The usefulness of Caco-2 cell monolayers in determining the intestinal drug absorption of potential drug candidates as such and from delivery systems, elucidating the underlying mechanisms thereof, presystemic metabolism, cellular uptake and cytotoxicological assessment has been exemplified in this review. The role of Caco-2 cell monolayers in studying the effectiveness, involved mechanism and toxicity of various excipients for drug absorption promotion has also been discussed.

Aim Our aim is to investigate the molecular mechanism of regulation of gene expression of drug metabolizing enzymes (DMEs) and transporters in diet-induced obesity. Main methods Adult male CD1 mice were fed diet containing 60% kcal fat (HFD) or 10% kcal fat (LFD) for 14 weeks. RNA levels of hepatic DMEs, transporters and their regulatory nuclear receptors (NRs) were analyzed by real-time PCR. Activation of cell-signaling components (JNK and NF-κB) and pro-inflammatory cytokines (IL-1β, IL-6 and TNFα) were measured in the liver. Finally, the pharmacodynamics of drugs metabolized by DMEs was measured to determine the clinical relevance of our findings. Key findings RNA levels of the hepatic phase I (Cyp3a11, Cyp2b10, Cyp2a4) and phase II (Ugt1a1, Sult1a1, Sultn) enzymes were reduced ~30-60% in HFD compared to LFD mice. RNA levels of Cyp2e1, Cyp1a2 and the drug transporters, multidrug resistance proteins, (Mrp)2, Mrp3 and multidrug resistant gene (Mdr)1b were unaltered in HFD mice. Gene expression of the NRs, PXR and CAR and nuclear protein levels of RXRα was reduced in HFD mice. Cytokines, JNK and NF-κB were induced in HFD mice. Thus reduction in hepatic gene expression in obesity may be modulated by cross-talk between NRs and inflammation-induced cell-signaling. Sleep time of Midazolam (Cyp3a substrate) was prolonged in HFD mice, while Zoxazolamine (Cyp1a2 and Cyp2e1 substrate)-induced sleep time was unaltered. Significance This study demonstrates that gene-specific reductions in DMEs can affect specific drugs metabolized by these enzymes, thus providing a rationale to monitor the effectiveness of drug therapy in obese individuals.

The aim of the investigation was to prepare and characterize microemulsion/mucoadhesive microemulsion of tacrine (TME/TMME), assess its pharmacokinetic and pharmacodynamic performances for brain targeting and for improvement in memory in scopolamine-induced amnesic mice. The TME was prepared by the titration method and characterized. Biodistribution of tacrine solution and formulations after intravenous and intranasal administrations were evaluated using 99m Tc as marker. From the data, the pharmacokinetic parameters, drug targeting efficiency, and direct nose-to-brain drug transport were calculated. To confirm drug localization in brain gamma scintigraphy in rabbits was performed. Lower Tmax values (60 min) after intranasal compared with intravenous administration (120 min) suggested selective nose-to-brain transport. The brain bioavailability of tacrine after intranasal TMME compared with intranasal tacrine solution was found to be 2-fold higher indicating larger extent of distribution of the drug to brain with intranasal TMME. Rabbit brain scintigraphy also showed higher uptake of drug into the brain after intranasal administration. The results demonstrated rapid and larger extent of transport of tacrine into the mice brain and fastest regain of memory loss in scopolamine-induced amnesic mice after intranasal TMME. Hence, results are suggestive of possible role of intranasal tacrine delivery in treating Alzheimer's patients.

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in-silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5,435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4,071 compounds with quantitative data is able to predict the remaining 1,364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in-silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery.