Antibodies as Carrier Molecules: Encapsulating Anti-Inflammatory

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Antibodies as Carrier Molecules: Encapsulating Anti-Inflammatory Drugs Inside Herceptine José Pedro Cerón-Carrasco, Horacio Perez-Sanchez, Jose Zuniga, and Alberto Requena J. Phys. Chem. B, Just Accepted Manuscript • DOI: 10.1021/acs.jpcb.7b10749 • Publication Date (Web): 29 Jan 2018 Downloaded from http://pubs.acs.org on January 30, 2018

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The Journal of Physical Chemistry

Antibodies as Carrier Molecules: Encapsulating Anti-inflammatory Drugs inside Herceptine José Pedro Cerón-Carrasco,∗,† Horacio Pérez-Sánchez,† José Zún˜iga,‡ and Alberto Requena∗,‡ Bioinformatics and High Performance Computing Research Group (BIO-HPC) Universidad Católica San Antonio de Murcia (UCAM) Campus de los Jerónimos, 30107, Murcia, Spain, and Departamento de Qu´ımica F´ısica, Universidad de Murcia, 30100 Murcia, Spain E-mail: [email protected]; [email protected]

∗

To whom correspondence should be addressed UCAM ‡ Dep. Qu´ımica F´ısica †

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Abstract The human epidermal growth factor receptor 2 (HER2) is over-expressed in about a third of breast cancer patients, with a strong involvement of cyclooxygenase-2 (COX-2) enzyme in the tumor progress. HER2 and COX-2 are consequently potential targets for inhibiting carcionogenesis. Herceptin (trastuzumab) is an antibody that partially blocks HER2 positive cancers at their initial stage. Unfortunately, the overall response rate to the single treatment with this antibody is still modest and, therefore, it needs to be improved by combining several chemotherapeutic agents. On the other hand, nonsteroidal anti-inflammatory drugs (NSAIDs) are designed to halt COX-2 functionality, so they might also exhort an anticancer activity. In this contribution, dual HerceptinNSAIDs drugs are designed using theoretical tools. More specifically, blind docking, molecular dynamics and quantum calculations are performed to assess the stability of 14 NSAIDs embedded inside Herceptin. Our calculations reveal the feasibility of improving the anti-tumor activity of the parent Herceptin by designing a dual HER2targeting with Etofenamate. That coupling mode might be used to further rationalize new clinical strategies beyond classical antibody dosages.

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Introduction The Human Epidermal Growth Factor Receptor 2 (HER2) is a transmembrane tyrosine kinase responsible for regulating normal cell growth and survival rate, along with other biological functions. 1 However, it has been shown that about 30% of patients with breast cancer overexpress this protein. 2 Cyclooxygenase-2 (COX-2) is another enzyme that contributes to many natural physiological processes such as hemostasis, platelet aggregation, kidney and gastric function. Recent clinical findings have shown that COX-2 is also aberrantly expressed in inflammatory breast carcinomas developed by HER2 positive patients. 3 Indeed, it has been shown that inflammation contributes largely to the tumor progression, probably due to the stimulation of prostanoid production. 4 Furthermore, the erroneous activation of HER2 and COX-2 is not only associated with the propagation of cancerous tissues but also correlates with more aggressive tumors and a poorer prognosis. 5,6 Consequently, HER2 and COX-2 have been proposed as two of the main anti-tumor targets for treating breast cancer as well as other cases, including ovarian, gastric, and prostate tumors. 7–13 Of all the possible novel anti-cancer drugs, antibodies are one of most attractive, since they can be biologically designed to recognize malignant cells. 14 As recently summarized by Ledford., 15 one can use the immune system as a weapon against cancers resistant to conventional indications, a technique known as immunotherapy, which is expected to be used in more than 60% of the cancer treatments in the next decade In this framework, the approval of Herceptin (trastuzumab) by US Food and Drug Administration (FDA) in 1998 laid the cornerstone in molecular cancer therapeutics for HER2-positive patients. 16 Herceptine is a humanized monoclonal antibody programmed to selectively interact with the HER2 oncoprotein, 17 which has become standard in the treatment of metastatic breast cancer, with a decrease of a 50% of the recurrence cases over a 20-month period. 18 Unfortunately, the long-term response to a single-Hercpetin treatment is still modest because tumors become resistant after the initial benefit, 19 so its activity needs to be enhanced by combining several chemotherapeutic agents in a drug cocktail. 20 This is especially required in patients with 3



high inflammatory biomarkers who respond less to the treatment with Herceptin. 21 A first approach to enhance the Herceptin properties is to covalently conjugate a cytotoxic drug (payload) to the antibody structure by means of a stable linker, which eventually leads to the corresponding antibody-drug conjugate (ADCs). 22 The antibody then plays a double function: it must recognize the cancer cell by binding to the tumor antigen, while simultaneously favoring the in situ delivery of the embedded drug by acting as a “Trojan horse”. Although the ADCs field has attracted the attention of many groups in the cancer therapy field, very few Herceptin-based ACDs have been approved by the FDA for cancer treatment to date. The most successful case is the so-called T-DM1, an ADC that combines Herceptin with the microtubule-inhibitory drug DM1, a derivative of maytansine. 23,24 Recent clinical trials have demonstrated that T-DM1 increases the survival rate and reduces the toxicity in patients with HER2-positive advanced breast cancer previously treated with trastuzumab. 25 T-DM1 is, however, an exception rather than a general rule, and most of the trials with ADCs have failed so far at the last clinical stages. As recently reviewed by Jackson, 26 preclinical data suggests that heterogeneity might be one of the main limiting factors in the therapeutic action of ADCs.The linkers used to attach the cytotoxic drug to the antibody usually react with lysines and cysteines residues located at the antibody surface. Since an antibody usually presents around 50 lysines and 12 cysteines, the use of non-specific conjugation methods yields heterogenous ADCs that vary in drug/antibody ratio and conjugation site structures. 27 Such heterogeneous conjugation at the surface induces self-assembling, low solubility, and/or instability due to side reactions like thiol exchange, which eventually lead to premature release. 26,28 It is possible to design site-specific conjugation by using more elaborated protocols based on engineered aminoacids, enzyme mediated or linker modification processes, although all them are at a very initial clinical stage. 29,30 An additional attractive approach is to use Herceptin as a non-covalent carrier, in which a small molecule is directly embedded in the antibody structure without the use of any additional molecule acting as a

