Design, Synthesis and Cytotoxic Activity of Novel Salicylaldehyde Hydrazones against Leukemia and Breast Cancer.

Despite the significant advancements in complex anticancer therapy, the search for new and more efficient specific anticancer agents remains a top priority in the field of drug discovery and development. Here, based on the structure-activity relationships (SARs) of eleven salicylaldehyde hydrazones with anticancer activities, we designed three novel derivatives. The compounds were tested in silico for drug-likeness, synthesized, and evaluated in vitro for anticancer activity and selectivity on four leukemic cell lines (HL-60, KE-37, K-562, and BV-173), one osteosarcomic cell line (SaOS-2), two breast adenocarcinomic cell lines (MCF-7 and MDA-MB-231), and one healthy cell line (HEK-293). The designed compounds were found to have appropriate drug likeness and showed anticancer activities in all cell lines tested; particularly, two of them exhibited remarkable anticancer activity in nanomolar concentrations on the leukemic cell lines HL-60 and K-562 and the breast cancer MCF-7 cells and extraordinary selectivity for the same cancer lines ranging between 164- and 1254-fold. The study also examined the effects of different substituents on the hydrazone scaffold and found that the 4-methoxy salicylic moiety, phenyl, and pyridinyl rings are the most appropriate for anticancer activity and selectivity of this chemical class.

Novel Arylsulfonylhydrazones as Breast Anticancer Agents Discovered by Quantitative Structure-Activity Relationships.

Breast cancer (BC) is the second leading cause of cancer death in women, with more than 600,000 deaths annually. Despite the progress that has been made in early diagnosis and treatment of this disease, there is still a significant need for more effective drugs with fewer side effects. In the present study, we derive QSAR models with good predictive ability based on data from the literature and reveal the relationships between the chemical structures of a set of arylsulfonylhydrazones and their anticancer activity on human ER+ breast adenocarcinoma and triple-negative breast (TNBC) adenocarcinoma. Applying the derived knowledge, we design nine novel arylsulfonylhydrazones and screen them in silico for drug likeness. All nine molecules show suitable drug and lead properties. They are synthesized and tested in vitro for anticancer activity on MCF-7 and MDA-MB-231 cell lines. Most of the compounds are more active than predicted and show stronger activity on MCF-7 than on MDA-MB-231. Four of the compounds (1a, 1b, 1c, and 1e) show IC50 values below 1 µM on MCF-7 and one (1e) on MDA-MB-231. The presence of an indole ring bearing 5-Cl, 5-OCH3, or 1-COCH3 has the most pronounced positive effect on the cytotoxic activity of the arylsulfonylhydrazones designed in the present study.

Galantamine-Curcumin Hybrids as Dual-Site Binding Acetylcholinesterase Inhibitors.

Galantamine (GAL) and curcumin (CU) are alkaloids used to improve symptomatically neurodegenerative conditions like Alzheimer's disease (AD). GAL acts mainly as an inhibitor of the enzyme acetylcholinesterase (AChE). CU binds to amyloid-beta (Aß) oligomers and inhibits the formation of Aß plaques. Here, we combine GAL core with CU fragments and design a combinatorial library of GAL-CU hybrids as dual-site binding AChE inhibitors. The designed hybrids are screened for optimal ADME properties and BBB permeability and docked on AChE. The 14 best performing compounds are synthesized and tested in vitro for neurotoxicity and anti-AChE activity. Five of them are less toxic than GAL and CU and show activities between 41 and 186 times higher than GAL.

Novel hits for acetylcholinesterase inhibition derived by docking-based screening on ZINC database.

The inhibition of the enzyme acetylcholinesterase (AChE) increases the levels of the neurotransmitter acetylcholine and symptomatically improves the affected cognitive function. In the present study, we searched for novel AChE inhibitors by docking-based virtual screening of the standard lead-like set of ZINC database containing more than 6 million small molecules using GOLD software. The top 10 best-scored hits were tested in vitro for AChE affinity, neurotoxicity, GIT and BBB permeability. The main pharmacokinetic parameters like volume of distribution, free fraction in plasma, total clearance, and half-life were predicted by previously derived models. Nine of the compounds bind to the enzyme with affinities from 0.517 to 0.735 µM, eight of them are non-toxic. All hits permeate GIT and BBB and bind extensively to plasma proteins. Most of them are low-clearance compounds. In total, seven of the 10 hits are promising for further lead optimisation. These are structures with ZINC IDs: 00220177, 44455618, 66142300, 71804814, 72065926, 96007907, and 97159977.

