Immobilised artificial membrane liquid chromatography vs liposome electrokinetic capillary chromatography: Suitability in drug/bio membrane partitioning studies and effectiveness in the assessment of the passage of drugs through the respiratory mucosa

This study pioneers a comparison of the application of biomimetic techniques, immobilised artificial membrane liquid chromatography (IAM LC) and liposome electrokinetic capillary chromatography (LEKC), for the prediction of pulmonary drug permeability. The pulmonary absorption profiles of 26 structurally unrelated drug-like molecules were evaluated using their IAM hydrophobicity index (CHI IAM) measured in IAM LC, and the logarithm of distribution constants (log KLEKC) derived from the LEKC experiments. Lipophilicity (phospholipids) parameters obtained from IAM LC and most LEKC analyses were linearly related to the n-octanol/water partitioning coefficients of the neutral forms (i.e., log Po/w values) to a moderate extent. However, the relationship with distribution coefficients at the experimental pH (7.4) (i.e., log D7.4) were weaker overall for IAM LC data and sigmoidal for some liposome compositions (phosphatidyl choline (PC): phosphatidyl inositol (PI) 85:15 mol% and 90:10 mol%) and concentrations (4 mM) in LEKC. This suggests that phospholipid partitioning supports both hydrophobic and electrostatic interactions occurring between ionised drugs and charged phospholipid moieties. The latter interactions are original when compared to those taking place in the more established n-octanol/water partitioning systems. A stronger correlation (R2 > 0.65) was identified between the LEKC retention parameters, and the experimental apparent lung permeability (i.e., log Papp values) as opposed to the values obtained by IAM LC. Therefore, LEKC offers unprecedented advantages over IAM LC in simulating cell membrane partitioning processes in the pulmonary delivery of drugs. Although LEKC has the advantage of more effectively simulating the electrostatic and hydrophobic forces in drug/pulmonary membrane interactions in vitro, the technique is unsuitable for analysing highly hydrophilic neutral or anionic compounds at the experimental pH. Conversely, IAM LC is useful for analysing compounds spanning a wider range of lipophilicity. Its simpler and more robust implementation, and propensity for high-throughput automation make it a favourable choice for researchers in drug development and pharmacological studies.

QupKake: Integrating Machine Learning and Quantum Chemistry for Micro-pKa Predictions

Accurate prediction of micro-pKa values is crucial for understanding and modulating the acidity and basicity of organic molecules, with applications in drug discovery, materials science, and environmental chemistry. This work introduces QupKake, a novel method that combines graph neural network models with semiempirical quantum mechanical (QM) features to achieve exceptional accuracy and generalization in micro-pKa prediction. QupKake outperforms state-of-the-art models on a variety of benchmark data sets, with root-mean-square errors between 0.5 and 0.8 pKa units on five external test sets. Feature importance analysis reveals the crucial role of QM features in both the reaction site enumeration and micro-pKa prediction models. QupKake represents a significant advancement in micro-pKa prediction, offering a powerful tool for various applications in chemistry and beyond.

An update on lipophilic efficiency as an important metric in drug design

INTRODUCTION

Lipophilic efficiency (LipE) and lipophilic metabolic efficiency (LipMetE) are valuable tools that can be utilized as part of a multiparameter optimization process to advance a hit to a clinical quality compound.

AREAS COVERED

This review covers recent, effective use cases of LipE and LipMetE that have been published in the literature over the past 5 years. These use cases resulted in the delivery of high-quality molecules that were brought forward to in vivo work and/or to clinical studies. The authors discuss best-practices for using LipE and LipMetE analysis, combined with lipophilicity-focused compound design strategies, to increase the speed and effectiveness of the hit to clinical quality compound optimization process.

EXPERT OPINION

It has become well established that increasing LipE and LipMetE within a series of analogs facilitates the improvement of broad selectivity, clearance, solubility, and permeability and, through this optimization, also facilitates the achievement of desired pharmacokinetic properties, efficacy, and tolerability. Within this article, we discuss lipophilic efficiency-focused optimization as a tool to yield high-quality potential clinical candidates. It is suggested that LipE/LipMetE-focused optimization can facilitate and accelerate the drug-discovery process.

Screening of bioactive compounds from selected mushroom species against putative drug targets in Mycobacterium tuberculosis: a multi-target approach

Mycobacterium tuberculosis (Mtb) is a notorious pathogen that causes one of the highest mortalities globally. Due to a pressing demand to identify novel therapeutic alternatives, the present study aims to focus on screening the putative drug targets and prioritizing their role in antibacterial drug development. The most vital proteins involved in the Biotin biosynthesis pathway and the Lipoarabinomannan (LAM) pathway such as biotin synthase (bioB) and alpha-(1->6)-mannopyranosyltransferase A (mptA) respectively, along with other essential virulence proteins of Mtb were selected as drug targets. Among these, the ones without native structures were modelled and validated using standard bioinformatics tools. Further, the interactions were performed with naturally available lead molecules present in selected mushroom species such as Agaricus bisporus, Pleurotus djamor, Hypsizygus ulmarius. Through Gas Chromatography-Mass Spectrometry (GC-MS), 15 bioactive compounds from the methanolic extract of mushrooms were identified. Further, 4 were selected based on drug-likeness and pharmacokinetic screening for molecular docking analysis against our prioritized targets wherein Benz[e]azulene from Pleurotus djamor illustrated a good binding affinity with a LF rank score of -9.036 kcal mol -1 against nuoM (NADH quinone oxidoreductase subunit M) and could be used as a prospective candidate in order to combat Tuberculosis (TB). Furthermore, the stability of the complex are validated using MD Simulations and subsequently, the binding free energy was calculated using MM-GBSA analysis. Thus, the current in silico analysis suggests a promising role of compounds extracted from mushrooms in tackling the TB burden.Communicated by Ramaswamy H. Sarma.

Synthesis of Metastable Calcium Carbonate Using Long-Chain Bisphosphonate Molecules

Cementation in construction materials primarily relies on the aqueous precipitation of minerals such as carbonates and silicates. The kinetics of nucleation and growth play a critical role in the development of strength and durability, yet our understanding of the kinetic controls governing phase formation and porosity reduction in cements remains limited. In this study, we synthesized bisphosphonate molecules with varying alkyl chain lengths and functional groups to investigate their impact on calcium carbonate precipitation. Through conductivity measurements, infrared spectroscopy, and thermogravimetric analysis, we uncovered the selective formation of polymorphs and the specific incorporation of these molecules within the carbonate matrix. Further, in situ atomic force microscopy revealed that these molecules influenced the morphology of the precipitates, indicating a possible effect on the ionic organization through sorption mechanisms. Interestingly, amorphous calcium carbonate (ACC), when formed in the presence of bisphosphonates, showed metastability for at least seven months without inhibiting further calcium carbonate precipitation. Our research sheds light on the diverse mechanisms by which organic additives can modify mineral nucleation and growth, offering valuable insights for the control and enhancement of carbonate-based cementation processes.

