Article pubs.acs.org/jmc
Evaluating the Differences in Cycloalkyl Ether Metabolism Using the Design Parameter “Lipophilic Metabolism Efficiency” (LipMetE) and a Matched Molecular Pairs Analysis Antonia F. Stepan,*,† Gregory W. Kauffman,† Christopher E. Keefer,‡ Patrick R. Verhoest,† and Martin Edwards§ †
Pfizer Worldwide Research & Development, 700 Main Street, Cambridge, Massachusetts 02139, United States Pfizer Worldwide Research & Development, Eastern Point Road, Groton, Connecticut 06340, United States § Pfizer Worldwide Research & Development, 10770 Science Center Drive, La Jolla, California 92121, United States ‡
S Supporting Information *
ABSTRACT: We have observed previously that modification of ring size and substitution pattern may be used as a strategy to mitigate the metabolic instability of cycloalkyl ethers. In this article, we introduce a medicinal chemistry design parameter named “lipophilic metabolism efficiency” (LipMetE) that indicates that these changes in metabolic stability can be largely ascribed to changes in lipophilicity. Our matched molecular pair analysis also indicates that this finding is a general phenomenon, widely observed across different chemotypes. It is our hope that both the LipMetE design parameter and the results from our pairwise analysis will be useful tools for medicinal chemists.
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INTRODUCTION Recently, we reported cycloalkane-containing sulfonamides such as compound 1 as a lead compounds for our γ-secretase inhibitor (GSI) program at Pfizer (Figure 1).1 While possessing
strategy did result in the anticipated reduction in clearance (without negatively impacting potency), two very clear trends emerged within this series of compounds (Figure 1). First, a reduction in unbound intrinsic clearance was observed as ring size was reduced, independent of the substitution pattern around the ring (see below for a discussion of unbound vs bound clearance). This is exemplified by comparing 2-THF 2 (CLint,u = 323 mL/min/kg) with 2-oxetane 3 (CLint,u = 83.9 mL/min/kg) and 3-THF 4 (CLint,u = 110 mL/min/kg) with 3oxetane 5 (CLint,u = 35.8 mL/min/kg), which reveals a 3−4fold reduction in CLint,u within each pair (Figure 1). We were also able to show that the oxidative metabolism of compounds 3 and 5 was predominantly due to CYP3A4 because coincubation of HLM with ketoconazole, a selective CYP3A4 inhibitor, decreased metabolite formation by greater than 80%. Second, and clearly more intriguing, is that 3-substituted and 4substituted analogues are more metabolically stable than their corresponding 2-substituted analogues. For example, it was found that the 3-substituted oxetane analogue (5) is greater than 2-fold more stable than the corresponding 2-substituted analogue (3). Preparation of gem-dimethyl oxetane 6 revealed that the metabolic stability of 3 can also be improved by blocking the metabolically labile α-position (6: CLint,u = 57.6 mL/min/kg).1b In this article, we report our findings from an in-depth investigation of these observations, focusing on two specific aspects. First, we set out to understand the balance between
Figure 1. Distinct differences in metabolism between cycloalkyl ethers of varying ring sizes and substitution patterns as observed on a γsecretase inhibitor program at Pfizer.
excellent in vitro potency for the lowering of Aβ42 in our whole cell assay [IC50 (Aβ42) = 11.7 nM], compound 1 displayed poor metabolic stability as demonstrated by high human liver microsomal unbound clearance (HLM CLint,u = 509 mL/ min/kg). In an attempt to increase metabolic stability through the reduction of lipophilicity by introduction of polarity, our lead optimization strategy aimed to replace the cyclobutyl moiety of 1 with cyclic ethers of varying ring size. While this © XXXX American Chemical Society
Received: May 28, 2013
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dx.doi.org/10.1021/jm4008642 | J. Med. Chem. XXXX, XXX, XXX−XXX
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Figure 2. Plot of unbound clearance, CLint,u, versus experimental log D, ElogD. The 45° lines represent different values of LipMetE. Compounds that parallel the same 45° line offer the same ratio of metabolic clearance to lipophilicity.
