Cross-column density functional theory-based quantitative structure-retention relationship model development powered by machine learning

Quantitative structure-retention relationship (QSRR) modeling has emerged as an efficient alternative to predict analyte retention times using molecular descriptors. However, most reported QSRR models are column-specific, requiring separate models for each high-performance liquid chromatography (HPLC) system. This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. Four machine learning methods-partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB)-were compared on a dataset of 360 retention times for 15 aromatic analytes. Molecular descriptors were calculated using density functional theory (DFT). Column characteristics like particle size and pore size and experimental conditions like temperature and gradient time were additionally used as descriptors. Results showed that the GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749 min on the test set. Feature analysis revealed that solvation energy (SE), HOMO-LUMO energy gap (∆E HOMO-LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. Our findings underscore the efficiency of ensemble methods, GB and RF models employing non-linear learners, in capturing local variations in retention times across diverse experimental setups. This study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models in optimizing chromatographic analysis.

3D-printed extraction devices fabricated from silica particles suspended in acrylate resin

In recent years, there has been an increasing worldwide interest in the use of alternative sample preparation methods. Digital light processing (DLP) is a 3D printing technique based on using UV light to form photo-curable resin layer upon layer, which results in a printed shape. This study explores the application of this technique for the development of novel drug extraction devices in analytical chemistry. A composite material consisting of a photocurable resin and C18-modified silica particles was employed as a sorbent device, demonstrating its effectiveness in pharmaceutical analysis. Apart from estimating optimal printing parameters, microscopic examination of the material surface, and sorbent powder to resin ratio, the extraction procedure was also optimised. Optimisation included the type and amount of sample matrix additives, desorption solvent, sorption and desorption times, and proper number of sorbent devices needed in extraction protocol. To demonstrate this method's applicability for sample analysis, the solid-phase extraction followed by gas chromatography coupled with mass spectrometry (SPE-GC-MS) method was validated for its ability to quantify benzodiazepine-type drugs. This evaluation confirmed good linearity in the concentration range of 50-1000 ng/mL, with R2 values being 0.9932 and 0.9952 for medazepam and diazepam, respectively. Validation parameters proved that the presented method is precise (with values ranging in-between 2.98 %-7.40 %), and accurate (88.81 % to 110.80 %). A negative control was also performed to investigate possible sorption properties of the resin itself, proving that the addition of C18-modified silica particles significantly increases the extraction efficiency and repeatability. The cost-effectiveness of this approach makes it particularly advantageous for single-use scenarios, eliminating the need for time-consuming sorbent-cleaning procedures, common in traditional solid-phase extraction techniques. Future optimisation opportunities include refining sorbent size, shape, and geometry to achieve lower limits of quantification. As a result of these findings, 3D-printed extraction devices can serve as a viable alternative to commercially available SPE or solid-phase microextraction (SPME) protocols for studying new sample preparation approaches.

Development of a 3D-Printable, Porous, and Chemically Active Material Filled with Silica Particles and its Application to the Fabrication of a Microextraction Device

We report on the first successful attempt to produce a silica/polymer composite with retained C18 silica sorptive properties that can be reliably printed using three-dimensional (3D) FDM printing. A 3D printer provides an exceptional tool for producing complex objects in an easy and inexpensive manner and satisfying the current custom demand of research. Fused deposition modeling (FDM) is the most popular 3D-printing technique based on the extrusion of a thermoplastic material. The lack of appropriate materials limits the development of advanced applications involving directly 3D-printed devices with intrinsic chemical activity. Progress in sample preparation, especially for complex sample matrices and when mass spectrometry is favorable, remains a vital research field. Silica particles, for example, which are commonly used for extraction, cannot be directly extruded and are not readily workable in a powder form. The availability of composite materials containing a thermoplastic polymer matrix and dispersed silica particles would accelerate research in this area. This paper describes how to prepare a polypropylene (PP)/acrylonitrile-butadiene-styrene (ABS)/C18-functionalized silica composite that can be processed by FDM 3D printing. We present a method for producing the filament as well as a procedure to remove ABS by acetone rinsing (to activate the material). The result is an activated 3D-printed object with a porous structure that allows access to silica particles while maintaining macroscopic size and shape. The 3D-printed device is intended for use in a solid-phase microextraction (SPME) procedure. The proposed composite's effectiveness is demonstrated for the microextraction of glimepiride, imipramine, and carbamazepine. The complex honeycomb geometry of the sorbent has shown to be superior to the simple tubular sorbent, which proves the benefits of 3D printing. The 3D-printed sorbent's shape and microextraction parameters were fine-tuned to provide satisfactory recoveries (33-47%) and high precision (2-6%), especially for carbamazepine microextraction.

