Basic pKa Research Articles

A preformulation study of marine alkaloid aaptamine: Physicochemical properties investigation and liposomal formulation development toward hepatocellular carcinoma treatment

AbstractHepatocellular carcinoma (HCC) is a global health concern with high prevalence and mortality. A marine alkaloid, AP‐427, has been reported to show potential for HCC treatment. However, its use is limited by low solubility and high toxicity. We aimed to investigate the preformulation parameters and develop AP‐427 liposomes to improve its clinical suitability. A stability‐indicating HPLC assay was established, and the physicochemical properties of AP‐427 were analyzed. Afterward, AP‐427 liposomes were prepared and characterized, and their cytotoxicity was evaluated. AP‐427 had a low solubility at physiological pH, a LogD of 2.56 ± 0.03, and a basic pKa of 3.24 ± 0.12. An entrapment efficiency of 52.71 ± 3.2% was achieved after optimization. The resulting AP‐427 liposomes were 147.2 ± 3.4 nm and stable up to three months when stored in a pellet form at 4°C. The crystallization of AP‐427 in liposomes became less ordered, and AP‐427 liposomes exhibited a controlled release fitted in Korsmeyer–Peppas model, indicating the release was driven by diffusion. Furthermore, AP‐427 liposomes showed a 3.6 times reduced cytotoxicity against HepG2 cells compared with free AP‐427, potentially enhancing its antitumor efficacy. In conclusion, the precise preformulation parameters advanced the AP‐427 liposomal formulation development, which showed potential for HCC treatment.Practical Applications: The aaptamine derivative AP‐427 has shown cytotoxic effects against hepatocellular carcinoma. However, the low solubility and high toxicity limit its clinical application. The present study aims to prepare liposomal formulation to solve the current problems. Results obtained from this study shed light on challenges related to drug solubility and have paved the way for the development of an effective AP‐427 liposomal formulation with promising application in hepatocellular carcinoma therapy.

Nonclassical Zwitterions as a Design Principle to Reduce Lipophilicity without Impacting Permeability.

The ionization of bioactive molecules impacts many ADME-relevant physicochemical properties, in particular, solubility, lipophilicity, and permeability. Ampholytes contain both acidic and basic groups and are distinguished as ordinary ampholytes and zwitterions. An influential review states that zwitterions only exist if the acidic pKa is significantly lower than the basic pKa. Through concordance of measured and calculated pKa and log P, we show that the zwitterionic behavior of several marketed drugs and natural products occurs despite a low or negative ΔpKa. These nonclassical zwitterions are characterized by a weak acidic and basic pKa and conjugation through an extended aromatic system, often including pseudorings via intramolecular hydrogen bonds. In contrast to most classical zwitterions, nonclassical zwitterions can exhibit excellent permeability. As permeability and lipophilicity are typically correlated, the combination of low lipophilicity and high permeability makes nonclassical zwitterions an attractive design principle in medicinal chemistry.

Wykorzystanie sekwencyjnego wirtualnego skriningu opartego na uczeniu maszynowym w poszukiwaniu nowych ligandów receptora 5-HT6

5-HT6 receptor takes part in learning and memory processes. For this reason, the use of ligands of this receptor in the treatment of neurodegenerative diseases such as Alzheimer's disease, depression or autism is being investigated. The development of machine learning (ML) and access to large compound databases allow for the increasing use of these methods in search of new drugs. The use of ML in pre-clinical tests allows for a reduction in time and costs of drug discovery. In this study, we used a sequential virtual screening approach in search of new structures with potential high affinity for the 5-HT6 receptor. Data from the ChEMBL database containing ligand binding affinities, measured as an inhibition constant (Ki), was used as the training dataset. Each step of the screening was based on machine learning models, the task of which was to classify compounds as potentially active and inactive. The first step included a ligand-based drug discovery (LBDD) approach, in which, using Klekota-Roth fingerprints and descriptors describing the chemical structure of the ligands, a classification model was developed to select a preliminary group of candidates from the Otava chemical compound database. In the second step, a structure-based drug discovery (SBDD) approach was used. For this purpose, compounds were docked to the homology model of the 5-HT6 receptor, developed using the AlphaFold algorithm and optimized by Induced-Fit Docking tool and molecular dynamics. Docking poses were scored by a trained Extra Trees classifier. Interactions of a reference ligand with 14 binding site residues were used as features for the trained model. The use of machine learning as a scoring function allowed to improve the virtual screening parameters compared to the Glide GScore scoring function. Based on the obtained model, it was also confirmed that the location of a ligand near the Ser5.43 and Phe5.38 residues is important for binding the compound to the receptor. The procedure has allowed to select 20 candidates with new chemical structures compared to known ligands. In addition, the obtained compounds had a relatively low basic pKa compared to known ligands and thus may be suspected to have a low affinity for hERG channels and good brain penetration.