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linker. 31 In this line, Gao, Zingaro et al. synthesized a novel metallodrug with large affinity for Herceptin, so it could be labeled without using a more complex stepwise synthesis. 32 The metallodrug is effectively retained within the antibody structure, which protects the “cargo” from side reactions and early deactivation, and the resulting dual drug-antibody exhibits a potent synergistic anticancer effect. 32 With the aim of enhancing currently available breast cancer treatments, in this contribution we assess the feasibility of combining the most used non-steroidal anti-inflammatory drugs (NSAIDs) with Herceptin by means of theoretical tools. Our approach is based on the strong correlation existing between inflammation and therapy efficiency, which suggests that NSAIDs may be combined to simultaneously target HER2 and COX-2. 33,34 In a previous work, Higgins and co-workers 35 studied the effects of two NSAID, Aspirin and Celecoxib, on breast cancer parameters, finding contradictory results on the efficicency of NSAIDs. Their clinical trial shows that neither Aspirin nor Celecoxib are associated with a positive effect in disease parameters. 35 This outcome indicates that it is not easy to predict/rationalize the effectiveness of a combined Herceptin-NSAID treatment, so the selection of the NSAID should be attempted with care. Accordingly, we conduct herein docking, quantum mechanical (QM), and molecular dynamics (MD) simulations to systematically assess the stability of a set of commonly used NSAID embedded in Herceptin in order to rank the most promising dual drugs.

Chemical models and computational details The computational protocol is initiated by obtaining the cartesian coordinates of the selected NSAIDs as deposited at the PubChem chemical library. 36 The training set was built with a wide-panel of 14 NSAIDs including carboxylic acids, carboxamides, oxicams, sulphonanilides and diaryl-substituted pyrazoles and furanones derivatives. Their raw structural data were next refined by assigning bond orders, stereochemistry, hydrogen atoms, and protonation

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F

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F

F N

N

H O

O

O

N

O H Aspirin O

N

O

O Etodolac

Epirizole

O

O

H

O

O O

H N

S

H

F

S O

F Etofenamate

Ibuprofen

O

O

N

N

O

H

O

F

O

H

Celecoxib

H

O H

N

N

N

O S O

H

H

N

O

O

N O

Meloxicam

Naproxen O

O

O

H

N

H

N

H N

O

O

O

S

H O

O

H

N

O O Nabumetone

Nepafenac

N+

Nimesulide

O-

O Ketorolac

H O O H

O

H

N

N

N

O

O S O

N O

Piroxicam

Oxaprozin

Figure 1: Non-steroidal anti-inflammatory drugs (NSAIDs) encapsulated into the Herceptin antibody. states at physiological pH with the LigPrep module 37 and the OPLS3 force field, 38 as implemented in the Small-Drug Design Suite of Schröridnger. 39 As expected, all carboxylic groups are predicted to be deprotonated at pH=7. The resulting structures are subsequently optimized at the M06-2X/6-31+G(d,p) level of theory. 40 Additional vibrational calculations are performed to confirm that stationary points are true minima in the potential energy surface

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(no imaginary frequencies). Partial atomic charges are computed within the Merz-SinghKollman ESP scheme 41,42 and subsequently implemented in docking and MD simulations. All QM calculations were carried out using Jaguar. 43,44 The optimized NSAIDs are next embedded into the experimental crystal structure of Herceptin, deposited at the Protein Data Bank (PDB) 45 with code 1N8Z (Fig. 2). 46 The PDB file contains the variable region of the antibody (Fab fragment) but not the constant region (Fc fragment is not included). However, the Fab fragment is enough to ensure that the encapsulation of NSAID does not disrupt the ability of the whole antibody to recognize the HER2 oncoprotein: if the NSAID is encapsulated far from the antigen binding site, the biological activity of the antibody is guaranteed. Furthermore, the Fab fragment might be used as a molecular carrier on its own, since it also reaches the cancer cell with a faster pharmacodynami: Herceptin needs one day compared to just a few hours for the Fab fragment. 47,48 Accordingly, the Herceptin(Fab) fragment extracted from the PDB data was subjected to further refinement using the Protein Preparation Wizard, 49 to include missing hydrogen atoms and assign all bond orders. The protonation states of all residues were computed with the PROPKA code. 50 A restricted optimization was finally carried out by minimizing all hydrogen atoms with the OPLS3 force field. 38 The resulting refined Herceptin structure was then used as the host in SiteMap and blind docking (BD) approaches. The former code scanned the whole protein structure for identifying binding sites that possessed the suitable shape and chemical properties for embedding small molecules,. 51,52 This code was applied as implemented in Schrödinger., 53 and used a library of compounds to determine all possible biding pockets compatible with each drug. The BD simulations were carried out according to the procedure described by Navarro and co-workers, 54 by searching for the global minimum of the potential energy surface with the genetic algorithm implemented in the Lead Finder program. 55,56 The size of the grid box for ligand BD was set to 120 ˚ A in each direction from the geometric center of the Herceptin model system. The dG score produced by Lead Finder was taken as the predicted value of

