2.2.10. Magnetic Nanoparticles

Magnetic elements (i.e., iron, nickel, cobalt, and their oxides) were utilized for the fabrication of nanomaterials for different medical applications [429,430] such as MRI, drug delivery, medical diagnostics, cancer therapy, biosensoring, and magneto-optic devices. Magnetic nanoparticles can be synthesized through different techniques including co-precipitation [431], microemulsion [432], hydrothermal synthesis [433], sol–gel process [434], polyol synthesis [435], flow injection [436], sonolysis/sonochemical methods [437], microwave irradiation [438], electrochemical synthesis [439], solvothermal method [440], chemical vapor deposition [441], laser pyrolysis [442], and green synthesis [443] using biomass or biotemplate.

Due to high magnetic flux density, magnetic nanoparticles were used for drug targeting [444] and bio-separation [445], including cell sorting [446]. Sun et al. [447] analyzed metallic, bi-metallic, magnetic cationic liposomes and superparamagnetic iron oxide nanoparticles for imaging and drug delivery. The surface of magnetic nanoparticles also needs to be functionalized to recognize specific targets (Figure 21) [448]. Polyethylene glycol (PEG) is one of the best polymers used for the functionalization of magnetic nanoparticles by surface modification [449]. Interestingly, surface modified magnetic nanoparticles reduce nonspecific interaction with biological molecules.

**Figure 21.** Surface modified magnetic nanoparticle.

Magnetic manipulation is another important advantage of magnetic nanoparticles [450]. It is done by labeling cells with magnetic nanoparticles that can easily be controlled by remote control or external magnetic field [451]. The magnetic nanoparticles, which are usually smaller than 10 nm can be easily transported through skin lipid matrix and hair follicles to the stratum granulosum, where it is condensing between corneocytes [452].

In orthopedic surgery, implant-associated infection is a serious issue, as stated in the previous sections. Infection around a bone graft can lead to serious illness or failure of surgery. Drug-loaded Fe3O4 composites promote cell adhesion, proliferation, and osteogenic differentiation of hBMSCs [453–455]. In stem cell therapy for bone regeneration, an application of these NPs is the magnetic targeting of stem cells to the deserved locations, known as magnetic homing of stem cells. For example, penetration of ferumoxide-labeled cells into porous hydroxyapatite ceramic implanted in a rabbit ulnar defect was significantly facilitated by this approach, which improved bone formation even in the chronic process [456].

2.2.11. Summary and Statistical Analysis of the Survey on Inorganic Nanobiomaterials

The survey on inorganic nanobiomaterials presented in Section 2.2 covered some 230 articles, practically the same as its counterpart for organic biomaterials not counting about 30 articles on fabrication techniques of silica and magnetic nanoparticles.

While the number of technical papers appears to be rather uniformly distributed among the ten types of inorganic nanomaterials considered in this survey, it should be noted that most studies focused on nanoparticles and their functionalization for drug/gene/therapy delivery, cell labeling, biosensing, and bioimaging (75%), followed by studies on development and fabrication of new composite materials and scaffolds (25%).

Gold and titania present the largest variety of nanostructures and the latter material may also be available in the form of nanotubes. Gold nanoparticles may represent the best solution for most applications in view of the possibility of controlling size and dimensions of nanostructures as well as for their special physical properties (for example, local plasmon resonance). However, massive utilization of GNPs is obviously limited by the high cost of gold. Silica and titania nanoparticles also are widely utilized as standalone materials or in combination with gold and silver nanoparticles.

Similar to what has been observed for organic nanobiomaterials, a rapidly growing research area in the field of inorganic nanobiomaterials for bone tissue engineering is to hybridize them with other materials (e.g., chitosan, PLA, PLGA, collagen, and hydroxyapatite) to enhance mechanical properties, biocompatibility and osteogenetic properties of the modified materials. Development of high-performance scaffolds comprised of multiple materials is the final stage of this complicated process.


*2.2. 2* × *2 Circularly Polarized Metasurface Array*

This section addresses the reconfiguration from the linear to circularly polarized RWMS array. Since RWMS is designed to excite surface waves on a single plane [11], circular polarization (CP) can be achieved by arranging orthogonal structures of RWMS fed with a 90-degree phase difference. To achieve a gain comparable to the linear RWMS, a 2 × 2 RWMS scheme is considered and designed, as displayed in Figure 4a.

(**a**) Structure is fed with four coaxial ports

(**b**) Structure is fed with feed network

**Figure 4.** 2 × 2 square ring elements embedded within metasurface and excited with (**a**) four coaxial probes, and (**b**) feed network. The feed network parameters are: *l*1 = 50.875 mm, *l*2 = 12 mm, *l*3 = 20 mm, *l*4 = 2.25 mm, *l*5 = 7.2 mm, *l*6 = 11.9 mm, *l*7 = 37.87 mm, *l*8 = 16.92 mm, *l*9 = 0.69 mm, *w*50 = 1.1 mm, *w*35 = 1.9 mm, *gs* = 2.74 mm, *v* = 1.143 mm, *wf* = *l f* = 6 mm, *gf* = 2.45 mm, *r*1 = 1.4 mm, *r*2 = 6.5 mm, and *t* = 0.79 mm.

A square grounded dielectric is modeled with a thickness *h* = 5.08 mm, and a length *lsub* = 133 mm. The spacing *s* along the *x*- and *y*-planes is 56 mm, which corresponds to 0.85*λo* at 4.5 GHz. The dielectric material, the dimensions of the ring elements, and the metasurface patches are identical to those given in the 1 × 4 array configurations. Similar to the linear array, the coupling between the ring elements is investigated first by individually feeding the ring elements with four coaxial probes. The simulated scattering parameters for 2 × 2 circular polarized structure are depicted in Figure 5a. The −10 dB fractional bandwidth is slightly less than that for the 1 × 4 array, with a bandwidth of 25% from 3.9 to 5.0 GHz. The mutual coupling between vertically/horizontally adjacent ports and diagonally orientated ports is less than −20 dB, which is lower than those observed for the 1 × 4 linear array. This is attributed to the orthogonality of the excited surface waves, which agrees with the findings in [41].

**Figure 5.** (**a**) S-parameters and (**b**) the broadside RHCP gain along with the axial ratio of the 2 × 2 RWMS scheme.

Once the coupling is examined, a parallel feeding network is designed to excite all four ring elements with a single coaxial feed, as illustrated in Figure 4b. The feed is designed at the center frequency (4.5 GHz) to split the power equally among the four ring elements with a 90◦ phase increment. The feed can dictate whether the sense of rotation of the CP is right hand (RHCP) or left hand (LHCP), depending on the orientation of the phase increment (clockwise or counterclockwise); in this work, the feed is modeled to realize RHCP. The feed is designed on Rogers RT/Duroid-5880 substrate, which has a thickness *hf* = 0.38 mm. Similar to the 1 × 4 linear array, the feeding substrate is stacked below the 2 × 2 circular array, and the outputs of the feed network are coupled to the ring elements using vias. The remaining dimensional parameters of the feeding network are detailed in the caption of Figure 4. When the feed network excites the 2 × 2 RWMS, the −10 dB matching bandwidth is larger than that obtained with the four coaxial feeds. This is attributed to the parallel feed structures

which do not provide any isolation between the ports; therefore, any reflected power from any port can couple through to other ports resulting in small power return back to the feed.

To further analyze the performance of the 2 × 2 RWMS structure, the broadside realized gain and the axial ratio are examined. The simulated results for the two different feed approaches are presented in Figure 5b.

When the structure is fed with individual probes, the realized gain is 13.5 dB at 4 GHz and reaches up to 15.8 dB at 4.8 GHz. Almost similar gain is attained when the feed network is utilized. However, there is a noticeable discrepancy in the axial ratio between the two feeding methods. For the individual feed approach, the broadside axial ratio (AR) is below 0.25 dB over the design frequency range, while it is only below 3 dB over a more narrow frequency range (3.9 GHz to 5.1 GHz) when the structure is excited with the feed network. The fluctuation in the axial ratio magnitude is due to the poor isolation of the considered feeding network, which leads to magnitude and phase errors for the power reached to each port [42].

The broadside realized gain at the center frequency (4.5 GHz) is around 14.5 dB, while it is only 10 dB for a 2 × 2 conventional array. This corresponds to the aperture efficiencies of 56% and 20% for the metasurface array and its conventional counterpart, respectively. Similar to the linear metasurface array, the high attained efficiency for the 2 × 2 metasurface array is due to the excitation of the radiating element's mode along with the surface waves' modes. This can be verified by examining the surface current distribution in Figure 6, which shows that both the radiating elements and the periodic patches contribute to the total radiation.

**Figure 6.** Surface current distribution at 4.5 GHz for the 2 × 2 metasurface array.

#### *2.3. Fabrication and Measurements*

To experimentally validate the simulated data, the proposed 2 × 2 RWMS circular array was fabricated, as depicted in Figure 7.

**Figure 7.** Top and bottom views of the fabricated prototype of the 2 × 2 RWMS array incorporated with the feed network.

Since linear and circular RWMS arrays share similar characteristics, only the circular array was considered for fabrication. The substrate materials and the dimensions of the fabricated design were identical to those specified in Figure 4. An edge mount SMA connector was soldered to the feed network substrate, as demonstrated in Figure 7. Four nylon screws were placed at the corner to tighten the feed and RWMS substrates together and avoid any misalignment.

The comparison of the measured and simulated results are displayed in Figures 8 and 9. The simulated and measured *S*11 and the axial ratio showed a good agreement; however, the broadside RHCP gain was about 1 dB lower than the simulated data. This deviation could be attributed to the fabrication tolerance and the stacking assembly error. The measured and simulated radiation patterns along different planes are displayed in Figure 9; a good agreement was observed between the simulated and measured data.

**Figure 8.** Comparison between simulated and measured values of the (**a**) reflection coefficient, (**b**) RHCP broadside gain, and axial ratio.

**Figure 9.** Simulated and measured radiation patterns at (**a**) 4 GHz along *xz*-plane, (**b**) 4 GHz along *yz*-plane, (**c**) 5 GHz along *xz*-plane and (**d**) 5 GHz along *yz*-plane.

