Antiviral Drug-Membrane Permeability: the ViralEnvelope and Cellular Organelles

Changjiang Liu,† Paolo Elvati,‡ and Angela Violi∗,†,‡,¶

†Biophysics Program, University of Michigan, Ann Arbor, MI 48109-2125, USA‡Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI

48109-2125, USA¶Department of Chemical Engineering, University of Michigan, Ann Arbor, MI

48109-2125, USA

E-mail: avioli@umich.edu

Abstract

To shorten the time required to find effec-tive new drugs, like antivirals, a key parame-ter to consider is membrane permeability, as acompound intended for an intracellular targetwith poor permeability will have low efficacy.Here, we present a computational model thatconsiders both drug characteristics and mem-brane properties for the rapid assessment ofdrugs permeability through the coronavirus en-velope and various cellular membranes. We an-alyze 79 drugs that are considered as potentialcandidates for the treatment of SARS-CoV-2and determine their time of permeation in dif-ferent organelle membranes grouped by viralbaits and mammalian processes. The computa-tional results are correlated with experimentaldata, present in the literature, on bioavailabil-ity of the drugs, showing a negative correlationbetween fast permeation and most promisingdrugs. This model represents an important toolcapable of evaluating how permeability affectsthe ability of compounds to reach both intendedand unintended intracellular targets in an accu-rate and rapid way. The method is general andflexible and can be employed for a variety ofmolecules, from small drugs to nanoparticles,as well to a variety of biological membranes.

Keywords

lipid bilayer, kinetic, permeation, molecular dy-namics, graphene quantum dots.

Introduction

The 2020 pandemic has brought the chasm be-tween the rapid spread of infectious diseasesand the relative slow pace of scientific discoveryto stark reality. This particular event is not thefirst, nor it will be the last. Since the turn ofthe century three different coronavirus threatshave surfaced and emerging infectious diseaseswill continue to be a threat for the foreseeablefuture. Current methods for studying betacoro-naviruses, responsible for the 2020 pandemic,are technically and time demanding. Viral iso-lation from field sample is rarely successful andreverse genetics recovery of recombinant virusis labor intensive and expensive. Similarly, thedesign and testing of new drugs is an extremelylengthy process. To this end, drug repurposing,where existing compounds that have alreadybeen tested safe in humans, are redeployed tocombat another disease, has emerged as a crit-ical strategy. As an example, Remdesivir, oneof the promising COVID-19 drug candidates atthe moment, is a broad-spectrum drug that wasoriginally targeted toward a number of viruses,

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such as SARS-CoV and MERS-CoV, but alsostudied to counter Ebola virus infection.

Multiple characteristics define the potency ofa drug, as well as its side effects. Amongthem, bioavailability - the fraction of a com-pound that reaches the intended target - isone of the hardest to estimate reliably andrapidly. Although several factors contribute toin defining bioavailability, passive permeabilitythrough different biological membranes is a crit-ical component since independent of the abilityof a drug to bind to its target, the inability toreach the intended target often translates intopoor in vivo efficacy and may require higherdosages with higher risks of side effects.1

The viral life cycle encompasses several cru-cial steps, starting with the attachment of thevirus to the cell and finishing with the release ofthe progeny virions from the host cell. SARS-CoV-2 targets cells through the viral struc-tural spike protein that binds to an enzymeon the host cell membranes. Following recep-tor binding, the virus uses host cell receptorsand endosomes to enter cells. Once inside, vi-ral polyproteins are synthesized that encode forthe replicase-transcriptase complex. The virusthen synthesizes RNA and structural proteinsleading to completion of assembly and releaseof viral particles. These viral lifecycle stepsprovide potential targets for drug therapy andmost of the drugs currently in clinical trials in-

hibit key components of the infection life-cycle,such as viral entry into the host cell, viral repli-cation, and viral RNA synthesis.2 These targetsare localized to particular subcellular compart-ments, yet current drug design strategies arefocused on general bioavailability and rarely ad-dress drug delivery to specific tissues or intra-cellular compartments. Therefore, knowledgeof the permeability of antiviral drugs, with ref-erence to both viral and mammalian cell com-partments, is vitally important from both apharmacokinetics and drug design standpoint.

