Voltammetric Determination of Binding Constant and Stoichiometry of

Jun 28, 2019 - Voltammetric Determination of Binding Constant and Stoichiometry of .... data for Φ vs log [FD], graphs and data for f1 vs [FD] and Δ...
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Voltammetric Determination of Binding Constant and Stoichiometry of Albumin (Human, Bovine, Ovine)−Drug Complexes Davide Ravelli,† Paola Isernia,‡ Andrea Acquarulo,† Antonella Profumo,† and Daniele Merli*,† †

Università degli Studi di Pavia, Dipartimento di Chimica, Viale Taramelli 12, 27100 Pavia, Italia Fondazione IRCCS Policlinico San Matteo, Servizio di Immunoematologia e Medicina Trasfusionale, Centro Lavorazione e Validazione Emocomponenti (CLV), Viale Camillo Golgi 19, 27100 Pavia, Italia

‡

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S Supporting Information *

ABSTRACT: The parameters characterizing the formation of complexes with albumin (in particular, human serum albumin (HSA)) are fundamental for the characterization of a drug for commercialization purposes and for the determination of common pharmacokinetic parameters. Electrochemical methods appear particularly attractive for the determination of the complexation constant, complex stoichiometry, and percentage of free/bound drug, due to the ease of operation and the wide availability. In this article, we propose an electrochemical method based on differential pulse voltammetry for the determination of albumin-drug interaction parameters, including the replacement of the drug-albumin adduct by a competitive compound, sulfanilamide. The formation of either single or multiple complexes between the considered drug and albumin has been considered. Typically, the method operates with a glassy carbon electrode in NaCl 0.9% as the supporting electrolyte.

T

Traditionally, the evaluation of the binding constants of a drug to plasma proteins is carried out through equilibrium dialysis, and this is commonly presented as the reference method. However, this approach suffers from many drawbacks, such as long equilibration times (typically 12−48 h) and the need to perform an initial set of studies intended to define the time needed for the system to reach equilibrium, and these have led to the development of alternative methods.8,9 All such approaches are useful for some drugs but fail with others, and the only data they provide is the bound drug fraction, not the stoichiometry of the complex, nor the binding constant. Moreover, all these alternative methods are generally expensive and time-consuming and require costly dedicated instrumentations. Some examples include (i) HPAC/FA, highperformance affinity chromatography/frontal analysis approach; (ii) PAMPA, parallel artificial membrane permeability assay; and (iii) ACE, affinity capillary electrophoresis.8 Furthermore, some other specific drawbacks are present, e.g., the PAMPA is unsuited for the analysis of very lipophilic drugs that might stay trapped in the membrane. On top of that, the membrane has to be chosen according to the compound studied, which can be problematic, and liquid chromatography−mass spectrometry (LC−MS) or LC−MS−MS instruments are needed.8 In the case of ACE techniques,10,11 the risk

he pharmacokinetics and pharmacodynamics of a drug are strongly influenced by its binding to the serum proteins, and the evaluation of the drug-binding ability to these macromolecules (in particular, to plasma proteins) is mandatory for the development and commercialization of an active principle.1,2 Plasma is a very complex matrix, consisting of more than 300 proteins, which show a dynamic concentration range spanning over more than 10 orders of magnitude. These proteins include albumin, as well as other less abundant derivatives that are now measured clinically, such as α, β, and γ globulins, fibrinogen, and clotting proteins. In fact, apart from classic “plasma proteins”, this fluid contains tissue proteins and distinct immunoglobulin sequences.3 Although there may be many components in plasma capable of binding drugs, two major proteins, namely, human serum albumin (HSA) and α1-acid glycoprotein (AGP), can bind a broad variety of drugs with an affinity sufficient to impart a significant effect on drug disposition and action. Indeed, while AGP mainly binds basic compounds, HSA, which shows a higher affinity for acidic compounds, can bind basic and neutral drugs as well. Globulins and lipoproteins may also play a role, albeit to a lower extent.4,5 Furthermore, some drugs are transported by selective proteins, such as thyroid hormones (bound to transthyretin)6 and steroids (transported by sex hormone-binding globulin (SHBG) and corticosteroid-binding globulin (CBG)).7 © XXXX American Chemical Society