Determining desired sorbent properties for proton-coupled electron transfer-controlled CO2 capture using an adaptive sampling-refined classifier

Electrochemical CO2 capture technologies have been found to consume less energy than the industry standard of thermal separations, but their real-world applicability requires that they also operate at comparable rates. Optimizing for both low energy demands and high capture rates is complicated by trade-offs between the two objectives and the many manipulable solution chemistry variables, including species type and concentration. Here, we computationally identified the solution chemistries that are most likely to outperform thermal separations in both energy demand and capture rate for electrochemical capture driven by proton-coupled electron transfer reactions by using an adaptive sampling contour estimation method. This approach provided high confidence inferences with few simulation runs by selecting the most informative conditions to test. We found that moderately basic pKa values of the reduced form of the redox-active compound were the most important variables for low energy and high rate CO2 capture.

ALipSol: An Attention-Driven Mixture-of-Experts Model for Lipophilicity and Solubility Prediction.

Lipophilicity (logD) and aqueous solubility (logSw) play a central role in drug development. The accurate prediction of these properties remains to be solved due to data scarcity. Current methodologies neglect the intrinsic relationships between physicochemical properties and usually ignore the ionization effects. Here, we propose an attention-driven mixture-of-experts (MoE) model named ALipSol, which explicitly reproduces the hierarchy of task relationships. We adopt the principle of divide-and-conquer by breaking down the complex end point (logD or logSw) into simpler ones (acidic pKa, basic pKa, and logP) and allocating a specific expert network for each subproblem. Subsequently, we implement transfer learning to extract knowledge from related tasks, thus alleviating the dilemma of limited data. Additionally, we substitute the gating network with an attention mechanism to better capture the dynamic task relationships on a per-example basis. We adopt local fine-tuning and consensus prediction to further boost model performance. Extensive evaluation experiments verify the success of the ALipSol model, which achieves RMSE improvement of 8.04%, 2.49%, 8.57%, 12.8%, and 8.60% on the Lipop, ESOL, AqSolDB, external logD, and external logS data sets, respectively, compared with Attentive FP and the state-of-the-art in silico tools. In particular, our model yields more significant advantages (Welch's t-test) for small training data, implying its high robustness and generalizability. The interpretability analysis proves that the atom contributions learned by ALipSol are more reasonable compared with the vanilla Attentive FP, and the substitution effects in benzene derivatives agreed well with empirical constants, revealing the potential of our model to extract useful patterns from data and provide guidance for lead optimization.

Acetic Acid as Processing Aid Dramatically Improves Organic Solvent Solubility of Weakly Basic Drugs for Spray Dried Dispersion Manufacture.

Many active pharmaceutical ingredients (APIs) in the pharmaceutical pipeline require bioavailability enhancing formulations due to very low aqueous solubility. Although spray dried dispersions (SDDs) have demonstrated broad utility in enhancing the bioavailability of such APIs by trapping them in a high-energy amorphous form, many new chemical entities (NCEs) are poorly soluble not just in water, but in preferred organic spray drying solvents, e.g., methanol (MeOH) and acetone. Spraying poorly solvent soluble APIs from dilute solutions leads to low process throughput and small particles that challenge downstream processing. For APIs with basic pKa values, spray solvent solubility can be dramatically increased by using an acid to ionize the API. Specifically, we show that acetic acid can increase API solubility in MeOH:H2O by 10-fold for a weakly basic drug, gefitinib (GEF, pKa 7.2), by ionizing GEF to form the transient acetate salt. The acetic acid is removed during drying, resulting in a SDD of the original GEF free base having performance similar to SDDs sprayed from solvents without acetic acid. The increase in solvent solubility enables large scale manufacturing for these challenging APIs by significantly increasing the throughput and reducing the amount of solvent required.

HERG Optimization of Benzofuro-Pyridine and Pyrazino-Indole Derivatives as MCHR1 Antagonists.

