Profiling of Disubstituted Chloroacetamides’ Potential Biological Activity by Liquid Chromatography

Abstract

Modern agriculture relies heavily on the use of pesticides, with one-third of them being herbicides. Chloroacetamides are the most widely used herbicides because of their high effectiveness, but their extensive use poses environmental challenges and threatens the health of living organisms due to toxicity risks. Since the pharmacokinetic behavior and toxicity of a compound are influenced by its lipophilicity, this essential physicochemical parameter for disubstituted chloroacetamides was determined in silico and experimentally through thin-layer chromatography on reversed phases (RPTLC C18/UV254s) in mixtures of water and distinct organic modifiers. The pharmacokinetic profile of chloroacetamides was analyzed by using the BOILED-Egg model. The correlation between the obtained chromatographic parameters and software-based lipophilicity, pharmacokinetic, and ecotoxicity predictors of the studied chloroacetamides was assessed by using linear regression, but more comprehensive insight was obtained through multivariate methods—Cluster Analysis and Principal Component Analysis. It was observed that the total number of carbon atoms in the structure of their molecules, along with the type of hydrocarbon substituents, are the most important factors affecting lipophilicity, pharmacokinetics, and potential toxicity to non-target organisms.

1. Introduction

Herbicides have an irreplaceable role in agriculture, as weeds are among the leading causes of crop damage, accounting for nearly 35% of total yield losses [1]. In recent decades, the rise in herbicide use has been paired with a growing resistance of weeds to current herbicides, making the development of new, potent, effective, and especially selective herbicides a major priority in agrochemistry [2]. Given that, alongside effectiveness, a future herbicide should also possess favorable pharmacokinetic properties, low toxicity to non-target organisms, rapid and easy degradability, and minimal persistence in the ecosystem. In modern agrochemistry, the alignment of the required and desired properties of a new herbicide with the optimization of time and financial resources is accomplished through the application of QSPR (Quantitative Structure–Property Relationship) and QSAR (Quantitative Structure–Activity Relationship) approaches [3,4]. The essential physicochemical parameter in these analyses is lipophilicity, which is shaped by two main structural factors—hydrophobicity and polarity—and describes how a compound is distributed between a non-polar (organic) phase and a polar (primarily aqueous) phase [5]. Evaluating the lipophilicity of a potential herbicide can help predict its mobility in the environment and its ability to pass through the biological membranes of non-target organisms. Also, since lipophilicity impacts the pharmacokinetic properties of a compound (absorption, distribution, metabolism, and excretion-ADME), understanding allows the prediction of a newly synthesized agent’s half-life, bioavailability, and toxicity [6,7,8]. The most widely accepted lipophilicity parameter is the partition coefficient (logP). In addition, numerous studies indicate that R_M⁰ and m parameters, derived from reversed-phase thin-layer chromatography (RPTLC), may act as alternative measures of a compound’s lipophilicity [9,10,11,12,13]. Chloroacetamides have been well-established in the herbicide market due to their broad-spectrum efficacy against various weed species, compatibility with other herbicidal agents, and their capacity to offer prolonged weed suppression, which helps minimize the frequency of application [14,15].

This study focuses on selected disubstituted derivatives of chloroacetamide, which have both hydrophilic and hydrophobic fragments in their structure (important for assessing the impact on their solubility and permeability). In the first step, data on their important molecular descriptors, lipophilicity, pharmacokinetic properties, and ecotoxicity were gathered by using in silico methods. In order to predict passive gastrointestinal absorption and brain access of examined chloroacetamides, the BOILED-Egg model (Brain Or Intestinal Estimate D permeation method) was applied. A radar chart was applied to evaluate their bioavailability in detail. Further, for the examined chloroacetamides, the RP-TLC parameters (R_M⁰ and m) were determined and correlated with the previously in silico-derived parameters of lipophilicity, pharmacokinetics, and ecotoxicity by using linear regression and multivariate methods (Cluster Analysis, CA, and Principal Component Analysis, PCA). In addition, multivariate methods provided understanding of the key factors influencing the biological activity of congeneric compounds with subtle structural differences.