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Figure 2: Representation of the experimentally resolved HER2–Herceptin(Fab) structure displayed as green and red-white cartoons, respectively. the ligand binding energy, which accounts for the Lennard-Jones term (LJ), metal interactions, solvation term, hydrogen bonds (H-bonds), electrostatic interactions, internal energy of the ligand, contributions to entropy due to ligand torsions, and a solvation penalty term. In that BD approach, multiple BD runs started around the geometric centers of all residues within the selected threshold. A histogram with the resulting distribution of binding energies was obtained, and the best 10 poses ranked in BD were retained for further refinement. We have previously demonstrated that even if BD provides a valuable initial description of the Herceptin domain interacting with an embedded drug, the scoring dG energies need to be improved with a higher level of theory to reach the most grounded biological conclusions. 31 Although the reliable prediction of any protein-ligand binding free energy is still a challenge for the computational chemistry community, the combined Molecular Mechanical/Generalized Born Surface Area (MM/GBSA) approach offers an affordable alternative for mimicking the binding reaction in large biosystems. 57,58 The MM/GBSA method is based on the difference between the free energies of the protein, ligand, and the complex in solution. 8


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The free energy for each species involved in the reaction (ligand, protein, and ligand-protein complex) is described as a sum of a gas-phase energy, polar and nonpolar solvation terms, and an entropy term (see Ref. 57 for further details). In our computational protocol, the MM-GBSA method is used to improve all poses obtained in the BD step by using the OPLS3 as implemented in Small-Drug Design Suite of Schrödinger. 39 All previous simulations account for the structure of the antibody as a rigid body. However, one might expect the Herceptin structure to re-adapt its spatial configuration once the NSAID molecule is encapsulated inside. To describe the Herceptin–NSAID binding mechanism better, the dynamic stability of the best ranked drugs on the scale of MM/GBSA values was finally assessed by locating the corresponding adducts in an orthorhombic box, with a buffer distance of 10 ˚ A in all directions, and by subsequently filling the box with water molecules SPC model and sodium cations to keep the system electronically balanced. Additional sodium and chloride ions were incorporated into the system to simulate the physiological salt concentration of 0.15 M NaCl. An initial full minimization was conducted for 2000 steps using the steepest descent method, with a convergence threshold of 1.0 kcal/mol/˚ A. The solvated systems were then relaxed throughout several stages that included a soluterestrained minimization, free-restrain minimization, NVT simulation of 24 ps at T=10 K, and NPT simulations at T=10 K and P=1 atm. For the production phase, the temperature was set to 300 K with the Nosé-Hoover algorithm, with a relaxation time of 1.0 ps. 59,60 Pressure was controlled at 1 bar with the Martyna–Tobias–Klein barostat using isotropic coupling and a relaxation time of 2.0 ps. 61 The RESPA integrator was used to integrate the motion equations with a 2.0 fs time step for bonded and near interactions, and a 6.0 fs time step for far interactions. 62 A cut-off of 9 ˚ A was applied to non-bonded interactions. Van der Waals interactions were evaluated using a cut-off radius of 9 ˚ A, and the electrostatic part was computed using the Particle Mesh Ewald (PME) 63 method with a tolerance of 10−9 . To be consistent with all the previous computational steps, MD simulations were run for 10 ns using the OPLS3 force field as implemented in Desmond code. 38,64

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Site 1

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Site 2

Site 4

Site 3

Site 5

Figure 3: SiteMap code identifies five binding pockets in the Herceptin structure: blue, red and yellow surfaces highlights H-bond donor, H-bond acceptor and hydrophobic regions within the binding site, respectively.

Results and discussion The search for the druggable binding sites on the Herceptin is first assessed with SiteMap. The refined crystal structure of Herceptin (i.e. the structure processed with the Protein Preparation Wizard workflow) is used as the starting model system. As discussed above, this algorithm scans the protein structure to identify pockets that can potentially accommodate a small molecule inside. The results obtained are summarized in Fig. 3. As observed, SiteMap identifies five possible binding pockets in the Herceptin structure, which are displayed as cubic boxes with colored surface-fingerprints. In this color scheme, blue surfaces 10


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represent regions suitable for occupancy by drugs with H-bonds donor groups, red surfaces highlight regions compatible with H-bond acceptors, and yellow surfaces localize areas in which hydrophobic groups can be located. A visual inspection reveals that Sites 2, 3 and 5 are mainly placed in the outer region of the protein, which is not the optimal place for a “payload” since (i) it presents less anchoring points (smaller surfaces) to retain the drug inside the Herceptin structure, e.g., lower druggability, and (ii) the interaction with solvent or surrounding biomolecules can induce an early release. Site 4 should also be discarded for designing a dual carrier-cargo system. Effectively, a comparison of Figs. 2 and 3 shows that Site 4 matches the antigen binding site and, consequently, if a small-drug is located in that region of Herceptin, its biological functionality cannot be guaranteed. Finally therefore we focus on Site 1, which is placed in the inner region of the antibody with no interaction with the antigen region. If a drug is embedded into that pocket, the interaction with solvent molecules or other surrounding biological molecules is avoided. In addition, Site 1 possesses the largest H-bond acceptor and donor regions as well as the largest hydrophobic ability due to the inner core-place, with all those parameters supporting better druggability in this region. SiteMap offers valuable results as it identifies potential binding sites on the Herceptin structure. However, it does not account for any specific drug. In contrast, all pockets are characterized according to general parameters like size, shape, and chemical properties. Our next computational step was, therefore, to use a BD approach to explicitly dock the NSAIDs of Fig. 1 into Herceptin. The best pose of each NSAID is collected in Fig. 4 for a visual inspection. It is noticeable that the binding preference for embedding largely depends on the NSAID chemical structure. As discussed above, the series of NSAID selected accounts for carboxylic acids, carboxamides, oxicams, sulphonanilides and diaryl-substituted pyrazoles and furanones derivatives, which explains the dissimilar behavior. DB results allow us to perform a first drug screening and to prioritize the most suitable functional groups. As showed in Fig. 4, one of the NSAIDs tested, Etodolac, is located in the region of Site