ADME/PK Insights of Crocetin: A Molecule Having an Unusual Chemical Structure with Druglike Features

Crocetin is a promising phyto-based molecule to treat Alzheimer's disease (AD). The chemical structure of crocetin is incongruent with various standard structural features of CNS drugs. As poor pharmacokinetic behavior is the major hurdle for any candidate to become a drug, we elucidated its druggable characteristics by implementing in silico, in vitro, and in vivo approaches, as limited ADME/PK information is available. Results demonstrate several attributes of crocetin based on rules of drug-likeness, lipophilicity, pKa, P-gp inhibitory activity, plasma stability, RBC partitioning, metabolic stability, CYP inhibitory action, blood-brain barrier (BBB) permeability, oral bioavailability, and pharmacokinetic interaction with marketed anti-Alzheimer's drugs (memantine, donepezil, galantamine, and rivastigmine). However, aqueous solubility, chemical stability, plasma protein binding, and P-gp induction are some concerns associated with this molecule that should be taken into consideration during its further development. Overall results indicate favorable ADME/PK behavior and potential druggable candidature of crocetin.

Early Stage In Vitro Bioprofiling of Potential Low-Molecular-Weight Organoboron Compounds for Boron Neutron Capture Therapy (BNCT)-Proposal for a Guide

Given the renewed interest in boron neutron capture therapy (BNCT) and the intensified search for improved boron carriers, as well as the difficulties of coherently comparing the carriers described so far, it seems necessary to define a basic set of assays and standardized methods to be used in the early stages of boron carrier development in vitro. The selection of assays and corresponding methods is based on the practical experience of the authors and is certainly not exhaustive, but open to discussion. The proposed tests/characteristics: Solubility, lipophilicity, stability, cytotoxicity, and cellular uptake apply to both low molecular weight (up to 500 Da) and high molecular weight (5000 Da and more) boron carriers. However, the specific methods have been selected primarily for low molecular weight boron carriers; in the case of high molecular weight compounds, some of the methods may need to be adapted.

Predicting the Accumulation of Ionizable Pharmaceuticals and Personal Care Products in Aquatic and Terrestrial Organisms

The extent to which chemicals bioaccumulate in aquatic and terrestrial organisms represents a fundamental consideration for chemicals management efforts intended to protect public health and the environment from pollution and waste. Many chemicals, including most pharmaceuticals and personal care products (PPCPs), are ionizable across environmentally relevant pH gradients, which can affect their fate in aquatic and terrestrial systems. Existing mathematical models describe the accumulation of neutral organic chemicals and weak acids and bases in both fish and plants. Further model development is hampered, however, by a lack of mechanistic insights for PPCPs that are predominantly or permanently ionized. Targeted experiments across environmentally realistic conditions are needed to address the following questions: (1) What are the partitioning and sorption behaviors of strongly ionizing chemicals among species? (2) How does membrane permeability of ions influence bioaccumulation of PPCPs? (3) To what extent are salts and associated complexes with PPCPs influencing bioaccumulation? (4) How do biotransformation and other elimination processes vary within and among species? (5) Are bioaccumulation modeling efforts currently focused on chemicals and species with key data gaps and risk profiles? Answering these questions promises to address key sources of uncertainty for bioaccumulation modeling of ionizable PPCPs and related contaminants. Environ Toxicol Chem 2024;43:502-512. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Weak base drug-induced endolysosome iron dyshomeostasis controls the generation of reactive oxygen species, mitochondrial depolarization, and cytotoxicity

Objectives

Approximately 75 % of marketed drugs have the physicochemical property of being weak bases. Weak-base drugs with relatively high pKa values enter acidic organelles including endosomes and lysosomes (endolysosomes), reside in and de-acidify endolysosomes, and induce cytotoxicity. Divalent cations within endolysosomes, including iron, are released upon endolysosome de-acidification. Endolysosomes are "master regulators of iron homeostasis", and neurodegeneration is linked to ferrous iron (Fe2+)-induced reactive oxygen species (ROS) generation via Fenton chemistry. Because endolysosome de-acidification-induced lysosome-stress responses release endolysosome Fe2+, it was crucial to determine the mechanisms by which a functionally and structurally diverse group of weak base drugs including atropine, azithromycin, fluoxetine, metoprolol, and tamoxifen influence endolysosomes and cause cell death.

Methods

Using U87MG astrocytoma and SH-SY5Y neuroblastoma cells, we conducted concentration-response relationships for 5 weak-base drugs to determine EC50 values. From these curves, we chose pharmacologically and therapeutically relevant concentrations to determine if weak-base drugs induced lysosome-stress responses by de-acidifying endolysosomes, releasing endolysosome Fe2+ in sufficient levels to increase cytosolic and mitochondria Fe2+ and ROS levels and cell death.

Results

Atropine (anticholinergic), azithromycin (antibiotic), fluoxetine (antidepressant), metoprolol (beta-adrenergic), and tamoxifen (anti-estrogen) at pharmacologically and therapeutically relevant concentrations (1) de-acidified endolysosomes, (2) decreased Fe2+ levels in endolysosomes, (3) increased Fe2+ and ROS levels in cytosol and mitochondria, (4) induced mitochondrial membrane potential depolarization, and (5) increased cell death; effects prevented by the endocytosed iron-chelator deferoxamine.

Conclusions

Weak-base pharmaceuticals induce lysosome-stress responses that may affect their safety profiles; a better understanding of weak-base drugs on Fe2+ interorganellar signaling may improve pharmacotherapeutics.

Leveraging DFT and Molecular Fragmentation for Chemically Accurate pKa Prediction Using Machine Learning

We present a quantum mechanical/machine learning (ML) framework based on random forest to accurately predict the pKas of complex organic molecules using inexpensive density functional theory (DFT) calculations. By including physics-based features from low-level DFT calculations and structural features from our connectivity-based hierarchy (CBH) fragmentation protocol, we can correct the systematic error associated with DFT. The generalizability and performance of our model are evaluated on two benchmark sets (SAMPL6 and Novartis). We believe the carefully curated input of physics-based features lessens the model's data dependence and need for complex deep learning architectures, without compromising the accuracy of the test sets. As a point of novelty, our work extends the applicability of CBH, employing it for the generation of viable molecular descriptors for ML.

In silico analysis of luteolin derivatives as antibacterial agents targeting DNA gyrase and CTX-M-15 extended-spectrum β-lactamase of Escherichia coli

Luteolin exhibited antibacterial activity against Escherichia coli and its chemical structure similar to that of ciprofloxacin (CPF) which works by inhibiting DNA gyrase. Filtrate from passion fruit extract containing luteolin and its derivatives could inhibit extended-spectrum β-lactamase (ESBL)-producing E. coli. Antibacterial compounds that can also inhibit ESBL will be valuable compounds to overcome the problem of resistant bacteria. This study aimed to ensure the potency of luteolin and luteolin derivatives targeting DNA gyrase and ESBL by in silico approach. Docking simulation of ligands L1-L14 was performed using AutoDock Vina, and pharmacokinetics and toxicity (absorption, distribution, metabolism, excretion, and toxicity) profiles were predicted by pKCSM online. The docking result revealed higher binding affinity on DNA gyrase (PDB.1KZN) of 12 luteolin derivatives (energy <-7.6 kcal/mol) compared to CPF and higher affinity (energy <-6.27 kcal/mol) of all compounds than clavulanic acid against ESBL CTX-M-15 (PDB.4HBU). The compounds could be absorbed through the human intestine moderately, which showed low permeability to blood-brain barrier, nontoxic and nonhepatotoxic. The most active luteolin glycoside (L6) is capable to inhibit DNA gyrase and ESBL from E. coli which provided the potential against resistant bacteria and was promoted as lead compounds to be developed further.

