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Mapping the Efficiency and Physicochemical Trajectories of Successful Optimizations Robert J Young, and Paul D Leeson J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.8b00180 • Publication Date (Web): 05 Apr 2018 Downloaded from http://pubs.acs.org on April 6, 2018

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Journal of Medicinal Chemistry

Mapping the efficiency and physicochemical trajectories of successful optimizations

Robert J. Young* and Paul D. Leeson*

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

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 are evident in contemporary practice and the impact on quality demonstrable. What is clear is that while targets are different, successful molecules are almost invariably amongst 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, whilst 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 two decades ago led practitioners to define useful chemical quality guidelines and specific criteria at each step, from the identification of active compounds, through to confirmed hits, then optimization to leads.2-3 Quality leads were sought, with the potential to deliver candidates in shortened timeframes, 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 attrition4 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 2000-10, 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 risk9 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. agonist or antagonist) for the desired target, whilst ensuring this can be achieved with acceptable pharmacokinetics, non-target 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


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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 behaviour.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 properties20-21 and complexity;22-23 the rule of 3 for fragments;24 measures of ligand efficiency;25-26 drug efficiency; 27 3-dimensionality as measured by fraction of sp3 carbon atoms28-29 and aromatic ring count.30-32 Taken together, a combination of dose, lipophilicity and solubility criteria has been established as an 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 properties,16 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 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 target-specific optimization, based on physicochemical properties and efficiency metrics,26 which we propose is more useful 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. 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 (LogP) 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 > 100nM) 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 datasets of both lead-drug and ‘start-finish’ optimization pairs have confirmed the underlying leadlike hypothesis that increases in calculated LogP (cLogP) and MW in optimization are dominant (Figure 1b) and established the concept that increased complexity reduces the likelihood of finding hits. Amongst the cited lead-to-drug optimization sets 22, 42 43 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 (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 (E,45 F46 and G,47 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, leads1 and patents48 of four pharmaceutical companies (Figure


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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 the largest median change (Figure 1c). Fragment based optimization40, 49-50 has come of age in the last 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 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 summarised41 clearly show increased MW relative to other starting points (Figure 1c). Optimization strategy and awareness of physical properties influences outcomes. For example, using lipophilic efficiency26 or simply lipophilicity itself26, 47 in optimization (entries I and H respectively, 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 2000-10, show increased physical properties versus older oral drugs (Figure 1a).47-48 We have hypothesised59 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 cut-off 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 2011-13 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 off-target labelled safety warnings.10 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 Amongst a number of measures proposed, ligand efficiency (LE, binding energy in kcal/mol per heavy atom) = pXC50 x 1.37 /heavy atom count (HAC)65 and lipophilic ligand efficiency (LLE or LipE, unitless) = pX50 – LogP (or LogD7.4)38 have attracted the most attention,


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as well as various criticisms,66-68 some of which provoked a response.25 LE, despite limitations, which include a non-linear 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 Amongst 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. Lipophilicity measures. Lipophilicity36 is the vital element in this review and the influence of both the partition coefficient, expressed as LogP, a constant, and the distribution coefficient at pH 7.4, Log D7.4, a pH-dependent value for ionizable compounds (LogP is the asymptote of the neutral form) are 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 LogD ~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


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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 a compound, the higher 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 LogP and LogD (at pH 7.4 and other pH values) are important in drug discovery.14 In general, LogD7.4 is important for ADME parameters while LogP is often important for target binding and selectivity.13 For oral drug candidates, values of LogD7.4 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 cLog D7.4 data are presented, although most LogD7.4 data were calculated using the most recent update of the GSK chromatographic LogD7.4 predictor. It should be noted that Chrom LogD7.4 values are typically 1.5 to 2 log units higher than the OW scale based on the deliberate calibration, chosen to avoid confusion between the methods.82 Examples are included which illustrate shortcomings with some calculations, but despite these issues, patterns are obvious; 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 LogD7.4 + aromatic ring count, ideal value 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 LogP in OW for poorly soluble compounds. Across all CHEMBL data there is an upwards 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 LogD7.4, and measured LogD7.4. 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.