Metabolic stability studies of lead compounds supported by separation techniques and chemometrics analysis

With metabolism being one of the main routes of drug elimination from the body (accounting for removal of around 75% of known drugs), it is crucial to understand and study metabolic stability of drug candidates. Metabolically unstable compounds are uncomfortable to administer (requiring repetitive dosage during therapy), while overly stable drugs increase risk of adverse drug reactions. Additionally, biotransformation reactions can lead to formation of toxic or pharmacologically active metabolites (either less-active than parent drug, or even with different action). There were numerous approaches in estimating metabolic stability, including in vitro, in vivo, in silico, and high-throughput screening to name a few. This review aims at describing separation techniques used in in vitro metabolic stability estimation, as well as chemometric techniques allowing for creation of predictive models which enable high-throughput screening approach for estimation of metabolic stability. With a very low rate of drug approval, it is important to understand in silico methods that aim at supporting classical in vitro approach. Predictive models that allow assessment of certain biological properties of drug candidates allow for cutting not only cost, but also time required to synthesize compounds predicted to be unstable or inactive by in silico models.

Drug affinity to human serum albumin prediction by retention of cetyltrimethylammonium bromide pseudostationary phase in micellar electrokinetic chromatography and chemically advanced template search descriptors

The development of high-throughput methods for the estimation of physicochemical and biological properties of drug candidates is highly desired in the pharmaceutical landscape. Affinity to plasma protein is one of the most important biological properties, which should be taken under concern during the design and assessment of future potential medicines. The main goal of this study was to develop a quantitative retention-activity relationship model, with rationalized in vivo and in silico approach to predict the affinity to human serum albumin (HSA), which is one of the most important plasma proteins. To achieve this goal, a set of 27 chemically diverse drugs with known affinity to HSA were analyzed by micellar electrokinetic chromatography (MEKC). The proposed model for HSA affinity assessment was based on retention in hexadecyltrimethylmonium bromide (CTAB) pseudostationary phase and chemically advanced template search (CATS) pharmacophore descriptors. The comparison of various regression methods, namely multiple linear regression (MLR), partial least squares regression (PLS), orthogonal partial least squares (OPLS), and support vector machine (SVM) were performed to develop a model with highest predictability. The obtained models are suitable for the prediction of drug affinity to human serum albumin using retention factor determined by MEKC and CATS descriptors, and only slightly differ in terms of coefficients of determination, Q² value calculated using leave-one-out cross-validation technique and root-mean-squared error of cross-validation (RMSECV) as well as root-mean-square error in prediction (RMSEP) obtained by external validation.

Molecular Docking Supplements an In vitro Determination of the Leading CYP Isoform for Arylpiperazine Derivatives

Background:

Molecular docking has often been used before to calculate in silico affinity of drugs towards their molecular target, but not to estimate leading CYP isoform responsible for metabolism of studied compounds.

Objective:

The aim of this study is to present molecular docking as a valid alternative for costly in vitro studies resulting in estimation of leading CYP isoform.

Method:

In vitro part was based on incubations of studied compounds with isolated CYP3A4 isoform followed by LC-MS analysis. The in silico stage consisted of docking three-dimensional models of the studied compounds with a three-dimensional model of the leading metabolizing isoform (CYP3A4), which was designated during the in vitro part of the study. XenoSite P450 metabolism prediction was also used to predict sites of metabolism and calculate probability values.

Results:

The calculated affinities showed a clear similarity when the in vitro results were compared with the calculated in silico affinity values. XenoSite CYP3A4 metabolism probability values also confirm significant participation of CYP3A4 in metabolism of studied compounds.

Conclusion:

Both molecular docking and XenoSite P450 metabolism prediction provide data that stands in agreement with in vitro studies, granting a more detailed spectrum on predicting CYP3A4 metabolism, and presenting molecular docking as a promising tool to cut costs and increase effectiveness in early drug development stages.

