponding median patent data48 for GlaxoSmithKline, Novartis, and Wyeth, 2000−2010]; (in black) leads from DNA-encoded libraries (calculated from the list in ref 41). (d) Median LE and LLE (based on cLogP) values of 480 target-assay pairs having ≥100 published compounds with potency values.26 Targets highlighted are discussed in this paper. Figure adapted from ref 26.

indicator of high quality oral candidates;10 such guidelines fit with the principle of lower systemic exposure to reduce toxicity risk. Predictive ADMET models provide correlations with single or multiple physical properties16,33,34 but show broad ranges of significance. There are limitations; for example, bulk physical properties do not differentiate ADME effects between enantiomers.35 However, in general, the probability of

progression though ADMET screens generally improves with optimal physical properties, of which lipophilicity,36 ionization state,37 and solubility have been shown to be the most important.14 Ten years after the Ro5 publication,5 we noted that the physicochemical properties of compounds in pharmaceutical company patents,38 while varying across the four companies examined, were inflated when compared with contemporary 6422



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molecules with fewer than 17 heavy atoms has been achieved,51 providing a source of inspiration for novel leadlike and fragment molecules and indeed overlapping with low MW druglike space. Fragment hit growth and optimization routinely use X-ray protein−ligand structures,52 which can result in improved druglike outcomes versus other methods of lead generation, if performed with concomitant control of physical properties.53 It can be easy to add lipophilicity to molecules to improve activity but not so easy to incorporate polar functionality without loss of activity; the thermodynamic basis of such interactions has been well-studied.54−56 There is great promise in DNA-encoded libraries57 (vide infra), although the hits recently summarized41 clearly show increased MW relative to other starting points (Figure 1c). Optimization strategy and awareness of physical properties influence outcomes. For example, using lipophilic efficiency26 or simply lipophilicity itself26,47 in optimization (entries I and H, respectively, of Figure 1b) resulted in a lowered lipophilicity, whereas there was an increase in lipophilicity (entry G, Figure 1b) when physical properties were not considered. In all these cases, median MW was increased. In entries G and H (Figure 1b), from the 2014 literature, there was a notable contrast in use of physical properties between industry (40% of papers) and academia (15%).47 These observations led us to question whether the community is sufficiently aware of property-based optimization. These concerns are supported by the observation that only 1.1% of 2015 papers from United States universities discussed physical properties, in comparison with 37.1% from other countries.58 Oral drugs invented post-1990, together with compounds from the patents of 18 pharmaceutical companies in the period 2000−2010, show increased physical properties versus older oral drugs (Figure 1a).47,48 We have hypothesized59 that the most time-resistant properties60 in oral drugs are the most important: for rule of 5 properties these are lipophilicity and hydrogen bond donor count. Practices leading to molecular inflation or obesity39,61,62 include (i) selecting potent but high MW and/or lipophilic hits from screens, (ii) focusing on optimization of in vitro potency rather than multiparameter optimization, (iii) using favored but limited chemical synthetic procedures,63 (iv) tactics used to obtain intellectual property on competitive targets, and (v) cultural organizational differences48 in research strategy and acceptance of physical property risk. External influences have a significant impact, including the appearance of targets that require larger and more lipophilic ligands,47 for example, antivirals and protein−protein interactions and disease risk−benefit considerations.10 The Ro5 may inadvertently have contributed to property inflation, as many misinterpreted a guideline for achieving acceptable permeation or absorption as a “rule” for drug discovery and that reaching its cutoff limits was acceptable practice. The newest oral drugs, invented post-2000, most closely reflect recent patent practice (Figure 1a), and in this group, risk−benefit considerations have increased influence on property inflation. For example, 34 of 60 oral drugs approved by the FDA in 2011−2013 had indications, such as cancer and human immunodeficiency virus (HIV), where the clinical need was commensurate with greater risk or dosing inconvenience. Compared to the other 26 drugs, these 34 showed greater lipophilicity and molecular weight, poorer solubility, greater likelihood of food-altering pharmacokinetics, and more offtarget labeled safety warnings.10

marketed oral drugs and could be contributing to increased risk of attrition (see Figure 1a for oral drug and patent trends). We asked if it could be that the widely accepted Ro5 might not yet be impacting drug discovery practices.39 In this Perspective, we develop the theme further, this time with a focus on targetspecif ic optimization, based on physicochemical properties and efficiency metrics,26 which we propose is more usef ul than pursuing absolute criteria such as the Ro5, establishing principles rather than rules. In addition to analyzing optimizations based on HTS output, we also consider starting points based on fragments40 and encoded library technology (ELT),41 some with direct comparisons to HTS.

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EVIDENT PRACTICES IN MEDICINAL CHEMISTRY OPTIMIZATIONS The concept of leadlikeness, published in 1999,20 was based on the authors’ experience that optimization frequently resulted in increased molecular properties, noticing that lower molecular weight (MW) leads, which could be increased in size, often proved simpler and faster to optimize compared with higher MW leads. The ideal screening library was proposed to have a MW of 100−350 and a 1-octanol−water partition coefficient (log P) of 1−3 so that hits could be “expanded” during optimization into the druglike space defined by the rule of 5. Compared to druglike molecules, smaller leadlike molecules are likely to have lower potency (typically >100 nM) but would be more likely to bind to targets and to offer more scope for multiparameter optimization. The emphasis on properties foreshadowed ligand efficiency measures: “The relative values of active compounds emerging from a high-throughput screen can usefully be assessed on the basis of biological activity per unit of molecular weight and lipophilicity.”20 Subsequent studies with larger data sets of both lead−drug and “start− finish” optimization pairs have confirmed the underlying leadlike hypothesis that increases in calculated log P (cLogP) and MW in optimization are dominant (Figure 1b) and established the concept that increased complexity reduces the likelihood of finding hits. Among the cited lead-to-drug optimization sets22,42−44 (entries A−D in Figure 1b), MW is increased in 78% of cases, more often than cLogP, which was increased in 58% of cases. Little or no change in cLogP is seen in studies (entries C and D in Figure 1b) respectively reporting lead−drug pairs since 199043 and first-in-class disclosures to follow-on drugs.44 Looking at literature-derived optimizations (entries E,45 F,46 and G47 of Figure 1b), it is striking that the median starting points are not leadlike, yet the magnitude of the property increases is nevertheless similar to the lead−drug pairs A and B. These observations are consistent with trends in progression of properties from HTS screening files, leads,1 and patents48 of four pharmaceutical companies (Figure 1c). The HTS file properties reflect a combination of more recent enhancement, including physical property restraints and leadlike compounds, as well as historical project compounds. Unsurprisingly fragments display the lowest physical properties and their optimization displays the largest median change (Figure 1c). Fragment based optimization40,49,50 has come of age in the past decade, exploiting the principle that many fewer compounds are required to identify hits (albeit much less potent ones) based on the principles22 underpinned by complexity. Fragment libraries are not large, usually a few thousand molecules, but the sampling of available chemical diversity is assisted because of the low MW. The articulation of all possible theoretical 6423



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EVIDENT CONTEMPORARY PRACTICES: EFFICIENCY METRICS AND LIPOPHILICITY CONTROL The compound quality literature has focused recently on ligand efficiency metrics, which quantify binding affinity per unit of physical property.26,64 Among a number of measures proposed, ligand efficiency (LE, binding energy in kcal/mol per heavy atom) = pXC50 × 1.37/heavy atom count (HAC)65 and lipophilic ligand efficiency (LLE or LipE, unitless) = pX50 − log P (or log D7.4)38 have attracted the most attention, as well as various criticisms,66−68 some of which provoked a response.25 LE, despite limitations that include a nonlinear relationship to HAC, has found utility in hit selection and, particularly, fragment based drug discovery.40,69 Because small molecule binding sites vary in size according to the target, optimal LE values are target-dependent.26,70 There is more general agreement that increasing LLE, an approximate measure of specificity and the quantitative embodiment of Hansch’s 30-year-old suggestion that “molecules should be made as hydrophilic as possible without loss of efficacy,”19 is important in lead optimization.71−73 LLE is linearly related to lipophilicity, and a thermodynamic basis for its application has been proposed.71,74 In general, reducing lipophilicity (and increasing LLE) tends to increase free drug levels;75 LLE has been correlated with drug efficiency, a measure utilizing potency and fraction of the dose that becomes available for drug action.27,76 Among marketed vascular endothelial growth factor receptor (VEGFR) kinase inhibitors, clinical response was linked to LLE,77 which could be exposure driven. An analysis of combined LE and LLE values of a set of 46 oral drugs acting on 25 targets, when compared with those of other molecules acting at the same targets, suggested that both of these efficiency metrics could be important in candidate drug selection.26 The mean fractions of molecules with LE and LLE better than the drug were 2.7% for GPCRs, proteases, PDEs, and others (17 targets), 22% for kinases (8 targets), and 1.5% for only in-class targets. Significantly in this analysis, LE and LLE contributed equally.26 The lower score for the kinases, predominantly cancer targets, could result from optimization of kinome selectivity profiles,78 as well as acceptance of higher risk−benefit ratios in the clinic. We use the “% better” LE/LLE metric in several examples (see later), but there are caveats in its use of literature data to note. First, published compounds are often selected from a larger group leading to possible bias; second, with sources such as CHEMBL, biological data frequently come from different assay formats; third, calculated lipophilicities may not be accurate (see below); fourth, there is possible redundancy since the measure uses potency twice, and HA count and lipophilicity often broadly correlate; and fifth, the value will change over time as new molecules are published. The “% better” measure is not a property or physicochemical parameter but a time-related guide to relative compound quality for the target in question. Median LE and LLE values vary substantially according to the target (Figure 1d), and in addition, significant correlations between potency and size or lipophilicity are infrequent.26 The results with drugs are consistent and suggest that the combination of values of size, lipophilicity, and in vitro potency, relative to optimal values for the specific target, can provide a useful measure of both druglikeness and progress in a drug discovery project.26 The obvious challenge for less or unprecedented targets is that the optimal property profile will

be unknown at the outset. Nevertheless, depending on the profile of the lead compounds, improved physical properties and optimization trajectories to achieve them can be identified from the outset. By following these principles, the “desirable property and efficiency envelope” of targets can be expanded to increase LE and LLE, as we have observed earlier with cholesterol ester transfer protein (CETP) inhibitors26,39 and factor Xa inhibitors.79 We further elaborate on the use of these concepts in the accompanying examples, exploring optimizations from a variety of hit sources including HTS, fragments, and DNA-encoded library technologies (ELT). In doing so, hits and leads were identified possessing a range of properties in terms of size, lipophilicity, and potency.

