Heterocycles 52: The Drug-Likeness Analysis of Anti-Inflammatory Thiazolo[3,2-b][1,2,4]triazole and Imidazo[2,1-b][1,3,4]thiadiazole Derivatives

Lipophilicity, a significant physicochemical parameter of bioactive molecules, along with absorption, distribution, metabolism, excretion parameters and toxicity risk, was investigated for 32 thiazolo[3,2-b][1,2,4]triazole and imidazo[2,1-b][1,3,4]thiadiazole derivatives with anti-inflammatory potential. The experimental lipophilicity study was carried out by reversed-phase thin-layer chromatography in a binary isopropanol-water mobile phase, and the obtained results were compared with the theoretical lipophilicity parameters estimated by various computational methods. Strong correlations were found between the experimental retention factors and calculated partition coefficients. A modified Petra/Osiris/Molinspiration analysis was performed on the previously synthesized compounds, using SwissADME, Osiris and Molinspiration web tools. The predicted in silico parameters highlighted the most promising compounds as potential drug candidates. The compounds showed good gastrointestinal absorption, moderate activity according to the bioactivity score (values situated between −1.25 and −0.06), and a safe toxicity profile. The results obtained in this study will contribute to lipophilicity studies and other future studies focused on modulating new drug candidates starting from thiazolo[3,2-b][1,2,4]triazole and imidazo[2,1-b][1,3,4]thiadiazole derivatives, which are important heterocycles in medicinal chemistry.


Introduction
Lipophilicity is a molecular descriptor of significant importance in the synthesis and development of new drugs.In the body, drugs undergo a series of biophysical and biochemical transformations, which can be divided into three steps: the pharmaceutical step (release of the drug substance from the pharmaceutical form), the pharmacokinetic step (absorption, distribution, metabolism, and excretion of the substance from the body) and the pharmacodynamic step (interaction of the substance with the receptor or enzyme) [1][2][3].One of the main factors that significantly impact these phases is the lipophilicity of the molecule.At the pharmacokinetic level, lipophilicity particularly influences transport processes such as intestinal absorption, protein binding, and distribution of molecules to various tissues and organs.In order to enter the systemic circulation, a substance must have an appropriate affinity for lipophilic biological membranes to cross them more rapidly.As a result of this process, bioactive molecules are distributed in the body, reach the site of action, and exert their pharmacological action [1,[3][4][5].
Lipophilicity is commonly described as the property of a substance to be distributed between two phases: a non-polar phase (organic phase) and a polar phase (usually the aqueous phase).The two-phase distribution system can be liquid-liquid (n-octanol/water partition coefficient) or solid-liquid (reversed-phase high-performance liquid chromatography-RP-HPLC or reversed-phase thin-layer chromatography-RP-TLC retention).The quantitative parameter describing the lipophilicity of a substance is the partition coefficient P, usually expressed on a logarithmic scale, Log P, which refers to the partition of unprotonated species of a compound in the organic and aqueous phases.The n-octanol/water system is frequently used for the evaluation of Log P to describe the solubility, intestinal absorption, transport across biological membranes, and toxicity of substances [2,4].
If a substance is protonated, the distribution coefficient D (Log D) is determined to evaluate lipophilicity.Log D comprises the contribution of all protonated and unprotonated species present at a given pH [6].
The classical method for determining Log P in the n-octanol/water system is the ex-tractive method, which is no longer used today due to numerous disadvantages, including increased solvent consumption and increased time for determinations compared to chromatographic methods [4].The partitioning of substances between a non-polar stationary phase and an aqueous mobile phase using RP-TLC or RP-HPLC is similar to the distribution of substances in biological systems.The reversed-phase (RP)-C18 silica gel is frequently used as the stationary phase due to its simple hydrophobic interaction with solutes in the mobile phase [3,7,8].
Lipophilicity not only affects the absorption, distribution, metabolism, clearance, and toxicity of compounds but also influences drug potency.The high lipophilicity of molecules does not correlate with their biological activity [2,9].
Petra/Osiris/Molinspiration (POM) analysis is an important bioinformatic method used to evaluate the physicochemical properties of molecules and to predict bioactivity, toxicity, and drug-likeness parameters.The POM analysis has been extensively used to predict the ability of compounds to serve as drug candidates.Petra software, developed by the research group of Prof. Dr. J. Gasteiger (University of Erlangen-Nuremberg, Germany), can be replaced by the SwissADME web tool [10] to predict pharmacokinetic properties (absorption, distribution, metabolism, excretion-ADME parameters), which are of great importance in drug discovery [11,12].The Osiris program completes the pharmacokinetic profiles of compounds by predicting the toxicity risk but also evaluates the drug score and drug-likeness properties [13].Molinspiration predicts compound bioactivity scores against human receptors, such as G protein-coupled receptors (GPCRs), ion channels, kinases, nuclear receptors, proteases, and enzymes, indicating the potential of a compound as a drug candidate [14].