Figure 1a-d. Optimization trends and physical properties. a.

Median cLogP and molecular weight of oral drugs and patented compounds. In black: oral 60

drugs by literature publication 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 48

compounds patented per target in 2000-10.


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b.

Median cLogP and molecular weight of ‘start to finish’ optimization pairs. In blue, leads to 42

22

43

st

drugs: A and B, historical drugs; C, drugs launched post-1990; D, 1 in class to follow-on 44

45

drug. In red, literature optimizations: E, early 2000’s literature; F, HTS hit-to-lead, 2000’s 46

47

literature; G, 2014 literature not using lipophilicity.

In green, property-aware optimizations:

47

H, 2014 literature using lipophilicity; I, late 2000’s literature using lipophilic ligand efficiency 26

(LLE). c.

Median cLogP and molecular weight of screening sets and derived compounds. In green: 54

fragment hits and their optimizations and fragment-derived candidate drugs (calculated from 40

the list in ref ). 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 1

48

GlaxoSmithKline, Novartis and Wyeth (leads) and corresponding median patent data for GlaxoSmithKline, Novartis and Wyeth, 2000-10]. In black: leads from DNA-encoded libraries 41

(calculated from the list in ref ). d.

Median LE and LLE (based on cLogP) values of 480 target-assay pairs having ≥ 100 published 26

compounds with potency values. Targets highlighted are discussed in this paper. Figure adapted from ref.

26



LLE 5

4

3

cLogP 8 8

p(Potency)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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3

cLogP

Figure 2. Generic potency versus lipophilicity plot, with with parallel lines showing constant LLE values and thresholds for an illustrative desirable potency of 10 nM or pIC50 > 8 and cLog P 11 hr), found post-discovery, may be a class effect.103 The structure of 1 bound to CCR5,104 demonstrates highly efficient interactions, reflecting the quality of the design process. Polar interactions occur with five of the seven available heteroatoms and non-polar interactions with each of the phenyl, tropane, isopropyl and cyclohexyl moieties. The optimization towards 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 sidechain to 11,106 then the diphenyl methane 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 250) was considered risky, and further optimization, achieved by balancing hERG inhibition, absorption and lipophilicity, provided the sulphonyl piperidyl candidate AZD 5672 5 (hERG 24 μM; hERG/free Cmax free 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 pursued; in vivo clearance in the rat was central to the endgame,92,93 where small molecular changes to identify 3 had significant impact.


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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 rat,110 led to the candidate sulfonamide GSK163929 4,94 albeit with a high predicted dose in man (900mg uid).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 guidance,72 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. Based on 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.


Journal of Medicinal Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Page 18 of 144

Patented Compounds with Common Pharmacophorea Candidate Drug Propertiesb

Company (Disease)

# Patents Median

Median

(compound n

cLogP

Mol Wt

No.

cLogP

LogDc

Mol Wt

2.0, 1.9

514, 510

% of all CCR5)

Pfizer (HIV)

3.0

495

309

7 (78%)

1, 2

3.3, 1.0

Merck & Co (HIV)

5.2

569

2467

17 (82%)

3

4.4

575

GlaxoSmithKline (HIV)

5.3

586

690

7 (46%)

4

5.8

696

AstraZeneca (RA)

3.0

586

1069

20 (87%)

5

2.3

1.5

640

Table 1. Physical properties of CCR5 antagonists and candidate drugs containing a common N-phenylpropylpiperidine pharmacophore, from 4 companies. a

From ref,38 b See Scheme 1. c Experimental values, refs.91, 95


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PK Parameters: Rat, Dog No.

Ref. Cl mL/min/kg

Vdss L/kg

t½ h

F%

1

74, 21

-, 4.3

2.3, 2.3

6, 42

91

2

201, 36

-, 10.5

1.4, 3.4

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, then does not increase further.


