Experimental versus theoretical log D_7.4, pK_a and plasma protein binding values for benzodiazepines appearing as new psychoactive substances

The misuse of benzodiazepines as new psychoactive substances is an increasing problem around the world. Basic physicochemical and pharmacokinetic data is required on these substances in order to interpret and predict their effects upon humans. Experimental log D7.4, pKa and plasma protein binding values were determined for 11 benzodiazepines that have recently appeared as new psychoactive substances (3‐hydroxyphenazepam, 4’‐chlorodiazepam, desalkylflurazepam, deschloroetizolam, diclazepam, etizolam, flubromazepam, flubromazolam, meclonazepam, phenazepam and pyrazolam) and compared with values generated by various software packages (ACD/I‐lab, MarvinSketch, ADMET Predictor and PreADMET). ACD/I‐LAB returned the most accurate values for log D7.4 and plasma protein binding while ADMET Predictor returned the most accurate values for pKa. Large variations in predictive errors were observed between compounds. Experimental values are currently preferable and desirable as they may aid with the future ‘training’ of predictive models for these new psychoactive substances

Simulation of remdesivir disposition and its drug interactions

Purpose: Remdesivir, a drug originally developed against Ebola virus, is currently recommended for patients hospitalized with coronavirus disease of 2019 (COVID-19). In spite of United States Food and Drug Administration’s recent assent of remdesivir as the only approved agent for COVID-19, there is limited information available about the physicochemical, metabolism, transport, pharmacokinetic (PK), and drug-drug interaction (DDI) properties of this drug. The objective of this in silico simulation work was to simulate the biopharmaceutical and DDI behavior of remdesivir and characterize remdesivir PK properties in special populations which are highly affected by COVID-19. Methods: The Spatial Data File format structures of remdesivir prodrug (GS-5734) and nucleoside core (GS-441524) were obtained from the PubChem database to upload into the GastroPlus software 9.8 version (Simulations Plus Inc., USA). The Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) Predictor and PKPlus modules of GastroPlus were used to simulate physicochemical and PK properties, respectively, in healthy and predisposed patients. Physiologically based pharmacokinetic (PBPK) modeling of GastroPlus was used to simulate different patient populations based on age, weight, liver function, and renal function status. Subsequently, these data were used in the Drug-Drug Interaction module to simulate drug interaction potential of remdesivir with other COVID-19 drug regimens and with agents used for comorbidities. Results: Remdesivir nucleoside core (GS-441524) is more hydrophilic than the inactive prodrug (GS-5734) with nucleoside core demonstrating better water solubility. GS-5734, but not GS-441524, is predicted to be metabolized by CYP3A4. Remdesivir is bioavailable and its clearance is achieved through hepatic and renal routes. Differential effects of renal function, liver function, weight, or age were observed on the PK profile of remdesivir. DDI simulation study of remdesivir with perpetrator drugs for comorbidities indicate that carbamazepine, phenytoin, amiodarone, voriconazole, diltiazem, and verapamil have the potential for strong interactions with victim remdesivir, whereas agents used for COVID-19 treatment such as chloroquine and ritonavir can cause weak and strong interactions, respectively, with remdesivir. Conclusions: GS-5734 (inactive prodrug) appears to be a superior remdesivir derivative due to its hepatic stability, optimum hydrophilic/lipophilic balance, and disposition properties. Remdesivir disposition can potentially be affected by different physiological and pathological conditions, and by drug interactions from COVID-19 drug regimens and agents used for comorbidities

Inductive effects in amino acids and peptides: Ionization constants and tryptophan fluorescence

Although inductive effects in organic compounds are known to influence chemical properties such as ionization constants, their specific contribution to the properties/behavior of amino acids and functional groups in peptides remains largely unexplored. In this study we developed a computationally economical algorithm for ab initio calculation of the magnitude of inductive effects for non-aromatic molecules. The value obtained by the algorithm is called the Inductive Index and we observed a high correlation (R2 = 0.9427) between our calculations and the pKa values of the alpha-amino groups of amino acids with non-aromatic side-chains. Using a series of modified amino acids, we also found similarly high correlations (R2 > 0.9600) between Inductive Indexes and two wholly independent chemical properties: i) the pKa values of ionizable side-chains and, ii) the fluorescence response of the indole group of tryptophan. After assessing the applicability of the method of calculation at the amino acid level, we extended our study to tryptophan-containing peptides and established that inductive contributions of neighboring side-chains are transmitted through peptide bonds. We discuss possible contributions to the study of proteins.Fil: Lara Popoca, Jesús. Universidad Nacional Autónoma de México; MéxicoFil: Thoke, Henrik S.. International and Interdisciplinary Research Network; DinamarcaFil: Stock, Roberto P.. Universidad Nacional Autónoma de México; MéxicoFil: Rudino Pinera, Enrique. Universidad Nacional Autónoma de México; MéxicoFil: Bagatolli, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra. Universidad Nacional de Córdoba. Instituto de Investigación Médica Mercedes y Martín Ferreyra; Argentin

Flavonoids in the Spotlight : Bridging the Gap between Physicochemical Properties and Formulation Strategies

Flavonoids are hydroxylated polyphenols that are widely distributed in plants with diverse health benefits. Despite their popularity, the bioavailability of flavonoids is often overlooked, impacting their efficacy and the comparison of products. The study discusses the bioavailability-related physicochemical properties of flavonoids, with a focus on the poorly soluble compounds commonly found in dietary supplements and herbal products. This review sums up the values of pKa, log P, solubility, permeability, and melting temperature of flavonoids. Experimental and calculated data were compiled for various flavonoid subclasses, revealing variations in their physicochemical properties. The investigation highlights the challenges posed by poorly soluble flavonoids and underscores the need for enabling formulation approaches to enhance their bioavailability and therapeutic potential. Compared to aglycones, flavonoid glycosides (with sugar moieties) tend to be more hydrophilic. Most of the reviewed aglycones and glycosides exhibit relatively low log P and high melting points, making them “brick dust” candidates. To improve solubility and absorption, strategies like size reduction, the potential use of solid dispersions and carriers, as well as lipid-based formulations have been discussed.Peer reviewe

