Inhibition Models for Human Cytochrome P450 Enzymes 1A2, 2C9, 2D6, and 3A4

The inhibitory potency of drugs against cytochrome P450 is important for the study of drug toxicities and drug-drug interactions.  ADMET Predictor™ CYP P450 inhibition classification package includes four global inhibition models for CYP 1A2, 2C9, 2D6, and 3A4 isoforms, as well as two substrate-specific inhibition models for Human Liver Microsome (HLM) CYP 3A4 with midazolam as substrate and recombinant expressed CYP 3A4 with testosterone as substrate.

The inhibitor/non-inhibitor cutoff values were set at K_i = 30 M for CYP 1A2 and K_i = 10 M for CYP 2D6. For the two substrate-specific CYP 3A4 inhibition models, the threshold value K_i = 20 M was used for HLM 3A4 and K_i = 1 M was used for recombinant 3A4.  Exact threshold values for the remaining models remain unknown, since the training databases for these models contained only binary indicators.

Performance of Inhibition Models of CYP P450 1A2, 2C9, 2D6, and 3A4

In addition, ADMET Predictor features two regression models for predicting the substrate-specific inhibition constant, K_i, values in microM for HLM CYP 3A4 with midazolam and recombinant expressed 3A4 with testosterone as substrates, respectively.

ADMET Predictor MET_3A4_Ki_mid (left) and MET_3A4_Ki_tes (right) Models Validation

Kinetic Models for Metabolism by Human Cytochrome P450 Enzymes 1A2, 2C9, 2C19, 2D6, and 3A4

Michaelis-Menten constant (K_m), a measure of the affinity of the enzyme for its substrate, and maximum metabolic rate (V_max), are two important parameters of the activities of cytochrome P450 enzymes which constitute a superfamily of hemoproteins involved in the drug metabolism in human body.  The ratio V_max/K_m, know as intrinsic clearance CL_int, is a measure of metabolic rate at zero substrate limit.

Within its Enslein Metabolism Module, ADMET Predictor provides a human CYP450 enzyme kinetic models package including K_m, V_max, and CL_int models for five important CYP isozymes 1A2, 2C9, 2C19, 2D6, and 3A4. These models were developed using Artificial Neural Network Ensemble (ANNE) methodology and 2D molecular descriptors.  The predicted parameter values are expected to be used in human physiological pharmacokinetic/pharmacodynamic (PK/PD) models for purposes of risk assessment and to support decision-making in drug discovery.  It is crucial to understand that these values reflect only hydroxylation mechanism facilitated by each enzyme.

Experimental K_m and V_max data for hydroxylation were compiled from the literature by Enslein Research, Inc., with careful examination of the original articles. The dataset contained substrates for each enzyme with kinetic parameters measured from in-vitro metabolic studies on cloned virus-infected cells expressing human enzyme-specific microsomes.

For the five logK_m models, squared correlation coefficient, R², was in the range of 0.714~0.923 and root-mean-squared error (RMSE) was in the range of 0.346~0.488 log units. For the five logV_max models, R² was in the range of 0.562~0.747 and RMSE was in the range of 0.307~0.714 log units.  Each of these numbers relates to an external test set.

ADMET Predictor MET_2C19_Km (left) and MET_2C19_Vmax (right) Models Validation

Probability of Metabolism by Human Uridine 5'-Diphosphate-Glucuronosyltransferases (UGT)

The UGT enzymes, distributed in various organs in human body, catalyze in Phase II metabolism the glucuronidation reaction (formation of a linkage between glucuronic acid and a nucleophile) leading to an easier elimination of xenobiotics. Some compounds are directly catalyzed by UGTs without ever having first been metabolized by Phase I enzymes. Most of the enzymes in humans are produced by the liver, but one, UGT 1A10, is generated by the GI tract.

We have developed classification QSAR models from literature data for seven UGT isozymes that cause Phase II drug metabolism: UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A9, UGT1A10 and UGT2B7. The number of compounds for these isozymes ranged from 101 to 149. The UGT models predict whether a compound will be metabolized by one or more of these enzymes and were developed using the Artificial Neural Network Ensemble (ANNE) training methodology. Independent test sets were used for validation of all models.

Performance of Classification Models for UGT-mediated Metabolism

For further information about licensing the ADMET Predictor Enslein Metabolism module, please contact:

Mr. John DiBella
Director, Marketing & Sales
661-723-7723 ext. 244
john.dibella@simulations-plus.com