Application of 3D-printed scabbard-like sorbent for sample preparation in bioanalysis expanded to 96-wellplate high-throughput format

Modern bioanalysis, which involves the quantitative and qualitative determination of small-molecule endogenous and exogenous substances in biological samples, is a powerful and useful tool that can generate valuable information related to many areas connected with human health and quality of life. Although LC-MS and GC-MS are widely viewed as the gold standards for many bioanalytical tasks, the scientific community has not abandoned its search for newer, more efficient, and more inexpensive methods of performing extraction as a sample preparation step before final analysis. Recent research showing the immense potential of 3D printing compelled our group to explore how this technology could be applied to techniques used in analytical chemistry. In particular, 3D printing offers three promising advantages: availability, low cost of materials and equipment, and the ability to fabricate objects of nearly any shape to suit the needs of a given application. Previously, we demonstrated that a commercial 3D material (LAY-FOMM) can function as a chemically active object that enables the reversible sorption of the antidiabetic drug, glimepiride, and endogenous steroids. In this report, we use a 3D printer to fabricate sorbents with a scabbard-like shape for use with a 96-blade system, which, along with the use of a 96-well plate, allows multiple extractions to be performed simultaneously. In order to assess the relative benefits of this 3D printed approach, we compare the performance of the proposed LAY-FOMM-based sorbent to that of the widely used C18 sorbent. Although the LAY-FOMM sorbent showed lower extraction recovery rates than the C18 sorbent, all of the other validation parameters suggest that it is suitable for use in high-throughput analysis of steroids in human plasma.

Synthesis of novel pyrido[1,2-c]pyrimidine derivatives with rigidized tryptamine moiety as potential SSRI and 5-HT1A receptor ligands

The study enabled obtaining a number of new derivatives of 4-aryl-pyrido[1,2-c]pyrimidine 9.1-9.27 having conformationally restricted tryptamine moiety. In vitro studies (RBA) have shown that derivatives 9.1, 9.2, 9.4, 9.7, 9.9, 9.14 and 9.27 exhibit high affinity to molecular targets 5-HT_1A receptor and SERT protein. In general, compounds with an unsubstituted or a para-substituted benzene ring of the pyrido[1,2-c]pyrimidine residue in the terminal part were characterized by higher binding ability, which can be justified by the greater flexibility of the structure. For the selected compounds 9.1, 9.7, 9.9 and 9.27, further in vitro, in vivo and metabolic stability tests were performed. The in vitro studies in the extended receptor profile (D₂, 5-HT_2A, 5-HT₆ and 5-HT₇) indicated their selectivity toward the 5-HT_1A receptor and SERT protein. The in vivo studies (8-OH-DPAT-induced hypothermia in mice, FST) revealed that the compound 9.1 has the properties of presynaptic agonist of the 5-HT_1A receptor, and compound 9.7 demonstrated the properties of a presynaptic antagonist of the 5-HT_1A receptor. Metabolic stability studies, in turn, showed that compounds 9.1, 9.7 and 9.9, having an unsubstituted indole residue, were more resistant to biotransformation reactions of the first pass phase than was compound 9.27 containing a 5-methoxy-substituted indole residue. The obtained results allowed further optimization of the structure.

Synthesis, molecular structure, and metabolic stability of new series of N'-(2-alkylthio-4-chloro-5-methylbenzenesulfonyl)-1-(5-phenyl-1H-pyrazol-1-yl)amidine as potential anti-cancer agents

A series of new N'-(2-alkylthio-4-chloro-5-methylbenzenesulfonyl)-1-(5-phenyl-1H-pyrazol-1-yl)amidine derivatives have been synthesized and evaluated in vitro by MTT assays for their antiproliferative activity against cell lines of colon cancer HCT-116, cervical cancer HeLa and breast cancer MCF-7. The studied compounds display selective activity mainly against HCT-116 and HeLa cells. Thus, five compounds show selective cytotoxic effect against HCT-116 (IC₅₀ = 3-10 μM) and HeLa (IC₅₀ = 7 μM). Importantly, the noticed values of IC₅₀ for four compounds are almost 4-fold lower for HeLa than non-malignant HaCaT cells. More-in-depth biological research revealed that the treatment of HCT-116 and HeLa with active compound resulted in increased numbers of cells in sub-G1 phase in a time dependent manner, while non-active derivative does not influence cell cycle. Metabolic stability assays using liver microsomes and NADPH provide important information on compounds susceptibility to phase 1 biotransformation reactions.