#### **3. Polarization Reconfigurable Holographic LWAs**

The advent of metasurfaces has facilitated the realization of surface-reactance modulation by varying the geometries of patches that are periodically arranged on a dielectric-covered ground plane. Patches with an inductive reactance support TM-mode surface waves while capacitive surface reactance support TE-mode [43]. The surface reactance of a metasurface unit cell depends on the frequency of operation and can be determined using the transverse-resonance method (TRM) [23].

The surface-reactance modulation function *Xs* required to form a pencil beam, in the desired elevation angle *θd* (from broadside) and the azimuth angle *φd* , is obtained using the holographic principle [4,23]

$$X\_{\mathbf{s}}(\mathbf{x},\mathbf{y}) = X\_{\text{avg}} \left\{ 1 + M e^{j\beta} \sqrt{(\mathbf{x} - \mathbf{x}\_{\varepsilon})^2 + (y - y\_{\varepsilon})^2} \, \mathrm{e}^{j\mathbf{k}\_0 \left( \mathbf{x} \sin \theta\_d \cos \phi\_d + y \sin \theta\_d \sin \phi\_d \right)} \right\} \tag{1}$$

where *Xavg* is the average surface reactance, *M* is the modulation index, (*xc*, *yc*) is the location of the source and *k*0 is the free-space phase constant. This leads to a holographic pattern which is a set of periodic ellipses that have a focus at the location of the source, as shown in Figure 10.

The metasurface forms a pencil beam with a linear polarization depending on the leaky-wave mode. The pencil beam is formed as a superposition of radiation from forward and backward leaky waves that move in opposite directions with a change in frequency. Hence, they have to be operated at the phase-crossover frequency, where the radiation from the forward and backward leaky waves interfere constructively.

**Figure 10.** The holographic pattern of the surface-reactance profile needed to form a pencil beam along (*θd* , *φd* ).

Different polarizations can be obtained by performing geometric modifications along the surface [25]. While operating at non-phase crossover frequencies, vertical and horizontal polarizations can be achieved by including a 180◦ phase shift on either side of the principal axes of the ellipses; a 180◦ phase shift along the minor axis results in vertical polarization, while that along the major axis leads to horizontal. A phase shift of 180◦ on either side of both the major and minor axes, along with a relative phase of 90◦ between them, leads to a circularly polarized pencil beam. The principal axes of the ellipses are expressed as

$$(\mathbf{x} - \mathbf{x}\_c)\cos\phi\_d + (y - y\_c)\sin\phi\_d = 0\tag{2}$$

$$(\left(\mathbf{x} - \mathbf{x}\_c\right)\sin\phi\_d - \left(\mathbf{y} - \mathbf{y}\_c\right)\cos\phi\_d = 0\tag{3}$$

The patterns pertaining to horizontal, vertical, and circular polarization are shown in Figure 11.

**Figure 11.** The holographic pattern of the surface-reactance profile needed to form a pencil beam with (**a**) horizontal, (**b**) vertical, and (**c**) circular polarizations.

Polarization reconfigurability can be achieved by feeding the metasurface with two sources instead of one [6]. The surface-reactance modulation function can be obtained as a superposition of the surface-reactance profile needed for each polarization that includes a 90◦ phase shift between them. Vertically- and horizontally-polarized beams can be formed when the metasurface is excited by the individual sources, and a circularly polarized beam is formed when excited by both sources, as illustrated in Figure 12.

This way, a single metasurface is capable of forming a pencil beam in the desired direction with a horizontal, vertical, or circular polarization. The E-plane radiation patterns demonstrating the polarization reconfigurability is shown in Figure 13.

**Figure 12.** A holographic metasurface, fed by two sources, that forms a pencil beam with three reconfigurable polarization states: vertical, horizontal, and circular polarizations [23].

**Figure 13.** *Cont*.

**Figure 13.** The E-plane radiation patterns obtained at 12 GHz when the antenna is excited by the (**a**) first, (**b**) second, and (**c**) both sources. 12 GHz. The metasurface is realized using Rogers RT/Duroid 5880 substrate of thickness 3.175 mm and a dielectric constant of 2.2. The average surface reactance and the modulation index are 255 and 31 ohms, respectively [23].

The metasurface forms a pencil beam in the desired direction with a vertical, horizontal, and circular polarization when excited by the first, second, and both sources, respectively.

The sense of rotation of the circular polarization depends on the 90◦ phase lead or lag between the two linear polarizations. A circularly-polarized beam with the other sense of rotation can be formed by externally including a 180◦ phase shift between the two feeds or by including another source and modulating the metasurface with the superimposition of the surface reactance pertaining to the three sources [6].

Employing metasurfaces and the principle of holography has resulted in a simple configuration of a multi-polarization high-gain LWA, which would otherwise be difficult to achieve. While the presence of multiple sources, yields the metasurface polarization diverse, internal reconfiguration of polarization can be potentially achieved by including switches between the patches. The difference between the surface-reactance profiles that lead to horizontal and vertical polarizations, is the inclusion of a 180◦ phase difference on either side of the major or minor axis of the ellipses. Electronic switches like PIN diodes can be used to include the 180◦ phase shift, thus resulting in internal reconfiguration; this yields to a metasurface, which is polarization reconfigurable. While having a switch in every unit cell would be expensive and cumbersome to process, the number of switches can be optimized by a judicious design.

#### **4. A Van Atta Retrodirective Microstrip Antenna Array with Low Backscattering**

While the previous two sections demonstrate the application of metasurfaces for surface-wave antenna arrays and leaky-wave antennas, this section focuses on the reconfiguration and improvement of van Atta arrays using AMC-based metasurfaces. In a van Atta reflectarray, the fields reradiated by the antennas are necessary while the fields scattered from the structure (i.e., structural mode) are undesired. Hence, metasurfaces are employed to eliminate these scattered fields from the structural mode of the van Atta retrodirective reflectarray.

#### *4.1. Theory of Passive Retrodirective Arrays*

A schematic diagram of a 1-D van Atta array is illustrated in Figure 14, where four array elements are connected using the two transmission lines (i.e., elements '1' and '4' are connected with each-other while element '2' is connected to element '3'). In order to direct the reradiated waves

back to the direction of the incident wave, these transmission lines must either be of equal lengths or the difference between their lengths should be a multiple of the wavelength. Since these two transmission lines transfer the signals received by elements '1' and '2' to elements '3' and '4', respectively; the lengths of the transmission lines connecting the antenna element should be kept equal to make the functioning frequency independent. Therefore both transmission lines have equal lengths, *l*1 = *l*2 = *l*. When a plane wave is incident obliquely upon the array at an angle *θi* , a phase delay ∆ between the antenna elements is introduced [44]

$$
\Delta = k\_0 d \sin \theta\_l \tag{4}
$$

**Figure 14.** A schematic diagram of a 1-D van Atta retrodirective array where four elements are used in the array.

The monostatic RCS of the retrodirective reflector, based on the reradiated fields only, can be derived following the approach in [38,39] and represented by

$$
\sigma^R = \frac{\lambda\_0^2}{4\pi} G\_0^2 \tag{5}
$$

where *G*0 is the gain of the array in the principle plane, *θ* is the incident/elevation angle, and *λ*0 is the free-space wavelength. The monostatic RCS (*σ R*) of Equation (5) is only based on the fields reradiated (**E R**) by the reflector array. However, the total RCS by the reflector array (*σ T* ) corresponds to the total radiated fields (**E T**) which can be determined by adding the reradiated fields (**E R**) and the scattered fields by the structure (**E S** ). Figure 15 shows a comparison of the ideal monostatic RCS between a retrodirective reflector (*σ R*) of Equation (5) and a flat perfect electric conductor (PEC) plate, where both have the same size.

The retrodirective response is obtained by having a planar array of 4 × 4 radiating elements, each of gain [*G*0(*θ*)] and with 0.5*λ*0 spacing between them. Thus, the total size of both the reflector array and the flat PEC plate is 2*λ*0 × 2*λ*0. It is clear from Figure 15 that the fully passive reflector array can offer a maximum reradiation towards the direction of incidence. However, due to the limitation of the patch antenna towards the grazing angles, the maximum reradiation pattern follows the radiation pattern of the single antenna.

**Figure 15.** Comparison of the ideal monostatic RCS between a retrodirective reflector (*σ R*) obtained by using (5) and a flat PEC.

#### *4.2. Design of the Patch Antenna and Its Feeding Network*

In principle, a retrodirective array can be implemented with any kind of antenna element where the bandwidth performance and beamwidth of the array will be directly impacted by the performance parameters of the single radiating element. Due to their design simplicity and their low profile, patch antennas are chosen to design the utilized retrodirective array.

#### 4.2.1. Aperture-Coupled Patch Antenna with Microstrip Line Feeding Network

Figure 16 illustrates the retrodirective reflector that is constructed using a 4 × 4 finite array of rectangular microstrip-patch antennas.

**Figure 16.** The geometry of the four by four finite array of rectangular microstrip patch antennas that are connected in the van Atta configuration in the *xz*-plane, (**a**) top. (**b**) bottom.

The elements are interconnected as per the van Atta configuration (i.e., illustrated in Figure 14) in the *xz*-plane. The feeding network is constructed using a 50 ohms microstrip transmission lines of equal length (i.e., *l* = *l*1 = *l*2). The dimensions of the square array along the *x*- and *y*-direction are 120 mm, corresponding to 2*λ*0; *λ*0 is the free-space wavelength at 5 GHz. The top side of the structure consists of 16 patch elements resonating at 5 GHz. With reference to Figure 16, the array spacing is *d* = 30 mm (0.5*λ*0), and the patch dimensions are *L* = 11 mm and *W* = 15.4 mm. All the elements are designed on Rogers-RO3006 dielectric substrate (thickness = 1.28 mm, *εr* = 6.15 and tan *δ* = 0.0022 at 10 GHz) backed by a PEC.

The above-mentioned design was (modeled in HFSS) illuminated by a plane wave incident along the principal plane (*xz*-plane) defined by *φi* = 0 ◦ , 180◦ and for 0 ◦ ≤ *θi* ≤ 90◦ . The configuration was simulated with two different lengths (*l*) of the transmission lines that are connecting the antenna elements. The total monostatic RCSs (*σ T* ) of the retrodirective array for the two transmission lines are depicted in Figure 17. The difference between the two responses is primarily due to the change in the phase of **E R** with respect to **E S** when the length of the transmission lines is changed.