Leveraging nitrogen occurrence in approved drugs to identify structural patterns

BACKGROUND

The process of drug development and discovery is costly and slow. Although an understanding of molecular design principles and biochemical processes has progressed, it is essential to minimize synthesis-testing cycles. An effective approach is to analyze key heteroatoms, including oxygen and nitrogen. Herein, we present an analysis focusing on the utilization of nitrogen atoms in approved drugs.

RESEARCH DESIGN AND METHODS

The present work examines the frequency, distribution, prevalence, and diversity of nitrogen atoms in a dataset comprising 2,049 small molecules approved by different regulatory agencies (FDA and others). Various types of nitrogen atoms, such as sp3-, sp2-, sp-hybridized, planar, ring, and non-ring are included in this investigation.

RESULTS

The results unveil both previously reported and newly discovered patterns of nitrogen atom distribution around the center of mass in the majority of drug molecules.

CONCLUSIONS

This study has highlighted intriguing trends in the role of nitrogen atoms in drug design and development. The majority of drugs contain 1-3 nitrogen atoms within 5Å from the center of mass (COM) of a molecule, with a higher preference for the ring and planar nitrogen atoms. The results offer invaluable guidance for the multiparameter optimization process, thus significantly contributing toward the conversion of lead compounds into potential drug candidates.

Halogenated Antimicrobial Agents to Combat Drug-Resistant Pathogens

Antimicrobial resistance presents us with a potential global crisis as it undermines the abilities of conventional antibiotics to combat pathogenic microbes. The history of antimicrobial agents is replete with examples of scaffolds containing halogens. In this review, we discuss the impacts of halogen atoms in various antibiotic types and antimicrobial scaffolds and their modes of action, structure-activity relationships, and the contributions of halogen atoms in antimicrobial activity and drug resistance. Other halogenated molecules, including carbohydrates, peptides, lipids, and polymeric complexes, are also reviewed, and the effects of halogenated scaffolds on pharmacokinetics, pharmacodynamics, and factors affecting antimicrobial and antivirulence activities are presented. Furthermore, the potential of halogenation to circumvent antimicrobial resistance and rejuvenate impotent antibiotics is addressed. This review provides an overview of the significance of halogenation, the abilities of halogens to interact in biomolecular settings and enhance pharmacological properties, and their potential therapeutic usages in preventing a postantibiotic era. SIGNIFICANCE STATEMENT: Antimicrobial resistance and the increasing impotence of antibiotics are critical threats to global health. The roles and importance of halogen atoms in antimicrobial drug scaffolds have been established, but comparatively little is known of their pharmacological impacts on drug resistance and antivirulence activities. This review is the first to extensively evaluate the roles of halogen atoms in various antibiotic classes and pharmacological scaffolds and to provide an overview of their ability to overcome antimicrobial resistance.

Acidic pH modulates Burkholderia cenocepacia antimicrobial susceptibility in the cystic fibrosis nutritional environment

IMPORTANCE

Burkholderia cenocepacia causes severe infections in cystic fibrosis (CF) patients. CF patients are prone to reoccurring infections due to the accumulation of mucus in their lungs, where bacteria can adhere and grow. Some of the antibiotics that inhibit B. cenocepacia in the laboratory are not effective for CF patients. A major contributor to poor clinical outcomes is that antibiotic testing in laboratories occurs under conditions that are different from those of sputum. CF sputum may be acidic and have increased concentrations of iron and zinc. Here, we used a medium that mimics CF sputum and found that acidic pH decreased the activity of many of the antibiotics used against B. cenocepacia. In addition, we assessed susceptibility to more than 500 antibiotics and found four active compounds against B. cenocepacia. Our findings give a better understanding of the lack of a relationship between susceptibility testing and the clinical outcome when treating B. cenocepacia infections.

Recent developments in photodynamic therapy and its application against multidrug resistant cancers

Recently, photodynamic therapy (PDT) has received a lot of attention for its potential use in cancer treatment. It enables the therapy of a multifocal disease with the least amount of tissue damage. The most widely used prodrug is 5-aminolevulinic acid, which undergoes heme pathway conversion to protoporphyrin IX, which acts as a photosensitizer (PS). Additionally, hematoporphyrin, bacteriochlorin, and phthalocyanine are also studied for their therapeutic potential in cancer. Unfortunately, not every patient who receives PDT experiences a full recovery. Resistance to different anticancer treatments is commonly observed. A few of the resistance mechanisms by which cancer cells escape therapeutics are genetic factors, drug-drug interactions, impaired DNA repair pathways, mutations related to inhibition of apoptosis, epigenetic pathways, etc. Recently, much research has been conducted to develop a new generation of PS based on nanomaterials that could be used to overcome cancer cells' multidrug resistance (MDR). Various metal-based, polymeric, lipidic nanoparticles (NPs), dendrimers, etc, have been utilized in the PDT application against cancer. This article discusses the detailed mechanism by which cancer cells evolve towards MDR as well as recent advances in PDT-based NPs for use against multidrug-resistant cancers.

Advantages of Multidimensional Biasing in Accelerated Dynamics: Application to the Calculation of the Acid pKa for Acetic Acid

The use of accelerated sampling methods such as metadynamics has shown a significant advantage in calculations that involve infrequent events, which would otherwise require sampling a prohibitive number of configurations to determine the difference in free energies between two or more chemically distinct states such as in the calculation of acid dissociation constants Ka. In this case, the most common method is to bias the system via a single collective variable (CV) representing the coordination number of the proton donor group, which yields results in reasonable agreement with experiments. Here we study the deprotonation of acetic acid using the reactive force field ReaxFF and observe a significant dependence of Ka on the simulation box size when biasing only the coordination number CV, which is due to incomplete sampling of the deprotonated state for small simulation systems and inefficient sampling for larger ones. Incorporating a second CV representing the distance between the H3O+ cation and the acetate anion results in substantially more efficient sampling, both accelerating the dynamics and virtually eliminating the computational box size dependence.

Adapting physiologically-based pharmacokinetic models for machine learning applications

Both machine learning and physiologically-based pharmacokinetic models are becoming essential components of the drug development process. Integrating the predictive capabilities of physiologically-based pharmacokinetic (PBPK) models within machine learning (ML) pipelines could offer significant benefits in improving the accuracy and scope of drug screening and evaluation procedures. Here, we describe the development and testing of a self-contained machine learning module capable of faithfully recapitulating summary pharmacokinetic (PK) parameters produced by a full PBPK model, given a set of input drug-specific and regimen-specific information. Because of its widespread use in characterizing the disposition of orally administered drugs, the PBPK model chosen to demonstrate the methodology was an open-source implementation of a state-of-the-art compartmental and transit model called OpenCAT. The model was tested for drug formulations spanning a large range of solubility and absorption characteristics, and was evaluated for concordance against predictions of OpenCAT and relevant experimental data. In general, the values predicted by the ML models were within 20% of those of the PBPK model across the range of drug and formulation properties. However, summary PK parameter predictions from both the ML model and full PBPK model were occasionally poor with respect to those derived from experiments, suggesting deficiencies in the underlying PBPK model.