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LIPOPHILICITY MEASURES Lipophilicity36 is the vital element in this review, and the influence of both the partition coefficient, expressed as log P, a constant, and the distribution coefficient at pH 7.4, log D7.4, a pH-dependent value for ionizable compounds (log P is the asymptote of the neutral form), is investigated.80 Historically, these values were defined for drug discovery as measurements between 1-octanol and aqueous buffers (OW). In the past decade, high throughput chromatographic methods have been developed and shown to be more reliable estimates of lipophilicity, irrespective of solubility;81 it was established at GSK that traditional shake flask 1-octanol−aqueous buffer measurements had an upper limit of log D ≈ 4 and were particularly unreliable for poorly soluble compounds.81 Importantly, clearer resolutions of lipophilicity-related observations were evident in comparison to OW measurements and predicted values, with the outcomes showing enhanced statistical significance between adjacent binned values in box−whisker plots.82 Lipophilicity is probably the most important driver of suboptimal profiles but is not the only important parameter. Many studies have established the principle that the more lipophilic is a compound, the higher is the risk of encountering problems due to one or all of low solubility, high clearance, poor absorption, high protein binding, and promiscuity.13,14,36 Additionally, chromatographic measurements of surrogates for membrane and protein binding can usefully generate free drug estimates.76 In different scenarios, both log P and log D (at pH 7.4 and other pH values) are important in drug discovery.14 In general, log D7.4 is important for ADME parameters while log P is often important for target binding and selectivity.13 For oral drug candidates, values of log D7.4 in the range ∼1−3 have been recommended to reduce known risks of downstream failure.33,34,36 By necessity, most compounds in this review are presented with calculated lipophilicity values. This is not always accurate, depending on the structure,83 although the continued assiduous curation of the BioByte database (www.biobyte.com) pioneered by Hansch and Leo provides an enduring gold standard in cLogP prediction. In some cases, ALogP, based on refinements of the Ghose/Crippen atomic contribution method,84,85 extracted with ChEMBL data, is also quoted. Additionally, some ChemAxon cLogD7.4 data are presented, although most log D7.4 data were calculated using the most recent update of the GSK chromatographic log D7.4 predictor. It should be noted that Chrom log D7.4 values are typically 1.5−2 log units higher than the OW scale based on the deliberate calibration, chosen to avoid confusion between the methods.82 Examples are included that illustrate shortcomings with some calculations, but despite these issues, patterns are obvious; 6424



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components,87 and Abraham and co-workers demonstrated that log P is dependent on other properties, namely, molecular size, polarity, and H-bonding acidity and basicity.88 A principal component analysis of ∼30 000 preclinical compounds indicated that the most meaningful nonredundant descriptors are MW, lipophilicity, hydrogen bond donors, and ionization state.16,17 Shape is also very likely to be important, as analyses of aromatic ring count30−32 and Fsp3 proportion28,29,60 have shown. The impact of MW in analyses is a subject of debate. MW per se has no influence on solubility in druglike molecules,82 but in combination with lipophilicity it affects permeability in CACO-2 cells and metabolic stability,33,34 and when lipophilicity is matched, high MW (>1000) and volume reduce passive permeability.89 The frequent correlation of MW with other parameters (e.g., lipophilicity and rotatable bond count) suggest it may also act as a surrogate for parameters not so well-defined or measured, for example, log P in OW for poorly soluble compounds. Across all CHEMBL data there is an upward trend in potency with increasing heavy atom count,15 but when analyzed by individual targets, significant potency versus size correlations are uncommon.26 In this Perspective, we show optimization trajectories using standard potency (in vitro) versus lipophilicity and size plots for many examples. The exemplified plots and analyses are intended to illustrate principles and not to be prescriptive in any way. Lipophilicity measures used include cLogP, PFI, chromatographic log D7.4, and measured log D7.4. LLE can be calculated using any of these properties. We also use LE versus LLE (based on cLogP) plots, which are a useful means of capturing overall progression of size, lipophilicity, and potency. LLEAT, which combines LE and LLE, is also employed.69 We use HA count as a size metric because it is used for LE calculation, but other measures of size including MW, molar volume, molar refraction, surface area, and shape can be used directly or be converted to efficiency measures. Examples are also given of “traffic light” visualization, highlighting changes to multiple properties. Users should apply whatever trajectory analysis works most effectively for their own specific targets, chemical series, and optimization issues faced.

where measured data are included, the differentiations are invariably clearer. Recommending best practice is logical: there is no substitute for generating measured data, so this should be gathered wherever practicable and compared to calculations within given series/programs. By establishing confidence in predictions and, if necessary, building local models on measurements, quality profiling of virtual compounds can be made to minimize synthesis of those analogs with little chance of progression. Additionally, the GSK Property Forecast Index (formally, PFI = chromatographic log D7.4 + aromatic ring count, ideal value is 8 and cLogP < 3. A case can be made for lower activity thresholds for more hydrophilic compounds, based on their likely higher drug efficiency.27

upper left box containing more potent compounds with lower lipophilicity. The parallel lines represent constant LLE (pIC50 − log P) values. The same principle applies should the X-axis be log D7.4, PFI, MW, heavy atom count (HA), or any physicochemical parameter or descriptor. Mapping drug discovery trajectories using various combinations of ligand efficiency metrics based on size and polar surface area has been discussed previously;86 interestingly the authors did not use LLE. In this respect, it is important to remember that the fundamental properties of molecular weight, polarity (hydrogen bonding and polar surface area), and lipophilicity, which are commonly used independently, are inter-related. Thus, Cramer had shown that many physicochemical properties are so highly related that they can be reduced to two main principal 6425



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Table 1. Physical Properties of CCR5 Antagonists and Candidate Drugs Containing a Common N-Phenylpropylpiperidine Pharmacophore, from Four Companies

a

From ref 38. bSee Scheme 1. cExperimental values, refs 91 and 95.

Table 2. PK Parameters of CCR5 Antagonists PK parameters: rat, dog compd 1 2 3 4 5

−1

Cl, mL min

kg

74, 21 201, 36 3.2/12, 3.5 22, − 38, 19

−1

Vdss, L/kg

t1/2, h

F, %

ref

−, 4.3 −, 10.5 0.28/0.6, 0.6

2.3, 2.3 1.4, 3.4 1.4/1.5, 3.4

5.0, 5.2

2.1, 3.9

6, 42 11 h), found postdiscovery, may be a class effect.103 The structure of 1 bound to CCR5104 demonstrates highly efficient interactions, reflecting the quality of the design process. Polar interactions occur with five of the seven available heteroatoms and nonpolar interactions with each of the phenyl, tropane, isopropyl, and cyclohexyl moieties. The optimization toward the AstraZeneca candidate, clinically evaluated for RA, had the advantage versus competitors of not requiring antiviral activity. From HTS hit 10,105 optimizing the amide side chain to 11,106 then the diphenylmethane substituents,106,107 each provided 100-fold potency improvements, leading to compounds such as 12 with improved rat pharmacokinetics (Scheme 1b).107 However, the hERG activity of 12 (7.3 μM; hERG/Cmax free ratio of 250) was considered risky, and further optimization, achieved by balancing hERG inhibition, absorption, and lipophilicity, provided the sulfonyl piperidyl candidate AZD 5672 5 (hERG 24 μM; hERG/Cmax free of 6400).95 The Merck candidate 392 was derived from multiple iterations of HTS hit 13 (Scheme 1c).108 Although LLE is improved in 3 versus 13, physical properties did not appear to be actively employed in its discovery. The physicochemical trajectory was shorter than competitors (Figure 3), but extensive changes to the lead structure were explored while preserving the common pharmacophore. Optimization of antiviral potency, selectivity, and pharmacokinetics was

neca’s 5 was ineffective clinically in RA, despite providing full blockade of CCR5 function in patients;97 other CCR5 antagonists98 including 1 also failed in RA.99 In vitro potency versus property trajectories of the initial hits and key lead compounds identified by each company are shown in Figure 3. The discovery of maraviroc 1 started from two molecules 6 and 7 chosen from a meager HTS output “neither of (which) could be considered to be ideal, having high molecular weight and lipophilicity, polypharmacology, weak binding affinity, and no measurable antiviral activity”.90 However, 6 was reasonably ligand efficient (LE = 0.29 (kcal/mol)/HA), giving a reason to believe; consequently, the two were merged to provide an amide lead 8,100 which displayed improved binding and antiviral activity (Scheme 1a). Next, reducing hERG inhibition and improving absorption of 8 led to triazole 9, and finally amide modification to 1 eliminated hERG activity while retaining other desirable properties.101 Restricting experimental log D7.4 in the range 1.5−2.5, controlling MW, and optimizing LE were drivers for optimization.90 The discovery of backup 2 followed similar principles.91 The discovery of 1 took 2.5 years and 965 analogs were made; the authors stated “at times it appeared impossible to achieve the delicate balance of antiviral activity, metabolic stability, absorption and ion channel activity.”90 A sentiment commonly felt in medicinal chemistry! It is notable that both 1 and 2 were progressed despite having poor rat pharmacokinetics (Table 2) and the oral bioavailability of 1 in humans turned out to be modest, at 23%.102 The 6426



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Scheme 1. Discovery of CCR5 Antagonists Based on the N-Phenylpropylpiperidine Pharmacophore (in Red), from Hit or Lead to Candidate Druga

a

Compounds selected are those highlighted in the inventors’ publications. (a) 1 maraviroc (Pfizer); (b) 5 AZD5672 (AstraZeneca); (c) 3 MrKA (Merck); (d) 4 GSK163929 (GSK). AV = antiviral potency. 6427



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Figure 3. Plots of CCR5 antagonist pIC50 values versus and cLogP (a) and heavy atom count (b). Values are from CHEMBL and the literature. The key compounds in Scheme 1 are connected to show the discovery trajectories for each candidate drug. The percentages of compounds in the background CCR5 data having better combined LE and LLE versus the candidate drugs are the following: maraviroc 1, 2.7%; AZD5672 5, 1.4%; MrKA 3, 14%; GSK 163929 4, 54%.