**Figure 17.** The monostatic RCSs of a retrodirective array with two different transmission line lengths compared to the monostatic RCS of a PEC plate of the same size.

Table 1 summarizes the values of the broadside RCS (*θi* = 0 ◦ ) of the retrodirective array for the two different lengths of transmission line. It is assumed that the phase of the scattered fields by the array structure (**E S** ) does not vary significantly with the change of length *l* of the transmission line (this will be verified in the next section). Thus, the total scattered (**E T**) will depend on the varying phase introduced by the transmission line. As seen in Table 1, for the first length of *l* = 145 mm, the two components (**E S** and **E R**) of **E T** added destructively near broadside incidence, while for the second length of *l* = 150 mm, the two components are in-phase and thus the total RCS is higher than the reradiated *σ R* obtained using Equation (5). With the current retrodirective setup (aperture-coupled patch antenna with microstrip line feeding network), it is extremely difficult to extract two components of the total scattered fields. In the next section, it will be shown that the virtual feeding network can be used to identify the two separate components of the total scattered field, and it can permit the targeting and cancellation of one of the components, especially the scattering by the structural mode, which is undesirable.

**Table 1.** The broadside (*θi* = 0 ◦ ) RCS of the retrodirective array for two different coaxial cable lengths.

#### 4.2.2. Aperture-Coupled Patch Antenna with Virtual Feeding Network

An alternative method for simulating the retrodirective reflector explained in Section 4.2.1 is to use a virtual feeding network. The basic concept of a virtual feeding network is to capture how much voltage (magnitude and phase) is received by each antenna element, and then feed the measured voltages to the respective antenna elements following the van Atta configuration shown in Figure 14. All the radiating elements of the array are terminated with an impedance equal to the input impedance of the antenna (ideally 50 ohms). Then, at the termination point, the received voltage

from the illuminating plane wave is determined by integrating along the electric field lines around the microstrip lines, similar to quasi-static simulators. This integration is performed based on the mode of propagation, which is Quasi-Transverse Electromagnetic (Quasi-TEM) as illustrated in Figure 18 [45]. The above-mentioned process is achieved by using a co-simulation between the full-wave simulator (Ansys Electromagnetic Suite/HFSS), and a Python code that is written to integrate the electric fields and then feed those calculated voltages again to their respective elements in the full-wave simulator. Figure 19 shows the structure that was configured in HFSS. Here, each of the antenna ports (i.e., transmitting antennas) is terminated with an impedance that is equal to the input impedance of the corresponding connected antenna (i.e., receiving antenna) to replicate the connection between them.

— E-fields - - - H-fields

**Figure 18.** Electric and magnetic field lines for the TEM mode of a coaxial line.

**Figure 19.** (**a**) Top and (**b**) bottom geometries of the 4 × 4 finite array of rectangular microstrip-patch antennas that are terminated and virtually connected in the van Atta configuration in the *xz*-plane.

Similar to the arrangement of Section 4.2.1, the structure is illuminated by a plane wave incident in the principal plane (*xz*-plane) defined by *φi* = 0 ◦ , 180◦ and for 0 ≤ *θi* ≤ 90◦ . Again, by using the Python script, the voltage captured by each antenna element at each incident angle is recorded and fed again to their counterpart reradiating element. This process allows for the decoupling of the total scattered fields **E T** into the two main components: reradiated fields **E R** and structural mode scattering **E S** ; their corresponding RCSs are shown in Figure 20. It can be shown that the phase of **E S** is primarily dependent on the structure, and it is not impacted by the phase introduced by the connecting cables. However, the phase of **E R** can be altered based on the connecting cables. Thus, to illustrate the impact of the phase difference (∆*RS*) at the broadside (*θi* ) between **E S** and **E R**, the total RCS by the retrodirective array is shown in Figure 20 when ∆*RS* = 0 ◦ and 180◦ , respectively.

**Figure 20.** The monostatic RCSs based on the reradiated, scattered, and the total fields when the phase difference (∆*RS*) between **E R** and **E S** is 0◦ and 180◦ .

#### *4.3. Retrodirective Reflector with Low Backscattering Using Metamaterials*

It was shown in the previous section that the scattered fields from the structure introduce ripples in the total RCS by the reflector, especially near the broadside, where the RCS of the structure is large. Ideally, **E S** can be canceled by a judiciously synthesized metasurface, like that of the widely-known concept of checkerboard metasurfaces [46].

Design of Metasurfaces Based on AMCs for RCS Reduction of Retrodirective Array

The RCS of the array structure can be reduced by placing an AMC, whose reflection is similar in magnitude but with a 180◦ phase difference compared to that of **E S** . As shown in Figure 20, the RCS of the structural mode is around −10 dBsm and the phase of its reflected fields is around 130◦ . Thus, an AMC surface of square patches is designed where their width and unit cell dimensions are selected to have a reflection phase of around −50◦ and an RCS of −10 dBsm, similar to the monostatic RCS corresponding to the structural mode of the antenna.

**Figure 21.** Geometries of (**a**) the finite array of the AMC and (**b**) the 4 × 4 array of rectangular microstrip patch antennas combined with the finite AMC array for canceling the RCS of the structure.

The geometries of the synthesized finite sized AMC metasurface are depicted in Figure 21a. As a result, as illustrated in Figure 21b, the final design of the proposed retrodirective reflector is obtained by placing the original antenna array of Figure 19 adjacent to the synthesized AMC of

Figure 21a. This configuration will introduce fields by the AMC with a phase difference of 180◦ compared to the phase of the scattered fields by the antenna structure.

This design is then illuminated by following a similar arrangement to the retrodirective reflector without the AMC, as explained previously in Section 4.2. The monostatic RCSs due to the antenna structure, reradiated fields, and the total fields are shown in Figure 22. It can be seen that regardless of the phase introduced by the connecting cables that impact the phase of the reradiated fields only, the monostatic RCS caused by the structure *σ S* is reduced at and near broadside. This causes the total RCS to have smaller ripples and the performance becomes less sensitive to the interference by the scattered fields of the structure.

The RCS, which corresponds to the fields scattered by the antenna structure, can be reduced by placing a metasurface with a similar RCS and 180◦ phase difference with respect to the reflected fields of the antenna array. The range of the phase difference can be relaxed to achieve an RCS reduction of at least 10 dB when the phase difference is maintained within (180 ± 37) ◦ [46,47]. Using this criterion, the RCS of various antennas has been reduced for more than 10 dB over a wide frequency band [46,48,49]; furthermore, a 10-dB RCS reduction is not necessary here. It is sufficient that the RCS of the scattered fields is lower than that of the reradiated fields by a certain factor based on the acceptable level of the ripples in the total field. Consequently, the phase-difference criterion of (180 ± 37) ◦ can be relaxed further, and a much broader operational bandwidth can be attained. In this model, the metasurface is designed to achieve the RCS-reduction at the operating frequency of the van Atta reflector to validate the concept, which can be applied over a broader frequency range.

The scanning capability of the van Atta reflector depends on the overall gain of the array (*G*0) as given by Equation (5), where *G*0 is determined by the radiation pattern of the single element and the spacing between them. Thus, individual radiating elements with broad beamwidths result in a reflector that responds to larger incident angles. However, antennas with broad beamwidths usually have a lower gain. As a figure of merit, the 10-dB beamwidth of the array is considered as its scanning range. In this design, the monostatic RCS plotted in Figure 22 shows that the 10-dB beamwidth of the designed array is about 120◦ in the principle scanning plane (i.e., *xz*-plane).

**Figure 22.** The monostatic RCSs based on the reradiated, scattered, and the total fields when the phase difference (∆*RS*) between **E R** and **E S** is 0◦ and 180◦ .

#### **5. Conclusions**

The application of metasurfaces in the reconfiguration and enhancement of the performance of surface-wave antenna arrays, holographic leaky-wave antennas, and retrodirective arrays is illustrated. Square ring-element arrays embedded within metasurfaces are proposed for linear and circular polarizations. The coupling between the ports and their effect on the broadside radiation are discussed

and highlighted. For practicality, parallel feed networks are incorporated with the two proposed designs. With the incorporation of metasurfaces, a high realized gain and a high aperture efficiency, compared to a conventional square ring array, are observed. A prototype was fabricated and a good agreement between the simulations and measurements was demonstrated.

Metasurface LWAs designed using the holographic principle are reviewed. In particular, polarization-diverse holographic metasurfaces that can form a pencil beam with a horizontal, vertical, or circular polarization are discussed. A potential technique to incorporate electronic reconfigurability is proposed.

As the third area of illustration, metasurfaces are employed to improve the monostatic RCS performance of retrodirective arrays. A co-simulation method that allows for decoupling the total scattered fields is introduced. Such a simulation allows the identification and cancellation of the undesired component in the scattered fields (those scattered by the antenna structure). It is shown that this work enables the use of retrodirective arrays comprised of simple patch antennas while simultaneously addressing the issue of backscattering by using metasurfaces based on AMCs.

**Author Contributions:** Conceptualization, S.R. and C.A.B.; methodology, M.A., M.A.A. and S.R.; software, M.A., M.A.A., and S.R.; validation, C.R.B., C.A.B. and A.Y.M.; formal analysis, S.R., A.Y.M. and C.A.B.; investigation, M.A., M.A.A. and S.R.; resources, M.A., M.A.A. and S.R.; data curation, M.A., M.A.A., S.R. and C.R.B.; writing—original draft preparation, M.A., M.A.A. and S.R.; writing—review and editing, C.A.B., S.R. and A.Y.M.; visualization, M.A., M.A.A. and S.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


For approximately one quarter of the patients receiving polytherapy treatment, it involves the coadministration of olanzapine in infratherapeutic doses for hypnotic purposes, or coadministration of quetiapine for the same purpose. Therefore, there was a small percentage (less than 10%) who truly receives neuroleptic polytherapy as such (Table 4).

**Table 4.** Infratherapeutic dosage percentage.

The criteria to decide what was considered to be the "minimum therapeutic dose" was based on their respective datasheets and the Sthal's Prescriber Guide. PGx: Pharmacogenetic Analysis.