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Rat PK

cLogP ALogP Drug cLogD7.4

% Better LE/LLE of Earliest priority n=1880 pCHEMBL 1st Patent values

F% Date to market

Human Dose

Cl mL/min/kg Vd L/Kg

cChromLogD t1/2 hr

Rucaparib 63

Olaparib 62

Veliparib 64

Niraparib 66

Talazoparib 67

3.0

7.4%

3.0

7.0%

0.6

2.6%

2.1

5.2%

1.2

8.9%

2.1

17%

11-Jan-99 US 199960115431

19-Dec-16

11%, 100% 12-Mar-03 19-Dec-14

2.0

30%

2.7

28%

0.9

1.0%

1.0

600 mg bid

GB 2003-5681

400 mg bid

34.3, 49 0.83, 1.18 0.53, 0.88

0.9%

61% 11-Apr-05 US 200560670204

Phase III

400 mg bid

33

-1.2

0.5%

0.5

1.1%

1.2

2.7

17%

65%

2.6

16%

10-Jan-07 27-Mar-17

-0.1

5.3%

1.7

11%

0.4

0.21%

2.5

11%

3.4

14%

28 6.9 3.4

3.7%

2.1

GB 2007-432

300 mg uid

3.6

56% 06-Aug-08 US 200861086687

Phase III

2 1 mg uid 0.3 2.25

Table 8. Lipophilicities, ‘% better’ LE and LLE values, discovery and marketing dates, and human doses of Poly(ADP-ribose) polymerase-1 (PARP-1) and PARP-2 inhibitors. ‘% 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.



vii). Human immunodeficiency virus (HIV) integrase inhibitors. A major challenge with this target was the identification of selective starting points.148 HIV integrase and hepatitis C virus (HCV) NS5b polymerase both use two magnesium ions in their catalytic active sites, and the breakthrough came when 4-aryl-2,4-diketobutanoic acids were initially discovered, from HTS, to inhibit both enzymes.149 Converting these structures to heterocycles possessing the dual metal chelating capability eventually provided the first integrase selective lead, 73, (Scheme 8)150 which was originally made in Merck’s HCV NS5b polymerase program,151 and optimization provided the first marketed HIV integrase inhibitor raltegravir 74.152 The design of the dolutegravir (75)153 154 lead 76155 was influenced by the earlier discovery of 73. In contrast, for elvitegravir (77), the designed quinolone lead 78156 was less potent than 73 and 76, possibly because it lacks an optimal second metal chelating site.156 The HA counts of all three leads are comparable and leadlike, and in each case, were increased in optimization, though with loss of LE for 74 and 75. The lipophilicities of 74 and 75 were reduced in optimization, increasing LLE by >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, 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 1.2 mL/min/kg, half-life 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 (400mg twice daily), it requires co-administration of a pharmacokinetic booster, ritonavir, to inhibit CYP3A4-induced oxidation, the major pathway of metabolism. Dolutegravir 75 ACS Paragon Plus Environment

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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: 75, 0.04%; 74, 3.5%; and 77, 22%. Dolutegravir 75 has the highest LLE value of the drugs, whether based on cLogP or experimental values. cLogP and measured chromatographic LogD7.482 values are respectively: dolutegravir -0.4 and 3.7; raltegravir 1.2 and 3.3; and elvitegravir 4.6 and 6.4. Follow-on drugs invariably benefit from preceding knowledge and results, and in this case, an advanced lead 76 with superior ligand efficiencies ultimately provided the better drug.

Scheme 8. HIV integrase drug discovery, showing key lead compounds and their properties.



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 shown in Scheme 8.


