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ACD/Percepta. Overview of the modules. PhysChem. Boiling Point/Vapor Pressure. Sigma. Features.
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ACD/Percepta Overview of the modules
Features LogP, pKa, and Solubility modules feature 2 different calculation algorithms: Classic and GALAS. For LogP, a consensus model based on these algorithms is also available. The models are TRAINABLE! All models provide quantitative estimates of prediction accuracy by the means of 95% confidence intervals (Classic), or Reliability Index (GALAS). pH-dependent predictions (LogD and LogS) are fully configurable, and the algorithms for calculation of individual components (LogP, pKa, intrinsic solubility), as well as their training options can be selected separately Water Solubility module provides two additional GALAS predictors: Solubility in pure water (LogSw) and Qualitative solubility Additionally, Classic models display full calculation protocol, and GALAS models – experimental values for 5 most similar structures from built-in DB
Module Features Calculates a set Abraham solvation properties of a solute: A, B, Bo, E, S, L, and V. Visualizes the contributions of each atom in the structure to the currently selected Abraham parameter in terms of color intensity of highlighted atoms. Lists up to 5 most similar structures from the Absolv database along with their experimental values of Abraham parameters and literature references Absolv Equations module enables the user to calculate various solvent-solvent or gas-solvent partition coefficients from more than 100 predefined Abraham type equations. Enter and use any custom equations using predicted Abraham solvation parameters of a solute as inputs
Module Features Predicts probability solubility of the compound in DMSO would exceed 20 mM threshold. TRAINABLE! RI (Reliability Index) values ranging from 0 to 1 provide an insight on the prediction accuracy Predictions are based on a data set of more than 20,000 compounds Displays structures of five mosts similar compounds from the SPECS library along with their experimental classification
Module Features Predicts several quantitative characteristics of the compounds’ transport efficiency across BBB: penetration rate (LogPS), Penetration extent (LogBB), Brain/plasma equilibration rate Classifies the compound as CNS permeable on non-permeable on the basis of above properties Visualizes the predictions using the 'traffic lights' scheme showing which parameters preclude brain delivery of the analyzed compound Allows altering the values of main physicochemical determinants to get an insight on structural changes needed to achieve desired permeation levels Displays a scatter plot showing the position of the analyzed compound relative to a set of well-known CNS and peripheral drugs. Displays carrier-mediated transport alerts for compounds that undergo facilitated diffusion or active efflux across BBB
Module Features • Calculates the following drug distribution-related properties: • %PPB – the cumulative percentage of the compound bound to human plasma proteins (albumin, alpha1-acid glycoprotein and others) TRAINABLE! • log KaHSA – human serum albumin affinity constants TRAINABLE! • Vd– the apparent Volume of Distribution Protein binding predictions are supplemented by Reliability Indices (RI) values ranging from 0 to 1, that provide an insight on the prediction accuracy Displays up to 5 similar structures from the respective training set along with the experimental %PPB, log KaHSA, or Vd values and corresponding literature references Displays textual comments highlinting, which plasma proteins contribute to binding, and physicochemical properties that influence tissue distribution
Module Features Predicts bioavailability (%F) after oral administration with the possibility to explore dose-dependence • Predicts a number of endpoints that affect oral bioavailability and visualizes their contributions with ‘traffic lights’: • Solubility (Dose/solubility ratio) • Stability in acidic media • Intestinal membrane permeability by passive or active transport • Likelihood of P-gp efflux • First pass metabolism in the liver Displays experimental %F values for up to 5 similar structures from Bioavailability DB, with literature references Dedicated Active Transport module provides additional information about compounds that cross intestinal barrier by carrier-mediated mechanisms (PepT1, ASBT, MCT1, amino acid transporters, etc.)
Module Features • Provides probabilistic predictive models that estimate: • Is the compound a P-gp substrate, and if so – is it a high affinity substrate? TRAINABLE! • Is the compound a P-gp inhibitor, and if so – is it a potent inhibitor? TRAINABLE! Predictions are supplemented by Reliability Indices (RI) values ranging from 0 to 1, that provide a quantitative insight on the prediction accuracy Additional knowledge-based models classify compounds as P-gp substrates/non-substrates, or P-gp inhibitors/non-inhibitors on the basis of relevant structural features and basic physicochemical parameters Displays experimental data for 5 most similar structures from P-gp DB, with literature references
Module Features • Calculates passive permeability across intestinal epithelium from the compounds’ physicochemical properties (such as LogP and pKa). Predicts: • %HIA – the extent of human intestinal absorption by oral route • Pe – permeability coefficient on jejunal and Caco-2 scales Estimates the relative contributions of transcellular and paracellular routes to overall permeability of the compound Allows altering the values of main physicochemical determinants to get an insight on structural changes needed to achieve desired absorption levels Allows simulating the influence of experimental conditions (pH and stirring) on Caco-2 permeability Displays the experimental values of the relevant properties for up to 3 similar structures from Absorption DB along with each prediction.
Module Features • Simulates the following dependencies: • %F–LogP • Cp(Max)–LogP • %F–Dose • Cp(Max)–Dose • Cp–Time Automatically estimates required physicochemical/pharmacokinetic input parameters, and also allows entering them manually to improve prediction accuracy Calculates maximum achievable plasma level and the corresponding time (Cp,max and Tmax), area under the concentration time curve (AUC) after oral and intravenous administration, as well as oral bioavailability (%F)