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Figure 4: Conformations of the top docked pose of each NSAID. Herceptin is displayed in cartoons and small-drugs are shown as sticks. Etodolac (shown in white blue) preferentially docks into region of Site 4, while Celecoxib and Oxaprozin (displayed as orange and yellow sticks, respectively) dock into Site 2. 4, so it interacts with the residues involved in the HER2 recognition function. Etodolac should therefore be discarded to preserve the properties of the native Herceptin antibody. Furthermore, two NSIADs (Celecoxib and Oxaprozin) are preferentially docked in the Site 2 region. As mentioned above, a ligand can be only poorly anchored in that region, so it is expected to produce a quick release of any small-drug embedded from Herceptin towards the solution. This is a noteworthy result that might shed light on previous trials in hospitals. According to the clinical study by Dang and co-workers, the combination Celecoxib and Herceptin is not active in breast cancer patients. 65 Our simulations indicate that Celecoxib cannot be efficiently loaded into the Herceptin structure; a computational result that might (at least partially) explain that poor synergic effect between both drugs. To the best of our

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Table 1: Computed Herceptin–NSAID binding energies within at the MM/GBSA level of theory. NSAID Etofenamate Etofenamate Etofenamate Nepafenac Nepafenac Naproxen Ketorolac Meloxicam Nepafenac Nimesulide

BD pose

MM/GBSA energy (kcal mol−1 )

01 02 03 01 02 01 01 01 02 01

-47.92 -35.41 -34.61 -34.59 -34.48 -34.04 -33.71 -33.63 -33.37 -33.29

knowledge, no equivalent clinical trails are available for Oxaprozin, although our predictions discard synergetic effects if that NSAIDs is combined with Herceptin. It is worth stressing that most BD poses lie on the inner region of Herceptin. This finding agrees with the better druggability of the region labeled as Site 4 by the SiteMap code. Consequently, although BD calculations are performed without any bias (i.e. they do not depend on the SiteMap results), both techniques provide consistent conclusions and provide further support to our biological predictions. Let us move on now in the level of theory by computing the Herceptin–NSAID binding energy within the MM/GBSA framework. These calculations are performed on the best 10 poses of each NSAID arising from BD simulations. The lowest (more negative) energetic MM/GBSA values. e.g., the most-likely binding spots, are listed in Table 1. According to the interaction energies computed, Etofenamate is the NSAID with the highest affinity for Herceptin, with an interaction energy of ca. −48 kcal mol−1 . Indeed, three of its BD poses are on the top-list. Nepafenac, Naproxen, Ketorocal, Meloxicam and Nimesulife are also well positioned candidates, although with a less intense interaction (ca. −34 kcal mol−1 ). As expected, Celecoxib is not among the best-ranked NSAIDs since it is located in the Site 2 region. The computed MM/GBSA interaction energy is only ca. −20 kcal mol−1 . It is also worth noticing that Aspirin, the most used NSAIDs, and probably the most

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14 RMSD and distances (in angstroms)

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d2

8

d3

Leu46

d1

6 Etofenamate

4 d4

2 Gly57

0 0

10

20

30

40

50

Time (ns)

Figure 5: Summary of the molecular dynamics trajectory. Left panel: plots correspond to the evolution of the root-mean-square (RMSD) of all residues [black line], the two Hbonds established with Leu46 [d2 and d1 as red and blue lines, respectively], the cation-π interaction with Lys45 [d3, green line] and the H-bond which appears between Etofenamate and Gly57 residue [d4, orange line]. All distances are given in angstroms. Right panel: sketch of the binding site extracted from the last snapshot of the MD production and the monitored distances.

prescribed drug, is not among the best candidates to be encapsulated either, since it has a predicted interaction energy of −30 kcal mol−1 which is about 20 kcal mol−1 lower in magnitude than the energy predicted for Etofenamate. It should be noticed that the absolute MM/GBSA energies have to be cautiously analyzed as more refined methods, such as free energy perturbation (FEP) calculations, are to be used to provide more accurate binding energy predictions. 66 Nevertheless, relative MM/GBSA energies can be used to prioritize NSAIDs in the design of stable bifunctional drugs, as they allow us to screen large libraries at an affordable computational costs. In order to confirm the dynamic stability of the Herceptin–Etofenamate system, additional MD simulations were conducted during 50 ns of trajectory. The evolution of the root-mean-square-deviation (RMSD) on all residues is used to monitor the stability of the dual drug. As illustrated in the left panel of Fig. 5, the RMSD value [black line] quickly