Received: May 3, 2019 Accepted: June 28, 2019 Published: June 28, 2019 A

DOI: 10.1021/acs.analchem.9b02088 Anal. Chem. XXXX, XXX, XXX−XXX

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Analytical Chemistry

Figure 1. Differential pulse voltammetry (DPV) of (a) furosemide and (b) ketoprofen recorded for supporting electrolyte (NaCl 0.9%), albumin (40 g·L−1), free drug (5 mg·L−1), and drug in the presence of albumin and after the addition of sulfanilamide. This experiment, described as experiment (a) in the text, allows the determination of the percentage amounts of bound and displaced drug.

diclofenac, furosemide, ketoprofen, L-dopa (levodopa), lorazepam, metoclopramide, nifedipine, nitrofurantoin, paroxetine, thalidomide. In addition, we have also studied the competition in the complexation of the drugs by addition of an electro-inactive molecule that is known for its ability to interact with albumin, i.e., sulfanilamide. When present in solution, the competitor should displace the bound drug from the complexation site and thus increase the fraction of the free drug in the plasma19−21 along with its analytical signal, accordingly.22

of protein adsorption on the capillary walls and the low detection limits of commonly used UV detectors are wellknown drawbacks.8 Finally, spectroscopic techniques12 have a low throughput and fail when very high- or very low-affinity processes are of concern, and in some cases large amounts of highly pure materials are required for accurate measurements.8 For these reasons, alternative approaches are highly desirable, in turn offering the possibility to compare data obtained with different methods and to address existing problems.8 Among the possible alternative methods, although electrochemical methods, and among them voltammetric ones, are well-known for their ability to determine the labile, i.e., free plus weakly bound, fraction of a molecule in solution in the presence of a complexing agent,13 their systematic use as a tool for the determination of plasma proteins−drug complexation stoichiometry and constants has not been explored so far. Indeed, electrochemical methods are not considered among those useful for the determination of these parameters in relation to pharmacokinetics.8,9 Nevertheless, robust mathematical approaches for the evaluation of these values with respect to pharmacologically inactive species interacting with pure albumin or other biologically relevant polymers, are reported in the literature,14−18 although often relegated to specialized journals. Electrochemical methods are characterized by versatility, high sensitivity, and simplicity. Compared to the above-mentioned techniques, electrochemical assays are simple and low-cost, offering a high throughput and fast response.18 In this paper, we discuss the use of a cheap and fast, yet robust and reliable, alternative approach based on a voltammetric method for the determination of the stoichiometry and complexation constant of selected drug molecules with two pure plasma proteins (bovine serum albumin (BSA) and ovalbumin (OVA)) and with whole human serum (HS). A necessary condition for the applicability of the described method is that the chosen drugs must be electroactive in a potential range compatible with the biologically mimicking conditions used in the experiments. Typically, 0.9% sodium chloride in water was used as the supporting electrolyte, operating with differential pulse voltammetry (DPV) and a glassy carbon electrode. Thus, we have investigated ten drugs belonging to different therapeutic categories, namely:

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EXPERIMENTAL SECTION Details on instruments, drugs, and albumin samples can be found in the Supporting Information, section “S.1 Materials and methods”. Two main goals were reached in this work, with two different electrochemical experiments conducted by employing the same drug. In detail, a first experiment (a) made possible the determination of the free fraction of the drug alone and in the presence of sulfanilamide, competing for the same binding site of the protein. Furthermore, a second experiment (b) allowed determining the stoichiometry and binding constant of the drug with the chosen complexing agent (BSA, OVA, human serum). For experiment (a), a DPV was recorded in 0.9% NaCl typically containing a 10−5 M drug concentration (see relevant examples in Figure 1). Albumin was then added to the voltammetric cell in large excess with respect to the drug, typically 10−3 M, and a new DPV was recorded. At this point, sulfanilamide was added to the voltammetric cell for the competition study, at a concentration similar to that of the drug (10−5 M).19 The percentage amounts of bound and displaced drug have been then calculated as follows:8 % bound drug =

ITD − IFD ·100 ITD

where TD stands for “total drug”, FD for “free drug”, and I stands for the current observed in each case. The amount of displaced drug is calculated as % displaced drug = B