In an effort to accelerate translation ofpromising drug candidates, as well as elimi-nate potentially ineffective or even harmful can-didate, we present a science-based model topredict the permeation of drug candidates forSARS-CoV-2 into coronavirus membrane andmammalian membranes.

Over the past decades many models have beendeveloped to estimate drugs’ permeability. Themost commonly used ones are the empirical”rule of five”,3 QSAR (Quantitative Structure-Activity Relationship)4 and QSPR (Quanti-tative Structure-Property Relationship) meth-ods.5 While QSAR/QSPR methods have showntheir efficacy in screening the binding of anti-viral drugs and targets, the predicted perme-ability is at best only accurate to an order ofmagnitude, mostly due to the incorporation oftoo many descriptors that are not related to

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Figure 2: Structural diagram of the coronavirus membrane with the anchored COVID-19 spikeprotein. A) Protein colored according to secondary structure (purple for alpha helix, red for betasheet, and gray for random coils). In the lipid bilayer, carbon are shown in gray, nitrogen in red,oxygen in blue, and phosphate in tan; cholesterol is shown uniformly in orange. Hydrogen atomsomitted for clarity. B) The anchoring region (part of the heptad repeated, the transmembraneregion, and the C-tails) of the spike protein in the membrane. C) The anchoring region of the spikeprotein colored by the residue type. Non-polar residues are colored in white, while polar residuesare in green.

the permeation problem, and the strong depen-dence on the quality of the training sets em-ployed.6 On the other end of the accuracy spec-trum, several methods have been proposed overthe years that leverage a physical description ofthe passive permeation process, like the inho-mogeneous solubility diffusion model and state-transition model.7 These models, that currentlyoften involve molecular dynamics (MD) simula-tions, are either computationally too expensivefor a rapid application or suffer from limited ac-curacy when drugs of various sizes and shapesor membranes with different lipid profiles areinvolved.

To bridge the gap between accuracy andease of use, we have recently introduced amodel to predict the rates of passive perme-ation of molecules and nanoparticles (up to sev-eral nm) into phosphatidylcholine membranes.8

The Low-Density Area (LDA) model, which cancombine both experimental and computationaldata, has shown accuracy equivalent to fully

atomistic MD methods with a much smallercomputational cost. Such a result was achievedobserving that the permeation process of smallcompounds is controlled by the formation oflow-density areas on the membrane surface, andby factorizing the permeation process into inde-pendent contributions of the membrane thermaland pressure fluctuations and characteristics ofthe molecules (size, shape and lipophilicity).

Leveraging the LDA model, here we report onthe permeation kinetics of promising drugs can-didates through coronavirus membranes, withthe overarching goal of providing inputs tohelp the design and selection of new or repur-posed drugs. Additionally, since the target ofmany drugs are located inside mammalian cells,we also report on the permeation kinetics ofthe same drugs through five mammalian mem-branes. A schematic of the overall approachand data sources used in this study is shown inFig. 1. The computations were obtained using acombination of molecular dynamics simulations

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in conjunction with an augmented LDA modelfor membranes. Seventy-nine drugs for SARS-CoV-2 were analyzed, that comprise FDA ap-proved drugs, compounds in clinical trials, andpreclinical compounds.2 The computational re-sults are compared with data available in theliterature on bioavailability and show the po-tential of the model to aid in the assessment ofdrugs’ permeability into a variety of organelles.