#### *3.3. Drug-Gene Conflicts*

The presence of the alleles listed in Table 5 of the CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A4, and CYP3A5 genes was analyzed in the Pharmacogenetics and Precision Medicine Unit of the University Hospital of Salamanca. Table 6 shows the frequency of occurrence of the estimated phenotypes from the detection of the different alleles in the sample. Notice that as a Precision Medicine-approach the model is adapted to the specific situation of the patient. Each patient was genotyped, selecting in each case the SNPs that could predict the metabolization of the prescribed therapy. Since each prescription can be different, SNPs can be different between patients.

**Table 5.** Pharmacogenetic testing. Genes and alleles included in the study.

Alleles listed using the PharmVar Haplotype nomenclature. \*: Allele. WT: Wild Type.

#### **Table 6.** Phenotype profile.

Allele Frequencies of the sample were similar to the NCBI dbSNP ALFA Project Frequencies [37]. PM: Poor Metabolizer. IM: Intermediate Metabolizer. EM: Extensive Metabolizer. UM: Ultrarapid Metabolizer. HI: Higher Inducibility.

The CYP1A2 gene was studied in 179 patients, with the most prevalent phenotype being HI (Higher Inducibility) (86.03%, *n* = 154). Among the 166 patients who were tested for the CYP2B6 gene, the most common phenotype was EM (Extensive Metabolizer) (50.00%, *n*. 83). In reference to the CYP2C9 and CYP2C19 genes (*n* = 183, *n* = 186), the majority phenotype was EM (57.38%, *n* = 105; 47.85%, *n* = 89). Most patients had an EM phenotype (85.95%, *n* = 159; 91.49%, *n* = 172) with respect to CYP2D6 cytochromes (*n* = 183) and CYP3A4 (*n* = 188), and PM (Poor Metabolizer) phenotype (87.85%, *n* = 159) was the most frequent with respect to CYP3A5 cytochrome (*n* = 187).

In total, taking into account the liver metabolism, mediated through the previously mentioned CYP450 system members, from both antipsychotic therapy (Table 7) [18,31,38] and concomitant therapy, 458 conflicts were discovered between the patient's prescription and genotype, with most conflicts associated with concomitant therapy occurring through cytochrome CYP3A5 (*n* = 57), and those primarily related to the metabolism of neuroleptics occurring through CYP1A2 (*n* = 80); taking into account the minor metabolizers of the studied antipsychotics, the cytochrome with the highest number of incidences found was CYP3A5 (*n* = 93) (Figure 3). The number of prescriptions of antipsychotics to patients with non-efficient metabolic phenotypes is summarized in Table 8. Following the conduct of the pharmacogenetic study, the total drug-gene conflicts were reduced by 11.02% (*p* < 0.05), based on a reduction in conflicts between neuroleptic therapy and the patient genotype of 19.80% (*p* < 0.05) considering all possible interactions, and 39.02% (*p* < 0.05) considering those that had a noticeable potential impact on the plasma levels of the drug involved. Moreover, with drug-drug interactions, there were more gene-drug interactions associated with non-psychiatric concomitant therapy.

**Table 7.** Antipsychotic therapy CYP450-mediated metabolism.

\* Almost null influence on plasma levels, # substrate inhibition, † suicide inhibition, ‡ inductor, ˆ inhibits CYP3A4.

**Figure 3.** Number of conflicts between the patients' genotype and the prescribed pharmacotherapy associated with each member of the CYP450 system studied. PGx: Pharmacogenetic Analysis.

**Table 8.** Number of cases (and percentage of the total of individuals presenting each specific phenotype) prescribed with and antipsychotic metabolized by an altered pathway.

HI: Higher Inducibility. PM: Poor Metabolizer. IM: Intermediate Metabolizer. UM: Ultrarapid Metabolizer.

#### *3.4. Clinical Impact*

The number of prescriptions for the most widely used atypical neuroleptic drugs, together with the prescribed minimum, maximum, and mean dose (mg/day), prior to and after intervention using method 5SPM, are listed in Tables 9 and 10. It is frequent to see doses beyond the recommended upper limit registered in the pharmacological data sheet of each drug, as frequently prescribe doses were increased by 20–30% before making the decision of considering the pharmacotherapy as a failure and choosing other neuroleptic [39]. As can be seen, the prescription of oral drugs whose metabolism is related to enzymes frequently altered in the study population, is reduced by between 20 and 100%, in favor of intramuscular prescriptions that either do not have liver metabolism, such as Paliperidone, which increases by 186.95%, or whose main route of metabolism is associated with an enzyme that usually has no alterations, such as Aripiprazole, whose prescriptions increase by 100%, predominantly metabolized by CYP3A4.

**Table 9.** Mean, Min, and Max Daily Dose prescribed to the study population.

Stahl Prescriber Guide dose ranges (mg/d): amisulpride (400–800), aripiprazole (15–20), asenapine (10–20), clozapine (300–450), olanzapine (10–20), quetiapine (400–800), risperidone (2–8). PGx: Pharmacogenetic Analysis.

To establish comparisons at the prescribed mean dose of antipsychotic drugs, chlorpromazine was established as a reference drug. The average dose of the different antipsychotics used in the sample per patient, and the average dose of chlorpromazine associated with them, is shown in Figure 4. The reduction by 50.88% (*p* < 0.05) of the mean dose of chlorpromazine per patient in mg/d following the application of the pharmacogenetic study is noteworthy, as an average each patient was prescribed 36.40 less chlorpromazine-corrected antipsychotic dose (384.94 mg/d) (Table 11) (*p* < 0.05). Likewise, the proportion of patients above 800 mg/d was reduced from 34.95% to 1.61% (*p* < 0.05), and the proportion of patients below 300 mg/d increases from 15.05% to 33.87% (*p* < 0.05). A reduction of more

than 20% in the dose of chlorpromazine was observed in 78.19% of the sample, achieving a reduction of more than 60% in 23.40% of the sample (*p* < 0.05).

**Table 10.** % of prescriptions of the different antipsychotics and presentations.

**Figure 4.** Antipsychotics mean dose and chlorpromazine conversion.

**Table 11.** Within-patient variation on antipsychotic daily dose.

**Table 11.** *Cont*.

Mean relative within-patient difference in daily dose comparing itself before and after the application of the *5-step Precision Medicine* (5SPM) model. CI: Confidence Interval

#### **4. Discussion**

A patient with prolonged severe mental illness usually has a heterogeneous and highly variable symptomatology, including positive, negative, affective, and cognitive symptomatology, which usually induces the prescriber to opt for the use of the combination of several drugs. However, polypharmacy is not a practice supported by scientific evidence and often carries an increased risk of adverse effects [8], which may imply less adherence to treatment and consequent therapeutic failure, with a higher number of hospital admissions and emergency care, and subsequently increased economic and personal expenditure [19].

It is necessary to know the adverse effects profile of neuroleptic treatment in order to perform a risk-benefit balance and thereby facilitate the individualization of the therapy. Polytherapy treatment is considered "justifiable" by some authors given the presence of a profile of adverse effects that hinders adherence to treatment by the patient, or that is detrimental to an underlying pathology of the individual, or when the therapeutic response has not been effective with the antipsychotics available for the specific case of the subject [40]. Consequently, knowledge of the pharmacogenetic profile of the patients becomes more relevant, so that monotherapy adapted to their metabolism can predominantly be applied, suitable for the treatment of symptomatic exacerbations and maintenance, reducing the presence of adverse effects [15].

This study is a retrospective descriptive analysis of 188 patients suffering from a prolonged serious mental illness, poor response to conventional treatment, which eventually results in polypharmacy, to whom the 5SPM model was applied. A PGx analysis was carried out aiming to study the main cytochromes involved in both the metabolism of the most commonly used atypical antipsychotics in their clinical centers, as well as in the metabolism of concomitant. It is important to clarify that, even being the application of the 5SPM Model intended as a multidisciplinary effort, the adjustment made on these patients' pharmacotherapy was mediated by the Psychiatry Department, therefore we discovered more interactions in the sample regarding non-psychiatric concomitanttherapy.

After making corrections on patient pharmacotherapy as part of the 5SPM model, our study revealed a significant reduction in the mean dose of antipsychotic (approximately 50%), which is justified both by the reduction in doses of neuroleptic administered, and by the reduction in approximately 60% of patients receiving two or more antipsychotics.

The pharmacogenetic profile of the population (CYP1A2 HI: 0.86; CYP2D6 EM: 0.85; CYP3A4 EM: 0.91) could explain the need to apply a higher dose with its consequent adverse effects in patients treated with olanzapine [41], clozapine [42], or asenapine [43], all related to CYP1A2, while favoring the use of aripiprazole or risperidone in those who have a wt-like haplotype in CYP2D6 [44–49] and CYP3A4 [44,50] cytochromes, a situation frequently found in our population.

The significant decrease in the mean dose of neuroleptic per patient should be discussed, together with the fact of the increase in the prescription of paliperidone and aripiprazole IM (DEPOT), the use of which doubles and almost triples from the beginning of the study. Aripiprazole is a neuroleptic whose metabolism develops predominantly in the liver, through the CYP450 system, specifically by CYP2D6 and CYP3A4 cytochromes which, in the vast majority of our patients (more than 80%), presented an EM phenotype, favoring its use. Paliperidone is an active metabolite of risperidone that has no liver metabolism. This, together with the fact that they are injectable presentations, favors the prevention of side effects and adherence to treatment, with its consequent positive effect when assessing therapeutic success. As mentioned above, there is a decrease in the use of olanzapine (Oral), aripiprazole (Oral), risperidone (Oral and IM), clozapine (Oral), quetiapine (Oral), asenapine (Oral), and paliperidone (Oral) in favor of these long-lasting injectable presentations, presenting better compliance, a potential decrease in hospital admissions, and a reduction in adverse effects. The preference in the use of these antipsychotics is largely determined by the profile of adverse effects associated with them, influenced by the rational approach that this study proposes. In our sample, DEPOT antipsychotics are prescribed frequently as monotherapy and the patients are administered a standardized dose by sanitary personnel, ensuring treatment compliance. Furthermore, gastrointestinal absorption variables, not always totally controlled in this type of patients (such as alcohol consumption), are nullified. One of the main problems of polytherapy is the potential presence of pharmacological interactions, which may lead to an alteration of the biological availability of one of the drugs involved, with the consequent alteration of the therapeutic effect on its target and the presence of possible adverse effects. Drug-induced phenoconversion during routine clinical practice remains a major public health issue (27). This phenomenon needs to be well addressed by precision medicine approaches focused in the drugs in the context of the patient prescription and oriented not only to genotype but considering drug–drug and gene–drug interactions. In fact phenoconversion has been previously called the Achilles' heel of personalized medicine reporting three principal concerns; drugs susceptible to phenoconversion, co-medications that can cause phenoconversion, and dosage amendments that need to be applied during and following phenoconversion (27).