Thiazolo[3,2-b][1,2,4]triazole and imidazo[2,1-b] [1,3,4]thiadiazole derivatives have been reported to exhibit various biological activities, such as anticonvulsant, analgesic and anti-inflammatory, antiproliferative, antimicrobial and enzyme inhibitory effects [15][16][17][18][19][20][21].We previously reported the analgesic and anti-inflammatory activities of some thiazolo[3,2b] [1,2,4]triazole and imidazo [2,1-b] [1,3,4]thiadiazole derivatives along with their low ulcerative risk [22,23].However, information on their pharmacokinetic profile is limited and more studies are needed in order to correlate it with the pharmacodynamic activity of molecules.Only knowing the ability of the compound to interact with receptors or enzymes is not sufficient for a molecule to be an efficient drug candidate but the pathway of the compound in the organism until it reaches the target site should also be considered.
The main objective of this study was to determine the lipophilicity of some thiazolo[3,2b] [1,2,4]triazoles and imidazo [2,1-b] [1,3,4]thiadiazoles, synthesized by us [22][23][24], by reversed-phase thin-layer chromatography and computational methods, together with their bioactivity and bioavailability parameters, highlighting the correlations between the parameters.In addition, lipophilicity evaluation along with ADME parameters, can reveal the most promising compounds as drug candidates and provide new insights and a better understanding of bioactivity and biological targets of compounds.
For all tested compounds, it was found that the lipophilicity variation profile (Figure 1) decreased linearly with the increase in isopropanol concentration in the mobile phase.With a 5% increase in isopropanol concentration in the mobile phase, the RM values decreased constantly, suggesting the absence of secondary interactions between the compounds and the stationary phase.As the compounds in the mobile phase travel through the silica pores, they are retained by the hydrocarbon moiety only through weak hydrophobic (van der Waals force) interactions [7].Among the investigated compounds, benzenesulfonamide derivatives 13a-16a (−0.
For all tested compounds, it was found that the lipophilicity variation profile (Figure 1) decreased linearly with the increase in isopropanol concentration in the mobile phase.With a 5% increase in isopropanol concentration in the mobile phase, the R M values decreased constantly, suggesting the absence of secondary interactions between the compounds and the stationary phase.As the compounds in the mobile phase travel through the silica pores, they are retained by the hydrocarbon moiety only through weak hydrophobic (van der Waals force) interactions [7].Among the investigated compounds, benzenesulfonamide derivatives 13a-16a (−0.555 ≤ mR M ≤ −0.013) and 13b-16b (−0.511 ≤ mR M ≤ −0.416) were the least lipophilic.Compounds containing bromine along with chlorine (6a, mR M = 0.324; 6b, mR M = 0.239) or fluorine (7a, mR M = 0.186), or only bromine atoms in their structure (2a, mR M = 0.200; 2b, mR M = 0.174; 10a, mR M = 0.168; 10b, mR M = 0.222), showed the highest lipophilicity.The Soczewinski Wachtmeister [25] regression equation estimates a quantitative distribution of compounds between a non-polar phase (stationary phase), equivalent to biological membranes, and a polar phase, characteristic of the aqueous extracellular environment of the organism.The increased isopropanol concentration in the mobile phase resulted in an important decrease in the R M values for the more lipophilic compounds (substituted with 4-bromophenyl, 4-chlorophenyl and 4-trifluoromethylphenyl).It can be pointed out that more lipophilic derivatives are more sensitive to changes in the concentration of the organic solvent in the mobile phase.showed the highest lipophilicity.The Soczewinski Wachtmeister [25] regression equation estimates a quantitative distribution of compounds between a non-polar phase (stationary phase), equivalent to biological membranes, and a polar phase, characteristic of the aqueous extracellular environment of the organism.The increased isopropanol concentration in the mobile phase resulted in an important decrease in the RM values for the more lipophilic compounds (substituted with 4-bromophenyl, 4-chlorophenyl and 4-trifluoromethylphenyl).It can be pointed out that more lipophilic derivatives are more sensitive to changes in the concentration of the organic solvent in the mobile phase.According to the literature data, PC(RM) parameters were also determined to characterize the lipophilicity of the compounds.The graphical representation of the first two main components PC1(RM) and PC2(RM), classifies the compounds according to their degree of lipophilicity.The first two principal components accounted for 99.63% (PC1(RM) 98.81% and PC2(RM) 0.82%) of the data variability in the case of compounds 1a-16a and 1b-16b (Figure 2).According to the loading factors (values ranging from −0.15 to 0.10), PC2 was not found to be statistically significant.It was only used for graphical representation.On the lipophilicity charts, it can be observed that the compounds are divided