ACD/ADME Suite

Learn more about the following modules and their capabilities:

P-gp Specificity
  • Knowledge-based models classify compounds as P-gp substrates or non-substrates, as well as P-gp inhibitors, or non-inhibitors.
  • Classification is made according to a set of rules based on the compound’s physicochemical properties (ionization, molecular size), biological class, etc.
  • The statistical models, employing a fragment-based approach, calculate probability for a compound to be a P-gp substrate or inhibitor
  • A reference database of >2300 compounds is provided with P-gp specificity data and literature references
Oral Bioavailability
  • Compounds are evaluated accounting for the influence of solubility, acid stability, passive absorption, first pass metabolism, P-gp efflux, and active transport.
  • Results are classified in a simple traffic-light pattern for quick visualization (red=problematic; amber=suspect; green=acceptable).
  • A searchable reference database is provided with oral bioavailability data for >700 compounds
Absolv
  • Absolv calculates solvation associated properties from Abraham-type equations and predicts calculation parameters necessary for those calculations:
    • H-bonding acidity and basicity (A, B, and BO)
    • Partitioning coefficient between gaseous phase and hexadecane (L)
    • Polarity/polarizability (S)
    • Excessive molar refraction (E)
    • McGowan volume (V)
  • Individual atom contributions are color mapped onto molecules for visual display
  • Various solvent-solvent or gas-solvent partition coefficients can be calculated from predefined Abraham-type equations, or custom equations using Abraham solvation parameters
  • An extensive reference database of >5000 compounds is provided, with referenced literature values of Abraham solvation parameters
Passive Absorption
  • Predicts the limiting effects of lipophilicity and ionization constant on the following absorption related properties:
    • Maximum achievable human intestinal absorption (HIA)
    • Jejunum permeability
    • Caco-2 permeability
    • Absorption rate
  • Estimates relative contributions of different transport routes for %HIA and Caco-2 permeability predictions
  • User-defined data can be added to improve prediction accuracy for proprietary chemical space; e.g., experimental logP, pKa data can be used to improve prediction of intestinal permeation rates; experimental conditions (pH, stirring rate) can be defined in the case of Caco-2 permeability prediction
  • A fully searchable Absorption database is provided with experimental data and literature references
Blood Brain Barrier Penetration
  • Provides reliable and easily interpreted predictions of passive BBB permeation rate (expressed as LogPS constants)
  • Calculates steady-state distribution ratio between brain tissue and plasma (LogBB) and unbound (i.e., pharmacologically active) fraction of drug in brain tissue
  • Provides a unified interface for evaluation of various parameters related to drug transfer across BBB and gives a qualitative estimate whether brain uptake of the analyzed compound is sufficient for CNS activity
  • User-defined data (e.g., experimental logP, pKa data) can be entered to improve prediction accuracy for proprietary chemical space
  • Values of the main physicochemical determinants (logP, pKa, hydrogen bonding, molecular size) may also be altered to simulate the influence of these properties on BBB transport potential
  • Special alerts are provided for compounds likely to undergo BBB transport by mechanisms other than passive diffusion
Distribution
  • Predictions are provided for the following:
    • Plasma protein bound fraction - trainable
    • Equilibrium binding constant to human serum albumin (logK^(HSA/a)) - trainable
    • Apparent volume of distribution for a compound dissolved in blood plasma (Vd)
    • Predictions of the extent of plasma protein binding and logK^(HSA/a) constants are supported by reliability estimation (calculated Reliability Index values)
  • Binding properties are derived from automatic calculation of underlying physicochemical properties, e.g., lipophilicity, ionization, and fragmental descriptors. A brief general description of the compound’s likely binding behaviour in plasma is provided.
P450 Inhibitors
  • Calculates the probability of a compound being an inhibitor of the five major drug metabolizing enzymes—CYP3A4, CYP2D6, CYP2C9, CYP2C19, and CYP1A2 (general inhibition Ki < 50 μM; and specific inhibition < 10 μM)
  • Displays experimental values for similar compounds for each isoform in the database
  • Trainable module
P450 Substrates
  • Calculates the probability of a compound being a being a substrate of the five major drug metabolizing enzymes—CYP3A4, CYP2D6, CYP2C9, CYP2C19, and CYP1A2 (Ki ≤ 50μM)
  • Displays experimental values for similar compounds for each isoforms in the database
  • Trainable module
P450 Regioselectivity
  • Predicts sites susceptible to metabolism by human liver microsomes on a compound
  • Predicts sites likely to be metabolized by each of the 5 major isoforms of CYP450—CYP3A4, CYP2D6, CYP2C9, CYP2C19, and CYP1A2
  • Graphically highlights metabolic ‘hotspots’
  • Proposes associated biotransformation reactions
  • Displays experimental values for similar compounds in the database
PK Explorer
  • Explore the inter-dependence of oral bioavailability, drug plasma levels, and dose
  • Alter the values of key physicochemical determinants (logP, pKa) and derived parameters (absorption, elimination rates) to investigate their effect on pharmacokinetics of the drug. Optionally, the effect of first-pass metabolism may be also included in the simulation.
  • Simulate the drug-plasma concentration vs. time curve