The application of 5SPM has helped us to significantly reduce interaction in our patients, either by an increase in the use of drugs not metabolized by the CYP450 system, the decrease in polytherapy previously exposed, or the possible didactic role on medical personnel that the implementation of pharmacogenetic analysis may have provided, regarding the role of the CYP450 system in antipsychotic metabolism. This methodology has led to the discovery of a large number of cases in which there was a problem in the pharmacotherapy due to the metabolism mainly mediated by CYP1A2. In the vast majority of cases, the dose administered per patient has been reduced, as well as cases of polytherapy. It should be noted that the use of DEPOT presentations of drugs such as paliperidone (not metabolized by the CYP450 system) or aripiprazole (whose predominant pathway within the CYP450 system is CYP3A4, which presents a wt-like genotype in the vast majority of the study population) has been the solution for the majority of problematic cases, because of the pharmacogenetic profile of the sample, even considering that each patient was studied individually attending its personal genetic implications. In conclusion, the success achieved in reducing the average dose of antipsychotic administered per patient, which has in turn facilitated the avoidance of a large number of adverse effects and potentially reduced the cost per patient, may be due to the combination of applying a methodology such as 5SPM, focused on the genetic and environmental circumstances of the patient alongside the rise of DEPOT prescriptions, which facilitate adherence to treatment and the use of standardized doses.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2075-4426/10/4/289/s1. Figure S1: 5 Step precision medicine model.

**Author Contributions:** Conceptualization, M.J.G.-S., M.Á.F.-M., and M.I.-G.; methodology, L.C.-L., I.R.-G., M.I.-G.; software, L.C.-L., I.R.-G.; validation, M.I.-G., M.B.G.-B., E.M.-V.; formal analysis, L.C.-L., I.R.-G.; investigation, L.C.-L., I.R.-G.; resources, S.S.-I., C.L., M.Á.F.-M.; data curation, L.C.-L., I.R.-G.; writing—original draft preparation, L.C.-L., I.R.-G.; writing—review and editing, M.I.-G., A.S.-M.; visualization, L.C.-L., I.R.-G., M.I.-G.; supervision, M.I.-G.; project administration, M.Á.F.-M., M.J.G.-S., and M.I.-G.; funding acquisition, M.Á.F.-M. and M.I.-G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

### **References**

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### *Article* **Impact of Drug-Gene-Interaction, Drug-Drug-Interaction, and Drug-Drug-Gene-Interaction on (es)Citalopram Therapy: The PharmLines Initiative**

#### **Muh. Akbar Bahar 1,2,\* , Pauline Lanting 3 , Jens H. J. Bos 1 , Rolf H. Sijmons 3 , Eelko Hak 1 and Bob Wil**ff**ert 1,4**

Received: 9 October 2020; Accepted: 14 November 2020; Published: 28 November 2020

**Abstract:** We explored the association between CYP2C19/3A4 mediated drug-gene-interaction (DGI), drug-drug-interaction (DDI) and drug-drug-gene-interaction (DDGI) and (es)citalopram dispensing course. A cohort study was conducted among adult Caucasians from the Lifelines cohort (167,729 participants) and linked dispensing data from the IADB.nl database as part of the PharmLines Initiative. Exposure groups were categorized into (es)citalopram starters with DGI, DDI and DDGI. The primary outcome was drug switching and/or dose adjustment, and the secondary was early discontinuation after the start of (es)citalopram. Logistic regression modeling was applied to estimate adjusted odd ratios with their confidence interval. We identified 316 (es)citalopram starters with complete CYP2C19/3A4 genetic information. The CYP2C19 IM/PM and CYP3A4 NM combination increased risks of switching and/or dose reduction (OR: 2.75, 95% CI: 1.03–7.29). The higher effect size was achieved by the CYP2C19 IM/PM and CYP3A4 IM combination (OR: 4.38, 95% CI: 1.22–15.69). CYP2C19/3A4 mediated DDIs and DDGIs showed trends towards increased risks of switching and/or dose reduction. In conclusion, a DGI involving predicted decreased CYP2C19 function increases the need for (es)citalopram switching and/or dose reduction which might be enhanced by co-presence of predicted decreased CYP3A4 function. For DDI and DDGI, no conclusions can be drawn from the results.

**Keywords:** (es)citalopram; drug-gene-interaction; drug-drug-interaction; drug-drug-gene-interaction; the PharmLines initiative

#### **1. Introduction**

Selective serotonin re-uptake inhibitors (SSRIs) such as citalopram and escitalopram ((es)citalopram) are among the first-line pharmacological options for depression in Europe and the US, and the use of SSRIs has increased considerably over the years [1,2]. However, reports showed that less than 50% of (es)citalopram users achieved disease symptom remission during their first treatment episode, and prognosis appeared unpredictable [3,4]. Such variable effectiveness may be explained by a large inter-individual pharmacokinetic variability among patients treated with

(es)citalopram [5,6]. This variability is known to be caused partly by differences in metabolic activity of drug metabolizing Cytochrome P450 (CYP) enzymes [7].

(Es)citalopram is primarily metabolized by the combination of CYP2C19 and CYP3A4 enzymes, and to a lesser extent by CYP2D6 enzyme [8,9]. Genetic polymorphisms are known to affect the catalytic activity of these enzymes. Some studies have investigated the role of *CYP2C19* and *CYP2D6* polymorphisms on the exposure as well as the clinical impact of (es)citalopram [7,10]. Such interaction between the drug treatment and genetic variation is referred to as drug-gene interaction (DGI) [11]. To the best of our knowledge, no previous studies have explored the impact of the DGI related to CYP3A4 polymorphisms, or its combination with *CYP2C19* polymorphisms, in (es)citalopram treatment. In addition, the concomitant administration of CYP2C19, CYP3A4, and/or CYP2D6 (CYP2C19/3A4/2D6) modulator drugs (inhibitor/inducer) produces a drug-drug-interaction (DDI) with (es)citalopram by affecting blood concentrations and hence modifying its effectiveness [12].

To make it even more complicated for treating physicians, (es)citalopram treatment may be affected by both genetics and drugs that modulate the activity of the metabolic pathways at the same time which potentially affect blood concentration even more unpredictably than DGI and DDI alone [13]. In other words, a drug-drug-gene-interaction (DDGI) is encountered when a DGI coincides with a DDI [14,15]. Generally, DDGIs show more pharmacokinetic diversity than DDIs and DGIs alone, since DDGIs concern several modes of interactions [15,16]. For example, a DDGI may involve the co-existence of a genetic polymorphism and a CYP-inhibitor for one CYP-enzyme or the co-presence of a genetic polymorphism in one or two metabolic pathways and a CYP modulator in another pathway [14,15].

Due to restricted study populations in trials and scarcity of health care databases with a possibility to link genetic and drug dispensing data, large-scale real-world pharmacogenetic studies are lacking on the impact of pharmacogenetic and drug interactions in general. Consequently, recent guidelines have only provided specific recommendations on the management of (es)citalopram-related DGIs and DDIs separately, but a knowledge gap remains regarding the pharmacotherapeutic management of DDGIs [17,18]. The PharmLines Initiative enables the unique linkage of genetic and drug data to perform an inception cohort study in a large population cohort which we used to explore the impact of DDIs, DGIs (specifically *CYP2C19*/*3A4* polymorphisms), and DDGIs on short-term first-time (es)citalopram therapy [19]. To mirror treatment success, proxy outcomes such as drug switching, dose adjustment, and an early discontinuation after the first prescription of (es)citalopram are used [20,21].

#### **2. Methods**

#### *2.1. Study Design, Setting and Data Sources*

This retrospective cohort study was performed using data from the PharmLines Initiative which links the Lifelines cohort and the University of Groningen prescription IADB.nl database, two large databases in the Northern part of the Netherlands [19].

The Lifelines cohort is a three-generation prospective cohort covering 167,729 Dutch participants from the Northern provinces of the Netherlands [22,23]. It was established with the aim to study 'complex interactions between environmental, phenotypic and genomic factors in the development of chronic diseases and healthy ageing' [22,23]. The participants from the Lifelines cohort generally represent the characteristics of the adult population of the Northern part of the Netherlands [24]. More comprehensive information about the Lifelines cohort can be found in the publications of Stolk et al. and Scholtens et al. [22,23].

The University of Groningen prescription database IADB.nl collected over 1.2 million prescriptions from 72 pharmacies. The information about gender, date of birth and four-digit postal codes (optional) from 730,000 recorded anonymous patients are available [25]. The prescription information of each participant is recorded such as dispensing date, Anatomical Therapeutic Chemical code (ATC code), quantity, duration, and DDD (defined daily dose) [25]. The participants recorded in the IADB.nl are

found to be representative of the general population in the Netherlands as whole [25]. The IADB.nl is a reliable database and has been used in many pharmacoepidemiological studies [26–28]

The linking process of these two databases was facilitated by a trusted third party, the Statistic Netherlands. The linkage was performed at the individual level and relied on combined information of postal code, date of birth, and gender. Once the selection process was completed, identifiers from each database were cleared and then, a new unique identifier (pseudoID) was assigned. Using the pseudoID, genetic and prescription information of the participants from the Lifelines cohort and the IADB.nl, respectively, could be combined. Details on the linking process has been published elsewhere [19].