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reaches a stable value of about 2 ˚ A during the initial 2 ns of the MD trajectory. This stability suggests that the embedding of Etofenamate does not induce a large perturbation in the overall structure of Herceptin, and it can therefore be easily accommodated within the carrier. An analysis of the ligand-protein contact along the trajectory shows that the interaction is initially governed by a cation-π interaction with Lys45 (d3) and two H-bonds (d2 and d1) established with Leu46 (see Fig. 5, right panel). As illustrated by the evolution of the contacts with Lys45, the cation-π interaction between the –NH3+ group (the residue is protonated at the physiological pH, according to PROPKA predictions) and the CF3 -phenyl ring of the Etofenamate are more flexible than the two H-bonds with Leu46 residue. The former geometrical parameters vary in the range of 4–9 ˚ A, with an average value of about 5 ˚ A. Figure 5 also shows that the H-bonds are somewhat more than stable at ca. 2 ˚ A, during first 20 ns. However, both interactions seem to be lost at the last MD window. Although such structural changes might indicate a release of the NSAID, a visual inspection reveals that the Heceptin-Etofenamate adduct is retained during all the simulation at Site 4 due to the rearrangement of the other close residues that interact with Etofoneamate. As illustrated in Fig. 5, a new H-bond is established with the Gly57 (d4), which is allowed to flipp towards the ligand after only 30 ns of trajectory, and consequently plays a pivotal role in the stability of the system. These results stress the importance of performing dynamic simulations in drug design, which is demonstrated to be a crucial step into the design of Herceptin bifunctional drugs. We therefore conclude that among all the small-drugs tested, Etofenamate is the NSAID most prone to be encapsulated in the Herceptin structure without altering its biological ability for HER2 recognition. Until now Herceptin had been proved to carry metallodrugs and alkaloids. 32,67 The reported data allow to include for the first time a NSAID to the list of payloads compatible with Herceptin.

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Conclusions and outlook In this work, we employ a wide panel of theoretical tools to ascertain the use of Herceptin, an antibody able to recognize the HER2 overexpressed protein in breast cancer cells, as a molecular carrier for non-steroidal anti-inflammatory drugs (NSAIDs). Our approach is based on the host ability of Herceptin, which is supported by it having a number of binding pockets compatible with the encapsulation of small-drug molecules. Indeed, SiteMap calculations reveal the presence of a inner pocket located far from the antigen region, which might be used to charge the antibody without altering its biological properties. Blind docking calculations are conducted to discard NSAIDs that are not compatible with this specific binding site. The resulting drugs list is ranked according to their MM/GBSA interactions energies. Finally, molecular dynamics simulations are performed to ensure the dynamic stability of the new dual drug. All accumulated theoretical results hint that Herceptin can be combined with Etofenamate (a clinically approved NSAID) to give a stable bifunctional drug. This conclusion is supported by three findings: (i) Etofenamate’s preferred binding pocket is not located in the antigen binding site but further inside the host structure, (ii) the Herceptin–Etofenamate complex has the largest interaction energy (ca. −48 kcal mol−1 ), and (iii) the dynamic stability of the cargo-carrier system with Etofenamate is retained inside the Herceptin. Other NSAIDs commonly used, such as Celecoxib, are shown to be non optimal in this task, since they are located at the solvent-exposed surface of antibody and can be quickly released to the solvent, whereas Aspirin presents a low interaction energy and should also be ruled out as a good choice. Finally, we would stress that the combined use of Herceptin and NSAIDs in immunological targeting needs to be designed with caution, and it is our hope that the results reported in this work pave the way for new routes for establishing synergic effects with Herceptin in the treatment of cancer.

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Acknowledgement This work has been partially funded by the Fundación Séneca–Agencia de Ciencia y Tecnolog´ıa de la Región de Murcia under Projects 19419/PI/14-1 and 19419/PI/14-2, and by the Ministerio de Econom´ıa y Competitividad under Project CTQ2016-79345-P. The MetDrugs Network (CTQ2015-70371-EDT) is acknowledged for providing opportunities for discussion. The research used resources from the Plataforma Andaluza de Bioinformática at the Universidad of Málaga, the CEN1 and Cabezon clusters installed at Universidad de Murcia, and the local Galileo cluster installed at Universidad Católica San Antonio de Murcia.

Supporting Information Available: (i) Cartesian coordinates for all NSAIDs at their QM optimized structures, (ii) refined Herceptin structure, and (iii) MM/GBSA of best 100 ranked poses. This information is available free of charge via the Internet at http://pubs.acs.org.

References (1) Yarden, Y.; Sliwkowski, M. X. Untangling the ErbB signalling network. Nat. Rev. Mol. Cell Biol. 2001, 2, 127–137. (2) Slamon, D.; Clark, G.; Wong, S.; Levin, W.; Ullrich, A.; McGuire, W. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 1987, 235, 177–182. (3) Subbaramaiah, K.; Norton, L.; W, W. G.; Dannenberg, A. J. Cyclooxygenase-2 is overexpressed in HER-2/neu-positive breast cancer. Evidence for involvement of AP-1 and PEA3. J. Biol. Chem. 2002, 277, 18649–18657. (4) Fouad, T. M.; Kogawa, T.; Reuben, J. M.; Ueno, N. T. In Inflammation and Cancer ; Aggarwal, B. B., Sung, B., Gupta, S. C., Eds.; Springer Basel: Basel, 2014; pp 53–73. 17