Results and Discussion

Molecular description of coron-avirus envelope

Coronaviridae is a family of large, enveloped,positive-stranded RNA viruses. The envelopeis derived from the host Endoplasmic Reticu-lum (ER) membrane and consists of a lipid bi-layer, in which structural proteins are anchored.The surface glycoprotein spike that forms largeprotrusions from the virus surface, binds to thehost-cell receptor and mediates viral entry.9,10

Since surface characteristics of the biologi-cal membranes determine the permeability ofdrugs, we developed a model to describe theanchoring of the spike protein to the lipid bi-layer. The spike contains three segments: alarge ectodomain, a single-pass transmembraneanchor, and a short intracellular tail. Theregion outside the viral core forms alpha he-lices where coiled-coil interactions, through hy-drophobic contact, help stabilize the chains.The tails near the C-terminal are mostly po-lar with charged residues that form randomcoils inside the viral core (i.e., the cytoplas-mic side after membrane fusion with the hostcell): interactions between the charged residuesand lipid head groups stabilize the end of thetransmembrane region of the spike protein. Adiagram of a section of the coronavirus enve-lope composed of lipid bilayer and the anchoredspike protein is shown in Fig. 2.

Based on existing data and estimated sec-ondary structure,11,12 we determined that thetransmembrane region of the COVID-19 spikeprotein is formed by the residues in the #1210-1235 range. The charge-charge interactions

among residues 1215:TYR, 1219:GLY, and1223:GLY contribute to hold the three chainstogether inside the hydrophobic region of themembrane lipids. The density of the spike pro-tein, used in our simulations, reflects the ex-perimental density of about 100 nm2 to 200 nm2

per transmembrane proteins (of any kind), es-timated from a viral envelop diameter of 80 nmto 120 nm13 and an approximate number of 800transmembrane proteins per virion.14 To reducethe computational cost, after the initial equili-bration run used to validate the stability of theprotein anchoring region, we considered onlypart of the protein (Fig. 2B and C) as the rest ofthe spike protein is far enough (5 nm or more) toaffect the formation and life time of low-densityareas on the viral membrane surface.

For the lipid bilayer, slightly higher concen-trations of sphingomyelin and phosphatidyli-nositol were found in the viral envelope, com-pared to the ER membrane from where coron-aviruses acquire their membrane envelope.15

Drugs’ Membrane Permeability

Figure 3: Ratios of times of entry for drugs indifferent membranes. The time of entry in ERmembrane is chosen as reference.

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Figure 4: Times of permeation for drugs candidates in different organelle membranes, groupedby A) viral baits, B) related mammalian processes when viral baits were unknown. Error barsrepresent one standard deviation. Tabulated data in the supplementary materials.

The LDA model describes the permeation asa gated entry process, regulated by the chanceof the molecule to find a low-density area onthe membrane surface and by the solubility ofthe drug in the hydrophobic phase. The fourparameters used in the model separate the con-tributions of the characteristics of the molecules(size, shape, solubility) from the properties ofthe membranes (lipid density distribution).

In addition to the viral envelope described be-fore (with and without spike protein), we stud-ied the surface properties of five membranescorresponding to various mammalian compart-ments (lysosome, plasma, Golgi, mitochondrialand endoplasmic reticulum membranes) usingall-atom molecular dynamics simulations.

Fig. 3 reports the distribution of the perme-ation times of the 79 drugs in all the membranes

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relative to the ER membrane, which was cho-sen as reference since the permeation in thismembrane is the fastest for all drugs. The re-sults show that the inclusion of the transmem-brane protein in the COVID-19 membrane hasa large impact on drug’s membrane permeabil-ity. The presence of the transmembrane proteinaffects the frequency, shape, and lifetime of low-density areas in the LDA model. Indeed, whencomparing the distribution of permeation timesfor ER and coronavirus membrane, with andwithout the spike protein, we observe a ∼40%increase in the permeation time due to the pres-ence of proteins and ∼10% due to the change inlipid composition. This effect is mostly due tothe reduced mobility of the lipids near a rigidobject similar to what has been observed forC60 fullerenes in lipid bilayers.16

While the presence of the spike protein in theCOVID-19 membrane results in an increasedstiffness, the viral membrane is still generallytwo times more permeable than plasma mem-brane and lysosomes (before accessible surfaceand concentration effects are considered). Al-though currently very few drugs are requiredto permeate through viral membranes, this dif-ference can potentially be exploited by otherclasses of compounds, like nanoparticles, whichcan amplify the effect of membrane stiffness dueto their relative large size. The individual timesof permeation of drug candidates grouped byviral baits or, when that is unknown, by bi-ological processes, are shown in Fig. 4. Theresults show a variety of timescales going fromnanoseconds to microseconds depending on thedrug and membrane.