#### *2.2. Study Population*

Adult Lifelines participants (Caucasian, 18 years and older) with available genetic information (*CYP2C19* and *CYP3A4* genes) and who had their first citalopram (N06AB04) or escitalopram (N06AB10) prescription recorded in the PharmLines Iniative were eligible. Those who were not prescribed any (es)citalopram for at least 180 days before starting their drug dispensing were included. If there were several periods of (es)citalopram dispensing, only the first dispensing period was included in the analysis. Date of the first (es)citalopram prescription was regarded as an index date which indicates the start of follow-up.

#### *2.3. Genotyping*

Genotyping for single-nucleotide polymorphism (SNP) of *CYP2C19* and *CYP3A4* genes in the Lifelines cohort was performed using the Illumina CytoSNP-12v2 array [22]. The genotype data was imputed by using the Genome of the Netherlands reference panel [22]. The quality of genotyping data was checked using the following requirements i.e., (i) the *p*-value of Hardy-Weinberg equilibrium distribution was > 1 × 10−4 , (ii) call rate of 95%, and (iii) minor allele frequency (MAF) was > 0.001 [22]. Additionally, principal component analysis was used to detect statistical outliers [22]. More detailed information on the genotyping process can be found in the publication of Scholtens et al. (2014) [22].

*CYP2C19* and *CYP3A4* genotypes were translated to haplotypes, which were used to predict corresponding phenotypes (Tables 1–4). Relevant haplotypes were selected and genotypes were translated to predicted phenotypes based on available information from the Dutch Pharmacogenetics Working Group (DPWG). Corresponding predicted phenotypes include poor metabolizer (PM), intermediate metabolizer (IM), and normal metabolizer (NM) for CYP2C19 and CYP3A4, and ultra-rapid metabolizer (UM) for CYP2C19.

**Table 1.** Pipeline translation table for *CYP2C19* with haplotypes and their Single Nucleotide Polymorphisms (SNPs) information.

**Table 2.** Pipeline translation table for *CYP3A4* with haplotypes and their SNP information.

**Table 3.** The translation of *CYP2C19* and *CYP3A4* haplotypes to their predicted metabolic activity.

**Table 4.** The translation of *CYP2C19* and *CYP3A4* haplotype combinations to their predicted phenotypes.

NM: Normal Metabolizer. IM: Intermediate Metabolizer. PM: Poor Metabolizer. UM: Ultrarapid Metabolizer.

#### *2.4. Definition of Exposures*

The exposure groups were defined as (es)citalopram users with a DGI, DDI, or DDGI. Participants who were predicted to be CYP2C19 UM, IM, or PM and/or CYP3A4 IM or PM and were prescribed (es)citalopram without co-prescription of CYP2C19/3A4/2D6 modulators (inhibitors/inducers) were classified as experiencing a DGI. For statistical power reasons, IM and PM groups were pooled into a combined IM/PM group, but we provided a sensitivity analysis for the separated IM and PM groups (Supplementary Materials S2).

Participants were classified to have a DDI when they were predicted as normal metabolizers (NM) of CYP2C19 and CYP3A4, and at the same time were co-prescribed a CYP2C19 and/or CYP3A4 and/or CYP2D6 modulator during the (es)citalopram treatment within a follow-up time frame of 90 days. A list of clinically relevant CYP2C19/3A4/2D6 modulators was based on *Commentaren Medicatiebewaking* (Health Base, NL) and the Flockhart tableTM (Supplementary S1) [32,33]. Only non-SSRI drugs were included as CYP2C19/3A4/2D6 modulators since our study population consists of first-time (es)citalopram users and it is uncommon to combine this with another SSRI drug in the early phase of drug treatments [34].

DDGI was defined as the occurrence of a DGI and DDI at the same time in which (es)citalopram patients with a CYP2C19/3A4 predicted deviating phenotype received a CYP2C19/3A4/2D6 modulator. The non-exposed reference group was defined as (es)citalopram users with a predicted normal

CYP2C19/3A4 and who were not prescribed any CYP2C19/3A4/2D6 modulator during first-time (es)citalopram treatment.

#### *2.5. Study Outcomes*

Study outcomes were drug switching, dose adjustment, and early discontinuation. The incidence of these outcomes within the time frame of a 90 day follow-up after the index date were identified. This time frame was used since the acute phase treatment of SSRIs is considered to be between 6 and 12 weeks after the start of drug treatment. A previous report indicated that about 70% of antidepressant users stopped their therapy within 90 days [35]. However, since interactions commonly have an immediate effect, the presence of the outcomes within the time frame of a 45 day follow-up after the index date were also explored (Supplementary S3) [21]. Drug switching was defined as patients having an early discontinuation of (es)citalopram as well as the prescription of another antidepressant, regardless of the class, within 120 days after the index date. The follow-up time frame was expanded for dispensing of other antidepressants from 90 to 120 days after the index date in order to accommodate the possible time gap between the dispensing of (es)citalopram and the new antidepressant [36,37]. Meanwhile, dose adjustment was defined as having a dose reduction or a dose elevation for at least 25% of the first dose within 90 days after the index date. Early discontinuation was defined as discontinuing the prescription of (es)citalopram within 90 days after the index date, having no further re-prescription of (es)citalopram for at least 180 days after the stop date as well as no switching as described previously. In the preliminary analysis the effects of exposure on drug switching and dose reduction were in the same direction, therefore the outcomes were combined. Analysis on the separated outcomes are provided in the Supplementary S2.

#### *2.6. Co-Variates*

The following co-variates were recorded to compare groups: age, gender, dose of (es)citalopram at the index date, number of co-prescriptions, and pre-defined drugs as a proxy for certain co-existing comorbidities (Supplementary S1). (Es)citalopram users had to have at least two prescriptions of these proxy medications within six months before or after the index date to be assumed as having a chronic condition of the potential comorbidities [38]. The presence of NSAIDs co-prescription during (es)citalopram prescription was checked within the time frame of 90 days since the combination of NSAIDs and SSRIs was reported to increase the risk of gastrointestinal bleeding [39]. The potential comorbidities were clustered into one group, namely 'potential comorbidities,' in order to increase the power of the calculation. The distribution for each potential comorbidity was compared separately between outcomes and none of them were statistically significant different (*p* < 0.05). Lastly, the distribution of the number of CYP2C19/3A4/2D6 modulator prescriptions during the use of (es)citalopram was compared, since a previous study indicated that the higher the number of CYP2C19/3A4/2D6 modulator prescriptions, the more alteration in the clearance of (es)citalopram [12].

#### *2.7. Statistical Analysis*

The Chi-square (or Fisher′ s exact test) and Mann-Whitney test were used to compare distribution of categorical and skewed distributed continuous variables between outcomes, respectively. Co-variates which differed significantly (*p* < 0.05) were entered into final multivariate logistic regression model to obtain adjusted odds ratio as measure of association (OR). We also provided adjusted *p*-values for false discovery rates due to multiple comparisons using the Benjamini-Hochberg method (*q*-values, with a *q* < 0.05 as the significance threshold). Since some participants did not have dosing information, a complete case analysis in cases of dosing comparison as well as dose adjustment analysis were performed. The baseline characteristics were compared between participants with complete information and participants without dosing information (Supplementary S2).

#### **3. Results**

Overall, 316 (es)citalopram users (median 45 years, 63% women) with *CYP2C19* and *CYP3A4* genetic information were available (Figure 1). Baseline characteristics of patients are displayed in Table 5. There were 32.6%, 7.3% and 4.4% of participants to have predicted CYP2C19 IM, PM, and UM, respectively, and there were 17.7% and 1.9% of our sample to have predicted CYP3A4 IM and PM, respectively. After combining both genetic information (regardless the presence of another exposure such as CYP modulators), we found that about 56% of the patients had at least one predicted deviating phenotype of CYP2C19 or CYP3A4. There were about 33%, 6%, 11%, and 4% of the participants having predicted CYP2C19 IM/PM + CYP3A4 NM, CYP2C19 IM/PM + CYP3A4 IM/PM, CYP2C19 NM + CYP3A4 IM/PM, and CYP2C19 UM + CYP3A4 NM/IM, respectively.

**Figure 1.** Selection of (es)citalopram first time users.

**Table 5.** *Cont.*

\* Excluded. NM: Normal Metabolizer. IM: Intermediate Metabolizer. PM: Poor Metabolizer. UM: Ultrarapid Metabolizer. DDD: Defined Daily Dose. CYP: Cytochrome P450.

Regardless of the number of prescribed CYP modulators, about 18% of the participants were exposed to CYP-modulators during (es)citalopram prescription and most of them were CYP2C19 inhibitors (13.9%). No combination of (es)citalopram with CYP2C19/3A4 inducer alone was identified. Two patients exposed to a combination of CYP modulators (one patient with a CYP2C19 and a CYP2D6 inhibitor, and one patient with a CYP2C19 inhibitor and a CYP3A4 inducer) were excluded since the number was too small to analyze. More than 60% of the participants had at least 20 mg citalopram or 10 mg escitalopram daily (≥ 1 Defined Daily Dose/DDD) at the start of their prescriptions. About 68% of the population had 1 to 2 potential comorbidities and about 78% of them used one to three different type of drugs during (es)citalopram prescription.

The more concomitant the CYP modulator used during (es)citalopram prescription, the more alteration in the (es)citalopram produced [12]. In our sample, we only found less than 10% of them using at least two concomitant CYP modulator at the same pathway. After looking on the combination of exposures (*CYP2C19*/*3A4* genotypes and CYP modulators) among our study population, we found that 9%, 47%, and 8.5% of participants were exposed to DDIs, DGIs, and DDGIs, respectively. Frequency of each type of DDGIs is presented in Table 6.

**Table 6.** Frequency of DDGI (overlapping condition of DDI and DGI).

NM: Normal Metabolizer. IM: Intermediate Metabolizer. PM: Poor Metabolizer. UM: Ultrarapid Metabolizer. Y: Yes. N: No.

There were 25 (7.9%), 7 (2.2%), 80 (25%), and 47 (15%) of (es)citalopram users experiencing drug switching, dose reduction, dose elevation, and early discontinuation, respectively. Number of co-prescriptions seemed to influence the rate of switching (*p* = 0.02). Female gender and a higher dose at the index date are less prevalent in the subgroup that experienced dose elevation of (es)citalopram (*p* = 0.003 and 0.002, respectively) (Table 7).