ADME Profiling in Drug Discovery and a New Path Paved on Silica

The drug discovery and development pipeline have more and more relied on in vitro testing and in silico predictions to reduce investments and optimize lead compounds. A comprehensive set of in vitro assays is available to determine key parameters of absorption, distribution, metabolism, and excretion, for example, lipophilicity, solubility, and plasma stability. Such test systems aid the evaluation of the pharmacological properties of a compound and serve as surrogates before entering in vivo testing and clinical trials. Nowadays, computer-aided techniques are employed not just in the discovery of new lead compounds but embedded as part of the entire drug development process where the ADME profiling and big data analyses add a new layer of complexity to those systems. Herein, we give a short overview of the history of the drug development pipeline presenting state-of-the-art ADME in vitro assays as established in academia and industry. We will further introduce the underlying good practices and give an example of the compound development pipeline. In the next step, recent advances at in silico techniques will be highlighted with special emphasis on how pharmacogenomics and in silico PK profiling can enhance drug monitoring and individualization of drug therapy

Computational Methods in Biomolecules:Study of Hydrophilic Interactions in Protein Folding & Constant-pH Molecular Simulation of pH Sensitive Lipid MORC16

Water molecules play a significant role in biological process and are directly involved with bio-molecules and organic compounds and ions. Recent research has focused on the thermal dynamics and kinetics of water molecules in solution, including experimental (infrared spectroscopy and Raman spectroscopy) and computational (Quantum Mechanics and Molecular Dynamics) approaches. The reason that water molecules are so unique, why they have such a profound influence on bio-activity, why water molecules show some anomalies compared to other small molecules, and where and how water molecules exert their influence on solutes are some of the areas under study. We studied some properties of hydrogen bond networks, and the relationship of these properties with solutes in water. Molecular dynamics simulation, followed by an analysis of “water bridges”, which represent protein-water interaction have been carried out on folded and unfolded proteins. Results suggest that the formation of transient water bridges within a certain distance helps to consolidate the protein, possibly in transition states, and may help further guide the correct folding of proteins from these transition states. This is supporting evidence that a hydrophilic interaction is the driving force of protein folding. Biological membranes are complex structures formed mostly by lipids and proteins. For this reason the lipid bilayer has received much attention, through computation and experimental studies in recent years. In this dissertation, we report results of a newly designed pH sensitive lipid MORC16, through all-atom and coarse-grained models. The results did not yield a MORC16 amphiphile which flips its conformation in response to protonation. This may be due to imperfect force field parameters for this lipid, an imperfect protonation definition, or formation of hydrogen bond does not responsible for conformation flip in our models. Despite this, some insights for future work were obtained

The Use of Computational Methods in the Toxicological Assessment of Chemicals in Food: Current Status and Future Prospects

A wide range of chemicals are intentionally added to, or unintentially found in, food products, often in very small amounts. Depending on the situation, the experimental data needed to complete a dietary risk assessment, which is the scientific basis for protecting human health, may not be available or obtainable, for reasons of cost, time and animal welfare. For example, toxicity data are often lacking for the metabolites and degradation products of pesticide active ingredients. There is therefore an interest in the development and application of efficient and effective non-animal methods for assessing chemical toxicity, including Quantitative Structure-Activity Relationship (QSAR) models and related computational methods. This report gives an overview of how computational methods are currently used in the field of food safety by national regulatory bodies, international advisory organisations and the food industry. On the basis of an international survey, a comprehensive literature review and a detailed QSAR analysis, a range of recommendations are made with the long-term aim of promoting the judicious use of suitable QSAR methods. The current status of QSAR methods is reviewed not only for toxicological endpoints relevant to dietary risk assessment, but also for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their reaction products. By referring to the concept of the Threshold of Toxicological Concern (TTC), the risk assessment context in which QSAR methods can be expected to be used is also discussed. This Joint Research Centre (JRC) Reference Report provides a summary and update of the findings obtained in a study carried out by the JRC under the terms of a contract awarded by the European Food Safety Authority (EFSA).JRC.DG.I.6-Systems toxicolog

Review of QSAR Models and Software Tools for predicting Biokinetic Properties

In the assessment of industrial chemicals, cosmetic ingredients, and active substances in pesticides and biocides, metabolites and degradates are rarely tested for their toxicologcal effects in mammals. In the interests of animal welfare and cost-effectiveness, alternatives to animal testing are needed in the evaluation of these types of chemicals. In this report we review the current status of various types of in silico estimation methods for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their metabolites/degradation products. The review was performed in a broad sense, with emphasis on QSARs and rule-based approaches and their applicability to estimation of oral bioavailability, human intestinal absorption, blood-brain barrier penetration, plasma protein binding, metabolism and. This revealed a vast and rapidly growing literature and a range of software tools. While it is difficult to give firm conclusions on the applicability of such tools, it is clear that many have been developed with pharmaceutical applications in mind, and as such may not be applicable to other types of chemicals (this would require further research investigation). On the other hand, a range of predictive methodologies have been explored and found promising, so there is merit in pursuing their applicability in the assessment of other types of chemicals and products. Many of the software tools are not transparent in terms of their predictive algorithms or underlying datasets. However, the literature identifies a set of commonly used descriptors that have been found useful in ADME prediction, so further research and model development activities could be based on such studies.JRC.DG.I.6-Systems toxicolog