by PC1(RM) based on their lipophilicity into two groups: Group A (1a-12a and 1b-12b), corresponding to the most lipophilic compounds, and Group B (13a-16a and 13b-16b), benzenesulfonamide derivatives, the least lipophilic compounds.Because of the benzenesulfonamide moiety, these compounds are more likely to form H-bonds with the mobile phase; therefore, their lipophilicity is lower than that of other derivatives.According to the literature data, PC(R M ) parameters were also determined to characterize the lipophilicity of the compounds.The graphical representation of the first two main components PC1(R M ) and PC2(R M ), classifies the compounds according to their degree of lipophilicity.The first two principal components accounted for 99.63% (PC1(R M ) 98.81% and PC2(R M ) 0.82%) of the data variability in the case of compounds 1a-16a and 1b-16b (Figure 2).According to the loading factors (values ranging from −0.15 to 0.10), PC2 was not found to be statistically significant.It was only used for graphical representation.On the lipophilicity charts, it can be observed that the compounds are divided by PC1(R M ) based on their lipophilicity into two groups: Group A (1a-12a and 1b-12b), corresponding to the most lipophilic compounds, and Group B (13a-16a and 13b-16b), benzenesulfonamide derivatives, the least lipophilic compounds.Because of the benzenesulfonamide moiety, these compounds are more likely to form H-bonds with the mobile phase; therefore, their lipophilicity is lower than that of other derivatives.

Computational Lipophilicity Parameters
The theoretical methods used to predict the lipophilicity of compounds are crucial in the early stages of the drug discovery process.Computational methods are also used to provide the estimated lipophilicity parameters when experimental methods cannot be applied [2,26].
The theoretical lipophilicity parameters were estimated by calculating different LogP

Computational Lipophilicity Parameters
The theoretical methods used to predict the lipophilicity of compounds are crucial in the early stages of the drug discovery process.Computational methods are also used to provide the estimated lipophilicity parameters when experimental methods cannot be applied [2,26].
The theoretical lipophilicity parameters were estimated by calculating different LogP values using various computational methods (Figure 3).

Computational Lipophilicity Parameters
The theoretical methods used to predict the lipophilicity of compounds are crucial in the early stages of the drug discovery process.Computational methods are also used to provide the estimated lipophilicity parameters when experimental methods cannot be applied [2,26].
The theoretical lipophilicity parameters were estimated by calculating different LogP values using various computational methods (Figure 3).The lipophilicity parameters estimated by calculating different LogP values using different computational methods are presented in Table S3 (Supplementary Materials).Among the predicted LogP methods, CLogP is widely accepted as the standard of computed lipophilicity.The most lipophilic thiazolo[3,2-b][1,2,4]triazoles were 7a (CLogP = 4.98) and 6a (CLogP = 4.86), followed by their corresponding imidazo[2,1- The lipophilicity parameters estimated by calculating different LogP values using different computational methods are presented in Table S3 (Supplementary Materials).Among the predicted LogP methods, CLogP is widely accepted as the standard of computed lipophilicity.1b-16b for the LogD calculated at pH = 1.7 (Figure 4).For these compounds, the LogDpH1.7 values were lower than LogP and other LogD values, thus the distribution range at this pH was lower, with the compounds absorbing better at pH ≥ 4.4.Bromine or trifluoromethyl substituted compounds presented the highest LogD values, having a good distribution in the body, and those substituted with the methoxy group showed the lowest LogD values, the distribution in the body being decreased.S4 (Supplementary Materials).

The Adapted POM Analysis
The SwissADME [10] platform is one of the main computational programs used to assess the pharmacokinetic, physicochemical, and drug-likeness behaviors of molecules.The chemical structures were drawn in Marvin JS by ChemAxon (Budapest, Hungary) and drug-likeness and ADME parameters were determined for each molecule (Table 1).According to the BOILED-Egg (Brain Or IntestinaL EstimateD Permeation) model (Figure 6), which sets a threshold of n-octanol/water partition coefficient (WLOGP) ≤ 5.88 and topological polar surface area (TPSA) ≤ 131.6Å, compounds that are situated in the white area are considered good drug candidates and can passively cross the gastrointestinal membranes, being easily absorbed.Compounds that are situated in the yellow area (1a, 1b, 2a, 2b, 4a, 4b, 5a, 5b, 8a, 8b, 9a, 9b, 10a, 10b) are easily absorbed from the gastrointestinal tract and passively cross the blood-brain barrier (BBB), with possible central effects.The sulfonamide derivatives (14a-16a, 14b-16b) and compounds 7a, 7b, situated in the gray area, presented lower gastrointestinal absorption after oral administration.

