ADMET Predictor™

Physicochemical & Biopharmaceutical (PCB)

What is the PCB Module?

ADMET Predictor’s PCB Module contains models for physicochemical property prediction. Each model was built by Simulations Plus scientists using artificial neural network ensemble (ANNE) technology.  The data sets were highly curated in order to generate highly accurate models.  The data for our pKa model (S+pKa) was recently expanded in a collaboration with Bayer scientists. This resulted in more accurate predictions for a set of 16,000 compounds that were not used to train the model.  Our logP (S+logP) and aqueous solubility (S+Sw) models have been ranked number one in peer reviewed journal articles.^1,2 The models in the PCB module are also available in the ADMET Predictor module of GastroPlus™ in order to perform PBPK simulations using in silico predicted properties.

Physicochemical property prediction

The image below lists the models in ADMET Predictor’s PCB Module.

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.