Obesity is a global epidemic associated with multiple severe diseases. Several pharmacotherapies have been investigated including the antagonists of melanin concentrating hormone receptor 1 (MCHR1). The design, synthesis, and biological studies of novel MCHR1 antagonists based on benzofuro-pyridine and pyrazino-indole scaffold was performed. We confirmed that fine-tuning lipophilicity and basic pKa by modifying the benzyl group and introducing different substituents on the aliphatic nitrogen sidechain decreases both hERG inhibition and metabolic clearance. We have succeeded to develop excellent in vitro parameters in the case of compounds 17 (4-[(5-chloropyridin-2-yl)methoxy]-1-[4-(2-hydroxyethyl)-8-oxa-4-azatricyclo[7.4.0.02 ,7 ]trideca-1(13),2(7),9,11-tetraen-11-yl]-1,2-dihydropyridin-2-one monohydrochloride) and 23 g (4-[(5-chloropyridin-2-yl)methoxy]-1-(1,2,3,4-tetrahydropyrazino[1,2-a]indol-8-yl)pyridin-2(1H)-one monohydrochloride), which can be considered as valuable tools for further pharmacological investigation.

Tricyclic Triazoles as σ1 Receptor Antagonists for Treating Pain.

The synthesis and pharmacological activity of a new series of 5a,7,8,8a-tetrahydro-4H,6H-pyrrolo[3,4-b][1,2,3]triazolo[1,5-d][1,4]oxazine derivatives as potent sigma-1 receptor (σ1R) ligands are reported. A lead optimization program aimed at improving the aqueous solubility of parent racemic nonpolar derivatives led to the identification of several σ1R antagonists with a good absorption, distribution, metabolism, and excretion in vitro profile, no off-target affinities, and characterized by a low basic pKa (around 5) that correlates with high exposure levels in rodents. Two compounds displaying a differential brain-to-plasma ratio distribution profile, 12lR and 12qS, exhibited a good analgesic profile and were selected as preclinical candidates for the treatment of pain.

Association between the glyco-metabolic adverse effects of antipsychotic drugs and their chemical and pharmacological profile: a network meta-analysis and regression.

Glyco-metabolic deteriorations are the most limiting adverse reactions to antipsychotics in the long term. They have been incompletely investigated and the properties of antipsychotics that determine their magnitude are not clarified.To rank antipsychotics by the magnitude of glyco-metabolic alterations and to associate it to their pharmacological and chemical properties, we conducted a network meta-analysis. We searched PubMed, Embase, and Psycinfo on 10 September 2020. We selected studies containing the endpoint-baseline difference or the distinct values of at least one outcome among glucose, HbA1c, insulin, HOMA-IR, triglycerides, total/HDL/LDL cholesterols. Of 2094 articles, 46 were included in network meta-analysis. Study quality was assessed by the RoB 2 and ROBINS-I tools. Mean differences (MD) were obtained by random-effects network meta-analysis; relations between MD and antipsychotic properties were analyzed by linear regressions. Antipsychotic properties investigated were acidic and basic pKa, polar surface area, polarizability, and occupancies of D2, H1, M1, M3, α1A, α2A, 5-HT1A, 5-HT2A, 5-HT2C receptors. We meta-analyzed 46 studies (11 464 patients); on average, studies lasted 15.47 weeks, patients had between 17.68 and 61.06 years of mean age and 61.64% were males. Olanzapine and clozapine associated with greater deteriorations, aripiprazole and ziprasidone with smaller deteriorations. Higher polarizability and 5-HT1A receptor occupancy were associated with smaller deteriorations, H1, M1, and M3 receptor occupancies with larger deteriorations. Drug rankings may guide antipsychotic switching toward metabolically safer drugs. Mechanistic insights may suggest improvements for combination therapies and drug development. More data are required regarding newer antipsychotics.

The influence of calculated physicochemical properties of compounds on their ADMET profiles

We analyzed the influence of calculated physicochemical properties of more than 20,000 compounds on their P-gp and BCRP mediated efflux, microsomal stability, hERG inhibition, and plasma protein binding. Our goal was to provide guidance for designing compounds with desired pharmacokinetic profiles. Our analysis showed that compounds with ClogP less than 3 and molecular weight less than 400 will have high microsomal stability and low plasma protein binding. Compounds with logD less than 2.2 and/or basic pKa larger than 5.3 are likely to be BCRP substrates and compounds with basic pKa less than 5.2 and/or acidic pKa less than 13.4 are less likely to inhibit hERG. Based on these results, compounds with MW < 400, ClogP < 3, basic pKa < 5.2 and acidic pKa < 13.4 are likely to have good bioavailability and low hERG inhibition.