IS − IFD ·100 IFD DOI: 10.1021/acs.analchem.9b02088 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry where S refers to the experiment run in the presence of sulfanilamide while I has the same meaning as above. For experiment (b), a calibration curve of the drug in 0.9% NaCl was built and subsequently the same analysis was repeated in a solution containing the complexing protein and 0.9% NaCl. Typically, the concentration of the drug spanned from 10−5 to 10−3 M, while the concentration of the protein (albumin) was in defect and was maintained at 100 mg·L−1. This value corresponds to (1−2.5) × 10−6 M, depending on the chosen albumin: OVA, BSA, or human serum. In the last case, human plasma was opportunely diluted, since the original concentration of albumin in the plasma used was 42 g·L−1. (See Supporting Information for further details.) We have chosen to work with whole human serum (HS), instead of with isolated human albumin (HSA), since human serum contains different proteins that can cooperate with HSA for the complexation of a given drug (e.g., TBG for thyroid hormones, globulins for basic drugs, etc.), so that a “serum binding constant” could be obtained. Indeed, since the same approach is usually considered in the literature9 (i.e., the evaluation of the binding constant to whole human serum rather than to HSA), the obtained values can be easily compared. The typical approach proposed in the literature for the determination of association constants exploits two different mathematical treatments, and the choice between them depends on a preliminary evaluation that establishes if the formation of a single or multiple complexes (i.e., more than one complex is present in solution under the operative conditions) occurs. We discuss in detail the mathematical approach in the Supporting Information. The relevant literature can also be considered for further details.17,22−24 Briefly, in the case of either single or multiple complexes, the method requires the acquisition of a calibration curve (ITD vs CDRUG, where TD stands for “total drug”) of the drug (where CDRUG is in the 10−3 to 10−5 M range) and the acquisition of a calibration curve for the drug (as before, IFD vs CDRUG, where FD stands for “free drug”) in the presence of albumin (typically 10−6 M), with a concentration range of the drug higher than the concentration of albumin (CALB). It is worth noting that we limited the present study to concentration ranges wherein a linear dependence applied to both the ITD vs CDRUG and the IFD vs CDRUG cases. Since the complex is not electro-active under operational conditions, for each point on the calibration curves, it is possible to take the punctual difference (ΔI = ITD − IFD) and then build the corresponding curve (showing a linear dependence, under our experimental conditions; see Figure S1) by plotting ΔI vs CDRUG. According to the method of Li16 and Sun,17,24 in the case of the single complex, the stoichiometry of the complex between ALB and drug (D) and the equilibrium constant βs of the reaction can be described as follows:

ΔI = K [ALB−mD]

(3)

[ALB] + [ALB−mD] = CALB

(4)

ΔImax − ΔI = K (CALB − [ALB−mD]) = K [ALB]

(5)

where ΔI is the difference between the peak current (Ip) as defined above and ΔImax represents the maximum value of the obtained differences; CALB, [ALB−mD] and [ALB] refer, respectively, to the total concentration of ALB, the concentration of the complex and the free concentration of ALB in the solution, while K is a proportionality constant. By arranging eqs 1, 3, and 5, eq 6 is obtained: ij yz ΔI zz = log β + m log [FD] logjjj s j ΔImax − ΔI zz k {

With CALB kept at 10

−6

(6)

M and with variation of the

(

concentration of the drug, the plot of Φ = log

ΔI ΔImax − ΔI

) as

a function of log [FD], i.e., the concentration of the free drug at the equilibrium, is linear and gives the values of log βs (intercept) and m (slope). See Figure 2 for the representative case of ketoprofen and OVA.

Figure 2. Relationship between Φ and log [FD] in the case of ketoprofen with OVA, exemplificative of the single complex formation situation.

In the case of multiple complexes formation,9 a nonlinear relation between Φ and log [FD] is found (see Figure 3), which implies that ALB and D form different complexes in steps. Thus, the resulting curve can be divided in two sectors, and two lines with slope corresponding to m1 and m2 are obtained, respectively (meaning that two complexes with different stoichiometries between ALB and D are formed). According to the literature, the equation that applies to the case of multiple complexes in solution is

βS

where ALB is albumin, D the drug, ALB−mD the complex, and m the stoichiometry of the complex. From this, the equilibrium constant βS can be obtained: [ALB−mD] [ALB][FD]m

(2)

Thus, it follows

ALB + mD V ALB−mD

βS =

ΔImax = KCALB

ΔI = (1)