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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 Figures S.1 and S.2). The spread of the ETL-derived 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 1.c). 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/beta 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 advisable to aim to truncate such structures during optimization. Table 9 also illustrates how much more lipophilic and aromatic these ELT hits are in comparison to the oral drug set and emphasizes their clear distinction from leadlike molecules. Attention to library design will be a key objective in future success of the technique; the careful design of the Vipergen Library that led to the P38 inhibitor (81) showed what can be achieved.158 As average molecular size increases (necessarily, in order to generate large numbers in the libraries), it should be noted that complexity increases and the chance of finding a hit reduces. At the levels ACS Paragon Plus Environment


typically screened, nM to pM concentrations, even if all reactions had worked perfectly, the number of instances of each structure are diminishing, and these represent a fraction of Avogadro’s number (1023), which remains a small sampling of the ~1060 apparently feasible druglike molecules.159

Figure 10. Plot of Calculated Chrom logD7.4 versus calculated molar refraction (cmr, a good surrogate for size, see supporting figures S.1 and S.2) for exemplars in the ELT review41 (X), overlaid on a GSK training set of marketed oral dug molecules with % bioavailability >30% (O) and 4-fold improvement in selectivity in the N-isopropyl purine 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, using 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 non-covalent187 dual mutant inhibitors will be revealing.


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Scheme 9. Issues overcome in the discoveries of EGFR double mutant candidates. Hinge binding sites shown by

.



AstraZeneca

Pfizer1

Pfizer2 LLED = 6

10.00

9.00

DM Cell pIC50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

8.00

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PF06459988 PF99 06747775 100

LLED = 5 LLED = 4 LLED = 3

7.00

6.00

Osimertinib 98 5.00

4.00 1.2

1.6

2

2.4

2.8

3.2

3.6

4

Measured LogD

Figure 19. Cellular EGFR double mutant potencies versus measured LogD for compounds cited in the discoveries of 98, 99 and 100. LLED = cell pIC50 – LogD7.4. The ‘% better’ LE and LLE values (based on cLogP), versus background data from CHEMBL (n=544, not shown) are: Osimertinib 98, 39%; PF06459988 99, 4.6%; PF-06747775 100, 0.5%.


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xii). 5’-Adenosine monophosphate-activated protein kinase (AMPK) activators. AMPK is a serine/threonine 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 Modelling 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 α1ß1γ1 isoform, located between the α- and βsubunits. There was variation in the location of the acidic groups of the three molecules, due to flexibility of interactions with three neighboring lysine residues located near the solvent front; 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 backwards 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, 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, yet ultimately provided compounds 106 and 110 with comparable LLE values. Merck did acknowledge the lack of LLE improvement in reaching 105189 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 optimization193 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).

Scheme 10. Discovery of AMPK kinase activators in Merck and Pfizer.


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Merck

Pfizer

0.5

PF06409577 110

0.45

0.4

MK-8277 106

104 MK-3903 105

LE

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


109

0.35

0.3

0.25

107

0.2 0

0.5

1

1.5

2

2.5

3

3.5

4

LLE

Figure 20. Trajectories in LE vs LLE space of the Merck and Pfizer AMPK kinase projects. Structures are shown in Scheme 10. LLE = α2ß1γ1 EC50 – cLogP. EC50 of the HTS hit 106 is approximated to 40 μM.



xiii). Observations on Kinase Inhibitors. To date, thirty-seven 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/back pocket and displacing so-called gatekeeper residues to achieve 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 ref78 (see supporting Table S.1 ) indicating little change of kinome selectivity with respect 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-3 beta (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.


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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 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 2nd generation of NS3/4A inhibitors are non-covalent macrocyclic structures with reduced human doses versus the 1st 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 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 properties,210 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 (c.f. Figure 10).33, 214


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Table 13. 1st Generation covalent HCV NS3/4A protease inhibitors. Telaprevir has aqueous solubility 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



Scheme 11. 2nd Generation macrocyclic HCV NS3/4A protease inhibitors and human doses. 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.


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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. % 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% and grazoprevir 116, 17%.