Recent advances on molecular dynamics-based techniques to address drug membrane permeability with atomistic detail

Several factors affect the passive membrane permeation of small molecules, including size, charge, pH, or the presence of specific chemical groups. Understanding these features is paramount to identifying or designing drug candidates with optimal ADMET properties and this can be achieved through experimental/knowledge-based methodologies or using computational approaches. Empirical methods often lack detailed information about the underlying molecular mechanism. In contrast, Molecular Dynamics-based approaches are a powerful strategy, providing an atomistic description of this process. This technique is continuously growing, featuring new related methodologies. In this work, the recent advances in this research area will be discussed.

Review of warming and acidification effects to the ecotoxicity of pharmaceuticals on aquatic organisms in the era of climate change

An increase in the temperature and the acidification of the aquatic environment are among the many consequences of global warming. Climate change can also negatively affect aquatic organisms indirectly, by altering the toxicity of pollutants. Models of climate change impacts on the distribution, fate and ecotoxicity of persistent pollutants are now available. For pharmaceuticals, however, as new environmental pollutants, there are no predictions on this issue. Therefore, this paper organizes the existing knowledge on the effects of temperature, pH and both stressors combined on the toxicity of pharmaceuticals on aquatic organisms. Besides lethal toxicity, the molecular, physiological and behavioral biomarkers of sub-lethal stress were also assessed. Both acute and chronic toxicity, as well as bioaccumulation, were found to be affected. The direction and magnitude of these changes depend on the specific pharmaceutical, as well as the organism and conditions involved. Unfortunately, the response of organisms was enhanced by combined stressors. We compare the findings with those known for persistent organic pollutants, for which the pH has a relatively low effect on toxicity. The acid-base constant of molecules, as assumed, have an effect on the toxicity change with pH modulation. Studies with bivalves have been were overrepresented, while too little attention was paid to producers. Furthermore, the limited number of pharmaceuticals have been tested, and metabolites skipped altogether. Generally, the effects of warming and acidification were rather indicated than explored, and much more attention needs to be given to the ecotoxicology of pharmaceuticals in climate change conditions.

Interaction of barley β-glucan with food dye molecules - An insight from pulse dipolar EPR spectroscopy

The interactions between dietary fibers (DFs) and small molecules are of great interest to food chemistry and nutrition science. However, the corresponding interaction mechanisms and structural rearrangements of DFs at the molecular level are still opaque due to the usually weak binding and the lack of appropriate techniques to determine details of conformational distributions in such weakly organized systems. By combining our previously established methodology on stochastic spin-labelling of DFs with the appropriately revised set of pulse electron paramagnetic resonance techniques, we present here a toolkit to determine the interactions between DFs and small molecules, using barley β-glucan as an example for neutral DF and a selection of food dye molecules as examples for small molecules. The proposed here methodology allowed us to observe subtle conformational changes of β-glucan by detecting multiple details of the local environment of the spin labels. Substantial variations of binding propensities were detected for different food dyes.

Investigating the bioaccumulation potential of anionic organic compounds using a permanent rainbow trout liver cell line

Permanent rainbow trout (Oncorhynchus mykiss) cell lines represent potential in vitro alternatives to experiments with fish. We here developed a method to assess the bioaccumulation potential of anionic organic compounds in fish, using the rainbow trout liver-derived RTL-W1 cell line. Based on the availability of high quality in vivo bioconcentration (BCF) and biomagnification (BMF) data and the substances' charge state at physiological pH, four anionic compounds were selected: pentachlorophenol (PCP), diclofenac (DCF), tecloftalam (TT) and benzotriazol-tert-butyl-hydroxyl-phenyl propanoic acid (BHPP). The fish cell line acute toxicity assay (OECD TG249) was used to derive effective concentrations 50 % and non-toxic exposure concentrations to determine exposure concentrations for bioaccumulation experiments. Bioaccumulation experiments were performed over 48 h with a total of six time points, at which cell, medium and plastic fractions were sampled and measured using high resolution tandem mass spectrometry after online solid phase extraction. Observed cell internal concentrations were over-predicted by KOW-derived predictions while pH-dependent octanol-water partitioning (DOW) and membrane lipid-water partitioning (DMLW) gave better predictions of cell internal concentrations. Measured medium and cell internal concentrations at steady state were used to calculate RTL-W1-based BCF, which were compared to DOW- or DMLW-based model approaches and in vivo data. With the exception of PCP, the cell-derived BCF best compared to DOW-based model predictions, which were higher than predictions based on DMLW. All methods predicted the in vivo BCF for diclofenac well. For PCP, the cell-derived BCF was lowest although all BCF predictions underestimated the in vivo BCF by ≥ 1 order of magnitude. The RTL-W1 cells, and all other prediction methods, largely overestimated in vivo BMF, which were available for PCP, TT and BHPP. We conclude that the RTL-W1 cell line can supplement BCF predictions for anionic compounds. For BMF estimations, however, in vitro-in vivo extrapolations need adaptation or a multiple cell line approach.

Targeting the Epidermal Growth Factor Receptor with Molecular Degraders: State-of-the-Art and Future Opportunities

Epidermal growth factor receptor (EGFR) is an oncogenic drug target and plays a critical role in several cellular functions including cancer cell growth, survival, proliferation, differentiation, and motility. Several small-molecule tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs) have been approved for targeting intracellular and extracellular domains of EGFR, respectively. However, cancer heterogeneity, mutations in the catalytic domain of EGFR, and persistent drug resistance limited their use. Different novel modalities are gaining a position in the limelight of anti-EGFR therapeutics to overcome such limitations. The current perspective reflects upon newer modalities, importantly the molecular degraders such as PROTACs, LYTACs, AUTECs, and ATTECs, etc., beginning with a snapshot of traditional and existing anti-EGFR therapies including small molecule inhibitors, mAbs, and antibody drug conjugates (ADCs). Further, a special emphasis has been made on the design, synthesis, successful applications, state-of-the-art, and emerging future opportunities of each discussed modality.

On hidden anisotropy of formally charged fragments

The proper and precise reproduction of the molecular electrostatic potential (MEP) is crucial to describe correctly electrostatic interactions in molecular modeling. Most of the classical molecular mechanics force fields for biomolecules and drug-like molecules use the atom-centered point charges to describe MEP. However, it has been systematically pointed out in literature that such an approximation is not always enough, and some groups, like amino group or heavy halogens, require the use of anisotropic model for better description of their MEP. At the same time, the formally charged groups have not been as extensively and systematically studied as their neutral counterparts. In this report, we demonstrate that the anisotropic models for formally charged groups do bring improvements in the reference MEP reproduction, that are comparable in magnitude to those for neutral groups.

Design, synthesis, molecular docking, and biological evaluation of coumarin-thymidine analogs as potent anti-TB agents

With the intent to discover new antituberculosis (TB) compounds, coumarin-thymidine analogs were synthesized using second-order nucleophilic substitution reactions of bromomethyl coumarin with thymidine. The newly synthesized coumarin-thymidine conjugates (1a-l) were characterized using IR, NMR, GC-MS, and CHN elemental analysis. The novel conjugates were found to exhibit potent anti-TB activity against the Mycobacterium tuberculosis H₃₇ Rv strain, with minimum inhibitory concentrations (MIC) of the active compounds ranging between 0.012 and 0.482 µM. Compound 1k was established as the most active candidate with a MIC of 0.012 µM. The toxicity study on HEK cells confirmed the nontoxic nature of compounds 1e, 1h, 1i, 1j, and 1k. Also, the most active compounds (1k, 1j, and 1e) were stable in the pH range from 2.5 to 10, indicating compatibility with the biophysical environment. Based on the pK_a studies, compounds 1k, 1j, and 1e are capable of crossing lipid-membrane barriers and acting on target cells. Molecular docking studies on the M. tuberculosis β-oxidation trifunctional enzyme (PDB ID: 7O4V) were conducted to investigate the mechanisms of anti-TB activity. All compounds showed excellent hydrogen binding interactions and exceptional docking scores against M. tuberculosis, which was in accordance with the results. Compounds 1a-l possessed excellent affinity to proteins, with binding energies ranging from -7.4 to -8.7 kcal/mol.

