Predict Pharmacokinetic Properties

• Gain insights into structure-property relationships
• Understand and modify the pharmacokinetic profile of lead compounds
  • Good/bad indicators for Lipinski’s rule-of-5, lead-likeness, cell permeability, plasma protein binding, CNS penetration, metabolic stability, and CYP inhibition
• Modify sets of structures with the interactive optimization tool
  • Generate libraries of analogs with substituent modifications based on an optimal property profile
  • Sort, filter, and prioritize hundreds of structural analogs according to your desired property profile
  • Create and use custom fragment libraries
  • Target synthetically accessible fragments with the built-in retrosynthesis tool

• Quantitatively predict blood-brain barrier (BBB) permeability.
  • Rate of brain penetration (logPS)
  • Extent of brain penetration (logBB)
  • Brain/plasma equilibration rate (log(PS * f_{u, brain}))
• Alter values for underlying physicochemical properties—logP, pK_a, unbound fraction in plasma—to investigate the effect on BBB permeability
• Visualize results
  • “Traffic light” indicators of permeability
  • Plot of the BBB penetration of compound under investigation with well-known CNS-active and peripherally-active drugs
• Alerts for compounds that undergo facilitated diffusion or active efflux across the BBB
• Submodules for LogPS and LogBB provide detailed prediction results
  • Up to 3 most similar structures in the internal library provided, with experimental values and literature references

• Calculate the probability of a compound exhibiting “general” (IC₅₀ < 50 μM) or “effective” (IC₅₀ < 10 μM) inhibition of 5 major human cytochrome P450 isoforms: 3A4, 2D6, 2C9, 2C19, 1A2
  • Results displayed as a bar plot with confidence intervals
• Estimates of prediction accuracy for each result
  • Reliability index and display of 5 most similar structures in the internal library with experimental values and literature references
• Train the algorithm with experimental data

• Calculate the probability that a compound will be metabolized by the cytochrome P450 (isoforms 3A4, 2D6, 2C9, 2C19, 1A2)
  • Results displayed as a bar plot with confidence intervals
• Estimates of prediction accuracy for each result
  • Reliability index and display of 5 most similar structures in the internal library with experimental values and literature references
• Train the algorithm with experimental data

Plasma Protein Binding

• Predict plasma-protein binding (PPB)
  • Percentage bound to human plasma proteins (% PPB)
  • Affinity constant to serum albumin (logK_a^HSA)
• List of plasma proteins contributing to binding
• Estimate of prediction accuracy
  • Reliability index and display of 5 most similar structures in the internal library with experimental values and literature references
• Train with experimental data

Volume of Distribution

• Calculate the extent of tissue binding and Volume of Distribution (V_d) based on a physiological Øie-Tozer model
• Alter values for key underlying properties—logP, and unbound fraction in plasma—to investigate the effect on distribution into the tissues
• Estimate of prediction accuracy
  • Display of 5 most similar structures in the internal library with experimental values and literature references

• Calculate the maximum recommended daily dose in humans (mg/kg/day)—for oral administration
• Estimate of prediction accuracy
  • Reliability index and display of 5 most similar drugs in the internal library with experimental values and literature references
• Indication of adverse effects on organs
• Indication of toxicity in mouse (for oral and intravenous administration)
• Alert for unreliable predictions

Active Transport

• Predict whether or not a compound is a likely substrate for oligopeptide transporter (PepT1), or bile acid transporter (ASBT)
• Predict carrier-mediated transport by other systems: monocarboxylic acid transporter MCT1, amino acid carriers, etc.
• Reference data for similar structures

Bioavailability

• Predict the percentage of drug that will reach systemic circulation after oral administration (%F)
• Explore dose-dependence
• See the contribution of endpoints that affect oral bioavailability: solubility, stability (pH <2), passive absorption, first-pass metabolism, P-gp efflux, active transport
  • Results displayed with color-coding to indicate good or poor bioavailability
• Estimate of prediction accuracy
  • Display of up to 5 similar structures in the internal library with experimental values and literature references

