Ligand Desolvation steers on-rate and impacts Drug Residence Time

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Ligand Desolvation steers on-rate and impacts Drug Residence Time of Heat shock protein 90 (Hsp90) Inhibitors Doris Alexandra Schuetz, Lars Richter, marta amaral, Melanie Grandits, Ulrich Graedler, Djordje Musil, Hans-Peter Buchstaller, Hans-Michael Eggenweiler, Matthias Frech, and Gerhard F. Ecker J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.8b00080 • Publication Date (Web): 27 Apr 2018 Downloaded from http://pubs.acs.org on April 27, 2018

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Journal of Medicinal Chemistry

Ligand Desolvation steers on-rate and impacts Drug Residence Time of Heat shock protein 90 (Hsp90) Inhibitors Doris A. Schuetz1,‡, Lars Richter1,‡, Marta Amaral2,3, Melanie Grandits1, Ulrich Grädler2, Djordje Musil2, HansPeter Buchstaller4, Hans-Michael Eggenweiler4, Matthias Frech2, Gerhard F. Ecker1,*

1

University of Vienna, Department of Pharmaceutical Chemistry, UZA 2, Althanstrasse 14, 1090 Vienna, Austria 2

Discovery Technologies, Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany 3

Instituto de Biologia Experimental e Tecnológica, Oeiras, Portugal 4

Medicinal Chemistry, Merck KGaA, 64293 Darmstadt, Germany

ABSTRACT Residence time - and more recently - the association rate constant kon are increasingly acknowledged as important parameters for in vivo efficacy and safety of drugs. However, their broader consideration in drug development is limited by a lack of knowledge how to optimize these parameters. In this study on a set of 176 heat shock protein 90 inhibitors, Structure-Kinetic relationships, X-ray crystallography and Molecular Dynamics simulations were combined to retrieve a concrete scheme how to rationally slow down on-rates. We discovered that an increased ligand desolvation barrier by introducing polar substituents resulted in a significant kon decrease. The slowdown was accomplished by introducing polar moieties to those parts of the ligand, which point towards a hydrophobic ACS Paragon Plus Environment

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cavity. We validated this scheme by increasing polarity of three Hsp90 inhibitors and observed a 9-, 13- and 45fold slowdown of on-rates and a 9-fold prolongation in residence time. This prolongation was driven by transition state destabilization rather than ground state stabilization.

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INTRODUCTION Early-stage drug discovery programs are still mostly guided by binding affinity optimization. However, in recent years it has been recognized that the lifetime of the binary drug target complex - the drug residence time is a key determinant for clinical success of drug candidates1–3. Based on the relationship between kinetic parameters and affinity, the residence time can be increased either by ground state (GS) stabilization4 or transition state (TS) destabilization (Figure 1)1–85–9 . According to the transition state theory10 the TS represents the highest barrier during the binding event. The binding free energy (∆GD) relates to the energy difference between the unbound and bound state and represents the GS. Improved interactions in the drug receptor complex (∆∆GD) stabilize the GS and thus can increase the ∆G‡off and the residence time (Figure 1). Destabilization of the TS by unfavorable interactions along the binding pathway (∆∆G‡on) can likewise increase ∆Goff and the residence time (Figure 1).

Figure 1. The effect of ground state (GS) and transition state (TS) (de)stabilization on residence time shown as free energy profiles of the binding event of a drug (D) to its receptor (R) to form a drug-receptor complex (DR). ∆G‡on relates to the energy barrier the drug has to overcome during binding. The sum of ∆G‡on and |∆GD| is the


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energy barrier, ∆G‡off, the drug has to overcome during the unbinding event. The equations for KD, kon and koff show the direct relation to their respective free energies, ∆GD, ∆G‡on and ∆G‡off.