2. Materials and Methods

2.1. In Silico Calculations

The structures of the examined chloroacetamides are presented in

Table 1. Structures of the examined disubstituted chloroacetamides.

	Derivative
1	N-ethyl-N-cyclohexyl- chloroacetamide
2	N-cyclohexyl-N-2-hexyl- chloroacetamide
3	N-cyclohexyl-N-cyclopropyl- chloroacetamide
4	N-cyclohexyl-N-n-hexyl- chloroacetamide
5	N-cyclohexyl-N-n-propyl- chloroacetamide
6	N-cyclohexyl-N-2-pentyl- chloroacetamide
7	N-2-butyl-N-cyclohexyl- chloroacetamide
8	N-cyclohexyl-N-methyl- chloroacetamide

[16,17].

For logP and logS calculations, as well as for the determination of molecular descriptors relevant to bioavailability, various software tools were employed, including ChemBioDraw 13.0, Molinspiration, SwissADME, MarvinSketch, Dragon Plus version 5.4, and ChemDoodle [18,19,20,21,22,23]. (Supplementary Material Tables S1–S3).

Ecotoxicity parameters, including effective concentration (EC₅₀, mgkg⁻¹) for various species (Algae, Daphnia, Medaka, and Minnow), were estimated using the PreADMET software, and they are listed in Table S4 [24].

2.2. Chromatographic Analysis

The chromatographic behavior of the studied chloroacetamides was examined by RPTLC. Approximately 2 mm³ of each freshly prepared ethanol-based solution in concentration 2 mg cm⁻³ was applied by capillary pipette on pre-conditioned commercial RP-18V/UV254 plates (5 cm × 10 cm, Macherey-Nagel GmbH & Co., Düren, Germany). On average, the typical spot diameter was 1 mm.

Four solvent systems (1-propanol–water, 2-propanol–water, tetrahydrofuran–water, and dioxane–water) were used to develop chromatograms. Thereby, the ascending technique was performed in the horizontal chromatographic chambers (TLC Development Chamber, for 20 cm × 20 cm plates, Macherey-Nagel, GmBH and Co., Duren, Germany) at room temperature without prior saturation of the chromatographic chamber with mobile phase vapors. The content of all the LC grade organic modifiers (Sigma-Aldrich, Saint-Quentin-Fallavier, France), φ, in the mobile phase was varied from 36% to 52% (v/v) in 4% increments. After chromatograms dried in a stream of air, detection of the studied chloroacetamides was performed under UV light at λ = 254 nm. All the studied derivatives were detected as dark spots. In all the mobile phases used, for each compound, the average R_f values of three measurements were calculated. According to the Bate-Smith equation, the R_M values were calculated [25]:

R_M = log (1/R_f − 1)

(1)

The linear relationship, as described by Soczewiński and Wachtmeister, between the obtained R_M values and the volume fraction of the organic solvent in the mobile phase (φ) yields two key chromatographic parameters [26]:

R_M = R_M⁰ + mφ

(2)

The intercept, R_M⁰ (chromatographic retention constant), corresponds to the extrapolated R_M value at 0% organic modifier. The slope of the regression line, m, reflects the compound’s specific hydrophobic surface area [27].

2.3. Statistical Calculations

Linear regression analysis was conducted using Origin v8.0, while multivariate statistical methods were performed with Statistica v14.1.0.8.

A data matrix comprising 8 rows (representing chloroacetamide compounds) and 25 columns (including chromatographic parameters, software-derived lipophilicity values, molecular descriptors, and toxicity/ecotoxicity predictors) was constructed for Cluster Analysis (CA) and Principal Component Analysis (PCA). To ensure equal weighting of all the variables, the dataset was standardized prior to applying these multivariate methods. Cluster Analysis was performed using the Ward method for clustering, with Euclidean distance employed as the measure of dissimilarity.