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 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 C2symmetry and is an effective pan-genotype HCV inhibitor.217 Despite being a bRo5 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 non-stoichiometric relative to NS5A, since the ratio of NS5A molecules to 125 at its EC50 in a replicon is estimated to be ~102103.216 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 sidechains of 125 were feasible, but the major


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differentiators are poly-aryl variants of the biphenyl spacer (Scheme 12) including an exotic high MW solution in pibrentasvir (130).226 Long half-lives in humans,218 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 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 mode of action suggest they are true outliers?227 The near-unchanged 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 N-methylated cyclic peptides showed a sharp fall in passive permeability at MW >1000, but also that MW 500-1000 could provide acceptable passive permeability.89 Amongst 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 LogD, aromatic ring count and number of rotatable bonds. While more data is needed to understand successful features of bRo5 space,212 we note that amongst 125 dipeptide sub-structures 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).



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% Drug

cLogP

Mol Wt

pEC50

LE

LLEa

GT-1a

better

Dose mg

LE &

uid

1st patent

b

LLE

Daclatasvir

Feb 21, 2008 4.7

739

10.3

0.26

5.6

1.2%

60

125

WO 2008021927 A2

Ledipasvir

Nov 18, 2010 6.7

889

10.5

0.22

3.8

12.7%

90

127

WO 2010132601 A1

Ombitasvir

Dec 15, 2011 8.0

894

10.9

0.23

2.9

14.7%

12.5

129

WO 2011156578 A1

Pibrentasvir

Jan 5, 2012 7.2

1113

11.7

0.20

4.5

12.7%

120

130

US 20120004196 A1

Elbasvir 126

Apr 5, 2012 6.7

882

11.4

0.24

4.7

4.9%

50 WO 2012041014 A1

Velpatasvir

May 24, 2012 5.7

883

10.9

0.23

5.2

4.9%

128

100 WO 2012068234 A2

Table 14. Physical properties and discovery timelines of HCV NS5A inhibitors. a Based on cLogP. b % compounds with better LE and LLE values amongst 356 daclatasvir (125) sub-structures from CHEMBL (the assay is phenotypic and CHEMBL searches include non NS5A antivirals).


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Scheme 12. Discovery of Daclatasvir 125 from screening hit 121.



Table 15. Structures of marketed HCV NS5A inhibitors. 126-130 followed after the first approved drug, Daclatasvir 125, and all drugs contain a common dipeptide carbamate motif, shown in red.


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0.3

124 0.28

Daclatasvir 125

0.26

Elbasvir 126 0.24

Ombitasvir 129

Velpatasvir Ledipasvir 128 127 Pibrentasvir 130

0.22

LE

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


121

0.2 0.18 0.16 0.14 0.12 0.1 -4

-3

-2

-1

0

1

2

3

4

5

6

7

8

LLE

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 shows 125 sub-structures from CHEMBL (n=356; the assay is phenotypic and full CHEMBL searches retrieve non NS5A antivirals).



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 two 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 136,232 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 to 6 years. Venetoclax 131 is highly potent with Ki ≤ 0.01nM for BCL-2.233 With experimental LogD7.4 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, 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= 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 dependency on potency with increasing physicochemical properties, which is rare amongst drug discovery targets.26 In this case, sub-nM potency required cLogP >8 and HA count >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 ‘%


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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 efficiency-optimization in the discovery of 131.



Scheme 13. Discovery, timelines (from patents) and optimization strategy of BCL-2 inhibitors.


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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



#

1

Target & activity; starting point source IRAK4 kinase inhibitor; fragment hit

Start point

Start properties pX50 cLogP, Mol Wt (HA) LLE, LE

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Optimized properties pX50 cLogP, Mol Wt (HA) LLE, LE

Δ LLE Δ LE

Comment

4.26 1.6, 179 (13) 2.7, 0.45 LLEAT 0.40

9.7 2.2, 361 (26) 7.7, 0.51 LLEAT 0.52

5.0 0.06

SBDD used; Candidate (PF06650833)