Absorption

• Predict passive permeability across jejunal epithelium based on logP and pK_a
  • Enter experimentally measured values to improve prediction
  • Alter logP and pK_a values to model the limiting effect of lipophilicity and ionization on intestinal permeation rate
• Calculate the extent of oral absorption (%HIA)
• Estimate relative contributions of transcellular and paracellular routes to overall %HIA
• Provides access to Absorption DB—a fully browsable and searchable database containing experimental data that was used for the development of the HIA model together with corresponding literature references
• Experimental values of the relevant properties for up to 3 similar structures from the internal set

Caco-2

• Calculate passive permeability across Caco-2 cell monolayers at specified pH and stirring conditions based on logP and pK_a values, or logD at specific pH
  • Adjust pH and stir rate
  • Alter logD, or logP and pK_a values to model the limiting effect of lipophilicity and ionization on Caco-2 permeation rate
• Information about the relative contributions of transcellular and paracellular routes to overall Caco-2 permeability
• Estimate of prediction accuracy
  • Display of up to 3 similar structures in the internal library with experimental information and literature references

P-gp Inhibitors

• Estimate the probability that a compound inhibits P-glycoprotein and that it is a potent inhibitor
• Classify a compound as an inhibitor or non-inhibitor of P-glycoprotein based on structural features and physicochemical properties
• Estimate of prediction accuracy
  • Reliability of prediction and display of 5 most similar structures in the internal library with experimental values and literature references
• Train with experimental data

P-gp Substrates

• Estimate the probability that a compound is a P-glycoprotein substrate and that it is a high-affinity substrate
• Classify a compound as a substrate or non-substrate of P-glycoprotein based on ionization, molecular size and compound class (peptide, alkaloid, anthracycline, etc.)
• Estimate of prediction accuracy
  • Reliability of prediction and display of 5 most similar structures in the internal library with experimental values and literature references
• Train with experimental data

Explore the dependency of various pharmacokinetic parameters on physicochemical properties

• Visualize the dependence of the following parameters on dose, logP, and pK_a (results provided as graphical plots. Use predicted values or enter experimentally derived values for logP and pK_a):
  • %F–LogP (the dependence of oral bioavailability on logP at a defined dose)
  • Cp(Max)–LogP (the dependence of maximum achievable drug concentration on logP at a defined dose)
  • %F–Dose (bioavailability–dose relationship)
  • Cp(Max)–Dose (maximum achievable drug plasma levels at different doses)
  • Cp–Time (simulation of plasma concentration-time curves for oral and intravenous administration)
• Estimate absorption (k_a), total body clearance (k_e), solubility in the gastrointestinal tract (Sol_GI), volume of distribution (V_d), and presystemic metabolism in the gut and liver (first-pass clearance) based on entered physicochemical property values
  • Alter any of these values to recalculate
  • Choose to ignore first-pass clearance
• Calculate maximum achievable plasma level (Cp(Max)) and the corresponding time (Tmax), area under the concentration-time curve (AUC) after oral and intravenous administration, and oral bioavailability (%F)

• Predict metabolic soft spots for metabolism by human liver microsomes (HLM) and the five major cytochrome P450 enzymes (1A2, 2C19, 2C9, 2D6, 3A4) to help:
  • Identify metabolic sites on new chemical entities
  • Guide synthesis of compounds with improved metabolic properties
  • Identify and elucidate likely metabolite structures
• Color mapping on structure indicates the probability of metabolism at each atom
• Probability score (0–1) for each atom as a likely site of metabolism
• View the expected metabolic reaction type at every atom: aliphatic or aromatic hydroxylation, N-dealkylation, O-dealkylation, or S-oxidation
• Estimate of prediction accuracy
  • Reliability of prediction and display of 5 most similar structures in the internal library. Indication of similarity of the metabolic site and experimental result (Metabolized, or Not Metabolized)