The Adapted POM Analysis
The SwissADME [10] platform is one of the main computational programs used to assess the pharmacokinetic, physicochemical, and drug-likeness behaviors of molecules.The chemical structures were drawn in Marvin JS by ChemAxon (Budapest, Hungary) and druglikeness and ADME parameters were determined for each molecule (Table 1).According to the BOILED-Egg (Brain Or IntestinaL EstimateD Permeation) model (Figure 6), which sets a threshold of n-octanol/water partition coefficient (WLOGP) ≤ 5.88 and topological polar surface area (TPSA) ≤ 131.6Å, compounds that are situated in the white area are considered good drug candidates and can passively cross the gastrointestinal membranes, being easily absorbed.Compounds that are situated in the yellow area (1a, 1b, 2a, 2b, 4a, 4b, 5a, 5b, 8a, 8b, 9a, 9b, 10a, 10b) are easily absorbed from the gastrointestinal tract and passively cross the blood-brain barrier (BBB), with possible central effects.The sulfonamide derivatives (14a-16a, 14b-16b) and compounds 7a, 7b, situated in the gray area, presented lower gastrointestinal absorption after oral administration.In the BOILED-Egg model, with the exception of 3b, 7a, 7b, substituted with trifluoromethyl moiety, thiazolo[3,2-b][1,2,4]triazole and imidazo[2,1-b][1,3,4]thiadiazole derivatives were not predicted to be substrates of P-glycoprotein (P-gp).
Most compounds serve as inhibitors of several cytochrome P450 isoenzymes (CYP1A2, CYP2C9, CYP2C19) and there are fewer predicted inhibitors of CYP2D6.Half of the imidazo[2,1-b][1,3,4]thiadiazole derivatives (1b, 4b, 8b, 9b, 10b, 12b, 13b, 16b) and five thiazolo[3,2-b][1,2,4]triazole derivatives (4a, 8a, 9a, 12a, 16a) were predicted to inhibit CYP3A4 isoenzyme.All compounds present no more than one rule violation, according to Lipinski, Ghose, Weber, Egan, and Muegge drug-likeness evaluation.The bioavailability score predicts the probability of a compound having at least 10% oral bioavailability in rats or measurable Caco-2 permeability.The results indicated a score of 0.55, meaning that there is a 55% chance for the compounds to have at least 10% bioavailability in rats.Evaluation of the Pan Assay INterference Structure (PAINS) and Brenk structural alert, lead-likeness, and synthetic accessibility score indicated no PAINS or Brenk structural alerts.The compounds are easy to synthesize according to the synthetic accessibility score, the obtained results were less than 3.35 with a maximum of 10 (very difficult to synthesize).
To assess the most promising drug candidates based on their bioactivity results, Molinspiration [14] 3).The bioactivity score for organic compounds indicates their level of activity.A bioactivity score > 0 suggests that the compound is active, a score between −5 and 0 denotes a moderately active compound, while a bioactivity score < −5 indicates that the compound is inactive.The higher the score value is, the greater the probability that the particular compound will be active.According to the results, the compounds are moderately active, with a bioactivity score situated between −1.25 and −0.06.MiLogP was determined by the methodology developed by Molinspiration as the sum of the fragment-based contributions and correction factors.The TPSA was calculated by the methodology proposed by Ertl [29], as the sum of fragment contributions, and O-and N-centered polar fragments were considered.TPSA provides identical results to 3D PSA (the correlation coefficient is 0.99), while the computation speed is 2-3 orders of magnitude faster.MiLogP and PSA are good descriptors for characterizing oral drug absorption, bioavailability and BBB permeability.According to Lipinski's rule, if LogP values are lower than 5, compounds are able to pass through biomembranes.Almost all compounds, except 2a, 2b, 3a, 3b, 6a, 6b, 7a, 7b, 8a, 8b, 10a, 10b, 11a and 11b, could easily cross the gastrointestinal membrane, according to the Pharmaceuticals 2024, 17, 295 9 of 21 Molinspiration prediction tool.Molecules with PSA ≥ 140Å are expected to exhibit poor intestinal absorption, but all tested compounds were below this limit.The theoretical methodologies employed for LogP prediction encompass a diverse range of algorithms.The primary categories of algorithms used for LogP calculations include substructure-based methods such as the atomic approach (cuts molecules down to single atom level and assumes that each atom contributes to the LogP parameter-ALOGP, ACLOGP, XLOGP2, XLOGP3, WLogP, LogP c , LogP v ), fragmental methods (cuts molecules into fragments and applies correction factors in order to compensate for intramolecular interactions-CLogP, miLogP), and property-based methods (utilize topological descriptors and other empirical approaches-ALOGPs, MLOGP, Silicos-IT, iLogP) [30].