8.19 1.1, 488 (36) 7.1, 0.31 LLEAT 0.38

6.1 0.07

SBDD used; in vitro proof of concept: weakly antiproliferative

Optimized compound

2

BCL-6 Inhibitor; fragment hit

3.16 2.2, 239 (18) 1.0, 0.24 LLEAT 0.19

3

BRD4 BD1 bromodomain inhibitor; published fragment

44% inh, 50μM 4.3, 186 (14) 2.0, 0.42 LLEAT 0.31

7.0 2.0, 468 (33) 5.0, 0.29 LLEAT 0.32

3.0 -0.13

4

BTK kinase inhibitor; fragment hit

5.46 0.9, 188 (14) 4.6, 0.53 LLEAT 0.56

7.55 2.4, 318 (24) 6.2, 0.43 LLEAT 0.46

1.6 -0.10


Ref

251

252

in vivo tool; SBDD used

253

in vivo tool; SBDD used

254

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5

BRD4 BET bromodomain; fragment hit optimization

6.05 3.3, 277 (21) 4.8, 0.39 LLEAT 0.42

8.82 3.1, 459 (32) 5.7, 0.38 LLEAT 0.36

0.9 -0.1

Candidate Phase I (ABBV-075, Mivebresib)

6

Renin inhibitor; earlier lead from FBDD & SBDD

8.34 4.0, 414 (30) 4.3, 0.38

8.68 3.2, 500 (36) 5.7, 0.33

1.4 -0.05

SBDD used; Candidate (TAK-272)

7

Factor D inhibitor; SBDD & fragment hits merged

4.7 4.5, 446 (32) 0.2, 0.20

8.22 2.2, 483 (31) 6.0, 0.36

5.8 0.16

SBDD used; in vivo tool: efficacy demonstrated

8

EZH2 Inhibitor; designed library (n=181)

4.24 1.4, 300 (21) 2.8, 0.28

9.15 2.4, 446 (30) 6.8, 0.42

4.0 0.14

In vivo tool

9

AR modulator nuclear receptor; designed library (n=51)

8.40 3.0, 297 (20) 5.4, 0.58

8.90 1.7, 350 (24) 7.2, 0.51

1.8 -0.07

Improved solubility & RAT PK

10

DGAT2 transferase inhibitor; partial screen

5.8 6.7, 444 (30) 2.0, 0.31

7.85 2.7, 440 (31) 5.2, 0.35

4.2 0.10

Preclinical candidate (PF06424439)


255

256

257

258

259

260


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11

ERK2 kinase inhibitor; kinase counter screen

6.01 0.8, 259 (19) 5.2, 0.43

8.3 -0.1, 293 (22) 8.4, 0.52

3.2 0.08

SBDD used; low permeability; no cellular potency

12

TarO inhibitor; screening hit

6.90 7.1, 481 (34) -0.2, 0.28

7.10 1.7, 476 (34) 4.4, 0.29

4.6 0.01

In vivo tool

13

mGluR5 GPCR NAM; HTS hit

6.71 1.6, 269 (19) 5.1, 0.48

8.21 1.5, 329 (24) 6.7, 0.47

1.6 -0.01

Candidate (PF06462894); mechanismbased toxicity

14

pan TRK kinase inhibitor (TRK A data); HTS hit

8.70 3.0, 451 (33) 5.7, 0.36

8.22 1.0, 480 (34) 7.2, 0.33

1.5 -0.03

Candidate (PF06273340) SBDD used DGF out; non-CNS

15

CYP11B2 inhibitor; HTS hit

7.92 2.4, 227 (16) 5.5, 0.68

8.64 2.7, 311 (23) 5.9, 0.51

0.4 -0.17

In vivo tool; fragment-like hit

16

PRC2-EED PPI; HTS hit

6.22 4.2, 364 (27) 2.0, 0.32

9.52 3.8, 487 (34) 5.7, 0.38

3.7 0.06

Tool compound; SBDD used


261

262

263

264

265

266

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p97 AAA ATPase inhibitor; HTS & Hit to lead

6.09 5.1, 396 (30) 1.0, 0.28

18

NIK kinase inhibitor; HTS plus literature insight

O H 2N

7.96 3.3, 413 (31) 4.7, 0.35

3.7 0.07

Candidate (CB-5083)

5.89 2.7, 246 (17) 2.2, 0.47 (LogD 2.5)

9.33 -1.0, 435 (32) 10.3, 0.40 (LogD 3.0)

8.1 -0.07

In vivo tool; SBDD used; Hit & optimized

19

Ghrelin GPCR inverse agonist; earlier lead

9.36 4.3, 549 (40) 5.1, 0.32

9.42 1.9, 573 (40) 7.5, 0.32

2.4 0.00

Overcomes mechanismbased CYP3A4 inh.