ΔI1β1[FD] + ΔI2β2[FD]2 + ... + ΔInβn[FD]n 1 + β1[FD] + β2[FD]2 + ... + βn[FD]n

(7)

where ΔI stands for the total decrease of Ip, as previously defined in the case of the single complex, while ΔI1... ΔIn stands for the decrease of Ip in each step, and [FD] for the

According to the literature,13 the following equations apply to the present case: C

DOI: 10.1021/acs.analchem.9b02088 Anal. Chem. XXXX, XXX, XXX−XXX

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Plotting f1 as a function of [FD] yields a new curve (see Figure S4), and according to the literature, it can be concluded that the tangent of the curve (f1 vs [FD]) intersects the ordinate axis at a1 and has a slope a2 for [FD] approaching to zero: lim f = ΔI1β1 = a1

x→0 1

lim

x→0

df1 d[FD]

(9)

= ΔI2β2 − ΔI1β12 = a 2

(10)

Substituting Z = 1/[FD] in eq 7 and multiplying for Zn/Zn yields eq 11: ΔI = Figure 3. Relationship between Φ and log [FD] in the case of ketoprofen with HS, exemplificative of the multiple complexes formation situation.

n

1 + β1[FD] + β2[FD] + ... + βn[FD]

(11)

lim ΔI = ΔIn = b1

(12)

z→0

ΔI1β1 + ΔI2β2[FD] + ... + ΔInβn[FD]n − 1 2

βn + βn − 1Z + ... + β1Z n − 1 + Z n

Thus, when ΔI is splotted as a function of Z (where Z = 1/ [FD]), another curve is obtained (see Figure S5). When Z approaches zero, the tangent to the curve (ΔI vs Z) intersects the ordinate axis at b1 with a slope b2.

equilibrium concentration of the free drug (not the total concentration of the drug). Defining f1 = ΔI/[FD] and introducing it into eq 7, eq 8 results: f1 =

ΔInβn + ΔIn − 1βn − 1Z + ... + ΔI1β1Z n − 1

lim

x→0

(8)

β dΔI = (ΔIn − 1 − ΔIn) n − 1 = b2 dZ βn

(13)

Table 1. Parameters Characterizing the Complexation of the Studied Drugs with OVA, BSA, and HSa

diclofenac - OVA diclofenac - BSA diclofenac - HS furosemide - OVA furosemide - BSA furosemide - HS ketoprofen - OVA ketoprofen - BSA ketoprofen - HS L-dopa - OVA L-dopa - BSA L-dopa - HS lorazepam - OVA lorazepam - BSA lorazepam - HS metoclopramide - OVA metoclopramide - BSA metoclopramide - HS nifedipine - OVA nifedipine - BSA nifedipine - HS nitrofurantoin - OVA nitrofurantoin - BSA nitrofurantoin - HS paroxetine - OVA paroxetine - BSA paroxetine - HS thalidomide - OVA thalidomide - BSA thalidomide - HS

complex type

complex stoichiometry (m) and affinity constant (log βs)

single single single single single single single multiple multiple multiple multiple multiple single single single single single single multiple multiple multiple multiple multiple multiple multiple multiple multiple single single single

m = 1, log β = 6.02 m = 1, log β = 5.08 m = 1, log β = 5.04 m = 2, log β = 7.56 m = 1, log β = 4.49 m = 1, log β = 4.04 m = 3, log β = 13.43 m1 = 1, log β1 = 4.80, m2 = 3, log β2 = 11.38 m1 = 1, log β1 = 5.20, m2 = 3, log β2 = 9.15 m1 = 1, log β1 = 3.58, m2 = 3, log β2 = 8.60 m1 = 1, log β1 = 3.47,m2 = 3, log β2 = 9.15 m1 = 1, log β1 = 4.90, m2 = 3, log β2 = 8.63 m = 3, log β = 10.40 m = 1, log β = 3.86 m = 1, log β = 4.61 m= 1, log β = 4.46 m= 2, log β = 6.10 m = 2, log β = 5.20 m1 = 1, log β1 = 4.87, m2 = 3, log β2 = 8.42 m1 = 1, log β1 = 4.37, m2 = 3, log β2 = 7.49 m1 = 1, log β1 = 4.42, m2 = 3, log β2 = 7.62 m1 = 1, log β1 = 5.17m2 = 4, log β2 = 8.97 m1 = 1, log β1 = 6.20, m2 = 4, log β2 = 9.20 m1 = 1, log β1 = 5.36, m2 = 4, log β2 = 9.35 m1 = 1, log β = 3.71, m2 = 3, log β2 = 23.41 m1 = 1, log β = 5.43, m2 = 4, log β2 = 18.76 m1 = 1, log β = 5.06, m2 = 4, log β2 = 24.02 m = 2, log β = 8.48 m = 2, log β = 9.30 m = 2, log β = 7.57