Beta-blockers in cardiac arrhythmias-Clinical pharmacologist's point of view

β-blockers is a vast group of antiarrhythmic drugs which differ in their pharmacokinetic and chemical properties. Some of them block β-adrenergic receptors selectively while the others work non-selectively. Consequently, they reduce the influence of the sympathetic nervous system on the heart, acting negatively inotropic, chronotropic, bathmotropic and dromotropic. Although they have been present in medicine since the beginning of the 1960s, they still play a crucial role in the treatment of cardiac arrhythmias. They are also first-line group of drugs used to control the ventricular rate in patients with the most common arrhythmia-atrial fibrillation. Previous reports indicate that infection with SARS-CoV-2 virus may constitute an additional risk factor for arrhythmia. Due to the aging of the population in developed countries and the increase in the number of patients with cardiac burden, the number of people suffering from cardiac arrhythmias will increase in the upcoming years. As a result the role of above-mentioned beta-blockers will remain significant. Particularly noteworthy is propranolol-the oldest beta adrenergic antagonist, which in recent years has found additional applications due to its unique properties. In this article, we reviewed the accessible literature and summarized the current guidelines on the use of beta-blockers in the treatment of cardiac arrhythmias.

Pharmacokinetics of Antimicrobials in Children with Emphasis on Challenges Faced by Low and Middle Income Countries, a Clinical Review

Effective antimicrobial exposure is essential to treat infections and prevent antimicrobial resistance, both being major public health problems in low and middle income countries (LMIC). Delivery of drug concentrations to the target site is governed by dose and pharmacokinetic processes (absorption, distribution, metabolism and excretion). However, specific data on the pharmacokinetics of antimicrobials in children living in LMIC settings are scarce. Additionally, there are significant logistical constraints to therapeutic drug monitoring that further emphasize the importance of understanding pharmacokinetics and dosing in LMIC. Both malnutrition and diarrheal disease reduce the extent of enteral absorption. Multiple antiretrovirals and antimycobacterial agents, commonly used by children in low resource settings, have potential interactions with other antimicrobials. Hypoalbuminemia, which may be the result of malnutrition, nephrotic syndrome or liver failure, increases the unbound concentrations of protein bound drugs that may therefore be eliminated faster. Kidney function develops rapidly during the first years of life and different inflammatory processes commonly augment renal clearance in febrile children, potentially resulting in subtherapeutic drug concentrations if doses are not adapted. Using a narrative review approach, we outline the effects of growth, maturation and comorbidities on maturational and disease specific effects on pharmacokinetics in children in LMIC.

Potentiometric Determination of Acid Dissociation Constants (pK a) for an Anticancer Pyrrole-Imidazole Polyamide

To optimize the pharmacological properties of an anticancer pyrrole-imidazole (Py-Im) polyamide (PIP-1), we characterized the acid dissociation constants of PIP-1, three other structurally related hairpin-shaped polyamides, and a cyclic polyamide bearing the same core sequence as PIP-1 via potentiometric titration. The acidities of the carboxylic acid at the C-terminus and the tertiary amine in the triamine linker remained very similar among the polyamides tested, whereas the pK a of the N-methylimidazole (Im) moieties varied with the peptide sequence and molecular architecture. A nearly 0.2 pH unit pK a shift of terminal Im toward the neutral state compared to internal Im was observed. Furthermore, according to the dissociation constants, a speciation diagram of PIP-1 as a function of pH is presented, which allows an assessment of the net charge and distribution of protonated species in the range of physiological pH.

Quantitative assessment of disintegration rate is important for predicting the oral absorption of solid dosage forms containing poorly soluble weak base drugs

This study aimed to develop a novel in silico modeling and simulation that considers the disintegration rate in the stomach to predict the in vivo performance of oral solid dosage forms with slow disintegration rates containing poorly soluble weak base drugs. Oxatomide and manidipine hydrochloride were used as model drugs. First, the in vitro disintegration rate and dissolution rate were determined in biorelevant media that simulate the gastrointestinal fluids in fasted humans using a USP apparatus II paddle dissolution tester. Next, the oral absorption of the dosage forms was predicted using the novel simulation model coupled with not only the dissolution rate but also the estimated disintegration rate. As the in vitro disintegration time was 45 min or longer for both drugs in Fasted State Simulated Gastric Fluid, the disintegration rate of these dosage forms was considered slow as immediate release (IR) tablets. While the predicted and observed pharmacokinetic profiles of both drugs were comparable using the new model, the conventional model, which did not consider the disintegration step, underestimated the oral absorption of both drugs. Thus, our novel simulation model coupled with the disintegration rate estimated from in vitro tests is promising for predicting the in vivo performance of oral solid dosage forms with slow disintegration rates containing poorly soluble weak base drugs.

Impact of Composition and Morphology of Ketoconazole-Loaded Solid Lipid Nanoparticles on Intestinal Permeation and Gastroplus-Based Prediction Studies

Ketoconazole (KTZ) is a potential oral antifungal agent to control systemic and local infections. This study addresses the impact of composition (tween 80 and compritol as CATO) and morphology on permeation (stomach, jejunum, and ileum) profiles of KTZ-loaded solid lipid nanoparticles (SLNs) in rats followed by in vivo pharmacokinetic prediction and simulation using GastroPlus. The selected formulations were characterized for size, size distribution, zeta potential, entrapment efficiency, total drug content, morphology, in vitro drug release, ex vivo permeation and drug deposition, penetration potential, and GastroPlus-based in vivo prediction in rats. The results showed that there was considerable impact of pH, composition (CATO and tween 80), size, total drug content, and entrapment efficiency on in vitro drug release and permeation across the stomach, jejunum, and ileum. Ex vivo findings suggested pH, composition, size, and permeability coefficient-dependent permeation of SLNs across the stomach, jejunum, and ileum. Confocal laser scanning microscopy (CLSM) confirmed a relatively high degree of penetration of the optimized formulation "K-SLN4" (66.1% across the stomach, 51.5% across the jejunum, and 47.9% across the ileum) as compared to KSUS (corresponding values of 21.7%, 18.2%, and 17.4%). Finally, GastroPlus predicted in vivo dissolution/absorption as 0.012 μg/mL of K-SLN4 as compared to KSUS (the drug suspension with 0.0058 μg/mL) and a total regional absorption of 80.0% by K-SLN4 as compared to 60.1% of KSUS. There was only an impact of dose on C _max (maximum plasma concentration) and area under the curve (AUC) in rats. Thus, the present strategy could be a promising alternative to parenteral and topical delivery systems for long-term therapy against systemic and local mycoses with high patient compliance.