In our dataset, participants with a predicted CYP2C19 IM phenotype had an increased risk of drug switching and/or dose reduction (aOR: 3.16, 95% CI: 1.41–7.09) but CYP2C19 PM did not show a comparable result (aOR: 0.54, 95% CI: 0.07–4.52) (Table 8). Meanwhile, both CYP2C19 IM and PM had a comparable trend on the risk of early discontinuation (aOR: 0.35, 95% CI: 0.15–0.79 and aOR: 0.41, 95% CI: 0.09–1.89, respectively) (Table 9).

Furthermore, there was an indication showing that co-presence of CYP3A4 IM/PM in individuals with CYP2C19 IM/PM increased the risk of switching and/or dose reduction of (es)citalopram to a larger extent than the combination of CYP2C19 IM/PM and CYP3A4 NM (aOR: 4.38, 95% CI: 1.22–15.69 and aOR: 2.75, 95% CI: 1.03–7.29, respectively). This effect might be facilitated by the combination of CYP2C19 IM and CYP3A4 IM since there was only one participant with CYP2C19 PM and no participants with CYP3A4 PM experiencing switching or dose reduction (Table 8). Meanwhile, CYP3A4 IM/PM in the co-presence of CYP2C19 NM did not seem to influence the risk of switching and/or dose reduction (aOR: 1.02, 95% CI: 0.19–5.24). No participants with the CYP2C19 UM and CYP3A4 NM/IM combination experienced drug switching and/or dose reduction and no significant association with early discontinuation as well as with dose elevation was observed (Tables 8–10).

DDIs seemed to increase the risk of drug switching and/or dose reduction (aOR: 2.82, 95% CI: 0.49–15.97), which was mainly facilitated by the co-presence of CYP2C19 inhibitors, but seemingly not to increase the risk of dose elevation and early discontinuation (Tables 8–10).

DDGIs also seemed to increase the risk of drug switching and/or dose reduction (aOR: 2.33, 95% CI: 0.42–12.78). However, there were only two participants with DDGIs experiencing drug switching or dose reduction, consisting of one participant with a DDGI affecting one pathway and the other one with a DDGI affecting two pathways (Supplementary S2). Consequently, a separated analysis of DDGIs based on the number of pathways affected produced comparable effect sizes (DDGI affecting one pathway: aOR: 2.52, 95% CI: 0.26–24.61; DDGI affecting two pathways: aOR: 2.17, 95% CI: 0.23–20.67).

Overall, there were no associations between the exposures and any outcomes tested reaching the statistical significance threshold of a false discovery rate-adjusted *p*-value (*q* > 0.05).

Analysis using a time frame of 45 days after the index date produced comparable results. CYP2C19 IM increased the risk of switching and the effect size was also larger in combination with CYP3A4 IM/PM (aOR: 6.41, 95% CI: 1.19–34.40) than with CYP3A4 NM (aOR: 2.66, 95% CI: 0.65–10.96). CYP2C19 IM seemingly increased the risk of dose reduction (aOR: 2.69, 95% CI: 0.43–16.96). Lastly, DDIs and DDGIs have a tendency to increase the risk of dose reduction and switching, respectively. Detailed data can be found in Supplementary S3.

**Table 7.**Baseline comparisons.

\* No start/stop date = 10; # no dose information = 94. NM: Normal Metabolizer. IM: Intermediate Metabolizer. PM: Poor Metabolizer. UM: Ultrarapid Metabolizer. DDD: Defined Daily Dose. CYP: Cytochrome P450. N: Number. IQR: Interquartile Range.

**Table 8.**Association between DDI, DGI, and DDGI with drug switching and/or dose reduction.

**Table 8.** *Cont.*

Adjusted for: \* CYP3A4 phenotypes, CYP modulator & N of co-prescriptions; \*\* CYP2C19 phenotypes, CYP modulator & N of co-prescriptions; \*\*\* CYP modulator & N of co-prescriptions; # CYP2C19 & CYP3A4 phenotypes & N of co-prescriptions; ˆ N of co-prescriptions. NM: Normal Metabolizer. IM: Intermediate Metabolizer. PM: Poor Metabolizer. UM: Ultrarapid Metabolizer. DDI: Drug-Drug interaction. DGI: Drug-Gene Interaction. DDGI: Drug-Drug-Gene Interaction (overlapping condition of DDI and DGI, please see Table 6: Frequency of DDGI). CYP: Cytochrome P450. NA: Not Available. OR: Odds Ratio. aOR: Adjusted Odds ratio. CI: Confidence Interval. Ref.: Reference.

#### **Table 9.**Association between DDI, DGI, and DDGI with early discontinuation.

**Table 9.** *Cont.*

Adjusted for: \* CYP3A4 phenotypes, CYP modulator & age; \*\* CYP2C19 phenotypes, CYP modulator & age; \*\*\* CYP modulator & age; # CYP2C19 & CYP3A4 phenotypes & age; ˆ age. NM: Normal Metabolizer. IM: Intermediate Metabolizer. PM: Poor Metabolizer. UM: Ultrarapid Metabolizer. DDI: Drug-Drug interaction. DGI: Drug-Gene Interaction. DDGI: Drug-Drug-Gene Interaction (overlapping condition of DDI and DGI, please see Table 6: Frequency of DDGI). CYP: Cytochrome P450. NA: Not Available. OR: Odds Ratio. aOR: Adjusted Odds ratio. CI: Confidence Interval. Ref.: Reference.

**Table 10.**Association between DDI, DGI, and DDGI with dose elevation.

**Table 10.** *Cont.*

Adjusted for: \* CYP3A4 phenotypes, CYP modulator, gender & dose at start; \*\* CYP2C19 phenotypes, CYP modulator, gender & dose at start; \*\*\* CYP modulator, gender & dose at start; # CYP2C19 & CYP3A4 phenotypes, gender & dose at start; ˆ gender & dose at start.NM: Normal Metabolizer. IM: Intermediate Metabolizer. PM: Poor Metabolizer. UM: Ultrarapid Metabolizer. DDI: Drug-Drug interaction. DGI: Drug-Gene Interaction. DDGI: Drug-Drug-Gene Interaction (overlapping condition of DDI and DGI, please see Table 6: Frequency of DDGI) CYP: Cytochrome P450. NA: Not Available. OR: Odds Ratio. aOR: Adjusted Odds ratio. CI: Confidence Interval. Ref.: Reference.

#### **4. Discussion**

In this explorative inception cohort study, we presented for both CYP2C19 and CYP3A4 the associations of DGI, DDI, and DDGI and the risk of switching or dose adjustments and early discontinuation in the first treatment episode of (es)citalopram. In our relatively small samples, we found an indication that participants with DGI involving predicted CYP2C19 IM tended to experience switching and/or dose reduction, instead of early discontinuation, regardless of the CYP3A4 predicted phenotype. For participants with DGI involving predicted CYP3A4 IM/PM, no influence on switching and/or dose reduction was found. Yet, the effect of CYP2C19 IM might be enhanced by the presence of CYP3A4 IM. DDI and DDGI might be associated with an increased risk of switching or dose reduction, but the associations were not significant with wide confidence intervals.

We found that participants with CYP2C19 IM were more likely to experience switching than those with NM. This is consistent with the study reported by Mrazek et al. which showed that individuals with CYP2C19 reduced catalytic function were less tolerant to citalopram than those with increased catalytic function [40]. We also found that (es)citalopram users with CYP2C19 IM tended to experience dose reductions more than those with CYP2C19 NM. Decreasing the maximum daily dose of (es)citalopram in patients with CYP2C19 IM by 25% of the normal maximum dose is recommended by the DPWG [41]. As a note, we possibly managed to find some associations on CYP2C19 IM and the outcomes because we had a large enough number of (es)citalopram users with the genotype (about 33% of the cohort).

Unfortunately, we did not find any significant association between patients with CYP2C19 PM and UM to the outcomes which was probably due to a limited sample size. Some clinical studies reported that patients with CYP2C19 PM were exposed to (es)citalopram blood concentration to a greater extent than CYP2C19 IM and that patients with CYP2C19 UM had a lower exposure to (es)citalopram compared to CY2C19 NMs [7]. Jukic et al. using about 2000 genotyped persons from the Oslo population found that escitalopram users with CYP2C19 UM and PM (33% of the study population) had a three times higher odds of switching to another antidepressant than those with CYP2C19 NM [20].

To the best of our knowledge, this is the first study to examine the impact of CYP3A4 alone and in combination with CYP2C19 on (es)citalopram treatment. Decreased function of CYP3A4 in the CYP2C19 NM participants did not seem to influence the outcomes, but might have increased the effect of CYP2C19 IM. A comparable trend of effects has been reported for CYP2D6. The effect of the CYP2D6 variant in individuals with CYP2C19 NM on the AUC of citalopram was limited. However, when there was a co-presence of CYP2C19 \*1/\*2 (IM), the influence of CYP2D6 \*1/\*4 (IM) became stronger [10].

In our dataset, there were about nine percent of (es)citalopram users exposed to potential DDIs. This might be because about 79% of our study population had at least one comorbidity and therefore, they used other drug(s) which might potentially interact with (es)citalopram. In the Lifelines population, the most prevalent potential CYP2C19 mediated DDI was citalopram and omeprazole [42]. Omeprazole was reported to increase s-citalopram plasma concentration by about 50% to 120% [43,44]. Therefore, it has been recommended that patients with omeprazole or esomeprazole should have a dose adjustment of (es)citalopram [45].

