logD pH Research Articles

Trivariate Linear Regression and Machine Learning Prediction of Possible Roles of Efflux Transporters in Estimated Intestinal Permeability Values of 301 Disparate Chemicals.

A system for predicting apparent bidirectional permeability (Papp) across Caco-2 cells of diverse chemicals has been reported. The present study aimed to investigate the relationship between in silico-generated Papp (from apical to basal side, Papp A to B) for 301 substances with diverse structures and a binary classification of the reported roles of efflux P-glycoprotein or breast cancer resistant protein. The in silico log(Papp A to B/Papp B to A) values of 70 substances with reported active efflux and 231 substances with no reported active efflux were significantly different (p < 0.01). The probabilities of active efflux transport estimated by trivariate analysis with log MW, log DpH 6.0, and log DpH 7.4 for the 70 active-efflux-positive compounds were higher than those of the other 231 substances (p < 0.01); the area under the corresponding receiver operating characteristic (ROC) curve was 0.81. Further probability values estimated using a machine learning algorithm with 30 chemical descriptors as inputs yielded an area under the ROC curve of 0.79. Using a secondary set of 52 efflux-positive and 48 efflux-negative medicines, the final trivariate-generated probabilities resulted in no significant differences between these binary groups (p = 0.09); however, the final machine learning model demonstrated a good area under the ROC curve of 0.79. Consequently, a combination of the previously established system for generating the permeability coefficients across intestinal monolayers (a continuous variable) and the currently proposed system for predicting the roles of additional active efflux (a binary classification) could prove useful; high accuracy was achieved by applying machine learning using in silico-generated chemical descriptors.

Investigation of drug partition kinetics to fat in simulated fed state gastric conditions based on drug properties.

The presence of fat in the gastric environment can affect the pharmacokinetic behaviour of drugs with mechanisms which have not been yet fully understood. The objective of the current study was to assess the drug partition to the lipid part of the fed gastric content under different emulsification conditions, using in vitro discriminating setups. The model drugs used in the study were selected on the basis of different physicochemical properties (lipophilicity, ionization, molecular weight and aqueous solubility) and different food effect observed in in vivo human studies. Fed State Simulated Gastric Fluid prepared with skimmed milk (FeSSGFsk) and anhydrous milk fat were used as surrogates for the aqueous and fat portions of the fed gastric environment respectively. An optimized biphasic model was developed so as to predict the differences in partition rate constants to fat, for model drugs of a wide range of the properties mentioned above. The experimental data and the use of statistical analysis revealed that molecular weight, molecular weight and log D pH 5 interaction and negative food effect act as negative factors to the rate constants of fat partition, while absence of food effect and logD pH 5 interaction with aqueous solubility affect the rate constants of partition to fat favourably.

Reactivity of 9-aminoacridine drug quinacrine with glutathione limits its antiprion activity.

Quinacrine-the drug based on 9-aminoacridine-failed in clinical trials for prion diseases, whereas it was active in in vitro studies. We hypothesize that aromatic nucleophilic substitution at C9 could be contributing factor responsible for this failure because of the transfer of acridine moiety from quinacrine to abundant glutathione. Here, we described the semi-large-scale synthesis of the acridinylated glutathione and the consequences of its formation on biological and biophysical activities. The acridinylated glutathione is one order of magnitude weaker prion protein binder than the parent quinacrine. Moreover, according to log DpH 7.4 , the glutathione conjugate is two orders of magnitude more hydrophilic than quinacrine. Its higher hydrophilicity and higher dsDNA binding potency will significantly decrease its bioavailability in membrane-like environment. The glutathione deactivates quinacrine not only directly but also decreases its bioavailability. Furthermore, the conjugate can spontaneously decompose to practically insoluble acridone, which is precipitated out from the living systems.

Development of a Novel Oral Cavity Compartmental Absorption and Transit Model for Sublingual Administration: Illustration with Zolpidem.