A comparison of experimentally determined lipophilicity parameters with a series of computationally estimated parameters revealed statistically significant correlations for both series of compounds 1a-16a (Table 4) and 1b-16b (Table 5).Thus, in the case of compounds 1a-16a, a very high negative correlation was observed between mR M parameters and enzyme inhibitor score (r = −0.92)and a high negative correlation between mR M and protease inhibitor score (r = −0.83).According to the bioactivity scores, the compounds were moderately active against protease and enzyme inhibition.In addition, the mR M values showed high positive correlations with the majority of computational lipophilicity parameters.Very high positive correlations were observed between R M0 parameters and most computational parameters (except iLogP and WLogP, where high and moderate positive correlations were observed).High negative correlations were identified between the drug score and some computational lipophilicity parameters (WLogP, r = −0.99,LogP V , r = −0.72),and a very high negative correlation was observed between WLogP and drug-likeness score (r = −0.92).
In the case of compounds 1b-16b, a very high negative correlation was observed between mR M parameters and enzyme inhibitor score (r = −0.93)and a high negative correlation between mR M and protease inhibitor score (r = −0.84).The mR M values showed a very high positive correlation only with iLogP (r = 0.95) and R M0 parameters presented very high correlations with the majority of computational parameters (except iLogP and WLogP).High negative correlations were identified between WLogP and drug-likeness, drug score (r = −0.92 and r = −0.98,respectively).In the two series of heterocyclic compounds 1a-16a and 1b-16b, the chromatographic lipophilicity parameters (R M , R M0 ) displayed no significant correlation or only a moderate correlation with WLogP.Among the atomic methods used to predict LogP, WLogP overestimated the lipophilicity of the 4-trifluoromethyl derivatives 3a, 3b, 7a, 7b, 11a, 11b, 15a, 15b, when compared to compounds substituted with the same R1 but different R2 (Table S3, Supplementary Materials).This method considers atomic contributions with no other correction factors and does not consider long-range interactions or intramolecular hydrogen bonds [10].The software takes into account only the chemical nature of the substituents, without regard for their position in the structure or other types of interactions.This could possibly account for the low correlation between retention factors and predicted WLogP.
The iLogP descriptor is a physics-based algorithm established on solvation-free energies in n-octanol and water and is a relatively new approach for lipophilicity calculation [10].Among the employed computational methods, iLogP predicted the lowest values and underestimated the lipophilicity of compounds.The correlation between the iLogP and the experimental R M0 values of imidazo[2,1-b] [1,3,4]thiadiazoles (1b-16b) was observed to be moderately positive, with a correlation coefficient of 0.75.The imidazo[2,1b] [1,3,4]thiadiazoles (1b-16b) are susceptible to protonation in acidic environments, which can lead to inaccurate iLogP predictions.
The presence of substituents on the bis-heterocyclic core influences the lipophilicity of compounds.The retention profile (Figure 1) of compounds 1a-16a, 1b-16b highlights their similar lipophilicity, having the same type of interactions with the stationary phase.In the representation of chromatographic lipophilicity profiles, it can be observed that the most lipophilic compounds are those containing chlorine and bromine in their structure (6a, 6b), followed by 2a, 2b and 10a, 10b also substituted with bromine, confirming that the presence of bromine atoms in a molecule increases lipophilicity.Benzenesulfonamide derivatives 13a-16a and 13b-16b were the least lipophilic compounds, the presence of 4-sulfonamide moiety increased the interaction of the compounds with the mobile phase, a mixture of isopropanol:water.The influence of different substituents on lipophilicity profile of compounds is illustrated in Figure 7.
The lipophilicity chart (Figure 2) highlights the distribution of the studied compounds according to their structural similarity, benzenesulfonamide derivatives 13a-16a, 13b-16b separated from other compounds due to their low lipophilicity.most lipophilic compounds are those containing chlorine and bromine in their structure (6a, 6b), followed by 2a, 2b and 10a, 10b also substituted with bromine, confirming that the presence of bromine atoms in a molecule increases lipophilicity.Benzenesulfonamide derivatives 13a-16a and 13b-16b were the least lipophilic compounds, the presence of 4sulfonamide moiety increased the interaction of the compounds with the mobile phase, a mixture of isopropanol:water.The influence of different substituents on lipophilicity profile of compounds is illustrated in Figure 7.The lipophilicity chart (Figure 2) highlights the distribution of the studied compounds according to their structural similarity, benzenesulfonamide derivatives 13a-16a, 13b-16b separated from other compounds due to their low lipophilicity.