Antimicrobial Polymers of Linear and Bottlebrush Architecture: Probing the Membrane Interaction and Physicochemical Properties

Polymeric antimicrobial peptide mimics are a promising alternative for the future management of the daunting problems associated with antimicrobial resistance. However, the development of successful antimicrobial polymers (APs) requires careful control of factors such as amphiphilic balance, molecular weight, dispersity, sequence, and architecture. While most of the earlier developed APs focus on random linear copolymers, the development of APs with advanced architectures proves to be more potent. It is recently developed multivalent bottlebrush APs with improved antibacterial and hemocompatibility profiles, outperforming their linear counterparts. Understanding the rationale behind the outstanding biological activity of these newly developed antimicrobials is vital to further improving their performance. This work investigates the physicochemical properties governing the differences in activity between linear and bottlebrush architectures using various spectroscopic and microscopic techniques. Linear copolymers are more solvated, thermo-responsive, and possess facial amphiphilicity resulting in random aggregations when interacting with liposomes mimicking Escheria coli membranes. The bottlebrush copolymers adopt a more stable secondary conformation in aqueous solution in comparison to linear copolymers, conferring rapid and more specific binding mechanism to membranes. The advantageous physicochemical properties of the bottlebrush topology seem to be a determinant factor in the activity of these promising APs.

Improving Small Molecule pKa Prediction Using Transfer Learning With Graph Neural Networks

Enumerating protonation states and calculating microstate pK_a values of small molecules is an important yet challenging task for lead optimization and molecular modeling. Commercial and non-commercial solutions have notable limitations such as restrictive and expensive licenses, high CPU/GPU hour requirements, or the need for expert knowledge to set up and use. We present a graph neural network model that is trained on 714,906 calculated microstate pK_a predictions from molecules obtained from the ChEMBL database. The model is fine-tuned on a set of 5,994 experimental pK_a values significantly improving its performance on two challenging test sets. Combining the graph neural network model with Dimorphite-DL, an open-source program for enumerating ionization states, we have developed the open-source Python package pkasolver, which is able to generate and enumerate protonation states and calculate pK_a values with high accuracy.

Pharmacokinetics of piperacillin and tazobactam in critically Ill patients treated with continuous kidney replacement therapy: A mini-review and population pharmacokinetic analysis

WHAT IS KNOWN AND OBJECTIVE

Timely and appropriate dosing of antibiotics is essential for the treatment of bacterial sepsis. Critically ill patients treated with continuous kidney replacement therapy (CKRT) often have physiologic derangements that affect pharmacokinetics (PK) of antibiotics and dosing may be challenging. We sought to aggregate previously published piperacillin and tazobactam (pip-tazo) pharmacokinetic data in critically ill patients undergoing CKRT to better understand pharmacokinetics of pip-tazo in this population and better inform dosing.

METHODS

The National Library of Medicine Database was searched for original research containing piperacillin or tazobactam clearance (CL) or volume of distribution (V) estimates in patients treated with CKRT. The search yielded 77 articles, of which 26 reported suitable estimates of CL or V. Of the 26 articles, 10 for piperacillin and 8 for tazobactam had complete information suitable for population pharmacokinetic modelling. Also included in the analysis was piperacillin and tazobactam PK data from 4 critically ill patients treated with CKRT in the Military Health System, 2 with burn and 2 without burn.

RESULTS AND DISCUSSION

Median and range of literature reported PK parameters for piperacillin (CL 2.76 L/hr, 1.4-7.92 L/hr, V 31.2 L, 16.77-42.27 L) and tazobactam (CL 2.34 L/hr, 0.72-5.2 L/hr, V 36.6 L, 26.2-58.87 L) were highly consistent with population estimates (piperacillin CL 2.7 L/hr, 95%CI 1.99-3.41 L/hr, V 25.83 22.07-29.59 L, tazobactam CL 2.49 L/hr, 95%CI 1.55-3.44, V 30.62 95%CI 23.7-37.54). The proportion of patients meeting pre-defined pharmacodynamic (PD) targets (median 88.7, range 71%-100%) was high despite significant mortality (median 44%, range 35%-60%). High mortality was predicted by baseline severity of illness (median APACHE II score 23, range 21-33.25). Choice of lenient or strict PD targets (ie 100%fT >MIC or 100%fT >4XMIC) had the largest impact on probability of target attainment (PTA), whereas presence or intensity of CKRT had minimal impact on PTA.

WHAT IS NEW AND CONCLUSION

Pip-tazo overexposure may be associated with increased mortality, although this is confounded by baseline severity of illness. Achieving adequate pip-tazo exposure is essential; however, risk of harm from overexposure should be considered when choosing a PD target and dose. If lenient PD targets are desired, doses of 2250-3375 mg every 6 h are reasonable for most patients receiving CKRT. However, if a strict PD target is desired, continuous infusion (at least 9000-13500 mg per day) may be required. However, some critically ill CKRT populations may need higher or lower doses and dosing strategies should be tailored to individuals based on all available clinical data including the specific critical care setting.

Virtual Screening Directly Identifies New Fragment-Sized Inhibitors of Carboxylesterase Notum with Nanomolar Activity

Notum is a negative regulator of Wnt signaling acting through the hydrolysis of a palmitoleoylate ester, which is required for Wnt activity. Inhibitors of Notum could be of use in diseases where dysfunctional Notum activity is an underlying cause. A docking-based virtual screen (VS) of a large commercial library was used to shortlist 952 compounds for experimental validation as inhibitors of Notum. The VS was successful with 31 compounds having an IC50 < 500 nM. A critical selection process was then applied with two clusters and two singletons (1-4d) selected for hit validation. Optimization of 4d guided by structural biology identified potent inhibitors of Notum activity that restored Wnt/β-catenin signaling in cell-based models. The [1,2,4]triazolo[4,3-b]pyradizin-3(2H)-one series 4 represent a new chemical class of Notum inhibitors and the first to be discovered by a VS campaign. These results demonstrate the value of VS with well-designed docking models based on X-ray structures.

Olivetolic acid, a cannabinoid precursor in Cannabis sativa, but not CBGA methyl ester exhibits a modest anticonvulsant effect in a mouse model of Dravet syndrome

Objective

Cannabigerolic acid (CBGA), a precursor cannabinoid in Cannabis sativa, has recently been found to have anticonvulsant properties in the Scn1a^+/- mouse model of Dravet syndrome. Poor brain penetration and chemical instability of CBGA limits its potential as an anticonvulsant therapy. Here, we examined whether CBGA methyl ester, a more stable analogue of CBGA, might have superior pharmacokinetic and anticonvulsant properties. In addition, we examined whether olivetolic acid, the biosynthetic precursor to CBGA with a truncated (des-geranyl) form, might possess minimum structural requirements for anticonvulsant activity. We also examined whether olivetolic acid and CBGA methyl ester retain activity at the epilepsy-relevant drug targets of CBGA: G-protein-coupled receptor 55 (GPR55) and T-type calcium channels.

Methods

The brain and plasma pharmacokinetic profiles of CBGA methyl ester and olivetolic acid were examined following 10 mg/kg intraperitoneal (i.p.) administration in mice (n = 4). The anticonvulsant potential of each was examined in male and female Scn1a^+/- mice (n = 17–19) against hyperthermia-induced seizures (10–100 mg/kg, i.p.). CBGA methyl ester and olivetolic acid were also screened in vitro against T-type calcium channels and GPR55 using intracellular calcium and ERK phosphorylation assays, respectively.