pursued; in vivo clearance in the rat was central to the end game,92,93 where small molecular changes to identify 3 had significant impact. At GlaxoSmithKline, CCR5 leads were generated using pharmacophore models based on known ligands.109 Tropane 14 was identified as an advanced lead with acceptable rat PK and good antiviral activity. Reducing hERG binding combined with optimization of exposure in the rat110 led to the candidate sulfonamide GSK163929 4,94 albeit with a high predicted dose in man (900 mg u.i.d.).110 Attention to physicochemical properties was not mentioned in publications, and the trajectory saw little change to LLE, in contrast to the outcomes in the Pfizer and AZ optimizations (Figure 3). Compounds 1 and 5 have better combined LE and LLE values versus 3 and 4 (Figure 3). While many marketed drugs have low ‘”% better” values, helping justify the measure, LE and LLE alone provide guidance72 but cannot precisely predict relative ADMET properties. For example, AZD 5672 5 and MrkA 3 both have better rat PK profiles than maraviroc 1 or its backup 2 (Table 2). However, the relative candidate drug physical properties are indicative of their originating patent estates (Table 1) and the discoveries of maraviroc 1 and AZD 5672 5 showed the largest changes in LE and LLE during optimization. On the basis of their relative efficiency values, 1 and 5 can be considered more optimized for CCR5 versus MrKA 3 and GSK163929 4; moreover, they also advanced further in the clinic and maraviroc 1 is a successful medicine. ii. Coagulation Factor Xa Inhibitors. Antithrombotic agents, specifically those targeting individual enzymes in the coagulation cascade, were heavily researched around the turn of the millennium, and several drugs are now marketed. Inhibition of the various trypsin-like serine proteases involved in the

cascade presented particular challenges for oral medicines; notably attention to physicochemical properties was a key driver toward success and the marketed drugs (rivaroxaban 15,111 apixaban 16,112 edoxaban 17,113 and betrixaban18114) are clearly differentiated from the many published inhibitors using efficiency and property metrics.79 For these plasma-based targets, better physicochemical properties demonstrably reduced plasma protein binding, which equated to a higher free fraction and consequent increased translation of intrinsic potency into anticoagulant activity. The identification of two clinical candidates 19115 and 20116 (Table 3) at GSK was achieved with close attention to physical properties, yet with hindsight and more up-to-date thinking, increased use of predictive methods would have circumnavigated several retrospectively identified blind alleys.117−119 The representative lead structure 21 was identified from a combinatorial array,120 possessing modest potency in a factor Xa assay and relatively poor physical properties. However, retrospective analysis indicated that 21 was reasonably ligand efficient, a metric not appreciated at the time. Minor structural changes identified homochiral 22 as a potent analog but with little demonstrable anticoagulant activity in term of prothrombin time extension; it was at this time that a data review established the benefits of reducing lipophilicity. Accordingly, a design guide of ACD cLogD7.4 < 3.5 was employed, leading to potent molecules with better translation of potency into anticoagulant activity and improved microsomal clearance. This culminated with the discovery of 19115 28 months and some 400 compounds after 21 was identified. 19 progressed to phase II trials before termination during a strategic review of the GSK portfolio. 6428



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Table 3. Exemplars Used in the Hit to Candidates Profiling of the GSK Factor Xa Program, with Calculated Physicochemical Properties (Using the Most Recent Versions of GSK Predictors) and Derived Efficiency Metricsa

a It is notable that all parameters for the candidates meet (or are very close to) cut-offs assigned to FXa molecules. LLEAT is the Astex ligand efficiency parameter,69 effectively LE corrected for lipophilicity, defined as 0.111 + (1.37 × LLE/HA).

Figure 4. (a) pKi50 vs cPFI and (b) LE vs LLE (pIC50 − cLogP) plot for representative factor Xa inhibitors described in ref 79, with four marketed drugs (diamonds) and illustrative examples from the BMS (blue) and GSK program highlighted (candidates, stars; hit, cross; others, circles). Betrixaban was the most recently approved drug, which is a low dose oral molecule (taken 80 mg u.i.d. with food) with relatively poor predicted physical properties, but has a high LLE of 6.3 (based on cLogP).

The identification of a second candidate took another 40 months and about 600 compounds, perhaps due to the seductive high potencies of the more aromatic-rich compounds such as 23117 and ignoring aspirational lipophilicity goals in some series, which diverted the group from the retrospectively plotted journey (Figure 4). Ultimately, semisaturated groups,

which bound in the S4 pocket, such as tetrahydroisoquinoline 24 and benzazepine 20 delivered desired levels of potency.116 The second candidate 20 necessitated use of the chlorothienylvinyl sulfonamide motif to achieve the required balance of potency, selectivity, and oral exposure. The mildly basic center in 20 contributed to low plasma protein binding and an 6429



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Table 4. Potency, Efficiency, and Calculated Physicochemical Data for the Four Marketed FXa Drugs and Two BMS Terminated Candidates, for Comparison with Table 3

Figure 5. PDE-V inhibitors (a) pIC50 vs cPFI and (b) LE vs LLE (cLogP derived) of PDE-V inhibitors in ChEMBL with three marketed drugs sildenafil, vardenafil, tadalafil (hits, ×; drugs, stars) and illustrative examples from the second-generation program (27 PF-489791 = triangle). It is interesting to note a relatively narrow band of ligand efficiency in the second-generation series once the two key substituents were identified.

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optimized by balancing of physicochemical and pharmacokinetic profiles.122 Noteworthy observations on the practice are that opportunities for modulation of physical properties were presented with both basic (e.g., 30) and acidic motifs, with a number of such variations retaining potency. This offered a range of physicochemical makeups, providing contrasting opportunities to tune the pharmacokinetic profile; the acyl sulfonamide is a good example of exploiting an acyclic carboxylic acid isostere. The clinical candidate 27 PF-489791 was suitable for once daily oral dosing with improved selectivity profile and notably occupies better efficiency space than the marketed drugs It is interesting to look further back at the differing trajectories that led to the discoveries of the marketed drugs sildenafil, vardenafil, and tadalafil, which predate widespread use of HTS and optimization “rules”. Nonetheless, the elaboration of the antiallergy agent zaprinast into sildenafil is an example of the optimization of a leadlike starting point.123 Thus, the modestly potent (PDE-V IC50 = 2 μM) but highly ligand efficient zaprinast was optimized to 31 (Scheme 3) with an increase in potency and maintenance of LE (Figure 5). Addition of the piperazine sulfonamide both dramatically improved potency and reduced lipophilicity, although it did decrease the LE of sildenafil. Vardenafil124 is a more potent close analog of sildenafil with consequent improved ligand efficiency. The discovery of tadalafil125 started from a highly aromatic and lipophilic, but highly ligand efficient hit, ethyl βcarboline-3-carboxylate 32, leading to advanced leads such as 33 with one of the carboline rings saturated and further lipophilicity added (33, cPFI = 9.1). Maintenance of the good potency while markedly reducing lipophilicity led to the lowdose, long-acting medicine tadalafil. iv. β-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE-1) Inhibitors. An important target for Alzheimer’s disease (AD), BACE-1 is an aspartyl protease that cleaves amyloid precursor protein, and inhibitors need to cross the blood−brain barrier. This requirement essentially rules out the potent but high MW substrate-based peptidomimetic inhibitors that were found initially.126 Achieving high potency with low MW and CNS-penetrant molecules appeared to be a major challenge, compounded by the large size of the BACE-1 binding site as shown by X-ray crystallography, with two aspartic acid residues in the catalytic site. However, the importance of the target for AD attracted massive drug design efforts126,127 and the breakthrough came from fragment screening, which identified low MW cyclic amidine hits which bind to the aspartate dyad.127 Subsequently, many cyclic and bicyclic amidine BACE-1 scaffolds have been identified,128 from which 13 molecules entered clinical trials of which five are still active; the six candidate drugs with published structures are shown in Table 5. Among the first fragments identified as BACE-1 inhibitors was the isocytosine 34 and its phenyl analog 35,129 which were starting points for the AstraZeneca structure-based drug design program leading to AZD3289 36 (Figure 9, Scheme 4).130 In contrast, directly optimizing potency and LE led to the dihydroisocytosine 37.131 To improve permeability and CNS penetration, other cyclic imine structures were explored, first hydantoins (38) and then aminoisoindoles (39), from which 36 emerged. The discovery trajectory shows that a sharp improvement in LLE from hydantoin 38 (Figure 6) was achieved while maintaining LE.

increased volume of distribution, contributing to an elongated half-life commensurate with once daily dosing (19 was projected as a twice daily, although formulation appeared to enable once daily). Interestingly, the ligand efficiency was improved with progression in the series, given reduced size and more optimal fit of the best ligands in the key binding pockets. With subsequent learnings, such as the value of chromatographic lipophilicity measures with improved predictions (established after completion of the work), plus an understanding of their interplay with aromatic ring count, goals could have been achieved with fewer molecules. Such practice would have been further enhanced with application of ligand efficiency measures, which were also not appreciated at the time. The trajectories of these FXa molecules have been reviewed previously,79 but it is interesting to compare the journey from BMS clinical assets DPC423 25 and razaxaban 26, all highly potent but with poorer properties (Table 4, Figure 4). This analysis also noted better translation of intrinsic FXa potency of the molecules with lower PFI into anticoagulant activity, attributed to lower plasma protein binding, such that the GSK compounds had similar 1.5× PT/aPPT extensions to apixaban 16 despite lesser intrinsic potency. iii. Phosphodiesterase-V (PDE-V) Inhibitors. The discovery of the second generation PDE-V inhibitor PF489791 27 presents an interesting trajectory (Figure 5 and Scheme 2), given the objectives for the program, including once Scheme 2. Representative Activity, Efficiency, and Calculated PFI Data for the Second Generation PDE-V Inhibitors That Led to PF-489791 27

daily dosing and enhanced PDE-subtype selectivity. The tactics employed gave a clinical candidate that compared favorably with first generation marketed drugs.121 The HTS hit 28 was reasonably potent but poorly ligand efficient and had poor properties. A focused library based on structural knowledge quickly improved properties and efficiency, in what could be termed a combinatorial fragmentation exercise using the quinazoline core to probe two binding vectors. Structureguided growth from the core-modified 29 led to many potent compounds with broadly similar ligand efficiencies, which were 6431



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Scheme 3. Representative Activity, Efficiency, and Calculated PFI Data for Hits and Key Intermediates En Route to the Three Marketed PDE-V Inhibitors

Table 5. BACE-1 Inhibitors with Known Structures That Have Reached Clinical Trialsa

a

The common cyclic imine is shown in red. The structures of E2609, JNJ-54861911, and CNP520, which are progressing in the clinic, have not been reported.

the proposed strategy, tracking optimal LE for the target and achieving an LLE increase of 6 units (Figure 6). All six known BACE-1 clinical candidates are high quality with “% better” LE/LLE values of 4 units leading to 74 and >1 unit to 75. In contrast, there was no change in LLE leading to 77, the most lipophilic of the three drugs; in contrast to 74 and 75, pharmacokinetic or metabolic optimization was not described in the discovery of 77. The potency of amide analogs of 74 in a HIV-1 strand transfer assay was correlated with lipophilicity;151 the reduction in lipophilicity in the discovery of raltegravir 74 was ultimately driven by the key observation that the thiophene in 73 could be removed without loss of potency. Subsequently,

Leadlike starting points provide opportunities for size increases during optimization while remaining within druglike bounds, and this trajectory is exemplified by all the PARP drugs (Figure 8). The origin of rucaparib 63142 and veliparib 64143 was the highly ligand efficient lead 61, and the impressive optimizations closely follow the “leading edge” of the potency versus HAC plot, even though LE values did reduce. This observed LE reduction is not surprising when the starting point 61 is essentially a fragment with the very high LE of 0.77 (kcal/ mol)/HA. The other drugs made use of the 2-phenyl group in 65 which increased affinity 10-fold versus 61, although this increase was insufficient to maintain LE144 or LLE. However, in all cases, LLE values were either increased or maintained when starting points and drugs are compared (Figure 8), and all drugs had cLogP values of 3 or less. In addition, compared with the background data, four of the five drugs have “% better” combined LLE (based on cLogP) and LE values of 10-fold reduction in clearance in the rat and >100-fold lower human dose (Table 8).147 6435



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Scheme 7. Key Compounds in the Discovery of Poly(ADP-ribose) Polymerase-1 (PARP-1) and PARP-2 Inhibitors, as an Example of Progression of Leadlike Chemical Starting Pointsa

a

(a) Discovery of olaparib (62) from the medium throughput screening hit, phthalazine-2-one 68.141 (b) Benzimidazole amides 61 and 65, reported in 2000,140 were developed to provide rucaparib (63)142 and inspired the discoveries of veliparib (64)143 and niraparib (66).145 Attempts to merge 68 and 65 by cyclizing 68 to an indolenine led instead to 72, which was optimized to talazoparib (67).146 Open arrows are design steps; closed arrows are optimization steps.