The software programs used for the estimation of LogP predicted different values depending on the compound structure and the complexity of the method used for calculation.The reliability of different computational programs used to predict LogP was also indicated by higher standard deviations (values between 0.56-1.02),thus suggesting that the accuracy of the theoretical method decreases with the complexity of the molecule [31]  ).Compounds substituted with R1 (green) are depicted in green (9a/9b, low lipophilicity; 13a/13b, the least lipophilic compounds), while those substituted with R2 (blue) are marked in blue (2a/2b, high lipophilicity; 4a/4b, low lipophilicity).Compounds substituted with both R1 and R2 are highlighted in purple (6a/6b, the most lipophilic compounds), light green (10a/10b, high lipophilicity), and light blue (12a/12b, low lipophilicity).
The software programs used for the estimation of LogP predicted different values depending on the compound structure and the complexity of the method used for calculation.The reliability of different computational programs used to predict LogP was also indicated by higher standard deviations (values between 0.56-1.02),thus suggesting that the accuracy of the theoretical method decreases with the complexity of the molecule [31] (Table S3, Supplementary Materials).The most lipophilic compounds evaluated by RP-TLC (6a, 6b) were also among those with the highest lipophilicity when LogP was estimated by theoretical methods.The strongest correlations (r ≥ 0.9, r 2 ≥ 0.9) between experimental R M0 and theoretical lipophilicity descriptors (ACLOGP, ALOGP, MLogP, XLOGP2, CLogP, LogP chem. ) of 1a-16a are presented in Figure 8. Thiazolo[3,2-b][1,2,4]triazoles (1a-16a) are relatively simple molecules with a range of 20-28 non-hydrogens atoms and the chromatographic lipophilicity parameter, R M0 , showed strong correlations with both types of substructure-based methods (atomic methods ALOGP, ACLOGP, XLOGP2 and fragmental method CLogP) used for predicting lipophilicity.(Table S3, Supplementary Materials).The most lipophilic compounds evaluated by RP-TLC (6a, 6b) were also among those with the highest lipophilicity when LogP was estimated by theoretical methods.The strongest correlations (r ≥ 0.9, r 2 ≥ 0.9) between experimental RM0 and theoretical lipophilicity descriptors (ACLOGP, ALOGP, MLogP, XLOGP2, CLogP, LogPchem.) of 1a-16a are presented in Figure 8. Thiazolo[3,2-b][1,2,4]triazoles (1a-16a) are relatively simple molecules with a range of 20-28 non-hydrogens atoms and the chromatographic lipophilicity parameter, RM0, showed strong correlations with both types of substructure-based methods (atomic methods ALOGP, ACLOGP, XLOGP2 and fragmental method CLogP) used for predicting lipophilicity.Imidazo[2,1-b][1,3,4]thiadiazoles (1b-16b), did not show as many strong correlations between R M0 and predicted lipophilicity parameters as their correspondents thiazolo[3,2-b][1,2,4]triazoles (1a-16a).Strong correlations were observed between R M0 and LogP chem., LogD pH1.7 and MLogP, suggesting that the structure of compounds plays a crucial role in lipophilicity (Figure 9).Imidazo[2,1-b][1,3,4]thiadiazoles (1b-16b), did not show as many strong correlations between RM0 and predicted lipophilicity parameters as their correspondents thiazolo[3,2b][1,2,4]triazoles (1a-16a).Strong correlations were observed between RM0 and LogPchem., LogDpH1.7 and MLogP, suggesting that the structure of compounds plays a crucial role in lipophilicity (Figure 9).The MLogP method takes into account the topological indices of molecules to supplement the deficiencies of the fragment and atom-based methods.The strong correlation between MlogP values and experimental R M0 of two series of heterocyclic compounds 1a-16a and 1b-16b suggests that more complex computational methods tend to produce results that are closer to experimental values.LogP chem.and LogD calculation method [27] considers the effect of the counterion concentration.The results demonstrated a strong correlation between the predicted values and R M0 , indicating that the protonation of a compound has an impact on its lipophilicity.
The ADME parameters determined by the SwissADME platform showed good gastrointestinal absorption for almost all studied compounds, except for benzenesulfonamide derivatives 14a-16a, 14b-16b, which are more hydrophilic.Compounds 7a and 7b, despite their high lipophilicity, were predicted to have low gastrointestinal absorption due to their interaction with P-gp (also called multidrug resistance protein-1), responsible for the efflux of xenobiotics from cells and associated with drug resistance [32].Higher lipophilicity (LogP > 5) determines lower solubility, higher permeability concerning gastrointestinal absorption and blood-brain barrier crossing, higher protein binding and affinity to metabolizing enzymes and efflux pumps, increasing compound turnover [2,33].Thiazolo[3,2-b][1,2,4]triazoles 4a, 8a, 9a, 12a, 16a, substituted with 4-methoxyphenyl moieties and half of the imidazo[2,1-b] [1,3,4]thiadiazole derivatives were predicted to inhibit CYP3A4 isoenzyme responsible for the metabolism of more than 50% of drugs.It is important to know if compounds could participate in pharmacokinetic drug-drug interactions, as they were predicted to inhibit some of the CYP450 isoenzymes, leading to unwanted side effects [9].