3. Results and Discussion

3.1. In Silico Evaluation of Chloroacetamides’ Bioactivity Parameters

3.1.1. Lipophilicity and Solubility

Computationally derived logP and logS values for the studied chloroacetamides are summarized in Table S1. Due to variations in the applied mathematical approach (atom-based, fragment-based, and whole molecule-based), the same derivatives have different logP values. Notably, across all computational approaches, derivative 2 and derivative 4, on average, exhibited the highest lipophilicity, while derivative 8 showed the lowest. Also, as expected, derivative 4 is classified as poorly to moderately soluble in water (insoluble < −10 < poorly < −6 < moderately < −4 < soluble < −2 very soluble < 0 < highly soluble), whereas derivative 8 exhibits the highest solubility [20]. These results can be attributed to the difference in the total number of carbon atoms in the structures of their molecules. Namely, a higher total number of carbon atoms gives derivatives a more pronounced hydrophobic character compared to other derivatives. However, compounds with the same total number of C atoms have similar lipophilicity but not solubility, which can be explained by the difference in the nature of the present substituent (derivative 2 and derivative 4).

3.1.2. Anticipation of the Pharmacokinetics and Toxicity Profile of Examined Disubstituted Chloroacetamides

The pharmacokinetic properties of the examined chloroacetamides are presented in Table S2, including data on gastrointestinal absorption (GI), blood–brain barrier (BBB) permeability, P-glycoprotein substrate activity (P-gp), and inhibition of CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4, as well as skin permeation (logK_sp).

Orally administered substances are absorbed through the gastrointestinal tract into the bloodstream, from where they may pass through the blood–brain barrier. The blood–brain barrier serves a protective role, limiting the entry of potentially harmful substances into the brain. P-gp pumps various substances out of enterocytes, which limits the absorption (bioavailability) of compounds into the systemic circulation, essentially acting as a defense mechanism against toxic substances, particularly in the brain. Cytochrome P450 (CYP450) enzymes are crucial for synthesizing cholesterol, steroids, prostacyclin, and thromboxane A2. They also play a key role in detoxifying foreign substances and metabolizing drugs. Over fifty different CYP450 enzymes have been identified, with six major enzymes—CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4—responsible for the metabolism of about 90% of all drugs. Although these enzymes are primarily located in the liver, they are also present in the small intestine, lungs, placenta, and kidneys, where they contribute to various metabolic processes [28,29]. The skin permeability coefficient (logK_sp) indicates compounds’ ability to penetrate the skin [30].

Table S2 clearly shows that all the studied chloroacetamide derivatives exhibit high gastrointestinal absorption. Additionally, they all can permeate the BBB, and none of these compounds are substrates for P-gp. These results suggest that once absorbed into the body, the studied chloroacetamide derivatives, in addition to their high bioavailability, may act as active agents in the central nervous system [31]. It is noticeable that none of the studied derivatives are inhibitors of CYP2C19, CYP2C9, CYP2D6, or CYP3A4. Also, only derivative 4 shows inhibition of CYP1A2, which would result in increasing the concentration of drugs metabolized by this enzyme and elevating the risk of adverse effects. As expected, the least lipophilic, derivative 8, shows the lowest possibility of passing through the skin, while the most lipophilic, derivative 4, exhibits the highest.

It is recognized that the theoretical bioavailability of a compound is optimized through a balanced combination of its lipophilicity and polarity, especially regarding water solubility (logS). Since lipophilicity and solubility are often opposing properties, the most acceptable way to reconcile them in the theoretical assessment of bioavailability was proposed by Egan [32]. Relying on two key descriptors, lipophilicity (−1 ≤ WlogP ≤ 5.88) and polarity of compound (0 Ų ≤ total polar surface area (TPSA) ≤ 131.6 Ų), Egan’s rule is summarized in a visual representation known as the BOILED-Egg model [33,34]. Given that this model offers a quick and easily reproducible approach to predict passive gastrointestinal absorption and brain penetration of small molecules, it was applied to study chloroacetamides. The obtained graph is shown in Figure 1.