20

mGluR2 GPCR PAM; earlier lead

7.2 4.2, 367 (27) 3.0, 0.37

7.8 2.0, 422 (31) 5.8, 0.34

2.8 -0.02

Improved CYP inh. & PK properties

4.1 0.06

2nd Candidate (AZD7986); no cardiac toxicity from aortic aldehyde binding

17

21

DPP1 covalent reversible inhibitor; earlier lead

N

N N

O

267

NH

6.0 2.8, 372 (28) 3.2, 0.29


8.40 1.1, 419 (33) 7.3, 0.35

268

269

270

271


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22

DLK, MAP3K12 kinase inhibitor; earlier lead

7.38 2.2, 393 (29) 5.2, 0.35

8.52 2.3, 423 (30) 6.2, 0.39

1.0 0.04

CNS-penetrant in vivo tool

23

EP1 GPCR receptor antagonist; earlier lead

8.03 4.7, 367 (26) 3.3, 0.42

8.96 3.4, 372 (26) 5.6, 0.47

2.3 0.06

In vivo tool; intraduodenal administration

24

H+, K+-ATPase blocker; earlier lead

8.03 3.9, 356 (25) 4.1, 0.44

7.31 1.4, 364 (25) 5.9, 0.40

1.8 -0.04

Long-acting in vivo tool

25

Neurokinin-3 GPCR antagonist; earlier oral lead

7.9 3.2, 422 (29) 4.7, 0.37

7.6 0.9, 358 (25) 6.7, 0.42

2.0 0.04

Candidate (ESN364) phase 2

26

Sigma 1 receptor; earlier lead

7.29 2.2, 330 (24) 5.1, 0.42

7.39 1.3, 311 (23) 6.1, 0.44

1.0 0.02

In vivo antinociceptive tool

27

mTORC1 & 2 kinase inhibitor; earlier lead

7.06 1.5, 485 (34) 5.6, 0.28

8.24 0.0, 518 (35) 8.2, 0.32


2.6, 0.04

Candidate (AZD3147); uses [pIC50pSolubility]

272

273

274

275

276

277

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WNK1 kinase; earlier lead

6.21 5.6, 438 (33) 0.6, 0.26

8.22 5.3, 481 (33) 2.9, 0.34

29

FFA1 GPCR agonist; earlier lead

6.70 4.5, 250 (19) 2.2, 0.47

7.69 3.8, 246 (18)

30

GSK-3β kinase inhibitor; earlier lead

7.92 0.7, 367 (27) 7.2, 0.40

8.15 0.5, 304 (22) 7.7, 0.51

28

2.3 0.08

Allosteric; in vivo tool; SBDD used

1.7 0.10

In vivo tool; cut-down & more potent agonist vs αlinolenic acid

0.5 0.11

In vivo tool; cut down & no CYP issues; CNS activity

8.0 3.6, 477 (34) 4.4, 0.32

7.85 2.2, 495 (35) 5.7, 0.31

1.3 -0.01

Candidate (AZD9362); improved CYP3A4 & solubility

32

JAK1 kinase inhibitor; earlier lead

8.82 2.2, 387 (27) 6.6, 0.45

10.0 0.7, 376 (26) 9.3, 0.53

2.7 0.08

JAK1 selective tool; improved hERG, PK, solubility

33

RORc nuclear receptor inverse agonists; earlier lead

7.24 3.3, 444 (31) 3.9, 0.32

8.52 2.8, 476 (33) 5.7, 0.35

1.8 0.03

in vitro tool; SBDD used

31

IGF-1R kinase inhibitor; earlier lead


278

279

280

281

282

283


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No reactive metabolites; improved microsomal stability Improved PK & CNS pen.; GI & CV side effects linked to M1

