Estimating the therapeutic potential of NSAIDs and linoleic acid-isomers supplementation against neuroinflammation

Nonsteroidal anti-inflammatory drugs (NSAIDs) regulate inflammation, which is associated with their role in preventing neurodegenerative diseases in epidemiological studies. It has sparked interest in their unconventional application for reducing neuroinflammation, opening up new avenues in biomedical research. However, given the pharmacological drawbacks of NSAIDs, the development of formulations with naturally antioxidant/anti-inflammatory dietary fatty acids has been demonstrated to be advantageous for the clinical translation of anti-inflammatory-based therapies. It includes improved blood-brain barrier (BBB) permeability and reduced toxicity. It permits us to speculate about the value of linoleic acid (LA)-isomers in preventing and treating neuroinflammatory diseases compared to NSAIDs. Our research delved into the impact of various factors, such as administration route, dosage, timing of intervention, and BBB permeability, on the efficacy of NSAIDs and LA-isomers in preclinical and clinical settings. We conducted a systematic comparison between NSAIDs and LA-isomers regarding their therapeutic effectiveness, BBB compatibility, and side effects. Additionally, we explored their underlying mechanisms in addressing neuroinflammation. Through our analysis, we've identified challenges and drawn conclusions that could propel advancements in treating neurodegenerative diseases and inform the development of future alternative therapeutic strategies.

Cytotoxic and Radiosensitising Effects of a Novel Thioredoxin Reductase Inhibitor in Brain Cancers

The thioredoxin (Trx) system, a key antioxidant pathway, represents an attractive target for cancer therapy. This study investigated the chemotherapeutic and radiosensitising effects of a novel Trx reductase (TrxR) inhibitor, IQ10, on brain cancer cells and the underlying mechanisms of action. Five brain cancer cell lines and a normal cell type were used. TrxR activity and expression were assessed by insulin reduction assay and Western blotting, respectively. IQ10 cytotoxicity was evaluated using growth curve, resazurin reduction and clonogenic assays. Radiosensitivity was examined using clonogenic assay. Reactive oxygen species levels were examined by flow cytometry and DNA damage assessed by immunofluorescence. Epithelial-mesenchymal transition (EMT)-related gene expression was examined by RT-PCR array. IQ10 significantly inhibited TrxR activity but did not affect Trx system protein expression in brain cancer cells. The drug exhibited potent anti-proliferative and cytotoxic effects against brain cancer cells under both normoxic and hypoxic conditions in both 2D and 3D systems, with IC₅₀s in the low micromolar range. It was up to ~ 1000-fold more potent than temozolomide. IQ10 substantially sensitised various brain cancer cells to radiation, with such effect being due, in part, to functional inhibition of TrxR, making cells less able to deal with oxidative stress and leading to increased oxidative DNA damage. IQ10 significantly downregulated EMT-associated gene expression suggesting potential anti-invasive and antimetastatic properties. This study suggests that IQ10 is a potent anticancer agent and could be used as either a single agent or combined with radiation, to treat brain cancers.

A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors

The highly-selective blood-brain barrier (BBB) prevents neurotoxic substances in blood from crossing into the extracellular fluid of the central nervous system (CNS). As such, the BBB has a close relationship with CNS disease development and treatment, so predicting whether a substance crosses the BBB is a key task in lead discovery for CNS drugs. Machine learning (ML) is a promising strategy for predicting the BBB permeability, but existing studies have been limited by small datasets with limited chemical diversity. To mitigate this issue, we present a large benchmark dataset, B3DB, complied from 50 published resources and categorized based on experimental uncertainty. A subset of the molecules in B3DB has numerical log BB values (1058 compounds), while the whole dataset has categorical (BBB+ or BBB-) BBB permeability labels (7807). The dataset is freely available at https://github.com/theochem/B3DB and https://doi.org/10.6084/m9.figshare.15634230.v3 (version 3). We also provide some physicochemical properties of the molecules. By analyzing these properties, we can demonstrate some physiochemical similarities and differences between BBB+ and BBB- compounds.

RP-18 TLC Chromatographic and Computational Study of Skin Permeability of Steroids

The skin permeability of steroids, as investigated in this study, is important because some of these compounds are, or could, be used in preparations applied topically. Several models of skin permeability, involving thin layer chromatographic and calculated descriptors, were generated and validated using K_p reference values obtained in silico and then tested on a group of solutes whose experimental K_p values could be found (log K_p^exp). The study established that the most applicable log K_p model is based on RP-18 thin layer chromatographic data (R_M) and the calculated descriptors V_M (molar volume) and PSA (polar surface area). Two less efficient, yet simple, equations based on PSA or V_M combined with HD (H-donor count) can be used with caution for rapid, rough estimations of compounds’ skin permeability prior to their chemical synthesis.

Comparison of statistical methods for predicting penetration capacity of drugs into human breast milk using physicochemical, pharmacokinetic and chromatographic descriptors

In silico methods are often used for predicting pharmacokinetic properties of drugs due to their simplicity and cost-effectiveness. This study evaluates the penetration of 83 active pharmaceutical ingredients into human breast milk with an experimental milk-to-plasma ratio (M/P) obtained from the literature. Multiple linear regression (MLR), partial least squares (PLS) and random forest (RF) regression methods were compared to uncover the relationship between physicochemical, pharmacokinetic and membrane crossing properties of active pharmaceutical ingredients (APIs) using their rapid reference measurement value (R_f values), thin-layer chromatography (TLC) data from albumin-impregnated plates. Molecular descriptors of APIs proven to be important for their crossing into breast milk, including protein binding, ionisation state and lipophilicity and TLC data, have been included in the development of the prediction models. The best regression results were achieved by MLR (r ² = 0.83 and r ² = 0.86, n = 28) and RF (r ² = 0.85, n = 58). In addition, the discriminant function analysis (DFA) was performed on acidic, basic and neutral drugs separately and showed a prediction accuracy of 93%, with M/P included as the discriminating variable.