Results

CBGA methyl ester exhibited relatively limited brain penetration (13%), although somewhat superior to that of 2% for CBGA. No anticonvulsant effects were observed against thermally induced seizures in Scn1a^+/- mice. Olivetolic acid also showed poor brain penetration (1%) but had a modest anticonvulsant effect in Scn1a^+/- mice increasing the thermally induced seizure temperature threshold by approximately 0.4°C at a dose of 100 mg/kg. Neither CBGA methyl ester nor olivetolic acid displayed pharmacological activity at GPR55 or T-type calcium channels.

Conclusions

Olivetolic acid displayed modest anticonvulsant activity against hyperthermia-induced seizures in the Scn1a^+/- mouse model of Dravet syndrome despite poor brain penetration. The effect was, however, comparable to the known anticonvulsant cannabinoid cannabidiol in this model. Future studies could explore the anticonvulsant mechanism(s) of action of olivetolic acid and examine whether its anticonvulsant effect extends to other seizure types.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42238-021-00113-w.

Functional block copolymer micelles based on poly (jasmine lactone) for improving the loading efficiency of weakly basic drugs

Functionalization of polymers is an attractive approach to introduce specific molecular forces that can enhance drug–polymer interaction to achieve higher drug loading when used as drug delivery systems. The novel amphiphilic block copolymer of methoxy poly(ethylene glycol) and poly(jasmine lactone) i.e., mPEG-b-PJL, derived from renewable jasmine lactone provides free allyl groups on the backbone thus, allowing flexible and facile post-synthesis functionalization. In this study, mPEG-b-PJL and its carboxyl functionalized polymer mPEG-b-PJL-COOH were utilised to explore the effect of ionic interactions on the drug–polymer behaviour. Various drugs with different pK_a values were employed to prepare drug-loaded polymeric micelles (PMs) of mPEG-b-PJL, mPEG-b-PJL-COOH and Soluplus® (polyvinyl caprolactam–polyvinyl acetate–polyethylene glycol graft copolymer) via a nanoprecipitation method. Electrostatic interactions between the COOH pendant on mPEG-b-PJL-COOH and the basic drugs were shown to influence the entrapment efficiency. Additionally, molecular dynamics (MD) simulations were employed to understand the polymer–drug interactions at the molecular level and how polymer functionalization influenced these interactions. The release kinetics of the anti-cancer drug sunitinib from mPEG-b-PJL and mPEG-b-PJL-COOH was assessed, and it demonstrated a sustainable drug release pattern, which depended on both pH and temperature. Furthermore, the cytotoxicity of sunitinib-loaded micelles on cancer cells was evaluated. The drug-loaded micelles exhibited dose-dependent toxicity. Also, haemolysis capacity of these polymers was investigated. In summary, polymer functionalization seems a promising approach to overcome challenges that hinder the application of polymer-based drug delivery systems such as low drug loading degree.

Block copolymer micelles with a functional core have been synthesized and evaluated for their drug delivery capability. High drug loading was observed due to strong ionic interactions, while cytotoxicity of polymers was found to be low.

Exploring the Relationship of Drug BCS Classification, Food Effect, and Gastric pH-Dependent Drug Interactions

Food effect (FE) and gastric pH-dependent drug-drug interactions (DDIs) are both absorption-related. Here, we evaluated if Biopharmaceutics Classification System (BCS) classes may be correlated with FE or pH-dependent DDIs. Trends in FE data were investigated for 170 drugs with clinical FE studies from the literature and new drugs approved from 2013 to 2019 by US Food and Drug Administration. A subset of 38 drugs was also evaluated to determine whether FE results can inform the need for a gastric pH-dependent DDI study. The results of FE studies were defined as no effect (AUC ratio 0.80-1.25), increased exposure (AUC ratio ≥1.25), or decreased exposure (AUC ratio ≤0.8). Drugs with significantly increased exposure FE (AUC ratio ≥2.0; N=14) were BCS Class 2 or 4, while drugs with significantly decreased exposure FE (AUC ratio ≤0.5; N=2) were BCS Class 1/3 or 3. The lack of FE was aligned with the lack of a pH-dependent DDI for all 7 BCS Class 1 or 3 drugs as expected. For the 13 BCS Class 2 or 4 weak base drugs with an increased exposure FE, 6 had a pH-dependent DDI (AUC ratio ≤0.8). Among the 16 BCS Class 2 or 4 weak base drugs with no FE, 6 had a pH-dependent DDI (AUC ratio ≤0.8). FE appears to have limited correlation with BCS classes except for BCS Class 1 drugs, confirming that multiple physiological mechanisms can impact FE. Lack of FE does not indicate absence of pH-dependent DDI for BCS Class 2 or 4 drugs. Graphical Abstract.

Computer Prediction of pK a Values in Small Molecules and Proteins

Accurately determining the acid dissociation constants (K _a or their logarithmic form, pK _a) of small molecules and large biomolecules has proven to be pivotal for the study different biological processes and developing new drugs. This Viewpoint summarizes some of the most common methodologies and recent advances described for pK _a prediction using computational techniques when experimental values are not easily accessible such as in proteins and/or for the screening of large libraries of new compounds.

Electron Donor-Acceptor Properties of Different Muscarinic Ligands: On the Road to Control Schizophrenia

Schizophrenia is a severe neuropsychiatric disorder that deteriorates perception, affection, and cognitive mental functions. The current treatments are mainly focused on the dopamine system, but the so-named dopamine hypothesis of schizophrenia fails to explain all the symptoms. Previous studies have shown that there is a reciprocal relationship between muscarinic acetylcholine receptors and dopamine receptor function. Some muscarinic ligands show antidopaminergic activity, and therefore, they should have some antipsychotic efficacy. In this work, conceptual density functional theory is employed to analyze the properties of acetylcholine's agonists, partial agonists, or antagonists. The aim is to establish a classification of the antipsychotic-like or pro-psychotic activities of these molecules based on the electron-donor and electron-acceptor properties. Most of the agonists and antagonists are better electron donors and worse electron acceptors than partial agonists. We found that acetylcholine antagonists that clinically promote psychotic symptoms are good electron-donor molecules, and acetylcholine agonists that clinically relieve symptoms of psychosis are good electron donors. These results represent a further advance on the road to understanding the charge-transfer properties of drugs used as possible treatments for schizophrenia.