Intraoral (IO) delivery is an alternative administration route to deliver a drug substance via the mouth that provides several advantages over conventional oral dosage forms. The purpose of this work was to develop and evaluate a novel, physiologically based oral cavity model for projection and mechanistic analysis of the clinical pharmacokinetics of intraoral formulations. The GastroPlus™ Oral Cavity Compartmental Absorption and Transit (OCCAT™) model was used to simulate the plasma concentration versus time profiles and the fraction and rate of intraoral drug transit/absorption for Intermezzo® sublingual tablets (zolpidem tartrate). The model was evaluated by the goodness-of-fit between simulated and observed concentrations and the deviation of key PK parameters (e.g., C max, T max, and AUC). In addition, a sensitivity analysis was conducted to demonstrate the interplay and impact of key modeling parameters on the fraction absorbed via oral mucosa (F a_IO). The OCCAT™ model captured the observed pharmacokinetics for Intermezzo® sublingual tablets (R (2) > 0.9). The predicted deviations (%) for C max, AUC0-inf, AUC0-20min, and T max were 5.7, 28.0, 11.8, and 28.6%, respectively, indicating good prediction accuracy. The model also estimated ~18% of total drug was absorbed via the IO route. Furthermore, the sensitivity analysis indicated that the F a_IO was not only associated with drug diffusivity and unbound fraction in epithelium tissue (f ut) but also depended on the physicochemical properties of compounds for IO delivery (e.g., solubility and logD pH = 7.4). The novel physiologically based IO absorption OCCAT™ model showed satisfactory performance and will be helpful to guide development of future intraoral formulations.

Attenuation of trace organic compounds (TOrCs) in bioelectrochemical systems

Microbial fuel cells (MFCs) and microbial electrolysis cells (MECs) are two types of microbial bioelectrochemical systems (BESs) that use microorganisms to convert chemical energy in wastewaters into useful energy products such as (bio)electricity (MFC) or hydrogen gas (MEC). These two systems were evaluated for their capacity to attenuate trace organic compounds (TOrCs), commonly found in municipal wastewater, under closed circuit (current generation) and open circuit (no current generation) conditions, using acetate as the carbon source. A biocide was used to evaluate attenuation in terms of biotransformation versus sorption. The difference in attenuation observed before and after addition of the biocide represented biotransformation, while attenuation after addition of a biocide primarily indicated sorption. Attenuation of TOrCs was similar in MFCs and MECs for eight different TOrCs, except for caffeine and trimethoprim where slightly higher attenuation was observed in MECs. Electric current generation did not enhance attenuation of the TOrCs except for caffeine, which showed slightly higher attenuation under closed circuit conditions in both MFCs and MECs. Substantial sorption of the TOrCs occurred to the biofilm-covered electrodes, but no consistent trend could be identified regarding the physico-chemical properties of the TOrCs tested and the extent of sorption. The octanol–water distribution coefficient at pH 7.4 (log DpH 7.4) appeared to be a reasonable predictor for sorption of some of the compounds (carbamazepine, atrazine, tris(2-chloroethyl) phosphate and diphenhydramine) but not for others (N,N-Diethyl-meta-toluamide). Atenolol also showed high levels of sorption despite being the most hydrophilic in the suite of compounds studied (log DpH 7.4 = −1.99). Though BESs do not show any inherent advantages over conventional wastewater treatment, with respect to TOrC removal, overall removals in BESs are similar to that reported for conventional wastewater systems, implying the possibility of using BESs for energy production in wastewater treatment without adversely impacting TOrC attenuations.

Impact of Physicochemical Profiling for Rational Approach on Drug Discovery

Solid-state characterization plays a vital role in lead optimization and candidate selection with the appropriate physicochemical properties for proper oral dosage formulation. Aqueous solubility is an important parameter in the successful development of oral dosage formulation since poor aqueous solubility limits absorption. In this study, we summarized an efficient approach using a small amount of sample for solid-state characterization, including thermodynamic solubility, which is defined as physicochemical profiling. By using the physicochemical profiling results of 75 anti-cancer drugs and clinical candidates, we examined the relationship between thermodynamic solubility and molecular structural parameters and assessed the effects of thermodynamic solubility on pharmacokinetic profile for rational soluble drug design. The Log DpH 7.4, aromatic ring count, and hydrogen bond count were good indicators for predicting sparingly soluble compounds that increase the lattice energy because of π-π stacking and hydrogen bonds, resulting in lowered thermodynamic solubility. The level of thermodynamic solubility in simulated intestinal fluid (pH 6.8) in the presence and absence of bile acid, which is required for minimal acceptable bioavailability (>30%), was 1 µg/mL and 10 µg/mL, respectively. Physicochemical profiling, which includes thermodynamic solubility considering its solid-state properties, contributes to rational lead optimization and efficient candidate selection for drug development.