literature log βs log β = 5.3834 log β = 5.735

log β = 4.4329 log β = 5.0937 log β = 4.0728

log β = 4.3638 log β = 3.4839

log β = 5.0226 log β = 4.4827

log β = 4.6340 log β = 6.8541

log β = 4.6342

log β = 4.031

% bound drug (found) 44 99 99 61 65 89 42 92 96 94 97 96 95 100 98 15 21 25 72 69 64 32 45 95 90 95 93 27 54 28

% bound drug (literature data)

9936

9036

9936

3036

8536

13−3036

60−9036

60−9936

9536

% displaced by sulfanilamide 16 12 15 12 45 37 40 48 52 0 0 0 0 9 10 0 0 0 0 0 0 0 9 7 0 0 0 30 36 40

Data reported are the mean of five independent measures. ±2 unit uncertainty on the last digit was found.

a

D

DOI: 10.1021/acs.analchem.9b02088 Anal. Chem. XXXX, XXX, XXX−XXX

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be seen, in some instances the drug is significantly displaced by sulfanilamide, as in the case of ketoprofen with BSA or human serum, while in other cases (metoclopramide, L-dopa, nifedipine, paroxetine) the displacement is negligible. Of course, our method, despite being not time-consuming and much more economical with respect to standard methods (e.g., radiolabeling, HPLC-MS), is not free from some drawbacks. In detail, the drug must be electroactive in the electrochemical window available for the water-based solution (typically 0.9% NaCl) and the chosen electrode. We have found that using different electrode types leads to comparable results; in particular, glassy carbon, platinum, or even hanging drop mercury electrode (HDME) can be used. For solid electrodes, an available potential window of −1 to +1.5 V vs Ag/AgCl/3 M NaCl can be expected. The DPV technique was proven to be the best choice in the considered cases, with respect to linear scan voltammetry and normal pulse voltammetry, due to the sensitivity of the technique and the compensation for the relatively high background currents deriving from the presence of albumin in solution.32 As for the supporting electrolyte, we found comparable results either when using 0.9% NaCl or other physiologically mimicking, nondenaturing solutions (in particular, 10 mM PBS, pH 7.2, or Britton−Robinson buffer).24 Organic-based supporting electrolytes were not tested, due to their denaturing capability toward albumin. Another possible limitation of the method is that the drug must be detectable when present in solution at reasonably high concentration (10−6 to 10−5 M), suitable for the analysis with DPV. No attempts were done to investigate drugs that are typically present in plasma at μg·L−1 concentration (e.g., digoxin). In this case, stripping techniques could perhaps be used,33 but their evaluation was beyond the scope of the present work.

Since in the considered cases only two complexes are formed (see Figure 3), solving the following equations, obtained from eqs 9, 10, 12, and 13, gives ΔI2 = b1 (ΔI1 − ΔI2)

(14)

β1 β2

= b2

(15)

And, then, the desired complexation constants: β1 = β2 =

a1b1 − a 2b2 a1b2 + b21

(16)

a 21 + a 2b1 a1b2 + b21

(17)

The method was applied with success to three different types of albumin (BSA, OVA, and human serum (HS)).