Although we did not find any significant associations between DDGI and the outcomes, this study is the first to explore the impact of complex DDGI on the (es)citalopram treatment at the population level. Generally, DDGI may come in two main scenarios [14,15]. Firstly, it may only affect one metabolic pathway of a drug, for example overlapping conditions between a CYP2C19 UM/IM/PM and a CYP2C19 inhibitor in (es)citalopram users. In this scenario, we might expect that the level of blood concentration of (es)citalopram in an individual with a CYP2C19 UM and a CYP2C19 inhibitor might be different from an individual with a CYP2C19 IM and a CYP2C19 inhibitor [15]. This is because the more the number of active allelic variants in the CYP450, the more difficult for their phenotypes to be converted by the co-presence of inhibitors [46]. The second main scenario is the alteration of two or even three metabolic pathways of a drug. The alteration can be a result of the presence of deviating genotypes in

one/two metabolic pathway(s) and the co-presence of CYP modulator in one/two other pathway(s). In this scenario, each possible combination of co-inhibition produced by genetic variation and CYP modulators might result in variation of (es)citalopram concentration in the blood [15]. Therefore, the effect of DDGI can vary depending on the scenario of interactions, the metabolic contribution of the inhibited pathway(s), and the potency of CYP modulators [15]. In this study, since we had only two patients with DDGI experiencing switching (Supplementary S2), we could not explore more about the impact of the different scenarios on the outcomes. It is plausible because the number of patients with DDI and DGI were limited, we could expect that the number of patients exposed to DDGI is even less. Hence, further study with a larger dataset is needed to provide solid evidence about the impact of DDGI in clinical practice which can be used to support the lack of pharmacotherapeutic management of DDGI in the current guidelines.

Since genotyping is still not a part of routine clinical testing, prescribers often have no indication about the genotype of the patients at the time of prescription. Consequently, the presence of DGI and DDGI related to (es)citalopram, exposing 56% of our study population, is potentially missed by health practitioners. Therefore, in order to avoid DGI and DDGI complex interaction, pre-emptive genotyping, inclusion of genetic information in electronic health records as well as a sophisticated computerized drug interaction surveillance system are needed in clinical practice.

Several potential limitations need to be discussed. First, we did not have data on the blood concentration of (es)citalopram as the best indicator to show the effect of interactions. Consequently, we could not ascertain the effect of DDI/DGI/DDGI on the citalopram metabolism and validate the associations between the exposures and the outcomes. In addition, we did not have information about the genotype status of *CYP2D6*. Therefore, we could not assess the combined effects of *CYP2C19*/*3A4*/*2D6* polymorphisms on (es)citalopram efficacy. *CYP2D6* is the most polymorphic CYP enzyme and the prevalence of people with CYP2D6 IM and PM genotypes in the Caucasian population is 40% and 10%, respectively [47]. Therefore, there might be some persons with *CYP2D6* polymorphisms among our participants. Despite its minor metabolic contribution on (es)citalopram disposition, *CYP2D6* polymorphism might corroborate the alteration of citalopram clearance in the presence of *CYP2C19* polymorphism [10]. It was reported in a small DGI study among healthy persons that one participant with CYP2C19 PM and CYP2D6 PM taking citalopram developed severe side effects and was withdrawn early before the study was completed [48]. Therefore, there was a possibility that the co-presence of combined *CYP2C19*/*3A4*/*2D6* polymorphisms might produce a substantial effect on citalopram disposition.

Furthermore, though, our study population from the PharmLines database is rather large (6379 participants), the statistical power of the study is relatively low to detect significant associations between multiple exposures as DGI, DDI, DDGI and outcomes. Therefore, the results of this study should be interpreted as hypothesis-generating rather than confirmative to explore potential effects of DGI, DDI, and DDGI on the prognosis of (es)citalopram treatment. Much larger studies are required to further confirm our findings. Lastly, about 30% of our dataset had no information about the dose of (es)citalopram. The missingness may probably not be related to other variables since it may be because pharmacists or pharmacy technicians forgot to include the dose information before sending the prescription data to the IADB.nl. In the baseline comparisons, we found that patients without dosing information were significantly older than those with complete information (Supplementary S2). Hence, we might underestimate the effect of age on the dose adjustments of (es)citalopram. Among those with complete information, age seemed not to influence the dose elevation or reduction of (es)citalopram (Table 7).

#### **5. Conclusions**

In conclusion, the predicted CYP2C19 IM phenotype increased the need of drug switching and/or dose reduction, and the co-presence of CYP3A4 IM enhanced these effects. Therefore, when patients

receive (es)citalopram, it is important to not only consider the genetic information for CYP2C19 but also the genetic status of CYP3A4 as well.

Despite the fact that DDI and DDGI showed trends towards increased risks of switching and/or dose reduction, no conclusions can be drawn from the results because there were great uncertainties surrounding the estimates. Therefore, further real-world studies with larger samples are needed to confirm the results.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2075-4426/10/4/256/s1, Table S1: List of comorbidities; Table S2.1: Separated analysis of drug switching and dose reduction; Table S2.2: Differentiation of DDGI affecting one and two pathways; Table S2.3: Sensitivity Analysis for specific combination of CYP2C19 & CYP3A4 Phenotypes; Table S2.4: Demographics of participants, discriminated between complete and missing dose information; Table S3.1: Frequency of DDGI for time frame of 45 days; Table S3.2: Baseline comparisons for time frame of 45 days; Table S3.3: Association between exposures and outcomes for time frame of 45 days.

**Author Contributions:** Conceptualization: M.A.B., P.L., J.H.J.B., R.H.S., E.H. and B.W.; Data curation, M.A.B., P.L. and J.H.J.B.; Formal analysis, M.A.B.; Investigation, M.A.B., P.L. and J.H.J.B.; Methodology, M.A.B., E.H. and B.W.; Project administration, J.H.J.B.; Resources, P.L. and J.H.J.B.; Software, J.H.J.B.; Supervision, R.H.S., E.H. and B.W.; Writing—original draft, M.A.B. and B.W.; Writing—review & editing, P.L., J.H.J.B., R.H.S. and E.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** The Lifelines Biobank initiative has been made possible by funds from FES (Fonds Economische Structuurversterking), SNN (Samenwerkingsverband Noord Nederland) and REP (Ruimtelijk Economisch Programma). The IADB.nl is funded by the University of Groningen. Muh. Akbar Bahar obtained a DIKTI scholarship from the Ministry of Research, Technology and Higher Education of Indonesia. The funding organizations had no role and influence in the study design and results.

**Acknowledgments:** The authors wish to acknowledge the services of the Lifelines Cohort Study, the contributing research centres delivering data to Lifelines, and all the study participants, and the participating IADB.nl pharmacies for kindly providing their data for research.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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*3.7. Hydrophilicity of Cotton Fabrics*

Hydrophilicity of cotton fabrics has been assessed by the amount of absorbed water. Hydrophilicity of untreated cotton fabric and cotton fabrics treated with ACR and [Cu2(ACR)(NO3)2] complex has been determined to be 112%, 95% and 86%, respectively. The results clearly indicated that the compounds induce an increase in hydrophobicity when applied to cotton fabric may be due to an increase in the number of hydrophobic groups on the surface of the treated cotton fabric. More effective hydrophobicity has been achieved with the cotton fabric treated with [Cu2(ACR)(NO3)2], if compared to that of ACR treated fabric, which effect can be explained by the enhanced lipophilicity of the [Cu2(ACR)(NO3)2] complex.

### *3.8. SEM Analysis*

*Pseudomonas aeruginosa* is an excellent biofilm producer, and it has been selected to investigate the efficacy of the cotton fabrics treated with the ACR and [Cu2(ACR)(NO3)2] in preventing the adhesion and biofilm formation [38]. In Figure 6 are presented SEM micrographs of untreated cotton fabrics and cotton fabrics treated with ACR and its [Cu2(ACR)(NO3)2] complex after 24 h of incubation. From the SEM images, it is clearly seen that a formation of P. aeruginosa biofilm on the untreated cotton fabric (Figure 6B). In this case, many bacterial cells are adhered to the cotton surface and embedded into extracellular bio matrix. On the other hand, a significant reduction of biofilm formation and bacterial the adhesion has been observed on the surface of the cotton fabric treated with ACR (Figure 6D). Only single bacterial cells are attached to the cotton surface of the cotton fabric treated with the complex [Cu2(ACR)(NO3)2] (Figure 6C). Thus, the deposition of ACR and [Cu2(ACR)(NO3)2] complex on the

cotton fabric prevents the formation and proliferation of a bacterial biofilm, allowing the production of antibacterial cotton fabric.

**Figure 6.** Scanning Electron Microscopy (SEM) micrograph of cotton fabrics tested against *P. aeruginosa* at magnification X 5000: (**A**) Cotton textile before treatments; (**B**) biofilm on untreated cotton textile; (**C**) absence of biofilm on cotton textile treated with [Cu2(ACR)(NO3)2]; (**D**) reduced biofilm formation on cotton textile treated with ACR.


**7. Conclusions**

The test results presented in this paper have confirmed both the effectiveness and versatility of the operation of the proposed algorithm. The change of the preset voltage value, made in the appropriate moment of the test disturbance, reduced the reactive power input at very low voltages. In a real electric power system, in the situation of an existing reactive power deficit resulting in a voltage drop, such a result of regulation would be very desirable

One of the reasons for the reactive power deficit in the HV network is the simultaneous increase in the demand for reactive power in many HV/MV substations in a given area. This deficit is manifested by a significant increase in the speed of voltage drop in the HV network and its extremely low level. As described in Section 6.1, both of these factors activate the algorithm proposed in the article. Should the

proposed solution be implemented in the majority of HV/MV substations, a significant increase in the level of its positive influence on the HV grid could be expected. Simultaneous reduction of reactive power consumption in many HV/MV substations may significantly reduce the risk of voltage collapse in the HV supply network.

The authors are currently conducting multi-variant simulation studies with the use of the real HV grid model, which will prove the correctness of the above statement. The research results will be published in the next paper.

To sum up, the author's algorithm is effective and owing to its simplicity, it has a high potential of practical application, as confirmed by the tests using a real transformer with a capability to change the voltage ratio under loading. The implementation of the algorithm makes it possible to increase the reserve of the voltage stability of the node, in which it is applied, thus mitigating the risk of a voltage collapse occurring.

**Author Contributions:** Conceptualization, R.M.; methodology, R.M.; software, R.M.; validation, R.M.; formal analysis, R.M., M.I. and P.M.; investigation, R.M.; resources, R.M.; writing—original draft preparation, R.M.; writing—review and editing, M.I. and P.M.; supervision, M.I. and P.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** The funding sponsors had no role in the design of the study, in the collection, analysis, or interpretation of the data, in the writing of the manuscript, or in the decision to publish the results. This research received no external funding. The APC was funded by the Gda ´nsk University of Technology.

**Conflicts of Interest:** The authors declare no conflict of interest.