Figure 1 illustrates that all the examined compounds fall within the yellow region (yolk), suggesting their potential to cross the blood–brain barrier. None of the studied chloroacetamide derivatives shows significant absorption in the gastrointestinal tract (represented by the empty white region). Also, for the compounds marked in red, it is predicted that they will not be eliminated by P-glycoprotein. Although the examined chloroacetamides fulfill Egan’s rule, the results obtained from the BOILED-Egg model indicate their reduced gastrointestinal absorption, suggesting poor bioavailability after oral administration.

Given the inconsistent information on the bioavailability of the studied chloroacetamides and the obvious influence of more than two factors, a radar chart was employed to further assess their bioavailability. This diagram, generated by SwissADME tools, visually represents six key physicochemical parameters linked to oral bioavailability, including lipophilicity, size, polarity, water solubility, degree of saturation, and flexibility. The optimal ranges are lipophilicity (−0.7 < XlogP₃ < 5), size (150Da < molecular weight (MW) < 500 Da), polarity (20 Ų < TPSA< 130 Ų), water solubility (−6 ≤ logS), saturation (0.25 < Csp³ < 1), and flexibility (0 < number of rotatable bonds < 9) [33]. Figure 2 presents the radar chart for the examined chloroacetamides.

Based on the data given in Tables S1–S3, as well as on the graphical representations obtained from the radar diagram, it can be concluded that all the analyzed derivatives fall within the red zone, indicating they meet the basic criteria for good human bioavailability [35]. These results indicate the need for additional research to assess the risk and safety of the application of new chloroacetamide-based herbicides. As an additional indicator of the previously mentioned issue, along with the increasing exposure of aquatic organisms to the harmful effects of herbicides, the effective concentrations (EC₅₀, mg kg⁻¹) of the studied chloroacetamide derivatives for different aquatic species, such as Algae, Daphnia, Medaka, and Minnow, were calculated (Table S4).

The data in Table S4 reveal that all the examined derivatives demonstrate the lowest toxicity to the Daphnia species. Generally, the highest toxicity among all the tested chloroacetamides has the most lipophilic derivative 4, while the less lipophilic derivative 8, with the lowest number of carbon atoms, exhibits the lowest toxicity.

3.2. Chromatographic Parameters in the Evaluation of Chloroacetamides’ Lipophilicity, Pharmacokinetics, and Ecotoxicity

The results of the chloroacetamides’ chromatographic analysis are presented in

Table 2. Chromatographic parameters of the tested chloroacetamides in applied protic modifiers.

Derivative	1-Propanol				2-Propanol
	RM0	m	r	sd	RM0	m	r	sd
1	1.883	−3.220	0.993	0.018	1.754	−2.644	0.997	0.008
2	2.627	−3.985	0.994	0.013	2.498	−3.376	0.996	0.013
3	2.055	−3.373	0.996	0.011	1.915	−2.778	0.999	0.006
4	2.748	−4.080	0.992	0.016	2.586	−3.454	0.994	0.014
5	2.092	−3.422	0.998	0.008	1.957	−2.846	0.998	0.008
6	2.522	−3.813	0.997	0.006	2.365	−3.217	0.993	0.015
7	2.385	−3.685	0.991	0.020	2.211	−3.039	0.999	0.005
8	1.686	−3.030	0.994	0.015	1.510	−2.518	0.996	0.008

and

Table 3. Chromatographic parameters of the tested chloroacetamides in applied aprotic modifiers.