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RESULTS AND DISCUSSION Table 1 gathers the binding constant and stoichiometry values for the formation of complexes between the considered drugs and OVA, BSA, or HS. Furthermore, the data obtained with our method are compared with literature values, when available. As apparent, the obtained values are in accordance with those already reported. Notably, the drug fraction bound to albumin is always strictly consistent with literature data, apart from the case of levodopa, in which significantly different values were found (96% vs 30%). The binding constant data are reported in Table 1 as well, and it is interesting to notice that similar trends have been obtained for HS and BSA, due to the structural homology between HSA and BSA.25 Generally speaking, good agreements between our data and literature data have been found, albeit only a handful of values are available in previous reports. For example, we could find the values for metoclopramide with BSA (log β found 6.10 versus log β literature 5.02),26 metoclopramide with HS (log β found 5.20 versus log β literature 4.48),27 and ketoprofen with HS (log β found 5.20 versus log β literature 4.07),28 showing good agreement among the available data, obtained through different approaches. In the case of furosemide, a logarithm of the binding constant with HS of 4.43 has been reported in the literature,29 in good agreement with our results (log β 4.04). It is worth noting, however, that a higher binding constant (log β 5.6)30 can be found as well. Thus, the wide range of data present in the literature for a defined drug− albumin complex shows that values differing by up to an order of magnitude from each other can be tolerated. In turn, this difference may be attributed to the different methods used for the evaluation and the chosen operative conditions. When we found multiple complexes with different log β values, often the value of β1 (describing the first complex formed) was comparable to the one described in the literature, as physiological conditions and conditions used by most Authors usually involved a great excess of ligand with respect to the drug, as in the case of the first complex we found. In some cases (e.g., thalidomide−HS), our results were quite different from those reported in the literature (log β found 7.57 vs log β literature 4.0),31 perhaps due to the fact that the literature value refers to clinically compromised patients with significant serum alterations.31 Concerning the competition of the chosen drug with respect to sulfanilamide, results are again presented in Table 1. As can

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CONCLUSIONS In conclusion, we have shown that an electrochemical method can be advantageously used for the determination of the interaction parameters between albumin and drugs: complexation constant, complexation stoichiometry, percentage of free drug, and replacement by a competitive substance, e.g., sulfanilamide. The method operates with a DPV technique using a glassy carbon electrode and physiological solution (NaCl 0.9%) as the supporting electrolyte. Thus, such a medium guarantees the conductivity of the solution and mimics physiological conditions, being nondenaturing with respect to albumin and isoosmolar with human serum. The method is rapid, effective (on the basis of the solid literature references), and inexpensive. Different new data regarding the complexation of several drugs with OVA, BSA, or HS are reported in this paper.

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.9b02088. Description of analytical procedures and mathematical treatment of electrochemical data, including graphs and data for peak current vs drug concentration, graphs and data for Φ vs log [FD], graphs and data for f1 vs [FD] and ΔI vs Z, (PDF) E

DOI: 10.1021/acs.analchem.9b02088 Anal. Chem. XXXX, XXX, XXX−XXX

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Analytical Chemistry

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(32) Gupta, V. K.; Jain, R.; Radhapyari, K.; Jadon, N.; Agarwal, S. Anal. Biochem. 2011, 408, 179−196. (33) Ghasemi, J.; Niazi, A.; Ghorbani, R. Anal. Lett. 2006, 39, 1159− 1169. (34) Wang, D.; Zhang, Y.; Liu, Y.-N.; Wang, J. J. Liq. Chromatogr. Relat. Technol. 2008, 31, 2077−2088. (35) Chamouard, J. M.; Barre, J.; Urien, S.; Houin, G.; Tillement, J. P. Biochem. Pharmacol. 1985, 34, 1695−1700. (36) Martindale - The Complete Drug Reference, 38th ed.; Brayfield, A., Ed.; Pharmaceutical Press: London, 2014. (37) Maruthamuthu, M.; Kishore, S. Proc. Indian Acad. Sci. (Chem. Sci.) 1987, 99, 187−193. (38) Machicote, R. G.; Pacheco, M. E.; Bruzzone, L. Spectrochim. Acta, Part A 2010, 77 (2), 466−472. (39) Yeggoni, D. P.; Subramanyam, R. Mol. BioSyst. 2014, 10 (12), 3101−3110. (40) Wang, X.; Liu, Y.; He, L. L.; Liu, B.; Zhang, S. Y.; Ye, X.; Jing, J. J.; Zhang, J. F.; Gao, M.; Wang, X. Food Chem. Toxicol. 2015, 78, 42− 51. (41) Zhang, Q.; Ni, Y. RSC Adv. 2017, 7, 39833−39841. (42) Kariv, I.; Cao, H.; Oldenburg, K. R. J. Pharm. Sci. 2001, 90, 580−587.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Davide Ravelli: 0000-0003-2201-4828 Daniele Merli: 0000-0003-3975-0127 Notes

The authors declare no competing financial interest.

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DOI: 10.1021/acs.analchem.9b02088 Anal. Chem. XXXX, XXX, XXX−XXX