Figure 8. Discovery trajectories of poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors. Data are pKi (n = 359) and pIC50 (n = 1520) values from CHEMBL. The discovery trajectories for the drugs in Scheme 7 are shown; all are IC50 values except for rucaparib where Ki values are used. Note that potency of >8 is achievable in the cLogP range −0.5 to 5.0. In contrast, the maximum achievable potency increases linearly as the HA count increases to ∼20 and then does not increase further.

polar basic and amidic replacements of the thiophene resulted in reduced protein binding, increased solubility, and good antiviral potency in a HIV spread assay.152 As a lead compound,

the dolutegravir precursor 76 had outstanding properties with high LLE and LE, potent antiviral activity, and candidate-like pharmacokinetics in rats: clearance of 1.2 mL min−1 kg−1, half6436



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Table 8. Lipophilicities, “% Better” LE and LLE Values, Discovery and Marketing Dates, and Human Doses of Poly(ADPribose) Polymerase-1 (PARP-1) and PARP-2 Inhibitorsa

a “% better” LE and LLE values are reasonably consistent with different lipophilicity measures except for talazoparib, which has a relatively low cLogP. Talazoparib has the lowest reported human dose as well as the lowest clearance in rats.

Scheme 8. HIV Integrase Drug Discovery, Showing Key Lead Compounds and Their Properties

life of 4.7 h, and bioavailability 53%.155 Moving from 76 to 75 was complex synthetically with the installation and optimization of the chiral tricyclic moiety,154 but it is the shortest “property journey” of the three drugs (Figure 9). Elvitegravir 77 is only available as a fixed combination, and while it has a lower dose (100 mg daily) than raltegravir 74 (400 mg twice daily), it requires coadministration of a pharmacokinetic booster, ritonavir, to inhibit CYP3A4-induced oxidation, the major pathway of metabolism. Dolutegravir 75 can claim to be the class leader, with the lowest dose (50 mg

daily) and better potencies against the major mutant integrase viruses. Dolutegravir 75 has the lowest LE and LLE “% better” metric, the values being the followng: 75, 0.04%; 74, 3.5%; 77, 22%. Dolutegravir 75 has the highest LLE value of the drugs, whether based on cLogP or experimental values. cLogP and measured chromatographic log D7.4 82 values are respectively the following: dolutegravir, −0.4 and 3.7; raltegravir, 1.2 and 3.3; elvitegravir, 4.6 and 6.4. Follow-on drugs invariably benefit from preceding knowledge and results, and in this case, an 6437



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Figure 9. HIV integrase trajectories shown on a background literature data of mean IC50 values (all HCV replicons/mutants) from CHEMBL (n = 1877): (a) potency vs cLogP; (b) potency vs HA count. Structures are shown in Scheme 8.

advanced lead 76 with superior ligand efficiencies ultimately provided the better drug. viii. Hits and Leads from Encoded Library Technology (ELT). The use of ELT is ever-expanding, and its impact has particularly been fueled by increasing structural diversity due to expansions in feasible on-DNA aqueous reactions and available monomers.157 The physicochemical quality of the libraries has improved, which is evident in the reduced lipophilicity of the hits being identified, even if MW generally remains high. Very large numbers of DNA-barcoded compounds can be rapidly screened for binding to immobilized protein with additional, informative selections made feasible using competition from known binders. It is interesting to compare the properties of the set of compounds described in a recent review of the published ELT hits,41 including exemplar compounds described in the BCATm (59, 60), RIP-1 (80), and P38 (81) sections. Figure 10 compares molar refraction and lipophilicity, a useful guide to oral absorption prediction, long established within GSK14 (molar refraction correlates with MW; see Supporting Information Figures S.1 and S.2). The spread of the ETLderived compound properties illustrates the challenges and opportunities that hits from the technique present.41 The ELT hits are characterized by, on average, increased size and lipophilicity when compared with a representative set of marketed oral drugs (Figure 10 and Figure 1c). Outliers in Figure 10 are larger molecules with oral exposure that is probably transporter-facilitated. Such molecules are almost invariably natural products, including the cardiac glycoside digoxin, tetracycline/β-lactam antibiotics, and macrolides. Macrocyclic compounds can change between hydrophobic and hydrophilic molecular conformations, depending on the local environment, which, inter alia, enables passage through membranes. Most of the rest of the ELT hit molecules exemplified are to the right of the plot in Figure 10, albeit in a region where drugs do reside, although it would be advisible to aim to truncate such structures during optimization. Table 9 also illustrates how much more lipophilic and aromatic these

Figure 10. Plot of calculated Chrom log D7.4 versus calculated molar refraction (cmr, a good surrogate for size; see Supporting Information Figures S.1 and S.2) for exemplars in the ELT review41 (×), overlaid on a GSK training set of marketed oral dug molecules with % bioavailability of >30% (green ○) and 30%, n = 372

ELT exemplars,n = 20

leadlike ranges

0.8 0.5 0.1 −0.1 401 406 10.2 9.8 1.1 1 1.9 1.7

2.5 2.5 2.2 2.1 312 300 8.3 8.40 1.58 2 4.0 3.7

4.3 4.3 3.9 4.0 592 517 13.9 14.3 3.9 4 7.9 7.6

2.0−4.0 1−3 100−350 4−11 1−3 4−6

Table 10. RIP-1 Activity (FP Assay), Lipophilicity, and Efficiency Data, Colored To Reflect Relative Quality for the Targeta

a

mChrom log P and mPFI values are measured. Calculated Chrom log P for 82, based on the 4H-1,2,4-triazole tautomer (with good agreement), but it is interesting to note that the 1H-1,2,4-triazole depicted appeared likely from structural work, which gives the following calculated data: cLogP = 2.6; cChrom log P = 3.7; cChrom log D7.4 = 4.5.

ix. Receptor Interacting Protein Kinase-1 (RIP-1) Inhibitors. The identification of the RIP-1 inhibitor 82 (Table 10),160 which is progressing in clinical trials, represents a triumph for hit identification using encoded library technology, where the hit 80 proved to be highly potent and ligand efficient when synthesized off-DNA.161 Strikingly, the candidate molecule 82 and hit 80 differed by only two atoms, although this modest change caused a profound improvement in measured physical properties and thus pharmacological behavior. Analysis of the trajectories followed illustrates some particularly pertinent learnings and indicators where application of improved in silico predictions of both activity and properties might reduce cycle times in future. Despite the seemingly modest changes, the optimization was achieved by a large team and several hundred compounds were synthesized, albeit with 18 months of the first off-DNA synthesis. This series of RIP-1 inhibitors binds at a unique allosteric region close to the ATP binding site in RIP-1, where an earlier series of RIP-1 inhibitors, known as “necrostatins”,162−164 were also found to bind, enabling exquisite kinome selectivity. The difficulty in securing selectivity over structurally and functionally related kinases is well-documented. A consequence of the necessary size inflation often required to achieve this is one reason that in LE/LLE analyses many kinase inhibitor drugs and candidates, acting by classical kinase hinge-binding

interactions, are not among the most optimal for the particular target.26 It is interesting to contrast the much lower efficiency and poorer physical properties of the “traditional” RIP-1 hinge binding inhibitors such as 83 (Table 10),165 perhaps reflecting the difficulties encountered in this approach (Figures 11, 12). Of course, the inherent binding dynamics of the distinct pockets are likely to be different; thus efficiency expectations and opportunities will differ, but the physicochemical properties of the active drugs will differentiate the desirability, assuming similar efficacy. Mapping trajectories and other parameters (such as selectivity) will help in understanding progress toward goals and ranking of compounds. Figures 12 and 13 illustrate the value of measured physical data in honing assessment of lipophilicity. Interestingly, the GSK Chrom log D7.4 predictor correlates very well with measurements82 for the successful series (Figure 14), posing the question as to whether stricter property based design might have reduced the number of compounds synthesized and profiled in optimization efforts. In this example, Chrom log D7.4 was more representative than cLogP, emphasizing the value in collecting and analyzing measured data. This can be used to build bespoke predictive models based on the measurements for a given series, with attention to the tautomeric forms used to generate predictions (see notes in Table 10). 6439



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Figure 11. RIP-1 FP pIC50 versus calculated PFI data: candidate 82 (purple star), ELT hit 80 (blue ×), analogs of these compounds in the hits to lead (yellow triangles) and LO disclosures (green circles). Hinge binding series related to the in vivo active 83 (red). The lines representing ligand lipophilic efficiency are based on pIC50 − cPFI.

Figure 13. RIP-1 LE vs LLE derived from measured Chrom log P data: star is 82, ELT hit X is 80, colors as in Figure 11. Cross lines are LE = 0.35 and LLE (pIC50 − mChrom log P) = 3.0 (lipophilicities not measured for 83 and analogs).

Figure 14. Measured vs predicted Chrom log D7.4 values in the necrostatin-site series including 82 and 80 (line of unity and line of best fit, r2 = 0.969). Colors as in Figure 11.