Derivative	Tetrahydrofuran (THF)				Acetonitrile (ACN)
	RM0	m	r	sd	RM0	m	r	sd
1	2.206	−3.876	0.995	0.007	1.225	−2.270	0.999	0.007
2	2.979	−4.631	0.991	0.012	1.878	−2.944	0.995	0.011
3	2.357	−3.977	0.999	0.007	1.280	−2.307	0.996	0.011
4	3.127	−4.715	0.997	0.010	1.964	−3.022	0.994	0.013
5	2.443	−4.110	0.998	0.006	1.370	−2.420	0.994	0.012
6	2.793	−4.424	0.994	0.009	1.730	−2.768	0.998	0.013
7	2.669	−4.360	0.993	0.014	1.516	−2.577	0.993	0.015
8	2.038	−3.724	0.997	0.008	1.185	−2.191	0.997	0.006

.

High regression coefficients (r) demonstrate the validity of the linear R_M–ϕ relationships within the selected experimental range.

The data shown in Table 2 and Table 3 reveal that different R_M⁰ values were obtained for the same compound in different organic modifiers. The highest R_M⁰ values were obtained in aprotic and nonpolar THF, and the lowest R_M⁰ values were characteristic of aprotic and polar acetonitrile. Furthermore, the order of the R_M⁰ values within the same group of modifiers was influenced by the polarity and dispersion interaction capabilities of the organic modifier [36]. Specifically, decreasing the polarity of protic modifiers and increasing their ability to form dispersion interactions led to lower R_M⁰ values. In contrast, for aprotic modifiers, reducing polarity and increasing dispersion interaction potential resulted in higher R_M⁰ values ((ε _(2-propanol) = 18) < (ε _(1-propanol) = 20); (ε _(THF) = 7.5) < (ε _{(acetonitrile)} = 37.5)). This behavior was also observed in earlier studies [37].

It is also evident that chloroacetamides’ retention is primarily influenced by the total number of carbon atoms in their structure, i.e., by the hydrophobic nature of the whole molecule. Specifically, in all applied systems, derivative 4 exhibited the highest chromatographic retention parameter (R_M⁰), owing to its higher total carbon atom count. In contrast, derivative 8 showed the weakest retention, as it contains a lower total number of carbon atoms compared to the other derivatives. These experimental data align with the in silico findings previously reported.

Moreover, the variation in the m values mirrors the trend observed in the R_M⁰ values. The linear correlation between R_M⁰ and m, as presented in Table S5, suggests that the chromatographic parameters are governed by the same physicochemical factors, indicating the congeneric nature of the studied chloroacetamide derivatives [38].

Building on previous research, it was hypothesized that RPTLC could be an effective tool for evaluating lipophilicity as well as pharmacokinetic behavior and ecotoxicity of the tested chloroacetamide derivatives [39]. This hypothesis was validated by correlating the chromatographic parameters (R_M⁰ and m) with software-derived values of logP/logK_sp and EC₅₀ by applying linear regression analysis. The obtained results are shown in

Table 4. The correlation matrix between chromatographic parameters and logP values of the studied chloroacetamides.

	logPcd	ClogP	milogP	logPms	MlogP	AlogP	NCNHET	AlogP98	XlogP2	WlogP	XlogP
1-propanol
RM0	0.976	0.982	0.975	0.984	0.992	0.986	0.991	0.991	0.972	0.987	0.988
m	0.983	0.989	0.976	0.988	0.994	0.992	0.994	0.996	0.978	0.992	0.994
2-propanol
RM0	0.977	0.984	0.976	0.984	0.996	0.987	0.995	0.994	0.978	0.989	0.989
m	0.989	0.994	0.991	0.992	0.992	0.996	0.994	0.997	0.977	0.995	0.997
THF
RM0	0.984	0.992	0.986	0.986	0.989	0.992	0.990	0.992	0.979	0.990	0.995
m	0.989	0.990	0.987	0.992	0.985	0.993	0.986	0.992	0.980	0.992	0.996
ACN
RM0	0.988	0.988	0.994	0.984	0.967	0.990	0.972	0.982	0.963	0.985	0.989
m	0.993	0.993	0.997	0.990	0.972	0.994	0.977	0.987	0.972	0.990	0.994

and

Table 5. The correlation matrix between chromatographic parameters and pharmacokinetic/ecotoxicity parameters of the examined chloroacetamides.