Albeit the bioavailability of a drug is af-fected by many factors, like metabolism rateand plasma protein binding,17 we find a nega-tive correlation between the bioavailability ofdrugs and the permeation time predicted bythe LDA model. For example, for drugs withinthe same group, Lisinopril (Fig. 4B, Cell En-try) is the least permeable compound as wellas the compound with the lowest bioavailabil-ity (25% based on experimental data18). Onthe other hand, Captopril, Nafamostat, andCamostat, for which we predict permeationtimes that are one order of magnitude shorter

than Lisinopril, have an estimated bioavail-ability of 100% 1. Similar observations canbe made for Rapamycin (Fig. 4A, N), whichhas lower bioavailability than Sapanisertib andSilmitasertib. While undeniably other factorsaffect the ability of a drug to reach the intendedtarget, these results show how useful membranepermeability predictions can be.

Interestingly, the permeation times of drugsthat are currently identified as most promisingcandidates against COVID-19 during their clin-ical trial19 are among the shortest compared toother drugs with similar targets. For example,drugs related to the disruption of viral transla-tion such as Ternatin-420 (Fig. 4B Translation),Zotatifin21 (Fig. 4A, Nsp2) and Plitidepsin22

(Fig. 4B, Translation), and sigma receptorssuch as Haloperidol23 and Compound PB2824

(Fig. 4A Nsp6 and Orf9c), Melperone, Clop-erastine,25 and Clemastine (Fig. 4B, ProteinProcessing), and the female hormone proges-terone (Fig. 4B, Viral transcription) are amongthe most permeable compounds in their re-spective categories. While a direct comparisonamong these drugs is not straightforward, asthey need to cross different membranes in or-der to reach their intended target, the time ofpermeation in their least permeable membrane(i.e., lysosome or plasma) is 40 ns on averagewith an upper bound of 62 ns, while the re-maining drugs require an average of 242 ns anda maximum of 4.6 µs.

Conclusion

In this paper, we present a model to computethe permeability of drugs that target varioussteps in the lifecycle of infection of SARS-CoV-2. The model is based on the LDA theory de-veloped by our group that describes the per-meation process as regulated by low-densityarea on the membrane surface and solubility ofdrugs in the hydrophobic phase of the mem-brane itself. Starting from the virus structure,we developed an atomistic model of the spikeprotein anchored to the lipid bilayer to studythe viral envelope. Molecular dynamics simu-

1data from http://drugbank.ca

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lations of the viral and five mammalian mem-branes were carried out to determine the dy-namics and characteristics of the membranes.The computational results were compared withdata present in the literature on bioavailability,and show that the LDA model can be appliedto predict the permeability of drugs throughvarious membranes and to gain insights on thedifferences between membranes and potentiallyorgans or organisms. From a drug design, aswell as health assessment perspective, this pre-dictive capability can facilitate compound eval-uation by ruling out candidates with low perme-ability or excessive permeability in unintendedorgans or organelles. Indeed, the LDA modelcan be used to estimate the absorption selectiv-ity of different targets (e.g., carcinogenic cells,organs) by molecules and nanoparticles withoutneed of large set of preexisting data, making itan invaluable tool for the comprehensive andfast validation pipeline of new compounds.

While the results reported in this paper fo-cus on drugs for SARS-CoV-2, the method isgeneral and flexible and can be employed, withother tools to shorten the effective responsetime to a global pandemic. Our work providesa means for scientific discovery to move closerto the speed of pandemic spreads and it can begeneralized to future events.

