Simulations Plus

NEW! The supersaturation (SupSatn), blood brain barrier penetration classification model (BBB_Filter), Pgp substrate (Pgp_Substr) and inhibition (Pgp_Inhib), and OATP1B1 inhibition (OATP1B1_Inh) models were rebuilt to improve their estimates of prediction confidence

Several new models based on data from the Extended Clearance Classification System journal article by Varma et al.³ were created for ADMET Predictor version 9.0

• NEW! A model to prediction high or low permeability in a low efflux MDCK assay (S+MDCK-LE)
• NEW! The Extended Clearance Classification System (ECCS) has been implemented
• NEW! S+CL_Mech model predicts if a compound’s major clearance pathway is renal, metabolic, or hepatic uptake
• NEW! S+CL_Renal, S+CL_Metab, S+CL_Uptake are binary classification models that predict if a compounds major clearance pathway is renal, metabolic, or hepatic uptake

NEW! The human plasma protein binding (hum_fup%) model was rebuilt with additional data

NEW! Plots of solubility and logD vs. pH

NEW! BCS⁴/DCS⁵ explorer window

Brief descriptions of the models are below.

¹ J. Pharm. Sci. 2008, 98, 861.

² a) Expert Opin. Drug Discov. 2006, 1, 31-52. b) Science of the Total Environment, 2013, 463-464, 781-789.

³ Varma MV, Steyn SJ, Allerton C, El-Kattan AF. “Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS).” Pharm Res 2015; 32:3785.

⁴ Amidon, GL, Lennernas H, Shah VP, Crison JR”A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability.” Pharm. Res. 1995; 12:413-20.

⁵ Butler JM and Dressman JB.  “The developability classification system: application of biopharmaceutics concepts to formulation development.” J. Pharm. Sci. 2010; 99(12):4940-54.

• Multiprotic pKa model (S+Acidic_pKa, S+Mixed_pKa, S+Basic_pKa) – a thermodynamically accurate multiprotic model for multiple ionization sites based on atomic descriptors and neural networks – not a database lookup!
• logP (S+logP, MlogP) – log of the octanol to water partition coefficient.  There are two models, an artificial neural network ensemble (S+logP) and Moriguchi (MlogP)
• logD (S+logD) – estimation of octanol-water distribution coefficient at user-defined pH
• Air-water partition (logHLC) – estimation of air-water partition coefficient (Henry’s Law constant) from US EPA data
• Inhibition of the hepatic OATP1B1 transporter in human (OATP1B1_Inh)
• Inhibition of the intestinal P-gp transporter in human (Pgp_Inh)
• Likelihood of intestinal efflux by P-gp transporter in human (Pgp_Substr)

• Human effective permeability (S+Peff) – jejunal Peff
• MDCK apparent permeability (S+MDCK) – in vitro Papp
• Corneal permeability (Perm_Cornea) – ocular permeability through rabbit cornea based on literature data obtained in vitro
• Skin permeability (Perm_Skin) – permeability through human skin of compounds dissolved in aqueous solution; based on literature data
• Blood-brain barrier permeation – there are two models, classification (BBB_Filter) and regression (LogBB).  The first classification model has a low cutoff such that compounds that are predicted to have low permeability have very little chance of penetrating the blood-brain barrier.  Compounds that are predicted to have high blood-brain barrier penetration should be evaluated with the regression model that predicts the blood to brain concentration ratio.

• Human and rat plasma protein binding as percent unbound (hum_fup% and rat_fup%)
• Human volume of distribution (Vd)
• Human and rat blood-to-plasma concentration ratio (RBP and RBP_rat)
• Fraction unbound in human liver microsomes (S+fumic)