	logKsp	Algae *	Daphnia *	Medaka *	Minnow *
1-propanol
RM0	0.984	0.976	0.993	0.992	0.930
m	0.991	0.978	0.995	0.988	0.919
2-propanol
RM0	0.984	0.979	0.996	0.992	0.926
m	0.995	0.981	0.993	0.986	0.915
THF
RM0	0.993	0.977	0.990	0.977	0.914
m	0.996	0.987	0.984	0.976	0.931
ACN
RM0	0.989	0.977	0.974	0.972	0.888
m	0.995	0.980	0.978	0.969	0.890

* derivative 8 is excluded.

.

The high values of the regression coefficient, r, given in Table 4 and Table S6, as well as in Table 5 and Table S7 (approximately r > 0.888), indicate that the chromatographic parameters (R_M⁰ and m) determined in all applied modifiers can be used as valid measures of the examined disubstituted chloroacetamide derivatives’ bioactivity parameters.

3.3. Multivariate Methods in Studying the Chloroacetamide Derivatives’ Biological Activity Parameters

A significant challenge in scientific research lies in analyzing, correlating, and drawing meaningful conclusions from the vast and diverse data, along with the complexity of the information they hold. This issue can be addressed through the application of multivariate techniques such as Cluster Analysis (CA) and Principal Component Analysis (PCA) [40,41,42]. These methods provide powerful tools for uncovering hidden patterns, reducing complexity, and enhancing the interpretability of large and intricate datasets.

3.3.1. CA Approach

CA results are shown in Figure 3 as a dendrogram of the analyzed parameters, and in Figure 4 as a dendrogram of the examined chloroacetamides.

From Figure 3, it is evident that the grouping of the chloroacetamides’ bioactivity parameters into two main clusters occurs. The first cluster includes the chromatographic parameter m determined in all the applied modifiers, toxicity parameters, and logS, while the second cluster consists of the chromatographic parameter R_M⁰, the software-derived lipophilicity parameter, and the pharmacokinetic predictor logK_sp. This division confirms that the grouped parameters within the first cluster are closely linked to the polarity of the studied compounds, and consequently, their ability to reach aquatic organisms and cause toxicity. The second cluster includes all the parameters related to lipophilicity or influenced by it, such as skin permeation.

Figure 4 reveals that the primary classification of the examined chloroacetamides is based on the total number of carbon atoms in their structure, which is linked to their lipophilicity. The first main cluster contains derivatives with more than 10 C atoms in structures of their molecules (2, 4, 6, and 7), while the second cluster includes derivatives with less than 10 C atoms (1, 3, 5, and 8). Within the second cluster, it is evident that derivative 8, with the shortest alkyl substituent, appears as an outlier. This demonstrates the ability of CA to reflect, compared to in silico and chromatographic analysis, not only the impact of the number of carbon atoms in the structure but also the influence of the nature of the substituent.

3.3.2. PCA Approach

By using PCA to decompose the original data matrix into loading vectors—lipophilicity parameters (both experimental and software-derived), solubility parameter, parameter of skin permeability, and toxicity parameters—irrelevant information is removed, and the data volume for analysis is significantly reduced. The score vectors represent the studied chloroacetamide derivatives. As shown in Figure S1, the first three principal components explain about 99.66% of the total variance. Figure 5 (loading plot) illustrates the grouping of the bioactivity parameters for the examined chloroacetamides.

PC1 primarily separates the bioactivity parameters of the tested chloroacetamides into two distinct groups. The first group, characterized by negative PC1, includes the chromatographic parameter m, logS, and toxicity parameters. The second group, described with positive PC1, includes the chromatographic parameter R_M⁰, software-derived logP values, and logK_sp. PCA results are similar to those previously obtained by CA, with one significant difference. Namely, PC2 separates the chromatographic parameter m determined in all the applied modifiers and logS from the ecotoxicity parameters.