Methods

Membrane simulations

We used NAMD26 version 2.13 with CHARMMForce Field27 version 36 for lipids, and TIP3P28

for water molecules. All MD simulations wereperformed with a timestep of 2 fs while keep-ing all the C–H and O–H bonds rigid viathe SHAKE algorithm.29 Long range electro-static interactions were modeled with the par-ticle mesh Ewald method30 using a 0.1 nm gridspacing, a tolerance of 10−6 and cubic inter-polation; cubic periodic boundary conditionswere applied. Temperature was kept constantat 310 K using a Langevin thermostat31 with atime constant of 1 ps, while pressure was kept at101.325 kPa using Langevin piston method32,33

with period of 50 fs and 25 fs decay. To accountfor the intrinsic anisotropy of the system, theproduction simulations were performed in theNPsT ensemble, where the x and y dimensionsof the periodic system (coplanar with the mem-brane) are allowed to vary independently fromthe z dimension. Non-bonded short-range inter-actions where smoothly switched to 0 between1 and 1.2 nm with a X-PLOR switch function.

The lipid compositions for cellular organelles(plasma, lysosome, Golgi, mitochondrial) weretaken from Horvath et al.,34 and the ER andviral membrane from van Genderen et al.15 Ta-ble S1 provides more details about the compo-sition of each membrane. Membranes withoutspike proteins had an approximate size of 8 nmby 8 nm, subject to round up to satisfy the re-quired imposed by the lipid composition. Themembranes with spike proteins were extendedto guarantee at least 4 nm of separation betweenthe closest atom of the spike protein with itsnearest periodic image, resulting in membranesof approximately 15.4 nm by 15.4 nm for thesystem with the entire protein and 9.5 nm by9.5 nm for the system with the anchoring regiononly. System were simulated in explicit solventto guarantee at least 6 nm of separation betweenthe membrane or protein and their periodic im-age from top and bottom. The lipids, protein,water molecules and ions were initially placedby the CHARMM-GUI membrane builder.35

Each membrane was equilibrated for at least30 ns in the NPsT ensemble described above.The membranes were assumed to be at equilib-rium once the time average of the area per lipidin a NPsT simulations was varying less than10% in 10 ns. The surface properties (LDAs)were then obtained by running a additional100 ns.

Spike protein in membrane

The main structure (residues 27 to 1146) of thespike protein can be found in the protein databank #6VSB, based on the cryo-EM structureof Wrapp et al.11 The secondary structure ofresidue 1146 to 1273 was instead based on theprediction of the C-I-TASSER model.12 Thesecondary structure of the alpha helices of the

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lower chunk was left unmodified, while the ran-dom coils were twisted to align the alpha helicesperpendicularly to the membrane. Simulationsof 30 ns show that the anchor is stable with nolipid rearrangement or vertical translation rel-ative to the membrane.

Drug properties

To better factor the drugs’ flexibility in themodel, their size/shape was estimated by con-sidering a large sample of conformations atroom temperature in water. For this purpose,we run molecular dynamics simulations usingthe CHARMM General Force Field36 to modelthe atomic forces and collected the molecule’sconformations every 20 ps. CGenFF37 softwarewas used to assist in the production of theneeded topology files. Each drug molecule waspresented at the center of an cubic box of 6 nmin each dimension, containing explicit solventof water and 0.15 M NaCl.

MD setup for drug simulation was the sameas the membrane simulations, except the use ofan isotropic ensemble (NPT). Each system wasminimized for 1000 steps, following 0.5 ns of re-laxation and 0.5 ns of production. Every 20 psthe molecule shape was estimated using the pro-tocol described previously8 and then used tocalculate the PLDA for the specific conforma-tion. The final value for PLDA was computedby averaging over the values obtained for allthe conformations. Finally, the partition coef-ficients of the drugs molecules in octanol andwater were taken from PubChem website usingexperimental values when available, or predic-tion from XLogP3 model38 otherwise.

Acknowledgement The authors are grate-ful to Dr. Matteo Masetti and Dr. J. ScottVanEpps for the discussions and suggestions.This work was supported by the University ofMichigan, BlueSky project.

Supporting Information Avail-

able

The following files are available free of charge.

Additional information about the drugs andmembranes involved in this study can be foundin the supplementary information. This mate-rial is available for download via the Internetat http://pubs.acs.org/.

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