Lapatinib was the first drug approved by FDA, whose label includes long residence time as mechanism of action. Lapatinib is only 1.6-fold more affine than Erlotinib, hence showing similar GS stabilization. However, Lapatinib dissociates 70-fold slower from the epidermal growth factor receptor (EGFR). The slower dissociation derives from the considerably higher energy barrier (TS) along the binding pathway, which Lapatinib has to overcome (Figure 1). As the energy barrier, and therefore the destabilization of the TS, is directly linked to kon, Lapatinib shows a 43-fold slower on-rate11. According to the principle of microscopic reversibility, barriers along the unbinding pathway are the same as on the binding pathway. Hence a better molecular understanding of the physicochemical features determining kon will eventually contribute to a more detailed model of the dissociation process. The importance of kon has been neglected for long, as it was considered to be invariant within lead-like series. Furthermore, any limitations could in theory be overcome by dosing at higher concentrations. Within the publicprivate partnership K4DD - Kinetics for Drug Discovery12- so far 387 drug-like compounds have been tested against pharmaceutically-relevant targets resulting in 847 sets of kon, koff and KD values13. Interestingly, the variance in affinities and on-rates was nearly identical. Both parameters cover seven orders of magnitude, which is in good agreement with Schoop and Dey’s observation of an overall on-rate range of 102-108 M-1s-1 6. Apart from its contribution to residence time, kon itself is emerging as a key parameter in drug design. Several studies have indicated that kon can be equally important for target occupancy as koff14,15,16. Most recently Sykes et al. published the indisputable clinical relevance of kon. Measured on- and off-rates for antipsychotic D2 receptor antagonists showed a significant relation between kon and extrapyramidal side effects (EPS) in the clinic. Interestingly, no correlation between koff and EPS was found17. Furthermore, fast kon has been shown to be of critical importance for the efficacy of the anticoagulants Ximelagatran and Dabigatran18, which is related to their rapid onset of action. Moreover, Walkup et al. introduced a mechanistic PD model, which relies on koff as well as kon values19.


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These findings ask for a deeper understanding of Structure-Kinetic relationship (SKR) in order to rationally optimize the on-rate. One great obstacle for a detailed analysis is the nature of the elusive, not directly observable TS that determines association kinetics20. In this context, molecular dynamics (MD) simulations are used to gain information about energy barriers during the binding event. These simulations have identified energetically costly conformational changes of the protein, including loop-helix conversions, during the binding event5,21–23. MD studies have also identified desolvation effects as a critical barrier in the binding event. Dror et al.24 reported hydrophobic ligand and receptor desolvation of the extracellular vestibule of the β2-adrenergic receptor as the main rate-limiting step in the ligand-receptor association. In another comprehensive analysis, Spagnuolo et al.8 introduced steric clashes based on a TS model, to successfully slow down the on-rate. In contrast to binding affinity data, which is abundantly accessible in the public domain, only limited sets of kinetic data from lead-like series are available for comprehensive SKR analysis. To contribute to a better SKR understanding, we analyzed a large collection of 176 kinetically characterized inhibitors of the model protein Hsp90, generated within the K4DD consortium12. Those inhibitors were derived from three lead-like series, with X-ray resolved binding modes. From SKR analysis and MD simulations we inferred a polar desolvation scheme to rationally slow down on-rates. Following that scheme we chemically modified three ligands and in fact observed a 9-, 13- and 45-fold slowdown in on-rates. Most interestingly, in addition to the slow on-rate, one compound showed a 9-fold prolongation in residence time, despite loss of affinity (GS destabilization).

RESULTS AND DISCUSSION The Hsp90 inhibitor dataset. The oncology target heat shock protein 90 (Hsp90) was an early example for the importance of binding kinetics for in vivo activity25. This target, amongst others, has been chosen by the K4DD consortium to shed light on the phenomenon of binding kinetics12. The N-terminal part of Hsp90 forms a flexible nucleotide binding site, which is in the focus of our study. In the framework of the K4DD project12 we had the opportunity to work with a highly consistent dataset of 176 Hsp90 inhibitors provided by Merck KGaA. In addition to SPR data, also eight newly resolved X-ray ACS Paragon Plus Environment

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structures were available for structural insight. This comprises a unique dataset, which offers the opportunity to systematically analyze molecular features driving binding kinetics. Chemistry-wise the dataset covers 73 indazole-, 74 resorcinol-, and 29 quinazoline-inhibitors. The indazole26 and resorcinol27 scaffolds are displayed in Figure 2A, B. In general, the molecular scaffolds vary on two substitution sites, R1 and R2. Co-crystals of the various scaffolds show good overlay of the R1 and R2 substitution sites (Figure S1), with R1 pointing into the hydrophobic binding cavity and R2 pointing towards the hydrophilic pocket entrance (Figure 2C, D). Global SKR analysis on the Hsp90 inhibitor dataset. A set of four easily interpretable physicochemical descriptors was computed for the entire Hsp90 dataset (n = 176) (See Supplementary Methods). We conducted Pearson correlation analysis of the descriptor set against on-rates. The analysis exhibited a significant negative correlation for molecular weight (R=-0.64; P

Ligand Desolvation steers on-rate and impacts Drug Residence Time

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