According to the five different drug-likeness prediction model systems (Lipinski, Ghose, Weber, Egan, Muegge), these compounds are likely to be good oral drug candidates in terms of bioavailability, presenting no more than one rule violation.
When assessed for bioactivity, the compounds were moderately active, with the results suggesting that the tested compounds have multiple mechanisms of action.They tend to act more as GPCRs ligands, ion channel modulators, kinase, and enzyme inhibitors, and less as nuclear receptor ligands and protease inhibitors.No significant differences were observed between the two groups of bioisosters.
Based on the results obtained after applying the modified POM analysis, eleven compounds (1a, 1b, 4a, 4b, 5b, 9a, 9b, 12a, 12b, 13a, 13b) out of 32 appear to be the most promising compounds, with the best pharmacokinetic properties, drug-likeness, bioactivity, and bioavailability score, having the potential to become orally administered drugs.The compounds demonstrated high gastrointestinal absorption, no rule violation of drug-likeness (Lipinski, Ghose, Weber, Egan, Muegge), moderate bioactivity score, no predicted toxicity effect and optimal drug-likeness score.
The correlations between the experimental lipophilicity parameters and bioactivity scores showed high negative correlations between mR M values and protease and enzyme inhibition bioactivity scores, indicating that an increase in lipophilicity of the compounds decreases their ability to inhibit proteases and enzymes.These results highlight the influence of lipophilicity on biological activity.
The results obtained in this study concerning the lipophilicity characterization of compounds and how it correlates with the pharmacokinetic and pharmacodynamic properties of thiazolo[3,2-b][1,2,4]triazoles and imidazo[2,1-b][1,3,4]thiadiazoles are considered preliminary data, contributing to more complex future studies.Our results, together with other literature-reported data, can be analyzed by machine learning methods such as multiple linear regression, random forests, support vector machine and artificial neural Compounds 1a, 1b, 4a, 4b, 5b, 9a, 9b, 12a, 12b, 13a, 13b had predicted CLogP values between 1 and 4, and it is generally known that compounds within this LogP range are more likely to present optimal physicochemical and ADME properties for orally administered drugs [9].Higher lipophilicity does not guarantee better biological activity and there are other factors to be considered when developing potential new drug candidates.
The results obtained in this study concerning the lipophilicity characterization of compounds and how it correlates with the pharmacokinetic and pharmacodynamic properties of thiazolo[3,2-b][1,2,4]triazoles and imidazo[2,1-b] [1,3,4]thiadiazoles are considered preliminary data, contributing to more complex future studies.Our results, together with other literature-reported data, can be analyzed by machine learning methods such as multiple linear regression, random forests, support vector machine and artificial neural networks, to evaluate the accuracy and reliability of predictive models and to identify connections between the compounds and biological targets [31].thiadiazoles 1b-16b.These compounds were synthesized in our laboratory and characterized using physicochemical and spectral methods ( 1 H-NMR, 13 C-NMR, IR, MS) [22][23][24].The anti-inflammatory and analgesic activity of the compounds was evaluated by in vivo and in silico methods [22,23].

Materials and Methods
The chromatographic plates were purchased from Merck (Darmstadt, Germany) and the solvents used were purchased from Sigma Aldrich (St. Louis, MO, USA) and Merck (Darmstadt, Germany).
The standard solutions (1 mg/mL) used to investigate the lipophilicity of the synthesized compounds were prepared in dichloromethane (1a-16a compounds) and dimethylsulfoxide (1b-16b compounds).From the obtained solutions, 2 µL was applied manually on the chromatographic plates, at 15 mm from the baseline and 5 mm from the edges of the plate at a spot distance of 12 mm.This procedure was repeated three times.The chromatographic plate upward elution was performed in a chromatographic chamber previously saturated for 30 min with mobile phase, at normal atmospheric pressure and room temperature.The migration distance of the eluent was 80 mm.The chromatographic plates were visually inspected under UV light at λ = 254 nm to identify the compounds.In order to minimize possible experimental errors, three measurements were performed for each mobile phase.The retention factors (R f ) were calculated as the average of three determinations in each case.