Figure 12. RIP-1 LE vs LLE data colored as Figure 11. Cross lines are LE = 0.35 and LLE (pIC50 − cLogP) = 5.0.

example 81158 (Table 11), identified by ELT, is predicted to possess reasonable properties although it is relatively large, with similar ligand efficiency to 84. Attention paid to properties and efficiency is clearly evident in the optimizations by Pfizer (86, 87)168−170 and GSK (88)171 in each analysis. To an extent, the Scios/J&J work to 89172 maintained ligand efficiency, but the relatively highly lipophilicity of the hit was not reduced. A modest change to ligand efficiency, mostly due to enhanced potency with little property modulation, was made during the Vertex optimization to 90.173 Potency, with no property modulation, appeared to be the main driver for the Boehringer trajectory to the allosteric inhibitor 91 from a small and lipophilic hit (Table 11).174 A modest drop in ligand efficiency was observed in a growth from 21 to 39 heavy atoms, with a modest growth in LLE (+0.9) in spite of an increase in cLogP of 3. The tactics of the GSK optimization leading to losmapimod 88 provided an interesting trajectory, achieved in a program that focused on properties and efficiency, delivering a molecule that progressed to phase III trials.171 The hit 92 (Table 12),175

x. P38α Mitogen-Activated Protein Kinase Inhibitors. With a pivotal role in inflammatory cell signaling, P38α kinase has been a broadly pursued target for autoimmune and inflammatory conditions and a number of candidates progressed to clinical trials.166 As a kinase target, it is unsurprising to observe a plethora of literature on prototypical large and predominantly aromatic molecules. Among reported advanced molecules, different thinking and tactics are evident in the optimization trajectories (see Table 11). Figures 15−18 show starting points that led to advanced molecules and clinical candidates, chosen to contrast different practices and attention (or not) to physicochemical properties and ligand efficiency. An early fragment approach from Astex gave two reasonably potent and efficient leads.167 However, with three and four aromatic rings contributing to high predicted PFI values, the Astex molecules 84 and 85 (Table 11) could not be described as among the “best”, although they were not fully optimized. This illustrates that even using fragment leads, attention to properties is continually required. Interestingly, the Vipergen 6440



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Table 11. Summary of P38α Inhibitors, Start Points, and Most Advanced Compounds with Their Properties, As Used in the Trajectories Analyses in Figures 15−18

efficiency; subsequently, further property modulation and finetuning of substituents delivered several molecules with encouraging profiles (e.g., 95, 96) 177,178 from which losmapimod 88 emerged with the best-balanced profile for progression. The trajectory of this optimization in Figure 15 could be described as orthogonal to the others reviewed. Losmapimod 88 is the only example with a marked reduction

identified by a pharmacophoric search, was not particularly attractive in terms of efficiency or properties, although an X-ray crystal structure of 92 bound to P38α indicated portions of the structure apparently contributing little to binding. Truncation led to 93,176 which showed a modest increase in potency but much improved efficiency and properties. Replacing the oxadiazole ring radically improved potency in 94 and improved 6441



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Figure 15. pIC50 vs HAC for the P38α compounds, illustrating the marked difference in tactics achieved in the GSK optimization to losmapimod (88) (green).

Figure 18. LE vs LLE for the P38α compounds, where the focus on properties and efficiency gave molecules from Pfizer (red) and GSK (green) (86, 87, 88) at the top right of the graph where the % better than values are optimal. In terms of LLE the Vipergen compound 81 scores well.

in HAC, while the design improved activity, thus giving much improved ligand efficiency. All the other examples improved on hit potency by addition of varying numbers of heavy atoms, with relatively modest shifts in LE during the optimizations. xi. Epidermal Growth Factor Receptor (EGFR) Tyrosine Kinase Inhibitors with Selectivity for the T790M/ L858R Double Mutant. The efficacy of the first generation of EGFR kinase inhibitors in non-small-cell lung cancer, including erlotinib and gefitinib, depends on the presence of an activating loop mutation (L858R), but disease progression is associated with a secondary mutation to the kinase domain “gatekeeper” residue T790M. Because side effects are associated with inhibition of wild-type (WT) EGFR, identification of selective T790M/L858R double mutant EGFR inhibitors versus WT became a highly attractive goal. In this example, optimization of LLE based on cellular potency inhibition was achieved despite the fact that this measure will be affected by target binding, access to the target, and competition with ATP. Osimertinib 98180 from AstraZeneca was approved in 2015, and Pfizer identified two candidate drugs, 99181 and 100182 (Scheme 9). All three compounds use a vinyl amide motif to enable covalent binding to EGFR, by targeting Cys-797. The discoveries of 98, 99, and 100 are exemplars of current drug discovery, employing structure-based design and multiparameter optimization of physical properties, reactivity, metabolism, pharmacokinetics, cross-kinase selectivity, and toxicity. In each case, the lead compounds 101, 102, and 103 were identified from legacy kinase projects. Optimal warhead electrophilicity was assessed directly using Ki/Kinact estimates or by a physicochemical model183 that measured half-lives in glutathione solution. The covalent strategy improved cell potency of leads 101 and 102 probably because it overcomes competition with intracellular ATP concentrations. A significant number of issues were overcome in optimization (summarized in Scheme 9, Figure 19). Despite optimization of log D and LLE being pursued in the discovery of 98, closely related compounds with higher LLE were rejected because of lack of kinase selectivity versus IGFR1 and INSR, as well as hERG binding.180 In common with many other marketed kinase inhibitors, 98 is therefore less optimized for its target according to the “% better” LE and LLE (based on

Figure 16. pIC50 vs cLogP for the P38α compounds, noting that except for the Boehringer molecule 91 (blue), most optimizations were achieved with modest increases in cLogP (or a decrease in one Pfizer example, 86 (red)). The Vipergen ELT product 81 (light purple) scores well in this analysis.

Figure 17. pIC50 vs cPFI for the P38α compounds, where the focus on properties was evident at Pfizer (red) and GSK (green). The Pfizer reductions (86, 87) (red) were achieved by lipophilicity modulation. Removal of two aromatic rings was the most notable feature of the optimization leading to 88 (green).

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Table 12. Activity, Efficiency, and Physicochemical Data on the Discovery of GSK P38α Candidate, Losmapimod 88, Emphasizing the Tactics of Using Structural Guidance to Cutting Down in Size, Then Optimizing, a Modest Lead 92 with Poor Properties and Efficiency

protein kinase composed of three subunits, which exists as 12 isoforms.188 It is a primary mechanism for maintenance of cellular energy homeostasis, and activators have potential for treatment of metabolic diseases. This target allows another look at the practices of drug discovery within Merck and Pfizer, where a number of interesting comparisons have been made previously.6,38,48 HTS in Merck failed to provide hits, but an “informer library”, containing molecules bridging fragment and leadlike space, provided the activator hit benzimidazole 104189 (Scheme 10). Optimization of 104 relied on increasing size and lipophilicity, with no less than three phenyl rings added to give 105.189 Despite having no measurable free fraction in plasma, 105 provided proof of concept in preliminary in vivo studies.190 Further development focused on increasing free fraction and access to muscle, leading to 106,190 where the sorbate moiety acts as a neutral carboxyl mimic, replacing the benzoic acid of 105. Compound 106 is a pan-AMPK activator, which in animal models improved glucose homeostasis but showed cardiovascular hypertrophy.191 In Pfizer, HTS provided the hit 107.192 Modeling overlays of 107 and A-769622 (108), a known AMPK activator,188 led to the hypothesis that the benzyl amide group of 107 was not required and instead a carboxyl group was predicted to mimic the acidic hydroxyl of 108. The resultant acid derivative 109 was indeed a more potent activator than 107, and further optimization led to the development candidate indole 110.192 X-ray crystallography showed that 110, 108, and an analog of Merck’s 105 all bind to the same allosteric site on the AMPK1 α1β1γ1 isoform, located between the α- and β-subunits. There was variation in the location of the acidic groups of the three

cLogP) metric (39% versus 544 double mutant EGFR inhibitors reported in CHEMBL). In contrast, 99 and 100, which have different hinge-binding modes (Scheme 9), both have higher LLE values and are more optimized (% better values are 4.6% and 0.5%, respectively). Structural features, including H-bonding, drive cross-kinase selectivity (see below) more than size or lipophilicity.184 98 contains a 2-anilino-6arylpyrimidine, a substructure frequently appearing in kinase inhibitors and that may be the source of selectivity issues in the 98 series. Improving kinase selectivity across a panel of 38 kinases was also required in the discovery of 100,182 where the 2-methoxy group in the pyrazole ring made a >4-fold improvement in selectivity in the N-isopropylpurine compounds. This was designed by targeting an interaction with Leu-792 in the hinge binding region, where many other kinases have a bulkier Tyr or Phe residue. This example shows that physical properties of kinase inhibitors may sometimes need to be compromised to some extent to achieve adequate kinome selectivity, but this tactic has not prevented progression of many molecules for cancer indications.78 However, both optimal physical properties and kinome selectivity can be achieved in alternative core scaffolds. Covalent kinase inhibitors using Michael acceptors can react with thiol-containing non-kinase targets. For example, by use of mass spectrometry, 98 was shown to bind to cathepsins while 100 does not.185 Ultimately, it is of course clinical efficacy that will decide value and the eventual comparisons of 98 with 100 and other progressing covalent186 or noncovalent187 dual mutant inhibitors will be revealing. xii. 5′-Adenosine Monophosphate-Activated Protein Kinase (AMPK) Activators. AMPK is a serine/threonine 6443



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Scheme 9. Issues Overcome in the Discoveries of EGFR Double Mutant Candidatesa

a

Hinge binding sites shown by blue arrows.

this aspect is likely to facilitate the binding of the carboxyl replacement in 106. The trajectories in LE and LLE space of the compounds in Scheme 10 are shown in Figure 20. The Merck program initially took a backward step in efficiency space with compound 105, which was then reversed significantly in 106. Because Pfizer hit 107 could be cut down, LE was greatly improved in 109, and yet both LE and LLE were improved further in 110. Interestingly, Merck’s hit 104 and the Pfizer lead 109 have the same LE and LLE values; yet their optimization proceeded along different paths and ultimately provided compounds 106 and 110 with comparable LLE values. Merck did acknowledge the lack of LLE improvement in reaching 105,189 and we note with interest that the organization is now often applying efficiency measures (e.g., Table 16, entries 12, 15, 20, 32). Pfizer was an early adopter of property optimization,193 and the use of efficiency metrics, especially LLE (or LiPE), appears to be routine (e.g., Table 16, entries 1, 7, 8, 10, 14, 15, 34, 35, 37). xiii. Observations on Kinase Inhibitors. To date, 37 licensed drugs and 243 clinical investigations have emerged from the many programs targeting the 518 kinases encoded in the human genome. Evaluation of the target landscape of the clinical molecules indicated that many are not so selective, explaining some off-target effects and highlighting potential for repurposing.78 The clear majority focused on the “traditional” approach of targeting the ATP-recognizing hinge motif of the active site, often reworking old hits and drawing on experience,194 then optimizing growth into the sugar/backpocket and displacing so-called gatekeeper residues to achieve

Figure 19. Cellular EGFR double mutant potencies versus measured log D for compounds cited in the discoveries of 98, 99, and 100. LLED = cell pIC50 − log D7.4. The “% better” LE and LLE values (based on cLogP) versus background data from CHEMBL (n = 544, not shown) are the following: osimertinib 98, 39%; PF-06459988 99, 4.6%; PF06747775 100, 0.5%.

molecules, due to flexibility of interactions with three neighboring lysine residues located near the solvent front; 6444