(5) Slamon, D.; Godolphin, W.; Jones, L.; Holt, J.; Wong, S.; Keith, D.; Levin, W.; Stuart, S.; Udove, J.; Ullrich, A.; et, a. Studies of the HER-2/neu proto-oncogene in human breast and ovarian cancer. Science 1989, 244, 707–712. (6) Howe, L. R.; Dannenberg, A. J. COX-2 Inhibitors for the Prevention of Breast Cancer. J. Mammary Gland Biol. Neoplasia 2003, 8, 31–43. (7) Soslow, R. A.; Dannenberg, A. J.; Rush, D.; Woerner, B. M.; Khan, K. N.; Masferrer, J.; Koki, A. T. COX-2 is expressed in human pulmonary, colonic, and mammary tumors. Cancer 2000, 89, 2637–2645. (8) Half, E.; Tang, X. M.; Gwyn, K.; Sahin, A.; Wathen, K.; Sinicrope, F. A. Cyclooxygenase-2 Expression in Human Breast Cancers and Adjacent Ductal Carcinoma in Situ. Cancer Res. 2002, 62, 1676–1681. (9) Holmes, M. D.; Chen, W. Y.; Schnitt, S. J.; Collins, L.; Colditz, G. A.; Hankinson, S. E.; Tamimi, R. M. COX-2 expression predicts worse breast cancer prognosis and does not modify the association with aspirin. Breast Cancer Res. Treat. 2011, 130, 657. (10) Nagaraja, V.; Eslick, G. D. HER2 expression in gastric and oesophageal cancer: a meta-analytic review. J. Gastrointest. Oncol. 2015, 6, 143–154. (11) Omar, N.; Yan, B.; Salto-Tellez, M. HER2: An emerging biomarker in non-breast and non-gastric cancers. Pathogenesis 2015, 2, 1 – 9. (12) Sharifi, N.; Salmaninejad, A.; Ferdosi, S.; Bajestani, A. N.; Khaleghiyan, M.; Estiar, M. A.; Jamali, M.; Nowroozi, M. R.; Shakoori, A. HER2 gene amplification in patients with prostate cancer: Evaluating a CISH-based method. Oncol. Lett. 2016, 12, 4651–4658. (13) Abrahao-Machado, L. F.; Scapulatempo-Neto, C. HER2 testing in gastric cancer: An update. World J. Gastroenterol. 2016, 22, 4619–4625. 18


Page 18 of 27

Page 19 of 27 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


(14) Trail, P.; Willner, D.; Lasch, S.; Henderson, A.; Hofstead, S.; Casazza, A.; Firestone, R.; Hellstrom, I.; Hellstrom, K. Cure of xenografted human carcinomas by BR96doxorubicin immunoconjugates. Science 1993, 261, 212–215. (15) Ledford, H. Cancer treatment: The killer within. Nature 2014, 508, 24–26. (16) Vogel, C. L.; Cobleigh, M. A.; Tripathy, D.; Gutheil, J. C.; Harris, L. N.; Fehrenbacher, L.; Slamon, D. J.; Murphy, M.; Novotny, W. F.; Burchmore, M.; Shak, S.; Stewart, S. J.; Press, M. Efficacy and Safety of Trastuzumab as a Single Agent in FirstLine Treatment of HER2-Overexpressing Metastatic Breast Cancer. J. Clin. Oncol. 2002, 20, 719–726, PMID: 11821453. (17) Hudis, C. A. Trastuzumab — Mechanism of Action and Use in Clinical Practice. N. Engl. J. Med. 2007, 357, 39–51, PMID: 17611206. (18) Stern, H. M. Improving Treatment of HER2-Positive Cancers: Opportunities and Challenges. Science Translational Medicine 2012, 4, 127rv2–127rv2. (19) Slamon, D. J.; Leyland-Jones, B.; Shak, S.; Fuchs, H.; Paton, V.; Bajamonde, A.; Fleming, T.; Eiermann, W.; Wolter, J.; Pegram, M.; Baselga, J.; Norton, L. Use of Chemotherapy plus a Monoclonal Antibody against HER2 for Metastatic Breast Cancer That Overexpresses HER2. N. Engl. J. Med. 2001, 344, 783–792, PMID: 11248153. (20) Valabrega, G.; Montemurro, F.; Aglietta, M. Trastuzumab: mechanism of action, resistance and future perspectives in HER2-overexpressing breast cancer. Ann. Oncol. 2007, 18, 977–984. (21) Alkhateeb, A. A.; Leitzel, K.; Ali, S. M.; Campbell-Baird, C.; Evans, M.; Fuchs, E.-M.; Köstler, W. J.; Lipton, A.; Connor, J. Elevation in Inflammatory Serum Biomarkers Predicts Response to Trastuzumab-Containing Therapy. PLoS ONE 2012, 7, e51379.