Quantitative Structure - Pharmacokinetics Relationships for Plasma Protein Binding of Basic Drugs.

PURPOSE: Binding of drugs to plasma proteins is a common physiological occurrence which may have a profound effect on both pharmacokinetics and pharmacodynamics. The early prediction of plasma protein binding (PPB) of new drug candidates is an important step in drug development process. The present study is focused on the development of quantitative structure - pharmacokinetics relationship (QSPkR) for the negative logarithm of the free fraction of the drug in plasma (pfu) of basic drugs. METHODS: A dataset includes 220 basic drugs, which chemical structures are encoded by 176 descriptors.  Genetic algorithm, stepwise regression and multiple linear regression are used for variable selection and model development. Predictive ability of the model is assessed by internal and external validation.  Results. A simple, significant, interpretable and predictive QSPkR model is constructed for pfu of basic drugs. It is able to predict 59% of the drugs from an external validation set within the 2-fold error of the experimental values with squared correlation coefficient of prediction 0.532, geometric mean fold error (GMFE) 1.94 and mean absolute error (MAE) 0.17. CONCLUSIONS: PPB of basic drugs is favored by the lipophilicity, the presence of aromatic C-atoms (either non-substituted, or involved in bridged aromatic systems) and molecular volume.  The fraction ionized as a base fB and the presence of quaternary C-atoms contribute negatively to PPB. A short checklist of criteria for high PPB is defined, and an empirical rule for distinguishing between low, high and very high plasma protein binders is proposed based. This rule allows correct classification of 69% of the very high binders, 71% of the high binders and 91% of the low binders in plasma. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

Quantitative Structure - Pharmacokinetic Relationships for Plasma Clearance of Basic Drugs with Consideration of the Major Elimination Pathway.

PURPOSE: The success of a new drug candidate is determined not only by its efficacy and safety, but also by proper pharmacokinetic behavior. The early prediction of pharmacokinetic parameters could save time and resources and accelerate drug development process. Plasma clearance (CL) is one of the key determinants of drug dosing regimen. The aim of the study is development of quantitative structure - pharmacokinetics relationships (QSPkRs) for the CL. METHODS: A dataset consisted of 263 basic drugs, which chemical structures were described by 154 descriptors.  Genetic algorithm, stepwise regression and multiple linear regression were used for variable selection and model development. Predictive ability of the models was assessed by internal and external validation.  Results. A number of significant QSPkR models for the CL were derived with respect to the primary elimination pathway (renal excretion, metabolism, or CYP3A4 mediated biotransformation), as well for the unbound clearance (CLu). The models were able to predict 52 - 80% of the drugs from external validation sets within the 2-fold error of the experimental values with geometric mean fold error 1.57 - 2.00. CONCLUSIONS: Plasma protein binding was the major restrictive factor for the CL of drugs, primarily cleared by metabolism.  The clearance was favored by lipophilicity and several structural features like OH-groups, aromatic rings, large hydrophobic centers, aliphatic groups, connected with electro-negative atoms, and non-substituted aromatic C-atoms. The presence of Cl-atoms and abundance of 6-member aromatic rings or fused rings had negative effect.  The presence of ether O-atoms contributed negatively to the CL of both metabolism and renally excreted drugs, and urine excretion was favored by the presence of 3-valence N-atoms. These findings give insight on the main structural features governing plasma CL of basic drugs and could serve as a guide for lead optimization in the drug development process. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

Quantitative Structure - Pharmacokinetics Relationships Analysis of Basic Drugs: Volume of Distribution.

PURPOSE: The early prediction of pharmacokinetic behavior is of paramount importance for saving time and resources and for increasing the success of new drug candidates. The steady-state volume of distribution (VDss) is one of the key pharmacokinetic parameters required for the design of a suitable dosage regimen. The aim of the study is to propose a quantitative structure - pharmacokinetics relationships (QSPkR) for VDss of basic drugs. METHODS: The data set consists of 216 basic drugs, divided to a modeling (n = 180) and external validation set (n = 36). 179 structural and physicochemical descriptors are calculated using validated commercial software. Genetic algorithm, stepwise regression and multiple linear regression are applied for variable selection and model development. The models are validated by internal and external test sets. RESULTS: A number of significant QSPkRs are developed. The most frequently emerged descriptors are used to derive the final consensus model for VDss with good explanatory (r2 0.663) and predictive ability (q2LOO-CV 0.606 and r2pred 0.593). The model reveals clear structural features determining VDss of basic drugs which are summarized in a short list of criteria for rapid discrimination between drugs with a large and small VDss. CONCLUSIONS: Descriptors like lipophilicity, fraction ionized as a base at pH 7.4, number of cycles and fused aromatic rings, presence of Cl and F atoms contribute positively to VDss, while polarity and presence of strong electrophiles have a negative effect.

Studies on drug-human serum albumin binding: the current state of the matter.

Human serum albumin (HSA) is the major plasma protein with vital functions acting as depot and career for many endogenous (fatty acids, bilirubin, etc.) and exogenous substances (drugs, nutrients, etc.) in the blood. Binding to HSA controls the free, active concentration of the drug and may affect considerably the overall pharmacodynamic and pharmacokinetic profile. Studies on drug - protein binding are important from both theoretical and practical point of view as they allow better understanding of the processes underlying drug disposition and elimination and the effect of several pathological states or co-administered drugs on drug delivery and efficacy. The present review focuses on the current state of drug - HSA binding studies. The major functions and consequences of drug - protein binding are described. The X-ray structure of HSA is discussed focusing on the location and the architecture of the primary drug and fatty acids binding sites. Some of the most commonly used methods for drug - HSA binding assay are presented together with examples for their application. The most extensive studied topics in the area are discussed including quantitative characterization of drug - HSA complexation, identification of the binding sites, stereoselectivity of drug - HSA interactions, and thermodynamic characterization of the binding process. A short section is devoted to in silico prediction of drug - HSA binding as an important step in drug design and development.

Quantitative structure--clearance relationships of acidic drugs.

Drug clearance is the most important of all pharmacokinetic parameters. It is affected significantly by the binding of drugs to serum proteins. Only the free (unbound) fraction of the drug is able to be cleared. The unbound clearance CLu is the clearance with reference to unbound drug in plasma Cu. CLu is independent of the plasma protein binding and depends only on drug chemical structure and properties. In the present study, the relationship between the unbound clearance CLu and the chemical structures of acidic drugs was modeled by a quantitative structure-clearance relationship (QSCLR) approach. The derived models were used to reveal the main structural features important for CLu. It was found that the lipophilicity of acidic drugs and the presence of substituents in the aromatic rings, cyano group, and/or nonpolar hydrogen atoms increase the rate of unbound clearance. The presence of sulfonyl groups, quaternary carbon atoms and/or eight-member ring system decreases the unbound clearance of drugs. Additionally, QSCLR models for renal, hepatic, and biliary clearances were derived.

Quantitative structure--plasma protein binding relationships of acidic drugs.

One of the most important factors, affecting significantly the overall pharmacokinetic and pharmacodynamic profile of a drug, is its binding to plasma protein (PPB). In the present study, we focus on a set of 132 diverse acidic drugs binding to plasma proteins to different extent and develop quantitative structure-plasma protein binding relationships (QSPPBR) to predict their unbound fraction in plasma (f(u)) using 178 molecular descriptors. QSPPBR models were derived after variable selection by genetic algorithm followed by stepwise regression and tested by cross- and external validation. The final model has r(2) value of 0.771, q(2) value of 0.737, and four outliers. It predicts 57% of the f(u) values with less than twofold error. According to the molecular descriptors selected as the most predictive for PPB, the lipophilicity of the drugs, the presence of aromatic rings, cyano groups, and H-bond donor-acceptor pairs increase the PPB, whereas the presence of tertiary carbon atoms, four-member rings, and iodine atoms decrease PPB. These descriptors were summarized into a short seven-item checklist of criteria responsible for PPB. The checklist could be used as a guide for evaluation of PPB of acidic drug candidates, similarly to the Lipinski's rule of five used for evaluation of oral permeability of drugs.