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Scheme 10. Discovery of AMPK Kinase Activators in Merck and Pfizer

to physical properties, which is contrary to the relationships evident for more general off-target effects.82,195 A notable milestone in efficiency-based design practice was noted with the improved clinical outcomes and lower dose of the VEGFR inhibitor axitinib.77 Subsequently, further examples emerged where efficiency and property-based design delivered clinical assets, as exemplified by the P38α examples above. The RIP-1 example shows a markedly different approach, exploiting an often searched for but rarely found allosteric pocket, enabling good properties, efficiency, and exquisite selectivity. The role of ELT here was key, and the Vipergen P38α example adds a further example of a potent molecule with reasonable properties. It is interesting to see an increasing number of smaller, highly efficient kinase inhibitors emerge for EGFR (covalent inhibitors, Scheme 9), IRAK-4 (Table 16 entry 1), GSK-3β (Table 16 entry 30), and JAK-1 (Table 16 entry 32), a common feature of these being highly engineered functionalized aliphatic heterocycles. Such motifs improved the quality of these molecules, and it will be interesting to follow their progression. xiv. Hepatitis C Virus: Nonstructural Protein 3/4A (NS3/4A) Protease Inhibitors. Spectacular recent contributions from medicinal chemistry teams enabled the discovery of several combination products, containing NS3/4A protease inhibitors and NS5A inhibitors, with hepatitis C virus (HCV) disease-curing capability.196 The first generation NS3/4A protease inhibitors boceprevir 111197 and telaprevir 112198 required very high doses or repeated administration and bind covalently to the active site serine (Table 13). Our survey of combined drug LE/LLE measures26 showed telaprevir 112 to be notably less ligand efficient than 111 and many other drugs; poor solubility (“less soluble than marble”198) and consequent challenges and delays in its development were significant issues.198 Nevertheless, telaprevir played an important role in

Figure 20. Trajectories in LE vs LLE space of the Merck and Pfizer AMPK kinase projects. Structures are shown in Scheme 10. LLE = α2β1γ1 EC50 − cLogP. EC50 of the HTS hit 106 is approximated to 40 μM.

potency and, sometimes, selectivity over the many related structures in the kinome. Mimicking ATP and the ease of growing from ensuing aromatic heterocycles using SNAr and cross-coupling methodologies was a fruitful tactic that delivered the first generation of drugs. The necessary increase in MW of molecules to achieve selectivity contributed to progressed molecules not necessarily being the best for target in terms of “% better” on LE vs LLE plots.26 This is corroborated by an analysis of the data in ref 78 (see Supporting Information Table S.1) indicating little change of kinome selectivity with respect 6445



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competitors. Two further drugs, simeprevir 119208 and paritaprevir 120,209 make direct use of the BILN-2061 113 macrocycle. These macrocyclic drugs break the Ro5, but this was necessary since sub-nM compounds for this target require heavy atom counts of >46 (Figure 21). In general, macrocycles do not express their full calculated physical properties210 because of intramolecular interactions, restricted solventaccessible surface areas, and constrained conformational flexibility; all these factors may assist permeability.211 While property-based design did not feature strongly, the optimizations inspired by BILN-2061 113 nevertheless resulted in reduced lipophilicity and increased LLE in all cases and increased LE in five of the six cases (Figure 21). Overall, the “% better” LE/LLE (based on cLogP) measure versus background inhibitors, while admittedly not spectacular, are in the top 20% and improved on telaprevir 112 (Scheme 11). This bRo5 target, like others (vide infra), therefore also seems to conform to the efficiency principles observed for targets with more druglike ligands. Several groups have tried to rationalize the observed effects of macrocycles and other bRo5 molecules, but general principles remain unclear.210,212,213 Such molecules likely lie outside the size/property domain of cytochrome P450s, and it has been suggested that bigger molecules necessarily require higher lipophilicity to achieve intestinal absorption (cf. Figure 10).33,214 xv. Hepatitis C Virus: Nonstructural Protein 5A (NS5A) Replication Complex Inhibitors. In searching for functional inhibitors of the HCV virus, using a cellular screen to identify inhibitors of the GT-1b replicon, Bristol-Myers Squibb found iminothiazolidinone 121 (Scheme 12), the only hit from in excess of one million compounds screened.216 While 121 did

Table 13. First Generation Covalent HCV NS3/4A Protease Inhibitorsa

a Telaprevir has aqueous solubility of 4.7 μg/mL, requiring spray-dried formulation.198 It is dosed with peginterferon alfa and ribavirin, with high fat food, and has a boxed warning for serious skin reactions.215

pioneering transformational HCV treatment regimes, although the risk of being replaced by more optimal compounds was clear and, indeed, marketing of telaprevir 112 was discontinued. The second generation of NS3/4A inhibitors are noncovalent macrocyclic structures with reduced human doses versus the first generation drugs (Scheme 11).199 These were all inspired by the progenitor macrocycle BILN-2061 (113),200 itself discovered from a linear hexapeptide inhibitor.199 In a key step, Merck scientists used structure-based design to open the macrocycle in 113 and form a new macrocycle between the carbamate and the pendant heterocycle.201 Additional replacement of the carboxyl of 113 with a sulfonamide isostere provided a new macrocyclic lead structure 114. Optimization of the carbamate to proline linker in 114202 provided the two Merck drugs vaniprevir 115203 and grazoprevir 116,204 as well as volixaprevir 117205,206 and glecaprevir 118207 from

Scheme 11. Second Generation Macrocyclic HCV NS3/4A Protease Inhibitors and Human Dosesa

a

Blue structures retain the BILN-2061 113 substructure, and red compounds have Merck substructure 114 in common. Simeprevir, paritaprevir, grazoprevir, voxilaprevir, and glecaprevir are approved by the FDA. Vaniprevir is approved in Japan. 6446



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Figure 21. Discovery trajectories of macrocyclic inhibitors of HCV NS3/4A protease from the lead BILN-2061 113: (a) potency vs cLogP; (b) potency vs HA count. The “% better” LE and LLE values versus the background of NS3/4A inhibitors from CHEMBL (n = 1305) are improved with the macrocycles versus the first generation drug telaprevir 112 (36%): simeprevir 119, 8.7%; glecaprevir 118, 11%; paritaprevir 120, 14%; voxilaprevir 117, 15%; grazoprevir 116, 17%.

Scheme 12. Discovery of Daclatasvir 125 from Screening Hit 121

not have desirable properties (MW = 587 and cLogP > 5) and readily epimerized, a minor change to the phenyl acetamide 122 encouragingly improved activity 100-fold.216 What followed is a tour de force of recent drug discovery.217 Compound 122 was unstable in solution, forming two dimeric isomers 123, which were biologically active. The hypothesis that the heterocycles in 123 served the purpose of spacers between the pair of key pharmacophoric dipeptide groups was confirmed when their inspired replacement with a biaryl linker

provided the highly potent, C2-symmetric lead 124 (Scheme 12). The optimization of 124216 still posed substantial challenges: reduction of lipophilicity and increasing solubility to improve ADME, removal of potentially mutagenic aniline nitrogens (imidazoles exploited as amide bioisosteres), refinement of the biaryl spacer, and optimization of the dipeptide. The trajectory in efficiency space is shown in Figure 22. The resulting drug, daclatasvir 125, retained C2-symmetry and is an effective pan-genotype HCV inhibitor.217 Despite being a bRo5 6447



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inhibitors, NS5A inhibitors are helping to cure HCV infection. This is indeed a triumph for medicinal chemistry.196 It is notable that all five follow-on drugs are highly potent with comparable doses, yet are even larger and more lipophilic than 125, resulting in lower LE and LLE values (Figure 22, Table 14). Do the NS5A drugs demonstrate that druglikeness extends well beyond the rule of five (bRo5), or does their unique modes of action suggest they are true outliers?227 The nearunchanged dipeptides imply strict requirements, and membrane permeability shows subtle SAR in the dipeptide moiety, where intramolecular H-bonding was proposed,228 a feature seen in other bRo5 molecules such as cyclosporin.212 Studies of Nmethylated cyclic peptides showed a sharp fall in passive permeability at MW > 1000 but also that MW 500−1000 could provide acceptable passive permeability.89 Among AbbVie’s bRo5 compounds,229 compiled from many projects and including 129 and 130, bioavailability was linked to physical properties familiar within Ro5 druglike space: optimal log D, aromatic ring count, and number of rotatable bonds. While more data are needed to understand successful features of bRo5 space,212 we note that among 125 dipeptide substructures reported to date in CHEMBL (n = 359), the drugs replicate the now-familiar pattern seen with other targets, lying within the top 15% having the best combined LE and LLE values (Figure 22, Table 14). xvi. B-Cell Lymphoma-2 (BCL-2) Inhibitors. Venetoclax 131 became the first marketed BCL-2 inhibitor in 2016 for treatment of chronic lymphocytic leukemia in patients who bear the 17p deletion genetic mutation. It is the culmination of 2 decades of committed research and development in AbbVie on this target,230 which depends on a protein−protein interaction and began with the identification of fragment hits 132 and 133 with different binding sites, using NMR (Scheme 13).231 The fragments were linked, then further optimization gave initial lead 134, from which the first clinical candidate ABT-737 135 was discovered. Improvements of bioavailability led to navitoclax 136232 and finally to venetoclax 131,233 which had improved selectivity (Scheme 13). Structure-based drug design played a key role in late optimization, leading to the introduction of an azaindole side chain in 130. From the patent dates, optimizing 134 to 131 took 5−6 years. Venetoclax 131 is highly potent with Ki ≤ 0.01 nM for BCL2.233 With experimental log D7.4 = 5.4, cLogP = 10.3, MW = 868, O + N = 14 and OH + NH = 3, it exists outside of bRo5 chemical space.212 Moreover, 131 is a zwitterion with pKa values of 4.3 and 8.0, a feature associated with increased attrition due to poor pharmacokinetics,4 and it contains an aniline and a nitro group which can bear risks for mutagenicity. It is a high risk oral candidate using the GSK guidelines of dose,

Figure 22. Trajectory in LE and LLE space for discovery of daclarasvir (125) and values for the other NS5A inhibitors derived from 125. The background data show 125 substructures from CHEMBL (n = 356; the assay is phenotypic and full CHEMBL searches retrieve non-NS5A antivirals).

drug, with modest amorphous solubility (0.014 mg/mL at pH 6.5), 125 had 50% bioavailability in rats. Liver is the target organ, and the liver to plasma ratio for 125 was ∼5 in the rat.218 Target engagement with NS5A protein was demonstrated using a biotinylated analog as a probe,216 suggesting 125 may act by inhibiting RNA binding to NS5A.219 Models of binding of 125 to crystallized dimeric NS5A domains have been proposed, but intriguingly, the mechanism of action of 125 is nonstoichiometric relative to NS5A, since the ratio of NS5A molecules to 125 at its EC50 in a replicon is estimated to be ∼102−103.216 Alternatively, 125 could have other antiviral actions. Other classes of NS5A inhibitors have been found,220 but none yet possess the antiviral efficacy of daclatasvir 125 analogs. The impact of 125 was profound: within ∼4 years of its discovery, competitors had patented no less than five future follow-on drugs 126−130221−225 derived from it (Table 15). Minor changes only to the dipeptide side chains of 125 were feasible, but the major differentiators are polyaryl variants of the biphenyl spacer (Scheme 12) including an exotic high MW solution in pibrentasvir (130).226 Long half-lives in humans218 together with very high potency, location of the target in the liver, and a unique biological mechanism all contribute to the success of these extraordinary molecules. In combination treatments with NS3/4A protease and NS5B polymerase

Table 14. Physical Properties and Discovery Timelines of HCV NS5A Inhibitors drug

cLogP

MW

pEC50 GT-1a

LE

LLEa

% better LE and LLEb

dose, mg u.i.d.

first patent

daclatasvir 125 ledipasvir 127 ombitasvir 129 pibrentasvir 130 elbasvir 126 velpatasvir 128

4.7 6.7 8.0 7.2 6.7 5.7

739 889 894 1113 882 883

10.3 10.5 10.9 11.7 11.4 10.9

0.26 0.22 0.23 0.20 0.24 0.23

5.6 3.8 2.9 4.5 4.7 5.2

1.2 12.7 14.7 12.7 4.9 4.9

60 90 12.5 120 50 100

Feb 21, 2008, WO 2008021927 A2 Nov 18, 2010, WO 2010132601 A1 Dec 15, 2011, WO 2011156578 A1 Jan 5, 2012, US 20120004196 A1 Apr 5, 2012, WO 2012041014 A1 May 24, 2012, WO 2012068234 A2

a

Based on cLogP. b% compounds with better LE and LLE values among 356 daclatasvir (125) substructures from CHEMBL (the assay is phenotypic, and CHEMBL searches include non-NS5A antivirals). 6448



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Table 15. Structures of Marketed HCV NS5A Inhibitorsa

a

126−130 followed after the first approved drug, daclatasvir 125, and all drugs contain a common dipeptide carbamate motif, shown in red.