findings in preclinical toxicology studies.6 In addition, highly lipophilic compounds are often susceptible to high HLM clearance. The in vivo manifestation of this oxidative lability is suboptimal pharmacokinetics7 (e.g., high blood clearance, poor oral bioavailability8) as well as high dose requirements to achieve a targeted efficacious concentration, thus further increasing the likelihood for adverse safety events.9 As a result, many contemporary design parameters that evaluate the druglikeness of compounds factor in lipophilicity. Two examples of these parameters are the “ligand-lipophilicity efficiency” (LLE),2 also reported as “lipophilic efficiency” LipE,3 and the “ligand-efficiency-dependent lipophilicity” (LELP).10 The LLE/ LipE parameter, given by eq 1, relates the lipophilicity to the in vitro potency (defined by the dissociation constant Ki) of a compound. Higher values of LipE represent a more efficient use of compound lipophilicity as a contribution to affinity to a protein target, with values above 5.0 typical for optimized compounds.10 LELP, given by eq 2, considers this relationship in a slightly different manner by balancing lipophilicity and potency using the design parameter “ligand efficiency” (LE), expressed in eq 3. LE removes the bias of molecular size on potency by normalizing with heavy atom count, thus allowing a direct comparison of activities across different chemotypes.
lipophilicity and chemical reactivity changes as drivers for metabolic stability variation. Second, we wanted to understand whether these trends are broadly applicable across multiple chemotypes or limited to only this specific series of GSIs. To address the first point, we employed a medicinal chemistry concept we call “lipophilic metabolism efficiency” (LipMetE) which we disclose in this article. Analogous to the design parameter “ligand lipophilicity efficiency” (LLE)2 or “lipophilic efficiency” (LipE),3 which describes the efficiency of a compound’s potency relative to its lipophilicity, LipMetE is an intuitive approach for describing the efficiency of a compounds metabolic stability relative to its lipophilicity. To answer the second question, we mined Pfizer’s high-throughput HLM screening data collection for the series of cyclic ether transformations utilized on the GSI program using a matched molecular pair (MMP) analysis. This methodology builds upon a previous publication that described Pfizer’s in-house implementation of MMP capabilities and the pursuit of design tools that leverage our vast collection of high-throughput absorption, distribution, metabolism, excretion, and toxicology (ADMET) data.4
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RESULTS AND DISCUSSION The Role of Lipophilicity in Clearance Mediated by CYP450 Enzymes. Highly lipophilic compounds very often come with higher associated risks in drug discovery, thus avoiding high compound lipophilicity is a principal design criteria when designing analogues or prioritizing chemical series.5 To this point, Hughes et al. reported that highly lipophilic compounds (ClogP > 3) with low polar surface area (TPSA < 75 Å2) have a 6-fold greater risk for adverse safety
LLE = LipE = − log10(K i or IC50) − log D
(1)
LELP = log P /LE
(2)
LE = −1.4 × log10 K i /(number of heavy atoms)
(3)
Given the often observed correlation between clearance and lipophilicity within a series of related compounds, and the B
dx.doi.org/10.1021/jm4008642 | J. Med. Chem. XXXX, XXX, XXX−XXX
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Scheme 1. Transformations Used to Investigate Changes in Microsomal Stability upon (a) Varying the Ring Size of Heterocycloalkyl Ethers (Transforms 1−6) and (b) Changing the Substitution around Heterocycloalkyl Ethers (Transforms 7− 11)
approximately around the same 45° LipMetE line (same LipMetE value), one can assume that changes in lipophilicity are a major driver for differences in clearance. On the other hand, if compounds with similar log D7.4 values traverse LipMetE lines, clearance is likely modulated for another reason, such as blocking a site of metabolism, modulating the substrate’s intrinsic affinity for the cytochrome (CYP) enzyme or a difference in chemical stability. The main purpose for evaluating the LipMetE plot is thus to examine series trends for clearance relative to lipophilicity and to spot the important outliers within a series, compounds with higher LipMetE than other series compounds. To examine the role of lipophilicity on the metabolism differences of the 19 cyclic ether GSI compounds, we observed where