Improving Dissolution Behavior and Oral Absorption of Drugs with pH-Dependent Solubility Using pH Modifiers: A Physiologically Realistic Mass Transport Analysis

Orally dosed drugs must dissolve in the gastrointestinal (GI) tract before being absorbed through the epithelial cell membrane. In vivo drug dissolution depends on the GI tract's physiological conditions such as pH, residence time, luminal buffers, intestinal motility, and transit and drug properties under fed and fasting conditions (Paixão, P. et al. Mol. Pharm. 2018 and Bermejo, et al. M. Mol. Pharm. 2018). The dissolution of an ionizable drug may benefit from manipulating in vivo variables such as the environmental pH using pH-modifying agents incorporated into the dosage form. A successful example is the use of such agents for dissolution enhancement of BCS class IIb (high-permeability, low-solubility, and weak base) drugs under high gastric pH due to the disease conditions or by co-administration of acid-reducing agents (i.e., proton pump inhibitors, H2-antagonists, and antacids). This study provides a rational approach for selecting pH modifiers to improve monobasic and dibasic drug compounds' dissolution rate and extent under high-gastric pH dissolution conditions, since the oral absorption of BCS class II drugs can be limited by either the solubility or the dissolution rate depending on the initial dose number. Betaine chloride, fumaric acid, and tartaric acid are examples of promising pH modifiers that can be included in oral dosage forms to enhance the rate and extent of monobasic and dibasic drug formulations. However, selection of a suitable pH modifier is dependent on the drug properties (e.g., solubility and pK_a) and its interplay with the pH modifier pK_a or pK_as. As an example of this complex interaction, for basic drugs with high pK_a and intrinsic solubility values and large doses, a polyprotic pH modifier can be expected to outperform a monoacid pH modifier. We have developed a hierarchical mass transport model to predict drug dissolution of formulations under varying pH conditions including high gastric pH. This model considers the effect of physical and chemical properties of the drug and pH modifiers such as pK_a, solubility, and particle size distribution. This model also considers the impact of physiological conditions such as stomach emptying rate, stomach acid and buffer secretion, residence time in the GI tract, and aqueous luminal volume on drug dissolution. The predictions from this model are directly applicable to in vitro multi-compartment dissolution vessels and are validated by in vitro experiments in the gastrointestinal simulator. This model's predictions can serve as a potential data source to predict plasma concentrations for formulations containing pH modifiers administered under the high-gastric pH conditions. This analysis provides an improved formulation design procedure using pH modifiers by minimizing the experimental iterations under both in vitro and in vivo conditions.

Data-driven equation for drug-membrane permeability across drugs and membranes

Drug efficacy depends on its capacity to permeate across the cell membrane. We consider the prediction of passive drug-membrane permeability coefficients. Beyond the widely recognized correlation with hydrophobicity, we additionally consider the functional relationship between passive permeation and acidity. To discover easily interpretable equations that explain the data well, we use the recently proposed sure-independence screening and sparsifying operator (SISSO), an artificial-intelligence technique that combines symbolic regression with compressed sensing. Our study is based on a large in silico dataset of 0.4 × 10⁶ small molecules extracted from coarse-grained simulations. We rationalize the equation suggested by SISSO via an analysis of the inhomogeneous solubility-diffusion model in several asymptotic acidity regimes. We further extend our analysis to the dependence on lipid-membrane composition. Lipid-tail unsaturation plays a key role but surprisingly contributes stepwise rather than proportionally. Our results are in line with previously observed changes in permeability, suggesting the distinction between liquid-disordered and liquid-ordered permeation. Together, compressed sensing with analytically derived asymptotes establish and validate an accurate, broadly applicable, and interpretable equation for passive permeability across both drug and lipid-tail chemistry.

Square root law model for the delivery and intestinal absorption of drugs: a case of hydrophilic captopril

The in vivo release and absorption of drugs are dependent on the interplay between many factors related to compound, formulation, and physiological properties. The mathematical models of oral drug absorption attempt to strike a balance between a complete description that takes into consideration as many independent factors as possible, and simple models that operate with fewer parameters, based mainly on critical factors. The latter models are by far more robust and easier to apply to predict the extent and sometimes even the rate of absorption. The present paper attempted to develop a simple model to describe the time course of absorption of the hydrophilic drug captopril (CPT) at the early phases of absorption, with implications mainly in the induction and early stages of achieving its therapeutic effect. As a phenomenological model, the instantaneous release of CPT was considered in the gastrointestinal fluid, leading to a constant drug concentration for a prolonged time, followed by a ‘long path diffusion’ inside the intestinal wall and a very low concentration at the interface intestinal wall-blood. These conditions regarding CPT concentration were translated into initial and boundary mathematical conditions for the diffusion equation in the intestinal wall. The solution of the diffusion equation led in the end to a square root law describing the dependence between the fraction of the drug absorbed and time. The model was successfully applied to data obtained in five bioequivalence studies: three comparing plasma levels achieved after the administration of a single dose of CPT 50 mg, one evaluating CPT pharmacokinetics after a 100 mg dose, and a fifth comparing CPT pharmacokinetics of two fixed-dose combinations of CPT 50 mg and hydrochlorothiazide 25 mg.

Spectroscopic Determination of Dissociation Constants of Some 4- nitrobenzaldehyde-4-substituted phenyl-1-carbonylhydrazones in Sodium Hydroxide Media

Purpose:

Hydrazones are a class of azomethines with a wide spectrum of pharmacological properties which are influenced by pH of the media. The purpose of this study was investigation of acid-base properties of five 4-nitrobenzaldehyde-4-substitutedphenyl1-carbonylhydrazones in sodium hydroxide media (14>pH>7).

Method:

The dissociation process was followed by UV-Vis spectroscopy, in the ethanol-water (V/V, 1:1) solutions, at room temperature. Semiempirical methods AM1 and PM3 were applied for determination of the deprotonation enthalpies.

Results:

The changes in the UV-Vis spectra, as well as the deprotonation enthalpies, suggested that the dissociation process for four investigated hydrazones with an amide group took place in one step. The exception with two dissociation steps was hydrazone with amide and hydroxyl group. The pH region of dissociation was from pH 10.8 to pH 11.6 for the first step and between pH 11.7 and pH 12.1 for the second step of dissociation. The influence of the ethanol on the UV-Vis spectra was eliminated by the method of Characteristic Vector Analyses (CVA). The stoichiometric dissociation constants were determined numerically (pKHA = n·pH + logI) and graphically (intercept of the dependence of logI on pH) from the absorbance data using experimental and reconstructed UV-Vis spectra, at three different ionic strengths. Thermodynamic dissociation constants were estimated graphically as an intercept of dependence of dissociation constant on the square root of the ionic strength.

Conclusion:

The obtained results demonstrated that the influence of the substituents on pKHA values was not significant, except for hydrazone with amide and hydroxyl group. Namely, the dissociation of the amide group of this hydrazone was retarded due to the influence of the phenolic group.

Structured edible lipid-based particle systems for oral drug-delivery

Oral administration is the most popular and patient-compliant route for drug delivery, though it raises great challenges due to the involvement of the gastro-intestine (GI) system and the drug bioavailability. Drug bioavailability is directly related to its ability to dissolve, transport and/or absorb through the physiological environment. A great number of drugs are characterized with low water solubility due to their hydrophobic nature, thus limiting their oral bioavailability and clinical use. Therefore, new strategies aiming to provide a protective shell through the GI system and improve drug solubility and permeability in the intestine were developed to overcome this limitation. Lipid-based systems have been proposed as good candidates for such a task owing to their hydrophobic nature which allows high drug loading, drug micellization ability during intestinal digestion due to the lipid content, and the vehicle physical protective environment. The use of edible lipids with high biocompatibility paves the bench-to-bedside translation. Four main types of structured lipid-based drug delivery systems differing in the physical state of the lipid phase have been described in the literature, namely emulsions, solid lipid nanoparticles, nanostructured lipid carriers, and oleogel-based particles. The current review provides a comprehensive overview of the different structured edible lipid-based oral delivery systems investigated up to date and emphasizes the contribution of each system component to the delivery performance, and the oral delivery path of lipids.

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pK_a for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pK_a challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pK_a challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pK_a values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pK_a prediction methods.