Affinity of Lipophilic Drugs to Mixed Lipid Aggregates in Simulated Gastrointestinal Fluids

To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.

Drug discovery projects in the pharmaceutical industry accumulate thousands of chemical structures and ten-thousands of data points from a dozen or more biological and pharmacological assays. A sufficient interpretation of the data requires understanding, which molecular families are present, which structural motifs correlate with measured properties, and which tiny structural changes cause large property changes. Data visualization and analysis software with sufficient chemical intelligence to support chemists in this task is rare. In an attempt to contribute to filling the gap, we released our in-house developed chemistry aware data analysis program DataWarrior for free public use. This paper gives an overview of DataWarrior's functionality and architecture. Exemplarily, a new unsupervised, 2-dimensional scaling algorithm is presented, which employs vector-based or nonvector-based descriptors to visualize the chemical or pharmacophore space of even large data sets. DataWarrior uses this method to interactively explore chemical space, activity landscapes, and activity cliffs.

This study was conducted to compare the luminal composition of the upper gastrointestinal tract in the fasted and fed states in humans, with a view toward designing in vitro studies to explain/predict food effects on dosage form performance. Twenty healthy human subjects received 250 mL water or 500 mL Ensure plus (a complete nutrient drink) through a nasogastric tube and samples were aspirated from the gastric antrum or duodenum for a period up to 3.5 h, depending on location/fluid combination. Samples were analyzed for polyethylene glycol, pH, buffer capacity, osmolality, surface tension, pepsin, total carbohydrates, total protein content, and bile salts. Following Ensure plus administration, gastric pH was elevated, buffer capacity ranged from 14 to 28 mmoL L-1 DeltapH-1 (vs. 7-18 mmol L-1 DeltapH-1), contents were hyperosmolar, gastric pepsin levels doubled, and surface tension was 30% lower than after administration of water. Post- and preprandial duodenal pH values were initially similar, but slowly decreased to 5.2 postprandially, whereas buffer capacity increased from 5.6 mmol L-1 DeltapH-1 (fasted) to 18-30 mmol L-1 DeltapH-1 (p 30%, bile salt levels were two to four times higher, luminal contents were hyperosmotic, and the presence of peptides and sugars was confirmed. This work shows that, in addition to already well characterized parameters (e.g., pH, and bile salt levels), significant differences in buffer capacity, surface tension, osmolality, and food components are observed pre-/postprandially. These differences should be reflected in test media to predict food effects on intralumenal performance of dosage forms.