Rationally designed N-phenylsulfonylindoles as a tool for the analysis of the non-basic 5-HT6R ligands binding mode

Among all of the monoaminergic receptors, the 5-HT6R has the highest number of non-basic ligands (approximately 5% of compounds stored in 25th version of ChEMBL database have the strongest basic pKa below 5, calculated using the Instant JChem calculator plugin). These compounds, when devoid of a basic nitrogen, exhibit high affinity and remarkable selectivity. Despite a decade of research, no clues have been given for explanation of such an intriguing phenomenon. Here, a series of analogs of four known 5-HT6R ligands, has been rationally designed to approach this issue. For each of the synthesized 42 compounds, a binding affinity for 5-HT6R has been measured, together with a selectivity profile against 5-HT1AR, 5-HT2AR, 5-HT7R and D2R. Performed induced fit docking and molecular dynamics experiments revealed that no particular interaction was responsible for the activity of non-basic compounds. In fact, a plain N-phenylsulfonylindole (1e) was found to possess a moderate (5-HT6R, Ki=159nM) affinity. No other monoaminergic receptor has as simple and selective ligand as this one. Thus, it is stated that it binds to the receptor solely based on its conformation and as such, possesses a minimum amount of features, required for binding. Also, any functional group able to form an additional interaction with the receptor increase the binding affinity, like in the case of two highly active non-basic compounds 3e and 5g (5-HT6R, Ki=65nM and 38nM, respectively).

Second basic pKa: An overlooked parameter in predicting phospholipidosis-inducing potential of diamines

In this paper, we present the phospholipidosis-inducing potential (PLIP) of forty fragment-sized diamines derived from N-benzyl-4-(methylamino)piperidine and discuss the relationship between their PLIP and the physicochemical properties. Our results demonstrate that the previously reported methods are not suitable for predicting the PLIP of fragment-sized diamines; the second basic pKa can distinguish PLIP-positive diamines from PLIP-negative diamines more accurately than ClogP or most basic pKa. To the best of our knowledge, this is the first report describing the relationship between PLIP and second basic pKa.

Open-source QSAR models for pKa prediction using multiple machine learning approaches

BackgroundThe logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affects chemical absorption, distribution, metabolism, excretion, and toxicity properties. Multiple proprietary software packages exist for the prediction of pKa, but to the best of our knowledge no free and open-source programs exist for this purpose. Using a freely available data set and three machine learning approaches, we developed open-source models for pKa prediction.MethodsThe experimental strongest acidic and strongest basic pKa values in water for 7912 chemicals were obtained from DataWarrior, a freely available software package. Chemical structures were curated and standardized for quantitative structure–activity relationship (QSAR) modeling using KNIME, and a subset comprising 79% of the initial set was used for modeling. To evaluate different approaches to modeling, several datasets were constructed based on different processing of chemical structures with acidic and/or basic pKas. Continuous molecular descriptors, binary fingerprints, and fragment counts were generated using PaDEL, and pKa prediction models were created using three machine learning methods, (1) support vector machines (SVM) combined with k-nearest neighbors (kNN), (2) extreme gradient boosting (XGB) and (3) deep neural networks (DNN).ResultsThe three methods delivered comparable performances on the training and test sets with a root-mean-squared error (RMSE) around 1.5 and a coefficient of determination (R2) around 0.80. Two commercial pKa predictors from ACD/Labs and ChemAxon were used to benchmark the three best models developed in this work, and performance of our models compared favorably to the commercial products.ConclusionsThis work provides multiple QSAR models to predict the strongest acidic and strongest basic pKas of chemicals, built using publicly available data, and provided as free and open-source software on GitHub.

Bridging in vitro dissolution and in vivo exposure for acalabrutinib. Part II. A mechanistic PBPK model for IR formulation comparison, proton pump inhibitor drug interactions, and administration with acidic juices

Acalabrutinib (Calquence®) 100 mg (bid) has received accelerated approval by FDA for the treatment of adult patients with mantle cell lymphoma (MCL) who have received at least one prior therapy. Acalabrutinib is a substrate of PgP and CYP3A4, with a significant fraction of drug metabolized by first pass gut extraction and 25% absolute bioavailability. The absorption of acalabrutinib is affected by stomach pH, with lower pharmacokinetic exposure observed following co-administration with proton pump inhibitors.During dissolution at pH values below its highest basic pKa, the two basic moieties of acalabrutinib react with protons from the aqueous solution, leading to a higher pH at the drug surface than in the bulk solution. A batch-specific product particle size distribution (P-PSD), was derived from dissolution data using a mechanistic model that was based on the understanding of surface pH and the contribution of micelles to the dissolution rate. P-PSD values obtained for various batches of acalabrutinib products in simple buffers, or in complex fluids such as fruit juices, were successfully integrated into a physiologically based pharmacokinetic (PBPK) model developed using GastroPlus v9.0™. The integrated model allowed the prediction of clinical pharmacokinetics under normal physiological stomach pH conditions as well as following treatment with proton pump inhibitors. The model also accounted for lower pharmacokinetic exposure that was observed when acalabrutinib was co-administered with the acidic beverages, grapefruit juice, (which contains CYP3A inhibitors), and orange drink (which does not contain CYP3A inhibitors), relative to administration with water.The integration of dissolution data in the PBPK model enables mechanistic understanding and the establishment of more robust in vitro-in vivo correlations (IVIVC) under a variety of conditions. The model can then distinguish the interplay between dissolution and first pass extraction and how in vivo stomach pH, saturation of gut PgP, and saturation or inhibition of gut CYP3A4, will impact the pharmacokinetics of acalabrutinib.

The discovery of novel 3-aryl-indazole derivatives as peripherally restricted pan-Trk inhibitors for the treatment of pain.

The design, synthesis, and biological evaluation of novel 3-aryl-indazole derivatives as peripherally selective pan-Trk inhibitors are described. Three strategies were used to obtain a potent compound exhibiting low central nervous system (CNS) penetration and high plasma exposure: 1) a structure-based drug design (SBDD) approach was used to improve potency; 2) a substrate for an efflux transporter for lowering brain penetration was explored; and 3) the most basic pKa (pKa-MB) value was used as an indicator to identify compounds with good membrane permeability. This enabled the identification of the peripherally targeted 17c with the potency, kinase-selectivity, and plasma exposure required to demonstrate in vivo efficacy in a Complete Freund's adjuvant (CFA)-induced thermal hypersensitivity model.

Predicting pKa for Small Molecules on Public and In-house Datasets Using Fast Prediction Methods Combined with Data Fusion.

Data fusion approach was investigated in the context of pKa prediction for 391 small molecules derived from a public data source as well as for 681 compounds from an internal corporate database. Four different pKa prediction methods (Simulations Plus ADMET-Predictor S+pKa, ACD/Labs Percepta Classic, ACD/Labs Percepta GALAS and Epik) were used to predict the most acidic or basic pKa for each of the compounds. By using data fusion, the median absolute error for the internal compounds was reduced from the best performing single model's value of 0.69 down to 0.50. In addition to the improved accuracy, data fusion also enabled predictions for all of the compounds in the dataset as individual methods failed on some of the molecules.

Preformulation and Evaluation of Tofacitinib as a Therapeutic Treatment for Asthma.

Preformulation studies on tofacitinib citrate, a small molecule JAK3 specific inhibitor, have not been previously reported in literature. We therefore conducted several preformulation studies on tofacitinib citrate, and its free base, to better understand factors that affect its solubility, stability, and solid-state characteristics. Further, the results of the preformulation studies helped facilitate the development of a nebulized formulation of tofacitinib citrate for inhalational delivery to house dust mite allergen-challenged, BALB/c mice as a potential treatment for eosinophilic asthma. The preformulation results indicated tofacitinib having a basic pKa of 5.2, with its stability dependent on pH, ionic strength, and temperature. Degradation of tofacitinib follows apparent first-order kinetics. In order to maximize stability of the drug, ionic strength and temperature should be minimized, with an optimal range pH between 2.0 and 5.0. Additionally, our findings demonstrate that tofacitinib citrate can successfully be nebulized at a suitable droplet size for inhalation (1.2 ± 0.2μm MMAD) through a nose-only chamber. Animals dosed with tofacitinib citrate demonstrated marked reductions in BAL eosinophils and total protein concentrations following HDM challenge. These data suggest that tofacitinib citrate represents the potential to be an effective therapy for eosinophilic asthma.

Resistance to lysosomotropic drugs used to treat kidney and breast cancers involves autophagy and inflammation and converges in inducing CXCL5.

Lysosomotropic agents such as sunitinib, lapatinib, and chloroquine belong to a drug family that is being used more frequently to treat advanced cancers. Sunitinib is standard care for metastatic renal cell carcinomas (mRCC) and lapatinib is used for trastuzumab/pertuzumab-refractory cancers. However, patients ineluctably relapse with a delay varying from a few months to a few years. To improve reactivity prior to relapse it is essential to identify the mechanisms leading to such variability. We showed previously that sunitinib became sequestered in lysosomes because of its basic pKa.Methods: Modifications to gene expression in response to sunitinib and in sunitinib resistant cells were analyzed by transcriptomic and proteomic analysis. ROS production was evaluated by FACS. Nuclear Factor kappa B (NFkB)-dependent transcriptional regulation of inflammatory gene expression was evaluated with a reporter gene. Correlation of CXCL5 with survival was analyzed with an online available data base (TCGA) and using a cohort of patients enrolled in the SUVEGIL clinical trial (NCT00943839).Results: We now show that sunitinib sequestration in lysosomes induced an incomplete autophagic process leading to activation of the NFkB inflammatory pathway. We defined a subset of inflammatory cytokines that were up-regulated by the drug either after an acute or chronic stimulus. One of the most up-regulated genes in sunitinib-resistant cells was the CXCL5 cytokine. CXCL5 was also induced in RCC by chloroquine and in a model of HER2 positive breast cancer cell lines after acute or chronic treatment with lapatinib. CXCL5 correlated to shorter survival in RCC and to the most aggressive forms of breast cancers. The levels of CXCL5 present in the plasma of patients treated with sunitinib were predictive of the efficacy of sunitinib but not of the VEGF-directed antibody bevacizumab.Conclusion: This translational study identified CXCL5 as a biomarker of efficacy of lysosomotropic drugs, a potential asset for personalized medicine.

UPLC-ESI-MS/MS method for the quantitative measurement of aliphatic diamines, trimethylamine N-oxide, and β-methylamino-l-alanine in human urine

This work describes a quantitative high-throughput analytical method for the simultaneous measurement of small aliphatic nitrogenous biomarkers, i.e., 1,6-hexamethylenediamine (HDA), isophoronediamine (IPDA), β-methylamino-l-alanine (BMAA), and trimethylamine N-oxide (TMAO), in human urine. Urinary aliphatic diamines, HDA and IPDA, are potential biomarkers of environmental exposure to their corresponding diisocyanates. Urinary BMAA forms as a result of human exposure to blue-green algae contaminated food. And, TMAO is excreted in urine due to the consumption of carnitine- and choline-rich diets. These urinary biomarkers represent classes of small aliphatic nitrogen-containing compounds (N-compounds) that have a high aqueous solubility, low logP, and/or high basic pKa. Because of the highly polar characteristics, analysis of these compounds in complex sample matrices is often challenging. We report on the development of ion-pairing chemistry based ultra-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (UPLC–ESI–MS/MS) method for the simultaneous measurement of these biomarkers in human urine. Chromatographic separation was optimized using heptafluorobutyric acid-(HFBA-) based mobile phase and a reversed-phase C18 column. All four analytes were baseline separated within 2.6 min with an overall run time of 5 min per sample injection. Sample preparation involved 4 h of acid hydrolysis followed by automated solid phase extraction (SPE) performed using strong cation exchange sorbent bed with 7 N ammonia solution in methanol as eluent. Limits of detection ranged from 0.05 ng/mL to 1.60 ng/mL. The inter-day and intra-day accuracy were within 10%, and reproducibility within 15%. The method is accurate, fast, and well-suited for biomonitoring studies within targeted groups, as well as larger population-based studies such as the U. S. National Health and Nutrition Examination Survey (NHANES).

Strategies for the synthesis of HBGl3, a glutamic acid derived ligand bearing phenolic and azacarboxylate donor groups at the nitrogen atom

The development of a route applicable to the preparation of acyclic glutamic acid-based chelating ligands bearing two different auxiliary donor groups linked to the nitrogen atom by methylene spacers is described and applied to the synthesis of the new polydentate ligand HBGl3, the first example of such a structure. The synthesis is accomplished using a strategy employing reductive amination and t-butyl ester protected intermediates. The most basic pKa values for the HBGl3 ligand have been estimated via potentiometric and UV–Visible titration techniques.