19



(22) Chari, R. V. J.; Miller, M. L.; Widdison, W. C. Antibody–Drug Conjugates: An Emerging Concept in Cancer Therapy. Angew. Chem. Int. Ed. 2014, 53, 3796–3827. (23) Lewis Phillips, G. D. et al. Targeting HER2-Positive Breast Cancer with TrastuzumabDM1, an Antibody-Cytotoxic Drug Conjugate. Cancer Res. 2008, 68, 9280–9290. (24) Junttila, T. T.; Li, G.; Parsons, K.; Phillips, G. L.; Sliwkowski, M. X. TrastuzumabDM1 (T-DM1) retains all the mechanisms of action of trastuzumab and efficiently inhibits growth of lapatinib insensitive breast cancer. Breast Cancer Res. Treat. 2011, 128, 347–356. (25) Verma, S.; Miles, D.; Gianni, L.; Krop, I. E.; Welslau, M.; Baselga, J.; Pegram, M.; Oh, D.-Y.; Diéras, V.; Guardino, E.; Fang, L.; Lu, M. W.; Olsen, S.; Blackwell, K. Trastuzumab Emtansine for HER2-Positive Advanced Breast Cancer. N. Engl. J. Med. 2012, 367, 1783–1791, PMID: 23020162. (26) Jackson, D. Y. Processes for Constructing Homogeneous Antibody Drug Conjugates. Org. Process Res. Dev. 2016, 20, 852–866. (27) Harris, L. J.; Larson, S. B.; Hasel, K. W.; McPherson, A. Refined Structure of an Intact IgG2a Monoclonal Antibody,. Biochem. 1997, 36, 1581–1597, PMID: 9048542. (28) Hamblett, K. J.; Senter, P. D.; Chace, D. F.; Sun, M. M. C.; Lenox, J.; Cerveny, C. G.; Kissler, K. M.; Bernhardt, S. X.; Kopcha, A. K.; Zabinski, R. F.; Meyer, D. L.; Francisco, J. A. Effects of Drug Loading on the Antitumor Activity of a Monoclonal Antibody Drug Conjugate. Clin. Cancer Res. 2004, 10, 7063–7070. (29) Hofer, T.; Thomas, J. D.; Burke, T. R.; Rader, C. An engineered selenocysteine defines a unique class of antibody derivatives. Proc. Nat. Acad. Sci. (PNAS) USA 2008, 105, 12451–12456.

20


Page 20 of 27

Page 21 of 27 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


(30) Farias, S. E.; Strop, P.; Delaria, K.; Galindo Casas, M.; Dorywalska, M.; Shelton, D. L.; Pons, J.; Rajpal, A. Mass Spectrometric Characterization of Transglutaminase Based Site-Specific Antibody–Drug Conjugates. Bioconjug. Chem. 2014, 25, 240–250, PMID: 24359082. (31) Cerón-Carrasco, J. P.; Cerezo, J.; Requena, A.; Z´ un ˜iga, J.; Contreras-Garc´ıa, J.; Chavan, S.; Manrubia-Cobo, M.; Pérez-Sánchez, H. E. Labelling Herceptin with a novel oxaliplatin derivative: a computational approach towards the selective drug delivery. J. Mol. Model. 2014, 20, 2401–2409. (32) Gao, J.; Liu, Y. G.; Liu, R.; Zingaro, R. Herceptin–Platinum(II) Binding Complexes: Novel Cancer-Cell-Specific Agents. ChemMedChem 2008, 3, 954–962. (33) Dannenberg, A. J.; Lippman, S. M.; Mann, J. R.; Subbaramaiah, K.; DuBois, R. N. Cyclooxygenase-2 and Epidermal Growth Factor Receptor: Pharmacologic Targets for Chemoprevention. J. Clin. Oncol. 2005, 23, 254–266, PMID: 15637389. (34) Crawford, S. Anti-inflammatory/antioxidant use in long-term maintenance cancer therapy: a new therapeutic approach to disease progression and recurrence. Ther. Adv. Med. Oncol. 2014, 6, 52–68. (35) Higgins, M.; Chapman, J.-A.; Ingle, J.; Sledge, G.; Budd, G.; Ellis, M.; Pritchard, K.; Clemons, M.; Badovinac, C. T.; Han, L.; Gelmon, K.; Rabaglio, M.; Elliott, C.; Shepherd, L.; Goss, P. Abstract P2-13-02: Effect of aspirin (ASP) or celecoxib (CC) use on outcomes in postmenopausal breast cancer patients randomized to adjuvant exemestane or anastrozole: NCIC CTG MA.27. Cancer Res, 2014, 72, P2–13–02–P2–13–02. (36) National Center for Biotechnology Information, https://pubchem.ncbi.nlm.nih.gov. (accessed June 19, 2017). (37) Schrödinger Release 2017-2: LigPrep. 2017; Schrödinger, LLC, New York, NY.

21



(38) Harder, E. et al. OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins. J. Chem. Theory Comput. 2016, 12, 281–296. (39) Schrödinger Release 2017-2: Small-Molecule Drug Discovery Suite. 2017; Schrödinger, LLC, New York, NY. (40) Zhao, Y.; Truhlar, D. G. Density Functionals with Broad Applicability in Chemistry. Acc. Chem. Res. 2008, 41, 157–167. (41) Singh, U. C.; Kollman, P. A. An approach to computing electrostatic charges for molecules. J. Comput. Chem. 1984, 5, 129–145. (42) Besler, B. H.; Merz, K. M.; Kollman, P. A. Atomic charges derived from semiempirical methods. J. Comput. Chem. 1990, 11, 431–439. (43) Bochevarov, A. D.; Harder, E.; Hughes, T. F.; Greenwood, J. R.; Braden, D. A.; Philipp, D. M.; Rinaldo, D.; Halls, M. D.; Zhang, J.; Friesner, R. A. Jaguar: A highperformance quantum chemistry software program with strengths in life and materials sciences. Int. J. Quantum Chem. 2013, 113, 2110–2142. (44) Schrödinger Release 2017-2: Jaguar. 2017; Schrödinger, LLC, New York, NY. (45) Sussman, J. L.; Lin, D.; Jiang, J.; Manning, N. O.; Prilusky, J.; Ritter, O.; Abola, E. Protein Data Bank (PDB): database of three-dimensional structural information of biological macromolecules. Acta Crystallographica Section D: Biological Crystallography 1998, 54, 1078–1084. (46) Cho, H.-S.; Mason, K.; Ramyar, K. X.; Stanley, A. M.; Gabelli, S. B.; Denney, D. W.; Jr,; Leahy, D. J. Structure of the extracellular region of HER2 alone and in complex with the Herceptin Fab. Nature 2003, 421, 756–760. (47) Scollard, D. A.; Chan, C.; Holloway, C. M.; Reilly, R. M. A kit to prepare 111 In-DTPAtrastuzumab (Herceptin) Fab fragments injection under GMP conditions for imaging 22