The distribution of the studied derivatives is illustrated in Figure 6 (score plot).

From Figure 6, it is evident that PC1 predominantly separates the studied chloroacetamide derivatives by the total number of carbon atoms in their structure. Derivatives with a higher number of carbon atoms (more than 10) have positive PC1 values, while the rest of the derivatives (fewer than 10 C atoms) show negative PC1 values. Additionally, the most lipophilic compound (derivative 4) is associated with the most positive PC1 value, while the least lipophilic compound (derivative 8) corresponds to the most negative PC1 value. The positive PC2 describes derivatives with the highest number of C atoms compared to other derivatives (2 and 4), as well as derivative 8, which appears as an outlier (the most negative PC1 and the most positive PC2 values).

Further analysis revealed a more detailed separation of the chloroacetamide derivatives by comparing the values of PC2 and PC3 (Figure 7).

Specifically, the PC2−PC3 relationship classifies the derivatives based on the type of hydrocarbon substituents. From Figure 7, derivative 3 and derivative 8 are described by the most negative PC2 and by the most positive PC2 values, respectively. These derivatives are distinct due to their structural features; in addition to having a low number of carbon atoms, they also possess unique substituents compared to the other compounds. Specifically, derivative 3 is the only one with a cycloalkyl substituent, while derivative 8 contains the smallest alkyl substituent among the analyzed derivatives.

PC3 recognizes subtle differences in the nature of the present substituent in the molecule of chloroacetamides. Namely, derivatives with straight alkyl substituents (1, 4, and 5) have negative PC3 values, while those with branched alkyl substituents (2, 6, and 7), as well as derivatives with cycloalkyl substituents, are characterized by positive PC3 values. Additionally, PC3 strongly distinguishes derivatives with the same total number of C atoms in molecules but with completely different types of hydrocarbon substituents (3 and 5). Interestingly, PC3 finely separates the derivatives with the same total number of C atoms in the molecule, i.e., substituents with the same number of C atoms in the alkyl chain but different in arrangement—straight (derivative 4) and branched alkyl substituent (derivative 2).

The obtained results underscore the power of PCA in revealing subtle but meaningful patterns among the examined compounds. These findings are highly relevant for the rational development of next-generation chloroacetamide-based herbicides because they emphasize the importance of a well-considered balance between structural and physicochemical properties in the design of new, effective, and safer candidates. Specifically, the number of carbon atoms in the molecule, along with lipophilicity and solubility, plays a crucial role in determining both the effectiveness and the toxicity of these compounds. PCA analysis reveals that even slight variations in the molecular structure—such as the nature of the substituents—can significantly impact a derivative’s environmental behavior and biological activity.

4. Conclusions

Chloroacetamide herbicides are extensively utilized in agriculture for their strong weed-control capabilities, but their structural resemblance to certain bioactive compounds raises important concerns. Prolonged exposure through environmental pathways such as contaminated water sources, soil, or food products can elevate the risk of health disruptions, particularly affecting the endocrine and nervous systems in humans and animals. Their chemical stability and mobility allow them to persist in ecosystems, which increases the likelihood of bioaccumulation and long-term ecological imbalance. A comprehensive understanding of the physicochemical properties of agrochemical substances is fundamental to their successful and responsible use in agriculture.

To explore the essential physicochemical properties that contribute to herbicidal activity, a set of chloroacetamide derivatives was examined by using both in silico modeling and chromatographic analysis. Multivariate statistical methods, applied during the initial evaluation, identified the total number of carbon atoms in the molecular structure—which correlates with lipophilicity—and the chemical nature of the substituents—which influences water solubility—as major factors affecting the behavior of these compounds.

Also, it was found that the chromatographic retention constant R_M⁰ proved to be a reliable indicator of lipophilicity and pharmacokinetic properties among the studied chloroacetamide derivatives. The chromatographic parameter m was also recognized as a potential indicator of ecotoxicity properties.