these analogues fall along the 45° LipMetE lines in Figure 2. For this set of closely related compounds, the LipMetE values vary from 0.6 for the 2-oxetane analogue (7) to 2.0 for the cyclobutyl analogue (1). Notably, the cycloheteroalkyl GSIs 2−5 and 7−18 (black squares) are close to the LipMetE line of 1.0 (LipMetE range is 0.6−1.5), with the outliers being the gem-dimethyl oxetanes 6 and 19. This distribution of LipMetE values is consistent with lipophilicity changes, as opposed to specific differences in chemical stability, being the major driving force behind the changes in metabolic stability highlighted in Figure 1 (apart from the two outliers 6 and 19 with clearly higher LipMetE values, vide supra). For instance, the LipMetE values of the 2-substituted 6-, 5-, and 4membered ring analogues 9, 2, and 3 are 1.5, 1.0, and 0.7, respectively, and this narrow range of values indicates that the lower clearance rates in the case of the smaller rings parallels their reduced ElogD (experimental log D) values14 (9: CLint,u > 576 mL/min/kg, ElogD = 4.30; 2: CLint,u = 323 mL/min/kg, ElogD = 3.50; 3: CLint,u = 83.9 mL/min/kg, ElogD = 2.60). The second question that Figure 2 can be used to answer is what role, if any, lipophilicity plays in the observed clearance differences between cyclic ether regioisomers. As illustrated with the very similar LipMetE values of compound pairs 9 ↔ 11 (9: LipMetE = 1.5; 11: LipMetE = 1.3), 2 ↔ 4 (2: LipMetE = 1.0; 4: LipMetE = 0.7), as well as 3 ↔ 5 (3: LipMetE = 0.7; 5: LipMetE = 0.9), the altered oxidative stability following regiochemical manipulations can also be attributed to changes
critical role that clearance plays in establishing dose, we believe a parameter that captures this relationship in an analogous fashion to LipE would be a useful addition to the medicinal chemistry design toolbox. We are therefore proposing a concept named “lipophilic metabolism efficiency” (LipMetE). The relationship for LipMetE is given by eq 4, where log D7.4 is the log D value at a pH of 7.4 and CLint,u represents the unbound intrinsic clearance. CLint,u is simply the bound intrinsic clearance (CLint,app) corrected for nonspecific binding (fraction unbound) in human liver microsomes ( f u,mic). This relationship is given by eq 5. A comparison of eqs 1 and 4 reveals the similarity of LipE and LipMetE: LipE describes the relationship of lipophilicity to potency, while LipMetE describes the relationship between lipophilicity and in vitro metabolic clearance in HLMs. LipMetE = log D7.4 − log10(CL int,u)
(4)
CL int,u = CL int,app/fu,mic
(5)
When available, experimental log D7.4 measurements are preferred, however, when this data is not available a validated, in silico calculation is a suitable surrogate.11 An additional consideration is the availability of in vitro, unbound intrinsic clearance data. CLint,u has been shown to predict in vivo clearance more accurately than bound intrinsic clearance in high lipophilicity space (log D > 3).12 Unfortunately, most drug discovery programs do not routinely collect microsomal binding data and therefore the use of an in silico microsomal binding model may be a suitable alternative. In the present work, we will use CLint,app obtained from our high-throughput HLM assay and the predicted f u,mic from our in-house microsomal binding model.13 Values for CLint,app, CLint,u, and predicted f u,mic can be found in the Supporting Information. Using LipMetE to Rationalize the Unbound Clearance of Cyclic Ethers. LipMetE can be used to discern the contribution of lipophilicity to metabolic stability from other factors, such as the intrinsic chemical stability of the compound. The relationship between lipophilicity and metabolic stability can be visualized by graphing log10(CLint,u) against log D7.4. If a set of related compounds with varying lipophilicity clusters C
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in the lipophilicity of the molecules. In another aspect, Figure 2 also illustrates the value of LipMetE plots at identifying outliers in which changes in clearance are not due to concomitant changes in lipophilicity. This is exemplified by the two gemdimethyl oxetanes 6 and 19 which have LipMetE values of 2.0, more than one unit higher than the corresponding unsubstituted 2-oxetanes 3 and 7 (3: LipMetE = 0.7; 7: LipMetE = 0.6). This graphical analysis highlights the successful application of a metabolic blocking approach,15 resulting in a decrease in clearance despite an increase in lipophilicity (6: CLint,u = 57.6 mL/min/kg, ElogD = 3.80; 19: 64.4 mL/min/kg, ElogD = 3.80; 3: 83.9 mL/min/kg, ElogD = 2.60; 7: CLint,u = 63.8 mL/ min/kg, ElogD = 2.60), a finding which can be explained with the modified clearance pathway of the gem-dimethyl substituted oxetanes confirmed in our previous publication.1 Finally, it is worth noting that the lead cyclobutyl compound (1) has a favorable LipMetE value (LipMetE = 2.0). LipMetE values typically span the range of −2 to 2 for small molecules, with higher LipMetE values generally being favorable. At a given log D, compounds with a high LipMetE value are metabolically more stable than compounds with a low LipMetE value. High LipMetE compounds are therefore attractive starting points for further optimization as they provide a wider log D range to improve upon properties such as potency and permeability in parallel with low clearance. Matched Molecular Pair Analysis. Second, we sought to investigate the generality of the trends in metabolic stability observed for the cyclic ether GSIs, 2−5 and 7−18. For this purpose, we employed a matched molecular pair (MMP) analysis. An MMP is defined as a pair of compounds that differs only by a small, well-characterized structural change,16,17 and the use of MMPs has gained traction as a medicinal chemistry tool to understand the impact of structural alterations on a variety of end points, such as aqueous solubility,18 metabolic stability,19 and melting point.20 Pfizer has developed an MMP database and toolset that has been described in an earlier publication.4 For this analysis, we used SMIRKS notation (Daylight) to mine the Pfizer MMP database, returning 557 MMPs for the 11 transformations given in Scheme 1 (Table 1). SMIRKS is a text-based convention for describing reactions and transformations using SMARTS and SMILES chemical representation. For each of the MMPs extracted, HLM clearance data was mapped to the pair of compounds and summary statistics were calculated at the transformation level (shown in Table 2). Transformations 1−6 cover ring contractions of cyclic ethers, while transformations 7−11 address regiochemical differences within the same ring system. No MMPs were found for transforms that introduce a gemdimethyl group on oxetanes in addition to compounds 3 and 6/ 19 (Figure 2). One concern when performing an MMP analysis is whether there is sufficient diversity among the pairs of compounds for a given transform, especially when the number of examples is limited. Diversity can be defined in a multitude of ways, and the approach we have taken is to examine the property and pharmacology space of the starting point compounds as a measure of (dis)similarity. For each transformation, the range and average log D and molecular weight were calculated and are included in Table 1. All but two of the transforms, 5 and 11, have a range of log D greater than three, while most span greater than five units. On the other hand, even those transformations with smaller ranges of log D demonstrate diversity in their molecular size and have molecular weight
Table 1. Pair Count and Diversity Representation for the 11 Cyclic Ether Matched Molecular Pairs Examined transform no.
no. of pairs
1
61
2
15
3
108
4
20
5
8
6
10
7
45
8
90
9
113
10
100
11
7
ClogD range (average) −0.1−5.3 (2.6) 1.5−4.2 (2.8) −0.8−5.4 (2.5) 0.5−4.5 (2.7) 2.4−4.4 (3.0) 0.1−4.2 (2.8) 0.5−5.7 (2.9) −0.3−5.7 (2.8) −1.6−5.4 (2.6) −1.2−8.2 (2.4) 1.9−2.8 (2.5)
molecular weight range (average)
therapeutic area count
project count
249−511 (404)
12
38
370−490 (438)
6
8
245−525 (401)
17
55
351−539 (412)
10
15
384−495 (438)
4
5
269−447 (384)
7
8
259−525 (389)
9
20
259−613 (403)
19
50
245−580 (404)
17
57
258−665 (405)
16
59
347−470 (398)
2
3
differences greater than 100. We also examined the number of internal research projects and therapeutic areas for which these compounds were originally prepared, as it has been shown that the property profile of compounds differs between protein classes21 and therapeutic areas.22 The rich diversity in pharmacology space further supports that each of the transforms is comprised of a representative cross-section of chemistry space. It is worth noting that even less common transformations such as 5 and 11 contain compounds from multiple projects across multiple therapeutic areas, suggesting sufficient structural diversity within these two sets of compounds. Table 2 summarizes the statistics for the 11 ring transformations outlined above and includes the mean response change (μ), the standard deviation of the response change (s), and the percentage of pairs showing an increase, decrease, or minimal change (“same”,