Page 22 of 27

Page 23 of 27 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


or radioimmunoguided surgery of HER2-positive breast cancer. Nucl. Med. Biol. 2011, 38, 129–136. (48) Hermanto, S.; Haryuni, R. D.; Ramli, M.; Mutalib, A.; Hudiyono, S. Preparation of F(ab’)2 trastuzumab fragment for radioimmunoconjugate synthesis of

177

Lu-DOTA-

F(ab’)2 -trastuzumab. J. Pharm. 2012, 2, 12–18. (49) Madhavi Sastry, G.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 2013, 27, 221–234. (50) Olsson, M. H. M.; Søndergaard, C. R.; Rostkowski, M.; Jensen, J. H. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions. J. Chem. Theory Comput. 2011, 7, 525–537, PMID: 26596171. (51) Halgren, T. New method for fast and accurate binding-site identification and analysis. Chem. Biol. Drug Des. 2007, 2, 146–148. (52) Halgren, T. A. Identifying and Characterizing Binding Sites and Assessing Druggability. J. Chem. Inf. Model. 2009, 49, 377–389, PMID: 19154148. (53) Schrödinger Release 2017-2: SiteMap. 2017; Schrödinger, LLC, New York, NY. (54) Navarro-Fernández, J.; Pérez-Sánchez, H.; Mart´ınez-Mart´ınez, I.; Meliciani, I.; Guerrero, J.; Vicente, V.; Corral, J.; Wenzel, W. In silico discovery of a compound with nanomolar affinity to antithrombin causing partial activation and increased heparin affinity. Journal of medicinal chemistry 2012, 55, 6403–6412. (55) Stroganov, O. V.; Novikov, F. N.; Stroylov, V. S.; Kulkov, V.; Chilov, G. G. Lead Finder: An Approach To Improve Accuracy of Protein?Ligand Docking, Binding Energy Estimation, and Virtual Screening. J. Chem. Inf. Model. 2008, 48, 2371–2385.

23



(56) Navarro-Fernandez, J.; Pérez-Sánchez, H.; Martinez-Martinez, I.; Meliciani, I.; Guerrero, J. A.; Vicente, V.; Corral, J.; Wenzel, W. In silico discovery of a compound with nanomolar affinity to antithrombin causing partial activation and increased heparin affinity. J. Med. Chem. 2012, 55, 6403–6412. (57) Kollman, P. A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, O.; Cieplak, P.; Srinivasan, J.; Case, D. A.; Cheatham, T. E. Calculating Structures and Free Energies of Complex Molecules:? Combining Molecular Mechanics and Continuum Models. Acc. Chem. Res. 2000, 33, 889–897, PMID: 11123888. (58) Greenidge, P. A.; Kramer, C.; Mozziconacci, J.-C.; Wolf, R. M. MM/GBSA Binding Energy Prediction on the PDBbind Data Set: Successes, Failures, and Directions for Further Improvement. Journal of Chemical Information and Modeling 2013, 53, 201– 209, PMID: 23268595. (59) Nosé, S. A Molecular Dynamics Method for Simulations in the Canonical Ensemble. Mol. Phys. 1984, 52, 255–268. (60) Hoover, W. G. Canonical Cynamics: Equilibrium Phase-Space Distributions. Phys. Rev. A 1985, 31, 1695–1697. (61) Martyna, G. J.; Tobias, D. J.; Klein, M. L. Constant Pressure Molecular Dynamics Algorithms. J. Chem. Phys. 1994, 101, 4177–4189. (62) Tuckerman, M.; Berne, B. J.; Martyna, G. J. Reversible Multiple Time Scale Molecular Dynamics. J. Chem. Phys. 1992, 97, 1990–2001. (63) Darden, T. A.; York, D. M.; Pedersen, L. G. Particle Mesh Ewald: An N–log(N) Method for Ewald Sums in Large Systems. J. Chem. Phys. 1993, 98, 10089–10092.

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Page 24 of 27

Page 25 of 27 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


(64) Desmond Molecular Dynamics System, version 4.4, D. E. Shaw Research, New York, NY, 2015. Maestro-Desmond Interoperability Tools, version 4.4, Schrödinger, New York, NY, 2015. (65) Dang, C. T.; Dannenberg, A. J.; Subbaramaiah, K.; Dickler, M. N.; Moasser, M. M.; Seidman, A. D.; D’Andrea, G. M.; Theodoulou, M.; Panageas, K. S.; Norton, L.; Hudis, C. A. Phase II Study of Celecoxib and Trastuzumab in Metastatic Breast Cancer Patients Who Have Progressed after Prior Trastuzumab-Based Treatments. Clin. Cancer Res. 2004, 10, 4062–4067. (66) Ciordia, M.; Pérez-Benito, L.; Delgado, F.; Trabanco, A A.; Tresadern, G. Application of Free Energy Perturbation for the Design of BACE1 Inhibitors. J. Chem. Inf. Model. 2016, 56, 1856–1871. (67) Yadav, A.; Sharma, S.; Yadav, V. K. Non-covalent carriage of anticancer agents by humanized antibody trastuzumab. J. Mol. Model. 2016, 22, 112–126.

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Antibodies as Carrier Molecules: Encapsulating Anti-Inflammatory

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