2.2.2. Study Validation

The targeted quantitative method was in-house validated complies with model-dependent performance characteristic covering specificity, selectivity, precision, repeatability, within-laboratory reproducibility, the calibration curve, detection limit (LOD), limit of quantification (LOQ), decision limit (CCα), detection capability (CCβ), and ruggedness according to the recommendation defined in Commission Decision 2002/657 / EC [34] and the reference guidelines in VICH GL49 [35].

The linearity of the quantitative method was determined for testosterone and testosterone ester analytes that were fortified into real pig plasma samples with increasing concentrations. The model samples were prepared according to the procedure described in Section 4.5. A matrix calibration curve was constructed based on the measured peak area ratios (Std. area/IS area) and the corresponding concentration levels. The parameters of the linear regression models were calculated by the least squares method (with a weight coefficient w = 1/2) based on ISO 11843: 2 [36]. Correlation coefficients (r), linear regression model parameters (y = a + bx) and critical curve limits (LOD, LOQ) were calculated and reported in Table 3. The calibration curve for 17β-testosterone, which was used to back-estimate the results of real plasma samples obtained during the experiment, was shown graphically (Supplementary Materials Figure S4). The complete standard area and internal standard area data that was used to calculate the 17β-testosterone calibration curve are presented in Supplementary Materials Table S4.

To determine the precision and repeatability (within-laboratory reproducibility) of the targeted analysis method, the standard deviation (SD) and variation coefficient (CV, %) were determined and calculated by repeated measurement of fortified plasma samples at two concentration levels. The calculated CV (*n* = 12) was less than 3.09% for a concentration level of 5 ng mL−<sup>1</sup> 17β-testosterone in plasma, demonstrating the good precision and repeatability required for confirmatory residual analyses by the Commission Decision 2002/657/EC. The results of the validation study for precision, repeatability, and other calculated statistics are shown in Supplementary Materials Table S5.


**Table 3.** Regression parameters of matrix calibration curves in the concentration range 0 to 80 ng mL−1.

Note: LOD and LOQ were estimated according to IUPAC (Direct Signal Method) methodology.

#### 2.2.3. Pharmacokinetic Profile of 17β-Testosterone

The experiment included targeted analysis of the primary testosterone metabolite in porcine plasma after a single i.m. administration and subsequent determination of the pharmacokinetic curve. Plasma concentrations of free 17β-testosterone for individual pigs were determined based on an estimation from the matrix calibration curve (see Supplementary Materials Table S6). The resulting plasma concentrations of 17β-testosterone were used to construct a pharmacokinetic curve, and a graph of concentration versus time is shown in Figure 3.

**Figure 3.** Plasma concentrations in pigs—time profile of testosterone after a single i.m. administration of 0.6 mL Sustanon 250 mg/mL inj.; the points on the curve represent the detected 17β-testosterone plasma concentrations in individual pigs.

#### *2.3. Metabolomic Study of Blood Plasma and Urine*

The obtained plasma and urine samples were processed in the laboratory as described in Section 4.2. The metabolomic profiles of the individual samples on day 14 after the administration of the hormonal preparation SUSTANON were measured as described in Section 4.4. Both groups of plasma and urine metabolomic profiles were processed for a comparison in XCMS software and, alternatively, using the SIEVE company software. Both variants of data processing identified the approximately corresponding number of ions ( *m*/*z*) of peaks or metabolites: 2500 ions were found in plasma and 1400 ions were found in urine. In both cases, the original number of ions in these data sets was further reduced based on a *p*-volume ≤ 0.05 for further statistical processing. The source data of sets X (n × m) after reduction each contained *n* = 21 rows (animal objects) and m = 254 columns of statistically significant identified peak areas or metabolites for plasma and m = 213 columns for urine, respectively. Datasets were transformed using two di fferent methods, i.e., column centering [37] and probabilistic quotient normalization (PQN) [38], and a natural logarithm was applied for their scaling before the subsequent multivariate statistical analysis. The hotelling T<sup>2</sup> test criterion did not identify any outlier in both data sets (Supplementary Materials Figures S5 and S6).

Multidimensional statistical methods such as principal component analysis (PCA), clustering analysis (CA) and orthogonal partial squares discriminant analysis (OPLS-DA) were applied for finding relationships between metabolomics datasets of plasma and urine. PCA score plots for plasma and urine samples (Figure 4) and a dendrogram from CA (Figure 5) visibly di fferentiated between the control group and the treated group of pigs after 17 beta-testosterone administration. The main graphical results from the OPLS-DA analysis of data matrix of X mass spectra of plasma and urine samples versus data matrix Y for binary variables (1 = group of treated pigs, 2 = control group) were generated by the proposed statistical model and are shown in Figure 6. Furthermore, the coe fficients R2(X) = 0.616, R2(Y) = 0.987 for the fit and Q2(Y) = 0.898 for prediction of the model (according to cross validation) were calculated by OPLS-DA analysis for plasma and the coe fficients R2(X) = 0.469, R2(Y) = 0.997 and Q2(Y) = 0.879 for urine data. The OPLS-DA permutation tests further confirmed that the proposed statistical models are correct and robust (Supplementary Materials Figures S7 and S8). A volcano plot and variable importance in the projection (VIP) plot and S-plot from OPLS-DA were employed to determine the most discriminating metabolites between the treatment group and the control group (Figure 7).

**Figure 4.** PCA Score plots for plasma (**A**) and urine (**B**) data matrix, blue ellipse, and blue point descriptions (K) represent statistically significantly different samples from the control group of pigs versus the treated (T) group of pigs; added urine labelling: M—male and F—female (Centering, STATISTICA).

**Figure 5.** CA dendrogram of matrix data objects for plasma (**A**) and urine (**B**), labels: K—control group, T—treated group and M—male, F—female (by Euclidean distance method, STATISTICA).

**Figure 6.** The OPLS-DA score plots for plasma (**A**) and urine (**B**) data matrix demonstrate robust discrimination between the control group of pigs marked with red colour and the group of treated pigs marked with blue colour (PQN scaling, R package). The control group indicated by red Pig number 8, 9, 12, 15, 21, 22, 23, and 24. The treated group indicated by blue Pig number 1, 2, 4, 5, 7, 11, 13, 14, 16, 25, 26, 27, and 28.

**Figure 7.** The OPLS-DA Vulcano plots for plasma (**A**) and urine (**B**) data matrix, only metabolites with the VIP scores above 2 were considered significant. A list of specific numbers of metabolites is given in Table 4.



Note: DHA—Dehydroandrosterone, DHEA—Dehydroepiandrosterone, DHT—5α-dihydrotestosterone.

#### *Metabolites* **2020**, *10*, 307