Scheme 13. Discovery, Timelines (from Patents), and Optimization Strategy of BCL-2 Inhibitors

solubility, and lipophilicity.10 The human dose of 131 is 400 mg,234 but the predictably poor solubilities of 0.0004 mg/mL (at pH 7.4) and 0.011 mg/mL (FaSSIF; dose to FASSIF solubility ratio of 36.4L) required an amorphous solid dispersion formulation, in which any conversion to the crystalline form would decrease bioavailability.235 Low-fat and

high-fat meals increase exposures of 131 by 3.4- and 5.2-fold respectively.236 The discovery trajectory (Figure 23) shows linear increases in cLogP and HA count, up to navitoclax 136; final optimization to 131 reduced size and lipophilicity.229 The background BCL-2 Ki data from CHEMBL show strong 6449



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Figure 23. Trajectory of the discovery of venetoclax (131): (a) potency vs cLogP; (b) potency vs HA count. Structures are shown in Scheme 13. The background data are BCL-2 compounds extracted from CHEMBL (n = 699). The Ki for 131 is reported to be 1 unit. Poor metabolism, notably phase II conjugation, is a risk but is context dependent and not always an issue.250

dependency on potency with increasing physicochemical properties, which is rare among drug discovery targets.26 In this case, sub-nM potency required cLogP > 8 and HA count of >55. This is most probably a result of the requirement to bind to the elongated, but shallow BH3 peptide binding site.237 Despite inherently reduced LE due to its size, 131 has a combined LE/LLE “% better” value versus background compounds of ≤8.3%, ≤3.0%, ≤1.6%, and ≤1.0%, where LLE is based on cLogP, ALogP, cLogD7.4, and cChromLogD, respectively. Hence the basic efficiency principles for optimized drugs we see with more druglike targets also appear to apply to this bRo5 target and indicate highly effective efficiencyoptimization in the discovery of 131. xvii. Recent Examples of Lipophilic Efficiency Optimization (Table 16). A brief survey of recent literature examples (published in 2015−2017) explicitly describing application of LLE helps illustrate the chemical strategies employed. The major strategies used in this set include adding an aromatic or aliphatic heterocycle, changing a phenyl to a heterocycle, and adding polar substituents. Optimizing heteroaromatics and polar groups is also common. Less common, but highly effective when it works out, is the cutting down in size or removal of a phenyl ring or other group. An example is the discovery of lorlatinib from the existing drug crizotinib (entry 37), through the construction of a macrocyclic lactam, thus allowing concomitant removal of a piperidine ring. In the morpholine-based GSK-3β kinase inhibitor, entry 30, a phenyl ring was effectively replaced by hydrogen, with an adjacent methylation used to improve affinity 10-fold. Together, the changes enabled incorporation of a more lipophilic distal 4-pyridyl substituent. The replacement of a phenyl with a polar hydroxyethyl motif features in the mTORC1/2 example, entry 27; here the interesting solubility efficiency metric, [pIC50 − pSolubility], was used. Fragment examples (entries 1−5) show the expected increase in MW while offering opportunities to demonstrate lipophilicity control. LLE improvements can come exclusively from cLogP reduction, as exemplified by the TarO inhibitor (entry 12), NK3 antagonist (entry 25), IGF-1R inhibitor (entry 6450



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Table 16. Recent Optimizations That Utilized Ligand Lipophilic Efficiency (LLE)a

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Table 16. continued

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Table 16. continued

a Of the 38 examples, 14 provided candidate drugs and 16 were in vivo tool compounds. Entries are categorized by hit or lead origin as follows: 1−7, fragment hits or fragment derived leads; 8 and 9, leads from designed libraries; 9 and 10, targeted screening hits; 10−18, HTS/screening hits; 19− 32, earlier identified leads; 34−36, literature or competitor-derived; 37 and 38, drug or clinical candidate derived. Median potency, cLogP, and MW start/finish values are 7.1/8.2, 3.3/2.1, and 380/427, respectively, consistent with trends in Figure 1b for lipophilicity and LLE optimizations. LLEAT, which is equal to 0.111 + (1.37 × LLE/HA), is included for the fragment hit optimizations69 in entries 1−5.

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CONCLUSIONS The examples we have shown illustrate differences in practices in optimization programs, set against a backdrop of attention to physicochemical quality and tracking of ligand efficiency measures. We note that successful optimizations tend to converge toward optimal ligand efficiency values for the target, and this occurs irrespective of whether physical properties are actively optimized or not. We propose that such monitoring of progress can speed up the optimization process by providing useful guidance to help objective decision-making while paying attention to the quality of measurements and predictions (Figure 24).

benchmark versus competitors. Creating potency versus property or LE versus LLE plots is simple to do. Predictions are necessary to prioritize decisions, and these should not be prescriptive but ensure a clear focus is kept on the destination. Knowing this allows a path to be mapped from the outset, which will differ in direction according to the properties of the chemical starting point. Starting points from screens are often lipophilic (cLogP ≈ 4),46,47 and increasing leadlikeness as an initial goal is the recommended approach. When it is deliberately sought, there is abundant evidence that it may not be difficult to radically reduce lipophilicity or increase sp3 character. Structure-based drug design offers the opportunity to maximize and tailor polar contacts with the target protein.289 Mapping of progress against a given target may also help differentiate binding modes when unusual SAR or marked shifts in efficiency might indicate a change in binding mode or kinetics. Ultimately, choosing series based on efficiency and identifying where other parameters (e.g., selectivity) can be used to differentiate between structures will pay dividends, not forgetting physical quality. Together, these are key elements of multiparameter optimization. Trends in properties of patented compounds encompassing oral drugs (Figure 1a) suggest medicinal chemists might have forgotten how to find small and hydrophilic molecules. We discussed above the reasons for molecular inflation: older drugs, which are smaller and less lipophilic than recent drugs, were largely discovered by mimicking natural ligands with optimization of in vivo activity. This approach is rarely used today because opportunities for exploitation of small, hydrophilic, endogenous transmitters, such as serotonin, dopamine, and acetylcholine, are becoming mined out. Low MW hits may be dismissed because they are not potent, and using LE to rank HTS output is necessary good practice. There is general agreement that leadlike compounds, giving “headroom” for optimization, should enhance HTS screening sets and have higher chances of success based on complexity criteria. While the evidence that leadlike hits accelerate candidate discovery remains anecdotal, the PARP and HIV integrase examples shown above demonstrate the value the approach. However, a clear value of low MW hits has been shown in fragment based drug discovery (FBDD),50 especially when using X-ray guided optimization with careful control of LE, and FBDD is delivering more druglike candidates versus HTS (Figure 1c). Libraries crossing over between fragment and leadlike space have found utility. All possible stable, synthetically feasible, and medicinal chemistry friendly molecules with HA count of up to 17,51 some 166.4 billion in total, have been identified, and exploiting this resource will assist selection of novel fragment and leadlike libraries. Without precompetitive disclosure of all compounds made in lead optimization campaigns, it is impossible to assess if practices really do expedite delivery. Anecdotally and in our experience, they do, but further improvement in physicochemical design practices would see fewer or, at the very least, better focused compounds made. Making fewer compounds with high probability of poor properties is good practice;290 it saves time and costs and is an ultimate implementation of green chemistry thinking.291 Screening larger, more complex molecules brings risks of poor solubility, false negatives, and assay interference, but could these be the best option for some difficult targets? Complex, high MW compounds also have much less chance of finding a fit and giving activity, but in this respect, ELT technology has

Figure 24. Optimization trajectories according to hit properties, exemplified with a generic potency versus lipophilicity plot showing “ideal” oral druglikeness range as the goal. Fragment (a) or leadlike (b) starting points can be “grown” into optimal druglike space. For typical HTS hits, seeking reduced lipophilicity from the outset (c) will develop improved leadlike characteristics and new SAR. Increasing potency by increasing lipophilicity of HTS hits (d) is risky, since lipophilicity will have to be reduced ultimately and could be difficult if potency depends on it. With potent lipophilic hits, seeking to reduce lipophilicity (e) is the most viable approach.

Successful case histories tell us something about the paths to success, which we believe is best set in the context of each specific target; generic, prescriptive, property, and eff iciency goals are not the way to proceed. In this respect our thinking has been influenced by the observation that while oral drugs have differing physicochemical profiles, they frequently possess optimal combined LE and LLE values versus the estate of published compounds active at their biological specific target.26 This applies also to several further examples shown here, including targets where the drugs lie in bRo5 space. Seeking optimum efficiencies and physicochemical properties are guiding principles and not rules. The likely achievable ligand efficiency is defined by the target,70 so optimizations should seek to maximize the LE vs LLE combination. The requirements for effective drug exposure by whatever route of administration is sought will constrain the physicochemical space available. Collection of key ADMET data pertinent to the program enables an assessment of progress toward these latter goals. Good practice entails using combinations of the various efficiency plots to assess progress, to map the journey, and to 6454