Prediction of steady-state volume of distribution of acidic drugs by quantitative structure-pharmacokinetics relationships.

The volume of distribution (VD) is one of the most important pharmacokinetic parameters of drugs. The present study employs quantitative structure-pharmacokinetics relationships (QSPkR) to derive models for VD prediction of acidic drugs. The steady-state volume of distribution (VD(ss)) values of 132 acidic drugs were collected, the chemical structures were described by 178 molecular descriptors, and QSPkR models were derived after variable selection by genetic algorithm and stepwise regression. Models were validated by cross-validation procedures and external test set. According to the molecular descriptors selected as the most predictive for VD(ss), the presence of seven- and nine-member cycles, atom type P(5+), SH groups, and large nonionized substituents increase the VD(ss), whereas atom types S(2+) and S(4+) and polar ionized substituents decrease it. Cross-validation and external validation studies on the QSPkR models derived in the present study showed good predictive ability with mean fold error values ranging from 1.58 (cross-validation) to 2.25 (external validation). The model performance is comparable to more complicated methods requiring in vitro or in vivo experiments and superior to the existing QSPkR models concerning acidic drugs. Apart from the prediction of VD in human, present models are also useful as a curator of available pharmacokinetic databases.

Molecular basis of sulindac competition with specific markers for the major binding sites on human serum albumin.

This study deals with the competitive interactions of sulindac (CAS 38194-50-2) with specific markers for the major binding areas on human serum albumin (HSA): phenylbutazone (CAS 50-33-9) and warfarin (CAS 129-06-6) for Site I, and diazepam (CAS 439-14-5) for Site II. The method used is high-performance liquid affinity chromatography with HSA-immobilized stationary phase. The affinity constants during the cobinding are determined, and the major thermodynamic parameters are calculated. Based on that the nature of the intermolecular interactions involved in the binding process is hypothesized. The cobinding of sulindac and phenylbutazone is simple competitive and is dominated by hydrogen bonds. The binding affinity of sulindac is significantly decreased by R-warfarin (anticooperative binding), and conformational changes resulting in alteration of the binding mechanisms (electrostatic as well as hydrophobic interactions) are supposed. S-Warfarin hardly affects the binding affinity of sulindac. Sulindac competes with diazepam for two types of binding site: high affinity Site II where the cobinding seems to be dominated by hydrophobic bonding, and low affinity Site I, the latter affinity being significantly decreased (anticooperative binding). These results contribute to a better understanding of the molecular mechanisms of the binding of the investigated drugs with HSA.

Thermodynamic characterization of the binding process of sulindac to human serum albumin.

This study deals with the molecular basis of the binding of the anti-inflammatory drug sulindac (CAS 38194-50-2) to human serum albumin (HSA) using high-performance liquid affinity chromatography. The chromatography was carried out using a HSA-immobilized column and a predominantly aqueous mobile phase (67 mmol/l sodium phosphate buffer/propan-1-ol, 94:6 v/v). A small quantity of sulidac was injected onto the column while increasing concentrations of the same drug were added to the mobile phase. The capacity factor k' serves as a measure for the binding affinity. The experiments were carried out at different temperatures in order to establish some thermodynamic parameters of the binding process. The values of the binding affinity constants decreased with the temperature. The free energy change was almost constant. Its large negative value suggests a spontaneous binding of sulidac to HSA. Both enthalpy and entropy changes of the binding process were also negative assuming donor-acceptor interactions between sulindac and its binding sites in HSA. Hydrogen bonds and salt linkages are supposed to make a major contribution to the binding of sulindac to HSA, while hydrophobic interactions seem to be of less importance.