Based on the R f values, a variety of parameters can be determined to estimate the lipophilicity of the compounds.The isocratic retention factor (R M ) was determined using the Bate-Smith and Westall equation [34]: where R f = x/y and x = the migration distance of the compound, and y = the migration distance of the solvent front.
There is usually a linear correlation between the R M values and the concentration of the organic component in the mobile phase.For a better estimation of lipophilicity, R M0 was determined as the extrapolated retention factor, considering pure water as the mobile phase (Soczewinski Wachtmeister equation) [25]: R M = R M0 + bC where b is the slope of the curve, which indicates the rate at which the solubility of the compound increases in the mobile phase, C is the concentration of the isopropanol in the mobile phase, and R M0 is the R M of the compound when the concentration of isopropanol is zero (extrapolated value).
To estimate the lipophilicity of the compounds, φ 0 , representing the chromatographic hydrophobicity index, was also determined.It corresponds to the percentage (by volume) of the organic component in the mobile phase and can be determined according to the following formula: In order to assess the lipophilicity of compounds, other more relevant lipophilicity parameters have been used, such as the arithmetic mean of the experimental retention factors (mR M ), and the score associated with the first principal component (PC1(R M )) obtained by applying principal component analysis (PCA) to the retention parameters R M [35,36].The determination of these parameters eliminates the errors that appear during the extrapolation procedure (when determining R M0 ) so that the results are more accurate.
The SwissADME calculations included five LogP estimations (iLogP, MLogP, XLOGP3, WLogP, Silicos-IT) used for lipophilicity evaluation.The Lipinski (rule of five) parameters, including LogP ≤ 5, molecular weight ≤ 500, number of hydrogen bond acceptors ≤ 10 and number of hydrogen bond donors ≤ 5, TPSA ≤ 140 Å were evaluated for all compounds to identify their drug-likeness potential [38].Compounds that violate more than one of these rules exhibit bioavailability problems.Also, Ghose, Weber, Egan and Muegge drug-likeness evaluation was determined along with other medicinal chemistry parameters such as Pan Assays INterference Structure-PAINS, Brenk structural alert-BRENK, lead-likeness-LL, synthetic accessibility score, to describe the compounds in vitro behavior that will influence their biological activity in vivo.
The Osiris evaluations included toxicity risks (mutagenic, tumorigenic, irritant, and reproductive effects), CLogP, solubility (LogS), drug-likeness prediction, and drug score, indicating the overall potential of the compounds as drug candidates [13].
The Molinspiration calculation included the topological polar surface area (TPSA), miLogP, and bioactivity score.The bioactivity of each compound was evaluated based on compound interactions as GPCR ligands, ion channel modulators, kinase inhibitors, nuclear receptor ligands, protease and other enzyme inhibitors [14].
POM analysis included selected pharmacokinetic, medicinal chemistry and druglikeness score parameters, and the parameters that showed similarity to those obtained from all software programs were used only from one.
The computational methods involved optimization of the geometry of each compound using ChemDraw Ultra v.12 or MarvinSketch v.19.22 (ChemAxon, Budapest, Hungary), followed by individual analysis of the structures.

Statistical Data Analysis
Principal component analysis (PCA) and correlations between the chromatographic lipophilicity parameters (mR M , R M0 ), computational lipophilicity parameters (various predicted LogP), bioactivity scores, drug scores and drug-likeness values were performed using the software package "Statistica v.12" ( StatSoft, Inc., Tulsa, OK, USA).For all investigated compounds, the correlation coefficient (r) was determined to assess the linear relationship and directions of the correlations between two variables.Values of r close to +1 indicate a strong positive correlation, those close to −1 indicate a strong negative correlation, and those closest to 0 indicate no relationship.If the correlation coefficient is squared, the resulting value (r 2 , the coefficient of determination) represents the strength of the relationship.The significance level was set at p ≤ 0.05.The pharmacokinetic profile of the compounds, described by ADME parameters, bioactivity, toxicity and drug-likeness behavior, were evaluated using a modified POM analysis.Our results suggest that investigated thiazolo[3,2-b][1,2,4]triazoles and imidazo[2,1-b] [1,3,4]thiadiazoles derivatives are not expected to present high toxicity risk, as they have a safe profile and are well absorbed after oral administration.Based on the modified POM analysis, compounds substituted with phenyl (1a, 1b), 4-methoxyphenyl (4a, 4b, 9a, 9b, 12a, 12b) and benzenesulfonamide moiety (13a, 13b) showed promising pharmacokinetic profiles and may be suitable candidates for drug development.Future artificial intelligence methodologies such as prediction of drug-target interaction are needed in order to fully explore the biological potential of compounds, identify connections between drugs and diseases, expanding the possibilities for addressing unmet medical needs.