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dependent on affinity and efficacy. The PD impact of longer target residence times was noted, something highlighted in section examples; this is notoriously hard to achieve or design for, but if secured, it can markedly improve PD efficacy.301 Dissociating PK and PD properties, if a strategy can be found, is a means to address otherwise poorly tractable targets and could provide exceptions to the general principles discussed here. An example is the use of bifunctional bRo5 molecules, Protacs, to degrade protein targets by via the ubiquitin proteasome system. The catalytic mode of action of Protacs may help compensate for their nondruglike properties.302 We rely on the benefits of institutional experience and learning,194,297 but the age of “big data” and machine learning is dawning for drug discovery.303 Synthesis prediction advances304 have clear potential not only for improving routes but also for providing fresh optimization opportunities. Chemists cannot know all available SAR data (in the broadest sense), but tools are becoming available that can do so and should assist better quality compound design and decision making. An early player is matched pair analysis, where companies have collaborated to pool learning.238 Further insights into important unsolved problems, such as better understanding of promiscuity,305 can be expected. In most hit optimizations, the final optimized molecule or candidate bears some structural resemblance to the hit, as shown by the bulk of the examples we cite. Remembering this is important during optimization, and while scaffold-hopping can be feasible, identifying structurally different leads usually requires a new hit identification campaign. Radically evolving a lead series to a different class happens rarely; it will probably take too long to achieve. Could machine learning in future reproducibly design novel starting points for any target? We do not know if the medicinal chemist will be replaced in the future by machines; it is currently acknowledged that chemist and machine together will always outcompete either one on its own.159 But to compete in the future, medicinal chemists will need to continue to exercise maximum design capability and efficiency.306 With attention to efficiency metrics and judicious implementation of lipophilicity predictions, enhanced by empirical data, better design can be achieved. It will be interesting to watch developments and learn how much the big data revolution might add over and above the principles and practices highlighted herein. We conclude with the prescient thoughts of the pioneering investigators of drug action, Brown and Fraser, published 150 years ago.12 “Although we cannot obtain a rational explanation of the connection between the chemical and physiological characters of a substance until we know more of the modus operandi of poisons, it might be supposed that a careful examination and comparison of known facts would lead to the discovery of some empirical law or laws by means of which we could deduce the action from the chemical constitution.” They conclude “This investigation has done little more than merely introduce us into a vast field of inquiry, but it has justified us in expecting that important fruits may be obtained by further and careful cultivation.” We now have many more known facts, and important fruits have appeared. Laws still elude us, yet continuing careful cultivation will undoubtedly yield more insights and guiding principles.

shown promise, as it offers much larger numbers of compounds to screen. In our experience, high MW hits with modest potency that cannot be “pruned” without dramatic loss of activity are very difficult to advance further. Designing high MW libraries to probe bRo5 space is sensible only if library members can demonstrate good ADME characteristics. Highly complex bRo5 molecules could theoretically have the advantage of reduced promiscuity, but there is insufficient knowledge for successful design principles to emerge so far. Natural product bRo5 drugs are chemically complex292 but generally hydrophilic,39,293 and the area is arguably underexploited.294 Nevertheless, drugs frequently rely on natural product partstructures292,295 and using libraries based on natural product scaffolds has long been attractive.296 The challenging synthesis often required appears to have deterred significant efforts within the pharmaceutical industry. Medicinal chemistry teams must deliver high quality candidates, which will not suffer development delay and shorten patent life for obvious reasons, such as poor solubility. The general success of HTS and FBDD suggests that perhaps fewer roadblocks exist in seeking viable chemical starting points than in optimizing them, with major challenges faced in seeking balanced biological, pharmacokinetic, and physical properties. Time frames are short, and most often, teams are small and therefore need to be efficient in their planning and execution. Obviously, it is desirable to reach optimized compounds by the shortest trajectory, making the fewest number of molecules possible to test each hypothesis (Figure 24). Developing detailed SAR understanding is attractive to medicinal chemists, but this may not necessarily progress projects. For example, increased LLE will be seen when a new polar group makes a favorable binding interaction, and by definition, that will change SAR. It is important to recognize that such changes can provide a “step-jump” to a new lead series, which often brings late stage lead optimization projects very close to candidate drugs.297 The appearance of quality guidance recognizing past problems should begin to make an impact on marketed drugs in the next few years. At the very least, we must now expect to see more targets clinically validated or disproved with quality molecules.11 If not, could patience with small molecule drug discovery soon run out? After all, the community has had the Ro5 and leadlikeness for 2 decades and ligand efficiency metrics for more than 1 decade. However, we do think that medicinal chemists are “upping” their game, as judged by the increased frequency of publications addressing the themes of this Perspective,298 including several of the excellent examples above. Dose prediction, which summarizes key preclinical data, combining potency, absorption, clearance, and volume of distribution in one valuable metric, is probably not used enough as an optimization tool. Better quality compounds progress more quickly through development because their human pharmacokinetics are more predictable;299 examples of the opposite case appear above, such as telaprevir and the extended timelines to secure venetoclax. The role of quantitative and translational pharmacology is to better understand the quantitative relationship between drug exposure, target engagement, efficacy, and safety across species in order to better predict human outcomes. In a recent review,300 it was interesting to note key variables that can be modulated by medicinal chemists to establish favorable pharmacokinetic− pharmacodynamic (PKPD) outcomes. All of the key PK drivers are controlled by physical properties, while PD is most 6455



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ASSOCIATED CONTENT

technology; FBDD, fragment-based drug design; hERG, human ether-a-go-go-related gene potassium channel; HAC, heavy atom count; HCV, hepatitis C virus; HIV, human immunodeficiency virus; HTS, high throughput screening; LE, ligand efficiency; LLE, ligand lipophilic efficiency; LLEAT, Astex LE corrected for lipophilicity; NMR, nuclear magnetic resonance; PD, pharmacodynamics; PFI, property forecast index; PK, pharmacokinetics; PKPD, pharmacokinetics−pharmacodynamics; Ro5, rule of five; SAR, structure−activity relationship; WT, wild type

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jmedchem.8b00180. Correlation of selectivity parameters, data of Figure 10 vs MW, and correlation of MW with refraction (PDF)

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AUTHOR INFORMATION

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Corresponding Authors

*R.J.Y.: e-mail, [email protected]. *P.D.L.: e-mail, [email protected].

REFERENCES

(1) Macarron, R.; Banks, M. N.; Bojanic, D.; Burns, D. J.; Cirovic, D. A.; Garyantes, T.; Green, D. V.; Hertzberg, R. P.; Janzen, W. P.; Paslay, J. W.; Schopfer, U.; Sittampalam, G. S. Impact of high-throughput screening in biomedical research. Nat. Rev. Drug Discovery 2011, 10 (3), 188−195. (2) Bleicher, K. H.; Bohm, H. J.; Muller, K.; Alanine, A. I. Hit and lead generation: beyond high-throughput screening. Nat. Rev. Drug Discovery 2003, 2 (5), 369−378. (3) Davis, A.; Keeling, D.; Steele, J.; Tomkinson, N.; Tinker, A. Components of successful lead generation. Curr. Top. Med. Chem. 2005, 5 (4), 421−439. (4) Waring, M. J.; Arrowsmith, J.; Leach, A. R.; Leeson, P. D.; Mandrell, S.; Owen, R. M.; Pairaudeau, G.; Pennie, W. D.; Pickett, S. D.; Wang, J.; Wallace, O.; Weir, A. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discovery 2015, 14 (7), 475−486. (5) Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 1997, 23, 3−25. (6) Lipinski, C. A. Druglike properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 2000, 44 (1), 235−249. (7) Abad-Zapatero, C. A sorcerer’s apprentice and the rule of five: from rule-of-thumb to commandment and beyond. Drug Discovery Today 2007, 12 (23−24), 995−997. (8) Morgan, P.; Van Der Graaf, P. H.; Arrowsmith, J.; Feltner, D. E.; Drummond, K. S.; Wegner, C. D.; Street, S. D. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discovery Today 2012, 17 (9−10), 419−424. (9) Cook, D.; Brown, D.; Alexander, R.; March, R.; Morgan, P.; Satterthwaite, G.; Pangalos, M. N. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat. Rev. Drug Discovery 2014, 13 (6), 419−431. (10) Bayliss, M. K.; Butler, J.; Feldman, P. L.; Green, D. V.; Leeson, P. D.; Palovich, M. R.; Taylor, A. J. Quality guidelines for oral drug candidates: dose, solubility and lipophilicity. Drug Discovery Today 2016, 21 (10), 1719−1727. (11) Morgan, P.; Brown, D. G.; Lennard, S.; Anderton, M. J.; Barrett, J. C.; Eriksson, U.; Fidock, M.; Hamren, B.; Johnson, A.; March, R. E.; Matcham, J.; Mettetal, J.; Nicholls, D. J.; Platz, S.; Rees, S.; Snowden, M. A.; Pangalos, M. N. Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat. Rev. Drug Discovery 2018, 17 (3), 167−181. (12) Brown, A. C.; Fraser, T. R. On the connection between chemical constitution and physiological action; with special reference to the physiological action of the salts of the ammonium bases derived from Strychnia, Brucia, Thebaia, Codeia, Morphia, and Nicotia. J. Anat. Physiol. 1868, 2 (2), 224−242. (13) Meanwell, N. A. Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety. Chem. Res. Toxicol. 2011, 24 (9), 1420−1456. (14) Young, R. J. Physical properties in drug design. In Tactics in Contemporary Drug Design; Meanwell, N. A., Ed.; Springer Berlin Heidelberg: Berlin, 2014; pp 1−68.

ORCID

Robert J. Young: 0000-0002-7763-0575 Paul D. Leeson: 0000-0003-0212-3437 Notes

The authors declare no competing financial interest. Biographies Robert J. Young joined Wellcome in 1990 after completing undergraduate and D.Phil. studies at Oxford University and a post doc at The Ben May Institute, University of Chicago. His subsequent medicinal chemistry career has seen many changes, mergers, and acquisitions at GlaxoWellcome/GSK, encompassing significant roles toward six development candidates before a move to early stage discovery (2006), with contributions across numerous H2L programs optimizing hits from HTS, fragments, and ELT methodologies. A long-term interest in physicochemical properties and partnership with Alan Hill produced impactful findings and well-cited publications. He is an author/inventor on >80 publications/patents, an elected member of the GSK Fellowship, Fellow of The Royal Society of Chemistry, and an Honorary Visiting Professor at the University of St. Andrews. Paul D. Leeson is a medicinal chemistry consultant with >35 years’ experience in major pharmaceutical companies: Smith, Kline and French; Merck Sharp and Dohme; Wyeth (USA); AstraZeneca; and GlaxoSmithKline. Since 2014 he has advised pharmaceutical companies, start-ups, and academia. At AstraZeneca (1997−2011) Paul was head of medicinal chemistry at the Charnwood site and he led AstraZeneca’s Global Chemistry Forum. Paul’s drug discovery contributions have been in the cardiovascular, neuroscience, respiratory, and inflammation therapy areas. He has a special interest in compound quality, and in 2014 he received the Nauta Award from the European Federation of Medicinal Chemistry. Paul has a Ph.D. from the University of Cambridge and holds an honorary professorship at the University of Nottingham.

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ACKNOWLEDGMENTS We thank so many colleagues and friends for their help and insights over the many years of gestation for this review, many of whom are in the cited references, although Natalie Theodoulou, Phil Harris, Brian Johns, Vipul Patel, Mike Hann, Hongfeng Deng, Ryan Bingham, Jennifer Borthwick, Sophie Bertrand, Andy Bell, Tony Wood, Darren Green, Chiara Zecchin, and Shenaz Bunally have made contributions to help us achieve our aims within.

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ABBREVIATIONS USED AD, Alzheimer’s disease; ADME, absorption, distribution, metabolism, and excretion; ADMET, absorption, distribution, metabolism, excretion, and toxicity; ATP, adenosine triphosphate; bRo5, beyond the rule of five; ELT, encoded library 6456



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