Fused Deposition Modeling Enables the Low-Cost Fabrication of Porous, Customized-Shape Sorbents for Small-Molecule Extraction

Fused deposition modeling, one of the most common techniques in three-dimensional printing and additive manufacturing, has many practical applications in the fields of chemistry and pharmacy. We demonstrate that a thermoplastic elastomer-poly(vinyl alcohol) (PVA) composite material (LAY-FOMM 60), which presents porous properties after PVA removal, is useful for the extraction of small-molecule drug-like compounds from water samples. The usefulness of the proposed approach is demonstrated by the extraction of glimepiride from a water sample, followed by LC-MS analysis. The recovery was 82.24%, with a relative standard deviation of less than 5%. The proposed approach can change the way of thinking about extraction and sample preparation due to a shift to the use of sorbents with customizable size, shape, and chemical properties that do not rely on commercial suppliers.

Novel 5-Substituted 2-(Aylmethylthio)-4-chloro-N-(5-aryl-1,2,4-triazin-3-yl)benzenesulfonamides: Synthesis, Molecular Structure, Anticancer Activity, Apoptosis-Inducing Activity and Metabolic Stability

A series of novel 5-substituted 2-(arylmethylthio)-4-chloro-N-(5-aryl-1,2,4-triazin-3-yl) benzenesulfonamide derivatives 27-60 have been synthesized by the reaction of aminoguanidines with an appropriate phenylglyoxal hydrate in glacial acetic acid. A majority of the compounds showed cytotoxic activity toward the human cancer cell lines HCT-116, HeLa and MCF-7, with IC50 values below 100 μM. It was found that for the analogues 36-38 the naphthyl moiety contributed significantly to the anticancer activity. Cytometric analysis of translocation of phosphatidylserine as well as mitochondrial membrane potential and cell cycle revealed that the most active compounds 37 (HCT-116 and HeLa) and 46 (MCF-7) inhibited the proliferation of cells by increasing the number of apoptotic cells. Apoptotic-like, dose dependent changes in morphology of cell lines were also noticed after treatment with 37 and 46. Moreover, triazines 37 and 46 induced caspase activity in the HCT-116, HeLa and MCF-7 cell lines. Selected compounds were tested for metabolic stability in the presence of pooled human liver microsomes and NADPH, both R² and Ar = 4-CF₃-C₆H₄ moiety in 2-(R²-methylthio)-N-(5-aryl-1,2,4-triazin-3-yl)benzenesulfonamides simultaneously increased metabolic stability. The results pointed to 37 as a hit compound with a good cytotoxicity against HCT-116 (IC50 = 36 μM), HeLa (IC50 = 34 μM) cell lines, apoptosis-inducing activity and moderate metabolic stability.

Prediction of overall in vitro microsomal stability of drug candidates based on molecular modeling and support vector machines. Case study of novel arylpiperazines derivatives

Other than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a compound in the drug development pipeline. The ideal drug candidate should be stable enough to reach its therapeutic site of action. Despite many recent excellent achievements in the field of computational methods supporting drug metabolism studies, a well-recognized procedure to model and predict metabolic stability quantitatively is still lacking. This study proposes a workflow for developing quantitative metabolic stability-structure relationships, taking a set of 30 arylpiperazine derivatives as an example. The metabolic stability of the compounds was assessed in in vitro incubations in the presence of human liver microsomes and NADPH and subsequently quantified by liquid chromatography-mass spectrometry (LC-MS). Density functional theory (DFT) calculations were used to obtain 30 models of the molecules, and Dragon software served as a source of structure-based molecular descriptors. For modeling structure-metabolic stability relationships, Support Vector Machines (SVM), a non-linear machine learning technique, were found to be more effective than a regression technique, based on the validation parameters obtained. Moreover, for the first time, general sites of metabolism for arylpiperazines bearing the 4-aryl-2H-pyrido[1,2-c]pyrimidine-1,3-dione system were defined by analysis of Q-TOF-MS/MS spectra. The results indicated that the application of one of the most advanced chemometric techniques combined with a simple and quick in vitro procedure and LC-MS analysis provides a novel and valuable tool for predicting metabolic half-life values. Given the reduced time and simplicity of analysis, together with the accuracy of the predictions obtained, this is a valid approach for predicting metabolic stability using structural data. The approach presented provides a novel, comprehensive and reliable tool for investigating metabolic stability, factors that affect it, and the proposed structures of metabolites at the same time. The performance of the DFT-SVM-based approach provides an opportunity to implement it in a standard drug development pipeline.