34

EP3 receptor GPCR antagonist; literature lead

8.36 5.1, 435 (28) 3.3, 0.41

7.93 1.2, 431 (30) 6.8, 0.36

3.5 -0.05

35

M1 PAMagonist; lead designed from competitor

7.54 2.5, 421 (31) 5.0, 0.33

7.22 3.0, 409 (29) 4.2, 0.34

-0.8 0.01

36

5-LO activating protein (FLAP) inhibitor; competitor patent

8.70 6.3, 466 (35) 2.4, 0.34

7.20 1.7, 434 (32) 5.5, 0.31

3.1 -0.03

Candidate (AZD6642)

37

ALK/ROS1 kinase inhibitor; drug (Crizotinib)

7.1 4.3, 450 (30) 2.8, 0.32

8.9 1.9, 406 (30) 7.0, 0.41

4.2 0.09

Candidate; (lorlatinib; PF06463922; Phase II) SBDD used

38

CETP inhibitor; Phase III compound (desPh torcetrapib)

4.3 (est.; 70% inh@ 100μM) 6.6, 512 (35) -2.3, 0.17

7.21 7.1, 711 (50) 0.1, 0.19

2.4 0.02

Candidate (TAP311) LogD7.4 = 5.2 LLED = 2.0

Table 16. Recent optimizations that utilised ligand lipophilic efficiency (LLE). Of the 38 examples, 14 provided candidate drugs and 16 in vivo tool compounds. Entries are categorized by hit or lead origin as follows: 1-7 fragment hits or fragment derived leads; 8-9 leads from designed libraries; 9-10 targeted screening hits; 10-18 HTS/screening hits; 19-32 earlier identified leads; 34-36 literature or competitor-derived; 37-38 drug or clinical candidate


284

285

286

287

288

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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 x LLE/HA), is included for the fragment hit optimizations69 in entries 1-5.



Conclusions. The examples we have shown illustrate differences in practices in optimization programs, set against a back-drop of attention to physicochemical quality and tracking of ligand efficiency measures. We note that successful optimizations tend to converge towards optimal ligand efficiency values for the target, and this occurs irrespectively 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. 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 efficiency goals are not the way to proceed. In this respect our thinking has been influenced by the observation that whilst 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 towards these latter goals. Good practice entails using combinations of the various efficiency plots to assess progress, map the journey, and to 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


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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 is approach 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. Whilst the evidence that leadlike hits accelerate candidate discovery remains anecdotal, the PARP and HIV integrase examples shown above demonstrate the value the approach. However, 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 pre-competitive 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 bring 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 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 complex,292 but generally hydrophilic,39 293 and the area is arguably underexploited.294 Nevertheless, drugs frequently rely on natural product part-structures292,295 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. Timeframes 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. 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’


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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 two decades and ligand efficiency metrics for more than one 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, whilst PD is most 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 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 non-druglike properties.302



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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 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


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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.



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.


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Abbreviations. 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 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, pharmacokineticspharmacodynamics; Ro5, rule of five; SAR, structure activity relationships; WT, wild type.

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, Andy Bell, Tony Wood, Darren Green, Chiara Zecchin, and Shenaz Bunally have made contributions to help us achieve our aims within.

Biographies. Rob 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 towards 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 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 PhD from the University of Cambridge and holds an honorary professorship at the University of Nottingham. Corresponding authors: emails [email protected] and [email protected]

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Table of contents graphic

Candidate

Potency

Optimization trajectories

Hit 3

Lead

Hit 2

Hit 1

Ligand Lipophilic Efficiency

Property


Mapping the Efficiency and Physicochemical ... - ACS Publications

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